Development of hepatic pathology in GBV-B-infected red-bellied tamarins (Saguinus labiatus).

GB virus B (GBV-B) is a new world monkey-associated flavivirus used to model acute hepatitis C virus (HCV) infection. Critical for evaluation of antiviral or vaccine approaches is an understanding of the effect of HCV on the liver at different stages of infection. In the absence of longitudinal human tissue samples at defined time points, we have characterized changes in tamarins. As early as 2 weeks post-infection histological changes were noticeable, and these were established in all animals by 6 weeks. Despite high levels of liver-associated viral RNA, there was reversal of hepatic damage on clearance of peripheral virus though fibrosis was demonstrated in four tamarins. Notably, viral RNA burden in the liver dropped to near undetectable or background levels in all animals which underwent a second viral challenge, highlighting the efficacy of the immune response in removing foci of replication in the liver. These data add to the knowledge of GBV-B infection in New World primates which can offer attractive systems for the testing of prophylactic and therapeutic treatments and the evaluation of their utility in preventing or reversing liver pathology.

Simian T-cell lymphotropic virus type I alters the proviral load and biodistribution of simian retrovirus type 2 in co-infected macaques, supporting advancement of immunosuppressive pathology.

The infection dynamics and pathology of a retrovirus may be altered by one or more additional viruses. To investigate this further, this study characterized proviral load, biodistribution and the immune response in Macaca fascicularis naturally infected with combinations of simian retrovirus type 2 (SRV-2) and simian T-cell lymphotropic virus type I (STLV-I). As the mesenteric lymph node (MLN) and the spleen have been implicated previously in response to retroviral infection, the morphology and immunopathology of these tissues were assessed. The data revealed a significant change in SRV-2 biodistribution in macaques infected with STLV-I. Pathological changes were greater in the MLN and spleen of STLV-I-infected and co-infected macaques compared with the other groups. Immune-cell populations in co-infected macaque spleens were increased and there was an atypical distribution of B-cells. These findings suggest that the infection dynamics of each virus in a co-infected individual may be affected to a different extent and that STLV-I appears to be responsible for enhancing the biodistribution and associated pathological changes in SRV-2 in macaques.

Horizontal acquisition and a broad biodistribution typify simian foamy virus infection in a cohort of Macaca fascicularis.

BACKGROUND: Foamy viruses are non-pathogenic in vivo and naturally infect all species of non-human primates (NHP). Simian foamy viruses (SFV) are highly prevalent in both free ranging and captive NHP but few longitudinal studies have been performed to assess the prevalence and biodistribution of SFV within captive NHP. METHOD: LTR and pol gene along with Gag antibody detection were undertaken to identify infection in a cohort of over 80 captive macaques. RESULTS: The prevalence of SFV was between 64% and 94% in different groups. Access to 23 dam-infant pairs allowed us to reveal horizontal transfer as the dominant route of SFV transmission in our cohort. Further, analysis of SFV from a range of tissues and blood revealed that macaques as young as six months old can be infected and that proviral biodistribution increases with age. CONCLUSIONS: These are the first data of this type for a captive cohort of cynomolgus macaques.

Prostate Cancer: Early Detection and Assessing Clinical Risk Using Deep Machine Learning of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data.

Detecting the presence of prostate cancer (PCa) and distinguishing low- or intermediate-risk disease from high-risk disease early, and without the need for potentially unnecessary invasive biopsies remains a significant clinical challenge. The aim of this study is to determine whether the T and B cell phenotypic features which we have previously identified as being able to distinguish between benign prostate disease and PCa in asymptomatic men having Prostate-Specific Antigen (PSA) levels < 20 ng/ml can also be used to detect the presence and clinical risk of PCa in a larger cohort of patients whose PSA levels ranged between 3 and 2617 ng/ml. The peripheral blood of 130 asymptomatic men having elevated Prostate-Specific Antigen (PSA) levels was immune profiled using multiparametric whole blood flow cytometry. Of these men, 42 were subsequently diagnosed as having benign prostate disease and 88 as having PCa on biopsy-based evidence. We built a bidirectional Long Short-Term Memory Deep Neural Network (biLSTM) model for detecting the presence of PCa in men which combined the previously-identified phenotypic features (CD8+CD45RA-CD27-CD28- (CD8+ Effector Memory cells), CD4+CD45RA-CD27-CD28- (CD4+ Effector Memory cells), CD4+CD45RA+CD27-CD28- (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA), CD3-CD19+ (B cells), CD3+CD56+CD8+CD4+ (NKT cells) with Age. The performance of the PCa presence 'detection' model was: Acc: 86.79 ( ± 0.10), Sensitivity: 82.78% (± 0.15); Specificity: 95.83% (± 0.11) on the test set (test set that was not used during training and validation); AUC: 89.31% (± 0.07), ORP-FPR: 7.50% (± 0.20), ORP-TPR: 84.44% (± 0.14). A second biLSTM 'risk' model combined the immunophenotypic features with PSA to predict whether a patient with PCa has high-risk disease (defined by the D'Amico Risk Classification) achieved the following: Acc: 94.90% (± 6.29), Sensitivity: 92% (± 21.39); Specificity: 96.11 (± 0.00); AUC: 94.06% (± 10.69), ORP-FPR: 3.89% (± 0.00), ORP-TPR: 92% (± 21.39). The ORP-FPR for predicting the presence of PCa when combining FC+PSA was lower than that of PSA alone. This study demonstrates that AI approaches based on peripheral blood phenotyping profiles can distinguish between benign prostate disease and PCa and predict clinical risk in asymptomatic men having elevated PSA levels.

Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data.

We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets. Herein, we profiled natural killer (NK) cell subsets in the blood of 72 asymptomatic men with Prostate-Specific Antigen (PSA) levels < 20 ng ml-1, of whom 31 had benign disease (no cancer) and 41 had prostate cancer. Statistical and computational methods identified a panel of eight phenotypic features ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]) that, when incorporated into an Ensemble machine learning prediction model, distinguished between the presence of benign prostate disease and prostate cancer. The machine learning model was then adapted to predict the D'Amico Risk Classification using data from 54 patients with prostate cancer and was shown to accurately differentiate between the presence of low-/intermediate-risk disease and high-risk disease without the need for additional clinical data. This simple blood test has the potential to transform prostate cancer diagnostics.

With an estimated 1.8 million new cases in 2018 alone, prostate cancer is the fourth most common cancer in the world. Catching the disease early increases the chances of survival, but this cancer remains difficult to detect. The best diagnostic test currently available measures the blood level of a protein called the prostate-specific antigen (PSA for short). Heightened amounts of PSA may mean that the patient has cancer, but 15% of individuals with prostate cancer have normal levels of the protein, and many healthy people can have high amounts of PSA. This blood test is therefore not widely accepted as a reliable diagnostic tool. Other methods exist to detect prostate cancer, yet their results are limited. A small piece of the prostate can be taken for analysis, but results from this invasive procedure are often incorrect. Scans can help to spot a tumor, but they are not accurate enough to be conclusive on their own. New tests are therefore urgently needed. Prostate cancer is often associated with changes in the immune system that can be detected through a blood test. In particular, the appearance of a type of white blood (immune) cells called natural killer cells may be altered. Yet, it was unclear whether measurements based on these cells could help to detect prostate cancer and assess the severity of the disease. Here, Hood, Cosma et al. collected and examined the natural killer cells of 72 participants with slightly elevated PSA levels and no other symptoms. Amongst these, 31 individuals had prostate cancer and 41 were healthy. These biological data were then used to produce computer models that could detect the presence of the disease, as well as assess its severity. The algorithms were developed using machine learning, where previous patient information is used to make prediction on new data. This work resulted in a new detection tool which was 12.5% more accurate than the PSA test in detecting prostate cancer; and in a detection tool that was 99% accurate in predicting the risk of the disease (in terms of clinical significance) in individuals with prostate cancer. Although these new approaches first need to be validated in the clinic before being deployed, they could ultimately improve the detection and diagnosis of prostate cancer, saving lives and reducing the need for further tests.

Tumor- and cytokine-primed human natural killer cells exhibit distinct phenotypic and transcriptional signatures.

An emerging cellular immunotherapy for cancer is based on the cytolytic activity of natural killer (NK) cells against a wide range of tumors. Although in vitro activation, or "priming," of NK cells by exposure to pro-inflammatory cytokines, such as interleukin (IL)-2, has been extensively studied, the biological consequences of NK cell activation in response to target cell interactions have not been thoroughly characterized. We investigated the consequences of co-incubation with K562, CTV-1, Daudi RPMI-8226, and MCF-7 tumor cell lines on the phenotype, cytokine expression profile, and transcriptome of human NK cells. We observe the downregulation of several activation receptors including CD16, CD62L, C-X-C chemokine receptor (CXCR)-4, natural killer group 2 member D (NKG2D), DNAX accessory molecule (DNAM)-1, and NKp46 following tumor-priming. Although this NK cell phenotype is typically associated with NK cell dysfunction in cancer, we reveal the upregulation of NK cell activation markers, such as CD69 and CD25; secretion of pro-inflammatory cytokines, including macrophage inflammatory proteins (MIP-1) α /ß and IL-1ß/6/8; and overexpression of numerous genes associated with enhanced NK cell cytotoxicity and immunomodulatory functions, such as FAS, TNFSF10, MAPK11, TNF, and IFNG. Thus, it appears that tumor-mediated ligation of receptors on NK cells may induce a primed state which may or may not lead to full triggering of the lytic or cytokine secreting machinery. Key signaling molecules exclusively affected by tumor-priming include MAP2K3, MARCKSL1, STAT5A, and TNFAIP3, which are specifically associated with NK cell cytotoxicity against tumor targets. Collectively, these findings help define the phenotypic and transcriptional signature of NK cells following their encounters with tumor cells, independent of cytokine stimulation, and provide insight into tumor-specific NK cell responses to inform the transition toward harnessing the therapeutic potential of NK cells in cancer.

Phenotype and Function of Activated Natural Killer Cells From Patients With Prostate Cancer: Patient-Dependent Responses to Priming and IL-2 Activation.

Background: Although immunotherapy has emerged as the "next generation" of cancer treatments, it has not yet been shown to be successful in the treatment of patients with prostate cancer, for whom therapeutic options remain limited to radiotherapy and androgen (hormone) deprivation therapy. Previous studies have shown that priming natural killer (NK) cells isolated from healthy individuals via co-incubation with CTV-1 cells derived from an acute lymphoblastic leukemia (ALL) enhances their cytotoxicity against human DU145 (metastatic) prostate cancer cells, but it remains unknown to what extent NK cells from patients with prostate cancer can be triggered to kill. Herein, we explore the phenotype of peripheral blood NK cells in patients with prostate cancer and compare the capacity of CTV-1 cell-mediated priming and IL-2 stimulation to trigger NK cell-mediated killing of the human PC3 (metastatic) prostate cancer cell line. Methods: The phenotype of resting, primed (co-incubation with CTV-1 cells for 17 h) and IL-2 activated (100 IU/ml IL-2 for 17 h) NK cells isolated from frozen-thawed peripheral blood mononuclear cell (PBMC) preparations from patients with benign disease (n = 6) and prostate cancer (n = 18) and their cytotoxicity against PC3 and K562 cells was determined by flow cytometry. Relationship(s) between NK cell phenotypic features and cytotoxic potential were interrogated using Spearman Rank correlation matrices. Results and Conclusions: NK cell priming and IL-2 activation of patient-derived NK cells resulted in similar levels of cytotoxicity, but distinct NK cell phenotypes. Importantly, the capacity of priming and IL-2 stimulation to trigger cytotoxicity was patient-dependent and mutually exclusive, in that NK cells from ~50% of patients preferentially responded to priming whereas NK cells from the remaining patients preferentially responded to cytokine stimulation. In addition to providing more insight into the biology of primed and cytokine-stimulated NK cells, this study supports the use of autologous NK cell-based immunotherapies for the treatment of prostate cancer. However, our findings also indicate that patients will need to be stratified according to their potential responsiveness to individual therapeutic approaches.

Immune-Phenotyping and Transcriptomic Profiling of Peripheral Blood Mononuclear Cells From Patients With Breast Cancer: Identification of a 3 Gene Signature Which Predicts Relapse of Triple Negative Breast Cancer.

Background: Interactions between the immune system and tumors are highly reciprocal in nature, leading to speculation that tumor recurrence or therapeutic resistance could be influenced or predicted by immune events that manifest locally, but can be detected systemically. Methods: Multi-parameter flow cytometry was used to examine the percentage and phenotype of natural killer (NK) cells, myeloid-derived suppressor cells (MDSCs), monocyte subsets and regulatory T (Treg) cells in the peripheral blood of of 85 patients with breast cancer (50 of whom were assessed before and after one cycle of anthracycline-based chemotherapy), and 23 controls. Transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) in 23 patients were generated using a NanoString gene profiling platform. Results: An increased percentage of immunosuppressive cells such as granulocytic MDSCs, intermediate CD14++CD16+ monocytes and CD127negCD25highFoxP3+ Treg cells was observed in patients with breast cancer, especially patients with stage 3 and 4 disease, regardless of ER status. Following neoadjuvant chemotherapy, B cell numbers decreased significantly, whereas monocyte numbers increased. Although chemotherapy had no effect on the percentage of Treg, MDSC and NK cells, the expression of inhibitory receptors CD85j, LIAR and NKG2A and activating receptors NKp30 and NKp44 on NK cells increased, concomitant with a decreased expression of NKp46 and DNAM-1 activating receptors. Transcriptomic profiling revealed a distinct group of 3 patients in the triple negative breast cancer (TNBC) cohort who expressed high levels of mRNA encoding genes predominantly involved in inflammation. The analysis of a large transcriptomic dataset derived from the tumors of patients with TNBC revealed that the expression of CD163, CXCR4, THBS1 predicted relapse-free survival. Conclusions: The peripheral blood immunome of patients with breast cancer is influenced by the presence and stage of cancer, but not by molecular subtypes. Furthermore, immune profiling coupled with transcriptomic analyses of peripheral blood cells may identify patients with TNBC that are at risk of relapse after chemotherapy.

Identifying the Presence of Prostate Cancer in Individuals with PSA Levels <20 ng ml-1 Using Computational Data Extraction Analysis of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data.

Determining whether an asymptomatic individual with Prostate-Specific Antigen (PSA) levels below 20 ng ml-1 has prostate cancer in the absence of definitive, biopsy-based evidence continues to present a significant challenge to clinicians who must decide whether such individuals with low PSA values have prostate cancer. Herein, we present an advanced computational data extraction approach which can identify the presence of prostate cancer in men with PSA levels <20 ng ml-1 on the basis of peripheral blood immune cell profiles that have been generated using multi-parameter flow cytometry. Statistical analysis of immune phenotyping datasets relating to the presence and prevalence of key leukocyte populations in the peripheral blood, as generated from individuals undergoing routine tests for prostate cancer (including tissue biopsy) using multi-parametric flow cytometric analysis, was unable to identify significant relationships between leukocyte population profiles and the presence of benign disease (no prostate cancer) or prostate cancer. By contrast, a Genetic Algorithm computational approach identified a subset of five flow cytometry features (CD8+CD45RA-CD27-CD28- (CD8+ Effector Memory cells); CD4+CD45RA-CD27-CD28- (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA); CD3-CD19+ (B cells); CD3+CD56+CD8+CD4+ (NKT cells)) from a set of twenty features, which could potentially discriminate between benign disease and prostate cancer. These features were used to construct a prostate cancer prediction model using the k-Nearest-Neighbor classification algorithm. The proposed model, which takes as input the set of flow cytometry features, outperformed the predictive model which takes PSA values as input. Specifically, the flow cytometry-based model achieved Accuracy = 83.33%, AUC = 83.40%, and optimal ROC points of FPR = 16.13%, TPR = 82.93%, whereas the PSA-based model achieved Accuracy = 77.78%, AUC = 76.95%, and optimal ROC points of FPR = 29.03%, TPR = 82.93%. Combining PSA and flow cytometry predictors achieved Accuracy = 79.17%, AUC = 78.17% and optimal ROC points of FPR = 29.03%, TPR = 85.37%. The results demonstrate the value of computational intelligence-based approaches for interrogating immunophenotyping datasets and that combining peripheral blood phenotypic profiling with PSA levels improves diagnostic accuracy compared to using PSA test alone. These studies also demonstrate that the presence of cancer is reflected in changes in the peripheral blood immune phenotype profile which can be identified using computational analysis and interpretation of complex flow cytometry datasets.

A Galaxy-based bioinformatics pipeline for optimised, streamlined microsatellite development from Illumina next-generation sequencing data.

Microsatellites are useful tools for ecologists and conservationist biologists, but are taxa-specific and traditionally expensive and time-consuming to develop. New methods using next-generation sequencing (NGS) have reduced these problems, but the plethora of software available for processing NGS data may cause confusion and difficulty for researchers new to the field of bioinformatics. We developed a bioinformatics pipeline for microsatellite development from Illumina paired-end sequences, which is packaged in the open-source bioinformatics tool Galaxy. This optimises and streamlines the design of a microsatellite panel and provides a user-friendly graphical user interface. The pipeline utilises existing programs along with our own novel program and wrappers to: quality-filter and trim reads (Trimmomatic); generate sequence quality reports (FastQC); identify potentially-amplifiable microsatellite loci (Pal_finder); design primers (Primer3); assemble pairs of reads to enhance marker amplification success rates (PANDAseq); and filter optimal loci (Pal_filter). The complete pipeline is freely available for use via a pre-configured Galaxy instance, accessible at https://palfinder.ls.manchester.ac.uk.

Changes in immune cell populations in the periphery and liver of GBV-B-infected and convalescent tamarins (Saguinus labiatus).

Flaviviruses related to hepatitis C virus (HCV) in suitable animal models may provide further insight into the role that cellular immunity contributes to spontaneous clearance of HCV. We characterised changes in lymphocyte populations in tamarins with an acute GBV-B infection, a hepatitis virus of the flaviviridae. Major immune cell populations were monitored in peripheral and intra-hepatic lymphocytes at high viraemia or following a period when peripheral virus was no longer detected. Limited changes in major lymphocyte populations were apparent during high viraemia; however, the proportions of CD3(+) lymphocytes decreased and CD20(+) lymphocytes increased once peripheral viraemia became undetectable. Intrahepatic lymphocyte populations increased at both time points post-infection. Distinct expression patterns of PD-1, a marker of T-cell activation, were observed on peripheral and hepatic lymphocytes; notably there was elevated PD-1 expression on hepatic CD4(+) T-cells during high viraemia, suggesting an activated phenotype, which decreased following clearance of peripheral viraemia. At times when peripheral vRNA was not detected, suggesting viral clearance, we were able to readily detect GBV-B RNA in the liver, indicative of long-term virus replication. This study is the first description of changes in lymphocyte populations during GBV-B infection of tamarins and provides a foundation for more detailed investigations of the responses that contribute to the control of GBV-B infection.