Identification of putative markers of triclabendazole resistance by a genome-wide analysis of genetically recombinant Fasciola hepatica

Despite years of investigation into triclabendazole (TCBZ) resistance in Fasciola hepatica, the genetic mechanisms responsible remain unknown. Extensive analysis of multiple triclabendazole-susceptible and -resistant isolates using a combination of experimental in vivo and in vitro approaches has been carried out, yet few, if any, genes have been demonstrated experimentally to be associated with resistance phenotypes in the field. In this review we summarize the current understanding of TCBZ resistance from the approaches employed to date. We report the current genomic and genetic resources for F. hepatica that are available to facilitate novel functional genomics and genetic experiments for this parasite in the future. Finally, we describe our own non-biased approach to mapping the major genetic loci involved in conferring TCBZ resistance in F. hepatica.

The application of metabolomic biomarker screening tools for improved Bovine Respiratory Disease diagnosis and management

Bovine Respiratory Disease (BRD) is considered to be one of the most significant causes of economic loss in cattle worldwide. The disease has multifactorial aetiology, where viral induced respiratory damage can predispose animals to developing secondary bacterial infections. Accurate identification of viral infected animals prior to the onset of bacterial infection is necessary to reduce the overuse of antimicrobial treatments and minimize further economic losses from reduced production capacity and death. This research focuses on Bovine Parainfluenza Virus Type 3 (BPIV-3), one of the viruses involved in generating BRD. Vaccination measures for BPIV-3 can induce a level of immunity preventing disease progression, however, not all animals respond equally and immunization can complicate disease diagnosis. Alternative diagnostic approaches are required to identify animals which fail to respond to vaccination during infection outbreaks and are therefore likely to be more susceptible to secondary bacterial infections. Mass spectrometry based metabolomics was employed to identify plasma markers capable of differentiating between vaccinated and non-vaccinated calves after challenge with BPIV-3. Differentiation of vaccinated and non-vaccinated study groups (n=6) was possible as early as day 2 post-BPIV-3 challenge up until day 20 using a panel of potential metabolite markers. This study illustrates the potential for metabolomics to provide more detailed information on animal vaccination status that could be used to develop tools for improved herd health management, reduce economic loss through rapid identification and isolation of animals without immune protection (improving herd level immunity) and help reduce the usage of antimicrobial therapeutic treatments in animals.

Proteomic identification of plasma proteins as markers of growth promoter abuse in cattle

Growth-promoting agents are continually misused for increasing animal growth and fraudulent gain in the meat industry, yet detection rates from conventional targeted testing for drug residues do not reflect this. This is because testing currently relies on direct detection of drugs or related metabolites and administrators of such compounds can take adaptive measures to avoid detection through the use of endogenous or unknown drugs, and low dose or combined mixtures. New detection methods are needed which focus on the screening of biological responses of an animal to such growth-promoting agents as it has been demonstrated that genomic, proteomic and metabolomics profiles are altered by xenobiotic intake. Therefore, an untargeted proteomics approach using comparative two-dimensional gel electrophoresis (2DE) was carried out to identify putative proteins altered in plasma after treatment with oestradiol, dexamethasone or prednisolone. Twenty-four male cattle were randomly assigned to four groups (n = 6) for experimental treatment over 40 days, namely a control group of non-treated cattle, and three groups administered 17β-oestradiol-3-benzoate (0.01 mg/kg, intramuscular), dexamethasone sodium phosphate (0.7 mg/day, per os) or prednisolone acetate (15 mg/day, per os), respectively. Plasma collected from each animal at day 25 post study initiation was subjected to proteomic analysis by 2DE for comparison of protein expression between treated and untreated animals. Analysis of acquired gel images revealed 22 plasma proteins which differed in expression by more than 50 % (p < 0.05) in treated animals compared to untreated animals. Proteins of interest underwent identification by LC–MS/MS analysis and were found to have associated roles in transport, blood coagulation, immune response and metabolism pathways. In this way, seven proteins are highlighted as novel biomarker candidates including transthyretin which is shown to be significantly increased in all treatment groups compared to control animals and potentially may find use as global markers of suspect anabolic practice.

Genetic assessment of parentage in the caridean rock shrimp Rhynchocinetes typus based on microsatellite markers

Over the past decade, the common rock shrimp, Rhynchocinetes typus H. Milne Edwards, 1837, has been the focus of extensive investigations on mating behaviour. The species is now perceived as a model system for the study of reproductive strategies and sexual conflict in crustaceans displaying external fertilization. Using molecular markers, the current study assesses whether social mating behaviour in common rock shrimp translates into true genetic parentage. In a large mesocosm tank with >200 individuals of both sexes, the analysis of 15 families (22 eggs per female) for three informative microsatellites unambiguously confirmed multiple paternity in 11 instances (73%) involving, in each case, two to four males. Where more than one male was identified siring a particular brood, reproductive skew was apparent towards a single individual. Results suggest that multiple paternity in this species results from subordinate male coercive behaviour, female solicitation of multiple male matings or a combination of both.

Post-diagnostic oral bisphosphonate use and colorectal cancer mortality: a population-based cohort study within the UK Clinical Practice Research Datalink

Background:We conducted the first study to investigate post-diagnostic oral bisphosphonates use and colorectal cancer-specific mortality.

Methods:Colorectal cancer patients were identified from the National Cancer Data Repository (1998–2007) and linked to the UK Clinical Practice Research Datalink, providing prescription records, and Office of National Statistics mortality data. Time-dependent Cox regression models investigated colorectal cancer-specific mortality in post-diagnostic bisphosphonate users.

Results:Overall, in 4791 colorectal cancer patients, there was no evidence of an association between bisphosphonate use and colorectal cancer-specific mortality (adjusted hazard ratio=1.11; 95% confidence interval 0.80, 1.54) or with drug frequency or type.

Conclusions:In this novel population-based cohort study, post-diagnostic bisphosphonate use was not associated with longer rates of colorectal cancer survival.

Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.

METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.

RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.

CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.

The prognostic value of the stem-like group in colorectal cancer using a panel of immunohistochemistry markers

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths in the Western world. It is becoming increasingly clear that CRC is a diverse disease, as exemplified by the identification of subgroups of CRC tumours that are driven by distinct biology. Recently, a number of studies have begun to define panels of diagnostically relevant markers to align patients into individual subgroups in an attempt to give information on prognosis and treatment response. We examined the immunohistochemical expression profile of 18 markers, each representing a putative role in cancer development, in 493 primary colorectal carcinomas using tissue microarrays. Through unsupervised clustering in stage II cancers, we identified two cluster groups that are broadly defined by inflammatory or immune-related factors (CD3, CD8, COX-2 and FOXP3) and stem-like factors (CD44, LGR5, SOX2, OCT4). The expression of the stem-like group markers was associated with a significantly worse prognosis compared to cases with lower expression. In addition, patients classified in the stem-like subgroup displayed a trend towards a benefit from adjuvant treatment. The biologically relevant and poor prognostic stem-like group could also be identified in early stage I cancers, suggesting a potential opportunity for the identification of aggressive tumors at a very early stage of the disease.

Hui and Walter's latent-class model extended to estimate diagnostic test properties from surveillance data: a latent model for latent data

Diagnostic test sensitivity and specificity are probabilistic estimates with far reaching implications for disease control, management and genetic studies. In the absence of 'gold standard' tests, traditional Bayesian latent class models may be used to assess diagnostic test accuracies through the comparison of two or more tests performed on the same groups of individuals. The aim of this study was to extend such models to estimate diagnostic test parameters and true cohort-specific prevalence, using disease surveillance data. The traditional Hui-Walter latent class methodology was extended to allow for features seen in such data, including (i) unrecorded data (i.e. data for a second test available only on a subset of the sampled population) and (ii) cohort-specific sensitivities and specificities. The model was applied with and without the modelling of conditional dependence between tests. The utility of the extended model was demonstrated through application to bovine tuberculosis surveillance data from Northern and the Republic of Ireland. Simulation coupled with re-sampling techniques, demonstrated that the extended model has good predictive power to estimate the diagnostic parameters and true herd-level prevalence from surveillance data. Our methodology can aid in the interpretation of disease surveillance data, and the results can potentially refine disease control strategies.

Molecular markers for predicting recurrence, progression and outcomes of bladder cancer (do the poster boys need new posters

Purpose of review
Molecular markers for bladder cancer recurrence and
progression continue to drive many research programmes.
Translating the laboratory findings into the clinical environment
where these markers are used in clinical decision making has
proved problematic. In the clinical arena, stage and grade are
still the main focus for decisions about patient management.
There is however an evolution in bladder cancer research from
single-marker/single-pathway research to a more global
assessment of the tumour cell with DNA microarrays and
proteomics.
Recent findings
In the last year, DNA microarray assessment has revealed
several interesting molecular markers such as p33ING1 and
DEK. Parallel ‘conventional’ single-pathway research has
focused on new novel markers such as HER2/neu, survivin and
matrix metalloproteinase 2 (MMP-2). Molecular markers that
have a long-standing association with bladder cancer
progression such as p53, E-cadherin and Ki-67 have been
reviewed by both single-marker studies and by microarray
studies and their status remains important.
Summary
It is an exciting time in the molecular biology research of bladder
cancer as the focus changes to assess the global genetic and
protein expression within tumour cells. From such a wealth of
information it is likely that molecular markers will make the
translation from benchside to bedside.