KIF5A and the contribution of susceptibility genotypes as a predictive biomarker for multiple sclerosis

There is increasing interest in the development of multiple sclerosis (MS) biomarkers that reflect central nervous system tissue injury to determine prognosis. We aimed to assess the prognostic value of kinesin superfamily motor protein KIF5A in MS by measuring levels of KIF5A in cerebrospinal fluid (CSF) combined with analysis of single nucleotide polymorphisms (SNPs; rs12368653 and rs703842) located within a MS susceptibility gene locus at chromosome 12q13–14 region. Enzyme-linked immunosorbent assay was used to measure KIF5A in CSF obtained from two independent biobanks comprising non-inflammatory neurological disease controls (NINDC), clinically isolated syndrome (CIS) and MS cases. CSF KIF5A expression was significantly elevated in progressive MS cases compared with NINDCs, CIS and relapsing–remitting MS (RRMS). In addition, levels of KIF5A positively correlated with change in MS disease severity scores (EDSS, MSSS and ARMSSS), in RRMS patients who had documented disease progression at 2-year clinical follow-up. Copies of adenine risk alleles (AG/AA; rs12368653 and rs703842) corresponded with a higher proportion of individuals in relapse at the time of lumbar puncture (LP), higher use of disease-modifying therapies post LP and shorter MS duration. Our study suggests that CSF KIF5A has potential as a predictive biomarker in MS and further studies into the potential prognostic value of analysing MS susceptibility SNPs should be considered

Reduced expression of mitochondrial fumarate hydratase in progressive multiple sclerosis contributes to impaired in vitro mesenchymal stromal cell-mediated neuroprotection

BACKGROUND: Cell-based therapies for multiple sclerosis (MS), including those employing autologous bone marrow-derived mesenchymal stromal cells (MSC) are being examined in clinical trials. However, recent studies have identified abnormalities in the MS bone marrow microenvironment. OBJECTIVE: We aimed to compare the secretome of MSC isolated from control subjects (C-MSC) and people with MS (MS-MSC) and explore the functional relevance of findings. METHODS: We employed high throughput proteomic analysis, enzyme-linked immunosorbent assays and immunoblotting, as well as in vitro assays of enzyme activity and neuroprotection. RESULTS: We demonstrated that, in progressive MS, the MSC secretome has lower levels of mitochondrial fumarate hydratase (mFH). Exogenous mFH restores the in vitro neuroprotective potential of MS-MSC. Furthermore, MS-MSC expresses reduced levels of fumarate hydratase (FH) with downstream reduction in expression of master regulators of oxidative stress. CONCLUSIONS: Our findings are further evidence of dysregulation of the bone marrow microenvironment in progressive MS with respect to anti-oxidative capacity and immunoregulatory potential. Given the clinical utility of the fumaric acid ester dimethyl fumarate in relapsing–remitting MS, our findings have potential implication for understanding MS pathophysiology and personalised therapeutic intervention

Oxidative stress-related biomarkers in multiple sclerosis:a review

Aim: To provide an up-to-date review of oxidative stress biomarkers in multiple sclerosis and thus identify candidate molecules with greatest promise as biomarkers of diagnosis, disease activity or prognosis. Method: A semi-systematic literature search using PubMed and other databases. Results: Nitric oxide metabolites, superoxide dismutase, catalase, glutathione reductase, inducible nitric oxide synthase, protein carbonyl, 3-nitrotyrosine, isoprostanes, malondialdehyde and products of DNA oxidation have been identified across multiple studies as having promise as diagnostic, therapeutic or prognostic markers in MS. Conclusion: Heterogeneity of study design, particularly patient selection, limits comparability across studies. Further cohort studies are needed, and we would recommend promising markers be incorporated into future clinical trials to prospectively validate their potential

The transcription factor PPARα is overexpressed and is associated with a favourable prognosis in IDH-wildtype primary glioblastoma

© 2016 John Wiley & Sons Ltd Aims: PPARα agonists are in current clinical use as hypolipidaemic agents and show significant antineoplastic effects in human glioblastoma models. To date however, the expression of PPARα in large-scale glioblastoma datasets has not been examined. We aimed to investigate the expression of the transcription factor PPARα in primary glioblastoma, the relationship between PPARα expression and patients’ clinicopathological features and other molecular markers associated with gliomagenesis. Methods and results: With protein immunoblotting techniques and reverse transcription quantitative real-time PCR, PPARα was found to be significantly overexpressed in glioblastoma compared with control brain tissue (P = 0.032 and P = 0.005). PPARA gene expression was found to be enriched in the classical glioblastoma subtype within The Cancer Genome Atlas (TCGA) dataset. Although not associated with overall survival when assessed by immunohistochemistry, cross-validation with the TCGA dataset and multivariate analyses identified PPARA gene expression as an independent prognostic marker for overall survival (P = 0.042). Finally, hierarchical clustering revealed novel, significant associations between high PPARA expression and a putative set of glioblastoma molecular mediators including EMX2, AQP4, and NTRK2. Conclusions: PPARα is overexpressed in primary glioblastoma and high PPARA expression functions as an independent prognostic marker in the glioblastoma TCGA dataset. Further studies are required to explore genetic associations with high PPARA expression and to analyse the predictive role of PPARα expression in glioblastoma models in response to PPARα agonists

Evaluation of the quality of RNA extracted from archival FFPE glioblastoma and epilepsy surgical samples for gene expression assays

AimsHistopathological tissue samples are being increasingly used as sources of nucleic acids in molecular pathology translational research. This study investigated the suitability of glioblastoma and control central nervous system (CNS) formalin-fixed paraffin embedded (FFPE) tissue-derived RNA for gene expression analyses.MethodsTotal RNA was extracted from control (temporal lobe resection tissue) and glioblastoma FFPE tissue samples. RNA purity (260/280 ratios) was determined and RNA integrity number (RIN) analysis was performed. RNA was subsequently used for RT-qPCR for two reference genes,18SandGAPDH.ResultsReference gene expression was equivalent between control and glioblastoma tissue when using RNA extracted from FFPE tissue, which has key implications for biological normalisation for CNS gene expression studies. There was a significant difference between the mean RIN values of control and glioblastoma FFPE tissue. There was no significant correlation between 260/280 or RIN values versus total RNA yield. The age of the tissue blocks did not influence RNA yield, fragmentation or purity. There was no significant correlation between RIN or 260/280 ratios and mean qPCR cycle threshold for either reference gene.ConclusionsThis study showed that routinely available CNS FFPE tissue is suitable for RNA extraction and downstream gene expression studies, even after 60 months of storage. Substantial RNA fragmentation associated with glioblastoma and control FFPE tissue blocks did not preclude downstream RT-qPCR gene expression analyses. Cross validation with both archival and prospectively collated FFPE specimens is required to further demonstrate that CNS tissue blocks can be used in novel translational molecular biomarker studies.</jats:sec

Ethical implications of AI in robotic surgical training: A Delphi consensus statement

CONTEXT: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them. OBJECTIVES: To provide ethical guidance on developing narrow AI applications for surgical training curricula. We define standardised approaches to developing AI driven applications in surgical training that address current recognised ethical implications of utilising AI on surgical data. We aim to describe an ethical approach based on the current evidence, understanding of AI and available technologies, by seeking consensus from an expert committee. EVIDENCE ACQUISITION: The project was carried out in 3 phases: (1) A steering group was formed to review the literature and summarize current evidence. (2) A larger expert panel convened and discussed the ethical implications of AI application based on the current evidence. A survey was created, with input from panel members. (3) Thirdly, panel-based consensus findings were determined using an online Delphi process to formulate guidance. 30 experts in AI implementation and/or training including clinicians, academics and industry contributed. The Delphi process underwent 3 rounds. Additions to the second and third-round surveys were formulated based on the answers and comments from previous rounds. Consensus opinion was defined as ≥ 80% agreement. EVIDENCE SYNTHESIS: There was 100% response from all 3 rounds. The resulting formulated guidance showed good internal consistency, with a Cronbach alpha of >0.8. There was 100% consensus that there is currently a lack of guidance on the utilisation of AI in the setting of robotic surgical training. Consensus was reached in multiple areas, including: 1. Data protection and privacy; 2. Reproducibility and transparency; 3. Predictive analytics; 4. Inherent biases; 5. Areas of training most likely to benefit from AI. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts to develop and reach content validation for guidance on ethical implications of AI in surgical training. Providing an ethical foundation for launching narrow AI applications in surgical training. This guidance will require further validation. PATIENT SUMMARY: As the role of AI in healthcare continues to expand there is increasing awareness of the potential pitfalls of AI and the need for guidance to avoid them.In this paper we provide guidance on ethical implications of AI in surgical training