Synthesising acid mine drainage to maintain and exploit indigenous mining micro-algae and microbial assemblies for biotreatment investigations

The stringent regulations for discharging acid mine drainage (AMD) has led to increased attention on traditional or emerging treatment technologies to establish efficient and sustainable management for mine effluents. To assess new technologies, laboratory investigations on AMD treatment are necessary requiring a consistent supply of AMD with a stable composition, thus limiting environmental variability and uncertainty during controlled experiments. Additionally, biotreatment systems using live cells, particularly micro-algae, require appropriate nutrient availability. Synthetic AMD (Syn-AMD) meets these requirements. However, to date, most of the reported Syn-AMDs are composed of only a few selected heavy metals without considering the complexity of actual AMD. In this study, AMD was synthesised based on the typical AMD characteristics from a copper mine where biotreatment is being considered using indigenous AMD algal-microbes. Major cations (Ca, Na, Cu, Zn, Mg, Mn and Ni), trace metals (Al, Fe, Ag, Na, Co, Mo, Pb and Cr), essential nutrients (N, P and C) and high SO₄ were incorporated into the Syn-AMD. This paper presents the preparation of chemically complex Syn-AMD and the challenges associated with combining metal salts of varying solubility that is not restricted to one particular mine site. The general approach reported and the particular reagents used can produce alternative Syn-AMD with varying compositions. The successful growth of indigenous AMD algal-microbes in the Syn-AMD demonstrated its applicability as appropriate generic media for cultivation and maintenance of mining microorganisms for future biotreatment studies.     

Delivering an accredited non-invasive prenatal diagnosis service for monogenic disorders and recommendations for best practice

The identification of cell-free fetal DNA circulating in maternal blood combined with technological developments, in particular next-generation sequencing, is enabling the development of safer prenatal diagnosis. While this technology has been widely applied as a highly sensitive screening test for aneuploidy, there has been relatively little clinical application for the diagnosis of monogenic disorders. In the UK, we have established non-invasive prenatal diagnosis (NIPD) as a clinical service for a range of inherited disorders. The results from NIPD do not require confirmation by invasive testing and are welcomed by patients and health professionals alike. Here, we describe the technical approaches used, current practice and outline recommendations for best practice when delivering an NIPD service from an accredited laboratory. © 2017 John Wiley & Sons, Ltd.     

Evaluation and classification of severity for 176 genes on an expanded carrier screening panel

Background

Disease severity is important when considering genes for inclusion on reproductive expanded carrier screening (ECS) panels. We applied a validated and previously published algorithm that classifies diseases into four severity categories (mild, moderate, severe, and profound) to 176 genes screened by ECS. Disease traits defining severity categories in the algorithm were then mapped to four severity-related ECS panel design criteria cited by the American College of Obstetricians and Gynecologists (ACOG).

Methods

Eight genetic counselors (GCs) and four medical geneticists (MDs) applied the severity algorithm to subsets of 176 genes. MDs and GCs then determined by group consensus how each of these disease traits mapped to ACOG severity criteria, enabling determination of the number of ACOG severity criteria met by each gene.

Results

Upon consensus GC and MD application of the severity algorithm, 68 (39%) genes were classified as profound, 71 (40%) as severe, 36 (20%) as moderate, and one (1%) as mild. After mapping of disease traits to ACOG severity criteria, 170 out of 176 genes (96.6%) were found to meet at least one of the four criteria, 129 genes (73.3%) met at least two, 73 genes (41.5%) met at least three, and 17 genes (9.7%) met all four.

Conclusion

This study classified the severity of a large set of Mendelian genes by collaborative clinical expert application of a trait-based algorithm. Further, it operationalized difficult to interpret ACOG severity criteria via mapping of disease traits, thereby promoting consistency of ACOG criteria interpretation.