The Dictyostelium model for mitochondrial disease

Mitochondrial diseases are a diverse family of genetic disorders caused by mutations affecting mitochondrial proteins encoded in either the nuclear or the mitochondrial genome. By impairing mitochondrial oxidative phosphorylation, they compromise cellular energy production and the downstream consequences in humans are a bewilderingly complex array of signs and symptoms that can affect any of the major organ systems in unpredictable combinations. This complexity and unpredictability has limited our understanding of the cytopathological consequences of mitochondrial dysfunction. By contrast, in Dictyostelium the mitochondrial disease phenotypes are consistent, measurable "readouts" of dysregulated intracellular signalling pathways. When the underlying genetic defects would produce coordinate, generalized deficiencies in multiple mitochondrial respiratory complexes, the disease phenotypes are mediated by chronic activation of an energy-sensing protein kinase, AMP-activated protein kinase (AMPK). This chronic AMPK hyperactivity maintains mitochondrial mass and cellular ATP concentrations at normal levels, but chronically impairs growth, cell cycle progression, multicellular development, photosensory and thermosensory signal transduction. It also causes the cells to support greater proliferation of the intracellular bacterial pathogen, Legionella pneumophila. Notably however, phagocytic and macropinocytic nutrient uptake are impervious both to AMPK signalling and to these types of mitochondrial dysfunction. Surprisingly, a Complex I-specific deficiency (midA knockout) not only causes the foregoing AMPK-mediated defects, but also produces a dramatic deficit in endocytic nutrient uptake accompanied by an additional secondary defect in growth. More restricted and specific phenotypic outcomes are produced by knocking out genes for nuclear-encoded mitochondrial proteins that are not required for respiration. The Dictyostelium model for mitochondrial disease has thus revealed consistent patterns of sublethal dysregulation of intracellular signalling pathways that are produced by different types of underlying mitochondrial dysfunction.     

Studies on Calf Diarrhoea in Mozambique: Prevalence of Bacterial Pathogens

The prevalence of diarrhoea in calves was investigated in 8 dairy farms in Mozambique at 4 occasions during 2 consecutive years. A total of 1241 calves up to 6 months of age were reared in the farms, and 63 (5%) of them had signs of diarrhoea. Two farms had an overall higher prevalence (13% and 21%) of diarrhoea. Faecal samples were collected from all diarrhoeal calves (n = 63) and from 330 healthy calves and analysed for Salmonella species, Campylobacter jejuni and enterotoxigenic Escherichia coli (ETEC). Salmonella spp. was isolated in only 2% of all calves. Campylobacter was isolated in 11% of all calves, irrespective of health condition, and was more frequent (25%) in one of the 2 diarrhoeal farms (p = 0.001). 80% of the isolates were identified as C. jejuni. No ETEC strains were detected among the 55 tested strains from diarrhoeal calves, but 22/55 (40%) strains from diarrhoeal calves and 14/88 (16%) strains from healthy calves carried the K99 adhesin (p = 0.001). 6,757 E. coli isolates were typed with a biochemical fingerprinting method (the PhenePlate?) giving the same E. coli diversity in healthy and diarrhoeal calves. Thus it was concluded: i) the overall prevalence of diarrhoea was low, but 2 farms had a higher prevalence that could be due to an outbreak situation, ii) Salmonella did not seem to be associated with diarrhoea, iii) Campylobacter jejuni was common at one of the 2 diarrhoeal farms and iv) ETEC strains were not found, but K99 antigen was more prevalent in E. coli strains from diarrhoeal calves than from healthy, as well as more prevalent in one diarrhoeal farm.     

A preliminary investigation into the relationship between functional movement screen scores and athletic physical performance in female team sport athletes

There is little research investigating relationships between the Functional Movement Screen (FMS) and athletic performance in female athletes. This study analyzed the relationships between FMS (deep squat; hurdle step [HS]; in-line lunge [ILL]; shoulder mobility; active straight-leg raise [ASLR]; trunk stability push-up; rotary stability) scores, and performance tests (bilateral and unilateral sit-and-reach [flexibility]; 20-m sprint [linear speed]; 505 with turns from each leg; modified T-test with movement to left and right [change-of-direction speed]; bilateral and unilateral vertical and standing broad jumps; lateral jumps [leg power]). Nine healthy female recreational team sport athletes (age = 22.67 ± 5.12 years; height = 1.66 ± 0.05 m; body mass = 64.22 ± 4.44 kilograms) were screened in the FMS and completed the afore-mentioned tests. Percentage between-leg differences in unilateral sit-and-reach, 505 turns and the jumps, and difference between the T-test conditions, were also calculated. Spearman''s correlations (p ≤ 0.05) examined relationships between the FMS and performance tests. Stepwise multiple regressions (p ≤ 0.05) were conducted for the performance tests to determine FMS predictors. Unilateral sit-and-reach positive correlated with the left-leg ASLR (r = 0.704-0.725). However, higher-scoring HS, ILL, and ASLR related to poorer 505 and T-test performance (r = 0.722-0.829). A higher-scored left-leg ASLR related to a poorer unilateral vertical and standing broad jump, which were the only significant relationships for jump performance. Predictive data tended to confirm the correlations. The results suggest limitations in using the FMS to identify movement deficiencies that could negatively impact athletic performance in female team sport athletes.     

Erratum

Neurotransmitter receptor trafficking and the regulation of synaptic strength. Traffic 2001:2(7):437–448.     

Identifying rare and common disease associated variants in genomic data using Parkinson’s disease as a model

Background

Genome-wide association studies have been successful in identifying common genetic variants for human diseases. However, much of the heritable variation associated with diseases such as Parkinson’s disease remains unknown suggesting that many more risk loci are yet to be identified. Rare variants have become important in disease association studies for explaining missing heritability. Methods for detecting this type of association require prior knowledge on candidate genes and combining variants within the region. These methods may suffer from power loss in situations with many neutral variants or causal variants with opposite effects.

Results

We propose a method capable of scanning genetic variants to identify the region most likely harbouring disease gene with rare and/or common causal variants. Our method assigns a score at each individual variant based on our scoring system. It uses aggregate scores to identify the region with disease association. We evaluate performance by simulation based on 1000 Genomes sequencing data and compare with three commonly used methods. We use a Parkinson’s disease case–control dataset as a model to demonstrate the application of our method.Our method has better power than CMC and WSS and similar power to SKAT-O with well-controlled type I error under simulation based on 1000 Genomes sequencing data. In real data analysis, we confirm the association of α-synuclein gene (SNCA) with Parkinson’s disease (p = 0.005). We further identify association with hyaluronan synthase 2 (HAS2, p = 0.028) and kringle containing transmembrane protein 1 (KREMEN1, p = 0.006). KREMEN1 is associated with Wnt signalling pathway which has been shown to play an important role for neurodegeneration in Parkinson’s disease.

Conclusions

Our method is time efficient and less sensitive to inclusion of neutral variants and direction effect of causal variants. It can narrow down a genomic region or a chromosome to a disease associated region. Using Parkinson’s disease as a model, our method not only confirms association for a known gene but also identifies two genes previously found by other studies. In spite of many existing methods, we conclude that our method serves as an efficient alternative for exploring genomic data containing both rare and common variants.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-014-0088-9) contains supplementary material, which is available to authorized users.     

Gene flow in environmental Legionella pneumophila leads to genetic and pathogenic heterogeneity within a Legionnaires’ disease outbreak

Background

Legionnaires’ disease is a severe form of pneumonia caused by the environmental bacterium Legionella pneumophila. Outbreaks commonly affect people with known risk factors, but the genetic and pathogenic complexity of L. pneumophila within an outbreak is not well understood. Here, we investigate the etiology of the major Legionnaires’ disease outbreak that occurred in Edinburgh, UK, in 2012, by examining the evolutionary history, genome content, and virulence of L. pneumophila clinical isolates.

Results

Our high resolution genomic approach reveals that the outbreak was caused by multiple genetic subtypes of L. pneumophila, the majority of which had diversified from a single progenitor through mutation, recombination, and horizontal gene transfer within an environmental reservoir prior to release. In addition, we discover that some patients were infected with multiple L. pneumophila subtypes, a finding which can affect the certainty of source attribution. Importantly, variation in the complement of type IV secretion systems encoded by different genetic subtypes correlates with virulence in a Galleria mellonella model of infection, revealing variation in pathogenic potential among the outbreak source population of L. pneumophila.

Conclusions

Taken together, our study indicates previously cryptic levels of pathogen heterogeneity within a Legionnaires’ disease outbreak, a discovery that impacts on source attribution for future outbreak investigations. Furthermore, our data suggest that in addition to host immune status, pathogen diversity may be an important influence on the clinical outcome of individual outbreak infections.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0504-1) contains supplementary material, which is available to authorized users.