Denis C. Bauer

A novel ENU-induced ankyrin-1 mutation impairs parasite invasion and increases erythrocyte clearance during malaria infection in mice

Genetic defects in various red blood cell (RBC) cytoskeletal proteins have been long associated with changes in susceptibility towards malaria infection. In particular, while ankyrin (Ank-1) mutations account for approximately 50% of hereditary spherocytosis (HS) cases, an association with malaria is not well-established, and conflicting evidence has been reported. We describe a novel N-ethyl-N-nitrosourea (ENU)-induced ankyrin mutation MRI61689 that gives rise to two different ankyrin transcripts: one with an introduced splice acceptor site resulting a frameshift, the other with a skipped exon. Ank-1(MRI61689/+) mice exhibit an HS-like phenotype including reduction in mean corpuscular volume (MCV), increased osmotic fragility and reduced RBC deformability. They were also found to be resistant to rodent malaria Plasmodium chabaudi infection. Parasites in Ank-1(MRI61689/+) erythrocytes grew normally, but red cells showed resistance to merozoite invasion. Uninfected Ank-1(MRI61689/+) erythrocytes were also more likely to be cleared from circulation during infection; the “bystander effect”. This increased clearance is a novel resistance mechanism which was not observed in previous ankyrin mouse models. We propose that this bystander effect is due to reduced deformability of Ank-1(MRI61689/+) erythrocytes. This paper highlights the complex roles ankyrin plays in mediating malaria resistance.

Three-dimensional disorganization of the cancer genome occurs coincident with long-range genetic and epigenetic alterations.

A three-dimensional chromatin state underpins the structural and functional basis of the genome by bringing regulatory elements and genes into close spatial proximity to ensure proper, cell-type-specific gene expression profiles. Here, we performed Hi-C chromosome conformation capture sequencing to investigate how three-dimensional chromatin organization is disrupted in the context of copy-number variation, long-range epigenetic remodeling, and atypical gene expression programs in prostate cancer. We find that cancer cells retain the ability to segment their genomes into megabase-sized topologically associated domains (TADs); however, these domains are generally smaller due to establishment of additional domain boundaries. Interestingly, a large proportion of the new cancer-specific domain boundaries occur at regions that display copy-number variation. Notably, a common deletion on 17p13.1 in prostate cancer spanning the TP53 tumor suppressor locus results in bifurcation of a single TAD into two distinct smaller TADs. Change in domain structure is also accompanied by novel cancer-specific chromatin interactions within the TADs that are enriched at regulatory elements such as enhancers, promoters, and insulators, and associated with alterations in gene expression. We also show that differential chromatin interactions across regulatory regions occur within long-range epigenetically activated or silenced regions of concordant gene activation or repression in prostate cancer. Finally, we present a novel visualization tool that enables integrated exploration of Hi-C interaction data, the transcriptome, and epigenome. This study provides new insights into the relationship between long-range epigenetic and genomic dysregulation and changes in higher-order chromatin interactions in cancer.

Early life events influence whole-of-life metabolic health via gut microflora and gut permeability.

Abstract The capacity of our gut microbial communities to maintain a stable and balanced state, termed ‘resilience’, in spite of perturbations is vital to our achieving and maintaining optimal health. A loss of microbial resilience is observed in a number of diseases including obesity, diabetes and metabolic syndrome. There are large gaps in our understanding of why an individual’s co-evolved microflora consortium fail to develop resilience thereby establishing a trajectory towards poor metabolic health. This review examines the connections between the developing gut microbiota and intestinal barrier function in the neonate, infant and during the first years of life. We propose that the effects of early life events on the gut microflora and permeability, whilst it is in a dynamic and vulnerable state, are fundamental in shaping the microbial consortia’s resilience and that it is the maintenance of resilience that is pivotal for metabolic health throughout life. We review the literature supporting this concept suggesting new potential research directions aimed at developing a greater understanding of the longitudinal effects of the gut microflora on metabolic health and potential interventions to recalibrate the ‘at risk’ infant gut microflora in the direction of enhanced metabolic health.

Cpipe: a shared variant detection pipeline designed for diagnostic settings

The benefits of implementing high throughput sequencing in the clinic are quickly becoming apparent. However, few freely available bioinformatics pipelines have been built from the ground up with clinical genomics in mind. Here we present Cpipe, a pipeline designed specifically for clinical genetic disease diagnostics. Cpipe was developed by the Melbourne Genomics Health Alliance, an Australian initiative to promote common approaches to genomics across healthcare institutions. As such, Cpipe has been designed to provide fast, effective and reproducible analysis, while also being highly flexible and customisable to meet the individual needs of diverse clinical settings. Cpipe is being shared with the clinical sequencing community as an open source project and is available at http://cpipeline.org.

VariantSpark: population scale clustering of genotype information

BackgroundGenomic information is increasingly used in medical practice giving rise to the need for efficient analysis methodology able to cope with thousands of individuals and millions of variants. The widely used Hadoop MapReduce architecture and associated machine learning library, Mahout, provide the means for tackling computationally challenging tasks. However, many genomic analyses do not fit the Map-Reduce paradigm. We therefore utilise the recently developed Spark engine, along with its associated machine learning library, MLlib, which offers more flexibility in the parallelisation of population-scale bioinformatics tasks. The resulting tool, VariantSpark provides an interface from MLlib to the standard variant format (VCF), offers seamless genome-wide sampling of variants and provides a pipeline for visualising results.ResultsTo demonstrate the capabilities of VariantSpark, we clustered more than 3,000 individuals with 80 Million variants each to determine the population structure in the dataset. VariantSpark is 80 % faster than the Spark-based genome clustering approach, adam, the comparable implementation using Hadoop/Mahout, as well as Admixture, a commonly used tool for determining individual ancestries. It is over 90 % faster than traditional implementations using R and Python.ConclusionThe benefits of speed, resource consumption and scalability enables VariantSpark to open up the usage of advanced, efficient machine learning algorithms to genomic data.

Adenosine monophosphate deaminase 3 activation shortens erythrocyte half-life and provides malaria resistance in mice.

The factors that determine red blood cell (RBC) lifespan and the rate of RBC aging have not been fully elucidated. In several genetic conditions, including sickle cell disease, thalassemia, and G6PD deficiency, erythrocyte lifespan is significantly shortened. Many of these diseases are also associated with protection from severe malaria, suggesting a role for accelerated RBC senescence and clearance in malaria resistance. Here, we report a novel, N-ethyl-N-nitrosourea-induced mutation that causes a gain of function in adenosine 5-monophosphate deaminase (AMPD3). Mice carrying the mutation exhibit rapid RBC turnover, with increased erythropoiesis, dramatically shortened RBC lifespan, and signs of increased RBC senescence/eryptosis, suggesting a key role for AMPD3 in determining RBC half-life. Mice were also found to be resistant to infection with the rodent malaria Plasmodium chabaudi. We propose that resistance to P. chabaudi is mediated by increased RBC turnover and higher rates of erythropoiesis during infection.

Mutation analysis of MATR3 in Australian familial amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that arises from the progressive degeneration of the motor neurons. Recently, mutations in the matrin 3 (MATR3) gene were described in both ALS and autosomal dominant distal myopathy with vocal cord and pharyngeal weakness. We sought to determine the prevalence of MATR3 mutations in Australian familial ALS (n = 106) using whole exome sequencing. No mutations were identified, indicating that MATR3 mutations are not a common cause of ALS in Australian familial cases with predominately European ancestry.

Gut permeability, its interaction with gut microflora and effects on metabolic health are mediated by the lymphatics system, liver and bile acid

There is evidence to link obesity (and metabolic syndrome) with alterations in gut permeability and microbiota. The underlying mechanisms have been questioned and have prompted this review. We propose that the gut barrier function is a primary driver in maintaining metabolic health with poor health being linked to gut leakiness. This review will highlight changes in intestinal permeability and how it may change gut microflora and subsequently affect metabolic health by influencing the functioning of major bodily organs/organ systems: the lymphatic system, liver and pancreas. We also discuss the likelihood that metabolic syndrome undergoes a cyclic worsening facilitated by an increase in intestinal permeability leading to gut dysbiosis, culminating in ongoing poor health leading to further exacerbated gut leakiness.

Genomics and personalised whole-of-life healthcare

Genome sequencing has the potential for stratified cancer treatment and improved diagnostics for rare disorders. However, sequencing needs to be utilised in risk stratification on a population scale to deepen the impact on the health system by addressing common diseases, where individual genomic variants have variable penetrance and minor impact. As the accuracy of genomic risk predictors is bounded by heritability, environmental factors such as diet, lifestyle, and microbiome have to be considered. Large-scale, longitudinal research programmes need to study the intrinsic properties between both genetics and environment to unravel their risk contribution. During this discovery process, frameworks need to be established to counteract unrealistic expectations. Sufficient scientific evidence is needed to interpret sources of uncertainty and inform decision making for clinical management and personal health.

Evaluation of computational programs to predict HLA genotypes from genomic sequencing data

Abstract Motivation Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative. Results In this study we evaluate the HLA typing accuracy and efficiency of five computational HLA typing methods by comparing their predictions against a curated set ofu2009>u20091000 published polymerase chain reaction-derived HLA genotypes on three different data sets (whole genome sequencing, whole exome sequencing and transcriptomic sequencing data). The highest accuracy at clinically relevant resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% accuracy by OptiType when limited to Class I genes only. We also observed variability between the tools for resource consumption, with runtime ranging from an average of 5u2009h (HLAminer) to 7u2009min (seq2HLA) and memory from 12.8u2009GB (HLA-VBSeq) to 0.46u2009GB (HLAminer) per sample. While a minimal coverage is required, other factors also determine prediction accuracy and the results between tools do not correlate well. Therefore, by combining tools, there is the potential to develop a highly accurate ensemble method that is able to deliver fast, economical HLA typing from existing sequencing data.