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Abstract Genetic management is a critical component of threatened species conservation. Understanding spatial patterns of genetic diversity is essential for evaluating the resilience of fragmented populations to accelerating anthropogenic threats. Nowhere is this more relevant than on the Australian continent, which is experiencing an ongoing loss of biodiversity that exceeds any other developed nation. Using a proprietary genome complexity reduction‐based method (DArTSeq), we generated a data set of 3239 high quality Single Nucleotide Polymorphisms (SNPs) to investigate spatial patterns and indices of genetic diversity in the koala (Phascolarctos cinereus), a highly specialised folivorous marsupial that is experiencing rapid and widespread population declines across much of its former range. Our findings demonstrate that current management divisions across the state of New South Wales (NSW) do not fully represent the distribution of genetic diversity among extant koala populations, and that care must be taken to ensure that translocation paradigms based on these frameworks do not inadvertently restrict gene flow between populations and regions that were historically interconnected. We also recommend that koala populations should be prioritised for conservation action based on the scale and severity of the threatening processes that they are currently faced with, rather than placing too much emphasis on their perceived value (e.g., as reservoirs of potentially adaptive alleles), as our data indicate that existing genetic variation in koalas is primarily partitioned among individual animals. As such, the extirpation of koalas from any part of their range represents a potentially critical reduction of genetic diversity for this iconic Australian species.

Abstract Background Past studies suggest that there are changes in peripheral blood cell gene expression in response to ischaemic stroke; however, the specific changes which occur during the acute phase are poorly characterised. The current study aimed to identify peripheral blood cell genes specifically associated with the early response to ischaemic stroke using whole blood samples collected from participants diagnosed with ischaemic stroke (n = 29) or stroke mimics (n = 27) following emergency presentation to hospital. Long non-coding RNA (lncRNA), mRNA and micro-RNA (miRNA) abundance was measured by RNA-seq, and the consensusDE package was used to identify genes which were differentially expressed between groups. A sensitivity analysis excluding two participants with metastatic disease was also conducted. Results The mean time from symptom onset to blood collection was 2.6 h. Most strokes were mild (median NIH stroke scale score 2.0). Ten mRNAs (all down-regulated in samples provided by patients experiencing ischaemic stroke) and 30 miRNAs (14 over-expressed and 16 under-expressed in participants with ischaemic stroke) were significantly different between groups in the whole cohort and sensitivity analyses. No significant over-representation of gene ontology categories by the differentially expressed genes was observed. Random forest analysis suggested a panel of differentially expressed genes (ADGRG7 and miRNAs 96, 532, 6766, 6798 and 6804) as potential ischaemic stroke biomarkers, although modelling analyses demonstrated that these genes had poor diagnostic performance. Conclusions This study provides evidence suggesting that the early response to minor ischaemic stroke is predominantly reflected by changes in the expression of miRNAs in peripheral blood cells. Further work in independent cohorts particularly in patients with more severe stroke is needed to validate these findings and investigate their clinical relevance.

Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct de novo patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the in vitro and in vivo applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.

Abstract Background Aedes albopictus is a highly invasive species and an important vector of dengue and chikungunya viruses. Indigenous to Southeast Asia, Ae. albopictus has successfully invaded every inhabited continent, except Antarctica, in the past 80 years. Vector surveillance and control at points of entry (PoE) is the most critical front line of defence against the introduction of Ae. albopictus to new areas. Identifying the pathways by which Ae. albopictus are introduced is the key to implementing effective vector surveillance to rapidly detect introductions and to eliminate them. Methods A literature review was conducted to identify studies and data sources reporting the known and suspected dispersal pathways of human-mediated Ae. albopictus dispersal between 1940–2020. Studies and data sources reporting the first introduction of Ae. albopictus in a new country were selected for data extraction and analyses. Results Between 1940–2020, Ae. albopictus was reported via various dispersal pathways into 86 new countries. Two main dispersal pathways were identified: (1) at global and continental spatial scales, maritime sea transport was the main dispersal pathway for Ae. albopictus into new countries in the middle to late 20th Century, with ships carrying used tyres of particular importance during the 1980s and 1990s, and (2) at continental and national spatial scales, the passive transportation of Ae. albopictus in ground vehicles and to a lesser extent the trade of used tyres and maritime sea transport appear to be the major drivers of Ae. albopictus dispersal into new countries, especially in Europe. Finally, the dispersal pathways for the introduction and spread of Ae. albopictus in numerous countries remains unknown, especially from the 1990s onwards. Conclusions This review identified the main known and suspected dispersal pathways of human-mediated Ae. albopictus dispersal leading to the first introduction of Ae. albopictus into new countries and highlighted gaps in our understanding of Ae. albopictus dispersal pathways. Relevant advances in vector surveillance and genomic tracking techniques are presented and discussed in the context of improving vector surveillance. Graphical Abstract

Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is a chronic, progressive, and growing worldwide health burden associated with mounting morbidity, mortality, and economic costs. Improvements in NTM-PD management are urgently needed, which requires a better understanding of fundamental immunopathology. Here, we examine temporal dynamics of the immune compartment during NTM-PD caused by Mycobacterium avium complex (MAC) and Mycobactereoides abscessus complex (MABS). We show that active MAC infection is characterized by elevated T cell immunoglobulin and mucin-domain containing-3 expression across multiple T cell subsets. In contrast, active MABS infection was characterized by increased expression of cytotoxic T-lymphocyte-associated protein 4. Patients who failed therapy closely mirrored the healthy individual immune phenotype, with circulating immune network appearing to ‘ignore’ infection in the lung. Interestingly, immune biosignatures were identified that could inform disease stage and infecting species with high accuracy. Additionally, programmed cell death protein 1 blockade rescued antigen-specific IFN-γ secretion in all disease stages except persistent infection, suggesting the potential to redeploy checkpoint blockade inhibitors for NTM-PD. Collectively, our results provide new insight into species-specific ‘immune chatter’ occurring during NTM-PD and provide new targets, processes and pathways for diagnostics, prognostics, and treatments needed for this emerging and difficult to treat disease.

Abstract Tuberculosis (TB) is one of the deadliest infectious diseases in the world. The metabolic disease type 2 diabetes (T2D) significantly increases the risk of developing active TB. Effective new TB vaccine candidates and novel therapeutic interventions are required to meet the challenges of global TB eradication. Recent evidence suggests that the microbiota plays a significant role in how the host responds to infection, injury and neoplastic changes. Animal models that closely reflect human physiology are crucial in assessing new treatments and to decipher the underlying immunological defects responsible for increased TB susceptibility in comorbid patients. In this study, using a diet‐induced murine T2D model that reflects the etiopathogenesis of clinical T2D and increased TB susceptibility, we investigated how the intestinal microbiota may impact the development of T2D, and how the gut microbial composition changes following a very low‐dose aerosol infection with Mycobacterium tuberculosis (Mtb). Our data revealed a substantial intestinal microbiota dysbiosis in T2D mice compared to non‐diabetic animals. The observed differences were comparable to previous clinical reports in TB patients, in which it was shown that Mtb infection causes rapid loss of microbial diversity. Furthermore, diversity index and principle component analyses demonstrated distinct clustering of Mtb‐infected non‐diabetic mice vs. Mtb‐infected T2D mice. Our findings support a broad applicability of T2D mice as a tractable small animal model for studying distinct immune parameters, microbiota and the immune‐metabolome of TB/T2D comorbidity. This model may also enable answers to be found to critical outstanding questions about targeted interventions of the gut microbiota and the gut‐lung axis.

Abstract Background Basenjis are considered an ancient dog breed of central African origins that still live and hunt with tribesmen in the African Congo. Nicknamed the barkless dog, Basenjis possess unique phylogeny, geographical origins and traits, making their genome structure of great interest. The increasing number of available canid reference genomes allows us to examine the impact the choice of reference genome makes with regard to reference genome quality and breed relatedness. Results Here, we report two high quality de novo Basenji genome assemblies: a female, China (CanFam_Bas), and a male, Wags. We conduct pairwise comparisons and report structural variations between assembled genomes of three dog breeds: Basenji (CanFam_Bas), Boxer (CanFam3.1) and German Shepherd Dog (GSD) (CanFam_GSD). CanFam_Bas is superior to CanFam3.1 in terms of genome contiguity and comparable overall to the high quality CanFam_GSD assembly. By aligning short read data from 58 representative dog breeds to three reference genomes, we demonstrate how the choice of reference genome significantly impacts both read mapping and variant detection. Conclusions The growing number of high-quality canid reference genomes means the choice of reference genome is an increasingly critical decision in subsequent canid variant analyses. The basal position of the Basenji makes it suitable for variant analysis for targeted applications of specific dog breeds. However, we believe more comprehensive analyses across the entire family of canids is more suited to a pangenome approach. Collectively this work highlights the importance the choice of reference genome makes in all variation studies.

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Background and Aims. The nacht domain, leucine-rich repeat, and pyrin domain-containing protein 3 (NLRP3) inflammasome is upregulated in human abdominal aortic aneurysm (AAA), but its pathogenic role is unclear. The aims of this study were firstly to examine whether the inflammasome was upregulated in a mouse model of AAA and secondly to test whether the inflammasome inhibitor colchicine limited AAA growth. Methods. AAA was induced in eight-week-old male C57BL6/J mice with topical application of elastase to the infrarenal aorta and oral 3-aminopropionitrile (E-BAPN). For aim one, inflammasome activation, abdominal aortic diameter, and rupture were compared between mice with AAA and sham controls. For aim two, 3 weeks after AAA induction, mice were randomly allocated to receive colchicine (n=28, 0.2 mg/kg/d) or vehicle control (n=29). The primary outcome was the rate of maximum aortic diameter increase measured by ultrasound over 13 weeks. Results. There was upregulation of NLRP3 markers interleukin- (IL-) 1β (median, IQR; 15.67, 7.11-22.60 pg/mg protein versus 6.87, 4.54-11.60 pg/mg protein, p=.048) and caspase-1 (109, 83-155 relative luminosity units (RLU) versus 45, 38-65 RLU, p

Background Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools—these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. Methods Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. Results As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as ‘benign’. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as ‘benign’. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. Conclusion In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences. While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences yet analysis workflows remain immature relative to other field such as DNASeq and RNASeq analysis pipelines. While software for detailing the composition of microbial communities using 16S rRNA marker genes is constantly improving, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs. Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics. MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

Abstract Background The targeted application of cutting-edge high-throughput molecular data technologies provides an enormous opportunity to address key health, economic and environmental issues in the tropics within the One Health framework. The Earth’s tropical regions are projected to contain > 50% of the world’s population by 2050 coupled with 80% of its biodiversity however these regions are relatively less developed economically, with agricultural productivity substantially lower than temperate zones, a large percentage of its population having limited health care options and much of its biodiversity understudied and undescribed. The generation of high-throughput molecular data and bespoke bioinformatics capability to address these unique challenges offers an enormous opportunity for people living in the tropics. Main In this review we discuss in depth solutions to challenges to populations living in tropical zones across three critical One Health areas: human health, biodiversity and food production. This review will examine how some of the challenges in the tropics can be addressed through the targeted application of advanced omics and bioinformatics and will discuss how local populations can embrace these technologies through strategic outreach and education ensuring the benefits of the One Health approach is fully realised through local engagement. Conclusion Within the context of the One Health framework, we will demonstrate how genomic technologies can be utilised to improve the overall quality of life for half the world’s population.

Summary: Background: Sensitive diagnostics are needed for effective management and surveillance of schistosomiasis so that current transmission interruption goals set by WHO can be achieved. We aimed to screen the Schistosoma haematobium secretome to find antibody biomarkers of schistosome infection, validate their diagnostic performance in samples from endemic populations, and evaluate their utility as point of care immunochromatographic tests (POC-ICTs) to diagnose urogenital schistosomiasis in the field. Methods: We did a biomarker identification study, in which we constructed a proteome array containing 992 validated and predicted proteins from S haematobium and screened it with serum and urine antibodies from endemic populations in Gabon, Tanzania, and Zimbabwe. Arrayed antigens that were IgG-reactive and a select group of antigens from the worm extracellular vesicle proteome, predicted to be diagnostically informative, were then evaluated by ELISA using the same samples used to probe arrays, and samples from individuals residing in a low-endemicity setting (ie, Pemba and Unguja islands, Zanzibar, Tanzania). The two most sensitive and specific antigens were incorporated into POC-ICTs to assess their ability to diagnose S haematobium infection from serum in a field-deployable format. Findings: From array probing, in individuals who were infected, 208 antigens were the targets of significantly elevated IgG responses in serum and 45 antigens were the targets of significantly elevated IgG responses in urine. Of the five proteins that were validated by ELISA, Sh-TSP-2 (area under the curve [AUC]serum=0·98 [95% CI 0·95–1·00]; AUCurine=0·96 [0·93–0·99]), and MS3_01370 (AUCserum=0·93 [0·89–0·97]; AUCurine=0·81 [0·72–0·89]) displayed the highest overall diagnostic performance in each biofluid and exceeded that of S haematobium-soluble egg antigen in urine (AUC=0·79 [0·69–0·90]). When incorporated into separate POC-ICTs, Sh-TSP-2 showed absolute specificity and a sensitivity of 75% and MS3_01370 showed absolute specificity and a sensitivity of 89%. Interpretation: We identified numerous biomarkers of urogenital schistosomiasis that could form the basis of novel antibody diagnostics for this disease. Two of these antigens, Sh-TSP-2 and MS3_01370, could be used as sensitive, specific, and field-deployable diagnostics to support schistosomiasis control and elimination initiatives, with particular focus on post-elimination surveillance. Funding: Australian Trade and Investment Commission and Merck Global Health Institute.