Unraveling the evolutionary origin of the complex Nuclear Receptor Element (cNRE), a cis-regulatory module required for preferential expression in the atrial chamber.

Cardiac function requires appropriate proteins in each chamber. Atria requires slow myosin to act as reservoirs, while ventricles demand fast myosin for swift pumping. Myosins are thus under chamber-biased cis-regulation, with myosin gene expression imbalances leading to congenital heart dysfunction. To identify regulatory inputs leading to cardiac chamber-biased expression, we computationally and molecularly dissected the quail Slow Myosin Heavy Chain III (SMyHC III) promoter that drives preferential expression to the atria. We show that SMyHC III gene states are orchestrated by a complex Nuclear Receptor Element (cNRE) of 32 base pairs. Using transgenesis in zebrafish and mice, we demonstrate that preferential atrial expression is achieved by a combinatorial regulatory input composed of atrial activation motifs and ventricular repression motifs. Using comparative genomics, we show that the cNRE might have emerged from an endogenous viral element through infection of an ancestral host germline, revealing an evolutionary pathway to cardiac chamber-specific expression.

Parallel use of human stem cell lung and heart models provide insights for SARS-CoV-2 treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe coronavirus disease 2019 (COVID-19). To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR-Cas9-mediated knockout of ACE2, we demonstrated that angiotensin-converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but that further processing in lung cells required TMPRSS2, while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems.

Parallel use of pluripotent human stem cell lung and heart models provide new insights for treatment of SARS-CoV-2.

SARS-CoV-2 primarily infects the respiratory tract, but pulmonary and cardiac complications occur in severe COVID-19. To elucidate molecular mechanisms in the lung and heart, we conducted paired experiments in human stem cell-derived lung alveolar type II (AT2) epithelial cell and cardiac cultures infected with SARS-CoV-2. With CRISPR- Cas9 mediated knock-out of ACE2, we demonstrated that angiotensin converting enzyme 2 (ACE2) was essential for SARS-CoV-2 infection of both cell types but further processing in lung cells required TMPRSS2 while cardiac cells required the endosomal pathway. Host responses were significantly different; transcriptome profiling and phosphoproteomics responses depended strongly on the cell type. We identified several antiviral compounds with distinct antiviral and toxicity profiles in lung AT2 and cardiac cells, highlighting the importance of using several relevant cell types for evaluation of antiviral drugs. Our data provide new insights into rational drug combinations for effective treatment of a virus that affects multiple organ systems. One-sentence summary: Rational treatment strategies for SARS-CoV-2 derived from human PSC models.

Human pluripotent stem cell-derived macrophages host Mycobacterium abscessus infection.

Human macrophages are a natural host of many mycobacterium species, including Mycobacterium abscessus (M. abscessus), an emerging pathogen affecting immunocompromised and cystic fibrosis patients with few available treatments. The search for an effective treatment is hindered by the lack of a tractable in vitro intracellular infection model. Here, we established a reliable model for M. abscessus infection using human pluripotent stem cell-derived macrophages (hPSC-macrophages). hPSC differentiation permitted reproducible generation of functional macrophages that were highly susceptible to M. abscessus infection. Electron microscopy demonstrated that M. abscessus was present in the hPSC-macrophage vacuoles. RNA sequencing analysis revealed a time-dependent host cell response, with differing gene and protein expression patterns post-infection. Engineered tdTOMATO-expressing hPSC-macrophages with GFP-expressing mycobacteria enabled rapid image-based high-throughput analysis of intracellular infection and quantitative assessment of antibiotic efficacy. Our study describes the first to our knowledge hPSC-based model for M. abscessus infection, representing a novel and accessible system for studying pathogen-host interaction and drug discovery.

CD90 Marks a Mesenchymal Program in Human Thymic Epithelial Cells In Vitro and In Vivo.

Thymic epithelium is critical for the structural integrity of the thymus and for T cell development. Within the fully formed thymus, large numbers of hematopoietic cells shape the thymic epithelium into a scaffold-like structure which bears little similarity to classical epithelial layers, such as those observed in the skin, intestine or pancreas. Here, we show that human thymic epithelial cells (TECs) possess an epithelial identity that also incorporates the expression of mesenchymal cell associated genes, whose expression levels vary between medullary and cortical TECs (m/cTECs). Using pluripotent stem cell (PSC) differentiation systems, we identified a unique population of cells that co-expressed the master TEC transcription factor FOXN1, as well as the epithelial associated marker EPCAM and the mesenchymal associated gene CD90. Using the same serum free culture conditions, we also observed co-expression of EPCAM and CD90 on cultured TECs derived from neonatal human thymus in vitro. Single cell RNA-sequencing revealed these cultured TECs possessed an immature mTEC phenotype and expressed epithelial and mesenchymal associated genes, such as EPCAM, CLDN4, CD90 and COL1A1. Importantly, flow cytometry and single cell RNA-sequencing analysis further confirmed the presence of an EPCAM+CD90+ population in the CD45- fraction of neonatal human thymic stromal cells in vivo. Using the human thymus cell atlas, we found that cTECs displayed more pronounced mesenchymal characteristics than mTECs during embryonic development. Collectively, these results suggest human TECs possess a hybrid gene expression program comprising both epithelial and mesenchymal elements, and provide a basis for the further exploration of thymus development from primary tissues and from the in vitro differentiation of PSCs.

Adult mouse fibroblasts retain organ-specific transcriptomic identity.

Organ fibroblasts are essential components of homeostatic and diseased tissues. They participate in sculpting the extracellular matrix, sensing the microenvironment, and communicating with other resident cells. Recent studies have revealed transcriptomic heterogeneity among fibroblasts within and between organs. To dissect the basis of interorgan heterogeneity, we compare the gene expression of murine fibroblasts from different tissues (tail, skin, lung, liver, heart, kidney, and gonads) and show that they display distinct positional and organ-specific transcriptome signatures that reflect their embryonic origins. We demonstrate that expression of genes typically attributed to the surrounding parenchyma by fibroblasts is established in embryonic development and largely maintained in culture, bioengineered tissues and ectopic transplants. Targeted knockdown of key organ-specific transcription factors affects fibroblast functions, in particular genes involved in the modulation of fibrosis and inflammation. In conclusion, our data reveal that adult fibroblasts maintain an embryonic gene expression signature inherited from their organ of origin, thereby increasing our understanding of adult fibroblast heterogeneity. The knowledge of this tissue-specific gene signature may assist in targeting fibrotic diseases in a more precise, organ-specific manner.

MonaGO: a novel gene ontology enrichment analysis visualisation system.

BACKGROUND: Gene ontology (GO) enrichment analysis is frequently undertaken during exploration of various -omics data sets. Despite the wide array of tools available to biologists to perform this analysis, meaningful visualisation of the overrepresented GO in a manner which is easy to interpret is still lacking. RESULTS: Monash Gene Ontology (MonaGO) is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing GO enrichment analysis and visualising the results. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is a unique platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. CONCLUSION: MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results.

Spatially resolved transcriptomics in immersive environments.

Spatially resolved transcriptomics is an emerging class of high-throughput technologies that enable biologists to systematically investigate the expression of genes along with spatial information. Upon data acquisition, one major hurdle is the subsequent interpretation and visualization of the datasets acquired. To address this challenge, VR-Cardiomics is presented, which is a novel data visualization system with interactive functionalities designed to help biologists interpret spatially resolved transcriptomic datasets. By implementing the system in two separate immersive environments, fish tank virtual reality (FTVR) and head-mounted display virtual reality (HMD-VR), biologists can interact with the data in novel ways not previously possible, such as visually exploring the gene expression patterns of an organ, and comparing genes based on their 3D expression profiles. Further, a biologist-driven use-case is presented, in which immersive environments facilitate biologists to explore and compare the heart expression profiles of different genes.

3D-cardiomics: A spatial transcriptional atlas of the mammalian heart.

Understanding the spatial gene expression and regulation in the heart is key to uncovering its developmental and physiological processes, during homeostasis and disease. Numerous techniques exist to gain gene expression and regulation information in organs such as the heart, but few utilize intuitive true-to-life three-dimensional representations to analyze and visualise results. Here we combined transcriptomics with 3D-modelling to interrogate spatial gene expression in the mammalian heart. For this, we microdissected and sequenced transcriptome-wide 18 anatomical sections of the adult mouse heart. Our study has unveiled known and novel genes that display complex spatial expression in the heart sub-compartments. We have also created 3D-cardiomics, an interface for spatial transcriptome analysis and visualization that allows the easy exploration of these data in a 3D model of the heart. 3D-cardiomics is accessible from http://3d-cardiomics.erc.monash.edu/.

A cis-regulatory-directed pipeline for the identification of genes involved in cardiac development and disease.

BACKGROUND: Congenital heart diseases are the major cause of death in newborns, but the genetic etiology of this developmental disorder is not fully known. The conventional approach to identify the disease-causing genes focuses on screening genes that display heart-specific expression during development. However, this approach would have discounted genes that are expressed widely in other tissues but may play critical roles in heart development. RESULTS: We report an efficient pipeline of genome-wide gene discovery based on the identification of a cardiac-specific cis-regulatory element signature that points to candidate genes involved in heart development and congenital heart disease. With this pipeline, we retrieve 76% of the known cardiac developmental genes and predict 35 novel genes that previously had no known connectivity to heart development. Functional validation of these novel cardiac genes by RNAi-mediated knockdown of the conserved orthologs in Drosophila cardiac tissue reveals that disrupting the activity of 71% of these genes leads to adult mortality. Among these genes, RpL14, RpS24, and Rpn8 are associated with heart phenotypes. CONCLUSIONS: Our pipeline has enabled the discovery of novel genes with roles in heart development. This workflow, which relies on screening for non-coding cis-regulatory signatures, is amenable for identifying developmental and disease genes for an organ without constraining to genes that are expressed exclusively in the organ of interest.

GWAS of stool frequency provides insights into gastrointestinal motility and irritable bowel syndrome.

Gut dysmotility is associated with constipation, diarrhea, and functional gastrointestinal disorders like irritable bowel syndrome (IBS), although its molecular underpinnings are poorly characterized. We studied stool frequency (defined by the number of bowel movements per day, based on questionnaire data) as a proxy for gut motility in a GWAS meta-analysis including 167,875 individuals from UK Biobank and four smaller population-based cohorts. We identify 14 loci associated with stool frequency (p ≤ 5.0 × 10-8). Gene set and pathway analyses detected enrichment for genes involved in neurotransmitter/neuropeptide signaling and preferentially expressed in enteric motor neurons controlling peristalsis. PheWAS identified pleiotropic associations with dysmotility syndromes and the response to their pharmacological treatment. The genetic architecture of stool frequency correlates with that of IBS, and UK Biobank participants from the top 1% of stool frequency polygenic score distribution were associated with 5× higher risk of IBS with diarrhea. These findings pave the way for the identification of actionable pathological mechanisms in IBS and the dysmotility syndromes.

Disease course following High Disease Activity Status revealed patterns in SLE.

BACKGROUND: We sought to examine the disease course of High Disease Activity Status (HDAS) patients and their different disease patterns in a real-world longitudinal cohort. Disease resolution till Lupus Low Disease Activity State (LLDAS) has been a general treatment goal, but there is limited information on this subset of patients who achieve this. METHODS: All consenting patients of the Monash Lupus Cohort who had at least 12 months of observation were included. HDAS was defined as SLEDAI-2K ≥ 10 ever, and HDAS episode as the period from the first HDAS clinic visit until attainment of LLDAS. We examined the associations of different HDAS patterns with the likelihood of damage accrual. RESULTS: Of 342 SLE patients, 151 experienced HDAS at least once, accounting for 298 HDAS episodes. The majority of HDAS patients (76.2%) experienced Recurrent HDAS (> 1 HDAS visit), and a smaller subset (47.7%) had Persistent HDAS (consecutive HDAS visits for longer than 2 months). Recurrent or Persistent HDAS patients were younger at diagnosis and more likely to experience renal or serositis manifestations; persistent HDAS patients were also more likely to experience neurological manifestations. Baseline SLEDAI greater than 10 was associated with longer HDAS episodes. Recurrent and Persistent HDAS were both associated with an increased likelihood of damage accrual. The total duration of HDAS episode greater than 2 years and experiencing multiple HDAS episodes (≥4) was also associated with an increased likelihood of damage accrual (OR 1.80, 95% CI 1.08-2.97, p = 0.02, and OR 3.31, 95% CI 1.66-13.26, p = 0.01, respectively). CONCLUSION: HDAS episodes have a highly variable course. Recurrent and Persistent HDAS, and longer duration of HDAS episodes, increased the risk of damage accrual. In addition to a major signifier of severity in SLE, its resolution to LLDAS can determine the subsequent outcome in SLE patients.

Towards spatio-temporally resolved developmental cardiac gene regulatory networks in zebrafish.

Heart formation in the zebrafish involves a rapid, complex series of morphogenetic events in three-dimensional space that spans cardiac lineage specification through to chamber formation and maturation. This process is tightly orchestrated by a cardiac gene regulatory network (GRN), which ensures the precise spatio-temporal deployment of genes critical for heart formation. Alterations of the timing or spatial localisation of gene expression can have a significant impact in cardiac ontogeny and may lead to heart malformations. Hence, a better understanding of the cellular and molecular basis of congenital heart disease relies on understanding the behaviour of cardiac GRNs with precise spatiotemporal resolution. Here, we review the recent technical advances that have expanded our capacity to interrogate the cardiac GRN in zebrafish. In particular, we focus on studies utilising high-throughput technologies to systematically dissect gene expression patterns, both temporally and spatially during heart development.

Algorithm for calculating high disease activity in SLE.

BACKGROUND: The ability to identify lupus patients in high disease activity status (HDAS) without knowledge of the SLEDAI could have application in selection of patients for treatment escalation or enrolment in trials. We sought to generate an algorithm that could calculate via model fitting the presence of HDAS using simple demographic and laboratory values. METHODS: We examined the association of high disease activity (HDA) with demographic and laboratory parameters using prospectively collected data. An HDA visit is recorded when SLEDAI-2K ≥10. We utilized the use of combinatorial search to find algorithms to build a mathematical model predictive of HDA. Performance of each algorithm was evaluated using multi-class area under the receiver operating characteristic curve and the final model was compared with the naïve Bayes classifier, and analysed using the confusion matrix for accuracy and misclassification rate. RESULTS: Data on 286 patients, followed for a median of 5.1 years were studied for a total of 5680 visits. Sixteen laboratory parameters were found to be significantly associated with HDA. A total of 216 algorithms were evaluated and the final algorithm chosen was based on seven pathology measures and three demographic variables. It has an accuracy of 88.6% and misclassification rate of 11.4%. When compared with the naïve Bayes classifier [area under the curve (AUC) = 0.663], our algorithm has a better accuracy with AUC = 0.829. CONCLUSION: This study shows that building an accurate model to calculate HDA using routinely available clinical parameters is feasible. Future studies to independently validate the algorithm will be needed to confirm its predictive performance.

Glucocorticoid gene signatures in systemic lupus erythematosus and the effects of type I interferon: a cross-sectional and in-vitro study.

BACKGROUND: Glucocorticoids, used as a therapy in systemic lupus erythematosus (SLE), interact with the cytoplasmic glucocorticoid receptor to modulate gene transcription. Minimising the use of glucocorticoids is a goal in SLE; however, pharmacological measures to support clinical guidelines are scarce. We evaluated glucocorticoid-regulated genes for their potential use as biomarkers of glucocorticoid exposure in SLE. We examined interactions between changes in gene expression that are induced by glucocorticoids and type I interferon. METHODS: Genes regulated by glucocorticoids and type I interferon were analysed in relation to glucocorticoid exposure in adult patients meeting the American College of Rheumatology criteria for SLE from three cross-sectional cohorts: a local cohort from a tertiary hospital in Melbourne, VIC, Australia, and two public datasets (GSE49454, Hospital de la Conception, Marseille, France, and GSE88884, patients enrolled in a large, multicentre clinical trial). RNA sequencing was done using RNA from healthy donor leucocytes treated with the glucocorticoid dexamethasone, or type I interferon, or both. FINDINGS: Glucocorticoid-regulated genes were analysed in a local SLE cohort (n=18) and public dataset GSE49454 (n=62). Five genes correlated with glucocorticoid dose in both cohorts and were combined to make a glucocorticoid gene signature. Validity of the glucocorticoid gene signature was tested in the public dataset GSE88884 (n=1756). A dose-dependent association was observed with glucocorticoid dose (p<0·0001), and the glucocorticoid gene signature had moderate ability to identify patients taking high-dose glucocorticoid (area under the curve [AUC]=0·77) although was less discriminatory when including all doses (AUC=0·69). We saw no effect of glucocorticoid dose on type I interferon -regulated gene expression. Patients with a high type I interferon gene signature had reduced glucocorticoid gene signature expression compared with patients with a low type I interferon gene signature matched for glucocorticoid dose, suggesting type I interferon inhibits glucocorticoid-stimulated gene expression. In RNA sequencing experiments, type I interferon impaired the expression of glucocorticoid-induced genes, whereas dexamethasone had minimal effect on the expression of type I interferon-stimulated genes. We identified genes regulated by dexamethasone but not affected by type I interferon; combined signatures using these genes also showed moderate ability to distinguish patients taking glucocorticoids. INTERPRETATION: A gene signature for glucocorticoid exposure was identified, but the substantial effect of type I interferon on glucocorticoid-induced genes might limit its application in SLE. These data confirm the insensitivity of type I interferon-regulated genes to glucocorticoids, and together support the concept that type I interferon has a role in glucocorticoid resistance in SLE. FUNDING: Lupus Research Alliance and Australian National Health and Medical Research Council.

From whole-mount to single-cell spatial assessment of gene expression in 3D.

Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.

High disease activity status suggests more severe disease and damage accrual in systemic lupus erythematosus.

OBJECTIVE: Disease severity in SLE is an important concept related to disease activity, treatment burden and prognosis. We set out to evaluate if high disease activity status (HDAS), based on ever attainment of a Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) disease activity score of ≥10, is an indicator for disease severity in SLE. METHODS: Using prospectively collected data, we assessed the association of HDAS with sociodemographic and disease characteristics and adverse clinical outcomes using logistic regression or generalised estimating equations. RESULTS: Of 286 patients with SLE, who were observed for a median (range) of 5.1 years (1-10.8 years), 43.7% experienced HDAS at least once during the observational period. Autoantibody positivity, particularly anti-dsDNA and anti-Sm positivity, were associated with increased likelihood of HDAS. Age ≥45 years at diagnosis was associated with reduced likelihood of HDAS (p=0.002). Patients with HDAS had higher Physician Global Assessment score (>1: OR 8.1, p<0.001) and were more likely to meet criteria for flare (mild/moderate flare: OR 4.4, p<0.001; severe flare: OR 17.2, p<0.001) at the time of experiencing HDAS. They were also more likely to have overall higher disease activity, as defined by time-adjusted mean SLEDAI-2K score in the highest quartile (OR 11.7, 95% CI 5.1 to 26.6; p>0.001), higher corticosteroid exposure (corticosteroid dose in highest quartile: OR 7.7, 95% CI 3.9 to 15.3; p<0.001) and damage accrual (OR 2.3, 95% CI 1.3 to 3.9; p=0.003) when compared with non-HDAS patients. CONCLUSIONS: HDAS is associated with more severe disease, as measured by higher disease activity across time, corticosteroid exposure and damage accrual. The occurrence of HDAS may be a useful prognostic marker in the management of SLE.

Utility of repeated antinuclear antibody tests: a retrospective database study.

BACKGROUND: Anti-nuclear antibody (ANA) testing is frequently used as a diagnostic or screening test in patients with inflammatory or musculoskeletal symptoms. The value of repeat testing is unclear. We sought to evaluate the frequency, utility, and cost of repeat ANA testing. The main objective was to assess the positive predictive value of a repeat ANA test for the diagnosis of rheumatological conditions associated with ANA. METHODS: In this retrospective cohort study, we analysed data from a single, multisite tertiary health network in Australia across a 7-year period. ANA and other autoimmune test results were obtained from the hospital pathology system with a positive ANA titre cutoff set at 1:160. Clinical information was sourced from clinical information systems on any patient who had a change in ANA result from negative to positive on repeat testing. The cost of repeated ANA testing was calculated using the Australian Government Medicare Benefits Schedule. FINDINGS: From March 19, 2011, to July 23, 2018, a total of 36 715 ANA tests were done in 28 840 patients at a total cost of US$675 029 (2018 equivalent). 14 058 (38·3%) of these ANA tests were positive. 7875 (21·4%) of the ordered tests were repeats in 4887 (16·9%) of the patients, among whom 2683 (54·9%) had initially negative tests, and 2204 (45·1%) had initially positive tests. 511 (19·0%) of the 2683 patients with initially negative tests had a positive result on at least one repeat test, with a median time to first positive result of 1·74 years (IQR 0·54-3·60). A change from negative to positive ANA was associated with a new diagnosis in only five (1·1%) of the 451 patients with clinical information available and no previous diagnosis of an ANA-associated rheumatological condition, yielding a positive predictive value of 1·1% (95% CI 0·4-2·7). INTERPRETATION: Repeat ANA testing after a negative result has low utility and results in high cost. FUNDING: Monash Health.

Novel Methods of Incorporating Time in Longitudinal Multivariate Analysis Reveals Hidden Associations With Disease Activity in Systemic Lupus Erythematosus.

Objective: Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. SLE is characterized by high inter-patient variability, including fluctuations over time, a factor which most biomarker studies omit from consideration. We investigated relationships between disease activity and biomarker expression in SLE, using novel methods to control for time-dependent variability, in a proof-of-concept study to evaluate whether doing so revealed additional information. Methods: We measured 4 serum biomarkers (MIF, CCL2, CCL19, and CXCL10) and 13 routine clinical laboratory parameters, alongside disease activity measured by the SLE disease activity index-2k (SLEDAI-2k), collected longitudinally. We analyzed these data with unsupervised learning methods via ensemble clustering, incorporating temporal relationships using dynamic time warping for distance metric calculation. Results: Data from 843 visits in 110 patients (median age 47, 83% female) demonstrated highly heterogeneous time-dependent relationships between disease activity and biomarkers. Unbiased magnitude-based hierarchical clustering of biomarker expression levels isolated a patient subset (n = 9) with distinctively heterogeneous expression of the 17 biological parameters, and who had MIF, CCL2, CCL19, and CXCL10 levels that were higher and more strongly associated with disease activity, based on leave-one-out cross-validated regression analysis. In the remaining subgroup, a time-dependent regression model revealed significantly stronger predictive power of biomarkers for disease activity, compared to a time-agnostic regression model. Despite no significant difference in simple magnitude, using dynamic time warping analysis to align longitudinal profiles revealed a large subset (n = 69) with significantly stronger associations between biological parameters and disease activity. This subgroup had significantly lower flare rates, disease activity and damage scores, suggesting this clustering is clinically meaningful. Conclusions: These results suggest associations between biological parameters and disease activity in SLE exist in a multi-dimensional time-dependent pattern, with implications for the analysis of biomarkers in SLE often used to identify therapeutic targets. Novel methods to analyse high-dimensional data and control for time-dependent variability may have broad utility in the study complex relationships between clinical and biological parameters.

Fly with the flock: immersive solutions for animal movement visualization and analytics.

Understanding the movement of animals is important for a wide range of scientific interests including migration, disease spread, collective movement behaviour and analysing motion in relation to dynamic changes of the environment such as wind and thermal lifts. Particularly, the three-dimensional (3D) spatial-temporal nature of bird movement data, which is widely available with high temporal and spatial resolution at large volumes, presents a natural option to explore the potential of immersive analytics (IA). We investigate the requirements and benefits of a wide range of immersive environments for explorative visualization and analytics of 3D movement data, in particular regarding design considerations for such 3D immersive environments, and present prototypes for IA solutions. Tailored to biologists studying bird movement data, the immersive solutions enable geo-locational time-series data to be investigated interactively, thus enabling experts to visually explore interesting angles of a flock and its behaviour in the context of the environment. The 3D virtual world presents the audience with engaging and interactive content, allowing users to 'fly with the flock', with the potential to ascertain an intuitive overview of often complex datasets, and to provide the opportunity thereby to formulate and at least qualitatively assess hypotheses. This work also contributes to ongoing research efforts to promote better understanding of bird migration and the associated environmental factors at the global scale, thereby providing a visual vehicle for driving public awareness of environmental issues and bird migration patterns.