Defining complex rule-based models in space and over time

Computational biology seeks to understand complex spatio-temporal phenomena across multiple levels of structural and functional organisation. However, questions raised in this context are difficult to answer without modelling methodologies that are intuitive and approachable for non-expert users. Stochastic rule-based modelling languages such as Kappa have been the focus of recent attention in developing complex biological models that are nevertheless concise, comprehensible, and easily extensible. We look at further developing Kappa, in terms of how we might define complex models in both the spatial and the temporal axes. In defining complex models in space, we address the assumption that the reaction mixture of a Kappa model is homogeneous and well-mixed. We propose evolutions of the current iteration of Spatial Kappa to streamline the process of defining spatial structures for different modelling purposes. We also verify the existing implementation against established results in diffusion and narrow escape, thus laying the foundations for querying a wider range of spatial systems with greater confidence in the accuracy of the results. In defining complex models over time, we draw attention to how non-modelling specialists might define, verify, and analyse rules throughout a rigorous model development process. We propose structured visual methodologies for developing and maintaining knowledge base data structures, incorporating the information needed to construct a Kappa rule-based model. We further extend these methodologies to deal with biological systems defined by the activity of synthetic genetic parts, with the hope of providing tractable operations that allow multiple users to contribute to their development over time according to their area of expertise. Throughout the thesis we pursue the aim of bridging the divide between information sources such as literature and bioinformatics databases and the abstracting decisions inherent in a model. We consider methodologies for automating the construction of spatial models, providing traceable links from source to model element, and updating a model via an iterative and collaborative development process. By providing frameworks for modellers from multiple domains of expertise to work with the language, we reduce the entry barrier and open the field to further questions and new research

A Bidirectional Collaboration Framework for Bio-Model Development

High-level graph data structures have gained favour in representing biologicalknowledge in a computationally executable form, but the information containedtherein must remain accessible to all users no matter their background. Bidirectionalgraph transformations may be used to synchronise and maintain the consistencyof these graph data structures as they evolve through the process of creatingand refining a bio-model knowledge base. We outline a bidirectional collaborationframework by which users with vastly differing backgrounds may contribute to thedevelopment and evolution of such a knowledge base, and examine a simple example to illustrate its merits. We also identify avenues for further research necessary to refine the framework. No prior biological knowledge is assumed

Single-cell RNA sequencing redefines the mesenchymal cell landscape of mouse endometrium

The endometrium is a dynamic tissue that exhibits remarkable resilience to repeated episodes of differentiation, breakdown, regeneration, and remodeling. Endometrial physiology relies on a complex interplay between the stromal and epithelial compartments with the former containing a mixture of fibroblasts, vascular, and immune cells. There is evidence for rare populations of putative mesenchymal progenitor cells located in the perivascular niche of human endometrium, but the existence of an equivalent cell population in mouse is unclear. We used the Pdgfrb‐BAC‐eGFP transgenic reporter mouse in combination with bulk and single‐cell RNA sequencing to redefine the endometrial mesenchyme. In contrast to previous reports we show that CD146 is expressed in both PDGFRβ + perivascular cells and CD31 + endothelial cells. Bulk RNAseq revealed cells in the perivascular niche which express the high levels of Pdgfrb as well as genes previously identified in pericytes and/or vascular smooth muscle cells (Acta2, Myh11, Olfr78, Cspg4, Rgs4, Rgs5, Kcnj8, and Abcc9). scRNA‐seq identified five subpopulations of cells including closely related pericytes/vascular smooth muscle cells and three subpopulations of fibroblasts. All three fibroblast populations were PDGFRα+/CD34 + but were distinct in their expression of Ngfr/Spon2/Angptl7 (F1), Cxcl14/Smoc2/Rgs2 (F2), and Clec3b/Col14a1/Mmp3 (F3), with potential functions in the regulation of immune responses, response to wounding, and organization of extracellular matrix, respectively. Immunohistochemistry was used to investigate the spatial distribution of these populations revealing F1/NGFR + cells in most abundance beside epithelial cells. We provide the first definitive analysis of mesenchymal cells in the adult mouse endometrium identifying five subpopulations providing a platform for comparisons between mesenchymal cells in endometrium and other adult tissues which are prone to fibrosis

Human and murine fibroblast single cell transcriptomics reveals fibroblast clusters are differentially affected by ageing, and serum cholesterol

Aims Specific fibroblast markers and in-depth heterogeneity analysis are currently lacking, hindering functional studies in cardiovascular diseases (CVDs). Here, we established cell-type markers and heterogeneity in murine and human arteries and studied the adventitial fibroblast response to CVD and its risk factors hypercholesterolaemia and ageing. Methods and results Murine aorta single-cell RNA-sequencing analysis of adventitial mesenchymal cells identified fibroblast-specific markers. Immunohistochemistry and flow cytometry validated platelet-derived growth factor receptor alpha (PDGFRA) and dipeptidase 1 (DPEP1) across human and murine aorta, carotid, and femoral arteries, whereas traditional markers such as the cluster of differentiation (CD)90 and vimentin also marked transgelin+ vascular smooth muscle cells. Next, pseudotime analysis showed multiple fibroblast clusters differentiating along trajectories. Three trajectories, marked by CD55 (Cd55+), Cxcl chemokine 14 (Cxcl14+), and lysyl oxidase (Lox+), were reproduced in an independent RNA-seq dataset. Gene ontology (GO) analysis showed divergent functional profiles of the three trajectories, related to vascular development, antigen presentation, and/or collagen fibril organization, respectively. Trajectory-specific genes included significantly more genes with known genome-wide associations (GWAS) to CVD than expected by chance, implying a role in CVD. Indeed, differential regulation of fibroblast clusters by CVD risk factors was shown in the adventitia of aged C57BL/6J mice, and mildly hypercholesterolaemic LDLR KO mice on chow by flow cytometry. The expansion of collagen-related CXCL14+ and LOX+ fibroblasts in aged and hypercholesterolaemic aortic adventitia, respectively, coincided with increased adventitial collagen. Immunohistochemistry, bulk, and single-cell transcriptomics of human carotid and aorta specimens emphasized translational value as CD55+, CXCL14+ and LOX+ fibroblasts were observed in healthy and atherosclerotic specimens. Also, trajectory-specific gene sets are differentially correlated with human atherosclerotic plaque traits. Conclusion We provide two adventitial fibroblast-specific markers, PDGFRA and DPEP1, and demonstrate fibroblast heterogeneity in health and CVD in humans and mice. Biological relevance is evident from the regulation of fibroblast clusters by age and hypercholesterolaemia in vivo, associations with human atherosclerotic plaque traits, and enrichment of genes with a GWAS for CVD

Plasma Proteomics of Renal Function: A Transethnic Meta-Analysis and Mendelian Randomization Study.

BACKGROUND: Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. METHODS: A cross-sectional study of 993 plasma proteins among 2882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified transethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR. RESULTS: In total, 57 plasma proteins were associated with eGFR, including one novel protein. Of these, 23 were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. CONCLUSIONS: In a discovery-replication setting, we identified 57 proteins transethnically associated with eGFR. The revealed causal relationships are an important stepping stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. This work was also supported by the Biomedical Research Program at Weill Cornell Medicine in Qatar, a program funded by the Qatar Foundation. K.S. is supported by Qatar National Research Fund (QNRF) grant no. NPRPC11-0115-180010. The Nord-Trøndelag Health Study (The HUNT Study) is a collaboration between HUNT Research Centre (Faculty of Medicine, Norwegian University of Science and Technology NTNU), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. The HUNT part of the project re-used protein data that was originally analysed and paid for by Somalogic Inc, CO, USA. Somalogic had no role in the design and conduct of the study; collection of phenotypic data, statistical analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Professor John Danesh is funded by the National Institute for Health Research [Senior Investigator Award]. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. RNA-sequencing experiments and kidney gene expression studies were supported by British Heart Foundation project grants [PG/17/35/33001 and PG/19/16/34270] and Kidney Research UK grants [ RP_017_20180302 and RP_013_20190305] to M.T. The German Diabetes Center is funded by the German Federal Ministry of Health (Berlin, Germany), the Ministry of Culture and Science of the state North Rhine-Westphalia (Düsseldorf, Germany), and grants from the German Federal Ministry of Education and Research (Berlin, Germany) to the German Center for Diabetes Research e.V. (DZD)

Life-Course Genome-wide Association Study Meta-analysis of Total Body BMD and Assessment of Age-Specific Effects.

Bone mineral density (BMD) assessed by DXA is used to evaluate bone health. In children, total body (TB) measurements are commonly used; in older individuals, BMD at the lumbar spine (LS) and femoral neck (FN) is used to diagnose osteoporosis. To date, genetic variants in more than 60 loci have been identified as associated with BMD. To investigate the genetic determinants of TB-BMD variation along the life course and test for age-specific effects, we performed a meta-analysis of 30 genome-wide association studies (GWASs) of TB-BMD including 66,628 individuals overall and divided across five age strata, each spanning 15 years. We identified variants associated with TB-BMD at 80 loci, of which 36 have not been previously identified; overall, they explain approximately 10% of the TB-BMD variance when combining all age groups and influence the risk of fracture. Pathway and enrichment analysis of the association signals showed clustering within gene sets implicated in the regulation of cell growth and SMAD proteins, overexpressed in the musculoskeletal system, and enriched in enhancer and promoter regions. These findings reveal TB-BMD as a relevant trait for genetic studies of osteoporosis, enabling the identification of variants and pathways influencing different bone compartments. Only variants in ESR1 and close proximity to RANKL showed a clear effect dependency on age. This most likely indicates that the majority of genetic variants identified influence BMD early in life and that their effect can be captured throughout the life course