Biology subjects

Zuliani, P.

Publications and source records attributed to Zuliani, P..

7 recordsLinked to original sources

bioRxiv2025

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

BackgroundDespite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. MethodsWe performed comprehensive RNA-sequencing of skin and blood (n=718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy. ResultsUsing two complementary methods for dimensionality reduction, we defined distinct but interconnected co-expression modules and factors within skin and blood that were significantly associated with disease phenotypes and disease severity, as measured by Psoriasis Area Severity Index (PASI). We identified a 14-gene signature negatively associated with BMI in nonlesional skin and disease severity in lesional skin, respectively. Genotype integration revealed that HLA-DQA1*01 and HLA-DRB1*15 genotypes were positively associated with baseline disease severity. Using Gaussian process regression followed by SHAP (SHapley Additive exPlanations), we defined two core drug independent and disease severity-associated gene modules in lesional skin - one positive, one negative - and a lesional 9-gene signature predictive of disease severity. Disease severity signatures in blood were only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status was independent of disease severity or drug. ConclusionsThese findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity. Plain Language SummaryPsoriasis is a common and debilitating skin disease, linked to multiple other inflammatory conditions. A lot is known about the mechanism of psoriasis and its inherited and external influences. Despite this, doctors cannot yet offer personalised treatments as it has been difficult to discover whether biological pathways are associated with disease severity, response to treatment or a persons likelihood of having other linked diseases. To help address this, we collected skin and blood samples and the personal characteristics of a group of people with severe psoriasis across the United Kingdom. Using computer-based methods, we discovered common biological processes underlying different psoriasis types, including genes that connect psoriasis severity with obesity, and another set of genes that help predict disease severity.

systems biology↗

bioRxiv2023

Computational Modelling of Immune Interaction and Epidermal Homeostasis in Psoriasis

Psoriasis is an incurable chronic inflammatory skin disease characterised by immune cytokine-stimulated epidermal hyperproliferation. This results in the skin becoming red with scaly plaques that can appear anywhere on the body, decreasing the quality of life for patients. Previous modelling studies of psoriasis have been limited to 2D models and lacked cell-cell interactions. We have developed a 3D agent-based model of epidermal cell dynamics to gain insights into how immune cytokine stimuli induces hyperproliferation in psoriasis to better understand disease formation and structural changes. Three main keratinocytes, stem, transit-amplifying (TA), differentiated and T cells, are modelled with proliferation and division governed by various nutrients and immune cytokines. Each cell has a set of attributes (growth rate, division probability, position, etc) whose values are governed by processes such as monod-based cellular growth model, probability-based division based on calcium and cytokine concentration and various forces to form the epidermal layers. The model has 2 steady states, healthy (non-lesional) and psoriatic (lesional) skin. Transition from healthy to psoriatic state is triggered by a temporary cytokine stimulus which causes hyperproliferation to occur, a hallmark of psoriasis. This results in the deepening of rete ridges and thickening of the epidermal structure. Model outputs has been validated against population ratios of stem, TA, differentiated, and T cells, cell cycle and turnover times in vivo. The model simulates the structural properties of epidermis, including layer stratification, formation of wave-like rete ridges, change in epidermal height and length of rete ridges from healthy to psoriasis. This has provided some insights on the complex spatio-temporal changes when transitioning between the 2 steady states and how a shot of temporary cytokine stimulus can induce different severity of psoriasis and alters proliferation between healthy and psoriatic skin in line with known literature. This provides the basis to study different cytokine simulation variations of psoriasis development and tracking of cell proliferation in the lab. It also provides a baseline to model the effects of psoriasis treatments such as narrowband-ultraviolet B (NB-UVB) or biologics and predict potential treatment outcomes for patients.

physiology↗

bioRxiv2022

Predicting partner fitness based on spatial structuring in a light-driven microbial community

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology (e.g., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function. Author SummaryMicrobial communities, play important, yet poorly understood roles in health and agriculture. As we develop a better understanding of how these communities interact together, as well as with their host organisms, there is growing interest in engineering communities with specific functions, such as for treating disease, personalized probiotics, or aiding plants with nutrient uptake. To better understand how these microbes interact with each other, we want to monitor the exchange of metabolites and the locations of the microbes, tasks which at present are technically challenging, if not impossible. Where experimental approaches for monitoring metabolites are limited, computational tools can enable greater access to the fate of both chemicals and microbes within a community. In this study, we developed a computerized model of a synthetic microbial community of two bacteria, one which performs photosynthesis and supplies sugar and another which consumes the sugar for growth. We showed that the relative level of sugar secretion regulates not only the steady-state support for the consumer partners growth, but also how the community changes with time. To determine the importance of spatial organization within the community, we fit a model and used it to predict colony growth. We anticipate that the synergy between experimental and computational approaches will improve our ability to design microbial communities with new functions.

bioengineering↗

bioRxiv2021

Investigating the growth of an engineered strain of Cyanobacteria with an Agent-Based Model and a Recurrent Neural Network

A computational framework combining Agent-Based Models (ABMs) and Deep Learning techniques was developed to help design microbial communities that convert light and CO2 into useful bioproducts. An ABM that accounts for CO2, light, sucrose export rate and cell-to-cell mechanical interactions was used to investigate the growth of an engineered sucrose-exporting strain of Synechococcus elongatus PCC 7942. The ABM simulations produced population curves and synthetic images of colony growth. The curves and the images were analyzed, and growth was correlated to nutrients availability and colonies initial spatial distribution. To speed up the ABM simulations, a metamodel based on a Recurrent Neural Network, RNN, was trained on the synthetic images of growth. This metamodel successfully reproduced the population curves and the images of growth at a lower computational cost. The computational framework presented here paves the road towards designing microbial communities containing sucrose-exporting Synechococcus elongatus PCC 7942 by exploring the solution space in silico first.

microbiology↗

bioRxiv2021

Individualised computational modelling of immune mediated disease onset, flare and clearance in psoriasis

Despite increased understanding about psoriasis pathophysiology, currently there is a lack of predictive computational models. We developed a personalisable ordinary differential equations model of human epidermis and psoriasis that incorporates immune cells and cytokine stimuli to regulate the transition between two stable steady states of clinically healthy (non-lesional) and disease (lesional psoriasis, plaque) skin. In line with experimental data, an immune stimulus initiated transition from healthy skin to psoriasis and apoptosis of immune and epidermal cells induced by UVB phototherapy returned the epidermis back to the healthy state. Notably, our model was able to distinguish disease flares. The flexibility of our model permitted the development of a patient-specific "UVB sensitivity" parameter that reflected subject-specific sensitivity to apoptosis and enabled simulation of individual patients clinical response trajectory. In a prospective clinical study of 94 patients, serial individual UVB doses and clinical response (Psoriasis Area Severity Index) values collected over the first three weeks of UVB therapy informed estimation of the "UVB sensitivity" parameter and the prediction of individual patient outcome at the end of phototherapy. An important advance of our model is its potential for direct clinical application through early assessment of response to UVB therapy, and for individualised optimisation of phototherapy regimes to improve clinical outcome. Additionally by incorporating the complex interaction of immune cells and epidermal keratinocytes, our model provides a basis to study and predict outcomes to biologic therapies in psoriasis. Author SummaryWe present a new computer model for psoriasis, an immune-mediated disabling skin disease which presents with red, raised scaly plaques that can appear over the whole body. Psoriasis affects millions of people in the UK alone and causes significant impairment to quality of life, and currently has no cure. Only a few treatments (including UVB phototherapy) can induce temporary remission. Despite our increased understanding about psoriasis, treatments are still given on a trial and error basis and there are no reliable computer models that can a) elucidate the mechanisms behind psoriasis onset or flare and b) predict a patients response to a course of treatment (e.g., phototherapy) and the likelihood of inducing a period of remission. Our computer model addresses both these needs. First, it explicitly describes the interaction between the immune system and skin cells. Second, our model captures response to therapy at the individual patient level and enables personalised prediction of clinical outcomes. Notably, our model also supports prediction of amending individual UVB phototherapy regimes based on the patients initial response that include for example personalised delivery schedules (i.e., 3x weekly vs. 5x weekly phototherapy). Therefore, our work is a crucial step towards precision medicine for psoriasis treatment.

systems biology↗

bioRxiv2019

Modelling bacterial twitching in fluid flows: a CFD-DEM approach

Bacterial habitats are often associated with fluid flow environments. There is a lack of models of the twitching motility of bacteria in shear flows. In this work, a three-dimensional modelling approach of Computational Fluid Dynamics (CFD) coupled with the Discrete Element Method (DEM) is proposed to study bacterial twitching on flat and groove surfaces under shear flow conditions. Rod-shaped bacteria are modelled as groups of spherical particles and Type IV pili attached to bacteria are modelled as dynamic springs which can elongate, retract, attach and detach. The CFD-DEM model of rod-shape bacteria is validated against orbiting of immotile bacteria in shear flows. The effects of fluid flow rate and surface topography on twitching motility are studied. The model can successfully predict upstream twitching motility of rod-shaped bacteria in shear flows. Our model can predict that there would be an optimal range of wall shear stress in which bacterial upstream twitching is most efficient. The results also indicate that when bacteria twitch on groove surfaces, they are likely to accumulate around the downstream side of the groove walls.

biophysics↗

bioRxiv2019

NUFEB: A Massively Parallel Simulator for Individual-based Modelling of Microbial Communities

We present NUFEB, a flexible, efficient, and open source software for simulating the 3D dynamics of microbial communities. The tool is based on the Individual-based Modelling (IbM) approach, where microbes are represented as discrete units and their behaviour changes over time due to a variety of processes. This approach allows us to study population behaviours that emerge from the interaction between individuals and their environment. NUFEB is built on top of the classical molecular dynamics simulator LAMMPS, which we extended with IbM features. A wide range of biological, physical and chemical processes are implemented to explicitly model microbial systems. NUFEB is fully parallelised and allows for the simulation of large numbers of microbes (107 individuals and beyond). The parallelisation is based on a domain decomposition scheme that divides the domain into multiple sub-domains which are distributed to different processors. NUFEB also offers a collection of post-processing routines for the visualisation and analysis of simulation output. In this article, we give an overview of NUFEBs functionalities and implementation details. We provide examples that illustrate the type of microbial systems NUFEB can be used to model and simulate.\n\nAuthor summaryIndividual-based Models (IbM) are one of the most promising frameworks to study microbial communities, as they can explicitly describe the behaviour of each cell. The development of a general-purpose IbM solver should focus on efficiency and flexibility due to the unique characteristics of microbial systems. However, available tools for these purposes present significant limitations. Most of them only facilitate serial computing for single simulation, or only focus on biological processes, but do not model mechanical and chemical processes in detail. In this work, we introduce the IbM solver NUFEB that addresses these shortcoming. The tool facilitates the modelling of much needed biological, chemical, physical and individual microbes in detail, and offers the flexibility of model extension and customisation. NUFEB is also fully parallelised and allows for the simulation of large complex microbial system. In this paper, we first give an overview of NUFEBs functionalities and implementation details. Then, we use NUFEB to model and simulate a biofilm system with fluid dynamics, and a large and complex biofilm system with multiple microbial functional groups and multiple nutrients.\n\nThis is a PLOS Computational Biology Software paper.

microbiology↗