Biology subjects

Forrest, S.

Publications and source records attributed to Forrest, S..

7 recordsLinked to original sources

bioRxiv2021

FXR inhibition reduces ACE2 expression, SARS-CoV-2 infection and may improve COVID-19 outcome

Prevention of SARS-CoV-2 entry in cells through the modulation of viral host receptors, such as ACE2, could represent a new therapeutic approach complementing vaccination. However, the mechanisms controlling ACE2 expression remain elusive. Here, we identify the farnesoid X receptor (FXR) as a direct regulator of ACE2 transcription in multiple COVID19-affected tissues, including the gastrointestinal and respiratory systems. We demonstrate that FXR antagonists, including the over-the-counter compound z-guggulsterone (ZGG) and the off-patent drug ursodeoxycholic acid (UDCA), downregulate ACE2 levels, and reduce susceptibility to SARS-CoV-2 infection in lung, cholangiocyte and gut organoids. We then show that therapeutic levels of UDCA downregulate ACE2 in human organs perfused ex situ and reduce SARS-CoV-2 infection ex vivo. Finally, we perform a retrospective study using registry data and identify a correlation between UDCA treatment and positive clinical outcomes following SARS-CoV-2 infection, including hospitalisation, ICU admission and death. In conclusion, we identify a novel function of FXR in controlling ACE2 expression and provide evidence that this approach could be beneficial for reducing SARS-CoV-2 infection, thereby paving the road for future clinical trials.

microbiology↗

bioRxiv2021

Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection, and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed ODE model. These results illustrate how realistic spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. SummaryA key question in SARS-CoV-2 infection is why viral loads and patient outcomes are so different across individuals. Because its difficult to see how the virus spreads in the lungs of infected people, we developed Spatial Immune Model of Coronavirus (SIMCoV), a computational model that simulates hundreds of millions of cells, including lung cells and immune cells. SIMCoV simulates how virus grows and then declines, and the simulations match data observed in patients. SIMCoV shows that when there are more initial infection sites, the virus grows to a higher peak. The model also shows how the timing of the immune response, particularly the T cell response, can affect how long the virus persists and whether it is ultimately cleared from the lungs. SIMCoV shows that the different viral loads in different patients can be explained by how many different places the virus is initially seeded inside their lungs. We explicitly add the branching airway structure of the lung into the model and show that virus spreads slightly faster than it would in a two-dimensional layer of lung cells, but much slower than traditional mathematical models based on differential equations. These results illustrate how realistic spatial computational models can improve understanding of how SARS-CoV-2 infection spreads in the lung.

immunology↗

bioRxiv2021

Inhibitory concentrations of ciprofloxacin induce an adaptive response promoting the intracellular survival of Salmonella Typhimurium

Antimicrobial resistance (AMR) is a pressing global health crisis, which has been fuelled by the sustained use of certain classes of antimicrobials, including fluoroquinolones. While the genetic mutations responsible for decreased fluoroquinolone (ciprofloxacin) susceptibility are known, the implications of ciprofloxacin exposure on bacterial growth, survival, and interactions with host cells are not well described. Aiming to understand the influence of inhibitory concentrations of ciprofloxacin in vitro, we subjected three clinical isolates of S. Typhimurium to differing concentrations of ciprofloxacin, dependent on their minimum inhibitory concentrations (MIC), and assessed the impact on bacterial growth, morphology, and transcription. We further investigated the differential morphology and transcription that occurred following ciprofloxacin exposure and measured the ability of ciprofloxacin-treated bacteria to invade and replicate in host cells. We found that ciprofloxacin-exposed S. Typhimurium are able to recover from inhibitory concentrations of ciprofloxacin, and that the drug induces specific morphological and transcriptional signatures associated with the bacterial SOS response, DNA repair, and intracellular survival. In addition, ciprofloxacin-treated S. Typhimurium have increased capacity for intracellular replication in comparison to untreated organisms. These data suggest that S. Typhimurium undergoes an adaptive response under ciprofloxacin perturbation that promotes cellular survival, a consequence that may justify more measured use of ciprofloxacin for Salmonella infections. The combination of multiple experimental approaches provides new insights into the collateral effects that ciprofloxacin and other antimicrobials have on invasive bacterial pathogens. ImportanceAntimicrobial resistance is a critical concern in global health. In particular, there is rising resistance to fluoroquinolones, such as ciprofloxacin, a first-line antimicrobial for many Gram-negative pathogens. We investigated the adaptive response of clinical isolates of Salmonella Typhimurium to ciprofloxacin, finding that the bacteria adapt in short timespans to high concentrations of ciprofloxacin in a way that promotes intracellular survival during early infection. Importantly, by studying three clinically relevant isolates, we were able to show that individual isolates respond differently to ciprofloxacin, and for each isolate, there was a heterogeneous response under ciprofloxacin treatment. The heterogeneity that arises from ciprofloxacin exposure may drive survival and proliferation of Salmonella during treatment and lead to drug resistance.

microbiology↗

bioRxiv2021

A Macro-scale Comparison Algorithm for Analysis of TCR Repertoire Completeness

Recent advances in biotechnology are beginning to generate whole immunome datasets, which will enable the comparison of immune repertoires between individuals, e.g., to assess immunocompetence. Existing algorithms cluster cell types based on the relative expression abundance of about 20 000 genes, but such algorithms have limited utility when comparing immunome datasets with many higher orders of magnitude (>1012) of variation, such as occurs in immunoreceptor sequences in highly polyclonal naive repertoires. In this paper we present a method for comparing immune repertoires by identifying macro-level features that are conserved between similar individuals. Our method allows us to detect some blind spots in naive populations and to assess whether a repertoire is likely complete by examining only a sample of its sequences. Author SummaryIn this paper we present a method for comparing the immune repertoire of different individuals. Repertoires are represented by a sample of genetic sequences. Our technique coarse grains each individuals data into groups, matches groups between individuals and finds significant differences.

bioinformatics↗

bioRxiv2021

High-content imaging to phenotype antimicrobial effects on individual bacteria at scale

High-content imaging (HCI) is technique for screening multiple cells in high resolution to detect subtle morphological and phenotypic variation. The method has been commonly deployed on model eukaryotic cellular systems, often for screening new drugs and targets. HCI is not commonly utilised for studying bacterial populations but may be powerful tool in understanding and combatting antimicrobial resistance. Consequently, we developed a high-throughput method for phenotyping bacteria under antimicrobial exposure at the scale of individual bacterial cells. Imaging conditions were optimised on an Opera Phenix confocal microscope (Perkin-Elmer) and novel analysis pipelines were established for both Gram-negative bacilli and Gram-positive cocci. The potential of this approach was illustrated using isolates of Klebsiella pneumoniae, Salmonella enterica serovar Typhimurium, and Staphylococcus aureus. HCI enabled the detection and assessment of subtle morphological characteristics, undetectable through conventional phenotypical methods, that could reproducibly distinguish between bacteria exposed to different classes of antimicrobials with distinct modes of action (MOA). In addition, distinctive responses were observed between susceptible and resistant isolates. By phenotyping single bacterial cells, we observed intra-population differences, which may be critical in identifying persistence or emerging resistance during antimicrobial treatment. The work presented here outlines a comprehensive method for investigating morphological changes at scale in bacterial populations under specific perturbation. ImportanceHigh-content imaging (HCI) is a microscopy technique that permits the screening of multiple cells simultaneously in high resolution to detect subtle morphological and phenotypic variation. The power of this methodology is that is can generate large datasets comprised of multiple parameters taken from individual cells subjected to range of different conditions. We aimed to develop novel methods for using HCI to study bacterial cells exposed to a range of different antibiotic classes. Using an Opera Phenix confocal microscope (Perkin-Elmer) and novel analysis pipelines we created a method to study the morphological characteristics of Klebsiella pneumoniae, Salmonella enterica serovar Typhimurium, and Staphylococcus aureus when exposed to antibacterial drugs with differing modes of action. By imaging individual bacterial cells at high resolution and scale, we observed intra-population differences associated with different antibiotics. The outlined methods are highly relevant for how we begin to better understand and combat antimicrobial resistance.

microbiology↗

bioRxiv2020

Development of caecaloids to study host-pathogen interactions: new insights into immunoregulatory functions of Trichuris muris extracellular vesicles in the caecum

The caecum, an intestinal appendage in the junction of the small and large intestines, displays a unique epithelium that serves as an exclusive niche for a range of pathogens including whipworms (Trichuris spp). While protocols to grow organoids from small intestine (enteroids) and colon (colonoids) exist, the conditions to culture organoids from the caecum have yet to be described. Here, we report methods to grow, differentiate and characterise mouse adult stem cell-derived caecal organoids, termed caecaloids. We compare the cellular composition of caecaloids to that of enteroids identifying differences in intestinal epithelial cell (IEC) populations that mimic those found in the caecum and small intestine. The remarkable similarity in the IECs composition and spatial conformation of caecaloids and their tissue of origin enables their use as an in vitro model to study host interactions with important caecal pathogens. Thus, exploiting this system we investigated the responses of caecal IECs to extracellular vesicles (EVs) secreted/excreted by the intracellular helminth Trichuris muris. Our findings reveal novel immunoregulatory effects of whipworm EVs on the caecal epithelium, including the downregulation of responses to nucleic acid recognition and type-I interferon (IFN) signalling.

immunology↗

bioRxiv2020

A blueprint for the implementation of a validated approach for the detection of SARS-Cov2 in clinical samples in academic facilities

The COVID-19 pandemic is expanding at an unprecedented rate. As a result, diagnostic services are stretched to their limit, and there is a clear need for the provision of additional diagnostic capacity. Academic laboratories, many of which are closed due to governmental lockdowns, may be in a position to support local screening capacity by adapting their current laboratory practices. Here, we describe the process of developing a SARS-Cov2 diagnostic workflow in a conventional academic Containment Level 2 (CL2) laboratory. Our outline includes simple SARS-Cov2 deactivation upon contact, the methods for a quantitative real-time reverse transcriptase PCR (qRT-PCR) detecting SARS-Cov2, a description of process establishment and validation, and some considerations for establishing a similar workflow elsewhere. This was achieved under challenging circumstances through the collaborative efforts of scientists, clinical staff, and diagnostic staff to mitigate to the ongoing crisis. Within 14 days, we created a validated COVID-19 diagnostics service for healthcare workers in our local hospital. The described methods are not exhaustive, but we hope may offer support to other academic groups aiming to set up something comparable in a short time frame.

molecular biology↗