Infectious Disease in the Workplace: Quantifying Uncertainty in Transmission.

Understanding disease transmission in the workplace is essential for protecting workers. To model disease outbreaks, the small populations in many workplaces require that stochastic effects are considered, which results in higher uncertainty. The aim of this study was to quantify and interpret the uncertainty inherent in such circumstances. We assessed how uncertainty of an outbreak in workplaces depends on i) the infection dynamics in the community, ii) the workforce size, iii) spatial structure in the workplace, iv) heterogeneity in susceptibility of workers, and v) heterogeneity in infectiousness of workers. To address these questions, we developed a multiscale model: A deterministic model to predict community transmission, and a stochastic model to predict workplace transmission. We extended this basic workplace model to allow for spatial structure, and heterogeneity in susceptibility and infectiousness in workers. We found a non-monotonic relationship between the workplace transmission rate and the coefficient of variation (CV), which we use as a measure of uncertainty. Increasing community transmission, workforce size and heterogeneity in susceptibility decreased the CV. Conversely, increasing the level of spatial structure and heterogeneity in infectiousness increased the CV. However, when the model predicts bimodal distributions, for example when community transmission is low and workplace transmission is high, the CV fails to capture this uncertainty. Overall, our work informs modellers and policy makers on how model complexity impacts outbreak uncertainty. In particular: workforce size, community and workplace transmission, spatial structure and individual heterogeneity contribute in a specific and individual manner to the predicted workplace outbreak size distribution.

ILC3s restrict the dissemination of intestinal bacteria to safeguard liver regeneration after surgery.

It is generally believed that environmental or cutaneous bacteria are the main origin of surgical infections. Therefore, measures to prevent postoperative infections focus on optimizing hygiene and improving asepsis and antisepsis. In a large cohort of patients with infections following major surgery, we identified that the causative bacteria are mainly of intestinal origin. Postoperative infections of intestinal origin were also found in mice undergoing partial hepatectomy. CCR6+ group 3 innate lymphoid cells (ILC3s) limited systemic bacterial spread. Such bulwark function against host invasion required the production of interleukin-22 (IL-22), which controlled the expression of antimicrobial peptides in hepatocytes, thereby limiting bacterial spread. Using genetic loss-of-function experiments and punctual depletion of ILCs, we demonstrate that the failure to restrict intestinal commensals by ILC3s results in impaired liver regeneration. Our data emphasize the importance of endogenous intestinal bacteria as a source for postoperative infection and indicate ILC3s as potential new targets.

Primary Infection by E. multilocularis Induces Distinct Patterns of Cross Talk between Hepatic Natural Killer T Cells and Regulatory T Cells in Mice.

The larval stage of the helminthic cestode Echinococcus multilocularis can inflict tumor-like hepatic lesions that cause the parasitic disease alveolar echinococcosis in humans, with high mortality in untreated patients. Opportunistic properties of the disease have been established based on the increased incidence in immunocompromised patients and mouse models, indicating that an appropriate adaptive immune response is required for the control of the disease. However, cellular interactions and the kinetics of the local hepatic immune responses during the different stages of infection with E. multilocularis remain unknown. In a mouse model of oral infection that mimics the normal infection route in human patients, the networks of the hepatic immune response were assessed using single-cell RNA sequencing (scRNA-seq) of isolated hepatic CD3+ T cells at different infection stages. We observed an early and sustained significant increase in natural killer T (NKT) cells and regulatory T cells (Tregs). Early tumor necrosis factor (TNF)- and integrin-dependent interactions between these two cell types promote the formation of hepatic lesions. At late time points, downregulation of programmed cell death protein 1 (PD-1) and ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1)-dependent signaling suppress the resolution of parasite-induced pathology. The obtained data provide fresh insight into the adaptive immune responses and local regulatory pathways at different infection stages of E. multilocularis in mice.

A new mouse model of radiation-induced liver disease reveals mitochondrial dysfunction as an underlying fibrotic stimulus.

Background & Aims: High-dose irradiation is an essential tool to help control the growth of hepatic tumors, but it can cause radiation-induced liver disease (RILD). This life-threatening complication manifests itself months following radiation therapy and is characterized by fibrosis of the pericentral sinusoids. In this study, we aimed to establish a mouse model of RILD to investigate the underlying mechanism of radiation-induced liver fibrosis. Methods: Using a small animal image-guided radiation therapy platform, an irradiation scheme delivering 50 Gy as a single dose to a focal point in mouse livers was designed. Tissues were analyzed 1 and 6 days, and 6 and 20 weeks post-irradiation. Irradiated livers were assessed by histology, immunohistochemistry, imaging mass cytometry and RNA sequencing. Mitochondrial function was assessed using high-resolution respirometry. Results: At 6 and 20 weeks post-irradiation, pericentral fibrosis was visible in highly irradiated areas together with immune cell infiltration and extravasation of red blood cells. RNA sequencing analysis showed gene signatures associated with acute DNA damage, p53 activation, senescence and its associated secretory phenotype and fibrosis. Moreover, gene profiles of mitochondrial damage and an increase in mitochondrial DNA heteroplasmy were detected. Respirometry measurements of hepatocytes in vitro confirmed irradiation-induced mitochondrial dysfunction. Finally, the highly irradiated fibrotic areas showed markers of reactive oxygen species such as decreased glutathione and increased lipid peroxides and a senescence-like phenotype. Conclusions: Based on our mouse model of RILD, we propose that irradiation-induced mitochondrial DNA instability contributes to the development of fibrosis via the generation of excessive reactive oxygen species, p53 pathway activation and a senescence-like phenotype. Lay summary: Irradiation is an efficient cancer therapy, however, its applicability to the liver is limited by life-threatening radiation-induced hepatic fibrosis. We have developed a new mouse model of radiation-induced liver fibrosis, that recapitulates the human disease. Our model highlights the role of mitochondrial DNA instability in the development of irradiation-induced liver fibrosis. This new model and subsequent findings will help increase our understanding of the hepatic reaction to irradiation and to find strategies that protect the liver, enabling the expanded use of radiotherapy to treat hepatic tumors.

LIM protein Ajuba promotes liver cell proliferation through its involvement in DNA replication and DNA damage control.

The LIM-domain protein Ajuba is associated with cell proliferation, a fundamental process of tissue regeneration and cancer. We report that in the liver, Ajuba expression is increased during regeneration and in tumour cells and tissues. Knockout of Ajuba using CRISPR/Cas9 is embryonic lethal in mice. shRNA targeting of Ajuba reduces cell proliferation, delays cell entry into S-phase, reduces cell survival and tumour growth in vivo and increases expression of the DNA damage marker γH2AX. Ajuba binding partners include proteins involved in DNA replication and damage, such as SKP2, MCM2, MCM7 and RPA70. Taken together, our data support that Ajuba promotes liver cell proliferation associated with development, regeneration and tumour growth and is involved in DNA replication and damage repair.

Intraperitoneal microbial contamination drives post-surgical peritoneal adhesions by mesothelial EGFR-signaling.

Abdominal surgeries are lifesaving procedures but can be complicated by the formation of peritoneal adhesions, intra-abdominal scars that cause intestinal obstruction, pain, infertility, and significant health costs. Despite this burden, the mechanisms underlying adhesion formation remain unclear and no cure exists. Here, we show that contamination of gut microbes increases post-surgical adhesion formation. Using genetic lineage tracing we show that adhesion myofibroblasts arise from the mesothelium. This transformation is driven by epidermal growth factor receptor (EGFR) signaling. The EGFR ligands amphiregulin and heparin-binding epidermal growth factor, are sufficient to induce these changes. Correspondingly, EGFR inhibition leads to a significant reduction of adhesion formation in mice. Adhesions isolated from human patients are enriched in EGFR positive cells of mesothelial origin and human mesothelium shows an increase of mesothelial EGFR expression during bacterial peritonitis. In conclusion, bacterial contamination drives adhesion formation through mesothelial EGFR signaling. This mechanism may represent a therapeutic target for the prevention of adhesions after intra-abdominal surgery.

Regular testing of asymptomatic healthcare workers identifies cost-efficient SARS-CoV-2 preventive measures.

Protecting healthcare professionals is crucial in maintaining a functioning healthcare system. The risk of infection and optimal preventive strategies for healthcare workers during the COVID-19 pandemic remain poorly understood. Here we report the results of a cohort study that included pre- and asymptomatic healthcare workers. A weekly testing regime has been performed in this cohort since the beginning of the COVID-19 pandemic to identify infected healthcare workers. Based on these observations we have developed a mathematical model of SARS-CoV-2 transmission that integrates the sources of infection from inside and outside the hospital. The data were used to study how regular testing and a desynchronisation protocol are effective in preventing transmission of COVID-19 infection at work, and compared both strategies in terms of workforce availability and cost-effectiveness. We showed that case incidence among healthcare workers is higher than would be explained solely by community infection. Furthermore, while testing and desynchronisation protocols are both effective in preventing nosocomial transmission, regular testing maintains work productivity with implementation costs.

Single-cell and bulk transcriptomics of the liver reveals potential targets of NASH with fibrosis.

Fibrosis is characterized by the excessive production of collagen and other extracellular matrix (ECM) components and represents a leading cause of morbidity and mortality worldwide. Previous studies of nonalcoholic steatohepatitis (NASH) with fibrosis were largely restricted to bulk transcriptome profiles. Thus, our understanding of this disease is limited by an incomplete characterization of liver cell types in general and hepatic stellate cells (HSCs) in particular, given that activated HSCs are the major hepatic fibrogenic cell population. To help fill this gap, we profiled 17,810 non-parenchymal cells derived from six healthy human livers. In conjunction with public single-cell data of fibrotic/cirrhotic human livers, these profiles enable the identification of potential intercellular signaling axes (e.g., ITGAV-LAMC1, TNFRSF11B-VWF and NOTCH2-DLL4) and master regulators (e.g., RUNX1 and CREB3L1) responsible for the activation of HSCs during fibrogenesis. Bulk RNA-seq data of NASH patient livers and rodent models for liver fibrosis of diverse etiologies allowed us to evaluate the translatability of candidate therapeutic targets for NASH-related fibrosis. We identified 61 liver fibrosis-associated genes (e.g., AEBP1, PRRX1 and LARP6) that may serve as a repertoire of translatable drug target candidates. Consistent with the above regulon results, gene regulatory network analysis allowed the identification of CREB3L1 as a master regulator of many of the 61 genes. Together, this study highlights potential cell-cell interactions and master regulators that underlie HSC activation and reveals genes that may represent prospective hallmark signatures for liver fibrosis.

Loss of Claudin-3 Impairs Hepatic Metabolism, Biliary Barrier Function, and Cell Proliferation in the Murine Liver.

BACKGROUND & AIMS: Tight junctions in the liver are essential to maintain the blood-biliary barrier, however, the functional contribution of individual tight junction proteins to barrier and metabolic homeostasis remains largely unexplored. Here, we describe the cell type-specific expression of tight junction genes in the murine liver, and explore the regulation and functional importance of the transmembrane protein claudin-3 in liver metabolism, barrier function, and cell proliferation. METHODS: The cell type-specific expression of hepatic tight junction genes is described using our mouse liver single-cell sequencing data set. Differential gene expression in Cldn3-/- and Cldn3+/+ livers was assessed in young and aged mice by RNA sequencing (RNA-seq), and hepatic tissue was analyzed for lipid content and bile acid composition. A surgical model of partial hepatectomy was used to induce liver cell proliferation. RESULTS: Claudin-3 is a highly expressed tight junction protein found in the liver and is expressed predominantly in hepatocytes and cholangiocytes. The histology of Cldn3-/- livers showed no overt phenotype, and the canalicular tight junctions appeared intact. Nevertheless, by RNA-seq we detected a down-regulation of metabolic pathways in the livers of Cldn3-/- young and aged mice, as well as a decrease in lipid content and a weakened biliary barrier for primary bile acids, such as taurocholic acid, taurochenodeoxycholic acid, and taurine-conjugated muricholic acid. Coinciding with defects in the biliary barrier and lower lipid metabolism, there was a diminished hepatocyte proliferative response in Cldn3-/- mice after partial hepatectomy. CONCLUSIONS: Our data show that, in the liver, claudin-3 is necessary to maintain metabolic homeostasis, retention of bile acids, and optimal hepatocyte proliferation during liver regeneration. The RNA-seq data set can be accessed at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE159914.

Modelling strategies to organize healthcare workforce during pandemics: Application to COVID-19.

Protection of the healthcare workforce is of paramount importance for the care of patients in the setting of a pandemic such as coronavirus disease 2019 (COVID-19). Healthcare workers are at increased risk of becoming infected. The ideal organisational strategy to protect the workforce in a situation in which social distancing cannot be maintained remains to be determined. In this study, we have mathematically modelled strategies for the employment of the hospital workforce with the goal of simulating the health and productivity of the workers. The models were designed to determine if desynchronization of medical teams by dichotomizing the workers may protect the workforce. Our studies model workforce productivity and the efficiency of home office applied to the case of COVID-19. The results reveal that a desynchronization strategy in which two medical teams work alternating for 7 days increases the available workforce.

Synergistic effect of the TLR5 agonist CBLB502 and its downstream effector IL-22 against liver injury.

The toll-like receptor 5 (TLR5) agonist, CBLB502/Entolimod, is a peptide derived from bacterial flagellin and has been shown to protect against radiation-induced tissue damage in animal models. Here we investigated the protective mechanism of CBLB502 in the liver using models of ischemia-reperfusion injury and concanavalin A (ConA) induced immuno-hepatitis. We report that pretreatment of mice with CBLB502 provoked a concomitant activation of NF-κB and STAT3 signaling in the liver and reduced hepatic damage in both models. To understand the underlying mechanism, we screened for cytokines in the serum of CBLB502 treated animals and detected high levels of IL-22. There was no transcriptional upregulation of IL-22 in the liver, rather it was found in extrahepatic tissues, mainly the colon, mesenteric lymph nodes (MLN), and spleen. RNA-seq analysis on isolated hepatocytes demonstrated that the concomitant activation of NF-κB signaling by CBLB502 and STAT3 signaling by IL-22 produced a synergistic cytoprotective transcriptional signature. In IL-22 knockout mice, the loss of IL-22 resulted in a decrease of hepatic STAT3 activation, a reduction in the cytoprotective signature, and a loss of hepatoprotection following ischemia-reperfusion-induced liver injury. Taken together, these findings suggest that CBLB502 protects the liver by increasing hepatocyte resistance to acute liver injury through the cooperation of TLR5-NF-κB and IL-22-STAT3 signaling pathways.

Absolute quantification of transcription factors in human erythropoiesis using selected reaction monitoring mass spectrometry.

Quantitative changes in transcription factor (TF) abundance regulate dynamic cellular processes, including cell fate decisions. Protein copy number provides information about the relative stoichiometry of TFs that can be used to determine how quantitative changes in TF abundance influence gene regulatory networks. In this protocol, we describe a targeted selected reaction monitoring (SRM)-based mass-spectrometry method to systematically measure the absolute protein concentration of nuclear TFs as human hematopoietic stem and progenitor cells differentiate along the erythropoietic lineage. For complete details on the use and execution of this protocol, please refer to Gillespie et al. (2020).

The LIM Protein Ajuba Augments Tumor Metastasis in Colon Cancer.

Colorectal cancer, along with its high potential for recurrence and metastasis, is a major health burden. Uncovering proteins and pathways required for tumor cell growth is necessary for the development of novel targeted therapies. Ajuba is a member of the LIM domain family of proteins whose expression is positively associated with numerous cancers. Our data shows that Ajuba is highly expressed in human colon cancer tissue and cell lines. Publicly available data from The Cancer Genome Atlas shows a negative correlation between survival and Ajuba expression in patients with colon cancer. To investigate its function, we transduced SW480 human colon cancer cells, with lentiviral constructs to knockdown or overexpress Ajuba protein. The transcriptome of the modified cell lines was analyzed by RNA sequencing. Among the pathways enriched in the differentially expressed genes, were cell proliferation, migration and differentiation. We confirmed our sequencing data with biological assays; cells depleted of Ajuba were less proliferative, more sensitive to irradiation, migrated less and were less efficient in colony formation. In addition, loss of Ajuba expression decreased the tumor burden in a murine model of colorectal metastasis to the liver. Taken together, our data supports that Ajuba promotes colon cancer growth, migration and metastasis and therefore is a potential candidate for targeted therapy.

Absolute Quantification of Transcription Factors Reveals Principles of Gene Regulation in Erythropoiesis.

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

Bayesian correlation is a robust gene similarity measure for single-cell RNA-seq data.

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single-cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq. Recently, a Bayesian correlation scheme that assigns low similarity to genes that have low confidence expression estimates has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering three ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression. We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

Connexin-43-dependent ATP release mediates macrophage activation during sepsis.

Bacterial spillage into a sterile environment following intestinal hollow-organ perforation leads to peritonitis and fulminant sepsis. Outcome of sepsis critically depends on macrophage activation by extracellular ATP-release and associated autocrine signalling via purinergic receptors. ATP-release mechanisms, however, are poorly understood. Here, we show that TLR-2 and -4 agonists trigger ATP-release via Connexin-43 hemichannels in macrophages leading to poor sepsis survival. In humans, Connexin-43 was upregulated on macrophages isolated from the peritoneal cavity in patients with peritonitis but not in healthy controls. Using a murine peritonitis/sepsis model, we identified increased Connexin-43 expression in peritoneal and hepatic macrophages. Conditional Lyz2cre/creGja1flox/flox mice were developed to specifically assess Connexin-43 impact in macrophages. Both macrophage-specific Connexin-43 deletion and pharmacological Connexin-43 blockade were associated with reduced cytokine secretion by macrophages in response to LPS and CLP, ultimately resulting in increased survival. In conclusion, inhibition of autocrine Connexin-43-dependent ATP signalling on macrophages improves sepsis outcome.

Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.

Bayesian Correlation Analysis for Sequence Count Data.

Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.

On cross-conditional and fluctuation correlations in competitive RNA networks.

MOTIVATION: In competitive endogenous RNA (ceRNA) networks, different mRNAs targeted by the same miRNA can 'cross-talk' by absorbing miRNAs and relieving repression on the other mRNAs. This creates correlations in mRNA expression even without direct interaction. Most previous theoretical study of cross-talk has focused on correlations in stochastic fluctuations of mRNAs around their steady state values. However, the experimentally known examples of cross-talk do not involve single-cell fluctuations, but rather bulk tissue-level changes between conditions, such as due to differentiation or disease. In our study, we quantify for the first time both fluctuational and cross-conditional cross-talk in chemical kinetic models of miRNA-mRNA interaction networks. We study the parameter regions under which these different types of cross-talk arise, and how they are affected by network structure. RESULTS: We find that while a network may support both fluctuational and cross-conditional cross-talk, the parameter regimes under which each type of cross-talk tends to emerge are rather different. Consistent with previous studies, fluctuational cross-talk occurs when miRNA and mRNA expression levels are 'balanced', whereas cross-conditional cross-talk tends to emerge when average miRNA levels are high and average mRNA levels are low. Conversely, cross-conditional miRNA cross-talk-a little-discussed phenomenon-is greatest when miRNA levels are low and mRNA levels are high. We show that the parameter ranges where cross-talk is maximized can, to some degree, be predicted based on network structure. Indeed, we find that the dominant effect of network structure on correlations happens through the effect of network structure on the overall balance between miRNA and mRNA expression. However, it is not the only effect, as we find that the density of connections between miRNAs and mRNAs in larger networks increases cross-talk without altering the expression balance. CONCLUSION: Our results deepen the theoretical understanding of cross-talk in ceRNA networks, and have implications for the experimental identification of ceRNA cross-talk phenomena. AVAILABILITY AND IMPLEMENTATION: Simulation software available upon request. CONTACT: tperkins@ohri.ca.

Optimal architecture of differentiation cascades with asymmetric and symmetric stem cell division.

The role of symmetric division in stem cell biology is ambiguous. It is necessary after injuries, but if symmetric divisions occur too often, the appearance of tumours is more likely. To explore the role of symmetric and asymmetric division in cell populations, we propose a mathematical model of competition of populations, in which the stem cell expansion is controlled by fully differentiated cells. We show that there is an optimal fraction of symmetric stem cell division, which maximises the long-term survival probability of the organism. Moreover, we show the optimal number of stem cells in a tissue, and we show that number has to be small enough to reduce the probability of the appearance of advantageous malignant cells, and large enough to assure that the population will not be suppressed by stochastic fluctuations.