Agent-based model predicts that layered structure and 3D movement work synergistically to reduce bacterial load in 3D in vitro models of tuberculosis granuloma.

Tuberculosis (TB) remains a global public health threat. Understanding the dynamics of host-pathogen interactions within TB granulomas will assist in identifying what leads to the successful elimination of infection. In vitro TB models provide a controllable environment to study these granuloma dynamics. Previously we developed a biomimetic 3D spheroid granuloma model that controls bacteria better than a traditional monolayer culture counterpart. We used agent-based simulations to predict the mechanistic reason for this difference. Our calibrated simulations were able to predict heterogeneous bacterial dynamics that are consistent with experimental data. In one group of simulations, spheroids are found to have higher macrophage activation than their traditional counterparts, leading to better bacterial control. This higher macrophage activation in the spheroids was not due to higher counts of activated T cells, instead fewer activated T cells were able to activate more macrophages due to the proximity of these cells to each other within the spheroid. In a second group of simulations, spheroids again have more macrophage activation but also more T cell activation, specifically CD8+ T cells. This higher level of CD8+ T cell activation is predicted to be due to the proximity of these cells to the cells that activate them. Multiple mechanisms of control were predicted. Simulations removing individual mechanisms show that one group of simulations has a CD4+ T cell dominant response, while the other has a mixed/CD8+ T cell dominant response. Lastly, we demonstrated that in spheroids the initial structure and movement rules work synergistically to reduce bacterial load. These findings provide valuable insights into how the structural complexity of in vitro models impacts immune responses. Moreover, our study has implications for engineering more physiologically relevant in vitro models and advancing our understanding of TB pathogenesis and potential therapeutic interventions.

Mathematical modeling indicates that regulatory inhibition of CD8+ T cell cytotoxicity can limit efficacy of IL-15 immunotherapy in cases of high pre-treatment SIV viral load.

Immunotherapeutic cytokines can activate immune cells against cancers and chronic infections. N-803 is an IL-15 superagonist that expands CD8+ T cells and increases their cytotoxicity. N-803 also temporarily reduced viral load in a limited subset of non-human primates infected with simian immunodeficiency virus (SIV), a model of HIV. However, viral suppression has not been observed in all SIV cohorts and may depend on pre-treatment viral load and the corresponding effects on CD8+ T cells. Starting from an existing mechanistic mathematical model of N-803 immunotherapy of SIV, we develop a model that includes activation of SIV-specific and non-SIV-specific CD8+ T cells by antigen, inflammation, and N-803. Also included is a regulatory counter-response that inhibits CD8+ T cell proliferation and function, representing the effects of immune checkpoint molecules and immunosuppressive cells. We simultaneously calibrate the model to two separate SIV cohorts. The first cohort had low viral loads prior to treatment (≈3-4 log viral RNA copy equivalents (CEQ)/mL), and N-803 treatment transiently suppressed viral load. The second had higher pre-treatment viral loads (≈5-7 log CEQ/mL) and saw no consistent virus suppression with N-803. The mathematical model can replicate the viral and CD8+ T cell dynamics of both cohorts based on different pre-treatment viral loads and different levels of regulatory inhibition of CD8+ T cells due to those viral loads (i.e. initial conditions of model). Our predictions are validated by additional data from these and other SIV cohorts. While both cohorts had high numbers of activated SIV-specific CD8+ T cells in simulations, viral suppression was precluded in the high viral load cohort due to elevated inhibition of cytotoxicity. Thus, we mathematically demonstrate how the pre-treatment viral load can influence immunotherapeutic efficacy, highlighting the in vivo conditions and combination therapies that could maximize efficacy and improve treatment outcomes.

Phosphatidylserine clustering by the Ebola virus matrix protein is a critical step in viral budding.

Phosphatidylserine (PS) is a critical lipid factor in the assembly and spread of numerous lipid-enveloped viruses. Here, we describe the ability of the Ebola virus (EBOV) matrix protein eVP40 to induce clustering of PS and promote viral budding in vitro, as well as the ability of an FDA-approved drug, fendiline, to reduce PS clustering and subsequent virus budding and entry. To gain mechanistic insight into fendiline inhibition of EBOV replication, multiple in vitro assays were run including imaging, viral budding and viral entry assays. Fendiline lowers PS content in mammalian cells and PS in the plasma membrane, where the ability of VP40 to form new virus particles is greatly lower. Further, particles that form from fendiline-treated cells have altered particle morphology and cannot significantly infect/enter cells. These complementary studies reveal the mechanism by which EBOV matrix protein clusters PS to enhance viral assembly, budding, and spread from the host cell while also laying the groundwork for fundamental drug targeting strategies.

Mechanisms of phosphatidylserine influence on viral production: A computational model of Ebola virus matrix protein assembly.

Ebola virus (EBOV) infections continue to pose a global public health threat, with high mortality rates and sporadic outbreaks in Central and Western Africa. A quantitative understanding of the key processes driving EBOV assembly and budding could provide valuable insights to inform drug development. Here, we use a computational model to evaluate EBOV matrix assembly. Our model focuses on the assembly kinetics of VP40, the matrix protein in EBOV, and its interaction with phosphatidylserine (PS) in the host cell membrane. It has been shown that mammalian cells transfected with VP40-expressing plasmids are capable of producing virus-like particles (VLPs) that closely resemble EBOV virions. Previous studies have also shown that PS levels in the host cell membrane affects VP40 association with the plasma membrane inner leaflet and that lower membrane PS levels result in lower VLP production. Our computational findings indicate that PS may also have a direct influence on VP40 VLP assembly and budding, where a higher PS level will result in a higher VLP budding rate and filament dissociation rate. Our results further suggest that the assembly of VP40 filaments follow the nucleation-elongation theory, where initialization and oligomerization of VP40 are two distinct steps in the assembly process. Our findings advance the current understanding of VP40 VLP formation by identifying new possible mechanisms of PS influence on VP40 assembly. We propose that these mechanisms could inform treatment strategies targeting PS alone or in combination with other VP40 assembly steps.

The Role of Biofilms, Bacterial Phenotypes, and Innate Immune Response in Mycobacterium avium Colonization to Infection.

Mycobacterium avium complex (MAC), is known for colonizing and infecting humans following inhalation of the bacteria. MAC pulmonary disease is notoriously difficult to treat and prone to recurrence. Both the incidence and prevalence MAC pulmonary disease have been increasing globally. MAC is well known to form biofilms in the environment. In vitro, these biofilms have been shown to aid MAC in epithelial cell invasion, protect MAC from phagocytosis, and cause premature apoptosis in macrophages. In vivo, the system of interactions between MAC, biofilms and host macrophages is complex, difficult to replicate in vitro and in animal models, has not been fully characterized. Here we present a three-dimensional agent-based model of a lung airway to help understand how these interactions evolve in the first 14 days post-bacterial inhalation. We parameterized the model using published data and performed uncertainty analysis to characterize outcomes and parameters' effects on those outcomes. Model results show diverse outcomes, including wide ranges of macrophage recruitment levels, and bacterial loads and phenotype distribution. Though most bacteria are phagocytosed by macrophages and remain intracellular, there are also many simulations in which extracellular bacteria continue to drive the colonization and infection. Initial parameters dictating host immune levels, bacterial loads introduced to the airway, and biofilm conditions have significant and lasting impacts on the course of these results. Additionally, though macrophage recruitment is key for suppressing bacterial loads, there is evidence of significant excess recruitment that fail to impact bacterial numbers. These results highlight a need and identify a path for further exploration into the inhalation events in MAC infection. Early infection dynamics could have lasting impacts on the development of nodular bronchiectatic or fibrocavitary disease as well as inform possible preventative and treatment intervention targeting biofilm-macrophage interactions.

Mathematical modeling of N-803 treatment in SIV-infected non-human primates.

Immunomodulatory drugs could contribute to a functional cure for Human Immunodeficiency Virus (HIV). Interleukin-15 (IL-15) promotes expansion and activation of CD8+ T cell and natural killer (NK) cell populations. In one study, an IL-15 superagonist, N-803, suppressed Simian Immunodeficiency Virus (SIV) in non-human primates (NHPs) who had received prior SIV vaccination. However, viral suppression attenuated with continued N-803 treatment, partially returning after long treatment interruption. While there is evidence of concurrent drug tolerance, immune regulation, and viral escape, the relative contributions of these mechanisms to the observed viral dynamics have not been quantified. Here, we utilize mathematical models of N-803 treatment in SIV-infected macaques to estimate contributions of these three key mechanisms to treatment outcomes: 1) drug tolerance, 2) immune regulation, and 3) viral escape. We calibrated our model to viral and lymphocyte responses from the above-mentioned NHP study. Our models track CD8+ T cell and NK cell populations with N-803-dependent proliferation and activation, as well as viral dynamics in response to these immune cell populations. We compared mathematical models with different combinations of the three key mechanisms based on Akaike Information Criterion and important qualitative features of the NHP data. Two minimal models were capable of reproducing the observed SIV response to N-803. In both models, immune regulation strongly reduced cytotoxic cell activation to enable viral rebound. Either long-term drug tolerance or viral escape (or some combination thereof) could account for changes to viral dynamics across long breaks in N-803 treatment. Theoretical explorations with the models showed that less-frequent N-803 dosing and concurrent immune regulation blockade (e.g. PD-L1 inhibition) may improve N-803 efficacy. However, N-803 may need to be combined with other immune therapies to countermand viral escape from the CD8+ T cell response. Our mechanistic model will inform such therapy design and guide future studies.

In Vitro Granuloma Models of Tuberculosis: Potential and Challenges.

Despite intensive research efforts, several fundamental disease processes for tuberculosis (TB) remain poorly understood. A central enigma is that host immunity is necessary to control disease yet promotes transmission by causing lung immunopathology. Our inability to distinguish these processes makes it challenging to design rational novel interventions. Elucidating basic immune mechanisms likely requires both in vivo and in vitro analyses, since Mycobacterium tuberculosis is a highly specialized human pathogen. The classic immune response is the TB granuloma organized in three dimensions within extracellular matrix. Several groups are developing cell culture granuloma models. In January 2018, NIAID convened a workshop, entitled "3-D Human in vitro TB Granuloma Model" to advance the field. Here, we summarize the arguments for developing advanced TB cell culture models and critically review those currently available. We discuss how integrating complementary approaches, specifically organoids and mathematical modeling, can maximize progress, and conclude by discussing future challenges and opportunities.

Fluoroquinolone Efficacy against Tuberculosis Is Driven by Penetration into Lesions and Activity against Resident Bacterial Populations.

Fluoroquinolones represent the pillar of multidrug-resistant tuberculosis (MDR-TB) treatment, with moxifloxacin, levofloxacin, or gatifloxacin being prescribed to MDR-TB patients. Recently, several clinical trials of "universal" drug regimens, aiming to treat drug-susceptible and drug-resistant TB, have included a fluoroquinolone. In the absence of clinical data comparing their side-by-side efficacies in controlled MDR-TB trials, a pharmacological rationale is needed to guide the selection of the most efficacious fluoroquinolone. The present studies were designed to test the hypothesis that fluoroquinolone concentrations (pharmacokinetics) and activity (pharmacodynamics) at the site of infection are better predictors of efficacy than the plasma concentrations and potency measured in standard growth inhibition assays and are better suited to determinations of whether one of the fluoroquinolones outperforms the others in rabbits with active TB. We first measured the penetration of these fluoroquinolones in lung lesion compartments, and their potency against bacterial populations that reside in each compartment, to compute lesion-centric pharmacokinetic-pharmacodynamic (PK/PD) parameters. PK modeling methods were used to quantify drug penetration from plasma to tissues at human-equivalent doses. On the basis of these metrics, moxifloxacin emerged with a clear advantage, whereas plasma-based PK/PD favored levofloxacin (the ranges of the plasma AUC/MIC ratio [i.e., the area under the concentration-time curve over 24 h in the steady state divided by the MIC] are 46 to 86 for moxifloxacin and 74 to 258 for levofloxacin). A comparative efficacy trial in the rabbit model of active TB demonstrated the superiority of moxifloxacin in reducing bacterial burden at the lesion level and in sterilizing cellular and necrotic lesions. Collectively, these results show that PK/PD data obtained at the site of infection represent an adequate predictor of drug efficacy against TB and constitute the baseline required to explore synergies, antagonism, and drug-drug interactions in fluoroquinolone-containing regimens.

Comparing efficacies of moxifloxacin, levofloxacin and gatifloxacin in tuberculosis granulomas using a multi-scale systems pharmacology approach.

Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim. GranSim is a hybrid agent-based computational model that simulates granuloma formation and function, FQ plasma and tissue pharmacokinetics and pharmacodynamics and is based on extensive in vitro and in vivo data. We treat in silico granulomas with recommended daily doses of each FQ and compare efficacy by multiple metrics: bacterial load, sterilization rates, early bactericidal activity and efficacy under non-compliance and treatment interruption. GranSim reproduces in vivo plasma pharmacokinetics, spatial and temporal tissue pharmacokinetics and in vitro pharmacodynamics of these FQs. We predict that MXF kills intracellular bacteria more quickly than LVX and GFX due in part to a higher cellular accumulation ratio. We also show that all three FQs struggle to sterilize non-replicating bacteria residing in caseum. This is due to modest drug concentrations inside caseum and high inhibitory concentrations for this bacterial subpopulation. MXF and LVX have higher granuloma sterilization rates compared to GFX; and MXF performs better in a simulated non-compliance or treatment interruption scenario. We conclude that MXF has a small but potentially clinically significant advantage over LVX, as well as LVX over GFX. We illustrate how a systems pharmacology approach combining experimental and computational methods can guide antibiotic selection for TB.

Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions.

Granulomas are a hallmark of tuberculosis. Inside granulomas, the pathogen Mycobacterium tuberculosis may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding M. tuberculosis metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of M. tuberculosis are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes in vivo, we present a multiscale in silico model of granuloma formation in tuberculosis. The model comprises host immunity, M. tuberculosis metabolism, M. tuberculosis growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to in vivo data from nonhuman primates and rabbits and apply the model to predict M. tuberculosis population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term M. tuberculosis survival in granulomas. We used virtual M. tuberculosis knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below ∼66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite- and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.

A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment.

While active tuberculosis (TB) is a treatable disease, many complex factors prevent its global elimination. Part of the difficulty in developing optimal therapies is the large design space of antibiotic doses, regimens and combinations. Computational models that capture the spatial and temporal dynamics of antibiotics at the site of infection can aid in reducing the design space of costly and time-consuming animal pre-clinical and human clinical trials. The site of infection in TB is the granuloma, a collection of immune cells and bacteria that form in the lung, and new data suggest that penetration of drugs throughout granulomas is problematic. Here we integrate our computational model of granuloma formation and function with models for plasma pharmacokinetics, lung tissue pharmacokinetics and pharmacodynamics for two first line anti-TB antibiotics. The integrated model is calibrated to animal data. We make four predictions. First, antibiotics are frequently below effective concentrations inside granulomas, leading to bacterial growth between doses and contributing to the long treatment periods required for TB. Second, antibiotic concentration gradients form within granulomas, with lower concentrations toward their centers. Third, during antibiotic treatment, bacterial subpopulations are similar for INH and RIF treatment: mostly intracellular with extracellular bacteria located in areas non-permissive for replication (hypoxic areas), presenting a slowly increasing target population over time. Finally, we find that on an individual granuloma basis, pre-treatment infection severity (including bacterial burden, host cell activation and host cell death) is predictive of treatment outcome.

A mathematical model of the initial interaction between Mycobacterium tuberculosis and macrophages.

There is a large body of literature describing molecular level interactions between Mycobacterium tuberculosis (Mtb) and macrophages. Macrophages initiate a range of anti-bacterial mechanisms in response to infection, and Mtb is capable of surviving and circumventing many of these responses. We apply a computational approach to ask: what are the effects on the cellular level of these opposing interactions? The model considers the interplay between bacterial killing and the pathogen's interference with macrophage function. The results reveal an oscillating balance between host and pathogen, but the balance is transient and varies in length, indicating that stochasticity in the bacterial population or host response could contribute to the diverse incubation periods observed in exposed individuals. The model captures host and strain variation and gives new insight into host-pathogen compatibility and co-evolution.

Evaluation of fendiline treatment in VP40 system with nucleation-elongation process: a computational model of Ebola virus matrix protein assembly.

Ebola virus (EBOV) infection is threatening human health, especially in Central and West Africa. Limited clinical trials and the requirement of biosafety level-4 laboratories hinder experimental work to advance our understanding of EBOV and the evaluation of treatment. In this work, we use a computational model to study the assembly and budding process of EBOV and evaluate the effect of fendiline on these processes in the context of fluctuating host membrane lipid levels. Our results demonstrate for the first time that the assembly of VP40 filaments may follow the nucleation-elongation theory, as this mechanism is critical to maintaining a pool of VP40 dimers for the maturation and production of virus-like particles (VLPs). We further find that this nucleation-elongation process is likely influenced by fluctuating phosphatidylserine (PS), which can complicate the efficacy of lipid-targeted therapies like fendiline, a drug that lowers cellular PS levels. Our results indicate that fendiline-induced PS reduction may actually increase VLP production at earlier time points (24 h) and under low fendiline concentrations (≤2 µM). However, this effect is transient and does not change the conclusion that fendiline generally decreases VLP production. In the context of fluctuating PS levels, we also conclude that fendiline can be more efficient at the late stage of VLP budding relative to earlier phases. Combination therapy with a VLP budding step-targeted drug may therefore further increase the treatment efficiency of fendiline. Finally, we also show that fendiline-induced PS reduction more effectively lowers VLP production when VP40 expression is high. Taken together, our results provide critical quantitative information on how fluctuating lipid levels (PS) affect EBOV assembly and egress and how this mechanism can be disrupted by lipid-targeting molecules like fendiline. IMPORTANCE: Ebola virus (EBOV) infection can cause deadly hemorrhagic fever, which has a mortality rate of ~50%-90% without treatment. The recent outbreaks in Uganda and the Democratic Republic of the Congo illustrate its threat to human health. Though two antibody-based treatments were approved, mortality rates in the last outbreak were still higher than 30%. This can partly be due to the requirement of advanced medical facilities for current treatments. As a result, it is very important to develop and evaluate new therapies for EBOV infection, especially those that can be easily applied in the developing world. The significance of our research is that we evaluate the potential of lipid-targeted treatments in reducing EBOV assembly and egress. We achieved this goal using the VP40 system combined with a computational approach, which both saves time and lowers cost compared to traditional experimental studies and provides innovative new tools to study viral protein dynamics.

Impact of Ebola virus nucleoprotein on VP40 virus-like particle production: a computational approach.

Ebola virus (EBOV) matrix protein VP40 can assemble and bud as virus-like particles (VLPs) when expressed alone in mammalian cells. Nucleoprotein (NP) could be recruited to VLPs as inclusion body (IB) when co-expressed, and increase VLP production. However, the mechanism behind it remains unclear. Here, we use a computational approach to study NP-VP40 interactions. Our simulations indicate that NP may enhance VLP production through stabilizing VP40 filaments and accelerating the VLP budding step. Further, both the relative timing and amount of NP expression compared to VP40 are important for the effective production of IB-containing VLPs. We predict that relative NP/VP40 expression ratio and time are important for efficient production of IB-containing VLPs. We conclude that disrupting the expression timing and amount of NP and VP40 could provide new avenues to treat EBOV infection. This work provides quantitative insights into EBOV proteins interactions and how virion generation and drug efficacy could be influenced.

In silico agent-based modeling approach to characterize multiple in vitro tuberculosis infection models.

In vitro models of Mycobacterium tuberculosis (Mtb) infection are a valuable tool for examining host-pathogen interactions and screening drugs. With the development of more complex in vitro models, there is a need for tools to help analyze and integrate data from these models. To this end, we introduce an agent-based model (ABM) representation of the interactions between immune cells and bacteria in an in vitro setting. This in silico model was used to simulate both traditional and spheroid cell culture models by changing the movement rules and initial spatial layout of the cells in accordance with the respective in vitro models. The traditional and spheroid simulations were calibrated to published experimental data in a paired manner, by using the same parameters in both simulations. Within the calibrated simulations, heterogeneous outputs are seen for bacterial count and T cell infiltration into the macrophage core of the spheroid. The simulations also predict that equivalent numbers of activated macrophages do not necessarily result in similar bacterial reductions; that host immune responses can control bacterial growth in both spheroid structure dependent and independent manners; that STAT1 activation is the limiting step in macrophage activation in spheroids; and that drug screening and macrophage activation studies could have different outcomes depending on the in vitro culture used. Future model iterations will be guided by the limitations of the current model, specifically which parts of the output space were harder to reach. This ABM can be used to represent more in vitro Mtb infection models due to its flexible structure, thereby accelerating in vitro discoveries.

Microglial roles in Alzheimer's disease: An agent-based model to elucidate microglial spatiotemporal response to beta-amyloid.

Alzheimer's disease (AD) is characterized by beta-amyloid (Aß) plaques in the brain and widespread neuronal damage. Because of the high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aß plaques. In response to AD pathology, microglia are innate phagocytotic immune cells that transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aß dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent-based model to characterize the spatiotemporal interactions between microglia and Aß. Our model qualitatively reproduces mouse data trends where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p < 0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. In addition, in silico behavioral knockout simulations show that phagocytosis knockouts have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knockouts had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis, and replication of activated microglia have complex impacts on plaque volume and coverage, whereas microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.

Reduced macrophage killing of M. avium drives infection risk in post-menopausal patients.

Non-tuberculous mycobacterial (NTM) infections, and Mycobacterium avium Complex (MAC) in particular, affect women at nearly twice the rate of men, and post-menopausal patients are at higher risk than pre-menopausal patients. The reasons for the disproportionate number of cases in women and post-menopausal patients remain unclear. One possibility is that menopause-associated immunological changes contribute to higher MAC prevalence post-menopause compared to pre-menopause. Menopause-associated immune disruption includes increased cytokine and chemokine production, and reduced cytotoxicity and phagocytosis in macrophages. Here we use an agent-based model of bacterial and host immune interactions in the airway to translate the combined impact of menopause-associated cellular immune disruptions to tissue scale outcomes. Our simulations indicate that menopause-associated immune disruptions can result in increased macrophage recruitment. However, this increase in macrophage number is unable to overcome functional deficits in macrophage phagocytosis and killing, since the post-menopausal simulations also show increased bacterial loads. Post-menopausal conditions are also associated with a lower number of cleared infections, and more simulations that have predominantly extracellular bacteria. Taken together, our work quantifies the potential impact of menopause-associated disruptions of innate immune functions on early MAC infection progression. Our findings will support the development of new therapies targeted to this high-risk group of patients.

Agent-based model indicates chemoattractant signaling caused by Mycobacterium avium biofilms in the lung airway increases bacterial loads by spatially diverting macrophages.

Incidence and prevalence of MAC infections are increasing globally, and reinfection is common. Thus, MAC infections present a significant public health challenge. We quantify the impact of MAC biofilms and repeated exposure on infection progression using a computational model of MAC infection in lung airways. MAC biofilms aid epithelial cell invasion, cause premature macrophage apoptosis, and limit antibiotic efficacy. In this computational work we develop an agent-based model that incorporates the interactions between bacteria, biofilm, and immune cells. In this computational model, we perform virtual knockouts to quantify the effects of the biofilm sources (deposited with bacteria vs. formed in the airway), and their impacts on macrophages (inducing apoptosis and slowing phagocytosis). We also quantify the effects of repeated bacterial exposures to assess their impact on infection progression. Our simulations show that chemoattractants released by biofilm-induced apoptosis bias macrophage chemotaxis towards pockets of infected and apoptosed macrophages. This bias results in fewer macrophages finding extracellular bacteria, allowing the extracellular planktonic bacteria to replicate freely. These spatial macrophage trends are further exacerbated with repeated deposition of bacteria. Our model indicates that interventions to abrogate macrophages' apoptotic responses to bacterial biofilms and/or reduce frequency of patient exposure to bacteria will lower bacterial load, and likely overall risk of infection.

Evaluation of Fendiline Treatment in VP40 System with Nucleation-Elongation Process: A Computational Model of Ebola Virus Matrix Protein Assembly.

Ebola virus (EBOV) infection is threatening human health, especially in Central and West Africa. Limited clinical trials and the requirement of biosafety level-4 (BSL-4) laboratories hinders experimental work to advance our understanding of EBOV and evaluation of treatment. In this work, we use a computational model to study the assembly and budding process of EBOV and evaluate the effect of fendiline on these processes. Our results indicate that the assembly of VP40 filaments may follow the nucleation-elongation theory, as it is critical to maintain a pool of VP40 dimer for the maturation and production of virus-like particles (VLPs). We further find that the nucleation-elongation process can also be influenced by phosphatidylserine (PS), which can complicate the efficacy of fendiline, a drug that lowers cellular PS levels. We observe that fendiline may increase VLP production at earlier time points (24 h) and under low concentrations (≤ 2 µM). But this effect is transient and does not change the conclusion that fendiline generally decreases VLP production. We also conclude that fendiline can be more efficient at the stage of VLP budding relative to earlier phases. Combination therapy with a VLP budding step-targeted drug may further increase the treatment efficiency of fendiline. Finally, we also show that fendiline has higher efficacy when VP40 expression is high. While these are single-cell level results based on the VP40 system, it points out a potential way of fendiline application affecting EBOV assembly, which can be further tested in experimental studies with multiple EBOV proteins or live virus.

"I Think I Am Getting There" Understanding the Computational Identity of Engineering Students Participating in a Computationally Intensive Thermodynamics Course.

In response to the growing computational intensity of the healthcare industry, biomedical engineering (BME) undergraduate education is placing increased emphasis on computation. The presence of substantial gender disparities in many computationally intensive disciplines suggests that the adoption of computational instruction approaches that lack intentionality may exacerbate gender disparities. Educational research suggests that the development of an engineering and computational identity is one factor that can support students' decisions to enter and persist in an engineering major. Discipline-based identity research is used as a lens to understand retention and persistence of students in engineering. Our specific purpose is to apply discipline-based identity research to define and explore the computational identities of undergraduate engineering students who engage in computational environments. This work will inform future studies regarding retention and persistence of students who engage in computational courses. Twenty-eight undergraduate engineering students (20 women, 8 men) from three engineering majors (biomedical engineering, agricultural engineering, and biological engineering) participated in semi-structured interviews. The students discussed their experiences in a computationally-intensive thermodynamics course offered jointly by the Biomedical Engineering and Agricultural & Biological Engineering departments. The transcribed interviews were analyzed through thematic coding. The gender stereotypes associated with computer programming also come part and parcel with computer programming, possibly threatening a student's sense of belonging in engineering. The majority of the participants reported that their computational identity was "in the making." Students' responses also suggested that their engineering identity and their computational identity were in congruence, while some incongruence is found between their engineering identity and a creative identity as well as between computational identity and perceived feminine norms. Responses also indicate that students associate specific skills with having a computational identity. This study's findings present an emergent thematic definition of a computational person constructed from student perceptions and experiences. Instructors can support students' nascent computational identities through intentional mitigation of the gender stereotypes and biases, and by framing assignments to focus on developing specific skills associated with the computational modeling processes.