Biology subjects

Kirschner, D.

Publications and source records attributed to Kirschner, D..

6 recordsLinked to original sources

bioRxiv2026

A tissue time machine to forward-predict single-timepoint data applied to tuberculosis granulomas

Recent advances have improved understanding of infection; however many detailed datasets are based on single timepoints post-infection and cannot generate longitudinal insights needed to predict infection outcomes. For example, pulmonary infection with Mycobacterium tuberculosis (Mtb) initiates a complex system of immune and bacterial interactions that leads to granuloma formation, yet single snapshots in time need elaboration long-term. We develop a new approach, the tissue time machine, applied to predict outcomes of non-human primate granulomas from initiation to endpoint using single timepoint tissue datasets including high-dimensional spatial features. This involves identifying granuloma digital partners, simulations most closely resembling real granulomas from a virtual library of over 50,000. By analyzing digital partners, we predict infection outcomes of 22/28 granulomas, and that Mtb-specific, IFN-{gamma}-producing T cell recruitment rates influence uncertainty for the 6/28. This method is applicable to any system that allows for mechanistic predictions over time using heterogeneous, incomplete, and high-dimensional datasets.

systems biology↗

bioRxiv2026

In vitro dormancy models improve ability to predict treatment response in severe marmoset tuberculosis lesions

Tuberculosis lesions are structurally and physiologically heterogeneous, creating microenvironments that restrict antibiotic penetration and alter bacterial susceptibility. This heterogeneity remains a major impediment to shortening tuberculosis treatment, as it is difficult to predict which regimens will sterilize bacteria in these hard-to-treat lesion niches. Many in vitro systems and commonly used animal models fail to recapitulate the necrotic, caseous lesion pathology associated with treatment failure and relapse. Here, we show that resource-efficient in vitro assays engineered to mimic key lesion microenvironments, including those with lipid-rich, caseum-like matrices with controlled oxygenation and pH, generate drug-response metrics that predict lesion-level treatment responses in a non-human primate model. We stratified individual marmoset lesions by baseline 2-deoxy-2-[18F] fluoro-D-glucose ([18F] FDG) positron emission tomography/computed tomography (PET/CT) features, then quantified drug potency and interactions across three lipid-induced dormancy (LIDs) conditions under both equipotent and lesion pharmacokinetic-informed dosing schemes. LIDs-based and pharmacokinetic-informed measurements aligned more closely with treatment responses in severe lesions than conventional in vitro data and recapitulated interaction patterns consistent with known regimen performance. Integrating imaging-derived and in vitro features in multivariate, sequential computational models improved predictive accuracy over baseline imaging alone and identified a subset of informative in vitro metrics. Together, these results establish a scalable framework linking lesion-mimicking assays to lesion-specific outcomes, enabling earlier, more cost-effective prioritization of pre-clinical regimens aimed at sterilizing hard-to-treat tuberculosis lesions and shortening therapy.

molecular biology↗

bioRxiv2026

A Comparison of Mechanisms Driving Lesion Outcomes during Lung Tumor and Tuberculosis Granuloma Formation

Small cell lung cancer (SCLC) and tuberculosis (TB) are both deadly diseases that present with spatially complex lung lesions. These lesions share many similarities, including several key spatial interactions between T cells and macrophages. Both SCLC and TB present with significant heterogeneity, both in terms of progression of disease and responses to treatment; current experimental methods have few tools to investigate the spatiotemporal evolution of these lesions within human lungs. We have applied our computational agent-based model, GranSim, to extensively study heterogeneity of TB granuloma scale formation, infection outcome and treatment in detail. We introduce TumorSim, an analogous agent-based model designed to understand the heterogeneity of SCLC lung tumors. TumorSim mechanistically and spatio-temporally captures immune-tumor interactions, many of which are well-studied in isolation, including cytokine-based recruitment of adaptive cells and PD1/PDL1-based inhibition of cytotoxic T-cell activity. Drawing from known lung immunology as well as literature on lung tumor responses, we define and explore a wide set of parameters to characterize TumorSim behavior using global sensitivity analysis. We compare factors that drive dynamics of both SCLC tumors and TB granulomas. As model validation, sensitivity analysis captures several well-known correlates of improved SCLC outcomes including macrophage-mediated cytotoxic T-cell recruitment. Surprisingly, both models predict a two-phase formation process occurring with an abrupt change in tumor/granuloma dynamics upon arrival of adaptive immune cells into the lung from lung-draining lymph nodes. Simulations suggest that while CCL5 is associated with improved tumor control later during tumor growth, CCL5 plays a pro-tumor role early during tumor growth by recruiting regulatory T cells. We also find that, similar to virtual TB granulomas, TumorSim tumors are increased in volume when immunosuppressive mechanisms outweigh pro-inflammatory responses. This novel tumor model can serve as a basis for future studies on lung tumor-immune dynamics to study both immunotherapeutics and anti-cancer drugs.

cancer biology↗

bioRxiv2025

Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden

Pulmonary infection after inhalation of Mycobacterium tuberculosis (Mtb) causes tuberculosis (TB). TB presents with lung granulomas - complex spheroidal structures composed of immune cells and bacteria. Granulomas often have centralized caseum (necrotic tissue) where mycobacteria are quarantined, complicating and prolonging multi-antibiotic regimens. Determining which antibiotic regimens are optimal for reducing treatment time and toxicity is a goal of recent TB eradication campaigns. Clinical trials are expensive and challenging, making it difficult to untangle which host-pathogen interactions drive the heterogeneous infection and treatment outcomes observed at between-host and within-host scales. To determine responses to antibiotic regimens, we simulate treatments in HostSim, our whole-host mechanistic, multi-scale computational model of Mtb-infection. HostSim tracks dynamics of pulmonary Mtb-infection over molecular, cellular, tissue, organ, and whole-host scales. We create a heterogenous virtual cohort, comprising distinct hosts, for virtual clinical trials. We represent drug treatments by newly-integrating pharmacokinetics / pharmacodynamics into HostSim, simulating treatment with commonly-prescribed TB antibiotic regimens (e.g., HRZE or BPaL). Our approach allows us to identify both (1) which hosts/granulomas most improve with treatment, and (2) which mechanisms influence outcome heterogeneity. By tracking experimental and clinical measurements, we virtually recreate several drug rankings from literature. We find that many methods of ranking treatment efficacy are strongly influenced by the definition of improvement used and, in some cases, the detection threshold of CFU. Other rankings depend on initial bacterial burden of hosts/granulomas. Our work suggests that metrics for regimen optimality may be orthogonal, which could explain seemingly-contradictory findings from prior studies.

systems biology↗

bioRxiv2021

Meta-analysis shows both congruence and complementarity of DNA metabarcoding to traditional methods for biological community assessment

Assessment of the diversity and composition of biological communities is central to studies in ecology as well as for ecological monitoring. Historically, individual taxonomic groups have been assessed separately, while for an understanding of the state and change of biodiversity under ongoing global change an integrated assessment would be necessary. DNA metabarcoding has been proposed to be a highly promising approach especially for the assessment of aquatic communities, and numerous studies have investigated the consistency of this new technique with traditional morpho-taxonomic approaches. These individual studies have used DNA metabarcoding to assess diversity and community structure of aquatic organisms both in marine and freshwater systems globally over the last decade. However, a systematic analysis of the comparability and effectiveness of DNA-based community assessment across all of these studies has hitherto been lacking. Here we performed the first meta-analysis of all available studies comparing traditional methods and DNA metabarcoding to measure and assess biological diversity of key aquatic groups, including microorganisms, macroinvertebrates, and fish. Across 215 datasets, we found that DNA metabarcoding provides diversity estimates (richness) that are globally consistent to those obtained using traditional methods. DNA metabarcoding also generates species inventories that are highly congruent with traditional methods for fish. Contrastingly, however, species inventories of microorganisms and macroinvertebrates obtained by DNA metabarcoding showed pronounced differences to traditional methods, missing some taxa but at the same time detecting otherwise overseen diversity. Our results indicate that DNA metabarcoding is efficient to estimate local and regional richness. The method is generally sufficiently advanced to study the composition of fish communities and replace more invasive traditional methods. For smaller organisms, like macroinvertebrates and microorganisms, DNA metabarcoding may continue to give complementary rather than identical estimates compared to traditional approaches. Systematic and comparable data collection will increase the understanding of different aspects of this complementarity, and increase the effectiveness of the method and adequate interpretation of the results.

ecology↗

bioRxiv2020

Lateral and longitudinal fish eDNA distribution in dynamic riverine habitats

Assessing the status and distribution of fish populations in rivers is essential for management and conservation efforts in these dynamic habitats. Currently, methods detecting environmental DNA (eDNA) are being established as an alternative and/or complementary approach to the traditional monitoring of fish species. In lotic systems, a sound understanding of hydrological conditions and their influence on the local target detection probability and DNA quantity is key for the interpretation of eDNA-based results. However, the effect of seasonal and diurnal changes in discharge and the comparability of semi-quantitative results between species remain hardly addressed. We conducted a cage experiment with four fish species (three salmonid and one cyprinid species) in a glacier-fed, fish-free river in Tyrol (Austria) during summer, fall, and winter discharge situations (i.e. 25-fold increase from winter to summer). Each season, water samples were obtained on three consecutive days at 13 locations downstream of the cages including lateral sampling every 1-2 m across the wetted width. Fish eDNA was quantified by species-specific endpoint PCR followed by capillary electrophoresis. Close to the cages, lateral eDNA distribution was heterogenous and mirrored cage placement within the stream. In addition to the diluting effect of increased discharge, longitudinal signal changes within the first 20 m were weakest at high discharge. For downstream locations with laterally homogenous eDNA distribution, the signals decreased significantly with increasing distance and discharge. Generally, the eDNA of the larger-bodied salmonid species was less frequently detected, and signal strengths were lower compared to the cyprinid species. This study exemplifies the importance of hydrological conditions for the interpretation of eDNA-based data across seasons. To control for heterogenous eDNA distribution and enable comparisons over time, sampling schemes in lotic habitats need to incorporate hydrological conditions and species traits.

ecology↗