Biology subjects

Rudolf, F.

Publications and source records attributed to Rudolf, F..

8 recordsLinked to original sources

bioRxiv2026

Nuclear receptor LRH-1 regulates early T cell development in mice

T cell development in the thymus requires tightly coordinated transcriptional programs that regulate lineage commitment, proliferation and differentiation. While key transcription factors controlling these processes have been extensively characterized, the contribution of the low expressed nuclear receptor Liver Receptor Homolog 1 (LRH-1, Nr5a2) in T cell development remains unexplored. Here, we investigated the role of LRH-1 in thymocyte maturation using an inducible ex vivo deletion system and in vivo Lck-Cre- and CD4-Cre-mediated LRH-1 knockout mouse models. We demonstrate that inducible LRH-1 deletion impairs early thymocyte development, identifying LRH-1 as a critical regulator of the double negative (DN)2/DN3 to DN4 transition. Early Lck-Cre-mediated deletion of LRH-1, but not CD4-Cre-mediated deletion at the double positive stage, resulted in markedly reduced thymic size and cellularity, indicating a stage-specific requirement for LRH-1 during thymopoiesis. Lck-Cre-mediated LRH-1 deletion led to a decreased frequency of mature CD4 T cells in peripheral lymphoid organs, while the remaining mature T cells were predominantly Cre reporter-negative and therefore escaped LRH-1 deletion. CD4 T cells that escaped Cre-mediated LRH-1 deletion exhibited impaired T cell activation marker expression and cytokine secretion. In vivo, these defects resulted in attenuated T cell effector function and compromised regulatory T cell-mediated protection in a T cell transfer model of colitis, indicating impaired effector and regulatory T cell function under (patho)physiological conditions. Collectively, our findings identify LRH-1 as a critical, previously unrecognized regulator of early thymocyte development, and establish its essential role in shaping functional peripheral CD4 T cell-mediated immune responses.

immunology↗

bioRxiv2025

Activation of nuclear receptors correlates with tuberculosis severity and is a target for host-directed therapy

The immune response to Mycobacterium tuberculosis is accompanied by metabolic adaptations that fuel host immunity, but that are exploited by the pathogen to ensure persistence and growth. Nuclear receptors, such as liver-X-receptors (LXR), orchestrate macrophage immunometabolic adaptations to infection and globally associate with tuberculosis (TB) protection. Here, we show that the "signalling by nuclear receptors" (SNR) pathway is detected in the whole blood of TB patients and that its expression correlates with disease severity. Accordingly, we also show that the activation of the LXR pathway progressively increases in the lungs of M. tuberculosis-infected C57BL/6 and C3HeB/FeJ mice. Pharmacologic activation of LXR, specifically at the chronic stage of infection, improved infection outcomes and significantly prolonged the survival of the highly susceptible C3HeB/FeJ mice. Common to both mouse models and to in vitro macrophage infections, LXR activation enhanced bacterial control together with an increase in extracellular cholesterol levels. We propose that progressive LXR activation is required to fine-tune host cholesterol availability during M. tuberculosis infections and restrict access to this nutrient during chronic stages of infections. Collectively, we identify the SNR pathway as a potential biomarker of TB severity and timely LXR activation as a candidate host-directed therapy.

immunology↗

bioRxiv2022

Phenotypic Heterogeneity in the DNA Replication Stress Response Revealed by Quantitative Protein Dynamics Measurements

Cells respond to environmental stressors by activating programs that result in protein abundance and localization changes. The DNA damage and DNA replication stress responses have been heavily studied and provide exemplars of the roles of protein localization and abundance regulation in proper cellular stress response. While vast amounts of data have been collected to describe the dynamics of yeast proteins in response to numerous external stresses, few have assessed and compared both protein localization kinetics and phenotypic heterogeneity in the same context, particularly during DNA replication stress. We developed a robust yet simple quantification scheme to identify and measure protein localization change events (re-localization) and applied it to the 314 yeast proteins whose subcellular distribution changes following DNA replication stress. We captured different kinetics of protein re-localization, identified proteins with localization changes that were not detected in previous analyses, and defined the extent of heterogeneity in stress-induced protein re-localization. Our imaging platforms and analysis pipeline enables efficient measurements of protein localization phenotypes for single cells over time and will guide future work in elucidating the biological parameters that govern cellular heterogeneity.

systems biology↗

bioRxiv2021

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

Experimental studies of cell growth, inheritance, and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking--assigning identical cells on consecutive images to a track--is often challenging due to imperfect segmentation, moving cells, or focus drift, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance measures the similarity between cells in two consecutive images by comparing the structural information contained in the low frequencies of a Fourier transform. We show that it is broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses the concept to reject unlikely assignments, thereby substantially increasing tracking performance on published and newly generated long-term data sets from various species. For S. cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator. It demonstrates how highly precise tracking can help uncover previously undescribed single-cell biology.

systems biology↗

bioRxiv2021

A single-cell atlas of lymphocyte adaptive immune repertoires and transcriptomes reveals age-related differences in convalescent COVID-19 patients

COVID-19 disease outcome is highly dependent on adaptive immunity from T and B lymphocytes, which play a critical role in the control, clearance and long-term protection against SARS-CoV-2. To date, there is limited knowledge on the composition of the T and B cell immune receptor repertoires [T cell receptors (TCRs) and B cell receptors (BCRs)] and transcriptomes in convalescent COVID-19 patients of different age groups. Here, we utilize single-cell sequencing (scSeq) of lymphocyte immune repertoires and transcriptomes to quantitatively profile the adaptive immune response in COVID-19 patients of varying age. We discovered highly expanded T and B cells in multiple patients, with the most expanded clonotypes coming from the effector CD8+ T cell population. Highly expanded CD8+ and CD4+ T cell clones show elevated markers of cytotoxicity (CD8: PRF1, GZMH, GNLY; CD4: GZMA), whereas clonally expanded B cells show markers of transition into the plasma cell state and activation across patients. By comparing young and old convalescent COVID-19 patients (mean ages = 31 and 66.8 years, respectively), we found that clonally expanded B cells in young patients were predominantly of the IgA isotype and their BCRs had incurred higher levels of somatic hypermutation than elderly patients. In conclusion, our scSeq analysis defines the adaptive immune repertoire and transcriptome in convalescent COVID-19 patients and shows important age-related differences implicated in immunity against SARS-CoV-2.

immunology↗

bioRxiv2021

Single-cell sequencing of plasma cells from COVID-19 patients reveals highly expanded clonal lineages produce specific and neutralizing antibodies to SARS-CoV-2

Isolation and characterization of antibodies in COVID-19 patients has largely focused on memory B cells, however it is the antibody-secreting plasma cells that are directly responsible for the production of serum antibodies, which play a critical role in controlling and resolving SARS-CoV-2 infection. To date there is little known about the specificity of plasma cells in COVID-19 patients. This is largely because plasma cells lack surface antibody expression, which complicates their screening. Here, we describe a technology pipeline that integrates single-cell antibody repertoire sequencing and high-throughput mammalian display screening to interrogate the specificity of plasma cells from 16 convalescent COVID-19 patients. Single-cell sequencing allows us to profile antibody repertoire features in these patients and identify highly expanded clonal lineages. Mammalian display screening is employed to reveal that 37 antibodies (out of 132 candidates) derived from expanded plasma cell clonal lineages are specific for SARS-CoV-2 antigens, including antibodies that target the receptor binding domain (RBD) with high affinity and exhibit potent neutralization of SARS-CoV-2. One Sentence SummarySingle-cell antibody repertoire sequencing and high-throughput screening identifies highly expanded plasma cells from convalescent COVID-19 patients that produce SARS-CoV-2-specific antibodies capable of potent neutralization.

immunology↗

bioRxiv2020

Whi5 is diluted and protein synthesis does not dramatically increase in pre-Start G1

In their manuscript, Litsios et al.1 report a new model for how cell growth and biosynthetic activity control the G1/S transition in budding yeast. In essence, Litsios et al. claim that Start is driven by an increasing concentration of the G1 cyclin Cln3 due to a dramatic acceleration of protein synthesis in pre-Start G1 and not by the dilution of the cell cycle inhibitor Whi5. While we previously reported that Start was in part driven by cell growth during G1 diluting out the Start inhibitor Whi52, Litsios et al. report that Whi5 remains at constant concentration during G1, and changes in Whi5 concentration therefore do not contribute to Start. Since Litsios et al. directly contradict several key points of our own model of how cell growth triggers Start, we decided to investigate their claims and data. More specifically, we decided to investigate Litsios et al.s three major claims: O_LIWhi5 concentration remains constant during G1 C_LIO_LICln3 concentration strongly increases prior to Start C_LIO_LIGlobal protein synthesis rates increase by 2-3 fold prior to Start C_LI We investigated each of these three claims and found that the evidence presented by Litsios et al. does not support their claims due to inadequate analysis methods and flaws in their experiments.

molecular biology↗

bioRxiv2018

A simple and flexible computational framework for inferring sources of heterogeneity from single-cell dynamics

The availability of high-resolution single-cell data makes data analysis and interpretation an important open problem, for example, to disentangle sources of cell-to-cell and intra-cellular variability. Nonlinear mixed effects models (NLMEs), well established in pharmacometrics, account for such multiple sources of variations, but their estimation is often difficult. Single-cell analysis is an even more challenging application with larger data sets and models that are more complicated. Here, we show how to leverage the quality of time-lapse microscopy data with a simple two-stage method to estimate realistic dynamic NLMEs accurately. We demonstrate accuracy by benchmarking with a published model and dataset, and scalability with a new mechanistic model and corresponding dataset for amino acid transporter endocytosis in budding yeast. We also propose variation-based sensitivity analysis to identify time-dependent causes of cell-to-cell variability, highlighting important sub-processes in endocytosis. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

systems biology↗