Biology subjects

Pfeil, J.

Publications and source records attributed to Pfeil, J..

9 recordsLinked to original sources

bioRxiv2026

Label-free detection of individual virus-infected cells using deep learning

Numerous applications in research and medicine rely on reliable identification and quantification of virus-infected cells. Current methods either apply reporter viruses, that often differ from clinical isolates (e. g. cell tropism, immune evasion) or staining approaches, that prevent live-cell experiments and may introduce biases through manual counting. We present a deep learning model for the label-free identification of virus-infected cells on light microscopy images (VAIruScope). To overcome limitations, our pipeline enables an automated quantification of virus-infected cells based on the recognition of cytopathic effects. The method was applied to different cell models and four clinically relevant prototype viruses representing RNA- (influenza A virus), DNA- (human cytomegalovirus, herpes simplex virus-1) and retroviruses (human immunodeficiency virus-1). VAIruScope identified infected cells achieving classification accuracies of up to 96 %. As proof-of-concept, the method was validated using electron microscopy for a wild-type HSV-1. VAIruScope may be applicable to live-cell imaging to investigate infection dynamics.

bioinformatics↗

bioRxiv2025

The temperature sensitivity of arboviral disease extrinsic incubation periods: a systematic review

BackgroundArboviral diseases are an increasing global public health concern, driven by both human and environmental factors. A key parameter shaping transmission is the extrinsic incubation period (EIP)--the time between a mosquito acquiring a virus and becoming infectious--which is strongly influenced by temperature. However, the temperature-EIP relationship remains poorly characterized across arboviruses and mosquito species. MethodsWe conducted the first systematic review of laboratory studies evaluating temperature effects on EIP-related outcomes across all reported mosquito-arbovirus pairings. We searched three databases and extracted data on transmission efficiency (TE), applying linear regression models adjusted for diurnal temperature range and viral dose. Studies where TE could not be extracted were synthesized narratively. ResultsOur synthesis included 60 studies covering 17 arboviral diseases and 20 mosquito species. We found substantial heterogeneity in temperature effects on TE. While CHIKV, ZIKV, DENV, and WNV generally showed increased TE and shorter EIP at higher temperatures, many viruses--such as SINV, USUV, and BATV--exhibited no clear trends, often due to limited data and small sample sizes. Even across different vectors of the same virus, findings varied widely, reflecting both biological differences and inconsistent experimental designs. ConclusionsThese findings reveal major gaps in our understanding of climate-sensitive arbovirus transmission. Standardization of vector competence experiments and expanded research on neglected viruses are urgently needed. Environmental cofactors beyond temperature--such as humidity and variability--should also be incorporated to improve modelling and support climate-resilient intervention strategies. Author summaryThe burden of disease caused by arboviruses is increasing globally, largely driven by rapid urbanization, climate change, and global mobility. The transmission dynamics of arboviruses are modulated by a parameter known as the extrinsic incubation period, which is driven by environmental factors, especially temperature. However, the extent and direction of this effect has not been well characterized for all viruses, as data is limited and has not been synthesized for many neglected tropical diseases. Our study is the first systematic review to examine the existing evidence of laboratory studies across all studied mosquito species and virus pairings and to provide a comprehensive dataset for future modelling studies. We find that there is significant heterogeneity, not only between viruses, but also within individual vector-virus combinations, and that many arboviruses show no temperature effect. Based on these results, we propose that existing research efforts be standardized, and further research is conducted on emerging and re-emerging arboviruses for better intervention strategies and modelling predictions.

microbiology↗

bioRxiv2024

tauX: A Gene Expression Ratio Strategy to Improve Machine Learning Applications in Precision Medicine

Machine learning algorithms identify patterns that would otherwise be difficult to observe in high-dimensional molecular and clinical data. For this reason, machine learning has the potential to have a profound impact on clinical decision making and drug target discovery. Nevertheless, there remain considerable technical challenges in adapting these tools for clinical use. These challenges include clinical feature engineering, model selection, and defining optimal strategies for model training. For cancer care, RNA sequencing of patient tumor biopsies has already proven to be a powerful molecular assay to characterize tumor-intrinsic and -extrinsic phenotypes influencing therapeutic response. To improve the predictive performance of RNA-sequencing data, we developed the tauX machine learning framework to refine gene expression features and improve the performance of machine learning algorithms. The tauX framework uses aggregated ratios of positively and negatively associated predictive genes to simplify the prediction task. We showed a significant improvement in predictive performance using a large database of synthetic gene expression profiles. We also show how the tauX framework can be used to elucidate the mechanisms of response and resistance to checkpoint blockade therapy using data from the Stand Up to Cancer (SU2C) Lung Response Cohort and The Cancer Genome Atlas (TCGA). The tauX strategy achieved superior predictive performance compared to models built upon established feature engineering strategies or widely used cancer gene expression signatures. The tauX framework is available as a freely deployable docker container (https://hub.docker.com/r/pfeiljx/taux).

bioinformatics↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2021

Reversible brain edema in experimental cerebral malaria is associated with transcellular blood-brain barrier disruption and delayed microhemorrhages

Brain swelling occurs in cerebral malaria (CM) and may either reverse or result in fatal outcome. It is currently unknown how brain swelling in CM reverses, as investigations have been hampered by inadequate animal models. In this study, we show that reversible brain swelling in experimental murine cerebral malaria (ECM) can be induced reliably after single vaccination with radiation-attenuated sporozoites as revealed by in vivo high-field (9.4T) magnetic resonance imaging. Our results provide evidence that parenchymal fluid increase and consecutive brain swelling results from transcellular blood-brain barrier disruption (BBBD), as revealed by electron microscopy. This mechanism enables reversal of brain swelling but does not prevent persistent focal brain damage, evidenced by microhemorrhages, in areas of most severe BBBD. In a cohort of 27 pediatric and adult CM patients (n=4 fatal, n=23 non-fatal) two out of four fatal CM patients (50%) and 8 out of 23 non-fatal CM patients (35%) showed microhemorrhages on MRI at clinical field strength of 1.5T, emphasizing the translational potential of the experimental model. Our data suggest that targeting transcellular BBBD may represent a promising adjunct therapeutic approach in cerebral brain swelling to reduce edema and may ultimately lead to a reduced permanent brain damage and a better longtime neurological outcome. Author summaryBrain swelling, which occurs in diseases such as cerebral malaria, is not necessarily fatal, and may reverse. Even upon reversal of brain swelling, neurological sequelae can still occur. The factors contributing to the reversibility of brain edema are not known, and treatment options remain therefore limited. Identifying the mechanisms leading to such reversibility could inform clinical management aimed at decreasing brain swelling and consecutive brain injury. Here we introduce a reproducible and simple animal model that allows comprehensive in vivo studies of reversible brain swelling in cerebral malaria at the peak of disease and upon recovery. We identify a specific type of blood-brain barrier disruption (BBBD) as a mechanism that occurs in brain swelling. We show that BBBD can reverse, but also highlight remaining brain damage in areas of most severe BBBD. As the ECM model introduced here bares crucial similarities to the CM in humans, our findings open strategies to study new therapeutic avenues and point to compounds that specifically target transcellular BBBD to reduce brain edema, and increase survival rates.

neuroscience↗

bioRxiv2020

Prevention of EAE by PEGylated Antigenic Peptide

The treatment of autoimmune disorders such as multiple sclerosis (MS) so far relies largely on the use of non-specific immunosuppressive drugs, which are not able to cure the disease. Presently, approaches to induce antigen-specific tolerance e.g. by peptide-based tolerogenic "inverse" vaccines regain interest. We previously have shown that coupling of peptides to carriers can enhance their capacity to induce regulatory T cells in vivo. We here investigated in an experimental autoimmune encephalomyelitis (EAE) model for chronic MS (MOG C57BL/6) whether the tolerogenic potential of immunodominant myelin T cell epitopes can be improved by conjugation to the synthetic carrier polyethylene glycol (PEG). Indeed, preventive administration of the PEGylated antigenic peptide could almost completely protect mice from EAE development, which was accompanied by reduced immune cell infiltration in the central nervous system (CNS). Depletion of Tregs abrogated the protective effect indicating that Tregs play a crucial role in induction of antigen-specific tolerance in EAE. Treatment during the acute phase was safe and did not induce immune activation. However, treatment at the peak of disease was not affecting the disease course, suggesting that either induction of Tregs is not occurring in the highly inflamed situation, or that the immune system is refractory to regulation in this condition. Thus, PEGylation of antigenic peptides is an effective and feasible strategy to improve tolerogenic (Treg-inducing) peptide-based vaccines, but application in overt disease might require modifications or combination therapies that simultaneously suppress effector mechanisms.

immunology↗

bioRxiv2019

Framework for determining accuracy of RNA sequencing data for gene expression profiling of single samples

BackgroundThe accuracy of gene expression as measured by RNA sequencing (RNA-Seq) is dependent on the amount of sequencing performed. However, some types of reads are not informative for determining this accuracy. Unmapped and non-exonic reads do not contribute to gene expression quantification. Duplicate reads can be the product of high gene expression or technical errors. FindingsWe surveyed bulk RNA-Seq datasets from 2179 tumors in 48 cohorts to determine the fractions of uninformative reads. Total sequence depth was 0.2-668 million reads (median (med.) 61 million; interquartile range (IQR) 53 million). Unmapped reads constitute 1-77% of all reads (med. 3%; IQR 3%); duplicate reads constitute 3-100% of mapped reads (med. 27%; IQR 30%); and non-exonic reads constitute 4-97% of mapped, non-duplicate reads (med. 25%; IQR 21%). Informative reads--Mapped, Exonic, Non-duplicate (MEND) reads--constitute 0-79% of total reads (med. 50%; IQR 31%). Further, we find that MEND read counts have a 0.22 Pearson correlation to the number of genes expressed above 1 Transcript Per Million, while total reads have a correlation of -0.05. ConclusionsSince the fraction of uninformative reads vary, we propose using only definitively informative reads, MEND reads, for the purposes of asserting the accuracy of gene expression measured in a bulk RNA-Seq experiment. We provide a Docker image containing 1) the existing required tools (RSeQC, sambamba and samblaster) and 2) a custom script. We recommend that all results, sensitivity studies and depth recommendations use MEND units.

genomics↗

bioRxiv2019

ProTECT: Prediction of T-cell Epitopes for Cancer Therapy

Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies (ACTs) and Peptide Vaccines (PVs) to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patients HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC (pMHC) binding prediction, and ranking of final candidates. We demonstrate ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, and compare it with published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 minutes per sample when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect.

bioinformatics↗

bioRxiv2019

Genomic landscape of 261 childhood cancer patient-derived xenograft models

Accelerating cures for children with cancer remains an immediate challenge due to extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs) from high-risk childhood cancers, many refractory to current standard-of-care treatments. Here, we genomically characterize 261 PDX models from 29 unique pediatric cancer malignancies and demonstrate faithful recapitulation of histologies, subtypes, and refine our understanding of relapsed disease. Expression and mutational signatures are used to classify tumors for TP53 and NF1 inactivation, as well as impaired DNA repair. We anticipate that these data will serve as a resource for pediatric oncology drug development and guide rational clinical trial design for children with cancer.\n\nHighlightsO_LIMultiplatform genomic analysis defines landscape of 261 pediatric cancer patient derived xenograft (PDX) models\nC_LIO_LIPediatric patient derived xenografts faithfully recapitulate relapsed disease\nC_LIO_LIInferred TP53 pathway inactivation correlates with pediatric cancer copy number burden\nC_LIO_LISomatic mutational signatures predict impaired DNA repair across multiple histologies\nC_LI

genomics↗