Biology subjects

Dempfle, A.

Publications and source records attributed to Dempfle, A..

4 recordsLinked to original sources

bioRxiv2026

Multi-Lab Testing of Early Preclinical Discoveries Identifies Promising Treatments

A fundamental challenge in drug development is the frequent failure of early laboratory research to translate into clinical benefit. One promising solution is to confirm findings from exploratory single-laboratory studies across multiple laboratories before clinical testing. We investigated this approach following the conduct of preclinical multi-laboratory studies across different fields of medicine. For this, we evaluated effect sizes, experimental rigor, and a set of criteria to identify determinants of confirmation success. When tested under increased rigor, only a fraction of multi-laboratory studies confirmed the initial results. The underlying effect size reduction was associated with outcome-relevant experimental differences between exploratory and confirmatory stages. In summary, multi-laboratory studies proved highly informative and served as an effective filter for promising treatments.

scientific communication and education↗

bioRxiv2025

The dynamic role of HLA proteins on compositional alterations of T-cell repertoires in inflammatory bowel disease

BackgroundInflammatory bowel disease (IBD), which is characterised by genetic predispositions and dysregulated immune responses, is rapidly emerging as a global health challenge. Genetic variations in the human leukocyte antigen (HLA) region are strongly associated with IBD; nonetheless, the functional consequences of this variation on the composition of T-cell receptors remain poorly understood. MethodsWe conducted comprehensive CDR3-QTL mapping using T-cell receptor beta (TRB) repertoires paired with HLA allotypes from 1,973 individuals, including 1,201 individuals with IBD and 772 healthy controls (HCs), to explore the role of the HLA allelic variants on TRB composition. Using network analyses, we defined key CDR3 motifs of public clones that were linked to risk HLA alleles for chronic inflammatory diseases. ResultsWe identified novel sites within both HLA class I and class II proteins that were strongly linked to TRB amino acid composition - cdr3QTLs, in both HCs and individuals with IBD. Those sites in HLA-DRB1 and HLA-DQ had stronger effects on CDR3 composition than did the disease in the IBD cohort. In HCs, but not in UC or CD, the strongest HLA signals that affected expanded clones, overlapped with primary CD risk loci from GWAS, e.g., DRB1 site 70 and DQA1 site 25. The strongest CD-specific effects on TRB composition were found in HLA-B, especially at sites that modulate viral responses (e.g., HLA-B sites 9, 67). Finally, the main risk HLA alleles for chronic inflammatory diseases clustered together based on the physicochemical properties of residues mapped to cdr3QTLs, suggesting that risk alleles might exert similar effects on the TRB repertoire. ConclusionStructurally, the main cdr3QTLs in both HLA class I/II are located in peptide-binding sites or sites contacting TCRs, highlighting their direct and antigen-mediated influences on TRB repertoires. Our findings suggest that cdr3QTLs in HLA class I exert IBD-specific effects on the TRB composition, influencing the dysregulated T cell responses implicated in IBD pathogenesis, possibly on the earlier stages of T cell development. While HLA class II cdr3QTLs show universal effects and strong associations with T-cell receptors, irrespective of disease.

immunology↗

bioRxiv2023

Evolution of E. coli in a mouse model of inflammatory bowel disease leads to a disease-specific bacterial genotype and trade-offs with clinical relevance

Inflammatory bowel disease (IBD) is a persistent inflammatory condition that affects the gastrointestinal tract and presents significant challenges in its management and treatment. Despite the knowledge that within-host bacterial evolution occurs in the intestine, the disease has rarely been studied from an evolutionary perspective. In this study, we aimed to investigate the evolution of resident bacteria during intestinal inflammation and whether- and how disease-related bacterial genetic changes may present trade-offs with potential therapeutic importance. Here, we perform an in vivo evolution experiment of E. coli in a gnotobiotic mouse model of IBD, followed by multiomic analyses to identify disease-specific genetic and phenotypic changes in bacteria that evolved in an inflamed versus a non-inflamed control environment. Our results demonstrate distinct evolutionary changes in E. coli specific to inflammation, including a single nucleotide variant that independently reached high frequency in all inflamed mice. Using ex vivo fitness assays, we find that these changes are associated with a higher fitness in an inflamed environment compared to isolates derived from non-inflamed mice. Further, using large-scale phenotypic assays, we show that bacterial adaptation to inflammation results in clinically relevant phenotypes, which intriguingly include collateral sensitivity to antibiotics. Bacterial evolution in an inflamed gut yields specific genetic and phenotypic signatures. These results may serve as a basis for developing novel evolution-informed treatment approaches for patients with intestinal inflammation.

evolutionary biology↗

bioRxiv2022

Stratification of responses to tDCS intervention in a healthy paediatric population based on resting-state EEG profiles

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of applications in both the clinical and cognitive psychology domains. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to timely and costly treatments with ineffective results if this variability is not taken into account. We propose the usage of electroencephalography (EEG) for the analysis and prediction of individual responses to tDCS. In this context the application of machine learning can be of enormous help. We analysed resting-state EEG activity to identify subgroups of participants with an homogeneous electrophysiological profile and their response to different tDCS interventions. The study described herein, which focuses on healthy controls, was conducted within a clinical trial for the development of treatments based on tDCS for age-matched children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). We have studied a randomized, double-blind, sham-controlled tDCS intervention in 56 healthy children and adolescents aged 10-17, applied in 2 parallel groups over 2 target regions, namely left Dorsolateral Prefrontal Cortex (lDLPFC) and right Inferior Frontal Gyrus (rIFG). Cognitive behavioural tasks were used to both activate particular brain areas during the stimulation and to assess the impact of the intervention afterwards. We have implemented an unsupervised learning approach to stratify participants based on their resting-state EEG spectral features before the tDCS application. We have then applied a correlational analysis to identify EEG profiles associated with tDCS subject response to the specific stimulation sites and the presence or not of concurrent tasks during the intervention. In the results we found specific digital electrophysiological profiles that can be associated to a positive response, whereas subjects with other profiles respond negatively or do not respond to the intervention. Findings suggest that unsupervised machine learning procedures, when associated with proper visualization features, can be successfully used to interpret and eventually to predict responses of individuals to tDCS treatment.

neuroscience↗