Biology subjects

Kestler, H. A.

Publications and source records attributed to Kestler, H. A..

8 recordsLinked to original sources

bioRxiv2025

An improved epigenetic age estimation with TFMethyl Clock reveals DNA methylation changes during aging in transcription factor binding sites

Methylation-based epigenetic clocks are among the most accurate tools for predicting chronological age. Although DNA methylation (DNAm) at genomic CpG sites is linked to various regulatory mechanisms, the biological interpretability of epigenetic clocks remains surprisingly limited. One primary mechanism by which DNAm is thought to influence gene regulation is by modulating transcription factor binding activity. In this study, we examine established epigenetic clocks to assess the regulatory potential of their predictive CpGs during the aging process. Our analysis reveals that generally most CpG sites used by epigenetic clocks do not overlap known transcription factor binding sites (TFBSs), indicating that changes in TFBS dynamics may not account for prediction accuracy of these models. On the other hand, by identifying age-associated CpGs that overlap TFBSs, we identified transcription factors that may be involved in the aging process. Specifically, the TFBSs of ZBED1, NFE2, CEBPB, FOXP1, EGR1, SP1, PAX5, and MAZ were particularly enriched for age-associated CpGs, while RBPJ, NFIC, RELA, IKZF1, STAT3, and USF2 were significantly protected against methylation changes. By focusing on TFBS-associated CpGs, combined with additional feature selection and engineering steps, we developed an alternative, TFMethyl Clock model, outperforming several existing approaches. Target genes of model-selected, age-predictive CpGs are enriched in the interleukin-1b production and long- chain fatty acid metabolism pathways. In contrast, these CpGs themselves are enriched mainly at binding sites of NR2C2 TF. Furthermore, approximately three-fourths of the target genes downstream of age- predictive CpGs exhibit significant age-related changes, suggesting that our approach captures deeper insights into possible methylation-driven biological aging processes. Our findings demonstrate that incorporating regulatory loci into the design of epigenetic predictors may provide mechanistic insights into the aging process while maintaining or even improving the predictive power.

bioinformatics↗

bioRxiv2025

Pancreatic cancer patient-derived organoids capture therapy response and tumor evolution

Patient-derived organoids (PDOs) reflect parental tumor features and may represent promising avatars for prognosticating drug response. Here, we recruited 169 patients with pancreatic cancer (PC) and established a living biobank including 83 pharmacotyped PDOs isolated from primary and metastatic, treatment-naive and pretreated PCs. In a core facility setting, the pharmacotyping success rate was 61.5%, with an unmet turnaround time of 32 days. Forty-six patients who underwent a total of 94 therapeutic lines were analyzed, resulting in a pharmacotyping-patient response matching rate of 73.4%. Sensitivity, specificity, positive and negative predictive values were 85.0%, 64.8%, 64.2%, and 85.4%, respectively. Tracing clonal evolution in longitudinal biopsies uncovered therapy-induced genetic alterations and single-nucleus multiomics identified transcriptomic and epigenetic changes associated with abnormal FGF signaling during treatment in one particular tracked study case. Our findings highlight the potential of PDOs as robust tools for drug response prediction and patient modeling to advance functional precision medicine.

cancer biology↗

bioRxiv2022

Non-aging despite high mutation rate - genomic insights into the evolution of Hydra

Hydra is a genus of freshwater polyps with remarkable regeneration abilities and a non-senescent phenotype under laboratory conditions. Thus, this animal is particularly interesting for aging research. Here, we gained insights into Hydras recent genetic evolution by genome sequencing of single cells and whole individuals. Despite its extreme longevity, Hydra does not show a lower somatic mutation rate than humans or mice. While we identify biological processes that have evolved under positive selection in animals kept in optimal laboratory conditions for decades, we found no signs of strong negative selection during this tiny evolutionary window. Interestingly, we observe the opposite pattern for the preceding evolution in the wild over a longer time period. Moreover, we found evidence that Hydra evolution in captivity was accompanied and potentially accelerated by loss of heterozygosity. Processes under positive selection in captive animals include pathways associated with Hydras simple nervous system, its nucleic acid metabolic process, cell migration, and hydrolase activity. Genes associated with organ regeneration, regulation of mRNA splicing, histone ubiquitination, and mitochondrial fusion were identified as highly conserved in the wild. Remarkably, several of the processes under strongest selection are closely related to those considered essential for the exapted, i. e. not brought about by natural selection, feature: Hydras non-aging.

evolutionary biology↗

bioRxiv2020

Dynamic characteristics rather than static hubs are important in biological networks

Biological processes are rarely a consequence of single protein interactions but rather of complex regulatory networks. However, interaction graphs cannot adequately capture temporal changes. Among models that investigate dynamics, Boolean network models can approximate simple features of interaction graphs integrating also dynamics. Nevertheless, dynamic analyses are time-consuming and with growing number of nodes may become infeasible. Therefore, we set up a method to identify minimal sets of nodes able to determine network dynamics. This approach is able to depict dynamics without calculating exhaustively the complete network dynamics. Applying it to a variety of biological networks, we identified small sets of nodes sufficient to determine the dynamic behavior of the whole system. Further characterization of these sets showed that the majority of dynamic decision-makers were not static hubs. Our work suggests a paradigm shift unraveling a new class of nodes different from static hubs and able to determine network dynamics.

bioinformatics↗

bioRxiv2019

Exploring Deep Physiological Models for Nociceptive Pain Recognition

Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T0vs.T4) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

bioinformatics↗

bioRxiv2018

Identification of biological mechanisms by semantic classifier systems

The interpretability of a classification model is one of its most essential characteristics. It allows for the generation of new hypotheses on the molecular background of a disease. However, it is questionable if more complex molecular regulations can be reconstructed from such limited sets of data. To bridge the gap between complexity and interpretability, we replace the de novo reconstruction of these processes by a hybrid classification approach partially based on existing domain knowledge. Using semantic building blocks that reflect real biological processes these models were able to construct hypotheses on the underlying genetic configuration of the analysed phenotypes. As in the building process, also these hypotheses are composed of high-level biology-based terms. The semantic information we utilise from gene ontology is a vocabulary which comprises the essential processes or components of a biological system. The constructed semantic multi-classifier system consists of expert base classifiers which each select the most suitable term for characterising their assigned problems. Our experiments conducted on datasets of three distinct research fields revealed terms with well-known associations to the analysed context. Furthermore, some of the chosen terms do not seem to be obviously related to the issue and thus lead to new, hypotheses to pursue.\n\nAuthor summaryData mining strategies are designed for an unbiased de novo analysis of large sample collections and aim at the detection of frequent patterns or relationships. Later on, the gained information can be used to characterise diagnostically relevant classes and for providing hints to the underlying mechanisms which may cause a specific phenotype or disease. However, the practical use of data mining techniques can be restricted by the available resources and might not correctly reconstruct complex relationships such as signalling pathways.\n\nTo counteract this, we devised a semantic approach to the issue: a multi-classifier system which incorporates existing biological knowledge and returns interpretable models based on these high-level semantic terms. As a novel feature, these models also allow for qualitative analysis and hypothesis generation on the molecular processes and their relationships leading to different phenotypes or diseases.

systems biology↗

bioRxiv2017

Naked mole-rat transcriptome signatures of socially-suppressed sexual maturation and links of reproduction to aging

Naked mole-rats (NMRs) are eusocially organized in colonies. Although breeders carry the additional metabolic load of reproduction, they are extremely long-lived and remain fertile throughout their lifespan. Comparative transcriptome analysis of ten organs from breeders and non-breeders of the eusocial long-lived NMR and the polygynous shorter-lived guinea pig provide comprehensive and unbiased molecular evidence that sexual maturation in NMR is socially suppressed. After transition into breeders, transcriptomes are markedly sex-specific, show pronounced feedback signaling via gonadal steroids and have similarities to reproductive phenotypes in African cichlid fish. Further, NMRs show functional enrichment of status-related expression differences associated with aging. Lipid metabolism and oxidative phosphorylation - molecular networks known to be linked to aging - were identified among most affected gene sets. Further, a transcriptome pattern associated with longevity is reinforced in NMR breeders contradicting the disposable soma theory of aging and potentially contributing to their exceptional long life- and healthspan.

systems biology↗

bioRxiv2017

Long-lived rodents reveal signatures of positive selection in genes associated with lifespan and eusociality

The genetic mechanisms that determine lifespan are poorly understood. Most research has been done on short lived animals and it is unclear if these insights can be transferred to long-lived mammals like humans. Some African mole-rats (Bathyergidae) have life expectancies that are multiple times higher than similar sized and phylogenetically closely related rodents. We obtained genomic and transcriptomic data from 17 rodent species and systematically scanned eleven lineages associated with the evolution of longevity and eusociality for positively selected genes (PSGs). The set of 319 PSGs contains regulators of mTOR and is enriched in functional terms associated with (i) processes that are regulated by the mTOR pathway, e.g. translation, autophagy and mitochondrial biogenesis, (ii) the immune system and (iii) antioxidant defense. Analyzing gene expression of PSGs during aging in the long-lived naked mole-rat and up-regulation in the short-lived rat, we found a pattern fitting the antagonistic pleiotropy theory of aging.

evolutionary biology↗