Biology subjects

Juhasz, J.

Publications and source records attributed to Juhasz, J..

4 recordsLinked to original sources

bioRxiv2026

Circadian clock regulates intestinal epithelial cell differentiation via NOTCH/Hes1 oscillations

The circadian clock regulates diverse cellular processes, including intestinal epithelial cell (IEC) proliferation. However, mechanisms regulating clock-dependent IEC differentiation remain unknown. We performed a time course RNA-Seq using the mouse small intestine and identified NOTCH signaling, a key mechanism regulating IEC differentiation, as one of the pathways under the control of circadian rhythms. Using mouse enteroids, we discovered that a NOTCH reporter, Hes1-luciferase, exhibits both ultradian or circadian oscillations depending on the stemness of mouse enteroids. Furthermore, single-cell analysis of Hes1-mCherry revealed that the period of Hes1 oscillations varies widely, but circadian rhythms modulate the number of Hes1-mCherry+ cells in the population. Finally, we show that Paneth cell numbers fluctuate over the circadian cycle, suggesting that circadian clock-regulated Hes1 drives the circadian dynamics of IEC composition. Our study provides a deeper insight into circadian regulation of IEC differentiation, which will be critical for applications of chronotherapies for digestive diseases.

molecular biology↗

bioRxiv2025

Your Brain Doesn't Look a Day Past 70! Cross-Sectional Associations with Brain-Predicted Age in the Cognitively-Intact Oldest-Old

The cognitively-intact oldest-old (85+) may be the most-resilient members of their birth cohort; due to survivorship effects (e.g., depletion of susceptibles), risk factors associated with brain aging biomarkers in younger samples may not generalize to the oldest-old. We evaluated associations between established aging-related risk factors and brain-predicted age difference (brainPAD) in a cross-sectional cognitively-intact oldest-old sample. Additionally, we evaluated brainPAD-cognition associations to characterize brain maintenance vs. cognitive reserve in our sample. Oldest-old adults (N = 206; 85-99 years; MoCA > 22 or neurologist evaluation) underwent T1-weighted MRI; brainPAD was generated with brainageR, such that more-positive brainPAD reflected relatively advanced brain aging. Sex, educational attainment, alcohol and smoking history, exercise history, BMI, cardiovascular and metabolic disease history, and anticholinergic medication burden were self-reported. Global cognitive z-score and coefficient of variation were derived from the NACC UDS 3.0 cognitive battery; crystallized-fluid discrepancy was derived from the NIH Toolbox Cognitive Battery. Mean brainPAD was -7.99 (SD: 5.37; range: -24.50, 6.03). Women showed more-delayed brain aging than men (B = -2.35, 95% CI = - 4.28, -0.41, p = 0.018). No other exposures were associated with brainPAD. BrainPAD was not associated with any cognitive variable. These findings suggest that cognitively-intact oldest-old adults may be atypically-resistant to risk factors associated with aging in younger samples, consistent with survivorship effects in aging. Furthermore, brainPAD may have limited explanatory value for cognitive performance in cognitively-intact oldest-old adults, potentially due to high cognitive reserve. Overall, our findings highlight the impact of survivorship effects on brain aging research. HighlightsO_LIBrain-predicted age difference was assessed in cognitively-intact oldest-old ([≥]85) C_LIO_LIMean brain-predicted age corresponded to an 8-year delay in brain aging C_LIO_LIBrain age in oldest-old was not associated with self-reported health history C_LIO_LIBrain age was not associated with cognitive performance C_LI

neuroscience↗

bioRxiv2024

ProkBERT PhaStyle: Accurate Phage Lifestyle Prediction with Pretrained Genomic Language Models

BackgroundPhage lifestyle prediction, i.e. classifying phage sequences as virulent or temperate, is crucial in biomedical and ecological applications. Phage sequences from metagenome or metavirome assemblies are often fragmented, and the diversity of environmental phages is not well known. Current computational approaches often rely on database comparisons and machine learning algorithms that require significant effort and expertise to update. We propose using genomic language models for phage lifestyle classification, allowing efficient direct analysis from nucleotide sequences without the need for sophisticated preprocessing pipelines or manually curated databases. MethodsWe trained three genomic language models (DNABERT-2, Nucleotide Transformer, and ProkBERT) on datasets of short, fragmented sequences. These models were then compared with dedicated phage lifestyle prediction methods (PhaTYP, DeePhage, BACPHLIP) in terms of accuracy, prediction speed, and generalization capability. ResultsProkBERT PhaStyle consistently outperforms existing models in various scenarios. It generalizes well for out-of-sample data, accurately classifies phages from extreme environments, and also demonstrates high inference speed. Despite having up to 20 times fewer parameters, it proved to be better performing than much larger genomic language models. ConclusionsGenomic language models offer a simple and computationally efficient alternative for solving complex classification tasks, such as phage lifestyle prediction. ProkBERT PhaStyles simplicity, speed, and performance suggest its utility in various ecological and clinical applications.

bioinformatics↗

bioRxiv2023

ProkBERT Family: Genomic Language Models for Microbiome Applications

Machine learning offers transformative capabilities in microbiology and microbiome analysis, deciphering intricate microbial interactions, predicting functionalities, and unveiling novel patterns in vast datasets. This enriches our comprehension of microbial ecosystems and their influence on health and disease. However, the integration of machine learning in these fields contends with issues like the scarcity of labeled datasets, the immense volume and complexity of microbial data, and the subtle interactions within microbial communities. Addressing these challenges, we introduce the ProkBERT model family. Built on transfer learning and self-supervised methodologies, ProkBERT models capitalize on the abundant available data, demonstrating adaptability across diverse scenarios. The models learned representations align with established biological understanding, shedding light on phylogenetic relationships. With the novel Local Context-Aware (LCA) tokenization, the ProkBERT family overcomes the context size limitations of traditional transformer models without sacrificing performance or the information rich local context. In bioinformatics tasks like promoter prediction and phage identification, ProkBERT models excel. For promoter predictions, the best performing model achieved an MCC of 0.74 for E. coli and 0.62 in mixed-species contexts. In phage identification, they all consistently outperformed tools like VirSorter2 and DeepVirFinder, registering an MCC of 0.85. Compact yet powerful, the ProkBERT models are efficient, generalizable, and swift. They cater to both supervised and unsupervised tasks, providing an accessible tool for the community. The models are available on GitHub and HuggingFace.

bioinformatics↗