Ziolkowski, P. | Search bioRxiv

bioRxiv2025

Linker Histones Enhance Robustness in Diurnal Transcription Dynamics.

Linker histone H1 is crucial for chromatin organization and gene expression in Arabidopsis thaliana, influencing development and stress responses. To explore its role in diurnal gene regulation, we examined H1-deficient plants and found that H1 is essential for maintaining rhythmic gene expression. Genes losing synchronization often contained NAC transcription factor binding sites, indicating H1 may affect their accessibility. Nuclear imaging revealed that H1 subtly modulates nuclear size and chromatin distribution across the photoperiod. Epigenetic analysis showed typical diurnal changes--declines in H3K4me3 and active RNA Pol II in the evening and increases in H3K27me3. In H1 mutants, these patterns persisted but with elevated H3K4me3 and RNA Pol II (Ser2P) levels at night and in the morning. These results suggest that H1 fine-tunes chromatin and transcriptional rhythms, contributing to the temporal coordination of gene activity in response to environmental and developmental signals.

plant biology↗

bioRxiv2024

Development of a Deep Learning model Tailored for HER2 Detection in Breast Cancer to aid pathologists in interpreting HER2-Low cases

IntroductionOver 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study, we evaluated the performance of a deep learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells staining intensity and completeness of membrane staining. MethodsWe trained a DL model on a multi-centric cohort of breast cancer cases with HER2- immunohistochemistry scores (n=299). The model was validated on 2 independent multi- centric validation cohorts (n=369 and n=92), with all cases reviewed by 3 senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. ResultsThe models concordance with the ground truth (ICC = 0.77 [0.68 - 0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the 3 senior pathologists (ICC = 0.45 [0.17 - 0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93%- 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumors respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumor-infiltrating lymphocytes, and necrosis, whilst some artifacts such as non- specific background cytoplasmic stain in the cytoplasm of tumor cells also cause discrepancy. ConclusionDeep learning can support pathologists interpretation of difficult HER2-low cases. Morphological variables and some specific artifacts can cause discrepant HER2-scores between the pathologist and the DL Model.

pathology↗