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Zachariah, E.

Publications and source records attributed to Zachariah, E..

3 recordsLinked to original sources

Hypoxia-induced metastatic heterogeneity in pancreatic cancer

In most solid tumors, hypoxia constitutes a defining microenvironmental feature that reprograms malignant cells into a highly metastatic state by driving cellular plasticity and exacerbating chromosomal instability (CIN). However, the mechanisms by which cancer cells concurrently co-opt these elements of hypoxic adaptation to promote metastasis remains poorly understood. Here, we report that hypoxia promotes metastasis by suppressing the JmjC-containing histone lysine demethylase Kdm8. CRISPR/Cas9-mediated targeting of Kdm8 in a Kras;Trp53-driven mouse model of pancreatic ductal adenocarcinoma (PDA) robustly rewires the malignant cell transcriptomic programs, leading to a profound loss of the epithelial morphology and widespread metastatic disease. In PDA patients, a high KDM8-induced gene signature is associated with reduced metastatic burden and better survival in advanced disease. Notably, Kdm8 suppression in normoxia recapitulates key aspects of the global epigenetic and transcriptomic rewiring, mitotic spindle defects, and CIN induced by hypoxia. Moreover, disruption of Kdm8s demethylase activity phenocopies Kdm8 loss, whereas expression of hypermorphic Kdm8 variants resistant to hypoxic suppression markedly reduces metastasis beyond the levels achieved by the wildtype protein. Through the suppression of Kdm8 demethylase function, hypoxia unleashes a potent metastatic program by simultaneously advancing cellular plasticity and CIN.

cell biology↗

A Spatial and Temporal Transcriptomic Atlas of Mouse Intestinal Regeneration

Background & AimsThe intestinal epithelium exhibits a remarkable capacity for regeneration following injury. However, the spatial and temporal dynamics of the injury-repair cycle remain incompletely understood. MethodsWe employ spatial transcriptomics to create an atlas of the damage and repair response to ionizing radiation in the mouse intestine. We map molecular events driving epithelial recovery over a six-day period and 23 biological samples, spanning the early apoptotic response to tissue remodeling and repair. ResultsThe datasets capture mRNA of 19,042 genes in [~]26 million bins at 2{micro}m resolution. Analysis revealed transcriptional patterns and niche signals that would remain undetected in bulk or single-cell approaches, including a non-random activation of interferon-target genes. Temporal shifts in cytokine and growth factor gene expression, particularly in the crypt and lower villus regions, corroborate published studies and reveal new predictions of the mechanisms governing intestinal healing. Global transcriptional upregulation was observed in the regenerating epithelium, suggesting hypertranscription is a hallmark of intestinal repair. Furthermore, we observe altered cellular differentiation trajectories and villus patterning at the early stages of regeneration. ConclusionsTogether, our work provides a detailed spatiotemporal map of intestinal regeneration at subcellular resolution and nearly whole-genome scale. These data lay the groundwork for future discoveries and therapeutic strategies to enhance epithelial repair in inflammatory bowel diseases and other gastrointestinal pathologies or in response to side-effects of cancer therapies.

molecular biology↗

Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model Integration

Cognitive linguistics posits that language underpins human thought, and this principle has influenced the study and development of large language models (LLMs). In particular, several studies have investigated the metabolic costs of sentence formation using neuroimaging techniques such as positron emission tomography, functional magnetic resonance imaging, electroencephalography (EEG), and imagined speech reconstruction (ISR). In this study, EEG data corresponding to imagined English-language speech phonemes were used for ISR, in combination with an LLM trained on an abridged autobiography. The LLM-generated text responses guided the synthesis of EEG data from relevant phonemes, which were then used to estimate corresponding metabolic activity, and the changes in simulated neurometabolic and electrical parameters were visually represented. Notably, introducing pseudorandom variance significantly (p < 0.001) enhanced the models ability to reflect biological variability. Future directions include expanding the ISR system with lightweight or locally run LLMs, incorporating training data from larger and more diverse populations, and utilizing truly random variability sources. Further optimization for broader hardware compatibility and implementation--such as neural phantoms, emotional context integration, or human-computer interaction platforms--offer promising pathways for advancement. Overall, this work establishes a foundation for the next generation of biologically inspired, modular, and adaptable ISR systems for both research and practical applications.

neuroscience↗