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Biology subjects

Karz, A.

Publications and source records attributed to Karz, A..

3 recordsLinked to original sources

The carnosinase dipeptidase CNDP1 is a novel metabolic vulnerability in brain metastasis

Brain metastatic cells undergo metabolic adaptations, such as increased reliance on oxidative phosphorylation. Integrating proteomic and transcriptomic profiling of patient samples revealed a consistent upregulation of the carnosine dipeptidase-1 (CNDP1) in brain versus extracranial metastases. Carnosine is an abundant metabolite in brain and muscle, known to exert anti-proliferative effects on cancer cells. Here, we demonstrate that CNDP1 inhibition suppresses both the establishment and maintenance of melanoma brain metastasis while its ectopic expression is sufficient to confer brain metastatic potential to poorly metastatic cells. CNDP1 suppression results in activation of the Integrated Stress Response via Heme-Regulated Inhibitor Kinase and reprogrammed translation towards preferential expression of mitochondrial and survival transcripts. We further show that CNDP1 upregulation supports mitochondrial activity by limiting the levels of its substrate carnosine, a copper ionophore, thus protecting metastatic cells from carnosine-induced copper toxicity. Our studies reveal a novel metabolic adaptation during brain metastasis, which can be leveraged for therapeutic purposes.

cancer biology↗

Unveiling common transcriptomic features between melanoma brain metastases and neurodegenerative diseases

Melanoma represents a critical clinical challenge due to its high incidence rates and unfavorable clinical outcomes. This type of skin cancer presents unique adaptability to the brain microenvironment, but its underlying molecular mechanisms are poorly understood. To further characterize its tumor neurobiology, we explore the relation between the transcriptional profiles of melanoma brain metastasis (MBM) and the neurodegenerative diseases Alzheimers disease, Parkinsons disease, and multiple sclerosis. Through an in silico approach, we unveiled the neurodegenerative signature of MBM when compared to melanoma non-brain metastasis (53 dysregulated genes enriched in 11 functional terms) and to non tumor-bearing brain controls (195 dysregulated genes, mostly involved in development and cell differentiation, chromatin remodeling and nucleosome organization, and translation). Two genes, ITGA10 and DNAJC6, emerged as key potential markers, as they are dysregulated in both scenarios. Lastly, we developed a user-friendly web tool (https://bioinfo.cipf.es/metafun-mbm/) as an open source, so that any user can interactively delve into the results.

cancer biology↗

MetFinder: a neural network-based tool for automated quantitation of metastatic burden in histological sections from animal models

Diagnosis of most diseases relies on expert histopathological evaluation of tissue sections by an experienced pathologist. By using standardized staining techniques and an expanding repertoire of markers, a trained eye is able to recognize disease-specific patterns with high accuracy and determine a diagnosis. As efforts to study mechanisms of metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate effects on tumor burden resulting from specific interventions. However, current methods of quantifying tumor burden are low in either resolution or throughput. Artificial neural networks, which can perform in-depth image analyses of tissue sections, provide an opportunity for automated recognition of consistent histopathological patterns. In order to increase the outflow of data collection from preclinical studies, we trained a deep neural network for quantitative analysis of melanoma tumor content on histopathological sections of murine models. This AI-based algorithm, made freely available to academic labs through a web-interface called MetFinder, promises to become an asset for researchers and pathologists interested in accurate, quantitative assessment of metastasis burden.

cancer biology↗