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Kazazian, K.

Publications and source records attributed to Kazazian, K..

2 recordsLinked to original sources

Transcriptomic profiling of a novel gastric implantation model identifies mechanisms and pathways that drive implantation into explanted human peritoneum

Epithelial cancers such as stomach and ovary cancer tend to metastasize to the peritoneum, often leading to intractable disease and poor survival. Currently, the mechanisms that enable gastric cancer cells to penetrate the mesothelium, and to implant, invade, and survive in the peritoneal niche are poorly understood. To investigate these mechanisms, we developed a novel human peritoneal explant model. Briefly, fresh peritoneal tissue samples from abdominal surgery patients were cultured on top of a layer of GFP-labeled human gastric adenocarcinoma cells (AGS); 2% of these cells implanted into the peritoneum. The transcriptomic profile of the implanted AGS cells was compared to the profile of AGS cells that failed to implant using RNA sequencing. Differentially expressed genes were enriched significantly (fold change>2) for genes that enable cell adhesion, motility, and membrane depolarization. We compared this list of genes with a previously identified peritoneal metastasis whole exome sequencing dataset (SRP043661). Upon further analysis based on subcellular localization, cell adhesion and cytoskeletal organization, we found nine core "peritoneal implantation" genes. We functionally validated these genes with CRISPR knockout and assessed peritoneal implantation and invasion using the human peritoneal explant model described above. From these data we identified ADAM12 as a key player of peritoneal metastasis. Knock out of ADAM12 significantly impaired peritoneal metastasis in vivo and ex vivo. Exploration of three publicly available independent datasets indicated that ADAM12 is indeed clinically relevant in peritoneal metastasis. To explore the role of ITGA{beta}1 in mediating cell-matrix interactions in the presence of ADAM12, we performed ITGA{beta}1 pull-down assays followed by mass spectrometry analysis in ADAM12 WT cells. ADAM12 KO cells show a marked disruption of the ITGA{beta}1 interactome in GCa cells. Key cytoskeletal proteins such as MYH14, MYH10, MYH9, ACTA2, SPTN1 and TPM1-TPM4 were found to be interactors with ITGA{beta}1 in ADAM12 WT cells but not ADAM12 KO cells. Our approach and the new data identify a distinct peritoneal metastasis gene set that facilitates implantation and invasion of gastric cancer cells within the peritoneum. Disruption of these pathways with peritoneal-directed therapies has the potential to improve survival in patients with high-risk primary gastric cancer.

cancer biology↗

Improved short-channel regression for mapping resting-state functional connectivity networks using functional near-infrared spectroscopy

Resting-state functional connectivity (rsFC) is an attractive biomarker of brain function that can vary with brain injury. The simplicity of resting-state protocols coupled with the main features of functional near-infrared spectroscopy (fNIRS), such as portability and versatility, can facilitate the monitoring of unresponsive patients in acute settings at the bedside. However, accurately mapping rsFC networks is challenging due to signal contamination from non-neural components, such as scalp hemodynamics and systemic physiology. Physiological noise may be mitigated through the use of short channels which may be able to provide sufficient information to eliminate the need for additional measurement devices, decreasing the complexity of the experimental setup. To this end, we examined the extent to which systemic physiology is embedded in the short-channel data and improved short-channel regression to account for temporal heterogeneity in the scalp hemodynamics. Our findings indicate that using temporal shifts in the short-channel data increases the agreement, by 70% on average, between short-channel regression and regression that includes short channels and physiological recordings. Overall, this method decreases the need for additional physiological recordings when mapping rsFC networks, providing a viable alternative when such measurements are not available or feasible.

neuroscience↗