KLK6 mediates stemness and metabolism of gastric carcinoma cells via the PI3K/AKT/mTOR signaling pathway

Zhou, Dong; He, Ying; Li, Hengping; Huang, Weidong

doi:10.3892/ol.2021.13085

“…Among the top 25 genes is GDF15, a marker of DUOX2+ WAE-like and WAE-like cells, which is highly expressed in KPT-treated cells, where these cell types are more abundant due to the greater efficiency of Paneth cell differentiation [ 24 , 25 ]. Conversely, KLK6 is highly expressed in the control-treated population, which has been shown to mediate the multipotency of intestinal stem cells [ 26 , 27 ].…”

Section: Resultsmentioning

confidence: 99%

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks

Shahir,

Stanley,

Purvis

2024

BMC Bioinformatics

0

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With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

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“…Among the top 25 genes is GDF15, a marker of DUOX2+ WAE-like and WAE-like cells, which is highly expressed in KPT-treated cells, where these cell types are more abundant due to the greater efficiency of Paneth cell differentiation (Zhang et al, 2019; Yang et al, 2021). Conversely, KLK6 is highly expressed in the control-treated population, which has been shown to mediate the multipotency of intestinal stem cells (Schrader et al, 2015; Zhou et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA Sequencing Data Using Graph Neural Networks

Shahir

¹

,

Stanley

²

,

Purvis

³

2023

Preprint

1

0

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With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

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“…To our knowledge, such a regulatory mechanism has not been investigated previously. Klk6 is involved in activating proliferation, extracellular matrix remodeling, and tumor invasion, while also promoting the growth and invasion of gastric cancer cells [ 43 , 44 ], as well as ovarian and colorectal cancer [ 45 ]. In salivary gland tumors, this kallikrein was found to be downregulated [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Exposure to Supraphysiological Levels of Testosterone Impacts Rat Submandibular Gland Proteome

Valente-Santos,

Vitorino,

Sousa-Mendes

et al. 2023

IJMS

1

0

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The salivary glands play a central role in the secretion of saliva, whose composition and volume affect oral and overall health. A lesser-explored dimension encompasses the possible changes in salivary gland proteomes in response to fluctuations in sex hormone levels. This study aimed to examine the effects of chronic exposure to testosterone on salivary gland remodeling, particularly focusing on proteomic adaptations. Therefore, male Wistar rats were implanted with subcutaneous testosterone-releasing devices at 14 weeks of age. Their submandibular glands were histologically and molecularly analyzed 47 weeks later. The results underscored a significant increase in gland mass after testosterone exposure, further supported by histologic evidence of granular duct enlargement. Despite increased circulating sex hormones, there was no detectable shift in the tissue levels of estrogen alpha and androgen receptors. GeLC-MS/MS and subsequent bioinformatics identified 308 proteins in the submandibular glands, 12 of which were modulated by testosterone. Of note was the pronounced upregulation of Klk3 and the downregulation of Klk6 and Klk7 after testosterone exposure. Protein–protein interaction analysis with the androgen receptor suggests that Klk3 is a potential target of androgenic signaling, paralleling previous findings in the prostate. This exploratory analysis sheds light on the response of salivary glands to testosterone exposure, providing proteome-level insights into the associated weight and histological changes.

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KLK6 mediates stemness and metabolism of gastric carcinoma cells via the PI3K/AKT/mTOR signaling pathway

Cited by 8 publications

References 37 publications

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks

Cellograph: a semi-supervised approach to analyzing multi-condition single-cell RNA-sequencing data using graph neural networks

Cellograph: A Semi-supervised Approach to Analyzing Multi-condition Single-cell RNA Sequencing Data Using Graph Neural Networks

Long-Term Exposure to Supraphysiological Levels of Testosterone Impacts Rat Submandibular Gland Proteome

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