Targeting hepatic kisspeptin receptor ameliorates nonalcoholic fatty liver disease in a mouse model.

Nonalcoholic fatty liver disease (NAFLD), the most common liver disease, has become a silent worldwide pandemic. The incidence of NAFLD correlates with the rise in obesity, type 2 diabetes, and metabolic syndrome. A hallmark featureof NAFLD is excessive hepatic fat accumulation or steatosis, due to dysregulated hepatic fat metabolism, which can progress to nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis. Currently, there are no approved pharmacotherapies to treat this disease. Here, we have found that activation of the kisspeptin 1 receptor (KISS1R) signaling pathway has therapeutic effects in NAFLD. Using high-fat diet-fed mice, we demonstrated that a deletion of hepatic Kiss1r exacerbated hepatic steatosis. In contrast, enhanced stimulation of KISS1R protected against steatosis in wild-type C57BL/6J mice and decreased fibrosis using a diet-induced mouse model of NASH. Mechanistically, we found that hepatic KISS1R signaling activates the master energy regulator, AMPK, to thereby decrease lipogenesis and progression to NASH. In patients with NAFLD and in high-fat diet-fed mice, hepatic KISS1/KISS1R expression and plasma kisspeptin levels were elevated, suggesting a compensatory mechanism to reduce triglyceride synthesis. These findings establish KISS1R as a therapeutic target to treat NASH.

G protein-coupled kisspeptin receptor induces metabolic reprograming and tumorigenesis in estrogen receptor-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly metastatic and deadly disease. TNBC tumors lack estrogen receptor (ERα), progesterone receptor (PR), and HER2 (ErbB2) and exhibit increased glutamine metabolism, a requirement for tumor growth. The G protein-coupled kisspeptin receptor (KISS1R) is highly expressed in patient TNBC tumors and promotes malignant transformation of breast epithelial cells. This study found that TNBC patients displayed elevated plasma kisspeptin levels compared with healthy subjects. It also provides the first evidence that in addition to promoting tumor growth and metastasis in vivo, KISS1R-induced glutamine dependence of tumors. In addition, tracer-based metabolomics analyses revealed that KISS1R promoted glutaminolysis and nucleotide biosynthesis by increasing c-Myc and glutaminase levels, key regulators of glutamine metabolism. Overall, this study establishes KISS1R as a novel regulator of TNBC metabolism and metastasis, suggesting that targeting KISS1R could have therapeutic potential in the treatment of TNBC.

KISS1/KISS1R and Breast Cancer: Metastasis Promoter.

Kisspeptins (KPs), peptide products of the kisspeptin-1 (KISS1) gene, are the endogenous ligands for the KISS1 receptor, KISS1R, which is a G protein-coupled receptor. In many human tumors, KISS1 functions as a metastasis-suppressor gene and KISS1/KISS1R signaling has antimetastatic and tumor-suppressor roles. On the contrary, emerging evidence indicates that the KP/KISS1R pathway plays detrimental roles in triple negative breast cancer (TNBC), the most difficult type of breast cancer to treat. TNBC patients initially respond to chemotherapy, but tumors acquire drug resistance and many patients relapse and die of metastases within a few years. In this review, we summarize recent developments in the understanding of the mechanisms by which KP/KISS1R signaling plays an adverse role in TNBC. This includes focusing on how KISS1R signaling regulates the cell cytoskeleton to induce tumor invadopodia formation and how KISS1R communicates with growth factor receptors such as the epidermal growth factor receptor, the receptor tyrosine kinase AXL, and transforming growth factor-ß to promote cell invasion, metastasis, and drug resistance.

KISS1/KISS1R in Cancer: Friend or Foe?

The KISS1 gene encodes KISS1, a protein that is rapidly processed in serum into smaller but biologically active peptides called kisspeptins (KPs). KISS1 and the KPs signal via the G-protein coupled receptor KISS1R. While KISS1 and KPs are recognized as potent positive regulators of the reproductive neuroendocrine axis in mammals, the first reported role for KISS1 was that of metastasis suppression in melanoma. Since then, it has become apparent that KISS1, KPs, and KISS1R regulate the development and progression of several cancers but interestingly, while these molecules act as suppressors of tumorigenesis and metastasis in many cancers, in breast and liver cancer they function as promoters. Thus, they join a small but growing number of molecules that exhibit dual roles in cancer highlighting the importance of studying cancer in context. Given their roles, KISS1, KPs and KISS1R represent important molecules in the development of novel therapies and/or as prognostic markers in treating cancer. However, getting to that point requires a detailed understanding of the relationship between these molecules and different cancers. The purpose of this review is therefore to highlight and discuss the clinical studies that have begun describing this relationship in varying cancer types including breast, liver, pancreatic, colorectal, bladder, and ovarian. An emerging theme from the reviewed studies is that the relationship between these molecules and a given cancer is complex and affected by many factors such as the micro-environment and steroid receptor status of the cancer cell. Our review and discussion of these important clinical studies should serve as a valuable resource in the successful development of future clinical studies.

A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem Interval.

Research on the human microbiome, the microbiota that live in, on, and around the human person, has revolutionized our understanding of the complex interactions between microbial life and human health and disease. The microbiome may also provide a valuable tool in forensic death investigations by helping to reveal the postmortem interval (PMI) of a decedent that is discovered after an unknown amount of time since death. Current methods of estimating PMI for cadavers discovered in uncontrolled, unstudied environments have substantial limitations, some of which may be overcome through the use of microbial indicators. In this project, we sampled the microbiomes of decomposing human cadavers, focusing on the skin microbiota found in the nasal and ear canals. We then developed several models of statistical regression to establish an algorithm for predicting the PMI of microbial samples. We found that the complete data set, rather than a curated list of indicator species, was preferred for training the regressor. We further found that genus and family, rather than species, are the most informative taxonomic levels. Finally, we developed a k-nearest- neighbor regressor, tuned with the entire data set from all nasal and ear samples, that predicts the PMI of unknown samples with an average error of ±55 accumulated degree days (ADD). This study outlines a machine learning approach for the use of necrobiome data in the prediction of the PMI and thereby provides a successful proof-of- concept that skin microbiota is a promising tool in forensic death investigations.