A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics.

Spatial transcriptomics technologies enable researchers to accurately quantify and localize messenger ribonucleic acid (mRNA) transcripts at a high resolution while preserving their spatial context. The identification of spatial domains, or the task of spatial clustering, plays a crucial role in investigating data on spatial transcriptomes. One promising approach for classifying spatial domains involves the use of graph neural networks (GNNs) by leveraging gene expressions, spatial locations, and histological images. This study provided a comprehensive overview of the most recent GNN-based methods of spatial clustering methods for the analysis of data on spatial transcriptomics. We extensively evaluated the performance of current methods on prevalent datasets of spatial transcriptomics by considering their accuracy of clustering, robustness, data stabilization, relevant requirements, computational efficiency, and memory use. To this end, we explored 60 clustering scenarios by extending the essential frameworks of spatial clustering for the selection of the GNNs, algorithms of downstream clustering, principal component analysis (PCA)-based reduction, and refined methods of correction. We comparatively analyzed the performance of the methods in terms of spatial clustering to identify their limitations and outline future directions of research in the field. Our survey yielded novel insights, and provided motivation for further investigating spatial transcriptomics.

Graph deep learning enabled spatial domains identification for spatial transcriptomics.

Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph deep learning (GDL) based spatial clustering approach is constructed in this paper. First, the deep graph infomax module embedded with residual gated graph convolutional neural network is leveraged to address the gene expression profiles and spatial positions in ST. Then, the Bayesian Gaussian mixture model is applied to handle the latent embeddings to generate spatial domains. Designed experiments certify that the presented method is superior to other state-of-the-art GDL-enabled techniques on multiple ST datasets. The codes and dataset used in this manuscript are summarized at https://github.com/narutoten520/SCGDL.

The locoregional adiponectin and its synergistic antitumor effect with HIF-1α blockade in TSCC.

Adiponectin (APN) is a kind of endogenous anti-tumor adipocytokine, which exerts its function by binding to its receptors (AdipoR1 and AdipoR2). However, hyperadiponectinemia is found in some pathophysiological processes without significant protective effect, which indicates the existence of APN resistance. Here, we aimed to investigate the locoregional expression of APN in tongue squamous cell carcinoma (TSCC) tissues, and to explore the potential regulatory mechanism of APN resistance under hypoxia. Consequently, we found that the protein expression of APN and AdipoR1, but not AdipoR2, was upregulated in the early stage of TSCC and after hypoxic treatment ex vivo and in vitro. Knockdown of HIF-1α decreased the level of APN and AdipoR1, and simultaneously, HIF-1α was identified as transcriptor of the APN. Intriguingly, a regenerative feedback of HIF-1α was unexpectedly detected after application of recombinant globular APN (gAPN), which most likely contributed to the APN resistance. Furthermore, HIF-1α blockade combined with gAPN has a prominent synergistic antitumor effect, which suggested an effective amelioration in APN resistance. In all, our study revealed the possible mechanism of APN resistance under hypoxia and provides a promising strategy of bi-target treatment with APN and HIF-1α for TSCC therapy.

PySmash: Python package and individual executable program for representative substructure generation and application.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

QSAR-assisted-MMPA to expand chemical transformation space for lead optimization.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

RSK2 protects human breast cancer cells under endoplasmic reticulum stress through activating AMPKα2-mediated autophagy.

Autophagy can protect stressed cancer cell by degradation of damaged proteins and organelles. However, the regulatory mechanisms behind this cellular process remain incompletely understood. Here, we demonstrate that RSK2 (p90 ribosomal S6 kinase 2) plays a critical role in ER stress-induced autophagy in breast cancer cells. We demonstrated that the promotive effect of RSK2 on autophagy resulted from directly binding of AMPKα2 in nucleus and phosphorylating it at Thr172 residue. IRE1α, an ER membrane-associated protein mediating unfolded protein response (UPR), is required for transducing the signal for activation of ERK1/2-RSK2 under ER stress. Suppression of autophagy by knockdown of RSK2 enhanced the sensitivity of breast cancer cells to ER stress both in vitro and in vivo. Furthermore, we demonstrated that inhibition of RSK2-mediated autophagy rendered breast cancer cells more sensitive to paclitaxel, a chemotherapeutic agent that induces ER stress-mediated cell death. This study identifies RSK2 as a novel controller of autophagy in tumor cells and suggests that targeting RSK2 can be exploited as an approach to reinforce the efficacy of ER stress-inducing agents against cancer.

UCH-L1-mediated Down-regulation of Estrogen Receptor α Contributes to Insensitivity to Endocrine Therapy for Breast Cancer.

Purpose: To determine the role of UCH-L1 in regulating ERα expression, and to evaluate whether therapeutic targeting of UCH-L1 can enhance the efficacy of anti-estrogen therapy against breast cancer with loss or reduction of ERα. Methods: Expressions of UCH-L1 and ERα were examined in breast cancer cells and patient specimens. The associations between UCH-L1 and ERα, therapeutic response and prognosis in breast cancer patients were analyzed using multiple databases. The molecular pathways by which UCH-L1 regulates ERα were analyzed using immunoblotting, qRT-PCR, immunoprecipitation, ubiquitination, luciferase and ChIP assays. The effects of UCH-L1 inhibition on the efficacy of tamoxifen in ERα (-) breast cancer cells were tested both in vivo and in vitro. Results: UCH-L1 expression was conversely correlated with ERα status in breast cancer, and the negative regulatory effect of UCH-L1 on ERα was mediated by the deubiquitinase-mediated stability of EGFR, which suppresses ERα transcription. High expression of UCH-L1 was associated with poor therapeutic response and prognosis in patients with breast cancer. Up-regulation of ERα caused by UCH-L1 inhibition could significantly enhance the efficacy of tamoxifen and fulvestrant in ERα (-) breast cancer both in vivo and in vitro. Conclusions: Our results reveal an important role of UCH-L1 in modulating ERα status and demonstrate the involvement of UCH-L1-EGFR signaling pathway, suggesting that UCH-L1 may serve as a novel adjuvant target for treatment of hormone therapy-insensitive breast cancers. Targeting UCH-L1 to sensitize ER negative breast cancer to anti-estrogen therapy might represent a new therapeutic strategy that warrants further exploration.

Polyethylene Glycol Ointment Alleviates Psoriasis-Like Inflammation Through Down-Regulating the Function of Th17 Cells and MDSCs.

Objective: To explore the possible mechanism of improving the imiquimod (IMQ)-induced psoriasis-like inflammation by using polyethylene glycol (PEG) ointment. Methods: We evaluated the appearance of psoriasis lesions by Psoriasis Area and Severity Index (PASI), observed the epidermal proliferation by histopathological staining and immunohistochemical staining, and explored the key molecules and signaling pathways of improving psoriasis-like inflammation treated with PEG ointment by RNA sequencing. Finally, we verified the expression of inflammatory cells and inflammatory factors by flow cytometry, immunohistochemical staining, and Q-PCR. Results: PEG ointment could improve the appearance of psoriasis lesions and the epidermis thickness of psoriasis mouse, inhibit the proliferation of keratinocytes, and down-regulate the relative mRNA levels of IL-23, IL-22, IL-6, IL-17C, IL-17F, S100A7, S100A8, S100A9, CXCL1, CXCL2, and IL-1ß in the skin lesions of psoriasis mouse by down-regulating the numbers of myeloid-derived suppressor cells (MDSCs) and T helper 17 (Th17) cells. Conclusion: PEG ointment could improve the IMQ-induced psoriasis-like inflammation by down-regulating the functions of Th17 cells and MDSCs.

Identification of phosphatidylcholine and lysophosphatidylcholine as novel biomarkers for cervical cancers in a prospective cohort study.

Metabolites are the end products of cellular regulatory processes. Squamous cervical cancer (SCC) can alter the level of certain small molecular metabolite in plasma through modulating gene expression. In this study, we identified two metabolites, phosphatidylcholine (PC) and lysophosphatidylcholine (LPC), which are significantly down- and upregulated in plasma of SCC as compared to uterine fibroid (UF) patients via ultra-performance liquid chromatographic-mass spectrometry (UPLC-MS). In external prospective cohort, our assay has a sensitivity of 93.2 %, a specificity of 91.3 %, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.972. The level of LPC is significantly higher in SCC than in UF patients. An opposite result was observed in PC level. Our findings suggest that the PC and lysoPC could be used as novel biomarkers to facilitate SCC diagnosis.

LAPTM4B-35 overexpression is an independent prognostic marker in endometrial carcinoma.

BACKGROUND: Lysosomal protein transmembrane 4 ß-35 (LAPTM4B-35), a novel oncoprotein that belongs to the mammalian 4-tetratransmembrane spanning protein superfamily, has been implicated in oncogenesis and cancer progression in several solid malignances. However, the expression of LAPTM4B-35 and its role in endometrial cancer progression remain unknown. MATERIALS AND METHODS: We investigated the expression of the LAPTM4B-35 protein by immunohistochemistry in 30 normal endometrium specimens and 165 endometrial carcinomas and analyzed its correlation with various clinicopathologic features, including patient outcome. RESULTS: LAPTM4B-35 immunoreactivity was overexpressed in endometrial carcinoma cases compared with normal endometrium (P < 0.001). High LAPTM4B-35 expression was found in 117 (70.91%) of these 165 carcinomas and was positively correlated with the International Federation of Gynecology and Obstetrics stage, histological grade, depth of myometrial invasion, lymph node metastasis, lymph vascular space involvement, and recurrence, but not with age and histological type. Patients with high LAPTM4B-35 expression had significantly poorer overall survival and disease-free survival compared with patients with low expression of LAPTM4B-35 (P = 0.001 and P = 0.002, respectively). Multivariate analysis showed that high LAPTM4B-35 expression was an independent prognostic factor for both overall survival and disease-free survival of patients with endometrial carcinoma (both P = 0.005). CONCLUSIONS: These results showed that high LAPTM4B-35 expression was associated with progression and prognosis of endometrial carcinoma.