SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition.

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses.

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.

BACKGROUND: Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods for identifying the DTIs are arduous, time-intensive, and financially burdensome. In addition, robust computational methods have been developed for predicting the DTIs and are widely applied in drug discovery research. However, advancing more precise algorithms for predicting DTIs is essential to meet the stringent standards demanded by drug discovery. RESULTS: We proposed a novel method called GSRF-DTI, which integrates networks with a deep learning algorithm to identify DTIs. Firstly, GSRF-DTI learned the embedding representation of drugs and targets by integrating multiple drug association information and target association information, respectively. Then, GSRF-DTI considered the influence of drug-target pair (DTP) association on DTI prediction to construct a drug-target pair network (DTP-NET). Next, we utilized GraphSAGE on DTP-NET to learn the potential features of the network and applied random forest (RF) to predict the DTIs. Furthermore, we conducted ablation experiments to validate the necessity of integrating different types of network features for identifying DTIs. It is worth noting that GSRF-DTI proposed three novel DTIs. CONCLUSIONS: GSRF-DTI not only considered the influence of the interaction relationship between drug and target but also considered the impact of DTP association relationship on DTI prediction. We initially use GraphSAGE to aggregate the neighbor information of nodes for better identification. Experimental analysis on Luo's dataset and the newly constructed dataset revealed that the GSRF-DTI framework outperformed several state-of-the-art methods significantly.

Deficiency in SLC25A15, a hypoxia-responsive gene, promotes hepatocellular carcinoma by reprogramming glutamine metabolism.

BACKGROUND & AIMS: The role of solute carrier family 25 member 15 (SLC25A15), a critical component of the urea cycle, in hepatocellular carcinoma (HCC) progression remains poorly understood. This study investigated the impact of SLC25A15 on HCC progression and its mechanisms. METHODS: We systematically investigated the function of SLC25A15 in HCC progression using large-scale data mining and cell, animal, and organoid models. Furthermore, we analyzed its involvement in reprogramming glutamine metabolism. RESULTS: SLC25A15 expression was significantly decreased in HCC tissues, and patients with low SLC25A15 levels had a poorer prognosis. Hypoxia-exposed HCC cells or tissues had lower SLC25A15 expression. A positive correlation between HNF4A, a transcription factor suppressed by hypoxia, and SLC25A15 was observed in both HCC tissues and cells. Modulating HNF4A levels altered SLC25A15 mRNA levels. SLC25A15 upregulated SLC1A5, increasing glutamine uptake. The reactive metabolic pathway of glutamine was increased in SLC25A15-deficient HCC cells, providing energy for HCC progression through additional lipid synthesis. Ammonia accumulation due to low SLC25A15 levels suppressed the expression of OGDHL (oxoglutarate dehydrogenase L), a switch gene that mediates SLC25A15 deficiency-induced reprogramming of glutamine metabolism. SLC25A15-deficient HCC cells were more susceptible to glutamine deprivation and glutaminase inhibitors. Intervening in glutamine metabolism increased SLC25A15-deficient HCC cells' response to anti-PD-L1 treatment. CONCLUSION: SLC25A15 is hypoxia-responsive in HCC, and low SLC25A15 levels result in glutamine reprogramming through SLC1A5 and OGDHL regulation, promoting HCC progression and regulating cell sensitivity to anti-PD-L1. Interrupting the glutamine-derived energy supply is a potential therapeutic strategy for treating SLC25A15-deficient HCC. IMPACT AND IMPLICATIONS: We first demonstrated the tumor suppressor role of solute carrier family 25 member 15 (SLC25A15) in hepatocellular carcinoma (HCC) and showed that its deficiency leads to reprogramming of glutamine metabolism to promote HCC development. SLC25A15 can serve as a potential biomarker to guide the development of precision therapeutic strategies aimed at targeting glutamine deprivation. Furthermore, we highlight that the use of an inhibitor of glutamine utilization can enhance the sensitivity of low SLC25A15 HCC to anti-PD-L1 therapy.

Antibiofilm Mechanisms of the Helical G3 Peptide against Staphylococcus epidermidis.

Antibacterial peptides (ABPs) have been recognized as promising alternatives to conventional antibiotics due to their broad antibacterial spectrum, high antibacterial activity, and low possibility of inducing bacterial resistance. However, their antibiofilm mechanisms have not yet reached a consensus. In this study, we investigated the antibiofilm activity of a short helical peptide G3 against Staphylococcus epidermidis, one of the most important strains of medical device contamination. Studies show that G3 inhibits S. epidermidis biofilm formation in a variety of ways. In the initial adhesion stage, G3 changes the properties of bacterial surfaces, such as charges, hydrophobicity, and permeability, by rapidly binding to them, thus interfering with their initial adhesion. In the mature stage, G3 prefers to target extracellular polysaccharides, leading to the death of outside bacteria and the disruption of the three-dimensional (3D) architecture of the bacterial biofilm. Such efficient antibiofilm activity of G3 endows it with great potential in the treatment of infections induced by the S. epidermidis biofilm.

Mesenchymal stem cells derived from CHIR99021 and TGF­ß induction remained on the colicomentum and improved cardiac function of a rat model of acute myocardium infarction.

Human induced pluripotent stem cells (hiPSCs) have been regarded as a potential stem cell source for cell therapy. However, the production of cells with mesenchymal potential from hiPSCs through spontaneous differentiation is time consuming and laborious. In the present study, the combined use of the GSK-3 inhibitor CHIR99021 and TGF-ß was used to obtain mesenchymal stem cell (MSC)-like cells from hiPSCs. During the induction process, the transcription of epithelial-mesenchymal transition (EMT)-related genes N-cadherin and Vimentin in the transformed cells was upregulated, whereas the transcription of E-cadherin and pluripotency-related transcription factors SOX2, OCT4 and NANOG did not change significantly. This indicated that whilst cells were pluripotent, EMT was initiated by the upregulation of transcription of EMT promoting genes. Both SMAD-dependent and independent signalling pathways were significantly activated by the combined induction treatment compared with the single factor induction. The hiPSC-derived MSC-like cells (hiPSC-MSCs) expressed MSC-related markers and acquired osteogenic, chondrogenic and adipogenic differentiation potentials. After being injected into the peritoneal cavity of rats, the hiPSC-MSCs secreted angiogenic and immune-regulatory factors and remained on the colicomentum for 3 weeks. Within an 11-week period, four intraperitoneal hiPSC-MSC injections (1x107 cells/injection) into acute myocardial infarction (AMI) model rats significantly increased the left ventricular ejection fraction, left ventricular fractional shortening and angiogenesis and significantly reduced scar size and the extent of apoptosis in the infarcted area compared with that of the control PBS injection. Symptoms of hiPSC-MSC-induced immune reaction or tumour formation were not observed over the course of the experiment in the hiSPC-MSC treated rats. In conclusion, the CHIR99021 and TGF-ß combined induction was a rapid and effective method to obtain MSC-like cells from hiPSCs and multiple high dose intraperitoneal injections of hiPSC-derived MSCs were safe and effective at restoring cardiac function in an AMI rat model.

Sono-promoted piezocatalysis and low-dose drug penetration for personalized therapy via tumor organoids.

Chemotherapy is a widely used cancer treatment, however, it can have notable side effects owing to the high-doses of drugs administered. Sonodynamic therapy (SDT) induced by sonosensitizers has emerged as a promising approach to treat cancer, however, there is limited research evaluating its therapeutic effects on human tumors. In this study, we introduced a dual therapy that combines low-dose chemotherapeutic drugs with enhanced sonodynamic therapy, utilizing barium titanate (BaTiO3, BTO) nanoparticles (NPs) as sonosensitizers to treat tumor organoids. We demonstrated that ultrasound could improve the cellular uptake of chemotherapy drugs, while the chemotherapeutic effect of the drugs made it easier for BTO NPs to enter tumor cells, and the dual therapy synergistically inhibited tumor cell viability. Moreover, different patient-derived tumor organoids exhibited different sensitivities to this therapy, highlighting the potential to evaluate individual responses to combination therapies prior to clinical intervention. Furthermore, this dual therapy exhibited therapeutic effects equivalent to those of high-dose chemotherapy drugs on drug-resistant tumor organoids and showed the potential to enhance the efficacy of killing drug-resistant tumors. In addition, the biosafety of the BTO NPs was successfully verified in live mice via oral administration. This evidence confirms the reliable and safe nature of the dual therapy approach, making it a feasible option for precise and personalized therapy in clinical applications.

Use of Machine Learning Tools in Evidence Synthesis of Tobacco Use Among Sexual and Gender Diverse Populations: Algorithm Development and Validation.

BACKGROUND: From 2016 to 2021, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations. OBJECTIVE: In order to expedite evidence synthesis and research gap discoveries, this pilot study has the following three aims: (1) to compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) to develop natural language processing (NLP) algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) to compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations. METHODS: We built a tobacco research domain-specific semantic database using data from 2993 paper abstracts from 4 leading tobacco-specific journals, with enrichment from other publicly available sources. We then trained an NLP model to extract named entities after learning patterns and relationships between words and their context in text, which further enriched the semantic database. Using this iterative process, we extracted and assessed studies relevant to LGBTQ+ tobacco control issues, further comparing our findings with an ongoing systematic review that also focuses on evidence synthesis for this demographic group. RESULTS: In total, 33 studies were identified as relevant to sexual and gender diverse individuals' nicotine and tobacco product use. Consistent with the ongoing systematic review, the NLP results showed that there is a scarcity of studies assessing policy impact on this demographic using causal inference methods. In addition, the literature is dominated by US data. We found that the product drawing the most attention in the body of existing research is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults, consistent with the research needs identified by the US health agencies. CONCLUSIONS: Our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. While future research is needed to statistically test the NLP tool's performance, there is potential for NLP tools to fundamentally transform the approach to evidence synthesis.

Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes.

Background: Soft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving. Objectives: The study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients. Methods: We retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study. Results: The AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p <0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p<0.01). Conclusion: The AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.

Dynamics of polarization-tuned mirror symmetry breaking in a rotationally symmetric system.

Lateral momentum conservation is typically kept in a non-absorptive rotationally symmetric system through mirror symmetry via Noether's theorem when illuminated by a homogeneous light wave. Therefore, it is still very challenging to break the mirror symmetry and generate a lateral optical force (LOF) in the rotationally symmetric system. Here, we report a general dynamic action in the SO(2) rotationally symmetric system, originating from the polarization-tuned mirror symmetry breaking (MSB) of the light scattering. We demonstrate theoretically and experimentally that MSB can be generally applied to the SO(2) rotationally symmetric system and tuned sinusoidally by polarization orientation, leading to a highly tunable and highly efficient LOF (9.22 pN/mW/µm-2) perpendicular to the propagation direction. The proposed MSB mechanism and LOF not only complete the sets of MSB of light-matter interaction and non-conservative force only using a plane wave but also provide extra polarization manipulation freedom.