Anticancer potential of grifolin in lung cancer treatment through PI3K/AKT pathway inhibition.

Objective: Grifolin is a natural secondary metabolite isolated from edible fruiting bodies of the mushroom Albatrellus confluens. Grifolin has antitumor activities in several types of cancer. We aimed to determine the effects of grifolin on lung cancer. Methods: We determined the proliferation, migration, invasion, and apoptosis of lung cancer cells using 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, Ethynyl deoxyuridine, colony formation, wound scratch, transwell, flow cytometry, and xenograft mouse assays. Molecular docking evaluated the binding relation between grifolin and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA). The levels of PIK3CA, AKT, and p-AKT were measured by western blot. Results: Grifolin (10, 20, or 40 µM) inhibited the proliferation, migration, and invasion of lung cancer cells, and induced cell cycle arrest and apoptosis. Grifolin also decreased CDK4, CDK6, and CyclinD1 expression and significantly decreased PIK3CA and p-AKT expression in lung cancer cells. These anticancer effects were abolished by 740Y-P. Conclusions: Grifolin regulates the PI3K/AKT pathway, thus inhibiting lung cancer progression.

The role of adiponectin in the association between abdominal obesity and type 2 diabetes: a mediation analysis among 232,438 Chinese participants.

Background: Adiposity and adipokines are closely associated with obesity-related metabolic abnormalities, but little is known regarding whether abdominal obesity is linked to type 2 diabetes mellitus (T2DM) through circulating adiponectin levels. Thus, this large-population-based study was designed to investigate the mediating effect of adiponectin in the relationship between abdominal obesity and T2DM. Methods: A total of 232,438 adults who lived in Dongguan, Guangdong Province, China, were enrolled in the present study. The circulating adiponectin concentrations were measured using latex-enhanced immunoturbidimetric assay. The association between circulating adiponectin and other clinical parameters was detected by Spearman's correlation analysis. Restricted cubic spline (RCS) regression was also used to address the non-linearity of the relationship between waist circumference and diabetes. Mediation analyses of circulating adiponectin were conducted using linear and logistic regression. Results: Subjects with abdominal obesity had lower levels of circulating adiponectin (P < 0.001). The circulating adiponectin value was inversely related to BMI (r = -0.370, P < 0.001), waist circumference (r = -0.361, P < 0.001), and fasting plasma glucose (r = -0.221, P < 0.001). The RCS plot showed a non-linear relation linking waist circumference with T2DM (P for non-linearity < 0.001). Patients with abdominal obesity presented 2.062 times higher odds of T2DM in comparison with those with non-abdominal obesity (odds ratio, 2.062; 95% confidence interval, 1.969-2.161) after adjusting for confounders. In the mediation analyses, the circulating adiponectin mediated the association between abdominal obesity and T2DM, with a mediation effect of 41.02% after adjustments. The above results were consistent in both men and women. Conclusion: The relationship between abdominal obesity and T2DM is mediated through circulating adiponectin level in adults, suggesting that circulating adiponectin might be a potential predictor for controlling the adverse progression from adiposity to T2DM.

Modulation of chronic obstructive pulmonary disease progression by antioxidant metabolites from Pediococcus pentosaceus: enhancing gut probiotics abundance and the tryptophan-melatonin pathway.

Chronic obstructive pulmonary disease (COPD), a condition primarily linked to oxidative stress, poses significant health burdens worldwide. Recent evidence has shed light on the association between the dysbiosis of gut microbiota and COPD, and their metabolites have emerged as potential modulators of disease progression through the intricate gut-lung axis. Here, we demonstrate the efficacy of oral administration of the probiotic Pediococcus pentosaceus SMM914 (SMM914) in delaying the progression of COPD by attenuating pulmonary oxidative stress. Specially, SMM914 induces a notable shift in the gut microbiota toward a community structure characterized by an augmented abundance of probiotics producing short-chain fatty acids and antioxidant metabolisms. Concurrently, SMM914 synthesizes L-tryptophanamide, 5-hydroxy-L-tryptophan, and 3-sulfino-L-alanine, thereby enhancing the tryptophan-melatonin pathway and elevating 6-hydroxymelatonin and hypotaurine in the lung environment. This modulation amplifies the secretion of endogenous anti-inflammatory factors, diminishes macrophage polarization toward the M1 phenotype, and ultimately mitigates the oxidative stress in mice with COPD. The demonstrated efficacy of the probiotic intervention, specifically with SMM914, not only highlights the modulation of intestine microbiota but also emphasizes the consequential impact on the intricate interplay between the gastrointestinal system and respiratory health.

Studying temporal dynamics of single cells: expression, lineage and regulatory networks.

Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures. Supplementary Information: The online version contains supplementary material available at 10.1007/s12551-023-01090-5.

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographic groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effect and meaningful condition effect, while perturbation prediction methods solely focus on condition effect, resulting in inaccurate gene expression predictions due to unaccounted batch effect. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effect from batch effect, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluate scDisInFact on both simulated and real datasets, and compare its performance with baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.

Mimicking pneumonia with septic shock: A case report and literature review.

Hydrochlorothiazide (HCTZ) is a commonly used diuretic antihypertensive drug that can cause electrolyte disorders, hyperglycemia and hyperuricemia as well as rare life-threatening adverse drug reactions. These include non-cardiogenic pulmonary edema, interstitial pneumonia, angioedema and aplastic anemia. The present report describes a case of a 59-year-old man who developed a hypersensitivity reaction to HCTZ. Specifically, the patient presented with symptoms of cough, chest tightness and shortness of breath, with pneumonic consolidation on chest CT and elevated levels of white blood cell count, neutrophil percentage, C-reactive protein and procalcitonin. A presumptive diagnosis of severe pneumonia was made initially. However, during the gradual recovery of the patient through treatment, he mistakenly ingested HCTZ containing losartan potassium intended for another patient, which resulted in symptoms similar to those observed upon admission. Upon further inquiry into the medical history, it was revealed that the patient had also taken irbesartan/HCTZ 4 h prior to hospitalization. There was no evidence of a pathogenic infection. Therefore, HCTZ-induced anaphylactic reaction was considered to be the most likely etiology for his severe shock. Treatments including epinephrine, methylprednisolone and respiratory support were administered. After 7 days, the patient was transferred from the Respiratory Intensive Care Unit [The Affiliated Jiangning Hospital of Nanjing Medical University (Nanjing, China)] to a general ward. During the follow-up, 12 months after advising the patient to discontinue HCTZ, there had been no recurrence of the aforementioned symptoms. At the time of publication, the patient is currently alive.

LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data.

Lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for inference of cell lineage and cell types at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expressions are emerging. Effectively incorporating the gene expression data requires a reasonable model of how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), which integrates lineage barcode and gene expression data using asymmetric cell division model and infers cell lineages and ancestral cell states using Neighbor-Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. With inferred ancestral states, LinRace can also show how a progenitor cell generates a large population of cells with various functionalities.

scMultiSim: simulation of single cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, hile also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data, many of them were not benchmarked before due to the lack of proper tools. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

The association of FGF-21 with the risk of newly diagnosed type-2 diabetes mellitus: a cross-sectional study in Southern China.

BACKGROUND: This study investigated the relationship between fibroblast growth factor 21 (FGF-21) and newly diagnosed type-2 diabetes mellitus (T2DM). METHODS: In this cross-sectional study, FGF-21 and T2DM risk were analyzed using restricted cubic splines with univariate or multivariate logistic regression analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated via logistic regression analysis. Cluster and subgroup analyses were conducted to evaluate the associations between FGF-21 and diabetes in different subpopulations. Nomograms and ROC curves were used to explore the clinical utility of FGF-21 in the diabetes assessment model. RESULTS: High levels of FGF-21 were significantly associated with a high risk of T2DM after adjusting for confounding factors in both the total population and subpopulations (P for trend < 0.001). In the total population, the ORs of diabetes with increasing FGF-21 quartiles were 1.00 (reference), 1.24 (95% CI 0.56-2.80; quartile 2), 2.47 (95% CI 1.18-5.33; quartile 3), and 3.24 (95% CI 1.53-7.14; quartile 4) in Model 4 (P < 0.001), and the trend was consistent in different subpopulations. In addition, compared with the model constructed with conventional noninvasive indicators, the AUC of the model constructed by adding FGF-21 was increased from 0.668 (95% CI: 0.602-0.733) to 0.715 (95% CI: 0.654-0.777), indicating that FGF-21 could significantly improve the risk-assessment efficiency of type-2 diabetes. CONCLUSION: This study demonstrated that a high level of circulating FGF-21 was positively correlated with diabetes, and levels of FGF-21 could be an important biomarker for the assessment of diabetes risk.

Ceftazidime-assisted synthesis of ultrasmall chitosan nanoparticles for biofilm penetration and eradication of Pseudomonas aeruginosa.

Pseudomonas aeruginosa (P. aeruginosa) infections present a grave threat to immunocompromised individuals, particularly those with cystic fibrosis due to the development of bacterial biofilms. In this study, we engineered self-assembling chitosan-ceftazidime nanoparticles (CSCE) capable of effectively penetrating biofilms and eradicating P. aeruginosa. The CSCE nanoparticles were synthesized through ionic cross-linking, combining negatively charged ceftazidime with positively charged chitosan, resulting in uniform nanoparticles measuring approximately 40 nm in diameter, exhibiting high dispersity and excellent biocompatibility. Remarkably, these nanoparticles exhibited significant inhibition of P. aeruginosa growth, reduced pyocyanin production, and diminished biofilm formation, achieving a maximum inhibition rate of 22.44%. Furthermore, in vivo investigations demonstrated enhanced survival in mice with abdominal P. aeruginosa infection following treatment with CSCE nanoparticles, accompanied by reduced levels of inflammatory cytokines Interleukin-6 (125.79 ± 18.63 pg/mL), Interleukin-17 (125.67 ± 5.94 pg/mL), and Tumor Necrosis Factor-α (135.4 ± 11.77 pg/mL). Critically, mice treated with CSCE nanoparticles showed no presence of bacteria in the bloodstream following intraperitoneal P. aeruginosa infection. Collectively, our findings highlight the potential of these synthesized nanoparticles as effective agents against P. aeruginosa infections.

LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data.

Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing have enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods for lineage reconstruction. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.

CLARIFY: cell-cell interaction and gene regulatory network refinement from spatially resolved transcriptomics.

MOTIVATION: Gene regulatory networks (GRNs) in a cell provide the tight feedback needed to synchronize cell actions. However, genes in a cell also take input from, and provide signals to other neighboring cells. These cell-cell interactions (CCIs) and the GRNs deeply influence each other. Many computational methods have been developed for GRN inference in cells. More recently, methods were proposed to infer CCIs using single cell gene expression data with or without cell spatial location information. However, in reality, the two processes do not exist in isolation and are subject to spatial constraints. Despite this rationale, no methods currently exist to infer GRNs and CCIs using the same model. RESULTS: We propose CLARIFY, a tool that takes GRNs as input, uses them and spatially resolved gene expression data to infer CCIs, while simultaneously outputting refined cell-specific GRNs. CLARIFY uses a novel multi-level graph autoencoder, which mimics cellular networks at a higher level and cell-specific GRNs at a deeper level. We applied CLARIFY to two real spatial transcriptomic datasets, one using seqFISH and the other using MERFISH, and also tested on simulated datasets from scMultiSim. We compared the quality of predicted GRNs and CCIs with state-of-the-art baseline methods that inferred either only GRNs or only CCIs. The results show that CLARIFY consistently outperforms the baseline in terms of commonly used evaluation metrics. Our results point to the importance of co-inference of CCIs and GRNs and to the use of layered graph neural networks as an inference tool for biological networks. AVAILABILITY AND IMPLEMENTATION: The source code and data is available at https://github.com/MihirBafna/CLARIFY.

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographical groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and the biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effects and meaningful condition effects, while perturbation prediction methods solely focus on condition effects, resulting in inaccurate gene expression predictions due to unaccounted batch effects. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effects from batch effects, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluated scDisInFact on both simulated and real datasets, and compared its performance to baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.

The effect of dietary carbohydrate and calorie restriction on weight and metabolic health in overweight/obese individuals: a multi-center randomized controlled trial.

BACKGROUND: Both low-carbohydrate (LC) and calorie-restricted (CR) diets have been shown to have metabolic benefits. However, the two regimens have yet to be thoroughly compared. We conducted a 12-week randomized trial to compare the effects of these diets separately and in combination on both weight loss and metabolic risk factors in overweight/obese individuals. METHODS: A total of 302 participants were randomized to LC diet (n = 76), CR diet (n = 75), LC + CR diet (n = 76), or normal control (NC) diet (n = 75) using a computer-based random number generator. The primary outcome was the change in body mass index (BMI). The secondary outcomes included body weight, waist circumference, waist-to-hip ratio, body fat, and metabolic risk factors. All participants attended health education sessions during the trial. RESULTS: A total of 298 participants were analyzed. BMI change over 12 weeks was - 0.6 (95% CI, - 0.8 to - 0.3) kg/m2 in NC, - 1.3 (95% CI, - 1.5 to - 1.1) kg/m2 in CR, - 2.3 (95% CI, - 2.6 to - 2.1) kg/m2 in LC, and - 2.9 (95% CI, - 3.2 to - 2.6) kg/m2 in LC + CR. LC + CR diet was more effective than LC or CR diet alone at reducing BMI (P = 0.001 and P < 0.001, respectively). Furthermore, compared with the CR diet, the LC + CR diet and LC diet further reduced body weight, waist circumference, and body fat. Serum triglycerides were significantly reduced in the LC + CR diet group compared with the LC or CR diet alone. Plasma glucose, homeostasis model assessment of insulin resistance, and cholesterol concentrations (total, LDL, and HDL) did not change significantly between the groups during the 12-week intervention. CONCLUSIONS: The reduction of carbohydrate intake without restricting caloric intake is more potent to achieve weight loss over 12 weeks when compared to a calorie-restricted diet in overweight/obese adults. The combination of restricting carbohydrate and total calorie intake may augment the beneficial effects of reducing BMI, body weight, and metabolic risk factors among overweight/obese individuals. TRIAL REGISTRATION: The study was approved by the institutional review board of Zhujiang Hospital of Southern Medical University and registered at the China Clinical Trial Registration Center (registration number: ChiCTR1800015156).

LinRace: single cell lineage reconstruction using paired lineage barcode and gene expression data.

Understanding how single cells divide and differentiate into different cell types in developed organs is one of the major tasks of developmental and stem cell biology. Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for the reconstruction of the cell division tree, and even the detection of cell types and differentiation trajectories at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expression data are emerging, aiming to improve the accuracy of lineage reconstruction. However, effectively incorporating the gene expression data requires a reasonable model on how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), a method that integrates the lineage barcode and gene expression data using the asymmetric cell division model and infers cell lineage under a framework combining Neighbor Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. Moreover, LinRace can output the cell states (cell types) of ancestral cells, which is rarely performed with existing lineage reconstruction methods. The information on ancestral cells can be used to analyze how a progenitor cell generates a large population of cells with various functionalities. LinRace is available at: https://github.com/ZhangLabGT/LinRace.

scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks.

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor's effect easily. We validated scMultiSim's simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

Rehabilomics: A state-of-the-art review of framework, application, and future considerations.

Rehabilomics is an important research framework that allows omics research built upon rehabilitation practice, especially in function evaluation, outcome prediction, and individualized rehabilitation. In the field of rehabilomics, biomarkers can serve as objectively measured indicators for body functioning, so as to complement the International Classification of Functioning, Disability, and Health (ICF) assessment. Studies on traumatic brain injury (TBI), stroke, and Parkinson's disease have shown that biomarkers (such as serum markers, MRI, and digital signals derived from sensors) are correlated with diagnosis, disease severity, and prognosis. Rehabilomics also examines a wide range of individual biological characteristics in order to develop personalized rehabilitation programs. Secondary prevention and rehabilitation of stroke have already adopted a rehabilomic approach to individualize treatment programs. Mechanisms of non-pharmacological therapies are expected to be unveiled in light of rehabilomics research. When formulating the research plan, learning from established databases is recommended and a multidisciplinary collaborative team is warranted. Although still in its infancy, the advancement and incorporation of rehabilomics has the potential to make a significant impact on public health.

The relationship between the CUN-BAE body fatness index and incident diabetes: a longitudinal retrospective study.

BACKGROUND: The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) index has been recommended as an ideal indicator of body fat and exhibited significant correlation with cardiometabolic risk factors. However, whether the CUN-BAE index correlates with incident diabetes in Asian populations is unknown. Therefore, this longitudinal study was designed to evaluate the association between baseline CUN-BAE index and type 2 diabetes mellitus (T2DM). METHODS: This retrospective longitudinal study involved 15,464 participants of 18-79 years of age in the NAGALA (NAfld in the Gifu Area Longitudinal Analysis) study over the period of 2004-2015. Cox proportional hazards regression was performed to test the relationship between the baseline CUN-BAE index and diabetes incidence. Further stratification analysis was conducted to ensure that the results were robust. The diagnostic utility of the CUN-BAE index was tested by the receiver operating characteristic (ROC) curve. RESULTS: Over the course of an average follow-up of 5.4 years, 373 (2.41%) participants developed diabetes. A higher diabetes incidence was associated with higher CUN-BAE quartiles (P for trend< 0.001). Each 1 unit increase in CUN-BAE index was associated with a 1.08-fold and 1.14-fold increased risk of diabetes after adjustment for confounders in males and females, respectively (both P < 0.001). Stratification analysis demonstrated a consistent positive correlation between baseline CUN-BAE and diabetes incidence. Moreover, based on ROC analysis, CUN-BAE exhibited a better capacity for diabetes prediction than both body mass index (BMI) and waist circumference (WC) in both sexes. CONCLUSIONS: The baseline CUN-BAE level was independently related to the incidence of diabetes. Increased adiposity determined by CUN-BAE could be used as a strong nonlaboratory predictor of incident diabetes in clinical practice.

scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.

Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.

miR-338-3p acts as a tumor suppressor in lung squamous cell carcinoma by targeting FGFR2/FRS2.

Background: Lung cancer refers to the occurrence of malignant tumors in the lung, and squamous cell carcinoma is one of the most common pathological types of non-small cell lung cancer. Studies have shown that microRNAs (miRNAs) play an important role in the occurrence, development, early diagnosis, and treatment of lung cancer. This study aimed to explore the role and possible mechanism of MicroRNA-338-3p (miR-338-3p) in lung squamous cell carcinoma (LUSC). Method: In this study, we compared 238 LUSC patients with relatively high miR-338-3p expression levels with 238 miR-338-3p expression levels in The Cancer Genome Atlas (TCGA)-LUSC dataset using first-line gene set enrichment analysis (GSEA). Second, the mRNA expression of miR-338-3p, FGFR2, and fibroblast growth factor receptor substrate 2 (FRS2) in 30 lung cancers and adjacent lung tissues was detected using quantitative real-time polymerase chain reaction (qRT-PCR). Finally, in vitro experiments were conducted, whereby the expression levels of miR-338-3p in lung cancer cells (H1703, SKMES1, H2170, H520) and normal lung epithelial cells (16HBE) were detected using qRT-PCR. miR-338-3p was overexpressed in lung cancer cells (H1703), and the cell proliferation (cell counting kit-8 [CCK8] assay), colony formation, cell apoptosis, cell cycle (BD-FACSVerse assay, Becton Dickinson, Bedford, MA, USA), cell invasion, and migration (Transwell assay, Thermo Fischer Corporation, Waltham, MA, USA) were detected. Results: We found that the expression of miR-338-3p was significantly reduced in LUSC tissues (p â€‹< â€‹0.001) and cancer cell lines (P < 0.01), and miR-338-3p was significantly negatively correlated with the expression of FGFR2 (P < 0.001) and FRS2 (P < 0.01). Furthermore, overexpression of miR-338-3p inhibited proliferation (P < 0.001), migration, and invasion (P < 0.001) of LUSC cell lines and increased apoptosis in the G1 phase (P < 0.001) and cell cycle arrest (P < 0.05). Conclusions: Our study demonstrates that miR-338-3p inhibits tumor cell proliferation and migration by targeting FGFR2 and FRS2 in LUSC. We believe that miR-338-3p may be a promising target for the treatment of LUSC.