Exercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes.

AIMS: Management of blood glucose fluctuation is essential for diabetes. Exercise is a key therapeutic strategy for diabetes patients, although little is known about determinants of glycemic response to exercise training. We aimed to investigate the effect of combined aerobic and resistance exercise training on blood glucose fluctuation in type 2 diabetes patients and explore the predictors of exercise-induced glycemic response. MATERIALS AND METHODS: Fifty sedentary diabetes patients were randomly assigned to control or exercise group. Participants in the control group maintained sedentary lifestyle for 2 weeks, and those in the exercise group specifically performed combined exercise training for 1 week. All participants received dietary guidance based on a recommended diet chart. Glycemic fluctuation was measured by flash continuous glucose monitoring. Baseline fat and muscle distribution were accurately quantified through magnetic resonance imaging (MRI). RESULTS: Combined exercise training decreased SD of sensor glucose (SDSG, exercise-pre vs exercise-post, mean 1.35 vs 1.10 mmol/L, p = .006) and coefficient of variation (CV, mean 20.25 vs 17.20%, p = .027). No significant change was observed in the control group. Stepwise multiple linear regression showed that baseline MRI-quantified fat and muscle distribution, including visceral fat area (ß = -0.761, p = .001) and mid-thigh muscle area (ß = 0.450, p = .027), were significantly independent predictors of SDSG change in the exercise group, as well as CV change. CONCLUSIONS: Combined exercise training improved blood glucose fluctuation in diabetes patients. Baseline fat and muscle distribution were significant factors that influence glycemic response to exercise, providing new insights into personalized exercise intervention for diabetes.

Multi-modal molecular determinants of clinically relevant osteoporosis subtypes.

Due to a rapidly aging global population, osteoporosis and the associated risk of bone fractures have become a wide-spread public health problem. However, osteoporosis is very heterogeneous, and the existing standard diagnostic measure is not sufficient to accurately identify all patients at risk of osteoporotic fractures and to guide therapy. Here, we constructed the first prospective multi-omics atlas of the largest osteoporosis cohort to date (longitudinal data from 366 participants at three time points), and also implemented an explainable data-intensive analysis framework (DLSF: Deep Latent Space Fusion) for an omnigenic model based on a multi-modal approach that can capture the multi-modal molecular signatures (M3S) as explicit functional representations of hidden genotypes. Accordingly, through DLSF, we identified two subtypes of the osteoporosis population in Chinese individuals with corresponding molecular phenotypes, i.e., clinical intervention relevant subtypes (CISs), in which bone mineral density benefits response to calcium supplements in 2-year follow-up samples. Many snpGenes associated with these molecular phenotypes reveal diverse candidate biological mechanisms underlying osteoporosis, with xQTL preferences of osteoporosis and its subtypes indicating an omnigenic effect on different biological domains. Finally, these two subtypes were found to have different relevance to prior fracture and different fracture risk according to 4-year follow-up data. Thus, in clinical application, M3S could help us further develop improved diagnostic and treatment strategies for osteoporosis and identify a new composite index for fracture prediction, which were remarkably validated in an independent cohort (166 participants).

Longitudinal metabolomics integrated with machine learning identifies novel biomarkers of gestational diabetes mellitus.

BACKGROUND: Evidence from longitudinal studies is crucial to enhance our understanding of the role of metabolites in the progression of gestational diabetes mellitus (GDM). Herein, a longitudinal untargeted metabolomic study was conducted to reveal the metabolomic profiles and biomarkers associated with the progression of GDM, and characterize the changing patterns of metabolites. METHODS: We collected serum samples at three trimesters from 30 patients with GDM and 30 healthy Chinese pregnant women with pre-pregnancy BMI, age, and parity matched, and untargeted metabolomic analysis was performed, followed by machine learning approaches that integrated bootstrap and LASSO. Cluster analysis was conducted to elucidate the patterns of metabolite changes. Pathway analyses were conducted to gain insights into the underlying pathways involved. RESULTS: A total of 32 metabolites, mainly belonging to amino acid and its derivatives, were significantly associated with GDM across three trimesters, and were clustered into three distinct patterns. Metabolites belonging to phosphatidylcholines, lysophosphatidylcholines, lysophosphatidic acids, and lysophosphatidylethanolamines were consistently upregulated, and 2,3-Dihydroxypropyl dihydrogen phosphate was downregulated in GDM group. Amino acid-related, glycerophospholipid, and vitamin B6 metabolism were enriched in multiple trimesters. The levels of allantoic acid, which was positively correlated with blood glucose, was consistently higher in GDM patients and exhibited good discriminatory ability for GDM in the early and mid-pregnancy. CONCLUSION: We identified and characterized distinct patterns of metabolites associated with GDM throughout pregnancy, and found that allantoic acid was a potential biomarker for early diagnosis of GDM.

CBX4 deletion promotes tumorigenesis under KrasG12D background by inducing genomic instability.

Chromobox protein homolog 4 (CBX4) is a component of the Polycomb group (PcG) multiprotein Polycomb repressive complexes 1 (PRC1), which is participated in several processes including growth, senescence, immunity, and tissue repair. CBX4 has been shown to have diverse, even opposite functions in different types of tissue and malignancy in previous studies. In this study, we found that CBX4 deletion promoted lung adenocarcinoma (LUAD) proliferation and progression in KrasG12D mutated background. In vitro, over 50% Cbx4L/L, KrasG12D mouse embryonic fibroblasts (MEFs) underwent apoptosis in the initial period after Adeno-Cre virus treatment, while a small portion of survival cells got increased proliferation and transformation abilities, which we called selected Cbx4-/-, KrasG12D cells. Karyotype analysis and RNA-seq data revealed chromosome instability and genome changes in selected Cbx4-/-, KrasG12D cells compared with KrasG12D cells. Further study showed that P15, P16 and other apoptosis-related genes were upregulated in the primary Cbx4-/-, KrasG12D cells due to chromosome instability, which led to the large population of cell apoptosis. In addition, multiple pathways including Hippo pathway and basal cell cancer-related signatures were altered in selected Cbx4-/-, KrasG12D cells, ultimately leading to cancer. We also found that low expression of CBX4 in LUAD was associated with poorer prognosis under Kras mutation background from the human clinical data. To sum up, CBX4 deletion causes genomic instability to induce tumorigenesis under KrasG12D background. Our study demonstrates that CBX4 plays an emerging role in tumorigenesis, which is of great importance in guiding the clinical treatment of lung adenocarcinoma.

Prognostic impact of metastatic patterns and treatment modalities on overall survival in lung squamous cell carcinoma: A population-based study.

This study aimed to investigate the impact of distinct metastasis patterns on the overall survival (OS) of individuals diagnosed with organ metastatic lung squamous cell carcinoma (LUSC). OS was calculated using the Kaplan-Meier method, and univariate and multivariate Cox regression analyses were conducted to further assess prognostic factors. A total of 36,025 cases meeting the specified criteria were extracted from the Surveillance, Epidemiology, and End Results database. Among these patients, 30.60% (11,023/36,025) were initially diagnosed at stage IV, and 22.03% (7936/36,025) of these individuals exhibited metastasis in at least 1 organ, including the liver, bone, lung, and brain. Among the 4 types of single metastasis, patients with bone metastasis had the lowest mean OS, at 9.438 months (95% CI: 8.684-10.192). Furthermore, among patients with dual-organ metastases, those with both brain and liver metastases had the shortest mean OS, at 5.523 months (95% CI: 3.762-7.285). Multivariate Cox regression analysis revealed that metastatic site is an independent prognostic factor for OS in patients with single and dual-organ metastases. Chemotherapy was beneficial for patients with single and multiple-organ metastases; although surgery was advantageous for those with single and dual-organ metastases, it did not affect the long-term prognosis of patients with triple organ metastases. Radiotherapy only conferred benefits to patients with single-organ metastasis. LUSC patients exhibit a high incidence of metastasis at the time of initial diagnosis, with significant differences in long-term survival among patients with different patterns of metastasis. Among single-organ metastasis cases, lung metastasis is the most frequent and is associated with the longest mean OS. Regarding treatment options, patients with single-organ metastasis can benefit from chemotherapy, surgery, and radiotherapy, and those with metastasis in 2 organs can benefit from chemotherapy and surgery. Patients with metastasis in more than 2 organs, however, can only benefit from chemotherapy. Understanding the variations in metastasis patterns assists in guiding pretreatment assessments and in determining appropriate therapeutic interventions for LUSC.

Genetic variants in epoxyeicosatrienoic acid processing and degradation pathways are associated with gestational diabetes mellitus.

AIM: To explore the genetic effects of CYP2C8, CYP2C9, CYP2J2, and EPHX2, the key genes involved in epoxyeicosatrienoic acid processing and degradation pathways in gestational diabetes mellitus (GDM) and metabolic traits in Chinese pregnant women. METHODS: A total of 2548 unrelated pregnant women were included, of which 938 had GDM and 1610 were considered as controls. Common variants were genotyped using the Infinium Asian Screening Array. Association studies of single nucleotide polymorphisms (SNPs) with GDM and related traits were performed using logistic regression and multivariable linear regression analyses. A genetic risk score (GRS) model based on 12 independent target SNPs associated with GDM was constructed. Logistic regression was used to estimate odds ratios and 95% confidence intervals, adjusting for potential confounders including age, pre-pregnancy body mass index, history of polycystic ovarian syndrome, history of GDM, and family history of diabetes, with GRS entered both as a continuous variable and categorized groups. The relationship between GRS and quantitative traits was also evaluated. RESULTS: The 12 SNPs in CYP2C8, CYP2C9, CYP2J2, and EPHX2 were significantly associated with GDM after adjusting for covariates (all P < 0.05). The GRS generated from these SNPs significantly correlated with GDM. Furthermore, a significant interaction between CYP2J2 and CYP2C8 in GDM (PInteraction = 0.014, ORInteraction= 0.61, 95%CI 0.41-0.90) was observed. CONCLUSION: We found significant associations between GDM susceptibility and 12 SNPs of the four genes involved in epoxyeicosatrienoic acid processing and degradation pathways in a Chinese population. Subjects with a higher GRS showed higher GDM susceptibility with higher fasting plasma glucose and area under the curve of glucose and poorer ß-cell function.

Identifying therapeutic biomarkers of zoledronic acid by metabolomics.

Zoledronic acid (ZOL) is a potent antiresorptive agent that increases bone mineral density (BMD) and reduces fracture risk in postmenopausal osteoporosis (PMOP). The anti-osteoporotic effect of ZOL is determined by annual BMD measurement. In most cases, bone turnover markers function as early indicators of therapeutic response, but they fail to reflect long-term effects. We used untargeted metabolomics to characterize time-dependent metabolic shifts in response to ZOL and to screen potential therapeutic markers. In addition, bone marrow RNA-seq was performed to support plasma metabolic profiling. Sixty rats were assigned to sham-operated group (SHAM, n = 21) and ovariectomy group (OVX, n = 39) and received sham operation or bilateral ovariectomy, respectively. After modeling and verification, rats in the OVX group were further divided into normal saline group (NS, n = 15) and ZOL group (ZA, n = 18). Three doses of 100 µg/kg ZOL were administrated to the ZA group every 2 weeks to simulate 3-year ZOL therapy in PMOP. An equal volume of saline was administered to the SHAM and NS groups. Plasma samples were collected at five time points for metabolic profiling. At the end of the study, selected rats were euthanatized for bone marrow RNA-seq. A total number of 163 compound were identified as differential metabolites between the ZA and NS groups, including mevalonate, a critical molecule in target pathway of ZOL. In addition, prolyl hydroxyproline (PHP), leucyl hydroxyproline (LHP), 4-vinylphenol sulfate (4-VPS) were identified as differential metabolites throughout the study. Moreover, 4-VPS negatively correlated with increased vertebral BMD after ZOL administration as time-series analysis revealed. Bone marrow RNA-seq showed that the PI3K-AKT signaling pathway was significantly associated with ZOL-mediated changes in expression (adjusted-p = 0.018). In conclusion, mevalonate, PHP, LHP, and 4-VPS are candidate therapeutic markers of ZOL. The pharmacological effect of ZOL likely occurs through inhibition of the PI3K-AKT signaling pathway.

Genomic Profiling Reveals the Variant Landscape of Sporadic Parathyroid Adenomas in Chinese Population.

OBJECTIVE: To define somatic variants of parathyroid adenoma (PA) and to provide novel insights into the underlying molecular mechanism of sporadic PA. METHODS: Basic clinical characteristics and biochemical indices of 73 patients with PA were collected. Whole-exome sequencing was performed on matched tumor-constitutional DNA pairs to detect somatic alterations. Functional annotation was carried out by ingenuity pathway analysis afterward. The protein expression of the variant gene was confirmed by immunohistochemistry, and the relationship between genotype and phenotype was analyzed. RESULTS: Somatic variants were identified in 1549 genes, with an average of 69 variants per tumor (range, 13-2109; total, 9083). Several novel recurrent somatic variants were detected, such as KMT2D (15/73), MUC4 (14/73), POTEH (13/73), CD22 (12/73), HSPA2 (12/73), HCFC1 (11/73), MAGEA1 (11/73), and SLC4A3 (11/73), besides the previously reported PA-related genes, including MEN1 (11/73), CASR (6/73), MTOR (4/73), ASXL3 (3/73), FAT1 (3/73), ZFX (5/73), EZH1 (2/73), POT1 (2/73), and EZH2 (1/73). Among them, KMT2D might be the candidate driver gene of PA. Crucially, 5 patients carried somatic mutations in CDC73, showed an aggressive phenotype similar to that of parathyroid carcinoma (PC), and had a decreased expression of parafibromin. Pathway analysis of recurrent potential PA-associated driver variant genes revealed functional enrichments in the signaling pathway of Notch. CONCLUSION: Our study expanded the pathogenic variant spectrum of PA and indicated that KMT2D might be a novel candidate driver gene and be considered as a diagnostic biomarker for PA. Meanwhile, CDC73 mutations might be an early developmental event from PA to PC. The results provided insights into elucidating the pathogenesis of parathyroid tumorigenesis and a certain basis for clinical diagnosis and treatment.

GPSM1 impairs metabolic homeostasis by controlling a pro-inflammatory pathway in macrophages.

G-protein-signaling modulator 1 (GPSM1) exhibits strong genetic association with Type 2 diabetes (T2D) and Body Mass Index in population studies. However, how GPSM1 carries out such control and in which types of cells are poorly understood. Here, we demonstrate that myeloid GPSM1 promotes metabolic inflammation to accelerate T2D and obesity development. Mice with myeloid-specific GPSM1 ablation are protected against high fat diet-induced insulin resistance, glucose dysregulation, and liver steatosis via repression of adipose tissue pro-inflammatory states. Mechanistically, GPSM1 deficiency mainly promotes TNFAIP3 transcription via the Gαi3/cAMP/PKA/CREB axis, thus inhibiting TLR4-induced NF-κB signaling in macrophages. In addition, we identify a small-molecule compound, AN-465/42243987, which suppresses the pro-inflammatory phenotype by inhibiting GPSM1 function, which could make it a candidate for metabolic therapy. Furthermore, GPSM1 expression is upregulated in visceral fat of individuals with obesity and is correlated with clinical metabolic traits. Overall, our findings identify macrophage GPSM1 as a link between metabolic inflammation and systemic homeostasis.

Superhydrophobic MXene Coating with Biomimetic Structure for Self-Healing Photothermal Deicing and Photoelectric Detector.

Two-dimensional transition metal carbides (Ti3C2Tx MXene) have emerged as new candidates for applications in multifunctional devices owing to their outstanding performance. However, these electronic devices are easily disturbed by water, breakage, oxidation during use, and limited energy resources. To solve these problems, herein, inspired by nature, a novel superhydrophobic, healable photothermal deicing and photodetector (SHPP) with a "papillary structure" is successfully fabricated for the first time, by a simple layer-by-layer assembly spraying process with 0D/1D/2D nanomaterials. As a result, the superhydrophobic modified 2D MXene coating (FM coating) on the SHPP sensor exhibits outstanding self-cleaning, long-term durability (>20 days), as well as excellent photothermal deicing performances under near-infrared light. Meanwhile, the unique semiembedded nano-ZnO/1D silver nanowire supports the sensor with desirable photoelectric performance with UV light and a fast response time (∼1 s), and good cycle stability. Moreover, benefiting from the transparent self-healing substrate, the photothermal deicing and photodetector properties can be restored at room temperature. The bioinspired structures and function mechanisms offer SHPP sensors great potential for the utilization of clean light energy, sensing, self-cleaning, anti-icing, and so forth.

Noninvasive detection and interpretation of gastrointestinal diseases by collaborative serum metabolite and magnetically controlled capsule endoscopy.

Gastrointestinal diseases are complex diseases that occur in the gastrointestinal tract. Common gastrointestinal diseases include chronic gastritis, peptic ulcers, inflammatory bowel disease, and gastrointestinal tumors. These diseases may manifest a long course, difficult treatment, and repeated attacks. Gastroscopy and mucosal biopsy are the gold standard methods for diagnosing gastric and duodenal diseases, but they are invasive procedures and carry risks due to the necessity of sedation and anesthesia. Recently, several new approaches have been developed, including serological examination and magnetically controlled capsule endoscopy (MGCE). However, serological markers lack lesion information, while MGCE images lack molecular information. This study proposes combining these two technologies in a collaborative noninvasive diagnostic scheme as an alternative to the standard procedures. We introduce an interpretable framework for the clinical diagnosis of gastrointestinal diseases. Based on collected blood samples and MGCE records of patients with gastrointestinal diseases and comparisons with normal individuals, we selected serum metabolite signatures by bioinformatic analysis, captured image embedding signatures by convolutional neural networks, and inferred the location-specific associations between these signatures. Our study successfully identified five key metabolite signatures with functional relevance to gastrointestinal disease. The combined signatures achieved discrimination AUC of 0.88. Meanwhile, the image embedding signatures showed different levels of validation and testing accuracy ranging from 0.7 to 0.9 according to different locations in the gastrointestinal tract as explained by their specific associations with metabolite signatures. Overall, our work provides a new collaborative noninvasive identification pipeline and candidate metabolite biomarkers for image auxiliary diagnosis. This method should be valuable for the noninvasive detection and interpretation of gastrointestinal and other complex diseases.

The mediating role of maternal metabolites between lipids and adverse pregnancy outcomes of gestational diabetes mellitus.

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, and the demographics of pregnant women have changed in recent decades. GDM is a metabolic disease with short- and long-term adverse effects on both pregnant women and newborns. The metabolic changes and corresponding risk factors should be of great significance in understanding the pathological mechanism of GDM and reducing the incidence of adverse pregnancy outcomes in patients with GDM. The well-known GDM-associated lipids used in clinical tests, such as triglyceride (TG), are thought to play a major role in metabolic changes during GDM, which have a potential causal relationship with abnormal pregnancy outcomes of GDM. Therefore, this study analyzed the relationship between clinical lipid indicators, metabolic profiles, and abnormal pregnancy outcomes in GDM through mediation analysis. By constructing a metabolic atlas of 399 samples from GDM patients in different trimesters, we efficiently detected the key metabolites of adverse pregnancy outcomes and their mediating roles in bridging abnormal lipids and adverse pregnancy outcomes in patients with GDM. Our study confirmed that TG and total cholesterol were independent risk factors for adverse pregnancy outcomes in patients with GDM. Several key metabolites as mediators (e.g., gamma-linolenic acid, heptadecanoic acid, oleic acid, palmitic acid, and palmitoleic acid) have been identified as potential biomarkers for adverse pregnancy outcomes in patients with GDM. These metabolites mainly participate in the biosynthesis of unsaturated fatty acids, which may shed new light on the pathology of GDM and provide insights for further exploration of the molecular mechanisms underlying adverse pregnancy outcomes.

Effects of Exercise Intervention on Type 2 Diabetes Patients With Abdominal Obesity and Low Thigh Circumference (EXTEND): Study Protocol for a Randomized Controlled Trial.

Introduction: Type 2 diabetes patients have abdominal obesity and low thigh circumference. Previous studies have mainly focused on the role of exercise in reducing body weight and fat mass, improving glucose and lipid metabolism, with a lack of evaluation on the loss of muscle mass, diabetes complications, energy metabolism, and brain health. Moreover, whether the potential physiological benefit of exercise for diabetes mellitus is related to the modulation of the microbiota-gut-brain axis remains unclear. Multi-omics approaches and multidimensional evaluations may help systematically and comprehensively correlate physical exercise and the metabolic benefits. Methods and Analysis: This study is a randomized controlled clinical trial. A total of 100 sedentary patients with type 2 diabetes will be allocated to either an exercise or a control group in a 1:1 ratio. Participants in the exercise group will receive a 16-week combined aerobic and resistance exercise training, while those in the control group will maintain their sedentary lifestyle unchanged. Additionally, all participants will receive a diet administration to control the confounding effects of diet. The primary outcome will be the change in body fat mass measured using bioelectrical impedance analysis. The secondary outcomes will include body fat mass change rate (%), and changes in anthropometric indicators (body weight, waist, hip, and thigh circumference), clinical biochemical indicators (glycated hemoglobin, blood glucose, insulin sensitivity, blood lipid, liver enzyme, and renal function), brain health (appetite, mood, and cognitive function), immunologic function, metagenomics, metabolomics, energy expenditure, cardiopulmonary fitness, exercise-related indicators, fatty liver, cytokines (fibroblast growth factor 21, fibroblast growth factor 19, adiponectin, fatty acid-binding protein 4, and lipocalin 2), vascular endothelial function, autonomic nervous function, and glucose fluctuation. Discussion: This study will evaluate the effect of a 16-week combined aerobic and resistance exercise regimen on patients with diabetes. The results will provide a comprehensive evaluation of the physiological effects of exercise, and reveal the role of the microbiota-gut-brain axis in exercise-induced metabolic benefits to diabetes. Clinical Trial Registration: http://www.chictr.org.cn/searchproj.aspx, identifier ChiCTR2100046148.

Identifying luminal and basal mammary cell specific genes and their expression patterns during pregnancy.

Mammary gland is present in all mammals and usually functions in producing milk to feed the young offspring. Mammogenesis refers to the growth and development of mammary gland, which begins at puberty and ends after lactation. Pregnancy is regulated by various cytokines, which further contributes to mammary gland development. Epithelial cells, including basal and luminal cells, are one of the major components of mammary gland cells. The development of basal and luminal cells has been observed to significantly differ at different stages. However, the underlying mechanisms for differences between basal and luminal cells have not been fully studied. To explore the mechanisms underlying the differentiation of mammary progenitors or their offspring into luminal and myoepithelial cells, the single-cell sequencing data on mammary epithelia cells of virgin and pregnant mouse was deeply investigated in this work. We evaluated features by using Monte Carlo feature selection and plotted the incremental feature selection curve with support vector machine or RIPPER to find the optimal gene features and rules that can divide epithelial cells into four clusters with different cell subtypes like basal and luminal cells and different phases like pregnancy and virginity. As representations, the feature genes Cldn7, Gjb6, Sparc, Cldn3, Cited1, Krt17, Spp1, Cldn4, Gjb2 and Cldn19 might play an important role in classifying the epithelial mammary cells. Notably, seven most important rules based on the combination of cell-specific and tissue-specific expressions of feature genes effectively classify the epithelial mammary cells in a quantitative and interpretable manner.

Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.

Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for machine learning on large biological datasets are learning an effective model with mechanistical explanation on the model determination and prediction. To satisfy such demands, we developed Vec2image, an explainable convolutional neural network framework for characterizing the feature engineering, feature selection and classifier training that is mainly based on the collaboration of principal component coordinate conversion, deep residual neural networks and embedded k-nearest neighbor representation on pseudo images of high-dimensional biological data, where the pseudo images represent feature measurements and feature associations simultaneously. Vec2image has achieved better performance compared with other popular methods and illustrated its efficiency on feature selection in cell marker identification from tissue-specific single-cell datasets. In particular, in a case study on type 2 diabetes (T2D) by multiple human islet scRNA-seq datasets, Vec2image first displayed robust performance on T2D classification model building across different datasets, then a specific Vec2image model was trained to accurately recognize the cell state and efficiently rank feature genes relevant to T2D which uncovered potential T2D cellular pathogenesis; and next the cell activity changes, cell composition imbalances and cell-cell communication dysfunctions were associated to our finding T2D feature genes from both population-shared and individual-specific perspectives. Collectively, Vec2image is a new and efficient explainable artificial intelligence methodology that can be widely applied in human-readable classification and prediction on the basis of pseudo image representation of biological deep sequencing data.

3MCor: an integrative web server for metabolome-microbiome-metadata correlation analysis.

MOTIVATION: The metabolome and microbiome disorders are highly associated with human health, and there are great demands for dual-omics interaction analysis. Here, we designed and developed an integrative platform, 3MCor, for metabolome and microbiome correlation analysis under the instruction of phenotype and with the consideration of confounders. RESULTS: Many traditional and novel correlation analysis methods were integrated for intra- and inter-correlation analysis. Three inter-correlation pipelines are provided for global, hierarchical and pairwise analysis. The incorporated network analysis function is conducive to rapid identification of network clusters and key nodes from a complicated correlation network. Complete numerical results (csv files) and rich figures (pdf files) will be generated in minutes. To our knowledge, 3MCor is the first platform developed specifically for the correlation analysis of metabolome and microbiome. Its functions were compared with corresponding modules of existing omics data analysis platforms. A real-world dataset was used to demonstrate its simple and flexible operation, comprehensive outputs and distinctive contribution to dual-omics studies. AVAILABILITYAND IMPLEMENTATION: 3MCor is available at http://3mcor.cn and the backend R script is available at https://github.com/chentianlu/3MCorServer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring Somatic Alteration Associating With Aggressive Behaviors of Papillary Thyroid Carcinomas by Targeted Sequencing.

Wisely differentiating high-risk papillary thyroid carcinoma (PTC) patients from low-risk PTC patients preoperatively is necessary when comes to making a personalized treatment plan. It is not easy to stratify the risk of patients according to sonography or lab results before surgery. This study aims to seek out potential mutation gene markers that may be helpful in stratifying the risk of PTC. A custom panel of 439 PTC relevant and classic tumor metabolic pathway relevant genes was designed. Targeted capture sequencing was performed on 35 pairs of samples from 35 PTC tumors and 35 para-tumor thyroid tissues obtained during surgery. Variant calling and detection of cancer gene mutations were identified by bio-information analysis. Ingenuity Pathway Analysis (IPA) was performed to do functional enrichment analysis of high-frequency mutant genes. Immunohistochemistry (IHC) was performed on 6 PTC patients to explore the expression of protein associated with interested genes. Event-free survival (EFS) was calculated to determine which genes might affect the prognosis of patients. We have identified 32 high-frequency mutant genes in PTC including BRAF. RBL2 was found to be significantly correlated to event-free survival, FOXO1, MUC6, PCDHB9, NOTCH1, FIZ1, and RTN1 were significantly associated with EFS, while BRAF mutant was not correlated to any of the prognosis indicators. Our findings in this study might open more choices when designing thyroid gene panels used in FNA samples to diagnose PTC and predict the potentially aggressive behavior of PTC.

Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population.

BACKGROUND: Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings. METHODS: Metabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices. RESULTS: This study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices. CONCLUSIONS: Metabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Performance assessment of sample-specific network control methods for bulk and single-cell biological data analysis.

In the past few years, a wealth of sample-specific network construction methods and structural network control methods has been proposed to identify sample-specific driver nodes for supporting the Sample-Specific network Control (SSC) analysis of biological networked systems. However, there is no comprehensive evaluation for these state-of-the-art methods. Here, we conducted a performance assessment for 16 SSC analysis workflows by using the combination of 4 sample-specific network reconstruction methods and 4 representative structural control methods. This study includes simulation evaluation of representative biological networks, personalized driver genes prioritization on multiple cancer bulk expression datasets with matched patient samples from TCGA, and cell marker genes and key time point identification related to cell differentiation on single-cell RNA-seq datasets. By widely comparing analysis of existing SSC analysis workflows, we provided the following recommendations and banchmarking workflows. (i) The performance of a network control method is strongly dependent on the up-stream sample-specific network method, and Cell-Specific Network construction (CSN) method and Single-Sample Network (SSN) method are the preferred sample-specific network construction methods. (ii) After constructing the sample-specific networks, the undirected network-based control methods are more effective than the directed network-based control methods. In addition, these data and evaluation pipeline are freely available on https://github.com/WilfongGuo/Benchmark_control.

Applying artificial intelligence in the microbiome for gastrointestinal diseases: A review.

For a long time, gut bacteria have been recognized for their important roles in the occurrence and progression of gastrointestinal diseases like colorectal cancer, and the ever-increasing amounts of microbiome data combined with other high-quality clinical and imaging datasets are leading the study of gastrointestinal diseases into an era of biomedical big data. The "omics" technologies used for microbiome analysis continuously evolve, and the machine learning or artificial intelligence technologies are key to extract the relevant information from microbiome data. This review intends to provide a focused summary of recent research and applications of microbiome big data and to discuss the use of artificial intelligence to combat gastrointestinal diseases.