@article {335, title = {Clinical Significance and Molecular Annotation of Cellular Morphometric Subtypes in Lower Grade Gliomas discovered by Machine Learning.}, journal = {Neuro Oncol}, year = {2022}, month = {2022 Jun 18}, abstract = {

BACKGROUND: Lower grade gliomas (LGG) are heterogenous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

METHODS: Cellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5- year overall survival (OS) of LGG patients. Tumor mutational burden (TMB), and immune cell infiltration between subtypes were analyzed using the Mann-Whitney test. The double-blinded validation for important immunotherapy-related biomarkers were executed using immunohistochemistry (IHC).

RESULTS: We developed a machine learning pipeline to extract CMBs from whole slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multi-center cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by immunohistochemical staining. In addition, the subtypes learned from LGG demonstrates translational impact on glioblastoma (GBM).

CONCLUSIONS: We developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune micro-environment, prognosis, and treatment response.

}, issn = {1523-5866}, doi = {10.1093/neuonc/noac154}, author = {Liu, Xiao-Ping and Jin, Xiaoqing and Ahmadian, Saman Seyed and Yang, Xu and Tian, Su-Fang and Cai, Yu-Xiang and Chawla, Kuldeep and Snijders, Antoine M and Xia, Yankai and van Diest, Paul J and Weiss, William A and Mao, Jian-Hua and Li, Zhi-Qiang and Vogel, Hannes and Chang, Hang} } @article {330, title = {Contribution of trace element exposure to gestational diabetes mellitus through disturbing the gut microbiome.}, journal = {Environ Int}, volume = {153}, year = {2021}, month = {2021 08}, pages = {106520}, abstract = {

BACKGROUND: A healthy gut microbiome is critical for glucose metabolism during pregnancy. In vivo studies indicate that trace element affects the composition and function of the gut microbiome and potentially leads to metabolic disorders but their relationships are largely unknown. We aimed to investigate whether the gut microbiome plays a role in the relationship between trace element exposure and gestational diabetes mellitus (GDM).

METHODS: In a prospective cohort study, serum levels of 22 trace elements and the fecal gut microbiome composition were assessed in 837 pregnant women in the second trimester between 22 and 24\ weeks of pregnancy prior to GDM diagnosis. Regression and mediation analysis were used to explore the link between element exposure, the gut microbiome, and GDM.

RESULTS: 128 pregnant women (15.3\%) were diagnosed with GDM. No individual trace elements were found significantly associated with GDM. In contrast, the composition of the gut microbiome was dramatically altered in women later diagnosed with GDM and characterized by lower alpha diversity and lower abundance of co-abundance groups (CAGs) composed of genera belonging to Ruminococcaceae, Coriobacteriales, and Lachnospiraceae. Rubidium (Rb) was positively associated with alpha diversity indices while mercury (Hg) and vanadium (V) showed negative associations. Elements including rubidium (Rb), thallium (Tl), arsenic (As), and antimony (Sb) were significantly correlated with GDM-related CAGs and mediation analysis revealed that Rb and Sb were inversely related to GDM risk by altering abundance levels of CAGs enriched for Lachnospiraceae, Coriobacteriales, and Ruminococcaceae.

CONCLUSION: Our study indicates that trace element exposure is associated with specific gut microbiome features that may contribute to GDM development, which could provide a new avenue for intervening in environmental exposure-related GDM.

}, keywords = {Diabetes, Gestational, Feces, Female, Gastrointestinal Microbiome, Humans, Pregnancy, Prospective Studies, Trace Elements}, issn = {1873-6750}, doi = {10.1016/j.envint.2021.106520}, author = {Zhang, Yuqing and Chen, Ting and Zhang, Yiyun and Hu, Qi and Wang, Xu and Chang, Hang and Mao, Jian-Hua and Snijders, Antoine M and Xia, Yankai} } @article {327, title = {Development and Validation of an Unsupervised Feature Learning System for Leukocyte Characterization and Classification: A Multi-Hospital Study}, volume = {129}, year = {2021}, month = {2021/06/01}, pages = {1837 - 1856}, abstract = {

The characterization and classification of white blood cells (WBC) are critical for the diagnosis of anemia, leukemia, and many other hematologic diseases. We developed WBC-Profiler, an unsupervised feature learning system for quantitative analysis of leukocytes. We demonstrate, through independent validation, that WBC-Profiler enables automatic extraction of complex and robust signatures from microscopic images without human-intervention and, thereafter, effective construction of interpretable leukocyte profiles, which decouples large scale complex leukocyte characterization from limitations in both human-based feature engineering/optimization and the end-to-end solutions provided by many modern deep neural networks. Further evaluation in a real-world clinical setting confirms that, compared with 23 clinicians from 8 hospitals (class-average-sensitivity, 0.798; class-average-specificity, 0.963; cell-average-timecost: 3.158\  s), WBC-Profiler performs with significantly improved accuracy and speed (class-average-sensitivity, 0.890; class-average-specificity, 0.980; cell-average-timecost: 0.375\  s). Our findings suggest that WBC-Profiler has the potential clinical implications.

}, isbn = {1573-1405}, url = {https://doi.org/10.1007/s11263-021-01449-9}, author = {Yan, Hong and Mao, Xuanyu and Yang, Xu and Xia, Yongquan and Wang, Chengbin and Wang, Junjun and Xia, Rui and Xu, Xuejing and Wang, Zhiqiang and Li, Zhiyang and Zhao, Xie and Li, Yan and Liu, Guoye and He, Li and Wang, Zhongyu and Wang, Zhiqiong and Li, Zhiqiang and Cai, Weidong and Shen, Han and Chang, Hang} } @article {329, title = {Genetic background influences the effect of thirdhand smoke exposure on anxiety and memory in Collaborative Cross mice.}, journal = {Sci Rep}, volume = {11}, year = {2021}, month = {2021 06 24}, pages = {13285}, abstract = {

Growing evidence indicates that thirdhand smoke (THS) exposure induces many adverse health effects. However, it is unclear how THS exposure affects behavior and how host genetic background modulates phenotypic changes. Here we used the Collaborative Cross (CC) mouse population-based model to assess behavioral alterations immediately after THS exposure from 4 to 9\ weeks of age. We first measured anxiety-like behavior in six strains using light/dark box combined with a custom multivariate mouse tracking system. We developed an anxiety risk scoring system based on anxiety-related traits and then evaluated the THS impact on them. THS exposure significantly decreased anxiety risk in CC019 (P = 0.002) and CC051 (P = 0.009), but increased anxiety risk in CC036 (P \< 0.001), while the other three strains did not show significant changes in anxiety-related traits. Such differences were driven by female mice for the six measures of anxiety-like behavior. Memory potential was measured in the same cohort of mice using the passive avoidance assay. Both THS-exposed male and female CC019 mice displayed significant memory loss compared to controls while no significant changes were found in the other five strains. This study provides strong evidence that THS exposure leads to strain-dependent changes in anxiety-like behavior and memory, suggesting that host genetic variations play a critical role in individual susceptibility to THS-induced effects.

}, keywords = {Animals, Anxiety, Collaborative Cross Mice, Female, Humans, Male, Memory, Risk Factors, Tobacco Smoke Pollution}, issn = {2045-2322}, doi = {10.1038/s41598-021-92702-1}, author = {He, Li and Wang, Pin and Schick, Suzyann F and Huang, Abel and Jacob, Peyton and Yang, Xu and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua and Chang, Hang and Hang, Bo} } @article {324, title = {Gut microbiome partially mediates and coordinates the effects of genetics on anxiety-like behavior in Collaborative Cross mice.}, journal = {Sci Rep}, volume = {11}, year = {2021}, month = {2021 01 11}, pages = {270}, abstract = {

Growing evidence suggests that the gut microbiome (GM) plays a critical role in health and disease. However, the contribution of GM to psychiatric disorders, especially anxiety, remains unclear. We used the Collaborative Cross (CC) mouse population-based model to identify anxiety associated host genetic and GM factors. Anxiety-like behavior of 445 mice across 30 CC strains was measured using the light/dark box assay and documented by video. A custom tracking system was developed to quantify seven anxiety-related phenotypes based on video. Mice were assigned to a low or high anxiety group by consensus clustering using seven anxiety-related phenotypes. Genome-wide association analysis (GWAS) identified 141 genes (264 SNPs) significantly enriched for anxiety and depression related functions. In the same CC cohort, we measured GM composition and identified five families that differ between high and low anxiety mice. Anxiety level was predicted with 79\% accuracy and an AUC of 0.81. Mediation analyses revealed that the genetic contribution to anxiety was partially mediated by the GM.\ Our findings indicate that GM partially mediates and coordinates the effects of genetics on anxiety.

}, keywords = {Animals, Anxiety, Behavior, Animal, Collaborative Cross Mice, Gastrointestinal Microbiome, Mice}, issn = {2045-2322}, doi = {10.1038/s41598-020-79538-x}, author = {Jin, X and Zhang, Y and Celniker, S E and Xia, Y and Mao, J-H and Snijders, A M and Chang, Hang} } @article {325, title = {Host genetics and gut microbiota cooperatively contribute to azoxymethane-induced acute toxicity in Collaborative Cross mice.}, journal = {Arch Toxicol}, volume = {95}, year = {2021}, month = {2021 03}, pages = {949-958}, abstract = {

Azoxymethane (AOM) is a widely used carcinogen to study chemical-induced colorectal carcinogenesis and is an agent for studying fulminant hepatic failure. The inter-strain susceptibility to acute toxicity by AOM has been reported, but its association with host genetics or gut microbiota remains largely unexplored. Here a cohort of genetically diverse Collaborative Cross (CC) mice was used to assess the contribution of host genetics and the gut microbiome to AOM-induced acute toxicity. We observed variation in AOM-induced acute liver failure across CC strains. Quantitative trait loci (QTL) analysis revealed three chromosome regions significantly associated with AOM toxicity. Genes located within these QTL, including peroxisome proliferator-activated receptor alpha (Ppara), were enriched for enzyme activator and nucleoside-triphosphatase regulator activity. We further demonstrated that the protein level of PPARα in liver tissues from sensitive strains was remarkably lower compared to levels in resistant strains, consistent with protective role of PPAR family in liver injury. We discovered that the abundance levels of gut microbial families Anaeroplasmataceae, Ruminococcaceae, Lactobacillaceae, Akkermansiaceae and Clostridiaceae were significantly higher in the sensitive strains\ compared to the resistant strains. Using a random forest classifier method, we determined that the relative abundance levels of these microbial families predicted AOM toxicity with the area under the receiver-operating curve (AUC) of 0.75. Combining the three genetic loci and five microbial families increased the predictive accuracy of AOM toxicity (AUC of 0.99). Moreover, we found that Ruminococcaceae and Lactobacillaceae acted as mediators between host genetics and AOM toxicity. In conclusion, this study shows that host genetics and specific microbiome members play a critical role in AOM-induced acute toxicity, which provides a framework for analysis of the health effects from environmental toxicants.

}, keywords = {Animals, Azoxymethane, Carcinogens, Chemical and Drug Induced Liver Injury, Collaborative Cross Mice, Gastrointestinal Microbiome, Liver Failure, Acute, Male, Mice, Quantitative Trait Loci, Species Specificity}, issn = {1432-0738}, doi = {10.1007/s00204-021-02972-x}, author = {Zhong, Chenhan and He, Li and Lee, Sun-Young and Chang, Hang and Zhang, Yuqing and Threadgill, David W and Yuan, Ying and Zhou, Fuling and Celniker, Susan E and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua} } @article {328, title = {Thirdhand cigarette smoke leads to age-dependent and persistent alterations in the cecal microbiome of mice.}, journal = {Microbiologyopen}, volume = {10}, year = {2021}, month = {2021 06}, pages = {e1198}, abstract = {

The gut microbiome composition is influenced by many factors including environmental exposures. Here, we investigated the effect of thirdhand cigarette smoke (THS) and exposure age on gut microbiome diversity. C57BL/6 mice were exposed to THS at human exposure relevant levels for three weeks during three different life stages: postnatal (0-3\ weeks of age), pubescent (4-7\ weeks of age), and adult (9-12\ weeks of age), respectively. Cecal microbiome profiles were assessed at 17\ weeks of age by 16S rRNA gene sequencing. We found that age at THS exposure strongly influenced the cecal microbiome composition. Although postnatal THS exposure significantly influenced the microbial composition, pubescent and adulthood exposures only had minor effects. The microbiome of postnatally THS-exposed mice significantly increased several degradation pathways that regulate glycolysis and pyruvate decarboxylation, and significantly decreased coenzyme A biosynthesis and pyrimidine deoxyribonucleoside salvage. Our results indicate that mouse postnatal development is particularly susceptible to persistent THS exposure effects on the gut microbiome.

}, keywords = {Age Factors, Animals, Bacteria, Cecum, Environmental Exposure, Female, Gastrointestinal Microbiome, Humans, Male, Mice, Mice, Inbred C57BL, Smoke, Tobacco, Tobacco Smoke Pollution}, issn = {2045-8827}, doi = {10.1002/mbo3.1198}, author = {He, Li and Zhou, Yan-Xia and Zhang, Yuqing and Hang, Bo and Chang, Hang and Schick, Suzaynn F and Celniker, Susan E and Xia, Yankai and Snijders, Antoine M and Mao, Jian-Hua} } @article {307, title = {Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles.}, journal = {mSphere}, volume = {5}, year = {2020}, month = {2020 01 08}, abstract = {

The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended. The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile.

}, issn = {2379-5042}, doi = {10.1128/mSphere.00763-19}, author = {Liang, Yali and Dong, Tianyu and Chen, Minjian and He, Lianping and Wang, Tingzhang and Liu, Xingyin and Hang Chang and Mao, Jian-Hua and Hang, Bo and Snijders, Antoine M and Xia, Yankai} } @article {301, title = {Machine Learning in Multimodal Medical Imaging.}, journal = {Biomed Res Int}, volume = {2017}, year = {2017}, month = {2017}, pages = {1278329}, keywords = {Animals, Humans, Image Processing, Computer-Assisted, Machine Learning}, issn = {2314-6141}, doi = {10.1155/2017/1278329}, author = {Xia, Yong and Ji, Zexuan and Krylov, Andrey and Hang Chang and Cai, Weidong} } @article {204, title = {Hematein, a casein kinase II inhibitor, inhibits lung cancer tumor growth in a murine xenograft model.}, journal = {Int J Oncol}, volume = {43}, year = {2013}, month = {2013 Nov}, pages = {1517-22}, abstract = {

Casein kinase II (CK2) inhibitors suppress cancer cell growth. In this study, we examined the inhibitory effects of a novel CK2 inhibitor, hematein, on tumor growth in a murine xenograft model. We found that in lung cancer cells, hematein inhibited cancer cell growth, Akt/PKB Ser129 phosphorylation, the Wnt/TCF pathway and increased apoptosis. In a murine xenograft model of lung cancer, hematein inhibited tumor growth without significant toxicity to the mice tested. Molecular docking showed that hematein binds to CK2α in durable binding sites. Collectively, our results suggest that hematein is an allosteric inhibitor of protein kinase CK2 and has antitumor activity to lung cancer.

}, keywords = {Animals, Apoptosis, Blotting, Casein Kinase II, Cultured, Enzyme Inhibitors, Female, Hematoxylin, Humans, Immunoenzyme Techniques, Inbred BALB C, Lung Neoplasms, Mice, Phosphorylation, Proto-Oncogene Proteins c-akt, T Cell Transcription Factor 1, Tumor Cells, Western, Wnt Proteins, Xenograft Model Antitumor Assays}, issn = {1791-2423}, doi = {10.3892/ijo.2013.2087}, author = {Hung, Ming-Szu and Xu, Zhidong and Chen, Yu and Smith, Emmanuel and Jiang-Hua Mao and Hsieh, David and Lin, Yu-Ching and Yang, Cheng-Ta and Jablons, David M and You, Liang} }