Browsing by Author "Liu, Xiaoming"

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    Different brain responses to electro-acupuncture and moxibustion treatment in patients with Crohn's disease
    (Nature Publishing Group, 2016-11-18) Bao, Chunhui; Liu, Peng; Liu, Huirong; Jin, Xiaoming; Calhoun, Vince D.; Wu, Luyi; Shi, Yin; Zhang, Jianye; Zeng, Xiaoqing; Ma, Lili; Qin, Wei; Zhang, Jingzhi; Liu, Xiaoming; Tian, Jie; Wu, Huangan; Department of Anatomy and Cell Biology, School of Medicine
    This study aimed to investigate changes in resting state brain activity in remissive Crohn's Disease (CD) patients after electro-acupuncture or moxibustion treatment. Fifty-two CD patients and 36 healthy subjects were enrolled, and 36 patients were equally and randomly assigned to receive either electro-acupuncture or moxibustion treatment for twelve weeks. We used resting state functional magnetic resonance imaging to assess Regional Homogeneity (ReHo) levels, and Crohn's Disease Activity Index (CDAI) and Inflammatory Bowel Disease Questionnaire (IBDQ) scores to evaluate disease severity and quality of life. The results show that (i) The ReHo levels in CD patients were significantly increased in cortical but decreased in subcortical areas, and the coupling between them was declined. (ii) Both treatments decreased CDAI, increased IBDQ scores, and normalized the ReHo values of the cortical and subcortical regions. (iii) ReHo changes in multiple cortical regions were significantly correlated with CDAI score decreases. ReHo changes in several subcortical regions in the electro-acupuncture group, and those of several cortical regions in the moxibustion group, were correlated with reduced CDAI. These findings suggest that both treatments improved cortex-subcortical coupling in remissive CD patients, but electro-acupuncture regulated homeostatic afferent processing network, while moxibustion mainly regulated the default mode network of the brain.
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    The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications
    (Biomed Central, 2019-02-04) Wu, Zhijin; Yan, Jingwen; Wang, Kai; Liu, Xiaoming; Guo, Yan; Zhi, Degui; Ruan, Jianhua; Zhao, Zhongming; BioHealth Informatics, School of Informatics and Computing
    The sixth International Conference on Intelligent Biology and Medicine (ICIBM) took place in Los Angeles, California, USA on June 10-12, 2018. This conference featured eleven regular scientific sessions, four tutorials, one poster session, four keynote talks, and four eminent scholar talks. The scientific program covered a wide range of topics from bench to bedside, including 3D Genome Organization, reconstruction of large scale evolution of genomes and gene functions, artificial intelligence in biological and biomedical fields, and precision medicine. Both method development and application in genomic research continued to be a main component in the conference, including studies on genetic variants, regulation of transcription, genetic-epigenetic interaction at both single cell and tissue level and artificial intelligence. Here, we write a summary of the conference and also briefly introduce the four high quality papers selected to be published in BMC Genomics that cover novel methodology development or innovative data analysis.
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    Polygenic Scores for Major Depressive Disorder and Risk of Alcohol Dependence
    (American Medical Association, 2017-11-01) Andersen, Allan M.; Pietrzak, Robert H.; Kranzler, Henry R.; Ma, Li; Zhou, Hang; Liu, Xiaoming; Kramer, John; Kuperman, Samuel; Edenberg, Howard J.; Nurnberger, John I., Jr.; Rice, John P.; Tischfield, Jay A.; Goate, Alison; Foroud, Tatiana M.; Meyers, Jacquelyn L.; Porjesz, Bernice; Dick, Danielle M.; Hesselbrock, Victor; Boerwinkle, Eric; Southwick, Steven M.; Krystal, John H.; Weissman, Myrna M.; Levinson, Douglas F.; Potash, James B.; Gelernter, Joel; Han, Shizhong; Biochemistry and Molecular Biology, School of Medicine
    Importance: Major depressive disorder (MDD) and alcohol dependence (AD) are heritable disorders with significant public health burdens, and they are frequently comorbid. Common genetic factors that influence the co-occurrence of MDD and AD have been sought in family, twin, and adoption studies, and results to date have been promising but inconclusive. Objective: To examine whether AD and MDD overlap genetically, using a polygenic score approach. Design, Settings, and Participants: Association analyses were conducted between MDD polygenic risk score (PRS) and AD case-control status in European ancestry samples from 4 independent genome-wide association study (GWAS) data sets: the Collaborative Study on the Genetics of Alcoholism (COGA); the Study of Addiction, Genetics, and Environment (SAGE); the Yale-Penn genetic study of substance dependence; and the National Health and Resilience in Veterans Study (NHRVS). Results from a meta-analysis of MDD (9240 patients with MDD and 9519 controls) from the Psychiatric Genomics Consortium were applied to calculate PRS at thresholds from P < .05 to P ≤ .99 in each AD GWAS data set. Main Outcomes and Measures: Association between MDD PRS and AD. Results: Participants analyzed included 788 cases (548 [69.5%] men; mean [SD] age, 38.2 [10.8] years) and 522 controls (151 [28.9.%] men; age [SD], 43.9 [11.6] years) from COGA; 631 cases (333 [52.8%] men; age [SD], 35.0 [7.7] years) and 756 controls (260 [34.4%] male; age [SD] 36.1 [7.7] years) from SAGE; 2135 cases (1375 [64.4%] men; age [SD], 39.4 [11.5] years) and 350 controls (126 [36.0%] men; age [SD], 43.5 [13.9] years) from Yale-Penn; and 317 cases (295 [93.1%] men; age [SD], 59.1 [13.1] years) and 1719 controls (1545 [89.9%] men; age [SD], 64.5 [13.3] years) from NHRVS. Higher MDD PRS was associated with a significantly increased risk of AD in all samples (COGA: best P = 1.7 × 10-6, R2 = 0.026; SAGE: best P = .001, R2 = 0.01; Yale-Penn: best P = .035, R2 = 0.0018; and NHRVS: best P = .004, R2 = 0.0074), with stronger evidence for association after meta-analysis of the 4 samples (best P = 3.3 × 10-9). In analyses adjusted for MDD status in 3 AD GWAS data sets, similar patterns of association were observed (COGA: best P = 7.6 × 10-6, R2 = 0.023; Yale-Penn: best P = .08, R2 = 0.0013; and NHRVS: best P = .006, R2 = 0.0072). After recalculating MDD PRS using MDD GWAS data sets without comorbid MDD-AD cases, significant evidence was observed for an association between the MDD PRS and AD in the meta-analysis of 3 GWAS AD samples without MDD cases (best P = .007). Conclusions and Relevance: These results suggest that shared genetic susceptibility contributes modestly to MDD and AD comorbidity. Individuals with elevated polygenic risk for MDD may also be at risk for AD.
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    Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program
    (Springer Nature, 2021) Taliun, Daniel; Harris, Daniel N.; Kessler, Michael D.; Carlson, Jedidiah; Szpiech, Zachary A.; Torres, Raul; Gagliano Taliun, Sarah A.; Corvelo, André; Gogarten, Stephanie M.; Kang, Hyun Min; Pitsillides, Achilleas N.; LeFaive, Jonathon; Lee, Seung-Been; Tian, Xiaowen; Browning, Brian L.; Das, Sayantan; Emde, Anne-Katrin; Clarke, Wayne E.; Loesch, Douglas P.; Shetty, Amol C.; Blackwell, Thomas W.; Smith, Albert V.; Wong, Quenna; Liu, Xiaoming; Conomos, Matthew P.; Bobo, Dean M.; Aguet, François; Albert, Christine; Alonso, Alvaro; Ardlie, Kristin G.; Arking, Dan E.; Aslibekyan, Stella; Auer, Paul L.; Barnard, John; Barr, R. Graham; Barwick, Lucas; Becker, Lewis C.; Beer, Rebecca L.; Benjamin, Emelia J.; Bielak, Lawrence F.; Blangero, John; Boehnke, Michael; Bowden, Donald W.; Brody, Jennifer A.; Burchard, Esteban G.; Cade, Brian E.; Casella, James F.; Chalazan, Brandon; Chasman, Daniel I.; Chen, Yii-Der Ida; Cho, Michael H.; Choi, Seung Hoan; Chung, Mina K.; Clish, Clary B.; Correa, Adolfo; Curran, Joanne E.; Custer, Brian; Darbar, Dawood; Daya, Michelle; de Andrade, Mariza; DeMeo, Dawn L.; Dutcher, Susan K.; Ellinor, Patrick T.; Emery, Leslie S.; Eng, Celeste; Fatkin, Diane; Fingerlin, Tasha; Forer, Lukas; Fornage, Myriam; Franceschini, Nora; Fuchsberger, Christian; Fullerton, Stephanie M.; Germer, Soren; Gladwin, Mark T.; Gottlieb, Daniel J.; Guo, Xiuqing; Hall, Michael E.; He, Jiang; Heard-Costa, Nancy L.; Heckbert, Susan R.; Irvin, Marguerite R.; Johnsen, Jill M.; Johnson, Andrew D.; Kaplan, Robert; Kardia, Sharon L. R.; Kelly, Tanika; Kelly, Shannon; Kenny, Eimear E.; Kiel, Douglas P.; Klemmer, Robert; Konkle, Barbara A.; Kooperberg, Charles; Köttgen, Anna; Lange, Leslie A.; Lasky-Su, Jessica; Levy, Daniel; Lin, Xihong; Lin, Keng-Han; Liu, Chunyu; Loos, Ruth J. F.; Garman, Lori; Gerszten, Robert; Lubitz, Steven A.; Lunetta, Kathryn L.; Mak, Angel C. Y.; Manichaikul, Ani; Manning, Alisa K.; Mathias, Rasika A.; McManus, David D.; McGarvey, Stephen T.; Meigs, James B.; Meyers, Deborah A.; Mikulla, Julie L.; Minear, Mollie A.; Mitchell, Braxton D.; Mohanty, Sanghamitra; Montasser, May E.; Montgomery, Courtney; Morrison, Alanna C.; Murabito, Joanne M.; Natale, Andrea; Natarajan, Pradeep; Nelson, Sarah C.; North, Kari E.; O'Connell, Jeffrey R.; Palmer, Nicholette D.; Pankratz, Nathan; Peloso, Gina M.; Peyser, Patricia A.; Pleiness, Jacob; Post, Wendy S.; Psaty, Bruce M.; Rao, D. C.; Redline, Susan; Reiner, Alexander P.; Roden, Dan; Rotter, Jerome I.; Ruczinski, Ingo; Sarnowski, Chloé; Schoenherr, Sebastian; Schwartz, David A.; Seo, Jeong-Sun; Seshadri, Sudha; Sheehan, Vivien A.; Sheu, Wayne H.; Shoemaker, M. Benjamin; Smith, Nicholas L.; Smith, Jennifer A.; Sotoodehnia, Nona; Stilp, Adrienne M.; Tang, Weihong; Taylor, Kent D.; Telen, Marilyn; Thornton, Timothy A.; Tracy, Russell P.; Van Den Berg, David J.; Vasan, Ramachandran S.; Viaud-Martinez, Karine A.; Vrieze, Scott; Weeks, Daniel E.; Weir, Bruce S.; Weiss, Scott T.; Weng, Lu-Chen; Willer, Cristen J.; Zhang, Yingze; Zhao, Xutong; Arnett, Donna K.; Ashley-Koch, Allison E.; Barnes, Kathleen C.; Boerwinkle, Eric; Gabriel, Stacey; Gibbs, Richard; Rice, Kenneth M.; Rich, Stephen S.; Silverman, Edwin K.; Qasba, Pankaj; Gan, Weiniu; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Papanicolaou, George J.; Nickerson, Deborah A.; Browning, Sharon R.; Zody, Michael C.; Zöllner, Sebastian; Wilson, James G.; Cupples, L. Adrienne; Laurie, Cathy C.; Jaquish, Cashell E.; Hernandez, Ryan D.; O'Connor, Timothy D.; Abecasis, Gonçalo R.; Epidemiology, Richard M. Fairbanks School of Public Health
    The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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    Transgenic ferret models define pulmonary ionocyte diversity and function
    (Springer Nature, 2023) Yuan, Feng; Gasser, Grace N.; Lemire, Evan; Montoro, Daniel T.; Jagadeesh, Karthik; Zhang, Yan; Duan, Yifan; Levlev, Vitaly; Wells, Kristen L.; Rotti, Pavana G.; Shahin, Weam; Winter, Michael; Rosen, Bradley H.; Evans, Idil; Cai, Qian; Yu, Miao; Walsh, Susan A.; Acevedo, Michael R.; Pandya, Darpan N.; Akurathi, Vamsidhar; Dick, David W.; Wadas, Thaddeus J.; Joo, Nam Soo; Wine, Jeffrey J.; Birket, Susan; Fernandez, Courtney M.; Leung, Hui Min; Tearney, Guillermo J.; Verkman, Alan S.; Haggie, Peter M.; Scott, Kathleen; Bartels, Douglas; Meyerholz, David K.; Rowe, Steven M.; Liu, Xiaoming; Yan, Ziying; Haber, Adam L.; Sun, Xingshen; Engelhardt, John F.; Medicine, School of Medicine
    Speciation leads to adaptive changes in organ cellular physiology and creates challenges for studying rare cell-type functions that diverge between humans and mice. Rare cystic fibrosis transmembrane conductance regulator (CFTR)-rich pulmonary ionocytes exist throughout the cartilaginous airways of humans1,2, but limited presence and divergent biology in the proximal trachea of mice has prevented the use of traditional transgenic models to elucidate ionocyte functions in the airway. Here we describe the creation and use of conditional genetic ferret models to dissect pulmonary ionocyte biology and function by enabling ionocyte lineage tracing (FOXI1-CreERT2::ROSA-TG), ionocyte ablation (FOXI1-KO) and ionocyte-specific deletion of CFTR (FOXI1-CreERT2::CFTRL/L). By comparing these models with cystic fibrosis ferrets3,4, we demonstrate that ionocytes control airway surface liquid absorption, secretion, pH and mucus viscosity-leading to reduced airway surface liquid volume and impaired mucociliary clearance in cystic fibrosis, FOXI1-KO and FOXI1-CreERT2::CFTRL/L ferrets. These processes are regulated by CFTR-dependent ionocyte transport of Cl- and HCO3-. Single-cell transcriptomics and in vivo lineage tracing revealed three subtypes of pulmonary ionocytes and a FOXI1-lineage common rare cell progenitor for ionocytes, tuft cells and neuroendocrine cells during airway development. Thus, rare pulmonary ionocytes perform critical CFTR-dependent functions in the proximal airway that are hallmark features of cystic fibrosis airway disease. These studies provide a road map for using conditional genetics in the first non-rodent mammal to address gene function, cell biology and disease processes that have greater evolutionary conservation between humans and ferrets.