Jun Ma

Baylor College of Medicine, Houston, Texas, United States

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Publications (13)53.65 Total impact

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    ABSTRACT: Humans and their microbiomes have coevolved as a physiologic community composed of distinct body site niches with metabolic and antigenic diversity. The placental microbiome has not been robustly interrogated, despite recent demonstrations of intracellular bacteria with diverse metabolic and immune regulatory functions. A population-based cohort of placental specimens collected under sterile conditions from 320 subjects with extensive clinical data was established for comparative 16S ribosomal DNA-based and whole-genome shotgun (WGS) metagenomic studies. Identified taxa and their gene carriage patterns were compared to other human body site niches, including the oral, skin, airway (nasal), vaginal, and gut microbiomes from nonpregnant controls. We characterized a unique placental microbiome niche, composed of nonpathogenic commensal microbiota from the Firmicutes, Tenericutes, Proteobacteria, Bacteroidetes, and Fusobacteria phyla. In aggregate, the placental microbiome profiles were most akin (Bray-Curtis dissimilarity <0.3) to the human oral microbiome. 16S-based operational taxonomic unit analyses revealed associations of the placental microbiome with a remote history of antenatal infection (permutational multivariate analysis of variance, P = 0.006), such as urinary tract infection in the first trimester, as well as with preterm birth <37 weeks (P = 0.001).
    Science translational medicine 05/2014; 6(237):237ra65. · 10.76 Impact Factor
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    ABSTRACT: Data from animal models show that in utero exposure to a maternal high-fat diet (HFD) renders susceptibility of these offspring to the adult onset of metabolic syndrome. We and others have previously shown that epigenetic modifications to histones may serve as a molecular memory of the in utero exposure, rendering the risk of adult disease. Because mice heterozygous for the Glut4 gene (insulin sensitive glucose transporter) born to wild-type (WT) mothers demonstrate exacterbated metabolic syndrome when exposed to an HFD in utero, we sought to analyze the genome-wide epigenetic changes that occur in the fetal liver in susceptible offspring. WT and Glut4(+/-) (G4(+/-)) offspring of WT mothers that were exposed either to a control or an HFD in utero were studied. Immunoblotting was used to measure hepatic histone modifications of fetal and 5-week animals. Chromatin immunoprecipitation (ChIP) followed by hybridization to chip arrays (ChIP-on-chip) was used to detect genome-wide changes of histone modifications with HFD exposure. We found that levels of hepatic H3K14ac and H3K9me3 significantly increased with HFD exposure in WT and G4(+/-) fetal and 5-week offspring. Pathway analysis of our ChIP-on-chip data revealed differential H3K14ac and H3K9me3 enrichment along pathways that regulate lipid metabolism, specifically in the promoter regions of Pparg, Ppara, Rxra, and Rora. We conclude that HFD exposure in utero is associated with functional alterations to fetal hepatic histone modifications in both WT and G4(+/-) offspring, some of which persist up to 5 weeks of age.
    American journal of obstetrics and gynecology 05/2014; 210(5):463.e1-463.e11. · 3.28 Impact Factor
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    ABSTRACT: Although our microbial community and genomes (the human microbiome) outnumber our genome by several orders of magnitude, to what extent the human host genetic complement informs the microbiota composition is not clear. The Human Microbiome Project (HMP) Consortium established a unique population-scale framework with which to characterize the relationship of microbial community structure with their human hosts. A wide variety of taxa and metabolic pathways have been shown to be differentially distributed by virtue of race/ethnicity in the HMP. Given that mtDNA haplogroups are the maternally derived ancestral genomic markers and mitochondria's role as the generator for cellular ATP, characterizing the relationship between human mtDNA genomic variants and microbiome profiles becomes of potential marked biologic and clinical interest. We leveraged sequencing data from the HMP to investigate the association between microbiome community structures with its own host mtDNA variants. 15 haplogroups and 631 mtDNA nucleotide polymorphisms (mean sequencing depth of 280X on the mitochondria genome) from 89 individuals participating in the HMP were accurately identified. 16S rRNA (V3-V5 region) sequencing generated microbiome taxonomy profiles and whole genome shotgun sequencing generated metabolic profiles from various body sites were treated as traits to conduct association analysis between haplogroups and host clinical metadata through linear regression. The mtSNPs of individuals with European haplogroups were associated with microbiome profiles using PLINK quantitative trait associations with permutation and adjusted for multiple comparisons. We observe that among 139 stool and 59 vaginal posterior fornix samples, several haplogroups show significant association with specific microbiota (q-value < 0.05) as well as their aggregate community structure (Chi-square with Monte Carlo, p < 0.005), which confirmed and expanded previous research on the association of race and ethnicity with microbiome profile. Our results further indicate that mtDNA variations may render different microbiome profiles, possibly through an inflammatory response to different levels of reactive oxygen species activity. These data provide initial evidence for the association between host ancestral genome with the structure of its microbiome.
    BMC Genomics 04/2014; 15(1):257. · 4.40 Impact Factor
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    ABSTRACT: Dysbiosis of the microbiome has been associated with type II diabetes mellitus, obesity, inflammatory bowel disorders, and colorectal cancer, and recently, the Human Microbiome Project Consortium has helped to define a healthy microbiome. Now research has begun to investigate how the microbiome is established, and in this article, we will discuss the maternal influences on the establishment of the microbiome. The inoculation of an individual's microbiome is highly dependent on the maternal microbiome, and changes occur in the maternal microbiome during pregnancy that may help to shape the neonatal microbiome. Further, we consider how mode of delivery may shape the developing microbiome of a neonate, and we end by discussing how the microbiome may impact preterm birth and the possibility of bacterial colonization of the placenta. Although the current literature demonstrates that the transformation of the maternal microbiome during pregnancy effects the establishment of the neonatal microbiome, further research is needed to explore how the microbiome shapes our metabolism and developing immune system.
    Seminars in Reproductive Medicine 01/2014; 32(1):14-22. · 3.21 Impact Factor
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    ABSTRACT: Whole genome shotgun sequencing (WGS) has been increasingly recognized as the most comprehensive and robust approach for metagenomics research. When compared with 16S-based metagenomics, it offers the advantage of identification of species level taxonomy and the estimation of metabolic pathway activities from human and environmental samples. Several large-scale metagenomic projects have been recently conducted or are currently underway utilizing WGS. With the generation of vast amounts of data, the bioinformatics and computational analysis of WGS results become vital for the success of a metagenomics study. However, each step in the WGS data analysis, including metagenome assembly, gene prediction, taxonomy identification, function annotation, and pathway analysis, is complicated by the shear amount of data. Algorithms and tools have been developed specifically to handle WGS-generated metagenomics data with the hope of reducing the requirement on computational time and storage space. Here, we present an overview of the current state of metagenomics through WGS sequencing, challenges frequently encountered, and up-to-date solutions. Several applications that are uniquely applicable to microbiome studies in reproductive and perinatal medicine are also discussed.
    Seminars in Reproductive Medicine 01/2014; 32(1):5-13. · 3.21 Impact Factor
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    ABSTRACT: The intestinal microbiome is a unique ecosystem and an essential mediator of metabolism and obesity in mammals. However, studies investigating the impact of the diet on the establishment of the gut microbiome early in life are generally lacking, and most notably so in primate models. Here we report that a high-fat maternal or postnatal diet, but not obesity per se, structures the offspring's intestinal microbiome in Macaca fuscata (Japanese macaque). The resultant microbial dysbiosis is only partially corrected by a low-fat, control diet after weaning. Unexpectedly, early exposure to a high-fat diet diminished the abundance of non-pathogenic Campylobacter in the juvenile gut, suggesting a potential role for dietary fat in shaping commensal microbial communities in primates. Our data challenge the concept of an obesity-causing gut microbiome and rather provide evidence for a contribution of the maternal diet in establishing the microbiota, which in turn affects intestinal maintenance of metabolic health.
    Nature Communications 01/2014; 5:3889. · 10.74 Impact Factor
  • American Journal of Obstetrics and Gynecology 01/2013; 208(1):S250. · 3.88 Impact Factor
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    ABSTRACT: In 2005, the World Health Organization estimated that 9.6% or 12.9 million births worldwide were born preterm at <37 weeks of gestation and were accompanied by a mortality rate as high as 42% (http://www.who.int/bulletin/volumes/88/1/08-062554). Significant data suggesting that intrauterine infection is an important modifier for the risk of preterm birth have emerged over the past four decades. However, causative microbial culprits have yet to be identified, and interventional trials with antimicrobials have uniformly failed to demonstrate a significant benefit. To the contrary, treatment for clinically asymptomatic, commonly associated polymicrobial communities (i.e., bacterial vaginosis) has resulted in an increase in the rate of preterm birth. This article discusses the importance of vaginal microbiome and the variance in its composition during normal pregnancy. We will expand this discussion to include possible mechanisms that might trigger preterm birth in at-risk subjects. Finally, we will review why preterm birth may be an ideal forum with which to apply our rapidly expanding metagenomic sequencing and analytic pipelines to discern the role of host and microbe in the relative continuum of health and disease.
    American Journal of Perinatology 11/2012; · 1.57 Impact Factor
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    ABSTRACT: While current major national research efforts (i.e., the NIH Human Microbiome Project) will enable comprehensive metagenomic characterization of the adult human microbiota, how and when these diverse microbial communities take up residence in the host and during reproductive life are unexplored at a population level. Because microbial abundance and diversity might differ in pregnancy, we sought to generate comparative metagenomic signatures across gestational age strata. DNA was isolated from the vagina (introitus, posterior fornix, midvagina) and the V5V3 region of bacterial 16S rRNA genes were sequenced (454FLX Titanium platform). Sixty-eight samples from 24 healthy gravidae (18 to 40 confirmed weeks) were compared with 301 non-pregnant controls (60 subjects). Generated sequence data were quality filtered, taxonomically binned, normalized, and organized by phylogeny and into operational taxonomic units (OTU); principal coordinates analysis (PCoA) of the resultant beta diversity measures were used for visualization and analysis in association with sample clinical metadata. Altogether, 1.4 gigabytes of data containing >2.5 million reads (averaging 6,837 sequences/sample of 493 nt in length) were generated for computational analyses. Although gravidae were not excluded by virtue of a posterior fornix pH >4.5 at the time of screening, unique vaginal microbiome signature encompassing several specific OTUs and higher-level clades was nevertheless observed and confirmed using a combination of phylogenetic, non-phylogenetic, supervised, and unsupervised approaches. Both overall diversity and richness were reduced in pregnancy, with dominance of Lactobacillus species (L. iners crispatus, jensenii and johnsonii, and the orders Lactobacillales (and Lactobacillaceae family), Clostridiales, Bacteroidales, and Actinomycetales. This intergroup comparison using rigorous standardized sampling protocols and analytical methodologies provides robust initial evidence that the vaginal microbial 16S rRNA gene catalogue uniquely differs in pregnancy, with variance of taxa across vaginal subsite and gestational age.
    PLoS ONE 06/2012; 7(6):e36466. · 3.53 Impact Factor
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    ABSTRACT: Results of high throughput experiments can be challenging to interpret. Current approaches have relied on bulk processing the set of expression levels, in conjunction with easily obtained external evidence, such as co-occurrence. While such techniques can be used to reason probabilistically, they are not designed to shed light on what any individual gene, or a network of genes acting together, may be doing. Our belief is that today we have the information extraction ability and the computational power to perform more sophisticated analyses that consider the individual situation of each gene. The use of such techniques should lead to qualitatively superior results. The specific aim of this project is to develop computational techniques to generate a small number of biologically meaningful hypotheses based on observed results from high throughput microarray experiments, gene sequences, and next-generation sequences. Through the use of relevant known biomedical knowledge, as represented in published literature and public databases, we can generate meaningful hypotheses that will aide biologists to interpret their experimental data. We are currently developing novel approaches that exploit the rich information encapsulated in biological pathway graphs. Our methods perform a thorough and rigorous analysis of biological pathways, using complex factors such as the topology of the pathway graph and the frequency in which genes appear on different pathways, to provide more meaningful hypotheses to describe the biological phenomena captured by high throughput experiments, when compared to other existing methods that only consider partial information captured by biological pathways.
    BMC Bioinformatics 01/2012; 13 Suppl 2:S4. · 3.02 Impact Factor
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    ABSTRACT: Microbial metagenomic analyses rely on an increasing number of publicly available tools. Installation, integration, and maintenance of the tools poses significant burden on many researchers and creates a barrier to adoption of microbiome analysis, particularly in translational settings. To address this need we have integrated a rich collection of microbiome analysis tools into the Genboree Microbiome Toolset and exposed them to the scientific community using the Software-as-a-Service model via the Genboree Workbench. The Genboree Microbiome Toolset provides an interactive environment for users at all bioinformatic experience levels in which to conduct microbiome analysis. The Toolset drives hypothesis generation by providing a wide range of analyses including alpha diversity and beta diversity, phylogenetic profiling, supervised machine learning, and feature selection. We validate the Toolset in two studies of the gut microbiota, one involving obese and lean twins, and the other involving children suffering from the irritable bowel syndrome. By lowering the barrier to performing a comprehensive set of microbiome analyses, the Toolset empowers investigators to translate high-volume sequencing data into valuable biomedical discoveries.
    BMC Bioinformatics 01/2012; 13 Suppl 13:S11. · 3.02 Impact Factor
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    ABSTRACT: Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, current methods for gene set enrichment testing treat all genes equal. It is well-known that some genes, such as those responsible for housekeeping functions, appear in many pathways, whereas other genes are more specialized and play a unique role in a single pathway. Drawing inspiration from the field of information retrieval, we have developed and present here an approach to incorporate gene appearance frequency (in KEGG pathways) into two current methods, Gene Set Enrichment Analysis (GSEA) and logistic regression-based LRpath framework, to generate more reproducible and biologically meaningful results. Two breast cancer microarray datasets were analyzed to identify gene sets differentially expressed between histological grade 1 and 3 breast cancer. The correlation of Normalized Enrichment Scores (NES) between gene sets, generated by the original GSEA and GSEA with the appearance frequency of genes incorporated (GSEA-AF), was compared. GSEA-AF resulted in higher correlation between experiments and more overlapping top gene sets. Several cancer related gene sets achieved higher NES in GSEA-AF as well. The same datasets were also analyzed by LRpath and LRpath with the appearance frequency of genes incorporated (LRpath-AF). Two well-studied lung cancer datasets were also analyzed in the same manner to demonstrate the validity of the method, and similar results were obtained. We introduce an alternative way to integrate KEGG PATHWAY information into gene set enrichment testing. The performance of GSEA and LRpath can be enhanced with the integration of appearance frequency of genes. We conclude that, generally, gene set analysis methods with the integration of information from KEGG PATHWAY performs better both statistically and biologically.
    BMC Bioinformatics 03/2011; 12:81. · 3.02 Impact Factor
  • American Journal of Obstetrics and Gynecology. 208(1):S5.