Biology subjects

Chen, X.

Publications and source records attributed to Chen, X..

114 records · Page 7Linked to original sources

bioRxiv2017

A Comprehensive Assessment Of Reproducibility Of R-fMRI Metrics On The Impact Of Different Strategies For Multiple Comparison Correction And Small Sample Size

Concerns regarding reproducibility of resting-state functional magnetic resonance imaging (R-fMRI) findings have been raised. Little is known about how to operationally define R-fMRI reproducibility and to what extent it is affected by multiple comparison correction strategies and sample size. We comprehensively assessed two aspects of reproducibility, test-retest reliability and replicability, on widely used R-fMRI metrics in both between-subject contrasts of sex differences and within-subject comparisons of eyes-open and eyes-closed (EOEC) conditions. We noted permutation test with Threshold-Free Cluster Enhancement (TFCE), a strict multiple comparison correction strategy, reached the best balance between family-wise error rate (under 5%) and test-retest reliability / replicability (e.g., 0.68 for test-retest reliability and 0.25 for replicability of amplitude of low-frequency fluctuations (ALFF) for between-subject sex differences, 0.49 for replicability of ALFF for within-subject EOEC differences). Although R-fMRI indices attained moderate reliabilities, they replicated poorly in distinct datasets (replicability < 0.3 for between-subject sex differences, < 0.5 for within-subject EOEC differences). By randomly drawing different sample sizes from a single site, we found reliability, sensitivity and positive predictive value (PPV) rose as sample size increased. Small sample sizes (e.g., < 80 (40 per group)) not only minimized power (sensitivity < 2%), but also decreased the likelihood that significant results reflect \"true\" effects (PPV < 0.26) in sex differences. Our findings have implications for how to select multiple comparison correction strategies and highlight the importance of sufficiently large sample sizes in R-fMRI studies to enhance reproducibility.

neuroscience↗

bioRxiv2017

Cell proliferation depends on the direct binding between PKM2 and AKAP-Lbc

The M2 form of the glycolytic enzyme pyruvate kinase (PKM2) has generated much interest recently due to its important role in tumor metabolism. A yeast two-hybrid screen carried out by the Alliance for Cell Signaling suggests that PKM2 interacts with A-Kinase Anchoring Protein (AKAP)-Lbc.\n\nAKAP-Lbc (also known as AKAP13) is a scaffold protein that integrates signaling through multiple enzymes including protein kinases A and D and the small G protein Rho. AKAP-Lbc was originally identified in leukemic blast cells, and multiple reports implicate AKAP-Lbc in breast, prostate and thyroid cancers, however the role of AKAP-Lbc in cancer biology is not understood.\n\nCo-immunoprecipitation, pulldown and Bimolecular Fluorescence Complementation (BiFC) data indicate that PKM2 interacts with AKAP-Lbc. Mapping experiments indicate that PKM2 directly interacts with amino acid residues 1923-2817 of AKAP-Lbc. By disrupting the interaction between the two proteins with the expression of the AKAP-Lbc fragments, our data suggest that the binding between PKM2 and PKA plays a critical role in cell proliferation. The work indicates that the binding between AKAP-Lbc and PKM2 may be an important target to treat some cancers by reducing the cell proliferation.

biochemistry↗

bioRxiv2017

DATS: the data tag suite to enable discoverability of datasets

Todays science increasingly requires effective ways to find and access existing datasets that are distributed across a range of repositories. For researchers in the life sciences, discoverability of datasets may soon become as essential as identifying the latest publications via PubMed. Through an international collaborative effort funded by the National Institutes of Health (NIH)s Big Data to Knowledge (BD2K) initiative, we have designed and implemented the DAta Tag Suite (DATS) model to support the DataMed data discovery index. DataMeds goal is to be for data what PubMed has been for the scientific literature. Akin to the Journal Article Tag Suite (JATS) used in PubMed, the DATS model enables submission of metadata on datasets to DataMed. DATS has a core set of elements, which are generic and applicable to any type of datasets, and an extended set that can accommodate more specialized data types. DATS is a platform-independent model also available as a Schema.org annotated serialization to be used beyond DataMed, for example, in projects like DataCite.

bioinformatics↗

bioRxiv2017

The Effects of Sex and Diet on Physiology and Liver Gene Expression in Diversity Outbred Mice

Inter-individual variation in metabolic health and adiposity is driven by many factors. Diet composition and genetic background and the interactions between these two factors affect adiposity and related traits such as circulating cholesterol levels. In this study, we fed 850 Diversity Outbred mice, half females and half males, with either a standard chow diet or a high fat, high sucrose diet beginning at weaning and aged them to 26 weeks. We measured clinical chemistry and body composition at early and late time points during the study, and liver transcription at euthanasia. Males weighed more than females and mice on a high fat diet generally weighed more than those on chow. Many traits showed sex- or diet-specific changes as well as more complex sex by diet interactions. We mapped both the physiological and molecular traits and found that the genetic architecture of the physiological traits is complex, with many single locus associations potentially being driven by more than one polymorphism. For liver transcription, we find that local polymorphisms affect constitutive and sex-specific transcription, but that the response to diet is not affected by local polymorphisms. We identified two loci for circulating cholesterol levels. We performed mediation analysis by mapping the physiological traits, given liver transcript abundance and propose several genes that may be modifiers of the physiological traits. By including both physiological and molecular traits in our analyses, we have created deeper phenotypic profiles to identify additional significant contributors to complex metabolic outcomes such as polygenic obesity. We make the phenotype, liver transcript and genotype data publicly available as a resource for the research community.

genetics↗

bioRxiv2016

Mocap: Large-scale inference of transcription factor binding sites from chromatin accessibility

Differential binding of transcription factors (TFs) at cis-regulatory loci drives the differentiation and function of diverse cellular lineages. Understanding the regulatory interactions that underlie cell fate decisions requires characterizing TF binding sites (TFBS) across multiple cell types and conditions. Techniques, e.g. ChIP-Seq can reveal genome-wide patterns of TF binding, but typically requires laborious and costly experiments for each TF-cell-type (TFCT) condition of interest. Chromosomal accessibility assays can connect accessible chromatin in one cell type to many TFs through sequence motif mapping. Such methods, however, rarely take into account that the genomic context preferred by each factor differs from TF to TF, and from cell type to cell type. To address the differences in TF behaviors, we developed Mocap, a method that integrates chromatin accessibility, motif scores, TF footprints, CpG/GC content, evolutionary conservation and other factors in an ensemble of TFCT-specific classifiers. We show that integration of genomic features, such as CpG islands improves TFBS prediction in some TFCT. Further, we describe a method for mapping new TFCT, for which no ChIP-seq data exists, onto our ensemble of classifiers and show that our cross-sample TFBS prediction method outperforms several previously described methods.

bioinformatics↗

bioRxiv2016

An improved de novo pipeline for enrichment of high diversity mitochondrial genomes from Amphibia to high-throughput sequencing

We present an improved de novo pipeline by combining long-range polymerase chain reaction (LR-PCR) and capture hybridization for enriching mitochondrial DNA to high-throughput sequencing. We test a new set of primers and hybridizing long-range library (LR-HY) with 112 mitochondrial genomes (MtG) representing three orders, 12 families, 54 genera, and 106 species of Amphibia. The primers are used for obtaining wide taxonomic MtG amplicons to sequence directly and/or make probes for closely related species. LR-HY is compared to standard hybridization. The primers successfully amplify 82 MtGs from all three order, all families, 92.6% (50/54) of the genera, and 74.5% (79/106) of the species, despite some DNA degradation and gene rearrangement. We observe a significantly negative correlation between sequence depth and gene variation. The pattern of highly variable regions is separately distributed in different regions within the length of < 4 kb in the 33-pooled sample. We demonstrate that using 2 kb libraries generate deeper sequence coverage in the highly variable loci than using 400 bp libraries. In total, the pipeline successfully recovers 83 complete and 14 almost complete MtGs from 53 of 54 genera, including 14 MtGs had rearranged protein-coding genes. This universal primers combined with LR-HY is an efficient way to enrich complete MtGs across the entire Amphibia.

genomics↗