Biology subjects

Xie, W.

Publications and source records attributed to Xie, W..

8 recordsLinked to original sources

bioRxiv2018

Child Growth Predicts Brain Functional Connectivity and Future Cognitive Outcomes in Urban Bangladeshi Children Exposed to Early Adversities

BackgroundFaltered growth has been shown to affect 161 million children worldwide and derail cognitive development from early childhood. The neural pathways by which growth faltering in early childhood affects future cognitive outcomes remain unclear, which is partially due to the scarcity of research using both neuroimaging and sensitive behavioral techniques in low-income settings. We employed EEG to examine the association between growth faltering and brain functional connectivity and whether brain functional connectivity mediates the effect of early adversity on cognitive development.\n\nMethodsWe recruited participants from an urban impoverished neighborhood in Dhaka, Bangladesh. One sample consisted of 85 children whose EEG and growth measures (height for age, weight for age, and weight to height) were collected at 6 months and cognitive outcomes were assessed at 27 months. Another sample consisted of 115 children whose EEG and growth measures were collected at 36 months and IQ scores were assessed at 48 months. Path analysis was used to test the effect of growth measures on cognitive outcomes through brain functional connectivity.\n\nFindingsFaltered growth was found to be accompanied by overall increased functional connectivity in the theta and low-beta frequency bands for the 36-month-old cohort. For both cohorts, brain functional connectivity was negatively predictive of later cognitive outcomes at 27 and 48 months, respectively. Faltered growth was found to have a negative impact on childrens IQ scores in the older cohort, and this effect was found to be mediated by brain functional connectivity in the low-beta band.\n\nInterpretationThe association found between growth measures and brain functional connectivity may reflect a broad deleterious effect of malnutrition on childrens brain development. The mediation effect of functional connectivity on the relation between physical growth and later IQ scores provides the first experimental evidence that brain functional connectivity may mediate the effect of biological adversity on cognitive development.\n\nFundingBill and Melinda Gates Foundation (OPP1111625)

neuroscience↗

bioRxiv2018

Structural and biochemical characterization on the cognate and heterologous interactions of the MazEF-mt9 TA system

The toxin-antitoxin (TA) modules widely exist in bacteria, and their activities are associated with the persister phenotype of the pathogen Mycobacterium tuberculosis (M. tb). M. tb causes Tuberculosis, a contagious and severe airborne disease. There are ten MazEF TA systems in M. tb, which play important roles in stress adaptation. How the antitoxins antagonize toxins in M. tb or how the ten TA systems crosstalk to each other are of interests, but the detailed molecular mechanisms are largely unclear. MazEF-mt9 is a unique member among the MazEF families due to its tRNase activity, which is usually carried out by the VapC family toxins. Here we present the cocrystal structure of the MazEF-mt9 complex at 2.7 [A]. By characterizing the association mode between the TA pairs through various characterization techniques, we found that MazF-mt9 not only bound its cognate antitoxin, but also the non-cognate antitoxin MazE-mt1, a phenomenon that could be also observed in vivo. Based on our structural and biochemical work, we proposed that the cognate and heterologous interactions among different TA systems work together to relieve MazF-mt9s toxicity to M. tb cells, which may facilitate their adaptation to the stressful conditions encountered during host infection.\n\nIMPORTANCETuberculosis (TB) is one of the most severe contagious diseases. Caused by Mycobacterium tuberculosis (M. tb), it poses a serious threat to human health. Additionally, TB is difficult to cure because of the multipledrug-resistant (MDR) and extensively drug-resistant (XDR) M. tb strains. Toxin-antitoxin (TA) systems have been discovered to widely exist in prokaryotic organisms with diverse roles, normally composed of a pair of molecules that antagonize each other. M. tb has ten MazEF systems, and some of them have been proved to be directly associated with the genesis of persisters and drug-resistance of M. tb. We here report the MazEF-mt9 complex structure, and thoroughly characterized the interactions between MazF-mt9 with MazEs within or outside the MazEF-mt9 family. Our study not only revealed the crosstalks between TA families and its significance to M. tb survival but also offers insights into potential anti-TB drug design.

biochemistry↗

bioRxiv2018

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high -fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe.

systems biology↗

bioRxiv2018

Multiplexed imaging using same species primary antibodies with signal amplification

Immunofluorescence (IF) imaging using antibodies to visualize specific biomolecules is a widely used technique in both biological and clinical laboratories. Standard IF imaging methods using primary antibodies followed by secondary antibodies have low multiplexing capability due to limited availability of primary antibodies raised in different animal species. Here, we used a DNA-based signal amplification method, Hybridization Chain Reaction (HCR), to replace secondary antibodies to achieve multiplexed imaging using primary antibodies of the same species with superior signal intensity. To enable imaging with DNA-conjugated antibodies, we developed a new antibody staining protocol to minimize nonspecific binding of antibodies caused by conjugated DNA oligonucleotides. We also expanded the HCR hairpin pool from previously published 5 to 13 for highly multiplexed in situ imaging. We finally demonstrated multiplexed in situ protein imaging using the technique in both cultured cells and mouse retina sections.

cell biology↗

bioRxiv2018

DPP9 is an endogenous and direct inhibitor of the NLRP1 inflammasome that guards against human auto-inflammatory diseases

The inflammasome is a critical immune complex that activates IL-1 driven inflammation in response to pathogen- and danger-associated signals. Nod-like receptor protein-1 (NLRP1) is a widely expressed inflammasome sensor. Inherited gain-of-function mutations in NLRP1 cause a spectrum of human Mendelian diseases, including systemic autoimmunity and skin cancer susceptibility. However, its endogenous regulation and its cognate ligands are still unknown. Here we apply a proteomics screen to identify dipeptidyl dipeptidase, DPP9 as a novel interacting partner and a specific endogenous inhibitor of NLRP1 inflammasome in diverse primary cell types from human and mice. DPP9 inhibition via small molecule drugs, targeted mutations in its catalytic site and CRISPR/Cas9-mediated genetic deletion potently and specifically activate the NLRP1 inflammasome leading to pyroptosis and IL-1 processing via ASC and caspase-1. Mechanistically, DPP9 maintains NLRP1 in its monomeric, inactive state by binding to the auto-cleaving FIIND domain. NLRP1-FIIND is a self-sufficient DPP9 binding module and its disruption by a single missense mutation abrogates DPP9 binding and explains the aberrant inflammasome activation in NAIAD patients with arthritis and dyskeratosis. These findings uncover a unique peptidase enzyme-based mechanism of inflammasome regulation, and suggest that the DPP9-NLRP1 complex could be broadly involved in human inflammatory disorders.

immunology↗

bioRxiv2018

Optimizing Trait Predictability in Hybrid Rice Using Superior Prediction Models and Selective Omic Datasets

Hybrid breeding has dramatically boosted yield and its stability in rice. Genomic prediction further benefits rice breeding by increasing selection intensity and accelerating breeding cycles. With the rapid advancement of technology, other omic data, such as metabolomic data and transcriptomic data, are readily available for predicting genetic values (or breeding values) for agronomically important traits. In the current study, we searched for the best prediction strategy for four traits (yield, 1000 grain weight, number of grains per panicle and number of tillers per plant) of hybrid rice by evaluating all possible combinations of omic datasets with different prediction methods. We conclude that, in rice, the predictions using the combination of genomic and metabolomic data generally produce better results than single-omics predictions or predictions based on other combined omic data. Inclusion of transcriptomic data does not improve predictability possibly because transcriptome does not provide more information for the trait than the sum of genome and metabolome; rather, the computational complexity is substantially increased if transcriptomic data is included in the models. Best linear unbiased prediction (BLUP) appears to be the most efficient prediction method compared to the other commonly used approaches, including LASSO, SSVS, SVM-RBF, SVP-POLY and PLS. Our study has provided a guideline for selection of hybrid rice in terms of which types of omic datasets and which method should be used to achieve higher trait predictability.

genetics↗

bioRxiv2017

Advanced whole genome sequencing and analysis of fetal genomes from amniotic fluid

Amniocentesis is typically performed to identify large chromosomal abnormalities within the fetus. Here we demonstrate that it is feasible to generate an accurate whole genome sequence (WGS) of a fetus from an amniotic sample. DNA from cells and the amniotic fluid were isolated and sequenced from 31 amniocenteses. Concordance of variant calls between the two DNA sources and with parental libraries was high. Two fetal genomes were found to harbor potentially detrimental variants in CHD8 and LRP1, variations in these genes have been associated with Autism Spectrum Disorder (ASD) and Keratosis pilaris atrophicans, respectively. We also discovered drug sensitivities and carrier information of fetuses for a variety of diseases. In this study, we demonstrate for the first time the sequencing of the whole genome of fetuses from amniotic fluid and show that much more information than large chromosomal abnormalities can be gained from an amniocentesis.

genomics↗

bioRxiv2017

Opportunities And Obstacles For Deep Learning In Biology And Medicine

Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We examine applications of deep learning to a variety of biomedical problems--patient classification, fundamental biological processes, and treatment of patients--and discuss whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges. We find that deep learning has yet to revolutionize or definitively resolve any of these problems, but promising advances have been made on the prior state of the art. Even when improvement over a previous baseline has been modest, we have seen signs that deep learning methods may speed or aid human investigation. More work is needed to address concerns related to interpretability and how to best model each problem. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning powering changes at both bench and bedside with the potential to transform several areas of biology and medicine.

bioinformatics↗