Biology subjects

Ma, W.

Publications and source records attributed to Ma, W..

228 records · Page 13Linked to original sources

bioRxiv2019

Contrasting biogeographic patterns of bacterial and archaeal diversity in the top- and subsoils of temperate grasslands

Biogeographic patterns and drivers of soil microbial diversity have been extensively studied in the past few decades. However, most research has focused on the topsoil while the subsoil is assumed to have similar microbial diversity patterns as the topsoil. Here we compare patterns and drivers of microbial diversity in the top- (0-10 cm) versus subsoils (30-50 cm) of temperate grasslands in Inner Mongolia of China along an aridity gradient covering a ~1500-km transect from arid to mesic ecosystems. Counter to the conventional assumption, we find contrasting biogeographic patterns of diversity and influencing factors for different bacterial and archaeal groups and between depths. While bacterial diversity increases with increasing aridity, archaeal diversity decreases. Microbial diversity in the topsoil is most strongly influenced by aboveground vegetation, but is most strongly influenced by historical temperature anomaly since the Last Glacial Maximum (LGM) in the subsoil. Moreover, the biogeographic patterns of top-subsoil diversity difference varies for different microbial groups and is overall most strongly influenced by soil fertility difference between depths and historical temperature anomaly. These findings suggest that diversity patterns observed in the topsoil may not be readily applied to the subsoil horizons. For the subsoil in particular, historical climate plays a vital role in the distribution of various microbial groups. Overall, our study provides novel information for understanding and predicting soil microbial diversity patterns at depth.\n\nIMPORTANCEExploring the biogeographic patterns of soil microbial diversity is critical for understanding mechanisms underlying the response of soil processes to climate change. Using top- and subsoils from a ~1500-km temperature grassland transect, we find divergent patterns of microbial diversity and its determinants in the top-versus subsoils. Furthermore, we find important legacy effect of historical climate change on the microbial diversity of subsoil but not topsoil. Our findings challenge the conventional assumption of similar geographic patterns of soil microbial diversity along soil profiles and help to improve our understanding of how soil microbial communities may respond to future climate change in different regions with varied climate history.

microbiology↗

bioRxiv2019

LRRC33 is a novel binding and regulating protein of TGF-β1 function in human acute myeloid leukemia cells

Transforming growth factor - {beta}1 (TGF-{beta}1) is a versatile cytokine. It has context-dependent pro- and anti-cell proliferation functions. Activation of latent TGF-{beta}1 requires release of the growth factor from pro-complexes and is regulated through TGF-{beta} binding proteins. Two types of TGF-{beta} binding partners, latent TGF-{beta}-binding proteins (LTBPs) and leucine-rich-repeat-containing protein 32 (LRRC32), have been identified and their expression are cell specific. TGF-{beta}1 also plays important roles in acute myeloid leukemia (AML) cells. However, the expression of LTBPs and LRRC32 are lacking in myeloid lineage cells and the binding protein of TGF-{beta}1 in these cells are unknown. Here we show that a novel leucine-rich-repeat-containing protein family member, LRRC33, with high mRNA level in AML cells, to be the binding and regulating protein of TGF-{beta}1 in AML cells. Using two representative cell lines MV4-11 and AML193, we demonstrate that the protein expression of LRRC33 and TGF-{beta}1 are correlated. LRRC33 co-localizes and forms complex with latent TGF-{beta}1 protein on the cell surface and intracellularly in these cells. Similar as in other cell types, the activation of TGF-{beta}1 in MV4-11 and AML193 cells are also integrin dependent. We anticipate our study to be a starting point of more comprehensive research on LRRC33 as novel TGF-{beta} regulating protein and potential non-genomic based drug target for AML and other myeloid malignancy.

biochemistry↗

bioRxiv2018

Complement C3- and CR3-dependent microglial clearance protects photoreceptors in retinitis pigmentosa

Complement activation has been implicated as an inflammatory driver of neurodegeneration in retinal and brain pathologies. However, its involvement and influence of photoreceptor degeneration in retinitis pigmentosa (RP), an inherited, largely incurable blinding disease, is unclear. We discover that markedly upregulated retinal expression of multiple complement components coincided spatiotemporally with photoreceptor degeneration in both the rd10 mouse model and in human specimens of RP, with increased complement C3 expression and activation localizing to infiltrating microglia near photoreceptors. Genetic ablation of C3 in the rd10 background resulted in accelerated structural and functional photoreceptor degeneration and altered retinal expression of inflammatory genes. These effects were phenocopied by the genetic deletion of CR3, a microglia-expressed receptor for the C3 activation product C3b, implicating an adaptive microglial-mediation mechanism involving C3-CR3 interaction. Deficiency of either C3 or CR3 resulted in deficient microglial phagocytosis of apoptotic photoreceptors in vivo, as well as increased microglial neurotoxicity to photoreceptors in vitro. These findings demonstrate a novel adaptive role for complement activation in RP that facilitates microglial clearance of apoptotic photoreceptors, without which increased proinflammatory microglial neurotoxicity ensues. These positive contributions of complement via microglial-mediated mechanisms are important in the design of immunomodulatory therapeutic approaches to neurodegeneration.\n\nOne Sentence SummaryComplement activation mediates adaptive neuroprotection for photoreceptors by facilitating C3-CR3 dependent microglial clearance of apoptotic cells.

neuroscience↗

bioRxiv2018

Intake of dietary fiber, fruits, and vegetables, and risk of diverticulitis

Background & AimsAlthough low fiber intake has been considered a risk factor for diverticulitis, prospective evidence is limited and conflicting, with little known about variation in the protective effects according to food sources. We assessed the associations of intakes of fiber and major food sources of fiber including fruits and vegetables with risk of diverticulitis.\n\nMethodsWe followed 50,019 women in the Nurses Health Study (1990-2014) and 48,292 men in the Health Professionals Follow-up Study (1986-2014) who were free of diverticulitis, cancer, and inflammatory bowel disease at baseline. Incident diverticulitis was identified through self-report with validity confirmed by review of medical records.\n\nResultsDuring a mean follow-up time of 22 years, we documented 4,343 incident cases of diverticulitis in women and 1,142 cases in men. Compared to participants in the lowest quintile, the multivariable HRs (95% CIs) of diverticulitis in the highest quintile of total fiber intake were 0.86 (0.78-0.95; P-trend=0.002) among women and 0.63 (0.51-0.79; P-trend<0.001) among men. Fiber from different food sources, except for vegetable fiber in women, was associated with a decreased risk of diverticulitis. Furthermore, total whole fruit intake was associated with reduced risk of diverticulitis in both cohorts with a multivariable HR for diverticulitis of 0.95 (0.92-0.98; P-trend<0.001) in women and 0.91 (0.86-0.96; P-trend<0.001) in men for every serving increase of total whole fruit intake per day.\n\nConclusionsHigher intake of dietary fiber and fiber from different food sources are associated with a lower risk of diverticulitis. A greater intake of whole fruit is also associated with reduced risk.

epidemiology↗

bioRxiv2018

Multiscale causal network models of Alzheimer’s disease identify VGF as a key regulator of disease

Though discovered over 100 years ago, the molecular foundation of sporadic Alzheimers disease (AD) remains elusive. To elucidate its complex nature, we constructed multiscale causal network models on a large human AD multi-omics dataset, integrating clinical features of AD, DNA variation, and gene and protein expression into probabilistic causal models that enabled detection and prioritization of high-confidence key drivers of AD, including the top predicted key driver VGF. Overexpression of neuropeptide precursor VGF in 5xFAD mice partially rescued beta-amyloid-mediated memory impairment and neuropathology. Molecular validation of network predictions downstream of VGF was achieved, with significant enrichment for homologous genes identified as differentially expressed in 5xFAD brains overexpressing VGF versus controls. Our findings support a causal and/or protective role for VGF in AD pathogenesis and progression.\n\nOne sentence summaryVGF protects against Alzheimers disease

genomics↗

bioRxiv2018

Mechanical Coupling Coordinates the Co-elongation of Axial and Paraxial Tissues in Avian Embryos

Tissues undergoing morphogenesis impose mechanical effects on one another. How developmental programs adapt to or take advantage of these effects remains poorly explored. Here, using a combination of live imaging, modeling, and microsurgical perturbations, we show that the axial and paraxial tissues in the forming avian embryonic body coordinate their rates of elongation through mechanical interactions. First, a cell motility gradient drives paraxial presomitic mesoderm (PSM) expansion, resulting in compression of the axial neural tube and notochord; second, elongation of axial tissues driven by PSM compression and polarized cell intercalation pushes the caudal progenitor domain posteriorly; finally, the axial push drives progenitors to emigrate into the PSM to maintain tissue growth and cell motility. These interactions form an engine-like positive feedback loop, which ensures the tissue-coupling and self-sustaining characteristics of body elongation. Our results suggest a general role of inter-tissue forces in the coordination of complex morphogenesis involving distinct tissues.

developmental biology↗

bioRxiv2018

Wheat avenin-like protein and its significant Fusarium Head Blight resistant functions

Wheat Avenin-like proteins (TaALP) are atypical storage proteins belonging to the Prolamin superfamily. Previous studies on ALPs have focused on the proteins positive effects on dough strength, whilst no correlation has been made between TaALPs and the plant immune system. Here, we performed genome-wide characterization of ALP encoding genes in bread wheat. In silico analyses indicated the presence of critical peptides in TaALPs that are active in the plant immune system. Pathogenesis-related nucleotide motifs were also identified in the putative promoter regions of TaALP encoding genes. RT-PCR was performed on TaALP and previously characterised pathogenesis resistance genes in developing wheat caryopses under control and Fusarium graminearum infection conditions. The results showed that TaALP and NMT genes were upregulated upon F. graminearum inoculation. mRNA insitu hybridization showed that TaALP genes were expressed in the embryo, aleurone and sub-aleurone layer cells. Seven TaALP genes were cloned for the expression of recombinant proteins in Escherichia coli, which displayed significant inhibitory function on F. graminearum under anti-fungal tests. In addition, FHB index association analyses showed that allelic variations of two ALP genes on chromosome 7A were significantly correlated with FHB symptoms. Over-expression of an ALP gene on chromosome 7A showed an enhanced resistance to FHB. Yeast two Hybridization results revealed that ALPs have potential proteases inhibiting effect on metacaspases and beta-glucosidases. A vital infection process related pathogen protein, F. graminearum Beta-glucosidase was found to interact with ALPs. Our study is the first to report a class of wheat storage protein or gluten protein with biochemical functions. Due to its abundance in the grain and the important multi-functions, the results obtained in the current study are expected to have a significant impact on wheat research and industry.

molecular biology↗

bioRxiv2018

Mavacamten stabilizes a folded-back sequestered super-relaxed state of β-cardiac myosin

SummaryMutations in {beta}-cardiac myosin, the predominant motor protein for human heart contraction, can alter power output and cause cardiomyopathy. However, measurements of the intrinsic force, velocity and ATPase activityof myosin have not provided a consistent mechanism to link mutations to muscle pathology. An alternative modelpositsthat mutations in myosin affect the stability ofa sequestered, super-relaxed state (SRX) of the proteinwith very slow ATP hydrolysis and thereby change the number of myosin heads accessible to actin. Here, using a combination of biochemical and structural approaches, we show that purified myosin enters aSRX thatcorresponds to a folded-back conformation, which in muscle fibersresults insequestration of heads around the thick filament backbone. Mutations that cause HCM destabilize this state, while the small molecule mavacamtenpromotes it. These findings provide a biochemical and structural link between the genetics and physiology ofcardiomyopathywith implications for therapeutic strategies.

biochemistry↗

bioRxiv2017

DeepGS: Predicting phenotypes from genotypes using Deep Learning

MotivationGenomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.\n\nResultsWe present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.\n\nAvailabilityDeepGS is provided as an open source R package available at https://github.com/cma2015/DeepGS.

bioinformatics↗

bioRxiv2017

Assessment of the aging of the brown adipose tissue by 18F-FDG PET/CT imaging in the progeria mouse model LmnaG609G/G609G

Brown adipose tissue (BAT) is an important energy metabolic organ that is closely related to obesity, type 2 diabetes, and atherosclerosis. Aging is one of the most important determinants of BAT activity. In this study, we used 18F-FDG PET/CT imaging to assess the aging of the BAT in LmnaG609G/G609G mice. To evaluate the BAT activity, LmnaG609G/G609G and wild-type (WT) mice were injected with 18F-FDG, and PET/CT imaging was performed. The maximum standardized uptake value (SUVMax) of the BAT was measured and the target/nontarget (T/NT) values of BAT were calculated. The transcription and the protein expression levels of the uncoupling protein 1 (UCP1), beta3-adrenergic receptor ({beta}3-AR), and the PRdomain-containing16 (PRDM16), were measured by quantitative real-time polymerase chain reaction (RT-PCR) and Western blotting or immunohistochemical analysis. Apoptosis and cell senescence of the BAT, in WT and LmnaG609G/G609G mice, was detected by Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), and by CDKN2A/p16INK4a immunohistochemical staining, respectively. At 14 weeks of age, the BAT SUVMax and the expression levels of UCP1, {beta}3-AR and PRDM16 in LmnaG609G/G609G mice was significantly lower than that in WT mice. At the same time, the number of p16INK4a and TUNEL positively stained cells (%) increased in LmnaG609G/G609G mice. LmnaG609G/G609G mice are an ideal model for studying BAT aging. The aging characteristics and the aging mechanism of BAT in LmnaG609G/G609G mice can mimic normal BAT aging.

cell biology↗

bioRxiv2017

Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution

The folding and three-dimensional (3D) organization of chromatin in the nucleus critically impacts genome function. The past decade has witnessed rapid advances in genomic tools for delineating 3D genome architecture. Among them, chromosome conformation capture (3C)-based methods such as Hi-C are the most widely used techniques for mapping chromatin interactions. However, traditional Hi-C protocols rely on restriction enzymes (REs) to fragment chromatin and are therefore limited in resolution. We recently developed DNase Hi-C for mapping 3D genome organization, which uses DNase I for chromatin fragmentation. DNase Hi-C overcomes RE-related limitations associated with traditional Hi-C methods, leading to improved methodological resolution. Furthermore, combining this method with DNA capture technology provides a high-throughput approach (targeted DNase Hi-C) that allows for mapping fine-scale chromatin architecture at exceptionally high resolution. Hence, targeted DNase Hi-C will be valuable for delineating the physical landscapes of cis-regulatory networks that control gene expression and for characterizing phenotype-associated chromatin 3D signatures. Here, we provide a detailed description of method design and step-by-step working protocols for these two methods.\n\nHighlightsO_LIDNase Hi-C, a method for comprehensive mapping of chromatin contacts on a whole-genome scale, is based on random chromatin fragmentation by DNase I digestion instead of sequence-specific restriction enzyme (RE) digestion.\nC_LIO_LITargeted DNase Hi-C, which combines DNase Hi-C with DNA capture technology, is a high-throughput method for mapping fine-scale chromatin architecture of genomic loci of interest at a resolution comparable to that of genomic annotations of functional elements.\nC_LIO_LIDNase Hi-C and targeted DNase Hi-C provide the first high-throughput way to overcome the RE-digestion-associated resolution limit of 3C-based methods.\nC_LIO_LIStep-by-step whole-genome and targeted DNase Hi-C protocols for mapping global and local 3D genome architecture, respectively, are described.\nC_LI

genomics↗

bioRxiv2016

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding

MotivationTranscription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of adjacent base pairs, etc. Several methods have been developed to jointly account for DNA sequence and shape properties in predicting TF binding affinity. However, a limitation of these methods is that they typically require a training set of aligned TF binding sites.\n\nResultsWe describe a sequence+shape kernel that leverages DNA sequence and shape information to better understand protein-DNA binding preference and affinity. This kernel extends an existing class of k-mer based sequence kernels, based on the recently described di-mismatch kernel. Using three in vitro benchmark datasets, derived from universal protein binding microarrays (uPBMs), genomic context PBMs (gcPBMs) and SELEX-seq data, we demonstrate that incorporating DNA shape information improves our ability to predict protein-DNA binding affinity. In particular, we observe that (1) the k-spectrum+shape model performs better than the classical k-spectrum kernel, particularly for small k values; (2) the di-mismatch kernel performs better than the k-mer kernel, for larger k; and (3) the di-mismatch+shape kernel performs better than the di-mismatch kernel for intermediate k values.\n\nAvailabilityThe software is available at https://bitbucket.org/wenxiu/sequence-shape.git\n\nContactrohs@usc.edu, william-noble@uw.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗