Biology subjects

Lu, L.

Publications and source records attributed to Lu, L..

209 records · Page 12Linked to original sources

bioRxiv2018

Amino acids stimulate the endosome-to-Golgi trafficking through Ragulator and small GTPase Arl5

The endosome-to-Golgi or endocytic retrograde trafficking pathway is an important post-Golgi recycling route. We made a novel discovery that the retrograde trafficking of cargos is inhibited and stimulated by the absence and presence, respectively, of amino acids (AAs), especially glutamine. By testing components of the AA-stimulated mTORC1 signaling pathway, we demonstrated that SLC38A9, v-ATPase and Ragulator, but not Rag GTPases and mTORC1, are essential for the AA-stimulated trafficking. Arl5, an ARF-like family small GTPase, interacts with Ragulator in an AA-regulated manner and both Arl5 and its effector, the Golgi-associated retrograde protein complex (GARP), are required for the AA-stimulated trafficking. We have therefore identified a mechanistic connection between the nutrient signaling and the retrograde trafficking pathway, whereby SLC38A9 and v-ATPase sense AA-sufficiency and Ragulator functions as a guanine nucleotide exchange factor to activate Arl5, which, together with GARP, a tethering factor, probably facilitates the endosome-to-Golgi trafficking.

cell biology↗

bioRxiv2018

Small-scale soil microbial community heterogeneity linked to landforms on King George Island, maritime Antarctica

We analysed soil-borne microbial (bacterial, archaeal, and fungal) communities around the Fildes Region of King George Island, maritime Antarctica, which were divided into two groups according to soil elemental compositions and environmental attributes (soil chemical parameters and vegetation conditions) located in Holocene raised beach and Tertiary volcanic stratigraphy. Prokaryotic communities of the two groups were well separated; they predominantly correlated with soil elemental compositions, and were secondly correlated with environmental attributes (e.g., soil pH, total organic carbon, [Formula], and vegetation coverage; Pearson test, r = 0.59 vs. 0.52, both P < 0.01). The relatively high abundance of P, S, Cl, and Br in Group 1 was likely due to landform uplift. Lithophile-elements (Si, Al, Ca, Sr, Ti, V, and Fe) correlated with prokaryotic communities in Group 2 may originate from weathering of Tertiary volcanic rock. The elements and nutrients accumulated during formation of different landforms influenced the development of soils, plant growth, and microbial communities, and resulted in small-scale spatially heterogeneous biological distributions. We propose that the geological evolution of the Fildes Region was crucial to its microbial community development.\n\nIMPORTANCEThis current study analyzed soil-borne microbial communities around the Fildes Region of King George Island, maritime Antarctica, which were divided into two groups according to soil elemental compositions and environmental attributes. We provide new evidence for the crucial influence of landforms on small-scale structures and spatial heterogeneity of soil microbial communities.

microbiology↗

bioRxiv2018

Easy Hi-C: A simple efficient protocol for 3D genome mapping in small cell populations

Despite the growing interest in studying the mammalian genome organization, it is still challenging to map the DNA contacts genome-wide. Here we present easy Hi-C (eHi-C), a highly efficient method for unbiased mapping of 3D genome architecture. The eHi-C protocol only involves a series of enzymatic reactions and maximizes the recovery of DNA products from proximity ligation. We show that eHi-C can be performed with 0.1 million cells and yields high quality libraries comparable to Hi-C.

genomics↗

bioRxiv2017

Prediction of potential disease-associated microRNAs using structural perturbation method

MotivationThe identification of disease-related microRNAs(miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.\n\nResultsThe link predictability of bilayer network is higher than that of miRNA-disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA-disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.\n\nAvailability and Implementationhttps://github.com/lecea/SPM-code.git\n\nContactlinyuan.lv@gmail.com, zouquan@nclab.net.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2017

Correlation structure of grid cells is preserved during sleep

The network of grid cells in the medial entorhinal cortex forms a fixed reference frame for mapping physical space. The mechanistic origin of the grid representation is unknown, but continuous attractor network (CAN) models explain multiple fundamental features of grid-cell activity. An untested prediction of CAN grid models is that the grid-cell network should exhibit an activity correlation structure that transcends behavioural or brain states. By recording from MEC cell ensembles during navigation and sleep, we found that spatial phase offsets of grid cells predict arousal-state-independent spike rate correlations. Similarly, state-invariant correlations between conjunctive grid-head-direction and pure head-direction cells were predicted by their head-direction tuning offsets. Spike rates of grid cells were only weakly correlated across modules, and module scale relationships disintegrated during slow-save sleep, suggesting that modules function as independent attractor networks. Collectively, our observations suggest that network states in MEC are expressed universally across brain and behaviour states.

neuroscience↗

bioRxiv2017

HPCDb: an integrated database of pancreatic cancer

We have established a database of Human Pancreatic Cancer (HPCDb) through effectively mining, extracting, analyzing, and integrating PC-related genes, single-nucleotide polymorphisms (SNPs), and microRNAs (miRNAs), now available online at http://www.pancancer.org/. Data were extracted from established databases, [≥]5 published literature (PubMed), and microarray chips (screening of differentially expressed genes using limma package in R, |log2 fold change (FC)| > 1). Further, protein-protein interactions (PPIs) were investigated through the Human Protein Reference Database. miRNA-target relationships were also identified using the online software TargetScan. Currently, HPCDb contains 3284 genes, 120 miRNAs, 589 SNPs, 10,139 PPIs, and 3904 miRNA-target pairs. The detailed information on PC-related genes (e.g., gene identifier (ID), symbol, synonyms, full name, chip sets, expression alteration, PubMed ID, and PPIs), miRNAs (e.g., accession number, chromosome location, related disease, PubMed ID, and miRNA-target interactions), and SNPs (e.g., SNP ID, allele, gene, PubMed ID, chromosome location, and disease) is presented through user-friendly query interfaces or convenient links to NCBI GEO, NCBI PubMed, NCBI Gene, NCBI dbSNP, and miRBase. Overall, HPCDb provides biologists with relevant information on human PC-related molecules at multiple levels, helping to generate new hypotheses or identify candidate markers.

bioinformatics↗

bioRxiv2017

Phenotypic associations of consensus driver genes in hepatocellular carcinoma

Although driver genes in hepatocellular carcinoma (HCC) have been investigated in various previous genetic studies, prevalence of key driver genes among heterogeneous populations is unknown. Moreover, the phenotypic associations of these driver genes are poorly understood. This report aims to reveal the phenotypic impacts of a group of consensus driver genes in HCC. We used MutSigCV and OncodriveFM modules implemented in the IntOGen pipeline to identify consensus driver genes across six HCC cohorts comprising 1,494 samples in total. To access their global impacts, we used TCGA mutations and copy number variations to predict the transcriptomics data, under generalized linear models. We further investigated the associations of the consensus driver genes to patient survival, age, gender, race and risk factors. We identify 10 consensus driver genes across six HCC cohorts in total. Integrative analysis of driver mutations, copy number variations and transcriptomic data reveals that these consensus driver mutations and their copy number variations are associated with majority (62.5%) of the mRNA transcriptome, but only a small fraction (8.9%) of miRNAs. Genes associated with TP53, CTNNB1, and ARID1A mutations contribute to the tripod of most densely connected pathway clusters. These driver genes are significantly associated with patients overall survival. Some driver genes are significantly linked to HCC gender (CTNNB1, ALB, TP53 and AXIN1), race (TP53 and CDKN2A), and age (RB1) disparities. This study prioritizes a group of consensus drivers in HCC, which collectively show vast impacts on the phenotypes. These driver genes may warrant as valuable therapeutic targets of HCC.

bioinformatics↗

bioRxiv2017

High-Resolution Maps of Mouse Reference Populations

Genetic reference panels are widely used to map complex, quantitative traits in model organisms. We have generated new high-resolution genetic maps of 259 mouse inbred strains from recombinant inbred strain panels (C57BL/6J x DBA/2J, ILS/IbgTejJ x ISS/IbgTejJ, C57BL/6J x A/J) and chromosome substitution strain panels (C57BL/6J-Chr# , C57BL/6J-Chr# , C57BL/6JChr# ). We genotyped all samples using the Affymetrix Mouse Diversity Array with an average inter-marker spacing of 4.3kb. The new genetic maps provide increased precision in the localization of recombination breakpoints compared to the previous maps. Although the strains were presumed to be fully inbred, we found residual heterozygosity in 40% of individual mice from five of the six panels. We also identified de novo deletions and duplications, in homozygous or heterozygous state, ranging in size from 21kb to 8.4Mb. Almost two-thirds (46 out of 76) of these deletions overlap exons of protein coding genes and may have phenotypic consequences. Twenty-nine putative gene conversions were identified in the chromosome substitution strains. We find that gene conversions are more likely to occur in regions where the homologous chromosomes are more similar. The raw genotyping data and genetic maps of these strain panels are available at http://churchill-lab.jax.org/website/MDA.

genetics↗

bioRxiv2017

Establishment In Culture Of Expanded Potential Stem Cells

Mouse embryonic stem cells are derived from in vitro explantation of blastocyst epiblasts1,2 and contribute to both the somatic lineage and germline when returned to the blastocyst3 but are normally excluded from the trophoblast lineage and primitive endoderm4-6. Here, we report that cultures of expanded potential stem cells (EPSCs) can be established from individual blastomeres, by direct conversion of mouse embryonic stem cells (ESCs) and by genetically reprogramming somatic cells. Remarkably, a single EPSC contributes to the embryo proper and placenta trophoblasts in chimeras. Critically, culturing EPSCs in a trophoblast stem cell (TSC) culture condition permits direct establishment of TSC lines without genetic modification. Molecular analyses including single cell RNA-seq reveal that EPSCs share cardinal pluripotency features with ESCs but have an enriched blastomere transcriptomic signature and a dynamic DNA methylome. These proof-of-concept results open up the possibility of establishing cultures of similar stem cells in other mammalian species.

developmental biology↗

bioRxiv2017

Deep Learning based multi-omics integration robustly predicts survival in liver cancer

Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill in this gap, we present a deep learning (DL) based model on HCC that robustly differentiates survival subpopulations of patients in six cohorts. We build the DL based, survival-sensitive model on 360 HCC patients data using RNA-seq, miRNA-seq and methylation data from TCGA, which predicts prognosis as good as an alternative model where genomics and clinical data are both considered. This DL based model provides two optimal subgroups of patients with significant survival differences (P=7.13e-6) and good model fitness (C-index=0.68). More aggressive subtype is associated with frequent TP53 inactivation mutations, higher expression of stemness markers (KRT19, EPCAM) and tumor marker BIRC5, and activated Wnt and Akt signaling pathways. We validated this multi-omics model on five external datasets of various omics types: LIRI-JP cohort (n=230, C-index=0.75), NCI cohort (n=221, C-index=0.67), Chinese cohort (n=166, C-index=0.69), E-TABM-36 cohort (n=40, C-index=0.77), and Hawaiian cohort (n=27, C-index=0.82). This is the first study to employ deep learning to identify multi-omics features linked to the differential survival of HCC patients. Given its robustness over multiple cohorts, we expect this workflow to be useful at predicting HCC prognosis prediction.

bioinformatics↗

bioRxiv2017

Overlapping representation of primary tastes in a defined region of the gustatory cortex

Two-photon imaging was used to examine taste responses in neurons in a region of gustatory cortex defined by thalamic input. This area contained an overlapping representation of primary tastes, with neurons that were either narrowly or broadly responsive to taste stimuli. Analysis demonstrates that activity in the neuronal population in this area yields information about both taste quality and hedonics.

neuroscience↗