Biology subjects

Pan, W.

Publications and source records attributed to Pan, W..

70 records · Page 4Linked to original sources

bioRxiv2019

In vitro pancreatic islet cluster expansion facilitated by hormones and chemicals

Tissue regeneration, such as pancreatic islet tissue propagation in vitro, could serve as a promising strategy for diabetes therapy and personalized drug testing. However, such a protocol has not been realized yet. Propagation could be divided by two steps, which are: (1) expansion in vitro and (2) repeat passaging. Even the in vitro expansion of the islet has not been achieved to date. Here, we describe a method to enable the expansion of islet clusters isolated from pregnant mice or wild-type rats by employing a combination of specific regeneration factors and chemical compounds in vitro. The expanded islet clusters expressed insulin, glucagon and somatostatin, which are markers corresponding to pancreatic {beta} cells, cells and {delta} cells, respectively. These different types of cells grouped together, were spatially organized and functioned similarly to primary islets. Further mechanistic analysis revealed that forskolin in our recipe contributed to renewal and regeneration, whereas exendin4 was essential for preserving islet cell identity. Our results provide a novel method for the in vitro expansion of islet clusters, which is an important step forward in developing future protocols and medium used for islet tissue propagation in vitro. Such method is important for future regenerative diabetes therapies and personalized medicine using large amounts of pancreatic islets derived from the same person.

cell biology↗

bioRxiv2019

Some Statistical Consideration in Transcriptome-Wide Association Studies

Transcriptome-wide association study (TWAS) has become popular in integrating a reference eQTL dataset with an independent main GWAS dataset to identify (putatively) causal genes, shedding mechanistic insights to biological pathways from genetic variants to a GWAS trait mediated by gene expression. Statistically TWAS is a (two-sample) 2-stage least squares (2SLS) method in the framework of instrumental variables analysis for causal inference: in Stage 1 it uses the reference eQTL data to impute a genes expression for the main GWAS data, then in Stage 2 it tests for association between the imputed gene expression and the GWAS trait; if an association is detected in Stage 2, a (putatively) causal relationship between the gene and the GWAS trait is claimed. If a non-linear model or a generalized linear model (GLM) is fitted in Stage 2 (e.g. for a binary GWAS trait), it is known that using only imputed gene expression, as in standard TWAS, in general does not lead to a consistent (i.e. asymptotically unbiased) estimate for the causal effect; accordingly, a variation of 2SLS, called two-stage residual inclusion (2SRI), has been proposed to yield better estimates (e.g. being consistent under suitable conditions). Our main goal is to investigate whether it is necessary or even better to apply 2SRI, instead of the standard 2SLS. In addition, due to the use of imputed gene expression (i.e. with measurement errors), it is known that in general some correction to the standard error estimate of the causal effect estimate has to be applied, while in the standard TWAS no correction is applied. Is this an issue? We also compare one-sample 2SLS with two-sample 2SLS (i.e. the standard TWAS). We used the ADNI data and simulated data mimicking the ADNI data to address the above questions. At the end, we conclude that, in practice with the large sample sizes and small effect sizes of genetic variants, the standard TWAS performs well and is recommended.

genetics↗

bioRxiv2019

Integrative framework of cross-module deep biomarker for the prognosis of clear cell renal cell carcinoma: A retrospective TCGA project

PurposeWe aimed to integrate cross-module data for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) based on deep learning and to explore the relationship between deep features from images and eigengenes form gene data.\n\nExperimental designA total of 209 patients with ccRCC with computed tomography (CT), histopathological images and RNA sequences were enrolled. A deep biomarker-based integrative framework was proposed to construct a prognostic model. Deep features extracted from CT and histopathological images by using deep learning combined with eigengenes generated from functional genomic data were used to predict ccRCC prognosis. Furthermore, the relationship between deep features and eigengenes was explored, and two survival subgroups identified by integrative cross-module biomarkers were subjected to functional analysis.\n\nResultsThe model based on the integrative framework stratified two subgroups of patients with a significant prognostic difference (P = 6.51e-6, concordance index [C-index] = 0.808, 95% confidence interval [CI] = 0.728-0.888) and outperformed the prediction based on their individual biomarkers in the independent validation cohort (n = 70, gene data: C-index = 0.452, CI = 0.336-0.567; histopathological images: C-index = 0.677, CI = 0.577-0.776; CT images: C-index = 0.774, CI = 0.670-0.879). On the basis of statistical relationship, deep features correlated or complemented with eigengenes both enhanced the predictive performance of eigengenes (P = 0.439, correlated: C-index = 0.785, CI = 0.685-0.886; complemented: C-index = 0.778, CI = 0.683-0.872). The functional analysis of subgroups also exhibited reasonable results.\n\nConclusionThe model based on the integrative framework of cross-module deep biomarkers can efficiently predict ccRCC prognosis, and the framework with a code is shared to act as a reliable and powerful tool for further studies.

bioinformatics↗

bioRxiv2019

Geomagnetic field absence influences adult body weight of a migratory insect, potentially through appetite regulation

The geomagnetic field (GMF) is well documented for its essential role as a cue used in animal orientation or navigation. Recent evidence indicates that the absence of GMF can trigger stress-like responses such as reduced body weight, as we have previously shown in newly emerged adults of the brown planthopper, Nilaparvata lugens. To test the hypothesis that reduced feeding in the absence of the GMF leads to a decrease of N. lugens body weight, we compared magnetic responses in feeding behavior, glucose levels, and expression of magnetoreception- and appetite-related genes in brown planthopper nymphs exposed to either a near-zero magnetic field (NZMF, i.e., GMF absence) or typical GMF conditions. In addition to observing the expected responses in the expression of the potential magnetosensor cryptochromes, the food intake of 5th instar nymphs was significantly reduced in insects reared in the absence of GMF. Insects that exhibited reduced feeding reared in the absence of the GMF also had higher glucose levels which is associated with food aversion. Expression patterns of appetite-related neuropeptide genes were also altered in the absence of GMF in a manner consistent with diminishing appetite. These findings support the hypothesis that strong changes in GMF intensity can affect insect feeding behavior and underlying regulatory processes. Our results provide further evidence that magnetoreception and regulatory responses to GMF changes can affect a wide variety of biological processes.

animal behavior and cognition↗

bioRxiv2019

Geomagnetic field intensity may be a cue for the regulation of insect migration

Geomagnetic field (GMF) intensity can be used by some animals to determine their direction and position during migration. However, its role, if any, in mediating other migration-related phenotypes remains largely unknown. Here, we simulated variation in GMF intensity between two locations along the migration route of a nocturnal insect migrant, the brown planthopper Nilaparvata lugens, that varied by [~]5 T (GMF50T vs. GMF45T) in field intensity. After one generation of exposure, we tested for changes in key morphological, behavioural and physiological traits related to migratory performance including wing dimorphism, flight capacity and positive phototaxis. Our results showed that all three morphological and behavioural phenotypes responded to a small difference in magnetic field intensity between the simulated northern vs. southern locations in ways expected along the migratory route. Consistent magnetic responses in the expression of the phototaxis-related Drosophila-like cryptochrome 1 (Cry1) gene and levels of two primary energy substrates used during flight, triglyceride and trehalose, were also found. Our findings indicate GMF intensity can be a cue that regulates the expression of phenotypes critical for insect migration and highlight the unique role of magnetoreception as a trait that can help migratory insects express potentially beneficial phenotypes in geographically variable environments.

animal behavior and cognition↗

bioRxiv2019

OMGS: Optical Map-based Genome Scaffolding

Due to the current limitations of sequencing technologies, de novo genome assembly is typically carried out in two stages, namely contig (sequence) assembly and scaffolding. While scaffolding is computationally easier than sequence assembly, the scaffolding problem can be challenging due to the high repetitive content of eukaryotic genomes, possible mis-joins in assembled contigs and inaccuracies in the linkage information. Genome scaffolding tools either use paired-end/mate-pair/linked/Hi-C reads or genome-wide maps (optical, physical or genetic) as linkage information. Optical maps (in particular Bionano Genomics maps) have been extensively used in many recent large-scale genome assembly projects (e.g., goat, apple, barley, maize, quinoa, sea bass, among others). However, the most commonly used scaffolding tools have a serious limitation: they can only deal with one optical map at a time, forcing users to alternate or iterate over multiple maps. In this paper, we introduce a novel scaffolding algorithm called OMGS that for the first time can take advantages of multiple optical maps. OMGS solves several optimization problems to generate scaffolds with optimal contiguity and correctness. Extensive experimental results demonstrate that our tool outperforms existing methods when multiple optical maps are available, and produces comparable scaffolds using a single optical map. OMGS can be obtained from https://github.com/ucrbioinfo/OMGS

bioinformatics↗

bioRxiv2019

The Abnormal Oswald Ripening of Protein Nanofiber in Myofibrillar Protein Solution

In solutions of myofibrillar protein extracted from giant squid (Dosidicus gigas), the size-coarsening process of protein nanofiber is complex. At high temperature (25{degrees}C), nanofiber keeps growth but with two distinguishable patterns, slow rate at the initial stage with t0.2 and the fast one at the late stage with t2.3. The intersection of these two slopes is around 300 min. Meanwhile, protein concentration in solution enhances as well. These behaviors contradict to the prediction of Ostwald ripening. Thus, we call this process as abnormal. These abnormal behaviors come from the conformation change of some types of constitution protein molecules with chemical potential reduction when they dissolve from nanofiber to solution. On the other hand, low temperature (10{degrees}C) depresses this size growth. This observation suggests that temperature regulates protein molecule conformation change in nanofiber. The consequence of this abnormal Ostwald ripening process is that all protein molecules in nanofiber are redistributed. Protein molecules with the absence of conformation change in dissolution accumulate in nanofiber to cause it growing, while the rest concentrates in solution.

biophysics↗

bioRxiv2019

A novel algorithm for the collective integration of single cell RNA-seq during embryogenesis

Single cell RNA-seq (scRNA-seq) over specified time periods has been widely used to dissect the cell populations during mammalian embryogenesis. Integrating such scRNA-seq data from different developmental stages and from different laboratories is critical to comprehensively define and understand the molecular dynamics and systematically reconstruct the lineage trajectories. Here, we describe a novel algorithm to integrate heterogenous temporal scRNA-seq datasets and to preserve the global developmental trajectories. We applied this algorithm and approach to integrate 3,387 single cells from seven heterogenous temporal scRNA-seq datasets, and reconstructed the cell atlas of early mouse cardiovascular development from E6.5 to E9.5. Using this integrated atlas, we identified an Etv2 downstream target, Ebf1, as an important transcription factor for mouse endothelial development.

bioinformatics↗

bioRxiv2018

Short exon prediction based on multiscale products of a genomic-inspired multiscale bilateral filtering

Multiscale signal processing techniques such as wavelet filtering have proved to be particularly successful in predicting exon sequences. Traditional wavelet predictor is domain filtering, and enforces exon features by weighting nucleotide values with coefficients. Such a measure performs linear filtering and is not suitable for preserving the short coding exons and the exon-intron boundaries. This paper describes a short exon prediction framework that is capable of non-linearly processing DNA sequences while achieving high prediction rates. There are two key contributions. The first is the introduction of a genomic-inspired multiscale bilateral filtering (MSBF) which exploits both weighting coefficients in the spatial domain and nucleotide similarity in the range. Similarly to wavelet transform, the MSBF is also defined as a weighted sum of nucleotides. The difference is that the MSBF takes into account the variation of nucleotides at a specific codon position. The second contribution is the exploitation of inter-scale correlation in MSBF domain to find the inter-scale dependency on the differences between the exon signal and the background noise. This favourite property is used to sharp the important structures while weakening noise. Three benchmark data sets have been used in the evaluation of considered methods. By comparison with two existing techniques, the prediction results demonstrate that: the proposed method reveals at least improvement of 50.5%, 36.7%, 12.8%, 17.8%, 17.7%, 11.5% and 12.2% on the exons length of 1-49, 50-74, 75-99, 100-124, 125-149, 150-174 and 175-199, respectively. The MSBF of its nonlinear nature is good at energy compaction, which makes it capable of locating the sharp variations around short exons. The direct scale multiplication of coefficients at several adjacent scales obviously enhanced exon features while the noise contents were suppressed. We show that the non-linear nature and correlation-based property achieved in proposed predictor is greater than that for traditional filtering, which leads to better exon prediction performance. There are some possible applications of this predictor. Its good localization and protection of sharp variations will make the predictor be suitable to perform fault diagnosis of aero-engine.

genomics↗

bioRxiv2018

Anti-angiogenic effects of VEGF stimulation on endothelium deficient in phosphoinositide recycling

Anti-angiogenic therapies have generated significant interest for their potential to combat tumor growth (1-6). However, the ability of tumors to overproduce pro-angiogenic ligands and overcome targeted inhibitory therapies has hampered this approach (7, 8). A novel way to circumvent this problem might be to target the resynthesis of critical substrates consumed during intracellular transduction of pro-angiogenic signals in endothelial cells, thus harnessing the tumors own production of excess stimulatory ligands to deplete adjacent host endothelial cells of the capacity to respond to these signals (9-12). Here we show using zebrafish and human endothelial cells in vitro that endothelial cells deficient in CDP-diacylglycerol synthase 2 are uniquely sensitive to increased VEGF stimulation due to a reduced capacity to re-synthesize phosphoinositides, including phosphatidylinositol 4,5-bisphosphate (PIP2) a key substrate for VEGF signal transduction, resulting in VEGF-exacerbated defects in angiogenesis and angiogenic signaling (9-22). Using murine tumor allograft models (23) we show that either systemic or endothelial cell specific suppression of phosphoinositide recycling results in reduced tumor growth and reduced tumor angiogenesis. Our results suggest that inhibition of phosphoinositide recycling may provide a useful anti-angiogenic approach, and highlights the general potential of targeting the resynthesis of rate limiting signaling substrates as a valuable therapeutic strategy.\n\nSUMMARY STATEMENTTargeting phosphoinositide recycling during tumor angiogenesis provides a potentially uniquely effective anti-cancer therapy.

cell biology↗

bioRxiv2018

Validating genome-wide CRISPR-Cas9 function in the non-conventional yeast Yarrowia lipolytica

Genome-wide mutational screens are central to understanding the genetic underpinnings of evolved and engineered phenotypes. The widespread adoption of CRISPR-Cas9 genome editing has enabled such screens in many organisms, but identifying functional sgRNAs still remains a challenge. To address this limitation, we developed a methodology to quantify the cutting efficiency of each sgRNA in a genome-scale library in the biotechnologically important yeast Yarrowia lipolytica. Screening in the presence and absence of native DNA repair enabled high-throughput quantification of sgRNA function leading to the identification of high efficiency sgRNAs that cover 94% of genes. Library validation enhanced the classification of essential genes by identifying inactive guides that create false negatives and mask the effects of successful disruptions. Quantification of guide effectiveness also creates a dataset from which functional determinants of CRISPR-Cas9 can be identified. Finally, application of the library identified mutations that led to high lipid accumulation and eliminated pseudohyphal morphology.

synthetic biology↗

bioRxiv2018

Single cell RNAseq provides a molecular and cellular cartography of changes to the human endometrium through the menstrual cycle

In a human menstrual cycle, the endometrium undergoes remodeling, shedding, and regeneration, all of which are driven by substantial gene expression changes in the underlying cellular hierarchy. Despite its importance in human fertility and regenerative biology, mechanistic understanding of this unique type of tissue homeostasis remains rudimentary. We characterized the transcriptomic transformation of human endometrium at single cell resolution, dissecting the multidimensional cellular heterogeneity of this tissue across the entire natural menstrual cycle. We profiled the behavior of 6 endometrial cell types, including a previously uncharacterized ciliated epithelial cell type, during four major phases of endometrial transformation, and found characteristic signatures for each cell type and phase. We discovered that human window of implantation opens with an abrupt and discontinuous transcriptomic activation in the epithelia, accompanied with widespread decidualized feature in the stromal fibroblasts. These data reveal signatures in the luminal and glandular epithelia during epithelial gland reconstruction, and suggest a mechanism for adult gland formation.

genomics↗

bioRxiv2017

Recurrently Mutated Genes Differ between Leptomeningeal and Solid Lung Cancer Brain Metastases

PurposeBrain metastases from non-small cell lung cancer (NSCLC) engraft and grow either within the brain (solid) or diffusely on its surface (leptomeningeal disease; LMD). Routine clinical diagnostics have low sensitivity and provide no information about the underlying mutations. A recurrent mutation analysis of LMD and a comparison between solid and LMD NSCLC brain metastases have yet to be explored.\n\nExperimental DesignWe performed whole-exome sequencing (WES) on eight cerebrospinal fluid (CSF) specimens from NSCLC LMD patients. We compared our LMD sequencing data with a published data set of 26 NSCLC solid brain metastases to determine the relative mutation frequency. We then performed a retrospective chart review of an additional set of 44 NSCLC LMD patients to further evaluate LMD mutations and clinical prognosis.\n\nResultsSix (75%) LMD cases had mutations in EGFR, while none had KRAS mutations. Retrospective chart review revealed only 4 LMD cases (7.7%) with KRAS mutations, but 33 cases (63.5%) with EGFR mutations. TP53 was mutated in 4/8 LMD (50%) cases and 13/26 of solid metastasis (50%). The median interval for developing LMD from NSCLC was shorter in EGFR-mutant (16.3 mo) than wild-type (23.9 mo) patients (p = 0.017).\n\nConclusionsEGFR and TP53 mutations were frequent in LMD exomes (combined frequency 87.5%), suggesting that PCR-based mutation detection assays towards these two genes could be a useful complement to current diagnostics. Correlations of EGFR in LMD and KRAS in solid metastases suggest molecular distinctions or systemic treatment pressure underpinning differences in growth patterns within the brain.\n\nTranslational RelevanceLeptomeningeal disease is a diffuse, malignant, and incurable metastatic brain tumor that accounts for 5-10% of brain metastases. Patients with LMD do not undergo biopsy and their overall prognosis is poor (median survival 3 to 27 months), making it difficult to collect sufficient samples for recurrent mutation analysis. Standard diagnostic procedures (MRI and cytology) for LMD provide no genetic information. To understand the mutation landscape of LMD, we performed whole-exome sequencing on eight lung-derived LMD cases. We showed that mutations in EGFR occurred more frequently in LMD than solid brain metastases, but KRAS mutations were not present in LMD. Further, mutations in recurrent genes such as EGFR and TP53 could be reliably detected in CSF via droplet digital PCR. Targeted analysis of recurrent mutations thus presents a useful complement to the existing diagnostic toolkit, and differences in mutations between LMD and solid brain metastases suggest distinct molecular mechanisms for growth.

genomics↗

bioRxiv2017

A Noninvasive Molecular Clock for Fetal Development Predicts Gestational Age and Preterm Delivery

We performed a high time-resolution, longitudinal study of normal pregnancy development by measuring cell-free RNA (cfRNA) in blood from women during each week of pregnancy. Analysis of tissue-specific transcripts in these samples enabled us to follow fetal and placental development with high resolution and sensitivity, and also to detect gene-specific responses of the maternal immune system to pregnancy. We established a \"clock\" for normal pregnancy development and enabled a direct molecular approach to determine expected delivery dates with comparable accuracy to ultrasound, creating the basis for a portable, inexpensive fetal dating method. We also identified a related gene set that accurately discriminated women at risk for spontaneous preterm delivery up to two months in advance of labor, forming the basis of a potential screening test for risk of preterm delivery.

bioengineering↗

bioRxiv2017

A Novel And Efficient Algorithm For De Novo Discovery Of Mutated Driver Pathways In Cancer

Next-generation sequencing studies on cancer somatic mutations have discovered that driver mutations tend to appear in most tumor samples, but they barely overlap in any single tumor sample, presumably because a single driver mutation can perturb the whole pathway. Based on the corresponding new concepts of coverage and mutual exclusivity, new methods can be designed for de novo discovery of mutated driver pathways in cancer. Since the computational problem is a combinatorial optimization with an objective function involving a discontinuous indicator function in high dimension, many existing optimization algorithms, such as a brute force enumeration, gradient descent and Newton's methods, are practically infeasible or directly inapplicable. We develop a new algorithm based on a novel formulation of the problem as non-convex programming and nonconvex regularization. The method is computationally more efficient, effective and scalable than existing Monte Carlo searching and several other algorithms, which have been applied to The Cancer Genome Atlas (TCGA) project. We also extend the new method for integrative analysis of both mutation and gene expression data. We demonstrate the promising performance of the new methods with applications to three cancer datasets to discover de novo mutated driver pathways.

bioinformatics↗

bioRxiv2017

Humans are colonized by many uncharacterized and highly divergent microbes

Blood circulates throughout the entire body and contains molecules drawn from virtually every tissue, including the microbes and viruses which colonize the body. Through massive shotgun sequencing of circulating cell-free DNA from the blood, we identified hundreds of new bacteria and viruses which represent previously unidentified members of the human microbiome. Analysing cumulative sequence data from 1,351 blood samples collected from 188 patients enabled us to assemble 7,190 contiguous regions (contigs) larger than 1 kbp, of which 3,761 are novel with little or no sequence homology in any existing databases. The vast majority of these novel contigs possess coding sequences, and we have validated their existence both by finding their presence in independent experiments and by performing direct PCR amplification. When their nearest neighbors are located in the tree of life, many of the organisms represent entirely novel taxa, showing that microbial diversity within the human body is substantially broader than previously appreciated.

microbiology↗