Biology subjects

Chen, B.

Publications and source records attributed to Chen, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Cardiomyocyte prohibitin ablation reprograms cardiac metabolism revealing a pathogenic role for mTORC1 in dilated cardiomyopathy

Maintaining cardiac structure and function throughout the lifespan requires a delicate balance in carbon allocation between energetic and biosynthetic processes. At the nexus of this balance are prohibitins-1 and -2 (PHB1, 2) which form a ring-like complex in mitochondrial and plasma membranes responsible for coordinating cellular growth, metabolism and autophagy. Here we describe how ablation of the PHB complex in cardiomyocytes of adult mice (cPHB1KO) causes unrestrained mechanistic target of rapamycin complex 1 (mTORC1) activity and a Warburg-like reprogramming of glucose metabolism in heart toward enhanced de novo amino acid biosynthesis. These changes are accompanied by disruptions in mitochondrial Ca2+ handling and impaired autophagy, leading to severe dilated cardiomyopathy and mortality within 12 weeks. Using pharmacological and nutritional approaches, we further show that mTORC1 inhibition attenuates pathologic cardiac remodeling only in female cPHB1KO mice. Our findings illustrate novel mechanisms linking the PHB complex with altered carbon flux and pathogenesis of cardiomyopathy.

cell biology↗

bioRxiv2018

PUMILIO hyperactivity drives premature aging of Norad-deficient mice

Although numerous long noncoding RNAs (lncRNAs) have been identified, our understanding of their roles in mammalian physiology remains limited. Here we investigated the physiologic function of the conserved lncRNA Norad in vivo. Deletion of Norad in mice results in genomic instability and mitochondrial dysfunction, leading to a dramatic multi-system degenerative phenotype resembling premature aging. Loss of tissue homeostasis in Norad-deficient animals is attributable to augmented activity of PUMILIO proteins, which act as post-transcriptional repressors of target mRNAs to which they bind. Norad is the preferred RNA target of PUMILIO2 (PUM2) in mouse tissues and, upon loss of Norad, PUM2 hyperactively represses key genes required for mitosis and mitochondrial function. Remarkably, enforced Pum2 expression fully phenocopies Norad deletion, resulting in rapid-onset aging-associated phenotypes. These findings provide new insights and open new lines of investigation into the roles of noncoding RNAs and RNA binding proteins in normal physiology and aging.

molecular biology↗

bioRxiv2018

Comprehensive transcriptomic analysis of cell lines as models of primary tumor samples across 22 tumor types

Cancer cell lines are commonly used as models for cancer biology. While they are limited in their ability to capture complex interactions between tumors and their surrounding environment, they are a cornerstone of cancer research and many important findings have been discovered utilizing cell line models. Not all cell lines are appropriate models of primary tumors, however, which may contribute to the difficulty in translating in vitro findings to patients. Previous studies have leveraged public datasets to evaluate cell lines as models of primary tumors, but they have been limited in scope to specific tumor types and typically ignore the presence of tumor infiltrating cells in the primary tumor samples. We present here a comprehensive pan-cancer analysis utilizing approximately 9,000 transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 different tumor types. After adjusting for tumor purity in the primary tumor samples, we performed correlation analysis and differential gene expression analysis between the primary tumor samples and cell lines. We found that cell-cycle pathways are consistently upregulated in cell lines, while no pathways are consistently upregulated across the primary tumor samples. In a case study, we compared colorectal cancer cell lines with primary tumor samples across the colorectal subtypes and identified three colorectal cell lines that were derived from fibroblasts rather than tumor epithelial cells. Lastly, we propose a new set of cell lines panel, the TCGA-110, which contains the most representative cell lines from 22 different tumor types as a more comprehensive and informative alternative to the NCI-60 panel. Our analysis of the other tumor types are available in our web app (http://comphealth.ucsf.edu/TCGA110) as a resource to the cancer research community, and we hope it will allow researchers to select more appropriate cell line models and increase the translatability of in vitro findings.

bioinformatics↗

bioRxiv2018

Distribution of duck-origin parvovirus in Cherry Valley ducks in vivo over time and histopathological investigation

In 2015, we successfully isolated a strain of duck-origin parvovirus from Cherry Valley ducks, which we named QH-L01. In this study, duck-origin parvovirus in Cherry Valley ducks was quantified and localized by quantitative real-time PCR (qPCR) and immunohistochemistry (IHC), and pathological damage to the tissues and organs was observed by hematoxylin-eosin staining (HE staining). qPCR showed that the viral load was higher in the spleen, brain, lung, cecum, ileum, and duodenum over time. The results from IHC experiments showed positive reactions in hepatocytes, epithelium of the lung atrium, myocardial cells, goblet cells of the intestine, and brain cells. Primary histological examination revealed pulmonary lobule depletion and dilation in the lung as well as necrosis and erosion of the villus tips in the duodenum, ileum and cecum. This study is the first demonstration that duck-origin parvovirus can be transmitted from the spleen to the brain and lung, resulting in proliferation and dissemination of the virus to the cecum, ileum, duodenum and other tissues through the blood. The lung, duodenum, ileum and cecum may thus represent the main target tissues and organs for duck-origin parvovirus.

zoology↗

bioRxiv2018

Parental Allele-Specific Protein Synthesis Dynamics in Single Cells In Vivo

Allelic expression from each parent-of-origin is important as a backup and to ensure that enough protein products of a gene are produced. Thus far, it is not known how each cell throughout a tissue differs in parental allele expression at the level of protein synthesis. Here, we measure the expression of the Ribosomal protein L13a (Rpl13a) from both parental alleles simultaneously in single cells in the living animal. We use genome-edited Drosophila that have a quantitative reporter of protein synthesis inserted into the endogenous Rpl13a locus. We find that individual cells can have large (>10-fold) differences in protein expression between the two parental alleles. Cells can produce protein from only one allele oftentimes, and time-lapse imaging of protein production from each parental allele in each cell showed that the imbalance in expression from one parental allele over the other can invert over time. One sentence summaryParental allele-specific protein expression varies widely across cells and over time. HighlightsO_LIWe used genome editing to insert a quantifiable protein translation reporter into the endogenous Ribosomal protein L13a gene and thus track the protein expression of both parental alleles simultaneously in every single cell in the awake animal. C_LIO_LICells can have a large difference in protein expression for one parental allele over the other, and this can invert over time, and can occur in clusters of cells within a tissue. C_LIO_LIWe demonstrate the highly variable nature of heterozygous and homozygous definitions across single cells, and over time. C_LIO_LIOur study demonstrates a new paradigm that can be used to examine inherited epigenetic control of expression from a specific parent-of-origin allele across single clonal cells from a common progenitor in vivo. C_LI

cell biology↗

bioRxiv2018

Real-time structural dynamics of late steps in bacterial translation initiation visualized using time-resolved cryogenic electron microscopy

Bacterial translation initiation entails the tightly regulated joining of the 50S ribosomal subunit to an initiator transfer RNA (fMet-tRNAfMet)-containing 30S ribosomal initiation complex (IC) to form a 70S IC that subsequently matures into a 70S elongation-competent complex (70S EC). Rapid and accurate 70S IC formation is promoted by 30S IC-bound initiation factor (IF) 1 and the guanosine triphosphatase (GTPase) IF2, both of which must ultimately dissociate from the 70S IC before the resulting 70S EC can begin translation elongation1. Although comparison of 30S2-6 and 70S5,7-9 IC structures have revealed that the ribosome, IFs, and fMet-tRNAfMet can acquire different conformations in these complexes, the timing of conformational changes during 70S IC formation, structures of any intermediates formed during these rearrangements, and contributions that these dynamics might make to the mechanism and regulation of initiation remain unknown. Moreover, lack of an authentic 70S EC structure has precluded an understanding of ribosome, IF, and fMet-tRNAfMet rearrangements that occur upon maturation of a 70S IC into a 70S EC. Using time-resolved cryogenic electron microscopy (TR cryo-EM)10 we report the first, near-atomic-resolution view of how a time-ordered series of conformational changes drive and regulate subunit joining, IF dissociation, and fMet-tRNAfMet positioning during 70S EC formation. We have found that, within ~20-80 ms, rearrangements of the 30S subunit and IF2, uniquely captured in its GDP*Pi-bound state, stabilize fMet-tRNAfMet in its intermediate, 70S P/I, configuration7 and trigger dissociation of IF1 from the 70S IC. Within the next several hundreds of ms, dissociation of IF2 from the 70S IC is coupled to further remodeling of the ribosome that positions fMet-tRNAfMet into its final, P/P, configuration within the 70S EC. Our results demonstrate the power of TR cryo-EM to determine how a time-ordered series of conformational changes contribute to the mechanism and regulation of one of the most fundamental processes in biology.

molecular biology↗

bioRxiv2018

AICM: A Genuine Framework for Correcting Inconsistency Between Large Pharmacogenomics Datasets

The inconsistency of open pharmacogenomics datasets produced by different studies limits the usage of pharmacogenomics in biomarker discovery. Investigation of multiple pharmacogenomics datasets confirmed that the pairwise sensitivity data correlation between drugs, or rows, across different studies (drug-wise) is relatively low, while the pairwise sensitivity data correlation between cell-lines, or columns, across different studies (cell-wise) is considerably strong. This common interesting observation across multiple pharmacogenomics datasets suggests the existence of subtle consistency among the different studies (i.e., strong cell-wise correlation). However, significant noises are also shown (i.e., weak drug-wise correlation) and have prevented researchers from comfortably using the data directly. Motivated by this observation, we propose a novel framework for addressing the inconsistency between large-scale pharmacogenomics data sets. Our method can significantly boost the drug-wise correlation and can be easily applied to re-summarized and normalized datasets proposed by others. We also investigate our algorithm based on many different criteria to demonstrate that the corrected datasets are not only consistent, but also biologically meaningful. Eventually, we propose to extend our main algorithm into a framework, so that in the future when more data-sets become publicly available, our framework can hopefully offer a \"ground-truth\" guidance for references.

bioinformatics↗

bioRxiv2018

Kluyveromyces marxianus as a robust synthetic biology platform host

Throughout history, the yeast Saccharomyces cerevisiae has played a central role in human society due to its use in food production and more recently as a major industrial and model microorganism, because of the many genetic and genomic tools available to probe its biology. However S. cerevisiae has proven difficult to engineer to expand the carbon sources it can utilize, the products it can make, and the harsh conditions it can tolerate in industrial applications. Other yeasts that could solve many of these problems remain difficult to manipulate genetically. Here, we engineer the thermotolerant yeast Kluyveromyces marxianus to create a new synthetic biology platform. Using CRISPR-Cas9 mediated genome editing, we show that wild isolates of K. marxianus can be made heterothallic for sexual crossing. By breeding two of these mating-type engineered K. marxianus strains, we combined three complex traits- thermotolerance, lipid production, and facile transformation with exogenous DNA-into a single host. The ability to cross K. marxianus strains with relative ease, together with CRISPR-Cas9 genome editing, should enable engineering of K. marxianus isolates with promising lipid production at temperatures far exceeding those of other fungi under development for industrial applications. These results establish K. marxianus as a synthetic biology platform comparable to S. cerevisiae, with naturally more robust traits that hold potential for the industrial production of renewable chemicals.

synthetic biology↗

bioRxiv2018

An Organometallic Ir(III) Molecular Probe for Imaging Microtubules in Fluorescence and Electron Microscopy

We report a versatile cyclometalated Iridium (III) complex probe that achieves synchronous fluorescence-electron microscopy correlation to reveal microtubule ultrastructure in cells. The selective insertion of probe between repeated and {beta} units of microtubule triggers remarkable fluorescent enhancement, and high TEM contrast due to the presence of heavy Ir ions. The highly photostable probe allows live cell imaging of tubulin localization and motion during cell division with an resolution of 20 nm, and under TEM imaging reveals the {beta} unit interspace of 45[A] of microtubule in cells.

cell biology↗

bioRxiv2018

Sense-antisense gene overlap causes evolutionary retention of the few introns in Giardia genome and the implications

BackgroundIt is widely accepted that the last eukaryotic common ancestor (LECA) and early eukaryotes were intron-rich and intron loss dominated subsequent evolution, thus the presence of only very few introns in some modern eukaryotes must be the consequence of massive loss. But it is striking that few eukaryotes were found to have completely lost introns. Despite extensive research, the causes of massive intron losses remain elusive, and actually the reverse question - how the few introns are retained under the pressure of loss is equally significant but was rarely studied, except that it was conjectured that the essential functions of some introns prevent their loss. The extremely few (eight) spliceosome-mediated cis-spliced introns in the relatively simple genome of Giardia lamblia provide an excellent opportunity to explore this question.\n\nResultsOur investigation of the intron-containing genes and introns in Giardia found three types of intron distribution patterns: ancient intron in ancient gene, relatively new intron in ancient gene, and relatively new intron in relatively new gene, which can reflect to some extent the dynamic evolution of introns in Giardia. Not finding any special features or functional importance of these introns responsible for the retention, we noticed and experimentally verified that some intron-containing genes form sense-antisense gene pairs with functional genes on their complementary strands, and that the introns just reside in the overlapping regions.\n\nConclusionsIn Giardias evolution, despite constant pressure of intron loss, intron gain can still occur in both ancient and newly-evolved genes, but only a few introns have been retained; the evolutionary retention of introns is most likely not due to the functional constraint of the introns themselves but the causes outside of introns, such as the constraints imposed by other genomic functional elements overlapping with the introns. These findings can not only provide some clues to find new genomic functional elements -- in the areas overlapping with introngs, but suggest that \"functional constraint\" of introns may not be necessarily directly associated with intron loss and gain, or that the real functions or the way of functioning of introns are probably still outside of our current knowledge.

genomics↗

bioRxiv2018

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

BackgroundNormal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resources such as TCGA and TARGET do not provide matched tissue samples for every cancer or cancer subtype. The recent GTEx project has profiled samples from healthy individuals, providing an excellent resource for this field, yet the feasibility of using GTEx samples as the reference remains unanswered.\n\nMethodsWe analyze RNA-Seq data processed from the same computational pipeline and systematically evaluate GTEx as a potential reference resource. We use those cancers that have adjacent normal tissues in TCGA as a benchmark for the evaluation. To correlate tumor samples and normal samples, we explore top varying genes, reduced features from principal component analysis, and encoded features from an autoencoder neural network. We first evaluate whether these methods can identify the correct tissue of origin from GTEx for a given cancer and then seek to answer whether disease expression signatures are consistent between those derived from TCGA and from GTEx.\n\nResultsAmong 32 TCGA cancers, 18 cancers have less than 10 matched adjacent normal tissue samples. Among three methods, autoencoder performed the best in predicting tissue of origin, with 12 of 14 cancers correctly predicted. The reason for misclassification of two cancers is that none of normal samples from GTEx correlate well with any tumor samples in these cancers. This suggests that GTEx has matched tissues for the majority cancers, but not all. While using autoencoder to select proper normal samples for disease signature creation, we found that disease signatures derived from normal samples selected via an autoencoder from GTEx are consistent with those derived from adjacent samples from TCGA in many cases. Interestingly, choosing top 50 mostly correlated samples regardless of tissue type performed reasonably well or even better in some cancers.\n\nConclusionsOur findings demonstrate that samples from GTEx can serve as reference normal samples for cancers, especially those do not have available adjacent tissue samples. A deep-learning based approach holds promise to select proper normal samples.

bioinformatics↗

bioRxiv2018

Evaluating cancer cell lines as models for metastatic breast cancer

Metastasis is the most common cause of cancer-related death and, as such, there is an urgent need to discover new therapies to treat metastasized cancers. Cancer cell lines are widely-used models to study cancer biology and test drug candidates. However, it is still unknown to what extent they adequately resemble the disease in patients. The recent accumulation of large-scale genomic data in cell lines, mouse models, and patient tissue samples provides an unprecedented opportunity to evaluate the suitability of cell lines for metastatic cancer research. In this work, we used breast cancer as a case study. The comprehensive comparison of the genetic profiles of 57 breast cancer cell lines with those of metastatic breast cancer samples revealed substantial genetic differences. In addition, we identified cell lines that more closely resemble different subtypes of metastatic breast cancer. Surprisingly, a combined analysis of mutation, copy number variation and gene expression data suggested that MDA-MB-231, the most commonly used triple negative cell line for metastatic breast cancer research, had little genomic similarity with Basal-like metastatic breast cancer samples. We further compared cell lines with organoids, a new type of preclinical model which are becoming more popular in recent years. We found that organoids outperformed cell lines in resembling the transcriptome of metastatic breast cancer samples. However, additional differential expression analysis suggested that both types of models could not mimic the effects of tumor microenvironment and meanwhile had their own bias towards modeling specific biological processes. Our work provides a guide of cell line selection in metastasis-related study and sheds light on the potential of organoids in translational research.

bioinformatics↗

bioRxiv2018

Quantitative assessment of cell population diversity in single-cell landscapes

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such expansion or shrinkage, or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes, identification and definition of altered cell populations, and benchmarking batch correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions, and has broad utility for gaining insight on how cell populations respond to perturbations.

bioinformatics↗

bioRxiv2018

Antimicrobial Activity of Tetrabromobisphenol A (TBBPA) against Staphylococcus aureus Skin Infections

Tetrabromobisphenol A (TBBPA) is a brominated flame retardant with selective antimicrobial activity against Gram-positive bacteria. We show that TBBPA exerts bactericidal effects by damaging the cell wall and membrane of Staphylococcus aureus (SA) without inducing antimicrobial resistance. In vivo skin infection assays indicate that a low dose of TBBPA could contribute to wound closure and attenuate SA infection and inflammatory infiltration. TBBPA has potential for use as an antimicrobial agent against Gram-positive pathogens.

microbiology↗

bioRxiv2018

Quasi-neutral molecular evolution -- When positive and negative selection cancel out

In the absence of both positive and negative selection, DNA sequences evolve at the neutral rate, R = 1. Due to the prevalence of negative selection, R[~]1 is rarely achieved in organismal evolution. However, when R [~] 1 is observed, it does not necessarily indicate neutral evolution because positive and negative selection could be equally strong but in opposite directions - hereby referred to as quasi-neutrality. We now show that somatic-cell evolution could be the paradigm of quasi-neutral evolution for these reasons: 1) Quasi-neutrality is much more likely in small populations (size N < 50) than in large ones; 2) Stem cell population sizes in single niches of normal tissues, from which tumors likely emerges, have small Ns (usually < 50); 3) the genome-wide evolutionary rate across tissue types is close to R = 1; 4) Relative to the average of R [~] 1, many genes evolve at a much higher or lower rate, thus hinting both positive and negative selection; 5) When N < 50, selection efficacy decreases rapidly as N decreases even when the selection intensity stays constant; 6) Notably, N is smaller in the small intestine (SmI) than in the colon (CO); hence, the [~] 70 fold higher rate of phenotypic evolution (observed as cancer risk) in the latter can be explained by the greater efficacy of selection, which then leads to the fixation of more advantageous mutations and fewer deleterious ones in the CO. Under quasineutrality, positive and negative selection can be measured in the same system as the two forces are simultaneously present or absent.

evolutionary biology↗

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

Effects of CEPA and 1-MCP on flower bud differentiation of apple cv. ‘Nagafu No.2’ grafted on different rootstocks

The apple (Malus domestica Borkh.) has a relatively long juvenile period which prevent the fruit breeding. The understanding of the flowering system is important to improve breeding efficiency in the apple. In this context, 2-year-old \"Fuji\" apple cv. \"Nagafu No.2\" trees that were grafted on dwarf self-rooted rootstock M.26, vigorous rootstock M. sieversii and interstock M.26/M. sieversii, respectively. Spraying with clean water (as controls), 800 mg{middle dot}L-1 2-Chloroethylphosphonic acid (CEPA) and 2 L{middle dot}L-1 1-methylcyclopropene (1-MCP). The results showed that CEPA significantly repressed the vegetative growth attributed to the increase of the ABA and ZT synthesis, and the decrease of IAA synthesis in leaves and buds. However, there was no significant difference or significant inverse effect between 1-MCP and control. Furthermore, CEPA promoted flower formation, increased the flowering rate and advanced the blossom period for 2 days compared with the control, which accompanied by the accumulation of soluble sugar, glucose and sucrose, and the increase of -amylase (-AMY) and sucrose phosphate synthase (SPS) activities, and the decrease of the starch contents and sucrose synthase (SS) activities in leaves and buds. However, the blossom period was delayed for 2 days after spraying with 1-MCP. Finally, the expression of TFL1 was significantly repressed while the AP1 was significantly promoted in buds from M.26 and M.26/M. sieversii after spraying with CEPA, while the effect was not significant from M. sieversii. However, the expression levels of TFL1 and AP1 were not significantly different from the control after the application of 1-MCP. In spite of this, CEPA was more susceptible to easy-flowering M26, followed by M26/M. sieversii, and still less susceptible to difficult-flowering rootstock M. sieversii.\n\nAbbreviations

physiology↗

bioRxiv2018

CRISPR-Tag: an Efficient DNA Tagging System in Living Cells

Main textA lack of efficient tools to image non-repetitive genes in living cells has limited our ability to explore the functional impact of spatiotemporal dynamics of genes. Here, we addressed this issue by developing the CRISPR-Tag system as a new DNA tagging strategy to label protein-coding genes with high signal-to-noise ratio under wild-field fluorescence microscopy by using 1 to 4 highly active sgRNAs. The CRISPR-Tag, with minimal size of [~] 250 bp, represents an easily and broadly applicable technique to study spatiotemporal organization of genomic elements in living cells.

cell biology↗