Biology subjects

Fan, J.

Publications and source records attributed to Fan, J..

12 recordsLinked to original sources

bioRxiv2018

Right hemisphere superiority for executive control of attention

Over forty years have passed since the first evidence showing the unbalanced attentional allocation of humans across the two visual fields, and since then, a wealth of behavioral, neurophysiological, and clinical data increasingly showed a right hemisphere dominance for orienting of attention. However, inconsistent evidence exists regarding the right-hemisphere dominance for executive control of attention, possibly due to a lack of consideration of its dynamics with the alerting and orienting functions. In this study, we used a version of the Attentional Network Test with lateralized presentation of the stimuli to the left visual field (processed by the right hemisphere, RH) and right visual field (processed by the left hemisphere, LH) to examine visual field differences in executive control of attention under conditions of alerting or orienting. Analyses of behavioral performance (reaction time and error rate) showed a more efficient executive control (reduced conflict effect) in the RH compared to the LH for the reaction time, under conditions of increased alerting and of informative spatial orienting. These results demonstrate the right-hemisphere superiority for executive control, and that this effect depends on the activation of the alerting and orienting functions.

neuroscience↗

bioRxiv2018

Functional importance of JMY expression by Sertoli cells in mediating mouse spermatogenesis

Sertoli cells are crucial for spermatogenesis in the seminiferous epithelium because their actin cytoskeleton supports vesicle transport, cell junction, protein anchoring and spermiation. Here, we show that junction-mediating and regulatory protein (JMY), an actin regulating protein, also affects endocytic vesicle trafficking and Sertoli cell junction remodeling since disruption of these functions induced male subfertility in Sertoli cell-specific Jmy knockout mice. Specifically, these mice have: a) impaired BTB integrity and spermatid adhesion in the seminiferous tubules; b) high incidence of sperm structural deformity; c) reduced sperm count and poor sperm motility. Moreover, the cytoskeletal integrity in Sertoli cell-specific Jmy knockout mice was compromised along with endocytic vesicular trafficking. These effects impaired junctional protein recycling and reduced Sertoli cell junctions. In addition, JMY interaction with -actinin1 and Sorbs2 was related to JMY activity and in turn actin cytoskeletal organization. In summary, JMY affects control of spermatogenesis through regulating actin filament organization and endocytic vesicle trafficking in Sertoli cells.

cell biology↗

bioRxiv2018

5-Hydroxymethylcytosines from Circulating Cell-free DNA as Diagnostic and Prognostic Markers for Hepatocellular Carcinoma

The lack of highly sensitive and specific diagnostic biomarkers is a major contributor to the poor outcomes of patients with hepatocellular carcinoma (HCC), the second-most common cause of cancer deaths worldwide. We sought to develop a clinically convenient and minimally-invasive approach that can be deployed at scale for the sensitive, specific, and highly reliable diagnosis of HCC, and to evaluate the potential prognostic value of this approach. The study cohort comprised of 2,728 subjects, including HCC patients (n = 1,208), controls (n = 965) (572 healthy individuals and 393 patients with benign lesions), as well as patients with chronic hepatitis B infection (CHB) (n =291), liver cirrhosis (LC) (n = 110), and cholangiocarcinoma (CCC) (n = 154), was recruited from three major liver cancer hospitals in Shanghai, China, from July 2016 to November 2017. Circulating cell-free DNA (cfDNA) were collected from plasma samples from these individuals before surgery or any radical treatment. Applying our 5hmC-Seal technique, the summarized 5-hydroxymethylcytosine (5hmC) profiles in cfDNA were obtained. Molecular annotation analysis suggested that the profiled 5hmC loci in cfDNA were enriched with liver tissue-derived regulatory markers (e.g., H3K4me1). We showed that a weighted diagnostic score (wd-score) based on 117 genes detected using the summarized 5hmC profiles in cfDNA accurately distinguished HCC patients from controls (AUC = 95.1%; 95% CI, 93.6-96.5%) in the validation set, markedly outperformed -fetoprotein (AFP) with superior sensitivity. The wd-scores, which not only detected early BCLC stages (e.g., Stage 0: AUC = 96.2%; 95% CI,94.1-98.4%) and small tumors (e.g., < 2 cm: AUC = 95.7%; 95% CI: 93.6-97.7%), also showed high capacity for distinguishing HCC from non-cancer patients with CHB/LC (AUC = 80.2%; 95% CI, 75.8-84.6%). Moreover, the prognostic value of 5hmC markers in cfDNA was evaluated for HCC recurrence, showing that a weighted prognostic score (wp-score) based on 16 marker genes predicted the recurrence risk (HR = 6.67; 95% CI, 2.81-15.82, p < 0.0001) in 555 patients who have been followed up after surgery. In conclusion, we have developed and validated a robust 5hmC-based diagnostic model that can be applied routinely with clinically feasible amount of cfDNA (e.g., from ~2-5 mL of plasma). Applying this new approach in the clinic could significantly improve the clinical outcomes of HCC patients, for example by early detection of those patients with surgically resectable tumors or as a convenient disease surveillance tool for recurrence.

cancer biology↗

bioRxiv2018

The Acinetobacter baumannii Mla system and glycerophospholipid transport to the outer membrane

The outer membrane (OM) of Gram-negative bacteria serves as a selective permeability barrier that allows entry of essential nutrients while excluding toxic compounds, including antibiotics. The OM is asymmetric and contains an outer leaflet of lipopolysaccharides (LPS) or lipooligosaccharides (LOS) and an inner leaflet of glycerophospholipids (GPL). We screened Acinetobacter baumannii transposon mutants and identified a number of mutants with OM defects, including an ABC transporter system homologous to the Mla system in E. coli. We further show that this opportunistic, antibiotic-resistant pathogen uses this multicomponent protein complex and ATP hydrolysis at the inner membrane to promote GPL export to the OM. The broad conservation of the Mla system in Gram-negative bacteria suggests the system may play a conserved role in OM biogenesis. The importance of the Mla system to Acinetobacter baumannii OM integrity and antibiotic sensitivity suggests that its components may serve as new antimicrobial therapeutic targets.

microbiology↗

bioRxiv2018

A hypomorphic Stip1 allele reveals the requirement for chaperone networks in mouse development and aging

Chaperone networks are dysregulated with aging and neurodegenerative disease, but whether compromised Hsp70/Hsp90 chaperone function directly contributes to neuronal degeneration is unknown. Stress-inducible phosphoprotein-1 (STI1; STIP1; HOP) is a co-chaperone that simultaneously interacts with Hsp70 and Hsp90, but whose function in vivo remains poorly understood. To investigate the requirement of STI1-mediated regulation of the chaperone machinery in aging we combined analysis of a mouse line with a hypomorphic Stip1 allele, with a neuronal cell line lacking STI1 and in-depth analyses of chaperone genes in human datasets. Loss of STI1 function severely disturbed the Hsp70/Hsp90 machinery in vivo, and all client proteins tested and a subset of cochaperones presented decreased levels. Importantly, mice expressing a hypomorphic STI1 allele showed spontaneous age-dependent hippocampal neurodegeneration, with consequent spatial memory deficits. STI1 is a critical node for the chaperone network and it can contribute to age-dependent hippocampal neurodegeneration.

cell biology↗

bioRxiv2017

A Multi-Species Functional Embedding Integrating Sequence and Network Structure

A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment.\n\nIn this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HO_SCPLOWANDLC_SCPLOW (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space capture functional similarity across species, and the vectors themselves are useful for a variety of cross species tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HO_SCPLOWANDLC_SCPLOW-embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are co-located in HO_SCPLOWANDLC_SCPLOW-space both within and across species. Software for the HO_SCPLOWANDLC_SCPLOW algorithm is available at http://github.com/lrgr/HANDL.

bioinformatics↗

bioRxiv2017

Cross-Site Comparison of Ribosomal Depletion Kits for Illumina RNAseq Library Construction

Ribosomal RNA (rRNA) comprises at least 90% of total RNA extracted from mammalian tissue or cell line samples. Informative transcriptional profiling using massively parallel sequencing technologies requires either enrichment of mature poly-adenylated transcripts or targeted depletion of the rRNA fraction. The latter method is of particular interest because it is compatible with degraded samples such as those extracted from FFPE and also captures transcripts that are not poly-adenylated such as some non-coding RNAs. Here we provide a cross-site study that evaluates the performance of ribosomal RNA removal kits from Illumina, Takara/Clontech, Kapa Biosystems, Lexogen, New England Biolabs and Qiagen on intact and degraded RNA samples. We find that all of the kits are capable of performing significant ribosomal depletion, though there are differences in their ease of use. All kits were able to remove ribosomal RNA to below 20% with intact RNA and identify [~]14,000 protein coding genes from the Universal Human Reference RNA sample at >1FPKM. Analysis of differentially detected genes between kits suggests that transcript length may be a key factor in library production efficiency. These results provide a roadmap for labs on the strengths of each of these methods and how best to utilize them.

genomics↗

bioRxiv2017

RNA velocity in single cells

RNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity--the time derivative of RNA abundance--can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

genomics↗

bioRxiv2017

An Interpretable Framework for Clustering Single-Cell RNA-Seq Datasets

BackgroundWith the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering.\n\nResultsIn this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues. DendroSplit offers a novel perspective on the single-cell RNA-Seq clustering problem motivated by the definition of \"cell type,\" allowing us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. We analyze several landmark single-cell datasets, demonstrating both the methods efficacy and computational efficiency.\n\nConclusionDendroSplit offers a clustering framework that is comparable to existing methods in terms of accuracy and speed but is novel in its emphasis on interpretabilty. We provide the full DendroSplit software package at https://github.com/jessemzhang/dendrosplit.

bioinformatics↗

bioRxiv2017

Integrative Single-Cell Analysis By Transcriptional And Epigenetic States In Human Adult Brain

Detailed characterization of the cell types comprising the highly complex human brain is essential to understanding its function. Such tasks require highly scalable experimental approaches to examine different aspects of the molecular state of individual cells, as well as the computational integration to produce unified cell state annotations. Here we report the development of two highly scalable methods (snDrop-Seq and scTHS-Seq), that we have used to acquire nuclear transcriptome and DNA accessibility maps for thousands of single cells from the human adult visual and frontal cortex. This has led to the best-resolved human neuronal subtypes to date, identification of a majority of the non-neuronal cell types, as well as the cell-type specific nuclear transcriptome and DNA accessibility maps. Integrative analysis allowed us to identify transcription factors and regulatory elements shaping the state of different brain cell types, and to map genetic risk factors of human brain common diseases to specific pathogenic cell types and subtypes.

genomics↗

bioRxiv2017

UBiT2: a client-side web-application for gene expression data analysis

We present a purely client-side web-application, UBiT2 (User-friendly BioInformatics Tools), that provides installation-free, offline alignment, analysis, and visualization of RNA-sequencing as well as qPCR data. Analysis modules were designed with single cell transcriptomic analysis in mind. Using just a browser, users can perform standard analyses such as quality control, filtering, hierarchical clustering, principal component analysis, differential expression analysis, gene set enrichment testing, and more, all with interactive visualizations and exportable publication-quality figures. We apply UBiT2 to recapitulate findings from single cell RNA-seq and Fluidigm Biomark multiplex RT-qPCR gene expression datasets. UBiT2 is available at http://pklab.med.harvard.edu/jean/ubit2/index.html with open-source code available at https://github.com/JEFworks/ubit2.

bioinformatics↗

bioRxiv2017

Comparison of Principal Component Analysis and t-Stochastic Neighbor Embedding with Distance Metric Modifications for Single-cell RNA-sequencing Data Analysis

Recent developments in technological tools such as next generation sequencing along with peaking interest in the study of single cells has enabled single-cell RNA-sequencing, in which whole transcriptomes are analyzed on a single-cell level. Studies, however, have been hindered by the ability to effectively analyze these single cell RNA-seq datasets, due to the high-dimensional nature and intrinsic noise in the data. While many techniques have been introduced to reduce dimensionality of such data for visualization and subpopulation identification, the utility to identify new cellular subtypes in a reliable and robust manner remains unclear. Here, we compare dimensionality reduction visualization methods including principle component analysis and t-stochastic neighbor embedding along with various distance metric modifications to visualize single-cell RNA-seq datasets, and assess their performance in identifying known cellular subtypes. Our results suggest that selecting variable genes prior to analysis on single-cell RNA-seq data is vital to yield reliable classification, and that when variable genes are used, the choice of distance metric modification does not particularly influence the quality of classification. Still, in order to take advantage of all the gene expression information, alternative methods must be used for a reliable classification.

bioinformatics↗