Biology subjects

Lin, D.

Publications and source records attributed to Lin, D..

140 records · Page 8Linked to original sources

bioRxiv2018

Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal Regression

Brain functional networks identified from fMRI data can provide potential biomarkers for brain disorders. Group independent component analysis (GICA) is popular for extracting brain functional networks from multiple subjects. In GICA, different strategies exist for reconstructing subject-specific networks from the group-level networks. However, it is unknown whether these strategies have different sensitivities to group differences and abilities in distinguishing patients. Among GICA, spatio-temporal regression (STR) and spatially constrained ICA approaches such as group information guided ICA (GIG-ICA) can be used to propagate components (indicating networks) to a new subject that is not included in the original subjects. In this study, based on the same a priori network maps, we reconstructed subject-specific networks using these two methods separately from resting-state fMRI data of 151 schizophrenia patients (SZs) and 163 healthy controls (HCs). We investigated group differences in the estimated functional networks and the functional network connectivity (FNC) obtained by each method. The networks were also used as features in a cross-validated support vector machine (SVM) for classifying SZs and HCs. We selected features using different strategies to provide a comprehensive comparison between the two methods. GIG-ICA generally showed greater sensitivity in statistical analysis and better classification performance (accuracy 76.45{+/-}8.9%, sensitivity 0.74{+/-}0.11, specificity 0.79{+/-}0.11) than STR (accuracy 67.45{+/-}8.13%, sensitivity 0.65{+/-}0.11, specificity 0.71{+/-}0.11). Importantly, results were also consistent when applied to an independent dataset including 82 HCs and 82 SZs. Our work suggests that the functional networks estimated by GIG-ICA are more sensitive to group differences, and GIG-ICA is promising for identifying image-derived biomarkers of brain disease.

neuroscience↗

bioRxiv2018

Detecting spatiotemporal pattern of tuberculosis and the relationship between ecological environment and tuberculosis, a spatial panel data analysis in Guangxi, China

Guangxi is one of the provinces having the highest reported incidence of tuberculosis (TB) in China. However, spatial and temporal pattern and causation of the situation are still unclear. In order to detect the spatiotemporal pattern of TB and the association with ecological environment factors in Guangxi Zhuang autonomous region, China, We performed a spatiotemporal analysis with prediction using time series analysis, Morans I global and local spatial autocorrelation statistics, and space-time scan statistics, to detect temporal and spatial clusters. Spatial panel models were employed to identify the influence factors. The time series analysis shows that the number of reported cases peaked in spring and summer and decreased in autumn and winter with the annual reported incidence of 113.1/100,000 population. Morans I global statistics were greater than 0 (0.363 - 0.536) during the study period. The most significant hot spots were mainly located in the central part. The east part exhibited a low-low relation. By spacetime scanning, the clusters identified were similar to that of the local autocorrelation statistics, and were clustered toward the early of 2016. Duration of sunshine, per capita gross domestic product (PGDP), the recovery rate of TB and participation rate of new cooperative medical care insurance in rural areas had a significant negative association with TB. In conclusion, the reported incidence of TB in Guangxi remains high. The main cluster was located in the central part of Guangxi, a region where promoting the productivity, improving TB treatment pathway and strengthening environmental protective measures (increasing sunshine exposure) are urgently needed.

epidemiology↗

bioRxiv2018

A genetically-encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice

Dopamine (DA) is a central monoamine neurotransmitter involved in many physiological and pathological processes. A longstanding yet largely unmet goal is to measure DA changes reliably and specifically with high spatiotemporal precision, particularly in animals executing complex behaviors. Here we report the development of novel genetically-encoded GPCR-Activation-Based-DA (GRABDA) sensors that enable these measurements. In response to extracellular DA rises, GRABDA sensors exhibit large fluorescence increases ({Delta}F/F0[~]90%) with sub-second kinetics, nanomolar to sub-micromolar affinities, and excellent molecular specificity. Importantly, GRABDA sensors can resolve a single-electrical-stimulus evoked DA release in mouse brain slices, and detect endogenous DA release in the intact brains of flies, fish, and mice. In freely-behaving mice, GRABDA sensors readily report optogenetically-elicited nigrostriatal DA release and depict dynamic mesoaccumbens DA changes during Pavlovian conditioning or during sexual behaviors. Thus, GRABDA sensors enable spatiotemporal precise measurements of DA dynamics in a variety of model organisms while exhibiting complex behaviors.

neuroscience↗

bioRxiv2018

BAP1 Loss Predicts Therapeutic Vulnerability in Malignant Peritoneal Mesothelioma

BackgroundMalignant Peritoneal Mesothelioma (PeM) is a rare but frequently fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM yet exist. In the search for novel therapeutic target candidates in PeM, we performed a comprehensive integrative multi-omics analysis of 19 treatment-naive PeM tumors.\n\nResultsThe analysis identified PeM tumors with BAP1 loss to form a distinct molecular subtype characterized by distinct expression patterns of genes involved in chromatin remodeling, DNA repair pathway, and immune checkpoint receptor activation. This PeM subtype could potentially benefit from immune checkpoint, PARP, or HDAC inhibition therapies.\n\nConclusionsOur findings uncover BAP1 as a trackable prognostic and predictive biomarker, and refine PeM disease classification. This integrated molecular characterization provides a comprehensive foundation for developing PeM precision medicine.

cancer biology↗

bioRxiv2018

Mitochondrial Fusion Suppresses Pancreatic Cancer Growth via Reduced Oxidative Metabolism

Pancreatic cancer is a highly lethal disease whose aggressive biology that is driven by mitochondrial oxidative metabolism. Mitochondria normally form a network of fused organelles, but we find that patient-derived and genetically engineered murine pancreatic cancer cells exhibit highly fragmented mitochondria with robust oxygen consumption rates (OCR). When mitochondrial fusion was activated by the genetic or pharmacological inhibition Drp1, the morphology and metabolism of human and murine pancreatic cancer cells more closely resembled that of normal pancreatic epithelial cells. This reduced metabolism was correlated with slower tumor growth, fewer metastases, and enhanced survival in a syngeneic orthotopic model. Similarly, directly activating mitochondrial fusion by overexpression of Mfn2 also reduced tumor growth and metastases. Mitochondrial fusion in pancreatic cancer cells was associated with reduced mitochondrial mass and Complex I expression and function. Thus, these data suggest that enhancing mitochondrial fusion through Drp1 inhibition or enhanced Mfn2 expression or function has strong tumor suppressive activity against pancreatic cancer and may thus represent a highly novel and efficacious therapeutic target.

cancer biology↗

bioRxiv2018

Longitudinal single cell fate in hematopoiesis in vivo using cellular barcoding and DiSNE movie visualization

Identifying the progeny of many single progenitor cells simultaneously can be achieved by tagging progenitors with unique heritable DNA barcodes, and allows inferences of lineage relationships, including longitudinally. While this approach has shed new light on single cell fate heterogeneity, data interpretation remains a major challenge. In this study, we applied our developmental interpolated t-Distributed Stochastic Neighbor Embedding (DiSNE) movie approach to visualize the clonal dynamics of hematopoietic reconstitution in primates and identify novel developmental patterns, namely a potential cluster of hematopoietic progenitors with early T cell and later granulocyte production.\n\nKey pointsO_LIComplex single cell haematopoietic fate heterogeneity can be visualized and assessed with tSNE pie maps\nC_LIO_LIDiSNE movie visualization of in vivo haematopoiesis allows \"play back\" of the waves of haematopoiesis\nC_LIO_LIIdentification of novel hematopoietic progenitors with early T cell and later granulocyte production\nC_LI

developmental biology↗

bioRxiv2018

Unsupervised embedding of single-cell Hi-C data

Single-cell Hi-C (scHi-C) data promises to enable scientists to interrogate the 3D architecture of DNA in the nucleus of the cell, studying how this structure varies stochastically or along developmental or cell cycle axes. However, Hi-C data analysis requires methods that take into account the unique characteristics of this type of data. In this work, we explore whether methods that have been developed previously for the analysis of bulk Hi-C data can be applied to scHi-C data. In this work, we apply methods designed for analysis of bulk Hi-C data to scHi-C data in conjunction with unsupervised embedding. We find that one of these methods, HiCRep, when used in conjunction with multidimensional scaling (MDS), strongly outperforms three other methods, including a technique that has been used previously for scHi-C analysis. We also provide evidence that the HiCRep/MDS method is robust to extremely low per-cell sequencing depth, that this robustness is improved even further when high-coverage and low-coverage cells are projected together, and that the method can be used to jointly embed cells from multiple published datasets.

bioinformatics↗

bioRxiv2017

Sensory Detection of Aversive and Appetitive Stimuli by CRF neurons

Corticotropin-releasing factor (CRF), which is released from the paraventricular nucleus (PVN) of the hypothalamus, is essential for mediating stress response by activating the hypothalamic-pituitary-adrenal (HPA) axis. CRF-releasing PVN neurons receive inputs from multiple brain regions that convey stressful events, but their rapid neuronal dynamics on the timescale of behavior remain unknown. Here, our optical recordings of PVN CRF neuronal activity in freely behaving mice revealed that PVN CRF neurons are activated immediately by a range of stress-inducing aversive stimuli, including predator odor. By contrast, CRF neuronal activity starts to drop within a second of exposure to appetitive stimuli, such as food. These findings suggest that PVN CRF neurons receive real-time information about aversive and appetitive stimuli. Their role in mediating the sensory detection and regulating the neuroendocrine axis resembles those of AgRP/NPY neurons and SFO neurons that regulate neurophysiology responses and mediate sensory detection of food and water.

neuroscience↗

bioRxiv2017

Identification of a Small Molecule Inhibitor of the Aminoglycoside 6’-N-Acetyltransferase Type Ib [AAC(6’)-Ib] Using Mixture-Based Combinatorial Libraries

The aminoglycoside 6'-N-acetyltransferase type Ib [AAC(6)-Ib] is the most widely distributed enzyme among AAC(6)-I-producing Gram-negative pathogens and confers resistance to clinically relevant aminoglycosides including amikacin. This enzyme is therefore ideal to target with enzymatic inhibitors that could overcome resistance to aminoglycosides. The search for inhibitors was carried out using mixture-based combinatorial libraries, the scaffold ranking approach, and the positional scanning strategy. A library with high inhibitory activity had pyrrolidine pentamine scaffold and was selected for further analysis. This library contained 738,192 compounds with functionalities derived from 26 different amino acids (R1, R2 and R3) and 42 different carboxylic acids (R4) in four R group functionalities. The most active compounds all contained S-phenyl (R1 and R3) and S-hydromethyl (R2) functionalities at three locations and differed at the R4 position. The compound containing 3-phenylbutyl at R4 (compound 206) was a robust enzymatic inhibitor in vitro, in combination with amikacin potentiated the inhibition of growth of three resistant bacteria in culture, and improved survival when used as treatment of Galleria mellonella infected with aac(6)-Ib-harboring Klebsiella pneumoniae and Acinetobacter baumannii strains.

microbiology↗

bioRxiv2017

Estimating the scale of biomedical data generation using text mining

While the impact of biomedical research has traditionally been measured using bibliographic metrics such as citation or journal impact factor, the data itself is an output which can be directly measured to provide additional context about a publications impact. Data are a resource that can be repurposed and reused providing dividends on the original investment used to support the primary work. Moreover, it is the cornerstone upon which a tested hypothesis is rejected or accepted and specific scientific conclusions are reached. Understanding how and where it is being produced enhances the transparency and reproducibility of the biomedical research enterprise. Most biomedical data are not directly deposited in data repositories and are instead found in the publication within figures or attachments making it hard to measure. We attempted to address this challenge by using recent advances in word embedding to identify the technical and methodological features of terms used in the free text of articles methods sections. We created term usage signatures for five types of biomedical research data, which were used in univariate clustering to correctly identify a large fraction of positive control articles and a set of manually annotated articles where generation of data types could be validated. The approach was then used to estimate the fraction of PLOS articles generating each biomedical data type over time. Out of all PLOS articles analyzed (n = 129,918), ~7%, 19%, 12%, 18%, and 6% generated flow cytometry, immunoassay, genomic microarray, microscopy, and high-throughput sequencing data. The estimate portends a vast amount of biomedical data being produced: in 2016, if other publishers generated a similar amount of data then roughly 40,000 NIH-funded research articles would produce ~56,000 datasets consisting of the five data types we analyzed.\n\nOne Sentence SummaryApplication of a word-embedding model trained on the methods sections of research articles allows for estimation of the production of diverse biomedical data types using text mining.

bioinformatics↗

bioRxiv2017

Assessment of clonal kinetics reveals multiple trajectories of dendritic cell development

A thorough understanding of cellular development is incumbent on assessing the complexities of fate and kinetics of individual clones within a population. Here, we develop a system for robust periodical assessment of lineage outputs of thousands of transient clones and establishment of bona fide cellular trajectories. We appraise the development of dendritic cells (DCs) from barcode-labeled hematopoietic stem and progenitor cells (HSPCs) by serially measuring barcode signatures, and visualize this multidimensional data using novel developmental interpolated t-distributed stochastic neighborhood embedding (Di-SNE) time-lapse movies. We identify multiple cellular trajectories of DC development that are characterized by distinct fate bias and expansion kinetics, and determine that these are intrinsically programmed. We demonstrate that conventional DC and plasmacytoid DC trajectories are largely separated already at the HSPC stage. This framework allows systematic evaluation of clonal dynamics and can be applied to other steady-state or perturbed developmental systems.

systems biology↗

bioRxiv2017

Rice TSV3 Encoding Obg-like GTPase Protein is Essential for Chloroplast Development during the Early Leaf Stage under Cold Stress

The Spo0B-associated GTP-binding (Obg) proteins occupy a wide variety of roles in the viability of nearly all bacteria. Its detailed roles in higher plants have not yet been elucidated. A novel rice thermo-sensitive virescent mutant tsv3 was identified in this study that displayed albino phenotype at 20{degrees}C before the 3-leaf stage while being normal green at 32{degrees}C or even at 20{degrees}C after the 4-leaf stage. The mutant phenotype was aligned with altered chlorophyll (Chl) content and chloroplast development. Map-based cloning and complementation test showed that TSV3 encoded a kind of small GTP binding protein. Subcellular localization revealed that TSV3 was in chloroplast. TSV3 transcripts were highly expressed in leaves and weak or undetectable in other tissues, suggesting the tissue-specific expression. In tsv3 mutant, the transcriptional levels of certain genes associated with biogenesis of chloroplast ribosomes 50S subunit were severely decreased at the 3-leaf-stage under cold stress, but could be recovered to normal levels at a higher temperature (32{degrees}C). The observations from this study indicated that the rice nuclear-encoded TSV3 plays important roles in chloroplast development at early leaf stage under cold stress.

plant biology↗

bioRxiv2017

Dynamic response to initial stage blindness in visual system development

Sensitive periods and experience-dependent plasticity have become core issues in visual system development. Converging evidence indicates that visual experience is an indispensable factor in establishing mature visual system circuitry during sensitive periods and the visual system exhibits substantial plasticity when facing deprivation. The mechanisms that underlie the environmental regulation of visual system development and plasticity are of great interest but need further exploration. Here, we investigated a unique sample of human infants who experienced initial stage blindness (beginning at birth and lasting 2 to 8 months) before the removal of bilateral cataracts. Retinal thickness, axial length, refractive status, visual grating acuity and genetic integrity were recorded during the preoperative period or at surgery, and then during follow-up. The results showed that the development of the retina is malleable and associated with external environment influences. Our work supported that the retina might play critical roles in the development of the experience-dependent visual system and its malleability might partly contribute to the sensitive period plasticity.\n\nSUMMARY STATEMENTThe follow-up investigation of a group of human infants, who experienced initial stage blindness before the removal of bilateral cataracts, revealed that retinal development is associated with environment influences and its malleability might be a potential basis of plasticity.

developmental biology↗

bioRxiv2017

A pooled sequencing approach identifies a candidate meiotic driver in Drosophila

Meiotic drive occurs when a selfish element increases its transmission frequency above the Mendelian ratio by hijacking the asymmetric divisions of female meiosis. Meiotic drive causes genomic conflict and potentially has a major impact on genome evolution, but only a few drive loci of large effect have been described. New methods to reliably detect meiotic drive are therefore needed, particularly for discovering moderate-strength drivers that are likely to be more prevalent in natural populations than strong drivers. Here we report an efficient method that uses sequencing of large pools of backcross (BC1) progeny to test for deviations from Mendelian segregation genome-wide of single-nucleotide polymorphisms (SNPs) that distinguish the parental strains. We show that meiotic drive can be detected by a characteristic pattern of decay in distortion of SNP frequencies, caused by recombination unlinking the driver from distal loci. We further show that control crosses allow allele-frequency distortion caused by meiotic drive to be distinguished from distortion resulting from developmental effects. We used this approach to test whether chromosomes with extreme telomere-length differences segregate at Mendelian ratios, as telomeric regions are a potential hotspot for meiotic drive due to their roles in meiotic segregation and multiple observations of high rates of telomere sequence evolution. Using four different pairings of long and short telomere strains, we find no evidence that extreme telomere-length variation causes meiotic drive in Drosophila. However, we identify one candidate meiotic driver in a centromere-linked region that shows an ~8% increase in transmission frequency, corresponding to a ~54:46 segregation ratio. Our results show that candidate meiotic drivers of moderate strength can be readily detected and localized in pools of F1 progeny.

genetics↗