Biology subjects

Yin, L.

Publications and source records attributed to Yin, L..

88 records · Page 5Linked to original sources

bioRxiv2019

Integrated Multi-omic Framework of the Plant Response to Jasmonic Acid

Understanding the systems-level actions of transcriptional responses to hormones provides insight into how the genome is reprogrammed in response to environmental stimuli. Here, we investigate the signaling pathway of the hormone jasmonic acid (JA), which controls a plethora of critically important processes in plants and is orchestrated by the transcription factor MYC2 and its closest relatives in Arabidopsis thaliana. We generated an integrated framework of the response to JA that spans from the activity of master and secondary-regulatory transcription factors, through gene expression outputs and alternative splicing to protein abundance changes, protein phosphorylation and chromatin remodeling. We integrated time series transcriptome analysis with (phospho)proteomic data to reconstruct gene regulatory network models. These enable us to predict previously unknown points of crosstalk from JA to other signaling pathways and to identify new components of the JA regulatory mechanism, which we validated through targeted mutant analysis. These results provide a comprehensive understanding of how a plant hormone remodels cellular functions and plant behavior, the general principles of which provide a framework for analysis of cross-regulation between other hormone and stress signaling pathways.

plant biology↗

bioRxiv2019

Epitranscriptomic regulation of the response to the air pollutant naphthalene in mouse lungs: from the perspectives of specialized translation and tolerance linked to the writer ALKBH8

BackgroundThe epitranscriptomic writer Alkylation Repair Homolog 8 (ALKBH8) is a tRNA methyltransferase that modifies the wobble uridine of selenocysteine tRNA to promote the specialized translation, via stop codon recoding, of proteins that contain selenocysteine. Corresponding selenoproteins play critical roles in protecting against reactive oxygen species and environmental stress. Using a novel animal model deficient in Alkbh8, we have investigated the importance of epitranscriptomic systems in the response to naphthalene (NA), an abundant polycyclic aromatic hydrocarbon, glutathione depleter and lung toxicant found in tobacco smoke, gasoline and mothballs.\n\nObjectivesOur goal was to define the molecular reprogramming of Alkbh8 deficient (Alkbh8def) mice and evaluate the roles that the epitranscriptomic writer ALKBH8 and selenoproteins play in mitigating NA-induced toxicity and lung dysfunction.\n\nMethodsWe performed basal lung analysis and NA exposure studies using WT, Alkbh8def and Cyp2abfgs-null mice, the latter of which lack the cytochrome P450 enzymes required for NA bioactivation. We characterized gene expression, molecular markers of damage, viability and tolerance to NA.\n\nResultsUnder basal conditions, lungs from Alkbh8def mice have increased oxidation-reduction potential (ORP) and 8-isoprostane levels, and have reprogrammed at the molecular level to display increased stress response transcripts. In addition, the ALKBH8 writer deficient lungs have a decreased GSH/GSSG ratio. Alkbh8def mice are more sensitive to NA than WT, showing higher susceptibility to lung damage both at the cellular and molecular levels. WT mice develop a tolerance to NA after 3 days, defined as resistance to a high challenging dose after repeated exposures, which is absent in Alkbh8def mice, with writer deficient not surviving NA exposure.\n\nDiscussionWe conclude that the epitranscriptomic writer ALKBH8 plays a protective role against NA-induced lung dysfunction and promotes NA tolerance. Our work provides an early example of how epitranscriptomic systems can regulate the response to environmental stress in vivo.

molecular biology↗

bioRxiv2019

Characters of neoantigens in cancer immunotherapy

Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the "NP" rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better understand this rule, we solved several neoantigen/MHC structures. These structures showed that neoantigens that follow this rule not only increase peptide-MHC binding affinity but also create new TCR-binding features. These molecular insights highlight the value of immune-based classification in neoantigen studies and may enable the design of more effective cancer immunotherapies.

cancer biology↗

bioRxiv2019

Dysfunction of ventral tegmental area GABA neurons causes mania-like behavior

The ventral tegmental area (VTA), an important source of dopamine, regulates goal- and reward-directed and social behaviors, wakefulness and sleep. Hyperactivation of dopamine neurons generates behavioral pathologies. But any roles of non-dopamine VTA neurons in psychiatric illness have been little explored. Lesioning or chemogenetically inhibiting VTA GABAergic (VTAVgat) neurons generated persistent wakefulness with mania-like qualities: locomotor activity was increased; sensitivity to D-amphetamine was heightened; immobility times decreased on the tail suspension and forced swim tests; and sucrose preference increased. Furthermore, after sleep deprivation, mice with lesioned VTAVgat neurons did not catch up on the lost NREM sleep, even though they were starting from an already highly sleep-deprived baseline, suggesting that the sleep homeostasis process was bypassed. The mania-like behaviors, including the sleep loss, were reversed by the mood-stabilizing drug valproate, and re-emerged when valproate treatment was stopped. Lithium salts, however, had no effect. The mania like-behaviors partially depended on dopamine, because giving D1/D2/D3 receptor antagonists partially restored the behaviors, but also on VTAVgat projections to the lateral hypothalamus (LH). Optically or chemogenetically inhibiting VTAVgat terminals in the LH elevated locomotion and decreased immobility time during the tail suspension and forced swimming tests. VTAVgat neurons are centrally positioned to help set an animals (and humans) level of mental and physical activity. Inputs that inhibit VTAVgat neurons intensify wakefulness (increased activity, enhanced alertness and motivation), qualities useful for acute survival. Taken to the extreme, however, decreased or failed inhibition from VTAVgat neurons produces mania-like qualities (hyperactivity, hedonia, decreased sleep).

neuroscience↗

bioRxiv2019

Complete single neuron reconstruction reveals morphological diversity in molecularly defined claustral and cortical neuron types

Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits.

neuroscience↗

bioRxiv2019

Longevity of growth-arrested bacteria requires protein synthesis and a stringent response

Gram-negative bacteria in infections, biofilms and industrial settings often stop growing due to nutrient depletion, immune responses or environmental stresses. Bacteria in this state tend to be tolerant to antibiotics and are often referred to as dormant. Rhodopseudomonas palustris, a phototrophic -proteobacterium, can remain fully viable for more than four months when growth is arrested. Here, we show that protein synthesis, specific proteins involved in translation and a stringent response are required for this remarkable longevity. Because it can generate ATP from light during growth arrest, R. palustris is an extreme example of a bacterial species that will stay alive for long periods of time as a relatively homogeneous population of cells and it is thus an excellent model organism for studies of bacterial longevity. There is evidence that other Gram-negative species also continue to synthesize proteins during growth arrest and that a stringent response is required for their longevity as well. Our observations challenge the notion that growth-arrested cells are necessarily dormant and metabolically inactive, and suggest that such bacteria may have a level of metabolic activity that is higher than many would have assumed. Our results also expand our mechanistic understanding of a crucial but understudied phase of the bacterial life cycle.\n\nIMPORTANCEWe are surrounded by bacteria; but they do not completely dominate our planet despite the ability of many to grow extremely rapidly in the laboratory. This has been interpreted to mean that bacteria in nature are often in a dormant state. We investigated life in growth arrest of Rhodopseudomonas palustris, a proteobacterium that stays alive for months when it is not growing. We found that cells were metabolically active; they continued to synthesize proteins and mounted a stringent response, both of which were required for their longevity. Our results suggest that long-lived bacteria are not necessarily inactive but have an active metabolism that is well adjusted to life without growth.

microbiology↗

bioRxiv2019

Retrieval of whole human genome clinical variant information through search motors

The interpretation of variation in the human genome constitutes one of the most pressing challenges in biomedicine. There are many academic and proprietary resources that provide annotation, interpretation, scoring and knowledge of genetic variants under multiple access modalities. For these resources, information is available through portals, wikis, APIs, curated content and databases, and pipelines. Here we explore the use of search motors to provide facilitated access to the main sources of information that are used by clinical geneticists and researchers. We also support the browsing experience with natural language processing and automated summaries of available information. The resulting tool, ai-OMNI.com, is intrinsically flexible, expandable and intuitive, thus providing a different experience for querying the human genome for the consequences of variation.

genomics↗

bioRxiv2019

Uncovering complex disease subtypes by integrating clinical data and imputed transcriptome from genome-wide association studies: Applications in psychiatry and cardiovascular medicine

Classifying patients into clinically and biologically homogenous subgroups will facilitate the understanding of disease pathophysiology and development of more targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, however disease subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (such as those from GWAS) with clinical symptoms for disease subtyping.\n\nIn this study, we proposed a novel analytic framework capable of finding subgroups of complex diseases by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, hence the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modelled. We also proposed various new evaluation or validation metrics, such as a newly modified prediction strength measure to assess generalization of clustering performance. The proposed framework was applied to derive subtypes for schizophrenia, and to stratify subjects into different levels of cardiometabolic risks.\n\nOur framework was able to subtype schizophrenia patients with diverse prognosis and treatment response. We also applied the framework to the Northern Finland Cohort (NFBC) 1966 dataset, and identified high- and low cardiometabolic risk subgroups in a gender-stratified analysis. Our results suggest a more data-driven and biologically-informed approach to defining metabolic syndrome. The prediction strength was over 80%, suggesting that the cluster model generalizes well to new datasets. Moreover, we found that the genes blindly selected by the cluster algorithm are significantly enriched for known susceptibility genes discovered in GWAS of schizophrenia and cardiovascular diseases, providing further support to the validity of our approach. The proposed framework may be applied to any complex diseases, and opens up a new approach to patient stratification.

genetics↗

bioRxiv2019

Epistatic Networks Associated with Parent-of-Origin Effects on Metabolic Traits

Parent-of-origin effects (POE) are unexpectedly common in complex traits, including metabolic and neurological diseases. POE can also be modified by the environment, but the architecture of these gene-by-environmental effects on phenotypes remains to be unraveled. Previously, quantitative trait loci (QTL) showing context-specific POE on metabolic traits were mapped in the F16 generation of an advanced intercross between LG/J and SM/J inbred mice. However, these QTL were not enriched for known imprinted genes, suggesting another mechanism is needed to explain these POE phenomena. Here, we use a simple yet powerful F1 reciprocal cross model to test the hypothesis that non-imprinted genes can generate complex POE on metabolic traits through genetic interactions with imprinted genes. Male and female mice from a F1 reciprocal cross of LG/J and SM/J strains were fed either high or low fat diets. We generated expression profiles from three metabolically-relevant tissues: hypothalamus, white adipose, and liver. We identified two classes of parent-of-origin expression biases: genes showing parent-of-origin-dependent allele-specific expression and biallelic genes that are differentially expressed by reciprocal cross. POE patterns of both gene classes are highly tissue-and context-specific, sometimes occurring only in one sex and/or diet cohort in a particular tissue. We then constructed tissue-specific interaction networks among genes from these two classes of POE. A key subset of gene pairs show significant epistasis in the F16 LG/J x SM/J advanced intercross data in cases where the biallelic gene fell within a previously-identified metabolic POE QTL interval. We highlight one such interaction in adipose, between Nnat and Mogat1, which associates with POE on multiple adiposity traits. Both genes localize to the endoplasmic reticulum of adipocytes and play a role in adipogenesis. Additionally, expression of both genes is significantly correlated in human visceral adipose tissue. The genes and networks we present here represent a set of actionable interacting candidates that can be probed to further identify the machinery driving POE on complex traits.

systems biology↗

bioRxiv2019

Adenovirus-mediated expression of SIK1 improves hepatic glucose and lipid metabolism in type 2 diabetes mellitus rats

AIMIn this study, we investigated the role and mechanism of Salt-induced kinase 1 (SIK1) in regulation of hepatic glucose and lipid metabolism in a high-fat food (HFD) and streptozocin (STZ)-induced type 2 diabetes mellitus (T2DM) rat model.\n\nMethodsA diabetic rat model treated with HFD plus low-dose STZ was developed and was transduced to induce a high expression of SIK1 in vivo via a tail-vein injection of a recombinant adenoviral vector. The effects on hepatic glucogenetic and lipogenic gene expression, systemic metabolism and pathological changes were then determined.\n\nResultsIn T2DM rats, SIK1 expression was reduced in the liver. Overexpression of SIK1 improved hyperglycaemia, hyperlipidaemia and fatty liver, reduced the expression of cAMP-response element binding protein (CREB)-regulated transcription co-activator 2 (CRTC2), phosphoenolpyruvate carboxykinase (PEPCK), glucose-6-phosphatase (G6Pase), pS577 SIK1, sterol regulatory element binding-protein-1c (SREBP-1c) and its target genes, including acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS), and increased the expression of SIK1, pT182 SIK1 and pS171 CRTC2 in diabetic rat livers with the suppression of gluconeogenesis and lipid deposition.\n\nConclusionSIK1 plays a crucial role in the regulation of glucose and lipid metabolism in the livers of HFD/STZ-induced T2DM rats, where it suppresses hepatic gluconeogenesis and lipogenesis by regulating the SIK1/CRTC2 and SIK1/SREBP-1c signalling pathways. Strategies to activate SIK1 kinase in liver would likely have beneficial effects in patients with T2DM and nonalcoholic fatty liver disease (NAFLD).

molecular biology↗

bioRxiv2018

Identifying the diagnostic value of microRNA-421 in gastric cancer patients: a meta-analysis

ObjectivesEmerging evidence has shown that the expression level of microRNA-421 (miR-421) was significantly different between gastric cancer (GC) patients and healthy individuals. However, the diagnostic accuracy of miR-421 in the reports remains inconsistent. This meta-analysis aims to assess the diagnostic value of miR-421 in GC detection.\n\nMethodsAll related articles on miR-421 in GC diagnosis were retrieved until September 2018. The QUADAS-2 checklist was used to assess the methodological quality of each study. The diagnostic performance of miR-421 for GC were assessed by using Meta-DiSc 1.4 and STATA 14.0 statistical software.\n\nResultsA total of 172 GC patients and 154 healthy controls from three articles (four studies) were enrolled in this meta-analysis. The results of pooled sensitivity, specificity, diagnostic odds ratio (DOR) with 95% confidence interval (CI) were 0.90 (95% CI: 0.85 to 0.93), 0.83 (95% CI: 0.77 to 0.87), and 37.18 (95% CI: 8.61 to 160.49), respectively. The area under the summary receiver operating characteristic curve (SROC) was 0.8977.\n\nConclusionsThis study indicates that miR-421 could serve as a promising biomarker for GC detecting. Further studies are needed to verify the generalizability of these findings.

epidemiology↗

bioRxiv2018

One Model to Rule Them All: An Integrative Approach to Matrix-Based Analyses in Neuroimaging Connectomics

Network modeling in neuroimaging holds promise in probing the interrelationships among brain regions and potential clinical applications. Two types of matrix-based analysis (MBA) are usually seen in neuroimaging connectomics: one is the functional attribute matrix (FAM) of, for example, correlations, that measures the similarity of BOLD response patterns among a list of predefined regions of interest (ROIs). Another type of MBA involves the structural attribute matrix (SAM), e.g., describing the properties of white matter between any pair of gray-matter regions such as fractional anisotropy, mean diffusivity, axial and radial diffusivity. There are different methods that have been developed or adopted to summarize such matrices across subjects, including general linear models (GLMs) and various versions of graph theoretic analysis. We argue that these types of modeling strategies tend to be \"inefficient\" in statistical inferences and have many pitfalls, such as having strong dependence on arbitrary thresholding under conventional statistical frameworks.\n\nHere we offer an alternative approach that integrates the analyses of all the regions, region pairs (RPs) and subjects into one framework, called Bayesian multilevel (BML) modeling. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. This integrative approach avoids the multiple testing issue that typically plagues the conventional statistical analysis in neuroimaging, and it provides a principled way to quantify both the effect and its uncertainty at each region as well as for each RP. As a result, a unique feature of BML is that the effect at each region and the corresponding uncertainty can be estimated, revealing the relative strength or importance of each region; in addition, the effect at each RP is obtained along with its uncertainty as statistical evidence. Most importantly, the BML approach can be scrutinized for consistency through validation and comparisons with alternative assumptions or models. We demonstrate the BML methodology with a real dataset with 16 ROIs from 41 subjects, and compare it to the conventional GLM approach in terms of model efficiency, performance and inferences. Furthermore, we emphasize the notion of full results reporting through \"highlighting,\" instead of through the common practice of \"hiding.\" The associated program will be available as part of the AFNI suite for general use.

neuroscience↗

bioRxiv2018

Development of a novel signature of long noncoding RNAs as a prognostic biomarker for esophageal cancer

ObjectivesThis study aims to develop a lncRNA signature based on RNA-Seq data to predict overall survival in esophageal cancer patients.\n\nMethodsThe lncRNA expression profiles and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database on August 30, 2017. Differentially expressed lncRNAs were screened out between tumor tissues and adjacent normal tissues. The univariate and multivariate Cox regression models were used to develop a prognostic signature for all esophageal cancer patients. The receiver operating curve (ROC) was used to test the sensitivity and specificity of lncRNA signature. Survivals were compared via log-rank test. GO and KEGG enrichment analyses were used to explore the potential functions of prognostic lncRNAs.\n\nResultsWe identified two lncRNAs (RPL34-AS1 and GK3P) were significantly associated with the overall survival of the total 150 esophageal cancer patients. A novel two-lncRNA signature was constructed by Cox regression models. Signature low-risk cases showed better overall survival (median 625.560 days vs. 478.000 days, p = 0.002) than high-risk cases. Further analysis suggested that this two-lncRNA signature was independent of clinical characteristics. GO functional and KEGG pathway enrichment analyses revealed potential functional roles of the two prognostic lncRNAs in tumorigenesis.\n\nConclusionsOur findings suggest that the two-lncRNA signature may be a useful prognostic biomarker for predicting overall survival in esophageal cancer patients.

bioinformatics↗

bioRxiv2018

Strategies to improve photosynthetic nitrogen-use efficiency with no yield penalty: lessons from late-sown winter wheat

HighlightOptimal N allocation at several integration levels accounts for improved canopy PNUE while maintaining high grain yield in winter wheat\n\nAbstractImproving canopy photosynthetic nitrogen-use efficiency (PNUE) may maintain or even increase yield with reduced N input. In this study, later-sown winter wheat was studied to reveal the mechanism underlying improved canopy PNUE while maintaining high yield. N allocation at several levels was optimised in late-sown wheat plants. N content per plant increased. Increased N was allocated to the flag leaf and second leaf, and to ribulose-1, 5-bisphosphate carboxylase/oxygenase (Rubisco) in upper leaves. Constant or reduced N was allocated to leaf 3, leaf 4, and Rubisco in lower leaves. The specific green leaf area nitrogen (SLN) of upper leaves increased, while that of lower leaves remained unchanged or decreased. N allocation to the cell wall decreased in all leaves. As a result, the maximum carboxylation rate of upper leaves increased, and that of lower leaves remained constant or decreased. CO2 diffusion capacity was enhanced in all leaves. Outperformance by light-saturated net photosynthetic rate (Pmax) over SLN led to improved PNUE in upper leaves. Enhanced Pmax coupled with unchanged or decreased SLN resulted in improved PNUE in lower leaves. High yield was maintained because enhanced photosynthetic capacity at the leaf and whole plant levels compensated for reduced canopy leaf area.

cell biology↗

bioRxiv2018

Unraveling the genetic architecture of grain size in einkorn wheat through linkage and homology mapping, and transcriptomic profiling

HighlightGenome-wide linkage and homology mapping revealed 17 genomic regions through a high-density einkorn wheat genetic map constructed using RAD-seq, and transcription levels of 20 candidate genes were explored using RNA-seq.\n\nAbstractUnderstanding the genetic architecture of grain size is a prerequisite to manipulate the grain development and improve the yield potential in crops. In this study, we conducted a whole genome-wide QTL mapping of grain size related traits in einkorn wheat by constructing a high-density genetic map, and explored the candidate genes underlying QTL through homologous analysis and RNA sequencing. The high-density genetic map spanned 1873 cM and contained 9937 SNP markers assigned to 1551 bins in seven chromosomes. Strong collinearity and high genome coverage of this map were revealed with the physical maps of wheat and barley. Six grain size related traits were surveyed in five agro-climatic environments with 80% or more broad-sense heritability. In total, 42 QTL were identified and assigned to 17 genomic regions on six chromosomes and accounted for 52.3-66.7% of the phenotypic variations. Thirty homologous genes involved in grain development were located in 12 regions. RNA sequencing provided 4959 genes differentially expressed between the two parents. Twenty differentially expressed genes involved in grain size development and starch biosynthesis were mapped to nine regions that contained 26 QTL, indicating that the starch biosynthesis pathway played a vital role on grain development in einkorn wheat. This study provides new insights into the genetic architecture of grain size in einkorn wheat, the underlying genes enables the understanding of grain development and wheat genetic improvement, and the map facilitates the mapping of quantitative traits, map-based cloning, genome assembling and comparative genomics in wheat taxa.

genetics↗

bioRxiv2017

GVC: A superfast and universal genomic variant caller

Germline and somatic variant detection from human and cancer whole-genome sequencing data is a challenge task for genome-wide association study and cancer genomics in precision medicine. Many confounding factors contribute the difficulties including complexity of variant, sequencing and alignment error, tumor clonality and sample purity etc. Current genomic variant callers are too time-consuming to meet the requirement of clinical application in precision medicine. We developed superfast and universal Genomic Variant Caller (GVC), which can simultaneously detect various genomic variants including SNV, sINDEL and SV from personal and normal-cancer paired whole-genome/exome sequencing data within fifteen minutes. Whats more, it achieved higher sensitivity and precision than popular variant callers including GATK4, Mutect, NovoBreak in germline and somatic variant detection from NA12878 and ICGC-TCGA Dream Challenge Datasets respectuvely. It is worth mentioning that GVC achieved comparable performance in variant detection from NA12878 sequenced by three different high-throughput sequencing platforms including Illumina HiSeq2000, NovaSeq and BGISEQ-500.

bioinformatics↗