Biology subjects

Xue, C.

Publications and source records attributed to Xue, C..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Decoding the Variant-to-Function Relationship for LIPA, a Risk Locus for CAD

Translating human genomic discoveries into mechanistic insights requires linking genetic variations to candidate genes and their causal functional phenotypes. Genome-wide association studies have consistently identified LIPA as a risk locus for coronary artery disease (CAD), with previous expression quantitative trait loci (eQTL) analyses prioritizing LIPA as a candidate causal gene. However, functional studies elucidating the causal variants, regulatory mechanisms, target cell types, and their causal impact on atherosclerosis have been lacking. To address this gap, we applied functional genomics and experimental mouse models to establish the variant-to-function relationship at the LIPA locus. Our findings show that CAD risk alleles in the LIPA locus increase LIPA expression and enzyme activity specifically in monocytes/macrophages by enhancing PU.1 binding to an intronic enhancer region that interacts with the LIPA promoter. In myeloid Lipa-overexpressing mice, we observed larger atherosclerotic lesions accompanied by altered macrophage function, including increased macrophage accumulation due to enhanced monocyte recruitment, reduced neutral lipid accumulation, and upregulation of integrin and extracellular matrix pathways. Our work establishes a direct causal link between LIPA risk alleles and increased monocyte/macrophage LIPA that exacerbates atherosclerosis, bridging human functional genomic evidence to the mechanistic understanding of CAD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/516293v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@11ce287org.highwire.dtl.DTLVardef@15c201aorg.highwire.dtl.DTLVardef@13f90c9org.highwire.dtl.DTLVardef@3aa3be_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

Retinoic acid signaling modulates smooth muscle cell phenotypic switching in atherosclerosis through epigenetic regulation of gene expression

BACKGROUNDSmooth muscle cells (SMCs) substantially contribute to the development of atherosclerosis through a process called "phenotypic switching." Our previous work identified an SMC-derived intermediate cell type, termed "SEM" cells, which plays a crucial role in SMC transition to other cell types and in lesion development. Activation of retinoic acid (RA) signaling by all-trans retinoic acid (ATRA) attenuates atherosclerosis in mice coincident with suppression of SEM cell formation. However, the effect of RA signaling on advanced disease and the underlying molecular mechanisms by which RA modulates SMC transition to SEM cells are largely unknown. METHODSWe applied SMC lineage tracing atheroprone mice and biochemistry and cell and molecular biology techniques (e.g., RNA sequencing, quantitative reverse transcription PCR, co-immunoprecipitation, and chromatin immunoprecipitation-quantitative PCR) to reveal the regulatory mechanisms of RA signaling in SMC transition to SEM cells. RESULTSActivation of RA signaling with ATRA in established atherosclerosis significantly reduced SEM cells and lesion size while increasing fibrous cap thickness. Mechanistically, retinoic acid receptor alpha (RAR) directly targets the promoters of Ly6a and Ly6c1 in mouse SMCs, and activation of RA signaling recruits EZH2 to the regulatory elements triggering local H3K27me3. Distinct from a molecular model that reported for RA recruitment of HDAC1 during embryogenesis, RAR/EZH2 complex recruits SIRT1 and SIRT6, rather than classical HDACs, to the regulatory regions of key SEM cell marker genes. This subsequently reduces multiple acetylated histone modifications (e.g., H3K27ac, H3K4ac, H3K9ac, H3K14ac, H3K56ac) with recruitment of the transcription corepressor, NCOR1, to repress downstream SEM cell marker genes. CONCLUSIONSOur findings provide novel mechanistic insights into RA modulating SMC phenotypic switching in atherosclerosis, suggesting molecular targets for preventive and therapeutic interventions for atherosclerosis and its clinical complications.

pathology↗

bioRxiv2022

KAI2-KL signalling regulates root hair elongation under magnesium deficiency by activating auxin, ethylene, and nitric oxide biosynthesis and signalling genes.

Root hair elongation (RHL), which expands the absorptive surface area of the root, is a crucial adaptation in plants for survival under magnesium (Mg) deficient soil. Despite the significance of this trait, the molecular mechanism in Mg starvation regulating RHL is elusive. We demonstrated that karrikins regulate RHL under a limited supply of Mg via crosstalk with auxin, ethylene, and NO. We used KAI2-KL-signalling mutants, auxin, ethylene, and NO-related genes mutants Arabidopsis and pharmacological method to investigate the role of KAI2-KL-signalling, and its interaction with ethylene, auxin, and NO in the process of RHL in Mg deficient conditions. Mg deficiency could not enhance RHL in KAI2-KL-signalling mutants such as kai2 and max2 Arabidopsis. Interestingly, exogenous application of ethylene, nitric oxide, or auxin recovered RHL of kai2 and max2 Arabidopsis under Mg deficiency. In contrast, exogenous supplementation of KAR1 could not rescue RHL in auxin, ethylene, and NO-related mutants Arabidopsis. In conclusion, we suggest that karrikins signalling might regulate the RHL in response to low Mg by acting as an upstream signalling pathway of auxin, ethylene, and NO signalling.

plant biology↗

bioRxiv2022

A Long-Circulating Vector for Aptamers Based upon Polyphosphodiester-Backboned Molecular Brushes

Aptamers face challenges for use outside the ideal conditions in which they are developed. These difficulties are most palpable in vivo due to nuclease activities, rapid clearance, and off-target binding. Herein, we demonstrate that a polyphosphodiester-backboned molecular brush can suppress enzymatic digestion, reduce non-specific cell uptake, enable long blood circulation, and rescue the bioactivity of a conjugated aptamer in vivo. The backbone along with the aptamer is assembled via solid-phase synthesis, followed by installation of poly(ethylene glycol) (PEG) side chains using a two-step process with near-quantitative efficiency. The synthesis allows for precise control over polymer size and architecture. Consisting entirely of building blocks that are generally recognized as safe for therapeutics, this novel molecular brush is expected to provide a highly translatable route for aptamer-based therapeutics.

pharmacology and toxicology↗

bioRxiv2022

Genome-Wide DNA Methylation Profiling of the Failing Human Heart with Mechanical Unloading Identifies LINC00881 as an Essential Regulator of Calcium Handling in the Cardiomyocyte

BackgroundHuman heart failure is characterized by global alterations in the myocardial DNA methylation profile, yet little is known about epigenetic regulation of non-coding transcripts and potential reversibility of DNA methylation with left ventricular assist device (LVAD) support. MethodGenome-wide mapping of myocardial DNA methylation was performed in 36 patients with end-stage heart failure at the time of LVAD implant, 8 patients at the time of LVAD explant, and 7 non-failing controls using high-density bead array platform. Transcriptomic and functional studies were performed in human induced pluripotent stem cell derived cardiomyocytes (iPSCs). ResultsEtiology-specific analysis revealed 2079 differentially methylated positions (DMPs) in ischemic cardiomyopathy (ICM) and 261 DMPs in non-ischemic cardiomyopathy (NICM). 192 DMPs were common to ICM and NICM. Analysis of paired samples before and after LVAD support demonstrated reverse methylation of only 3.2% of HF-specific DMPs. Methylation-expression correlation analysis yielded several protein-coding genes that are hypomethylated and upregulated (HTRA1, FAM65A, FBXO16, EFCAB13, AKAP13, RPTOR) or hypermethylated and downregulated (TBX3) in ICM and NICM patients. A novel cardiac-specific super-enhancer lncRNA (LINC00881) is hypermethylated and downregulated in the failing human heart. LINC00881 is an upstream regulator of sarcomere and calcium channel gene expression including MYH6, CACNA1C, and RYR2. LINC00881 knockdown significantly reduced peak calcium amplitude in the beating human iPSCs. ConclusionsFailing human heart exhibits etiology-specific changes in DNA methylation including coding and non-coding regions, which are minimally reversible with mechanical unloading. Epigenetic reprogramming may be necessary to achieve transcriptional normalization and sustained clinical recovery from heart failure.

genomics↗

bioRxiv2022

A Genome-wide CRISPR Screen Identifies WDFY3 as a Novel Regulator of Macrophage Efferocytosis

AbstractsPhagocytic clearance of dying cells, termed efferocytosis, must occur efficiently to maintain homeostasis and prevent disease. Yet, our understanding of this important biological process remains incomplete. To search for novel regulators of efferocytosis, we performed a FACS-based genome-wide CRISPR knockout screen in primary murine macrophages. We identified a novel role for WDFY3 in efferocytosis by macrophages. WDFY3 deficiency in macrophages specifically impaired uptake, not binding, of apoptotic cells due to defective actin depolymerization. We further revealed that WDFY3 directly interacts with GABARAP, thus facilitating LC3 lipidation and subsequent lysosomal acidification to permit the degradation of apoptotic cell components. Although the C-terminus of WDFY3 was sufficient to rescue impaired degradation, full-length WDFY3 is still required for regulating uptake. Finally, WDFY3 is required for efficient efferocytosis in vivo in mice and in primary human macrophages. The work expands our knowledge of the mechanisms of macrophage efferocytosis, and more broadly, provides a general strategy for genome-wide CRISPR screen to interrogate complex functional phenotypes in primary macrophages. HighlightsO_LIFunctional readout for pooled genome-wide CRISPR screen in primary macrophages. C_LIO_LIWDFY3 is discovered as a regulator of macrophage efferocytosis in vitro and in vivo. C_LIO_LIWDFY3 deficiency led to impaired uptake, as opposed to binding, of apoptotic cells due to defective actin depolymerization. C_LIO_LIWDFY3 directly interacts with GABARAP, facilitating LC3 lipidation and subsequent lysosomal acidification to permit the degradation of apoptotic cell components. C_LIO_LIC-terminal WDFY3 is sufficient to regulate the degradation of engulfed apoptotic cells while full-length WDFY is required for regulating uptake. C_LI

cell biology↗

bioRxiv2021

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

genomics↗

bioRxiv2021

Three-dimensional drug screen identifies HDAC inhibitors as therapeutic agents in mTORC1-driven lymphangioleiomyomatosis

Lymphangioleiomyomatosis (LAM) is a rare disease involving cystic lung destruction by invasive LAM cells. These cells harbor loss-of-function mutations in TSC2, conferring constitutive mTORC1 signaling. Rapamycin is the only clinically approved disease-modifying treatment, but its action is cytostatic and disease progresses upon its withdrawal. There is a critical need to identify novel agents that prevent the invasive phenotype and/or eradicate the neoplastic LAM cells. Here, we employed novel cellular and extracellular models to screen for candidate therapeutics in a physiologically relevant setting. We observed that lung-mimetic hydrogel culture of pluripotent stem cell-derived diseased cells more faithfully recapitulates human LAM biology compared to conventional culture on two-dimensional tissue culture plastic. Leveraging our culture system, we conducted a three-dimensional drug screen using a custom 800-compound library, tracking cytotoxicity and invasion modulation phenotypes at the single cell level. We identified histone deacetylase (HDAC) inhibitors as a group of anti-invasive agents that are also selectively cytotoxic towards TSC2-/- cells. Unexpectedly, we observed that next generation ATP-competitive mTORC1/2 inhibitors potentiate invasion. We determined anti-invasive effects of HDAC inhibitors to be independent of genotype, while selective cell death is mTORC1-dependent and mediated by apoptosis. Drug performance was subsequently evaluated at the single cell level in zebrafish xenografts. We observed consistent therapeutic efficacy in vivo at equivalent concentrations to those used in vitro, substantiating HDAC inhibitors as potential therapeutic candidates for pursuit in patients with LAM. One Sentence SummaryWe performed a drug screen in 3D and discovered HDAC inhibitors exhibit therapeutic efficacy in models of the lung disease lymphangioleiomyomatosis.

cell biology↗

bioRxiv2021

MCGA: a multi-strategy conditional gene-based association framework integrating with isoform-level expression profiles reveals new susceptible and druggable candidate genes of schizophrenia

Linkage disequilibrium and disease-associated variants in non-coding regions make it difficult to distinguish truly associated genes from redundantly associated genes for complex diseases. In this study, we proposed a new conditional gene-based framework called MCGA that leveraged an improved effective chi-squared statistic to control the type I error rates and remove the redundant associations. MCGA initially integrated two conventional strategies to map genetic variants to genes, i.e., mapping a variant to its physically nearby gene and mapping a variant to a gene if the variant is a gene-level expression quantitative trait locus (eQTL) of the gene. We further performed a simulation study and demonstrated that the isoform-level eQTL was more powerful than the gene-level eQTL in the association analysis. Then the third strategy, i.e., mapping a variant to a gene if the variant is an isoform-level eQTL of the gene, was also integrated with MCGA. We applied MCGA to predict the potential susceptibility genes of schizophrenia and found that the potential susceptibility genes identified by MCGA were enriched with many neuronal or synaptic signaling-related terms in the Gene Ontology knowledgebase and antipsychotics-gene interaction terms in the drug-gene interaction database (DGIdb). More importantly, nine susceptibility genes were the target genes of multiple approved antipsychotics in DrugBank. Comparing the susceptibility genes identified by the above three strategies implied that strategy based on isoform-level eQTL could be an important supplement for the other two strategies and help predict more candidate susceptibility isoforms and genes for complex diseases in a multi-tissue context.

bioinformatics↗

bioRxiv2021

Dynamic task-belief is an integral part of decision-making

Natural decisions involve two seemingly separable processes: inferring the relevant task (task-belief) and performing the believed-relevant task. The assumed separability has led to the traditional practice of studying task-switching and perceptual decision-making individually. Here, we used a novel paradigm to manipulate and measure macaque monkeys task-belief, and demonstrated inextricable neuronal links between flexible task-belief and perceptual decision-making. We showed that in animals, but not artificial networks that performed as well or better than the animals, stronger task-belief is associated with better perception. Correspondingly, recordings from neuronal populations in cortical areas 7a and V1 revealed that stronger task-belief is associated with better discriminability of the believed-relevant but not the believed-irrelevant feature. Perception also impacts belief updating: noise fluctuations in V1 help explain how task-belief is updated. Our results demonstrate that complex tasks and multi-area recordings can reveal fundamentally new principles of how biology affects behavior in health and disease.

neuroscience↗

bioRxiv2021

Adipocyte Tribbles1 Regulates Plasma Adiponectin and Plasma Lipids in Mice

Multiple GWAS have identified SNPs in the 8q24 locus near the TRIB1 gene that significantly associate with plasma lipids and coronary artery disease. While subsequent studies have uncovered roles for hepatic and myeloid Trib1 in contributing to either plasma lipids or atherosclerosis, the causal tissue for these GWAS associations remains unclear. The same 8q24 SNPs significantly associate with plasma adiponectin levels in humans as well, suggesting a role for TRIB1 in adipose tissue. Here, we report that adipocyte-specific Trib1 knockout mice (Trib1_ASKO) have increased plasma adiponectin levels and decreased plasma cholesterol and triglycerides. We demonstrate that loss of Trib1 increases adipocyte production and secretion of adiponectin independent of the known TRIB1 function of regulating proteasomal degradation. RNA-seq analysis of adipocytes and livers from Trib1_ASKO mice suggests that alterations in adipocyte function underlie the plasma lipid changes observed in these mice. Secretomics and RNA-seq analysis revealed that Trib1_ASKO mice have increased production of Lpl and decreased production of Angptl4 in adipose tissue, and fluorescent substrate assays confirm an increase in adipose tissue Lpl activity, which likely underlies the observed triglyceride phenotype. In summary, we demonstrate here a novel role for adipocyte Trib1 in regulating plasma adiponectin, total cholesterol, and triglycerides in mice, confirming previous genetic associations observed in humans and providing a novel avenue through which Trib1 regulates plasma lipids and coronary artery disease.

physiology↗

bioRxiv2020

A global overview of single-cell type selectivity and pleiotropy in complex diseases and traits

After centuries of genetic studies, one of the most fundamental questions, i.e. in what cell types do DNA mutations regulate a phenotype, remains unanswered for most complex phenotypes. The current availability of hundreds of genome-wide association studies (GWASs) and single-cell RNA sequencing (scRNA-seq) of millions of cells provides a unique opportunity to address the question. In the present study, we firstly constructed an association landscape between over 20,000 single cell clusters and 997 complex phenotypes by a cross annotation framework with scRNA-seq expression profiles and GWAS summary statistics. We then performed an extensive overview of cell-type specificity and pleiotropy in human phenotypes and found most phenotypes (>90%) were moderately selectively associated with a limited number of cell types while a small fraction cell types (<10%) had strong pleiotropy in multiple phenotypes (~100). Moreover, we identified three cell type-phenotype mutual pleiotropy blocks in the landscape. The application of the single cell type-phenotype cross annotation framework (named SPA) also explained the T cell biased lymphopenia and suggested important supporting genes in severe COVID-19 from human genetics angle. All the cell type-phenotype association results can be queried and visualized at http://pmglab.top/spa.

genetics↗

bioRxiv2020

Systematic comparative analysis of Mendelian randomization methods for inferring causal genes of complex phenotypes and the application to psychiatric diseases

Isolating causal genes from enormous genome-wide association signals of complex phenotypes remains an open and challenging question. SMR (Summary-based Mendelian Randomization) is a widely used Mendelian randomization (MR) method for inferring causal genes by using a single expression quantitative trait locus (eQTL). In the present study, we explored more powerful MR methods based on multiple eQTLs. Among six representative multiple instrumental variable (IVs) based MR methods, original used in the epidemiological field, not all MR methods worked for the causal gene estimation. But we found the maximum-likelihood based MR method and weighted median-based MR method were preferable to the other four MR methods in terms of valid type 1 errors, acceptable statistical powers and robustness to linkage disequilibrium (LD) in eQTLs. Both of the MR methods were also much more powerful than the SMR. We recalibrated key parameters of the two MR methods in practices and developed a multiple IVs based MR analysis framework for causal gene estimation, named MACG and available at http://pmglab.top/kggsee. In the applications, MACG not only rediscovered many known causal genes of the schizophrenia and bipolar disorder, but also reported plenty of promising candidate causal genes. In conclusion, this study provided a powerful tool and encouraging exemplars of mining potential causal genes from huge amounts of GWAS signals with eQTLs.

bioinformatics↗

bioRxiv2020

Relaxed 3D genome conformation facilitates the pluripotent to totipotent state transition in embryonic stem cells

The 3D genome organization is crucial for gene regulation. Although recent studies have revealed a uniquely relaxed genome conformation in totipotent early blastmeres of both fertilized and cloned embryos, how weakened higher-order chromatin structure is functionally linked to totipotency acquisition remains elusive. Using low-input Hi-C, ATAC-seq, and ChIP-seq, we systematically examined the dynamics of 3D genome and epigenome during pluripotency-to-totipotency transition in mouse embryonic stem cells (ESCs). The totipotent 2-cell-embro-like cells (2CLCs) exhibit more relaxed chromatin architecture compared to ESCs, including global weakening of both enhancer-promoter interactions and TAD insulation. While the former leads to inactivation of ESC enhancers and down-regulation of pluripotent genes, the latter may facilitate contacts between the new enhancers arising in 2CLCs and neighboring 2C genes. Importantly, disruption of chromatin loops by depleting CTCF or cohesin promotes ESC to 2CLC transition. Our results thus establish a critical role of 3D genome organization in totipotency acquisition. HIGHLIGHTSO_LIGlobal weakening of the 3D genome conformation during ESC to 2CLC transition C_LIO_LILoss of enhancer-promoter loops and down-regulation of pluripotent genes in 2CLCs C_LIO_LIInactivation of ESC enhancers and formation of new enhancers in 2CLCs C_LIO_LIDisruption of chromatin loops by depleting CTCF or cohesin promotes 2CLC emergence C_LI

molecular biology↗

bioRxiv2020

Quantitative partition of the rhizosphere microbiota assembly processes

The soil microbial reservoir and plant recruitment are predominant forces determining microbiota assembly in rhizosphere (i.e. active and passive processes respectively), but to date, no straightforward method to evaluate the respective contribution of forces to the rhizospheric microbiota assembly rules is available. We propose herein a promising way to quantitatively partition the assembling forces of rhizosphere microbiota using ordination metrics. We anticipate that this new method can not only weight the plants individual contributions to microbiota assembly in rhizosphere, but can also indirectly provide a way to quantitatively evaluate soil health by the contribution from plant selection.

ecology↗

bioRxiv2019

Development and validation of a deep learning framework for Alzheimers disease classification

Alzheimers disease (AD) is the primary cause of dementia worldwide (1), with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and magnetic resonance imaging (MRI) to identify likely cases, yet effective practices remain variably-applied and lacking in sensitivity and specificity (2). Here we report an explainable deep learning strategy that delineates unique AD signatures from multimodal inputs of MRI, age, gender, and mini-mental state examination (MMSE) score. Our framework linked a fully convolutional network (FCN) to a multilayer perceptron (MLP) to construct high resolution maps of disease probability from local brain structure. This enabled precise, intuitive visualization of individual AD risk en route to accurate diagnosis. The model was trained using clinically-diagnosed AD and cognitively normal (NC) subjects from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset (n=417) (3), and validated on three independent cohorts: the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=382) (4), the Framingham Heart Study (FHS, n=102) (5), and the National Alzheimers Coordinating Center (NACC, n=582) (6). Model performance was consistent across datasets, with mean accuracy values of 0.966, 0.948, 0.815, and 0.916 for ADNI, AIBL, FHS and NACC, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n=11), and high-risk cerebral regions predicted by the model closely tracked postmortem histopathological findings. This framework provides a clinically-adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for AD diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

neuroscience↗

bioRxiv2019

ASEP: gene-based detection of allele-specific expression in a population by RNA-seq

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore wasting shared information across individuals. Failure to accommodate such shared information not only loses power, but also makes it difficult to interpret results across individuals. However, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to heterogeneity in ASE as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect accounting for multi-SNP correlations within the same gene. ASEP is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre-versus post-treatment). Extensive simulations have demonstrated the convincing performance of ASEP under a wide range of scenarios. We further applied ASEP to RNA-seq data of human macrophages, and identified genes showing evidence of differential ASE pre-versus post-stimulation, which were extended through findings in cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.

genomics↗

bioRxiv2019

Low rank mechanisms underlying flexible visual representations

Neuronal population responses to sensory stimuli are remarkably flexible. The responses of neurons in visual cortex depend on stimulus properties (e.g. contrast), processes that affect all stages of visual processing (e.g. adaptation), and cognitive processes (e.g attention or task switching). The effects of all of these processes on trial-averaged responses of individual neurons are well-described by divisive normalization, in which responses are scaled by the total stimulus drive. Normalization describes how a staggering variety of sensory, cognitive, and motor processes affect individual neurons (1), but whether different normalization processes could be mediated by the same mechanism remains poorly understood. We and others recently showed that attention has low rank effects on the covariability of populations of neurons in visual area V4 (2-4), which strongly constrains mechanistic models mechanism (2). We hypothesized that measuring changes in population covariability associated with other normalization processes could clarify whether they might share a mechanism. Our experimental design included measurements in multiple visual areas using four normalization processes. We found that contrast, adaptation, attention, and task switching affect the responses of populations of neurons in primate visual cortex in a similarly low rank way. These results suggest that a given circuit uses a common mechanism to perform many forms of normalization and likely reflect a general principle that applies to a wide range of brain areas and sensory, cognitive, or motor processes.

neuroscience↗