Biology subjects

Shang, J.

Publications and source records attributed to Shang, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2021

Cortical representation variability aligns with in-class variances and can help one-shot learning

Learning invariance across a set of transformations is an important step in mapping high-dimensional inputs to a limited number of classes. After understanding the set of\ invariances, can a new class be learned from one element? We propose a representation which can facilitate such learning: if the variability in representing individual elements across trials aligns with the variability among different elements in a class, then class boundaries learned from the variable representations of one element should be representative of the entire class. In this study, we test whether such a representation occurs in mouse visual systems. We use Neuropixels probes recording single unit activity in mice observing 200 repeats of natural movies taken from a set of 9 continuous clips. We observe that the trial-by-trial variability in the representation of individual frames is well aligned to the variability in representation of multiple frames from the same clip, but not well aligned to the variability among frames from different clips. Thus, the variable representations of images in the mouse cortex can be efficiently used to classify images into their clips. We compare these representations to those in artificial neural networks. We find that, when introducing noise in networks trained for classification (both feed-forward and recurrent networks), the variability in the representation of elements aligns with the in-class variance. The networks which best reproduce the in-vivo observed directions of variability were those trained on a hierarchical classification task. Taken together, these results point to a solution which the cortex can use for one-shot learning of a class: by using noise as a mechanism for generalization. This is a potential computational explanation for the high level of noise observed in the cortex.

neuroscience↗

bioRxiv2020

The Development of a Novel Nanobody Therapeutic for SARS-CoV-2

Combating the COVID-19 pandemic requires potent and low-cost therapeutics. We identified a novel series of single-domain antibodies (i.e., nanobody), Nanosota-1, from a camelid nanobody phage display library. Structural data showed that Nanosota-1 bound to the oft-hidden receptor-binding domain (RBD) of SARS-CoV-2 spike protein, blocking out viral receptor ACE2. The lead drug possessing an Fc tag (Nanosota-1C-Fc) bound to SARS-CoV-2 RBD with a Kd of 15.7picomolar ([~]3000 times more tightly than ACE2 did) and inhibited SARS-CoV-2 infection with an ND50 of 0.16microgram/milliliter ([~]6000 times more potently than ACE2 did). Administered at a single dose, Nanosota-1C-Fc demonstrated preventive and therapeutic efficacy in hamsters subjected to SARS-CoV-2 infection. Unlike conventional antibody drugs, Nanosota-1C-Fc was produced at high yields in bacteria and had exceptional thermostability. Pharmacokinetic analysis of Nanosota-1C-Fc documented a greater than 10-day in vivo half-life efficacy and high tissue bioavailability. Nanosota-1C-Fc is a potentially effective and realistic solution to the COVID-19 pandemic. Impact statementPotent and low-cost Nanosota-1 drugs block SARS-CoV-2 infections both in vitro and in vivo and act both preventively and therapeutically.

microbiology↗

bioRxiv2020

Structural Mechanism Underlying Ligand Binding and Activation of PPARγ

Ligands bind to an occluded orthosteric pocket within the nuclear receptor (NR) ligand-binding domain (LBD). Molecular simulations have revealed several theoretical ligand entry/exit pathways to the orthosteric pocket, but experimentally it remains unclear whether ligand binding proceeds through induced fit or conformational selection mechanisms. Using NMR spectroscopy lineshape analysis, we show that ligand binding to the peroxisome proliferator-activated receptor gamma (PPAR{gamma}) LBD involves a two-step induced fit mechanism including an initial fast step followed by slow conformational change. Surface plasmon resonance and isothermal titration calorimetry heat capacity analysis support the fast kinetic binding step and the conformational change after binding step, respectively. The putative initial ligand binding pose is suggested in several crystal structures of PPAR{gamma} LBD where a ligand is bound to a surface pore formed by helix 3, the {beta}-sheet, and the {Omega}-loop--one of several ligand entry sites suggested in previous targeted and unbiased molecular simulations. These findings, when considered with a recent NMR study showing the activation function-2 (AF-2) helix 12 exchanges in and out of the orthosteric pocket in apo/ligand-free PPAR{gamma}, suggest an activation mechanism whereby agonist binding occurs through an initial encounter complex with the LBD followed by transition of the ligand into the orthosteric pocket concomitant with a conformational change resulting in a solvent-exposed active helix 12 conformation.

biophysics↗

bioRxiv2020

Human Microbial Transplant Restores T Cell Cytotoxicity and Anti-Tumor Response to PD-L1 Blockade in Gnotobiotic Mice

Recent studies demonstrate that gut microbiota regulate tumor response to immune checkpoint blockade. Still, the mechanisms by which microbiota control tumor response to immunotherapy remain unclear. We colonized germ-free mice with cultured human-derived microbiota prior to tumor inoculation. While no human donor microbiota altered tumor growth, two distinct gut microbiota inhibited tumor response to anti-PD-L1. Colonization with non-responder microbiota led to reduced tumor immune cell infiltration and modified antigen presenting cell phenotype. RNA sequencing of tumor-infiltrating CD8+ T cells revealed enrichment for stem cell-like genes in non-responders and reduced effector-like expression conferring cytotoxic potential. Antibiotic depletion and microbiota transplant restored anti-PD-L1 response in non-responders, with expansion of effector cells and cytotoxicity. Concomitant blockade of TNF similarly improved response to anti-PD-L1 and increased cytotoxicity. These results demonstrate inhibitory roles for the microbiota in checkpoint blockade and reveal the potential for microbiota transplant and TNF blockade to overcome microbiota-mediated resistance.

immunology↗

bioRxiv2020

Constitutive androstane receptor directs T cell adaptation to bile acids in the small intestine

Bile acids (BAs) are lipid emulsifying metabolites synthesized in hepatocytes and maintained in vivo through enterohepatic circulation between the liver and small intestine1. As detergents, BAs can cause toxicity and inflammation in enterohepatic tissues2. Nuclear receptors maintain BA homeostasis in hepatocytes and enterocytes3, but it is unclear how mucosal immune cells tolerate high BA concentrations in the small intestine lamina propria (siLP). We previously reported that CD4+ T effector (Teff) cells upregulate expression of the xenobiotic transporter MDR1/ABCB1 in the siLP to prevent BA toxicity and suppress Crohns disease-like small bowel inflammation4. Here, we identify the nuclear xenobiotic receptor, constitutive androstane receptor (CAR/NR1I3), as a regulator of MDR1 expression in T cells, and safeguard against BA toxicity and inflammation in the small intestine. CAR was activated and induced large-scale transcriptional reprograming in Teff cells infiltrating the siLP, but not the colon. CAR induced expression of detoxifying enzymes and transporters in siLP Teff cells, as in hepatocytes, but also the key anti-inflammatory cytokine, Il10. Accordingly, CAR-deficiency in T cells exacerbated, whereas pharmacologic CAR activation suppressed, BA-driven ileitis in T cell-reconstituted Rag-/- mice. These data suggest that CAR acts locally in small intestinal T cells to detoxify BAs and resolve inflammation. Activation of this program offers an unexpected strategy to treat small bowel Crohns disease, and provides evidence of lymphocyte sub-specialization within the small intestine.

immunology↗

bioRxiv2020

Structural basis for heme-dependent NCoR binding to the transcriptional repressor REV-ERBβ

Heme is the endogenous ligand for the constitutively repressive REV-ERB nuclear receptors, REV-ERB (NR1D1) and REV-ERB{beta} (NR1D2), but how heme regulates REV-ERB activity remains unclear. While cellular studies indicate heme is required for the REV-ERBs to bind the corepressor NCoR and repress transcription, fluorescence-based biochemical assays and crystal structures suggest that heme displaces NCoR. Here, we show that heme artifactually influences detection of NCoR interaction in fluorescence-based assays. However, using fluorescence-independent methods, isothermal titration calorimetry and NMR spectroscopy, we demonstrate that heme directly increases REV-ERB{beta} ligand-binding domain (LBD) binding affinity for NCoR. We further report two crystal structures of REV-ERB{beta} LBD cobound to heme and NCoR peptides, which reveal the structural basis for heme-dependent NCoR binding to REV-ERB{beta}. By resolving previous contradictory biochemical, structural, and cellular studies, our findings should facilitate renewed progress toward understanding heme-dependent REV-ERB activity.

molecular biology↗

bioRxiv2020

CHEER: hierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning

ABSTRARCTThe fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used alignment-based methods are not ideal choices for detecting new viral species. In this work, we present a novel hierarchical classification model named CHEER, which can conduct read-level taxonomic classification from order to genus for new species. By combining k-mer embedding-based encoding, hierarchically organized CNNs, and carefully trained rejection layer, CHEER is able to assign correct taxonomic labels for reads from new species. We tested CHEER on both simulated and real sequencing data. The results show that CHEER can achieve higher accuracy than popular alignment-based and alignment-free taxonomic assignment tools. The source code, scripts, and pre-trained parameters for CHEER are available via GitHub: https://github.com/KennthShang/CHEER.

bioinformatics↗

bioRxiv2020

Adaptation supports short-term memory in a visual change detection task

The maintenance of short-term memories is critical for survival in a dynamically changing world. Previous studies suggest that this memory can be stored in the form of persistent neural activity or using a synaptic mechanism, such as with short-term plasticity. Here, we compare the predictions of these two mechanisms to neural and behavioral measurements in a visual change detection task. Mice were trained to respond to changes in a repeated sequence of natural images while neural activity was recorded using two-photon calcium imaging. We also trained two types of artificial neural networks on the same change detection task as the mice. Following fixed pre-processing using a pretrained convolutional neural network, either a recurrent neural network (RNN) or a feedforward neural network with short-term synaptic depression (STPNet) was trained to the same level of performance as the mice. While both networks are able to learn the task, the STPNet model contains units whose activity are more similar to the in vivo data and produces errors which are more similar to the mice. When images are omitted, an unexpected perturbation which was absent during training, mice often do not respond to the omission but are more likely to respond to the subsequent image. Unlike the RNN model, STPNet also produces a similar pattern of behavior. These results suggest that simple neural adaptation mechanisms may serve as an important bottom-up memory signal in this task, which can be used by downstream areas in the decision-making process. Author SummaryAnimals have to adapt to environments with rich dynamics and maintain multiple types of memories. In this study, we focus on a visual change detection task in mice which requires short-term memory. Learning which features need to be maintained in short-term memory can be realized in a recurrent neural network by changing connections in the network, resulting in memory maintenance through persistent activity. However, in biological networks, a large diversity of time-dependent intrinsic mechanisms are also available. As an alternative to persistent neural activity, we find that learning to make use of internal adapting dynamics better matches both the observed neural activity and behavior of animals in this simple task. The presence of a large diversity of temporal traces could be one of the reasons for the diversity of cells observed. We believe that both learning to keep representations of relevant stimuli in persistent activity and learning to make use of intrinsic time-dependent mechanisms exist, and their relative use will be dependent on the exact task.

neuroscience↗

bioRxiv2019

Genome-wide analysis of chalcone synthase (CHS) family from eggplant (Solanum melongena L.) in flavonoid biosynthetic pathway and expression pattern in response to heat stress

Enzymes of chalcone synthase (CHS) family participate in the synthesis of a series of secondary metabolites in plants, fungi and bacteria. CHS showed significant correlation to the accumulation patterns of anthocyanin. The peel color mainly determined by the content of anthocyanin, is a majority economic trait for eggplant affected by heat stress. A total of 7 CHS(SmCHS1-7) putative genes were identified in genome-wide of eggplant (S. melongena L.). The SmCHS genes distribute on 7 scaffolds and were classified into 3 clusters. Phylogenetic relationships analysis showed that 73 CHS genes from 7 Solanaceae species were classified into 10 groups. SmCHS5, SmCHS6 and SmCHS7 were continuously down-regulated under 38{degrees}C and 45{degrees}C treatment, while SmCHS4 was up-regulated under 38{degrees}C but little change at 45{degrees}C in peel. Expression profiles of anthocyanin biosynthesis key genes families showed that the PAL, 4CL and AN11 genes were mainly expressed in all five tissues. CHI, F3H, F35H, DFR, 3GT and bHLH1 genes were expressed in flower and peel. Under heat stress, 52 key genes expression level were reduced under heat stress. By contrast, expression patterns of eight key genes similar to SmCHS4 up-regulated at the 38{degrees}C-3h. Comparative analysis of putative CHS protein biochemical characteristics, cis-regulatory elements, regulatory network revealed that SmCHS genes family have conservation gene structure and functional diversification. SmCHS showed two or more expression patterns and execute multiple functions to regulate anthocyanin content. Combined with regulatory networks, it is possible to further understand the regulation mechanism of peel color in eggplant.

genomics↗

bioRxiv2019

CNLLRR: A Novel Low-Rank Representation Method for Single-cell RNA-seq Data Analysis

The development of single-cell RNA-sequencing (scRNA-seq) technology has enabled the measurement of gene expression in individual cells. This provides an unprecedented opportunity to explore the biological mechanisms at the cellular level. However, existing scRNA-seq analysis methods are susceptible to noise and outliers or ignore the manifold structure inherent in the data. In this paper, a novel method called Cauchy non-negative Laplacian regularized low-rank representation (CNLLRR) is proposed to alleviate the above problem. Specifically, we employ the Cauchy loss function (CLF) instead of the conventional norm constraints in the noise matrix of CNLLRR, which will enhance the robustness of the method. In addition, graph regularization term is applied to the objective function, which can capture the paired geometric relationships between cells. Then, alternating direction method of multipliers (ADMM) is adopted to solve the optimization problem of CNLLRR. Finally, extensive experiments on scRNA-seq data reveal that the proposed CNLLRR method outperforms other state-of-the-art methods for cell clustering, cell visualization and prioritization of gene markers. CNLLRR contributes to understand the heterogeneity between cell populations in complex biological systems.\n\nAuthor summaryAnalysis of single-cell data can help to further study the heterogeneity and complexity of cell populations. The current analysis methods are mainly to learn the similarity between cells and cells. Then they use the clustering algorithm to perform cell clustering or downstream analysis on the obtained similarity matrix. Therefore, constructing accurate cell-to-cell similarity is crucial for single-cell data analysis. In this paper, we design a novel Cauchy non-negative Laplacian regularized low-rank representation (CNLLRR) method to get a better similarity matrix. Specifically, Cauchy loss function (CLF) constraint is applied to punish noise matrix, which will improve the robustness of CNLLRR to noise and outliers. Moreover, graph regularization term is applied to the objective function, which will effectively encode the local manifold information of the data. Further, these will guarantee the quality of the cell-to-cell similarity matrix learned. Finally, single-cell data analysis experiments show that our method is superior to other representative methods.

bioinformatics↗

bioRxiv2019

A binning tool to reconstruct viral haplotypes from assembled contigs

MotivationInfections by RNA viruses such as Influenza, HIV still pose a serious threat to human health despite extensive research on viral diseases. One challenge for producing effective prevention and treatment strategies is high intra-species genetic diversity. As different strains may have different biological properties, characterizing the genetic diversity is thus important to vaccine and drug design. Next-generation sequencing technology enables comprehensive characterization of both known and novel strains and has been widely adopted for sequencing viral populations. However, genome-scale reconstruction of haplotypes is still a challenging problem. In particular, haplotype assembly programs often produce contigs rather than full genomes. As a mutation in one gene can mask the phenotypic effects of a mutation at another locus, clustering these contigs into genome-scale haplotypes is still needed.\n\nResultsWe developed a contig binning tool, VirBin, which clusters contigs into different groups so that each group represents a haplotype. Commonly used features based on sequence composition and contig coverage cannot effectively distinguish viral haplotypes because of their high sequence similarity and heterogeneous sequencing coverage for RNA viruses. VirBin applied prototype-based clustering to cluster regions that are more likely to contain mutations specific to a haplotype. The tool was tested on multiple simulated sequencing data with different haplotype abundance distributions and contig sizes, and also on mock quasispecies sequencing data. The benchmark results with other contig binning tools demonstrated the superior sensitivity and precision of VirBin in contig binning for viral haplotype reconstruction.\n\nAvailabilityhttps://github.com/chjiao/VirBin\n\nContactyannisun@cityu.edu.hk

bioinformatics↗

bioRxiv2019

Does the neuronal noise in cortex help generalization?

One remarkable feature of neuronal activity in the mammalian cortex is the high level of variability in response to repeated stimuli. First, we used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated presentations of natural movies. We find that even for their preferred moment in the movie clip, neurons have high variability which cannot be well captured by Gaussian or Poisson distributions. A large fraction of responses are better fit by log-normal or Gaussian mixture models with two components. These distributions are similar to activity distributions during training of deep neural networks using dropout. This poses the interesting hypothesis: is the role of cortical noise to help in generalization during learning?\n\nSecond, to ensure the robustness of our results we analyzed electrophysiological recordings in the same areas of mouse visual cortex, again using repeated natural movie presentations and found similar response distributions. To make sure that the trial-by-trial variations we observe are not due exclusively to the result of changes in state, we constructed a population coupling model, where each neurons activity is coupled to a low-dimension version of the activity of all other simultaneously recorded neurons. The population coupling model can capture global, brain-wide activity fluctuations that are state-dependent. The residuals from this model also show non-Gaussian noise distributions.\n\nThird, we ask a more specific question: is the noise in the cortex more likely to move the representation of the stimulus in-class versus out-of-class? To address this question, we analyzed the responses of neurons across trials from multiple sections of different movie clips. We observe that the noise in the cortex better aligns to in-class variations. We argue that a useful noise for learning generalizations is to move from representations of different exemplars in-class, similar to cortical noise.

neuroscience↗

bioRxiv2019

Quantitative Structural Assessment of Graded Receptor Agonism

Ligand-receptor interactions, which are ubiquitous in physiology, are described by theoretical models of receptor pharmacology. Structural evidence for graded-efficacy receptor conformations predicted by receptor theory has been limited, but is critical to fully validate theoretical models. We applied quantitative structure-function approaches to characterize the effects of structurally similar and structurally diverse agonists on the conformational ensemble of nuclear receptor peroxisome proliferator-activated receptor gamma (PPAR{gamma}). For all ligands, agonist efficacy is correlated to a shift in the conformational ensemble equilibrium from a ground state towards an active state, which is detected by NMR spectroscopy but not observed in crystal structures. For the structurally similar ligands, ligand potency is also correlated to efficacy and conformation, indicating ligand residence times among related analogs can influence receptor conformation and function. Our results derived from quantitative graded activity-conformation correlations provide new experimental evidence and a platform with which to extend and test theoretical models of receptor pharmacology to more accurately describe and predict ligand-dependent receptor activity.

biophysics↗

bioRxiv2018

Structure-function analysis of 1-hydroxypioglitazone, a major in vivo metabolite of the PPARγ agonist pioglitazone

The thiazolidinedione (TZD) pioglitazone (Pio) is an FDA-approved drug for type 2 diabetes mellitus that binds and activates the nuclear receptor peroxisome proliferator-activated receptor gamma (PPAR{gamma}). Although TZDs have potent antidiabetic effects, they also display harmful side effects that have necessitated a better understanding of their mechanisms of action. In particular, little is known about the effect of in vivo TZD metabolites on the structure and function of PPAR{gamma}. Here, we present a structure-function comparison of Pio and a major in vivo metabolite, 1-hydroxypioglitazone (PioOH). PioOH displayed a lower binding affinity and reduced potency in coregulator recruitment assays compared to Pio. To determine the structural basis of these findings, we solved an X-ray crystal structure of PioOH bound to PPAR{gamma} ligand-binding domain (LBD) and compared it to a published Pio-bound crystal structure. PioOH exhibited an altered hydrogen bonding network that could underlie its reduced affinity and potency compared to Pio. Solution-state structural analysis using NMR spectroscopy and hydrogen/deuterium exchange mass spectrometry (HDX-MS) analysis revealed that PioOH stabilizes the PPAR{gamma} activation function-2 (AF-2) coactivator binding surface better than Pio. In support of AF-2 stabilization, PioOH displayed stabilized coactivator binding in biochemical assays and better transcriptional efficacy (maximal transactivation response) in a cell-based assay that reports on the activity of the PPAR{gamma} LBD. These results, which indicate that Pio hydroxylation affects both its potency and efficacy as a PPAR{gamma} agonist, contribute to our understanding of PPAR{gamma}-binding drug metabolite interactions and may assist in future PPAR{gamma} drug design efforts.

biochemistry↗

bioRxiv2018

Defining a canonical ligand-binding pocket in the orphan nuclear receptor Nurr1

Nuclear receptor related 1 protein (Nurr1/NR4A2) is an orphan nuclear receptor that is considered to function without a canonical ligand-binding pocket. A crystal structure of the Nurr1 ligand-binding domain (LBD) revealed no physical space in the conserved region where other nuclear receptors with solvent accessible apo-protein ligand-binding pockets bind synthetic and natural ligands. Using solution NMR spectroscopy, hydrogen/deuterium exchange mass spectrometry, and molecular dynamics simulations, we show here that the putative canonical ligand-binding pocket in the Nurr1 LBD is dynamic with high solvent accessibility, exchanges between two or more conformations on the microsecond-to-millisecond timescale, and can expand from the collapsed crystalized conformation to allow binding of unsaturated fatty acids. These findings should stimulate future studies to probe the ligandability and druggability of Nurr1 for both endogenous and synthetic ligands, which could lead to new therapeutics for Nurr1-related diseases, including Parkinsons disease and schizophrenia.

biophysics↗

bioRxiv2018

Taxonomic Profiling and Populational Patterns of Bacterial Bile Salt Hydrolase (BSH) Genes on Worldwide Human Gut Microbiome

Bile salt hydrolase (BSH) in gut bacteria can hydrolyze conjugated bile salts to unconjugated bile acids and amino acids. Thereby play a crucially important role in host health by reducing serum cholesterol levels, preserving bile acids balance and regulating various metabolism as signaling molecules. Here we present the taxonomic identification of BSHs in human microbiota and elucidate the abundance and activity differences of various bacterial BSHs among 11 different populations. For the first time, we have revealed BSH are distributed in 154 intestinal bacterial strains within 33 genera in human microbiota. However, these BSHs present obviously differentiation for the sequence identity being from 28.6% to 100%, and the 32.7% bacteria strains having more than one paralogs of BSHs with dissimilarity. Therefore, we reclassified the BSHs from the different genera into 6 phylotypes basing on their phylogenetic tree, and demonstrate the significant abundance patterns of BSH phylotypes among different populations. From the enzyme activity comparison, the representative sequence of BSH-T3 was shown highest enzyme activity in 6 phylotypes. Meanwhile, BSH-T3 sequences which all distributed in Lactobacillus show highest abundance in Chinese and Austrian. The information illustrated by this study is useful for investigating the population differences of bile acid metabolism related diseases, and further giving a new suggestion on selection of probiotics or development of pharmaceutical proteins based upon the activity of BSH phylotypes to regulate host metabolism and maintain fitness.

genomics↗

bioRxiv2018

Cross-type Biomedical Named Entity Recognition with Deep Multi-Task Learning

MotivationState-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type.\n\nResultsWe propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora.\n\nAvailabilityOur source code is available at https://github.com/yuzhimanhua/lm-lstm-crf.\n\nContactxwang174@illinois.edu, xiangren@usc.edu.

bioinformatics↗

bioRxiv2018

Coincident binding of synthetic and natural ligands to the nuclear receptor PPARγ

Crystal structures of peroxisome proliferator-activated receptor gamma (PPAR{gamma}) have revealed overlapping binding modes for synthetic and natural/endogenous ligands, indicating competition for the orthosteric pocket. Here we show that cobinding of a synthetic ligand to the orthosteric pocket can push natural and endogenous PPAR{gamma} ligands (fatty acids) out of the orthosteric pocket towards an alternate ligand-binding site near the functionally important omega ({Omega}) loop. X-ray crystallography, NMR spectroscopy, all-atom molecular dynamics simulations, and mutagenesis coupled to quantitative functional assays reveal that synthetic ligand and fatty acid cobinding can form a \"ligand link\" to the {Omega} loop and synergistically affect the structure and function of PPAR{gamma}. These findings contribute to a growing body of evidence indicating ligand binding to nuclear receptors can be more complex than the classical one-for-one orthosteric exchange of a natural or endogenous ligand with a synthetic ligand.

biochemistry↗