Biology subjects

Huang, Y.-F.

Publications and source records attributed to Huang, Y.-F..

At least 19 recordsLinked to original sources

bioRxiv2026

Deciphering the epitranscriptomic code of RNA degradation with nanopore direct RNA sequencing

The precise regulation of RNA degradation is crucial for gene expression homeostasis, yet how multiple molecular features coordinate on a single transcript remains poorly understood. Here, we use nanopore direct RNA sequencing (DRS) to simultaneously track alternative isoforms, m6A modifications, and poly(A) tail dynamics at single-molecule resolution during a time course of RNA decay. We show that m6A regulates RNA degradation in a stoichiometry-dependent manner, where modification levels quantitatively modulate decay kinetics. Mechanistically, m6A is functionally coupled to deadenylation, promoting accelerated poly(A) tail shortening and coordinated RNA turnover. At the isoform level, we identify regional m6A clusters (RMCs) as structural elements that associate with isoform-selective degradation and remodel protein-coding potential. Furthermore, transcript splicing architecture is associated with distinct m6A deposition patterns, suggesting that gene structure encodes RNA decay kinetics through m6A-mediated regulation. A machine learning model integrating these multi-modal features highlights the central contribution of m6A and deadenylation in shaping RNA decay, while revealing substantial regulatory heterogeneity across transcripts. Collectively, our study deciphers the multi-layered, cooperative principles of RNA degradation and provides an epitranscriptomic perspective for understanding how RNA fate is encoded at the single-molecule resolution.

bioinformatics↗

bioRxiv2025

Cytosolic mannosyltransferases involved in the ER N-glycosylation pathway exhibit bacterial ancestry

N-glycosylation in eukaryotes begins with the assembly of a lipid-linked oligosaccharide on the endoplasmic reticulum membrane. As a pivotal post-translational protein modification, it is conserved across all three domains of life. However, the evolutionary origins of the N-glycosylation pathway remain a subject of ongoing debate in evolutionary biology, largely due to the limited availability of robust data regarding the evolutionary trajectories of the glycosyltransferases involved in this process. Here, we present phylogenetic analyses of the eukaryotic ALG1 and ALG2 mannosyltransferases (MTases), which are crucial for constructing the core trimannosyl Man3GlcNAc2 structure conserved in eukaryotic N-glycans. Our comprehensive phylogenetic study, combined with functional and structural analyses, suggests that the ALG2 MTase likely originated from a bacterial ancestor. This inference is further supported by the identification of sequential and functional ALG1 homologs exclusively within bacterial lineages, rather than in Asgard archaea or other archaeal groups. Our findings challenge the prevailing hypothesis that the eukaryotic N-glycosylation pathway primarily evolved from archaeal ancestors, instead suggesting a chimeric origin involving contributions from both bacterial and archaeal lineages. Synopsis O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/682550v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1ac81a8org.highwire.dtl.DTLVardef@746054org.highwire.dtl.DTLVardef@604bdborg.highwire.dtl.DTLVardef@14235d_HPS_FORMAT_FIGEXP M_FIG C_FIG The ALG glycosyltransferase (GTases) involved in the initial stage of eukaryotic N-glycosylation on the cytosolic side of the ER membrane exhibit chimeric evolutionary origins. LUCA: Last Universal Common Ancestor; LECA: Last Eukaryotic Common Ancestor. HighlightsO_LIThe evolutionary trajectories of cytosolic ALG mannosyltransferases (MTases) were studied C_LIO_LIThe ancestor of ALG2 exhibited in the LUCA but evolved its MTase activity in bacterial lineages C_LIO_LIThe ALG1 MTase originated from bacterial lineages C_LI

molecular biology↗

bioRxiv2024

A high-resolution haplotype pangenome uncovers somatic hybridization, recombination and intercontinental migration in oat crown rust

Basidiomycetes like rust fungi have complex reproductive cycles and dikaryotic life stages which influence their population structure and evolution. Puccinia coronata f. sp. avenae (Pca), the causal agent of oat crown rust, is a pathogen of global economic importance. To investigate the genetic diversity of the species, as well as the role of mating type system and nuclear exchange (somatic hybridization) in host adaptation of Pca we acquired whole genome sequencing data of Taiwanese and Australian isolates, adding to existing data for USA and South African populations. An atlas of 30 chromosome-level, fully-phased nuclear haplotypes from six USA isolates and nine Australian isolates was generated to capture the genomic composition of key oat crown rust lineages. This study provides evidence of nuclear exchange and recombination of haplotypes in both the USA and Australian Pca populations as mechanisms for the introduction of genetic diversity. The limitations of assuming clonal evolutionary history from virulence phenotyping is demonstrated by the detection of either sexual or cryptic genetic recombination in the Pca Australian population. Overall, the characterization of intercontinental migration of Pca at the haplotype level provides further impetus for molecular monitoring of rust pathogen populations on a global scale.

genomics↗

bioRxiv2024

Detection of oat crown rust disease in Taiwan (2019-2021)

Oat is a minor forage crop grown in Taiwan. Only a few historical records of oat rust disease have been reported in the country, therefore the pathogen population remains poorly characterized. A rust-like disease outbreak was detected at the Experimental Farm of National Taiwan University in 2019, which caused significant damage to the field experiments. To determine the identity of the pathogen responsible for this disease outbreak, we collected infected foliar material. Disease signs suggested infection by the oat crown rust fungus. Hence, common procedures in rust pathology were applied to confirm the identity of the pathogen with phenotypic and molecular diagnostic techniques. A total of 50 field samples from infected oat cultivars were collected in 2019 and five rust isolates were purified in 2020 and 2021. Phylogenetic analysis based on ITS sequences indicated that the pathogen was likely Puccinia coronata f. sp. avenae (Pca), which was further supported by the placement of Taiwanese isolate NTU-01 with other Pca representatives in a phylogenetic tree of Basidiomycete fungi. Phenotyping assays across 36 oat differential lines demonstrated that Taiwanese isolates are phenotypically similar with relatively limited virulence. This study presents the first molecular confirmation of Pca in Taiwan and reports the virulence profiles of Taiwanese Pca population.

microbiology↗

bioRxiv2023

Deep multiple-instance learning accurately predicts gene haploinsufficiency and deletion pathogenicity

Copy number losses (deletions) are a major contributor to the etiology of severe genetic disorders. Although haploinsufficient genes play a critical role in deletion pathogenicity, current methods for deletion pathogenicity prediction fail to integrate multiple lines of evidence for haploinsufficiency at the gene level, limiting their power to pinpoint deleterious deletions associated with genetic disorders. Here we introduce DosaCNV, a deep multiple-instance learning framework that, for the first time, models deletion pathogenicity jointly with gene haploinsufficiency. By integrating over 30 gene-level features potentially predictive of haploinsufficiency, DosaCNV shows unmatched performance in prioritizing pathogenic deletions associated with a broad spectrum of genetic disorders. Furthermore, DosaCNV outperforms existing methods in predicting gene haploinsufficiency even though it is not trained on known haploinsufficient genes. Finally, DosaCNV leverages a state-of-the-art technique to quantify the contributions of individual gene-level features to haploinsufficiency, allowing for human-understandable explanations of model predictions. Altogether, DosaCNV is a powerful computational tool for both fundamental and translational research.

bioinformatics↗

bioRxiv2022

Environmental luminance impairs socio-sexual recognition memory through a succinct retina to supraoptic nucleus circuit

Social memory between the same gender or even different gender is a complex and heavily modulated process in the nervous system. It is important for an individual to form social memory between the opposite sex to either increase mating opportunities with multiple partners or form monogamous pair bonding. Therefore, a specific neuronal circuit to regulate social sexual memory may enhance the mating opportunity for an individual. It has been shown that both the auditory and somatosensory systems could increase the activity of oxytocin neurons in the paraventricular nucleus to regulate social behaviors. Although light exposure could influence various forms of memory, such as fear and object memory, how luminance signals modulate social recognition memory remains unclear. Here, we show that acute light exposure could impair the socio-sexual recognition memory (SSRM) in male mice. Contrary to sound and touch, light stimulation could inhibit oxytocin neurons in the SON (SONOT) through M1 SON-projecting ipRGCs and GABAergic neurons in the peri-SON (pSONGABA). Optogenetic activation of SONOT neurons with channelrhodopsin is sufficient to enhance the SSRM performance in male mice, even under light conditions. Our results show that the visual system could modulate SSRM through a succinct ipRGCs-pSONGABA-SONOT neuronal circuitry. Together, we demonstrate a dedicated neuronal circuit of how luminance affects memory formation for an individual toward different sex through the oxytocin system, a powerful modulatory neurohormone in the central nervous system.

neuroscience↗

bioRxiv2022

Altered sequencing success at non-B-DNA motifs

Modern sequencing technologies are not error-free, and might have elevated error rates at some locations of the genome. A potential cause for such elevated error rates is the formation of alternative DNA structures (non-B DNA), such as G-quadruplexes (G4s), Z-DNA, or cruciform structures, during sequencing. Approximately 13% of the human genome has the potential to form such structures, which have been previously shown to affect the activity of DNA polymerases and helicases. Here we tested whether motifs with the potential to form non-B DNA (non-B motifs) influence the sequencing success of three major sequencing technologies--Illumina, Pacific Biosciences (PacBio) HiFi, and Oxford Nanopore Technologies (ONT). We estimated sequencing success by computing the rates of single-nucleotide, insertion, and deletion errors, as well as by evaluating mean read depth and mean base quality. Overall, all technologies exhibited altered sequencing success for most non-B motif types. Single-nucleotide error rates were generally increased for G-quadruplexes (G4s) and Z-DNA motifs in all three technologies. Illumina and PacBio HiFi deletion error rates were also increased for all non-B types except for Z-DNA motifs, while in ONT they were increased substantially only for G4 motifs. Insertion error rates for non-B motifs were highly elevated in Illumina, moderately elevated in PacBio HiFi, and only slightly elevated in ONT. Using Poisson regression modeling, we evaluated how non-B DNA motifs and other factors influence sequencing error profiles. Using the error rates at non-B motifs, we developed a probabilistic approach to determine the number of false-positive single-nucleotide variants (SNVs) in different sample size and variant frequency cutoff scenarios, as well as in previously generated sequencing data sets (1000Genomes, Simons Genome Diversity Project, and gnomAD). Overall, the effect of non-B DNA on sequencing should be considered in downstream analyses, particularly in studies with limited read depth--e.g., single-cell and ancient DNA sequencing, as well as sequencing of pooled population samples--and when scoring variants with low frequency (e.g., singletons). Because each sequencing technology analyzed has a unique error profile at non-B motifs, a combination of different technologies should be considered in future sequencing studies of such motifs, to maximize accuracy.

genomics↗

bioRxiv2022

Transcription factor binding sites are frequently under accelerated evolution in primates

Recent comparative genomic studies have identified many human accelerated elements (HARs) with elevated substitution rates in the human lineage. However, it remains unknown to what extent transcription factor binding sites (TFBSs) are under accelerated evolution in humans and other primates. Here, we introduce two pooling-based phylogenetic methods with dramatically enhanced sensitivity to examine accelerated evolution in TFBSs. Using these new methods, we show that more than 6,000 TFBSs annotated in the human genome have experienced accelerated evolution in Hominini, apes, and Old World monkeys. Although these TFBSs individually show relatively weak signals of accelerated evolution, they collectively are more abundant than HARs. Also, we show that accelerated evolution in Pol III binding sites may be driven by lineage-specific positive selection, whereas accelerated evolution in other TFBSs might be driven by nonadaptive evolutionary forces. Finally, the accelerated TFBSs are enriched around neurodevelopmental and pluripotency genes, suggesting that accelerated evolution in TFBSs may drive the divergence of neurodevelopmental processes between primates.

evolutionary biology↗

bioRxiv2021

A deep learning framework for predicting human essential genes from population and functional genomic data

Being able to predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve our ability to identify genes associated with genetic disorders. Numerous computational methods have recently been developed to predict human essential genes from population genomic data; however, the existing methods have limited power in pinpointing short essential genes due to the sparsity of polymorphisms in the human genome. Here we present an evolution-based deep learning model, DeepLOF, which integrates population and functional genomic data to improve gene essentiality prediction. Compared to previous methods, DeepLOF shows unmatched performance in predicting ClinGen haploinsufficient genes, mouse essential genes, and essential genes in human cell lines. Furthermore, DeepLOF discovers 109 potentially essential genes that are too short to be identified by previous methods. Altogether, DeepLOF is a powerful computational method to aid in the discovery of essential genes.

bioinformatics↗

bioRxiv2021

Mixture Density Regression reveals frequent recent adaptation in the human genome

How much genome differences between species reflect neutral or adaptive evolution is a central question in evolutionary genomics. In humans and other mammals, the prevalence of adaptive versus neutral genomic evolution has proven particularly difficult to quantify. The difficulty notably stems from the highly heterogeneous organization of mammalian genomes at multiple levels (functional sequence density, recombination, etc.) that complicates the interpretation and distinction of adaptive vs. neutral evolution signals. Here, we introduce Mixture Density Regressions (MDRs) for the study of the determinants of recent adaptation in the human genome. MDRs provide a flexible regression model based on multiple Gaussian distributions. We use MDRs to model the association between recent selection signals and multiple genomic factors likely to affect positive selection, if the latter was common enough in the first place to generate these associations. We find that a MDR model with two Gaussian distributions provides an excellent fit to the genome-wide distribution of a common sweep summary statistic (iHS), with one of the two distributions likely enriched in positive selection. We further find several factors associated with recent adaptation, including the recombination rate, the density of regulatory elements in immune cells, GC-content, gene expression in immune cells, the density of mammal-wide conserved elements, and the distance to the nearest virus-interacting gene. These results support that strong positive selection was relatively common in recent human evolution and highlight MDRs as a powerful tool to make sense of signals of recent genomic adaptation. Author SummaryOver the last 50,000 years, human populations have been exposed to selective pressures that can trigger adaptation in the genome. The search for signals of these selective events is however obscured by the substantial variation of factors that are relevant for the prevalence and detection of adaptation across the genome. Here, we analyze the impact of multiple factors on positive selection using a biologically meaningful approach that considers the influence of adaptative and non-adaptive processes in the human genome. Our results show that this novel approach is better suited to find genomic factors associated with recent positive selection compared to the classic regression or correlation approaches. We find multiple genomic functional elements associated with selection across the genome, including novel associations that emerge only after controlling for multiple confounding factors. This strongly suggests that adaptation was common enough in recent evolutionary times to produce a widespread correlation between functional elements and positive selection in the human genome. Note on the language used in this manuscriptIn this manuscript we use discrete population groups, such as the Yoruba. We want to emphasize that these discrete groups are only used for convenience and clarity when presenting our results, but in fact represent arbitrary human constructions, the same way that the boundaries of countries are arbitrary. There is an unbroken continuum and mixing of geographical ancestries across groups often identified as distinct populations across the world. The discrete groups we use are, as such, by no mean discrete genetic entities. Once grouped together, the grouped individuals only happen to be genetically more similar, with their ancestries coming from specific geographic locations more predominantly than individuals from the other groups. How much more predominantly is completely arbitrary.

evolutionary biology↗

bioRxiv2021

Extreme purifying selection against point mutations in the human genome

Genome sequencing of tens of thousands of humans has enabled the measurement of large selective effects for mutations to protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring similar selective effects in noncoding as well as in coding regions of the human genome. ExtRaINSIGHT estimates the prevalance of strong purifying selection, or "ultraselection" ({lambda}s), as the fractional depletion of rare single-nucleotide variants in target genomic sites relative to matched sites that are putatively free from selection, after controlling for local variation and neighbor-dependence in mutation rate. We show using simulations that{lambda} s is closely related to the average site-specific selection coefficient against heterozygous point mutations, as predicted at mutation-selection balance. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find strong evidence of ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. By contrast, we find weak evidence in other noncoding RNAs and transcription factor binding sites, and only modest evidence in ultraconserved elements and human accelerated regions. We estimate that ~0.3-0.5% of the human genome is ultraselected, implying ~0.3-0.4 lethal or nearly lethal de novo mutations per potential human zygote. Overall, our study sheds new light on the genome-wide distribution of fitness effects for new point mutations by combining deep new sequencing data sets and classical theory from population genetics.

evolutionary biology↗

bioRxiv2020

Dissecting genomic determinants of positive selection with an evolution-guided regression model

In evolutionary genomics, it is fundamentally important to understand how characteristics of genomic sequences, such as gene expression level, determine the rate of adaptive evolution. While numerous statistical methods, such as the McDonald-Kreitman test, are available to examine the association between genomic features and the rate of adaptation, we currently lack a statistical approach to disentangle the independent effect of a genomic feature from the effects of other correlated genomic features. To address this problem, I present a novel statistical model, the MK regression, which augments the McDonald-Kreitman test with a generalized linear model. Analogous to the classical multiple regression model, the MK regression can analyze multiple genomic features simultaneously to infer the independent effect of a genomic feature, holding constant all other genomic features. Using the MK regression, I identify numerous genomic features driving positive selection in chimpanzees. These features include well-known ones, such as local mutation rate, residue exposure level, tissue specificity, and immune genes, as well as new features not previously reported, such as gene expression level and metabolic genes. In particular, I show that highly expressed genes may have a higher adaptation rate than their weakly expressed counterparts, even though a higher expression level may impose stronger negative selection. Also, I show that metabolic genes may have a higher adaptation rate than their non-metabolic counterparts, possibly due to recent changes in diet in primate evolution. Overall, the MK regression is a powerful approach to elucidate the genomic basis of adaptation.

evolutionary biology↗

bioRxiv2019

Phylogenetic modeling of regulatory element turnover based on epigenomic data

Evolutionary changes in gene expression are often driven by gains and losses of cis-regulatory elements (CREs). The dynamics of CRE evolution can be examined using multi-species epigenomic data, but so far such analyses have generally been descriptive and model-free. Here, we introduce a probabilistic modeling framework for the evolution of CREs that operates directly on raw chromatin immunoprecipitation and sequencing (ChIP-seq) data and fully considers the phylogenetic relationships among species. Our framework includes a phylogenetic hidden Markov model, called epiPhyloHMM, for identifying the locations of multiply aligned CREs, and a combined phylogenetic and generalized linear model, called phyloGLM, for accounting for the influence of a rich set of genomic features in describing their evolutionary dynamics. We apply these methods to previously published ChIP-seq data for the H3K4me3 and H3K27ac histone modifications in liver tissue from nine mammals. We find that enhancers are gained and lost during mammalian evolution at about twice the rate of promoters, and that turnover rates are negatively correlated with DNA sequence conservation, expression level, and tissue breadth, and positively correlated with distance from the transcription start site, consistent with previous findings. In addition, we find that the predicted dosage sensitivity of target genes positively correlates with DNA sequence constraint in CREs but not with turnover rates, perhaps owing to differences in the effect sizes of the relevant mutations. Altogether, our probabilistic modeling framework enables a variety of powerful new analyses.

evolutionary biology↗

bioRxiv2019

Unified inference of missense variant effects and gene constraints in the human genome

A challenge in medical genomics is to identify variants and genes associated with severe genetic disorders. Based on the premise that severe, early-onset disorders often result in a reduction of evolutionary fitness, several statistical methods have been developed to predict pathogenic variants or constrained genes based on the signatures of negative selection in human populations. However, we currently lack a statistical framework to jointly predict deleterious variants and constrained genes from both variant-level features and gene-level selective constraints. Here we present such a unified approach, UNEECON, based on deep learning and population genetics. UNEECON treats the contributions of variant-level features and gene-level constraints as a variant-level fixed effect and a gene-level random effect, respectively. The sum of the fixed and random effects is then combined with an evolutionary model to infer the strength of negative selection at both variant and gene levels. Compared with previously published methods, UNEECON shows unmatched performance in predicting missense variants and protein-coding genes associated with autosomal dominant disorders, and feature importance analysis suggests that both gene-level selective constraints and variant-level predictors are important for accurate variant prioritization. Furthermore, based on UNEECON, we observe an unexpected low correlation between gene-level intolerance to missense mutations and that to loss-of-function mutations, which can be partially explained by the prevalence of disordered protein regions that are highly tolerant to missense mutations. Finally, we show that genes intolerant to both missense and loss-of-function mutations play key roles in the central nervous system and the autism spectrum disorders. Overall, UNEECON is a promising framework for both variant and gene prioritization.\n\nAuthor summaryNumerous statistical methods have been developed to predict deleterious missense variants or constrained genes in the human genome, but unified prioritization methods that utilize both variant- and gene-level information are underdeveloped. Here we present UNEECON, an evolution-based deep learning framework for unified variant and gene prioritization. By integrating variant-level predictors and gene-level selective constraints, UNEECON outperforms existing methods in predicting missense variants and protein-coding genes associated with dominant disorders. Based on UNEECON, we show that disordered proteins are tolerant to missense mutations but not to loss-of-function mutations. In addition, we find that genes under strong selective constraints at both missense and loss-of-function levels are strongly associated with the central nervous system and the autism spectrum disorders, highlighting the need to investigate the function of these highly constrained genes in future studies.

bioinformatics↗

bioRxiv2019

Characterizing RNA stability genome-wide through combined analysis of PRO-seq and RNA-seq data

The rate at which RNA molecules decay is a key determinant of cellular RNA concentrations, yet current approaches for measuring RNA half-lives are generally labor-intensive, limited in sensitivity, and/or disruptive to normal cellular processes. Here we introduce a simple method for estimating relative RNA half-lives that is based on two standard and widely available high-throughput assays: Precision Run-On and sequencing (PRO-seq) and RNA sequencing (RNA-seq). Our method treats PRO-seq as a measure of transcription rate and RNA-seq as a measure of RNA concentration, and estimates the rate of RNA decay required for a steady-state equilibrium. We show that this approach can be used to assay relative RNA half-lives genome-wide, with good accuracy and sensitivity for both coding and noncoding transcription units. Using a structural equation model (SEM), we test several features of transcription units, nearby DNA sequences, and nearby epigenomic marks for associations with RNA stability after controlling for their effects on transcription. We find that RNA splicing-related features are positively correlated with RNA stability, whereas features related to miRNA binding, DNA methylation, and G+C-richness are negatively correlated with RNA stability. Furthermore, we find that a measure based on U1-binding and polyadenylation sites distinguishes between unstable noncoding and stable coding transcripts but is not predictive of relative stability within the mRNA or lincRNA classes. We also identify several histone modifications that are associated with RNA stability. Together, our estimation method and systematic analysis shed light on the pervasive impacts of RNA stability on cellular RNA concentrations.

genomics↗

bioRxiv2019

Conservation and Divergence of Related Neuronal Lineages in the Drosophila Central Brain

Wiring a complex brain requires enormous cell specificity. This specificity is laid out via a developmental process where neural stem cells produce countless diverse neurons. To help elucidate this process and resolve the considerable dynamic specificity, we need to observe the development of multiple neuronal lineages. Drosophila central brain lineages are predetermined, comprised of a fixed set of neurons born in pairs in a specific order. To reveal specific roles of lineage identity, Notch-dependent sister fate specification, and temporal patterning in morphological diversification, we mapped approximately one quarter of the Drosophila central brain lineages. While we found large aggregate differences, we also discovered similar patterns of morphological specification and diversification. Lineage identity plus Notch state govern primary neuronal trajectories, whereas temporal fates diversify terminal elaborations in target-specific manners. In addition, we identified related lineages of analogous neuron types produced in similar temporal patterns. Two stem cells even yield identical series of dopaminergic neuron types, but with completely disparate sister neurons. These phenomena suggest that large changes in morphological diversity can be the consequence of relatively small differences in lineage fating. Taken together, this large-scale lineage mapping study reveals that relatively simple rules drive incredible neuronal complexity.

developmental biology↗

bioRxiv2018

Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease

A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here we introduce a unified population-genetic and machine-learning model, called Linear Allele-Specific Selection InferencE (LASSIE), for estimating the fitness effects of all potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features, and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. We show that this map is informative about both human evolution and disease.

genomics↗

bioRxiv2018

The ligand-mediated affinity of escort proteins determines the directionality of lipophilic cargo transport

Intracellular cargo transport is a highly dynamic process. In eukaryotic cells, the uptake and release of lipophilic ligands are executed by escort proteins. However, how these carriers control the directionality of cargo trafficking remains unclear. Here, we have elucidated the unliganded structure of an archetypal fatty acid-binding protein (FABP) and found that it possesses stronger binding affinity than its liganded counterpart towards empty nanodiscs. Titrating unliganded FABP and nanodiscs with long-chain fatty acids (LCFAs) rescued the broadening of FABP cross-peak intensities in HSQC spectra due to decreased protein-membrane interaction. Crystallographic studies revealed that the tails of bound LCFAs obstructed the charged interfaces of the FABP-nanodisc complexes. We conclude that the lipophilic ligands, by taking advantage of escort proteins with high conformational homogeneity and nanodiscs as the third interaction partner involved in this transport study, participate directly in the control of their own transportation in an irreversible, unidirectional fashion.

biophysics↗