Biology subjects

Huang, R.

Publications and source records attributed to Huang, R..

At least 127 records · Page 7Linked to original sources

bioRxiv2021

Behavioral and fMRI evidence that arousal enhances bottom-up attention and memory selectivity in young but not older adults

During a challenge or emotional experience, increases in arousal help us focus on the most salient or relevant details and ignore distracting stimuli. The noradrenergic system integrates signals about arousal states throughout the brain and helps coordinate this adaptive attentional selectivity. However, age-related changes in the noradrenergic system and attention networks in the brain may reduce the efficiency of arousal to modulate selective processing in older adults. In the current neuroimaging study, we examined age differences in how arousal affects bottom-up attention to category-selective stimuli differing in perceptual salience. We found a dissociation in how arousal modulates selective processing in the young and older brain. In young adults, emotionally arousing sounds enhanced selective incidental memory and brain activity in the extrastriate body area for salient versus non-salient images of bodies. Older adults showed no such advantage in selective processing under arousal. These age differences could not be attributed to changes in the arousal response or less neural distinctiveness in old age. Rather, our results suggest that, relative to young adults, older adults become less effective at focusing on salient over non-salient details during increases in emotional arousal.

neuroscience↗

bioRxiv2021

Retro Drug Design: From Target Properties to Molecular Structures

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

bioinformatics↗

bioRxiv2021

Chemoproteomic study uncovers HemK2/KMT9 as a new target for NTMT1 bisubstrate inhibitors

Understanding the selectivity of methyltransferase inhibitors is important to dissect the functions of each methyltransferase target. From this perspective, here we report a chemoproteomic study to profile the selectivity of a potent protein N-terminal methyltransferase 1 (NTMT1) bisubstrate inhibitor NAH-C3-GPKK (Ki, app = 7{+/-}1 nM) in endogenous proteomes. First, we describe the rational design, synthesis, and biochemical characterization of a new chemical probe 6, a biotinylated analogue of NAH-C3-GPKK. Next, we systematically analyze protein networks that may selectively interact with the biotinylated probe 6 in concert with the competitor NAH-C3-GPKK. Besides NTMT1, the designated NTMT1 bisubstrate inhibitor NAH-C3-GPKK was found to also potently inhibit a methyltransferase complex HemK2-Trm112 (also known as KMT9-Trm112), highlighting the importance of systematic selectivity profiling. Furthermore, this is the first potent inhibitor for HemK2/KMT9 reported to date. Thus, our studies lay the foundation for future efforts towards the development of selective inhibitors for NTMT1 and HemK2/KMT9. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/439666v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1bedeb5org.highwire.dtl.DTLVardef@f58a4org.highwire.dtl.DTLVardef@1847a17org.highwire.dtl.DTLVardef@6ba9eb_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

MQuad enables clonal substructure discovery using single cell mitochondrial variants

Mitochondrial mutations are increasingly recognised as informative endogenous genetic markers that can be used to reconstruct cellular clonal structure using single-cell RNA or DNA sequencing data. However, there is a lack of effective computational methods to identify informative mtDNA variants in noisy and sparse single-cell sequencing data. Here we present an open source computational tool MQuad that accurately calls clonally informative mtDNA variants in a population of single cells, and an analysis suite for complete clonality inference, based on single cell RNA or DNA sequencing data. Through a variety of simulated and experimental single cell sequencing data, we showed that MQuad can identify mitochondrial variants with both high sensitivity and specificity, outperforming existing methods by a large extent. Furthermore, we demonstrated its wide applicability in different single cell sequencing protocols, particularly in complementing single-nucleotide and copy-number variations to extract finer clonal resolution. MQuad is a Python package available via https://github.com/single-cell-genetics/MQuad.

bioinformatics↗

bioRxiv2021

Novel bisubstrate inhibitors for protein N-terminal acetyltransferase D

Protein N-terminal acetyltransferase D (NatD, NAA40) that specifically acetylates the alpha-N-terminus of histone H4 and H2A has been implicated in various diseases, but no inhibitor has been reported for this important enzyme. Based on the acetyl transfer mechanism of NatD, we designed and prepared a series of highly potent NatD bisubstrate inhibitors by covalently linking coenzyme A to different peptide substrates via an acetyl or propionyl spacer. The most potent bisubstrate inhibitor displayed an apparent Ki value of 1.0 nM. Biochemical studies indicated that bisubstrate inhibitor is competitive to the peptide substrate and noncompetitive to the cofactor, suggesting NatD undergoes an ordered Bi-Bi mechanism. We also demonstrated that these inhibitors are highly specific towards NatD, displaying about 1,000-fold selectivity over other closely related acetyltransferases. High-resolution crystal structures of NatD bound to two of these inhibitors revealed the molecular basis for their selectivity and inhibition mechanism, providing a rational path for future inhibitor development.

biochemistry↗

bioRxiv2020

Development of a continuous fluorescence-based assay for N-terminal acetyltransferase D

N-terminal acetylation catalyzed by N-terminal acetyltransferases (NATs) has various biological functions in protein regulation. N-terminal acetyltransferase D (NatD) is one of the most specific NAT with only histone H4 and H2A proteins as the known substrates. Dysregulation of NatD has been implicated in colorectal and lung cancer progression, implying its therapeutic potential in cancers. However, there is no reported inhibitor for NatD yet. To facilitate the discovery of small-molecule NatD inhibitors, we report the development of a fluorescence-based acetyltransferase assay in 384-well high-throughput screening (HTS) format through monitoring the formation of coenzyme A. The fluorescent signal is generated from the adduct in the reaction between coenzyme A and fluorescent probe ThioGlo4. The assay exhibited a Z-factor of 0.77 and a coefficient of variation of 6%, indicating it is a robust assay for HTS. A pilot screen of 1280 pharmacologically active compounds and subsequent validation identified two hits, confirming the application of this fluorescence assay in HTS.

biochemistry↗

bioRxiv2020

In-House Forecasting of Crop Latitude Adaptation through a Daylength-sensing-based Environment Adaptation Simulator (DEAS)

Global climate change necessitates the accelerated breeding of new crop varieties that can sustain yields in new environments. As a proxy for environmental adaptation, the selection of crops that can adapt to different latitudes is an appealing strategy. However, such selection currently involves a lengthy procedure that severely restricts the rapid breeding of varieties. Here, we aimed to combine molecular technologies with an in-house streamlined screening method to facilitate rapid selection for latitude adaptation. We established the Daylength-sensing-based Environment Adaptation Simulator (DEAS) to measure crop latitude adaptation via the transcriptional dynamics of florigen genes at different latitudes. We used different statistical approaches to demonstrate that DEAS predicts the florigen expression profiles in rice with high accuracy. Furthermore, we demonstrated the potential for application of DEAS in different crops. Incorporating DEAS into the breeding programs of conventional and underutilized crops could help meet the future needs for crop adaptation and promote sustainable agriculture.

plant biology↗

bioRxiv2020

Diversity of ACE2 and its interaction with SARS-CoV-2 receptor binding domain

COVID-19, the clinical syndrome caused by the SARS-CoV-2 virus, has rapidly spread globally causing tens of millions of infections and over a million deaths. The potential animal reservoirs for SARS-CoV-2 are currently unknown, however sequence analysis has provided plausible potential candidate species. SARS-CoV-2 binds to the angiotensin I converting enzyme 2 (ACE2) to enable its entry into host cells and establish infection. We analyzed the binding surface of ACE2 from several important animal species to begin to understand the parameters for the ACE2 recognition by the SARS-CoV-2 spike protein receptor binding domain (RBD). We employed Shannon entropy analysis to determine the variability of ACE2 across its sequence and particularly in its RBD interacting region, and assessed differences between various species ACE2 and human ACE2. As cattle are a known reservoir for coronaviruses with previous human zoonotic transfer, and has a relatively divergent ACE2 sequence, we compared the binding kinetics of bovine and human ACE2 to SARS-CoV-2 RBD. This revealed a nanomolar binding affinity for bovine ACE2 but an approximate ten-fold reduction of binding compared to human ACE2. Since cows have been experimentally infected by SARS-CoV-2, this lower affinity sets a threshold for sequences with lower homology to human ACE2 to be able to serve as a productive viral receptor for SARS-CoV-2.

molecular biology↗

bioRxiv2020

Beam image-shift accelerated data acquisition for near-atomic resolution single-particle cryo-electron tomography

Tomographic reconstruction of cryopreserved specimens imaged in an electron microscope followed by extraction and averaging of sub-volumes has been successfully used to derive atomic models of macromolecules in their biological environment. Eliminating biochemical isolation steps required by other techniques, this method opens up the cell to in-situ structural studies. However, the need to compensate for errors in targeting introduced during mechanical navigation of the specimen significantly slows down tomographic data collection thus limiting its practical value. Here, we introduce protocols for tilt-series acquisition and processing that accelerate data collection speed by an order of magnitude and significantly improve map resolution compared to existing approaches. We achieve this by using beam-image shift to multiply the number of areas imaged at each stage position, by integrating geometrical constraints during imaging to achieve high precision targeting, and by performing per-tilt astigmatic CTF estimation and data-driven exposure weighting to improve final map resolution. We validated our beam image-shift electron cryo-tomography (BISECT) approach by determining the structure of a low molecular weight target (~300kDa) at 3.6 [A] resolution where density for individual side chains is clearly resolved.

biochemistry↗

bioRxiv2020

Discovery of a Potent and Dual-Selective Bisubstrate Inhibitor for Protein Arginine Methyltransferase 4/5

Protein arginine methyltransferases (PRMTs) have been implicated in the progression of many diseases. Understanding substrate recognition and specificity of individual PRMT would facilitate the discovery of selective inhibitors towards future drug discovery. Herein, we reported the design and synthesis of bisubstrate analogues for PRMTs that incorporate a S-adenosylmethionine (SAM) analogue moiety and a tripeptide through an alkyl substituted guanidino group. Compound AH237 is a potent and selective inhibitor for PRMT4 and PRMT5 with a half-maximal inhibition concentration (IC50) of 2.8 nM and <1.5 nM, respectively. Computational studies provided a plausible explanation for the high potency and selectivity of AH237 for PRMT4/5 over other 40 methyltransferases. This proof-of-principle study outlines an applicable strategy to develop potent and selective bisubstrate inhibitors for PRMTs, providing valuable probes for future structural studies.

biochemistry↗

bioRxiv2020

Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2

Drug repurposing is a rapid approach to identifying therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drug and 49 investigational drugs. Among these confirmed compounds, the anti-SARS-CoV-2 activities of 230 compounds, including 38 approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set of drug repurposing screen for SARS-CoV-2 is useful for drug repurposing efforts including design of new drug combinations for clinical trials.

microbiology↗

bioRxiv2020

Massive-scale biological activity-based modeling identifies novel antiviral leads against SARS-CoV-2

The recent global pandemic caused by the new coronavirus SARS-CoV-2 presents an urgent need for new therapeutic candidates. While the importance of traditional in silico approaches such as QSAR in such efforts in unquestionable, these models fundamentally rely on structural similarity to infer biological activity and are thus prone to becoming trapped in the very nearby chemical spaces of already known ligands. For novel and unprecedented threats such as COVID-19 much faster and efficient paradigms must be devised to accelerate the identification of new chemical classes for rapid drug development. Here we report the development of a new biological activity-based modeling (BABM) approach that builds on the hypothesis that compounds with similar activity patterns tend to share similar targets or mechanisms of action. In BABM, compound activity profiles established on massive scale across multiple assays are used as signatures to predict compound activity in a new assay or against a new target. We first trained and validated this approach by identifying new antiviral lead candidates for Zika and Ebola based on data from ~0.5 million compounds screened against ~2,000 assays. BABM models were then applied to predict ~300 compounds not previously reported to have activity for SARS-CoV-2, which were then tested in a live virus assay with high (>30%) hit rates. The most potent compounds showed antiviral activities in the nanomolar range. These potent confirmed compounds have the potential to be further developed in novel chemical space into new anti-SARS-CoV-2 therapies. These results demonstrate unprecedented ability using BABM to predict novel structures as chemical leads significantly beyond traditional methods, and its application in rapid drug discovery response in a global public health crisis.

cell biology↗

bioRxiv2020

Ferroportin 3 is a dual-targeted mitochondrial/chloroplast iron exporter necessary for iron homeostasis in Arabidopsis

Mitochondria and chloroplasts are organelles with high iron demand that are particularly susceptible to iron-induced oxidative stress. Despite the necessity of strict iron regulation in these organelles, much remains unknown about mitochondrial and chloroplast iron transport in plants. Here, we propose that Arabidopsis Ferroportin 3 (FPN3) is an iron exporter dual-targeted to mitochondria and chloroplasts. FPN3 is expressed in shoots regardless of iron conditions, but its transcripts accumulate under iron deficiency in roots. fpn3 mutants cannot grow as well as wild type under iron-deficient conditions and shoot iron levels are reduced in fpn3 mutants compared to wild type. ICP-MS measurements show that iron levels in the mitochondria and chloroplasts are increased relative to wild type, consistent with the proposed role of FPN3 as a mitochondrial/plastid iron exporter. In iron deficient fpn3 mutants, abnormal mitochondrial ultrastructure was observed, whereas chloroplast ultrastructure was not affected, implying that FPN3 plays a critical role in the mitochondria. Overall, our study suggests that FPN3 is essential for optimal iron homeostasis. Significance statementIron homeostasis must be tightly controlled in the mitochondria and chloroplasts, but iron trafficking in these organelles is not fully understood. Our work suggests that FPN3 is an iron exporter required for maintaining proper iron levels in mitochondria and chloroplasts. Furthermore, FPN3 is necessary for the optimal growth and normal mitochondrial ultrastructure under iron deficiency. This study reveals the physiological role of FPN3 and advances our understanding of iron regulation in mitochondria and chloroplasts.

plant biology↗

bioRxiv2020

Berberine reverses multidrug resistance in Candida albicans by hijacking the drug efflux pump Mdr1p

Clinical use of antimicrobials faces great challenges from the emergence of multidrug resistant (MDR) pathogens. The overexpression of drug efflux pumps is one of the major contributors to MDR. It is considered as a promising approach to overcome MDR by reversing the function of drug efflux pumps. In the life-threatening fungal pathogen Candida albicans, the major facilitator superfamily (MFS) transporter Mdr1p can excrete many structurally unrelated antifungals, leading to multidrug resistance. Here we report a counterintuitive case of reversing multidrug resistance in C. albicans by using a natural product berberine to hijack the overexpressed Mdr1p for its own importation. Moreover, we illustrate that the imported berberine accumulates in mitochondria, and compromises the mitochondrial function by impairing mitochondrial membrane potential and mitochondrial Complex I. It results in the selective elimination of Mdr1p overexpressed C. albicans cells. Furthermore, we show that berberine treatment can prolong the mean survival time (MST) of mice with a blood-borne dissemination of Mdr1p overexpressed multidrug resistant candidiasis. This study provided a potential direction of novel anti-MDR drug discovery by screening for multidrug efflux pump converters.

microbiology↗

bioRxiv2020

Dissecting transcriptomic signatures of genotype x genotype interactions during the initiation of plant-rhizobium symbiosis

Rhizobia are ecologically important, facultative plant symbiotic microbes. In nature there exists large variability in the association of rhizobial strains and host plant of the same species. Here, we evaluated whether plant and rhizobial genotypes influence the initial transcriptional response of rhizobium following perception of host plant. RNA-sequencing of the model rhizobium Sinorhizobium meliloti exposed to root exudates or luteolin was performed in a combination of three S. meliloti strains and three Medicago sativa varieties. The response to root exudates involved hundreds of changes in the rhizobium transcriptome. Of the differentially expressed genes, expression of 35% were influenced by strain genotype, 16% by the plant genotype, and 29% by strain x host plant genotype interactions. We also examined the response of a hybrid S. meliloti strain, in which the symbiotic megaplasmid (~ 20% of the genome) was mobilized between two of the above-mentioned strains. Dozens of genes resulted up-regulated in the hybrid strain, indicative of nonadditive variation in the transcriptome. In conclusion, this study demonstrated that transcriptional responses of rhizobia upon perception of legumes is influenced by the genotypes of both symbiotic partners, and their interaction, suggesting a wide genetic spectrum of partner choice selection in plant-rhizobium symbiosis.

microbiology↗

bioRxiv2020

treeclimbR pinpoints the data-dependent resolution of hierarchical hypotheses

The arrangement of hypotheses in a hierarchical structure (e.g., phylogenies, cell types) appears in many research fields and indicates different resolutions at which data can be interpreted. A common goal is to find a representative resolution that gives high sensitivity to identify relevant entities (e.g., microbial taxa or cell subpopulations) that are related to a phenotypic outcome (e.g. disease status) while controlling false detections, therefore providing a more compact view of detected entities and summarizing characteristics shared among them. Current methods, either performing hypothesis tests at an arbitrary resolution or testing hypotheses at all possible resolutions leading to nested results, are suboptimal. Moreover, they are not flexible enough to work in situations where each entity has multiple features to consider and different resolutions might be required for different features. For example, in single cell RNA-seq data, an increasing focus is to find differential state genes that change expression within a cell subpopulation in response to an external stimulus. Such differential expression might occur at different resolutions (e.g., all cells or a small set of cells) for different genes. Our new algorithm treeclimbR is designed to fill this gap by exploiting a hierarchical tree of entities, proposing multiple candidates that capture the latent signal and pinpointing branches or leaves that contain features of interest, in a data-driven way. It outperforms currently available methods on synthetic data, and we highlight the approach on various applications, including microbiome and microRNA surveys as well as single cell cytometry and RNA-seq datasets. With the emergence of various multi-resolution genomic datasets, treeclimbR provides a thorough inspection on entities across resolutions and gives additional flexibility to uncover biological associations.

bioinformatics↗

bioRxiv2020

Intestinal inflammation modulates the expression of ACE2 and TMPRSS2 and potentially overlaps with the pathogenesis of SARS-CoV-2 related disease

The presence of gastrointestinal symptoms and high levels of viral RNA in the stool suggest active Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) replication within enterocytes. Here, in multiple, large cohorts of patients with inflammatory bowel disease (IBD), we have studied the intersections between Coronavirus Disease 2019 (COVID-19), intestinal inflammation and IBD treatment. A striking expression of ACE2 on the small bowel enterocyte brush border supports intestinal infectivity by SARS-CoV-2. Commonly used IBD medications, both biologic and non-biologic, do not significantly impact ACE2 and TMPRSS2 receptor expression in the uninflamed intestines. Additionally, we have defined molecular responses to COVID-19 infection that are also enriched in IBD, pointing to shared molecular networks between COVID-19 and IBD. These data generate a novel appreciation of the confluence of COVID-19- and IBD-associated inflammation and provide mechanistic insights supporting further investigation of specific IBD drugs in the treatment of COVID-19.

immunology↗

bioRxiv2020

Probing the Plasticity in the Active Site of Protein N-terminal Methyltransferase 1 Using Bisubstrate Analogs

The interactions of a series of bisubstrate analogs with protein N-terminal methyltransferase 1 (NTMT1) were examined to probe the molecular properties of the NTMT1 active site through biochemical characterization and structural studies. Our results indicate that a 2-C to 4-C atom linker enables its respective bisubstrate analog to occupy both substrate and cofactor binding sites of NTMT1, but the bisubstrate analog with a 5-C atom linker only interacts with the substrate binding site and functions as a substrate. Furthermore, the 4-C atom linker is the optimal and produces the most potent inhibitor (Ki, app = 130 {+/-} 40 pM) for NTMT1 to date, displaying over 100,000-fold selectivity over other methyltransferases and 3,000-fold even to its homolog NTMT2. This study reveals the molecular basis for the plasticity of the NTMT1 active site. Additionally, our study outlines a general guidance on the development of bisubstrate inhibitors for any methyltransferases.

biochemistry↗