Biology subjects

Huang, K.

Publications and source records attributed to Huang, K..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

The structure and diversity of the bacterial community in the overlying water of the Yangzong Lake in Yunnan

Bacteria are critical components of lake ecosystems, and understanding the characteristics of bacterial community structure and diversity indices is of significant importance for the analysis and management of lake ecology. In this study, overlying water samples were collected from Yangzong Lake in May, June, and July 2021. High-throughput sequencing and statistical analysis were employed to investigate the relationships between physicochemical factors, bacterial community structure, and diversity in the overlying water of Yangzong Lake. The results showed significant differences in oxidation-reduction potential among the sampling points. The order of oxidation-reduction potential was as follows: Yangzong DAHE > Yangzong Lake South > Yangzong Lake Middle > Yangzong Lake North, while other physicochemical factors exhibited minor differences. The number of OTUs and bacterial alpha diversity index in the overlying water of Yangzong Lake Middle were higher than those in Yangzong Lake North, Yangzong Lake South, and Yangzong DAHE. The dominant bacterial phyla in the overlying water were Actinobacteriota, Bacteroidota, and Proteobacteria. Actinobacteriota had the highest relative abundance of 34.63% in Yangzong Lake North, while Bacteroidota had the highest relative abundance of 33.79% in Yangzong Lake South, and Proteobacteria had the highest relative abundance of 27.29% in Yangzong Lake Middle. The dominant genera were hgcI_clade, CL500-29_marine_group, and Flavobacterium. Among them, hgcI_clade had the highest relative abundance of 37.56% in Yangzong Lake North, CL500-29_marine_group had the highest relative abundance of 35.22% in Yangzong Lake South, and Flavobacterium had the highest relative abundance of 42.02% in Yangzong Lake Middle. Correlation analysis revealed significant correlations between Campilobacterota at the phylum level and dissolved oxygen (DO) and total phosphorus (TP). At the genus level, Flavobacterium, Limnohabitans, and Pseudarcicella showed significant correlations with DO and TP.

microbiology↗

bioRxiv2024

Naa80 is required for actin N-terminal acetylation and normal hearing in zebrafish

Actin is a key component of the cytoskeleton of eukaryotic cells and is involved in numerous cellular functions. In animal cells, actins are uniquely N-terminally processed by a dedicated enzyme machinery to generate their mature acidic and acetylated forms. The final step of this maturation process involves N-terminal acetylation, a reaction catalyzed by NAA80 in humans. In human cell lines, N-terminal acetylation of actin plays a crucial role in maintaining normal cytoskeletal dynamics and cell motility. The physiological impact of actin N-terminal acetylation remains to be defined. Here, we developed a zebrafish naa80 knockout model and established that zNaa80 acetylates both muscle and non-muscle actins in vivo. Our in vitro investigation of purified zNaa80 unveiled a clear preference for acetylating N-termini derived from actins. Interestingly, zebrafish lacking actin N-terminal acetylation were viable and exhibited normal development, morphology and behaviour. In contrast, human individuals carrying pathogenic actin variants may present with hypotonia and hearing impairment. While zebrafish depleted for naa80 did not display any obvious muscle defects or abnormal muscle tissue, we found that they have abnormal inner ear development such as small otoliths and impaired response to sound stimuli. In sum, we have defined that zebrafish Naa80 N-terminally acetylates actins in vitro and in vivo and that actin N-terminal acetylation is essential for normal hearing in vivo.

biochemistry↗

bioRxiv2024

PVN-mPFC OT projections modulates pup-directed pup care or attacking in virgin mandarin voles

In many species, adult animals may exhibit caregiving or aggression towards conspecific offspring. The neural mechanisms underlying the infanticide and pup care remain poorly understood. Here, using monogamous virgin mandarin voles (Microtus mandarinus) that may exhibit pup care or infanticide, we found that more oxytocin (OT) neurons in the paraventricular nucleus (PVN) were activated during pup caring than infanticide. Optogenetic activation of OT neurons in the PVN facilitated pup-caring in male and female mandarin voles. In infanticide voles, optogenetic activation of PVN OT cells prolonged latency to approach and attack pups, whereas inhibition of these OT neurons facilitated approach and infanticide. In addition, OT release in the medial prefrontal cortex (mPFC) in pup-care voles increased upon approaching and retrieving pups, and decreased in infanticide voles upon attacking pups. Optogenetic activation of PVN OT neuron projections to the mPFC shortened the latency to approach and retrieve pups and facilitated the initiation of pup care, whereas inhibition of these projections had little effect. For pup-care females, neither activation nor inhibition of the fibers affected their behavior towards pups. In infanticide male and female voles, optogenetic activation of PVN-mPFC OT projection fibers prolonged the latency to approach and attack pups and suppressed the initiation of infanticide, whereas inhibition of these projections promoted approach and infanticide. Finally, we found that intraperitoneal injection of OT promoted pup care and inhibited infanticide behavior. It is suggested that the OT system, especially PVN OT neurons projecting to mPFC, modulates pup-directed behaviors and OT can be used to treat abnormal behavioral responses associated with some psychological diseases such as depression and psychosis.

neuroscience↗

bioRxiv2024

Deep Learning Behavioral Phenotyping System in the Diagnosis of Parkinson's Disease with Drosophila melanogaster

Drosophila Melanogaster is widely used as animal models for Parkinsons disease (PD) research. Because of the complexity of MoCap and quantitative assessment among Drosophila Melanogaster, however, there is a technical issue that identify PD symptoms within drosophila based on objective spontaneous behavioral characteristics. Here, we developed a deep learning framework generated from kinematic features of body posture and motion between wildtype and SNCAE46K mutant drosophila genetically modeled {square}-Syn, supporting clustering and classification of PD individuals. We record locomotor activity in a 3D-printed trap, and utilize the pre-analysis pose estimation software DeepLabCut (DLC) to calculate and generate numerical data representing the motion speed, tremor frequency, and limb motion of Drosophila Melanogaster. By plugging these data as the input, the diagnosis result (1/0) representing PD or WT as the output. Our result provides a toolbox which would be valuable in the investigation of PD progressing and pharmacotherapeutic drug development.

neuroscience↗

bioRxiv2024

Scaling laws of molecular residence time

Understanding the molecular residence autocorrelation function in liquid is of fundamental importance in physical and life science. Encoded in this function is not only the binding properties, but also the information of the liquid environment. Based on extensive in silico experiments and theoretical analysis, we reveal that power law residence scaling arises in both passive and active liquid, in contrast to the common sense of exponential decay. In simple homogeneous liquid, the scaling exponent depends solely on the system dimensionality. Such scaling law is robust against the superposition of diverse binding energies in single-phase liquid but can be breached if the system undergoes phase separation. Remarkably, in a dissipative system where phase separation is subject to non-equilibrium feedback controls, an anomalous power law emerges whose scaling exponent is in line with the puzzling residence scaling of transcription factors reported in recent experiments. Our results highlight the sensitivity of molecular residence to its surrounding liquid and suggest that active phase separation can serve as a scaling proofreading mechanism in gene regulation.

biophysics↗

bioRxiv2023

A compendium of Amplification-Related Gain Of Sensitivity (ARGOS) genes in human cancer

Chromosomal gains are among the most frequent somatic genetic alterations occurring in cancer. While the effect of sustained oncogene expression has been characterized, the impact of copy-number gains affecting collaterally-amplified "bystander" genes on cellular fitness remains less understood. To investigate this, we built a comprehensive map of dosage compensations across human cancers by integrating expression and copy number profiles from over 8,000 TCGA tumors and CCLE cell lines. Further, we analyzed the effect of gene overexpression across 17 human cancer ORF screens to provide an overview of genes that prove toxic to cancer cells when overexpressed. Combining these two independent approaches we propose a class of Amplification-Related Gain Of Sensitivity (ARGOS) genes. These genes are located in commonly amplified regions of the genome, have lower expression levels than expected by their copy-number status, and are toxic to cancer cells when overexpressed. We experimentally validated CDKN1A and RBM14 as high-confidence pan-cancer ARGOS genes in lung and breast cancer cell line models. We additionally suggest that RBM14s mechanism of toxicity involves altered DNA damage response and innate immune signaling processes following gene overexpression. Finally, we provide a comprehensive catalog of compensated, toxic, and ARGOS genes as a community resource.

cancer biology↗

bioRxiv2023

Psilocybin prevents activity-based anorexia in female rats by enhancing cognitive flexibility: contributions from 5-HT1A and 5-HT2A receptor mechanisms.

Psilocybin has shown promise for alleviating symptoms of depression and is currently in clinical trials for the treatment of anorexia nervosa (AN), a condition that is characterised by persistent cognitive inflexibility. Considering that enhanced cognitive flexibility after psilocybin treatment is reported to occur in individuals with depression, it is plausible that psilocybin could improve symptoms of AN by breaking down cognitive inflexibility. A mechanistic understanding of the actions of psilocybin is required to tailor the clinical application of psilocybin to individuals most likely to respond with positive outcomes. This can only be achieved using incisive neurobiological approaches in animal models. Here, we use the activity-based anorexia (ABA) rat model and comprehensively assess aspects of reinforcement learning to show that psilocybin (post-acutely) improves body weight maintenance in female rats and facilitates cognitive flexibility, specifically via improved adaptation to the initial reversal of reward contingencies. Further, we reveal the involvement of signalling through the serotonin (5-HT) 1A and 5-HT2A receptor subtypes in specific aspects of learning, demonstrating that 5-HT1A antagonism negates the cognitive enhancing effects of psilocybin. Moreover, we show that psilocybin elicits a transient increase and decrease in cortical transcription of these receptors (Htr2a and Htr1a, respectively), and a further reduction in the abundance of Htr2a transcripts in rats exposed to the ABA model. Together, these findings support the hypothesis that psilocybin could ameliorate cognitive inflexibility in the context of AN and highlight a need to better understand the therapeutic mechanisms independent of 5-HT2A receptor binding.

neuroscience↗

bioRxiv2023

Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors

Understanding a cells expression response to genetic perturbations helps to address important challenges in biology and medicine, including the function of gene circuits, discovery of therapeutic targets and cell reprogramming and engineering. In recent years, Perturb-seq, pooled genetic screens with single cell RNA-seq (scRNA-seq) readouts, has emerged as a common method to collect such data. However, irrespective of technological advances, because combinations of gene perturbations can have unpredictable, non-additive effects, the number of experimental configurations far exceeds experimental capacity, and for certain cases, the number of available cells. While recent machine learning models, trained on existing Perturb-seq data sets, can predict perturbation outcomes with some degree of accuracy, they are currently limited by sub-optimal training set selection and the small number of cell contexts of training data, leading to poor predictions for unexplored parts of perturbation space. As biologists deploy Perturb-seq across diverse biological systems, there is an enormous need for algorithms to guide iterative experiments while exploring the large space of possible perturbations and their combinations. Here, we propose a sequential approach for designing Perturb-seq experiments that uses the model to strategically select the most informative perturbations at each step for subsequent experiments. This enables a significantly more efficient exploration of the perturbation space, while predicting the effect of the rest of the unseen perturbations with high-fidelity. Analysis of a previous large-scale Perturb-seq experiment reveals that our setting is severely restricted by the number of examples and rounds, falling into a non-conventional active learning regime called "active learning on a budget". Motivated by this insight, we develop IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW, a novel active learning method that exploits rich and multi-modal prior knowledge in order to efficiently guide the selection of subsequent perturbations. Using prior knowledge for this task is novel, and crucial for successful active learning on a budget. We validate IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW using insilico benchmarking of active learning, constructed from a large-scale CRISPRi Perturb-seq data set. We find that IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW outperforms other active learning strategies by reaching comparable accuracy at only a third of the number of perturbations profiled as the next best method. Overall, our results demonstrate the potential of sequentially designing perturbation screens through IO_SCPLOWTERC_SCPLOWPO_SCPLOWERTC_SCPLOW.

bioinformatics↗

bioRxiv2023

A continental-scale analysis reveals the latitudinal gradient of stomatal density across amphistomatous species: Evolutionary history vs. present-day environment

Amphistomy is a potential method for increasing photosynthetic rate; however, latitudinal gradients of stomatal density across amphistomatous species and their drivers remain unknown. Here, the adaxial stomatal density (SDad) and abaxial stomatal density (SDab) of 486 amphistomatous species-site combinations, belonging to 32 plant families, were collected from China, and their total stomatal density (SDtotal) and stomatal ratio (SR) were calculated. Overall, these four stomatal traits did not show significant phylogenetic signals. There were no significant differences in SDab and SDtotal between woody and herbaceous species, but SDad and SR were higher in woody species than in herbaceous species. Besides, a significantly positive relationship between SDab and SDad was observed. We also found that stomatal density (including SDab, SDad, and SDtotal) decreased with latitude while SR increased with latitude, and temperature seasonality was the most important environmental factor driving it. Besides, evolutionary history (represented by both phylogeny and species) explained about 10-22 fold more of the variation in stomatal traits than the present-day environment (65.2%-71.1% vs. 2.9%-6.8%). Our study extended our knowledge of trait-environment relationships and highlighted the importance of evolutionary history in driving stomatal trait variability.

ecology↗

bioRxiv2023

ZHX2 alleviates vascular remodeling and smooth muscle cell proliferation by transcriptional regulation of GADD45G

ObjectiveThis study explores the role of ZHX2 in vascular remodeling, specifically focusing on its effects on VSMC proliferation, migration, and neointima formation following vascular injury. Methods and Results: Data from both human atherosclerotic samples and a mouse carotid injury model indicated a decrease in ZHX2 levels. In vivo, ZHX2 overexpression reduced neointima formation in mice subjected to carotid artery ligation. In vitro, ZHX2 inhibited the proliferation and migration of primary VSMCs. Conversely, specific knockout of ZHX2 in SMCs in vivo or knockdown of ZHX2 in primary VSMCs had opposite effects. RNA- seq analysis revealed that ZHX2 overexpression significantly affected the expression of cell cycle-related genes. Using Chromatin Immunoprecipitation Sequencing (ChIP-seq) and luciferase reporter assays, we demonstrated that ZHX2 plays a crucial role in the transcriptional regulation of GADD45G, identifying GADD45G as the downstream target responsible for ZHX2-mediated effects. Conclusions: ZHX2 emerges as a key player in pathological vascular remodeling, suppressing VSMC proliferation and migration through its regulatory impact on GADD45G transcription and cell cycle-related gene expression.

molecular biology↗

bioRxiv2023

A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions

The 5 UTR, a regulatory region at the beginning of an mRNA molecule, plays a crucial role in regulating the translation process and impacts the protein expression level. Language models have showcased their effectiveness in decoding the functions of protein and genome sequences. Here, we introduced a language model for 5 UTR, which we refer to as the UTR-LM. The UTR-LM is pre-trained on endogenous 5 UTRs from multiple species and is further augmented with supervised information including secondary structure and minimum free energy. We fine-tuned the UTR-LM in a variety of downstream tasks. The model outperformed the best-known benchmark by up to 42% for predicting the Mean Ribosome Loading, and by up to 60% for predicting the Translation Efficiency and the mRNA Expression Level. The model also applies to identifying unannotated Internal Ribosome Entry Sites within the untranslated region and improves the AUPR from 0.37 to 0.52 compared to the best baseline. Further, we designed a library of 211 novel 5 UTRs with high predicted values of translation efficiency and evaluated them via a wet-lab assay. Experiment results confirmed that our top designs achieved a 32.5% increase in protein production level relative to well-established 5 UTR optimized for therapeutics.

bioinformatics↗

bioRxiv2023

TransGeneSelector: A Transformer-based Approach Tailored for Key Gene Mining with Small Plant Transcriptomic Datasets

Gene mining, particularly from small sample sizes such as in plants, remains a challenge in life sciences. Traditional methods often omit significant genes, while deep learning techniques are hindered by small sample constraints and lack specialized gene mining approaches. This paper presents TransGeneSelector, the first deep learning method tailored for key gene mining in small transcriptomic datasets, ingeniously integrating data augmentation, sample filtering, and a Transformer-based classifier. Tested on Arabidopsis thaliana seeds germination classification using just 79 samples, it not only achieves classification performance on par with, if not superior to, Random Forest and SVM but also excels in identifying upstream regulatory genes that Random Forest might miss, and these pinpointed genes more accurately reflect the metabolic processes inherent in seed germination. TransGeneSelectors ability to mine vital genes from limited datasets signifies its potential as the current state-of-the-art in gene mining in small sample scenarios, providing an efficient and versatile solution for this critical research area.

bioinformatics↗

bioRxiv2023

A suite of engineered mice for interrogating psychedelic drug actions

Psychedelic drugs like lysergic acid diethylamide (LSD) and psilocybin have emerged as potentially transformative therapeutics for many neuropsychiatric diseases, including depression, anxiety, post-traumatic stress disorder, migraine, and cluster headaches. LSD and psilocybin exert their psychedelic effects via activation of the 5-hydroxytryptamine 2A receptor (HTR2A). Here we provide a suite of engineered mice useful for clarifying the role of HTR2A and HTR2A-expressing neurons in psychedelic drug actions. We first generated Htr2a-EGFP-CT-IRES-CreERT2 mice (CT:C-terminus) to independently identify both HTR2A-EGFP-CT receptors and HTR2A-containing cells thereby providing a detailed anatomical map of HTR2A and identifying cell types that express HTR2A. We also generated a humanized Htr2a mouse line and an additional constitutive Htr2A-Cre mouse line. Psychedelics induced a variety of known behavioral changes in our mice validating their utility for behavioral studies. Finally, electrophysiology studies revealed that extracellular 5-HT elicited a HTR2A-mediated robust increase in firing of genetically-identified pyramidal neurons--consistent with a plasma membrane localization and mode of action. These mouse lines represent invaluable tools for elucidating the molecular, cellular, pharmacological, physiological, behavioral, and other actions of psychedelic drugs in vivo.

neuroscience↗

bioRxiv2023

starTracer: An Accelerated Approach for Precise Marker Gene Identification in Single-Cell RNA-Seq Analysis

We introduce starTracer, a novel R package designed to enhance the specificity and efficiency of marker gene identification in single-cell RNA-seq data analysis. The package consists of two primary functional modules: "searchMarker" and "filterMarker". The "searchMarker" module, operating as an independent pipeline, exhibits superior flexibility by accepting a variety of input file types. Its primary output is a marker gene matrix, where genes are sorted by their potential to function as cluster-specific markers, with those exhibiting the greatest potential positioned at the top of the matrix for each respective cluster. In contrast, the "filterMarker" module is designed as a complementary pipeline to the Seurat "FindAllMarkers" function, providing a more accurate marker gene list for each cluster in conjunction with Seurat results. Benchmark analyses demonstrate that starTracer not only achieves excellent specificity in identifying marker genes compared to Seurat but also significantly surpasses it in processing speed. Impressively, the speed improvement ranges by 1~2 orders of magnitude compared to Seurat, as observed across three independent datasets. It is worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. Furthermore, the "filterMarker" reordering process considerably enhances Seurats marker matrix specificity. These advantages solidify starTracer as an invaluable tool for researchers working with single-cell RNA-seq data, merging robust accuracy with exceptional speed.

bioinformatics↗

bioRxiv2023

Inferring cell trajectories of spatial transcriptomics via optimal transport analysis

The integration of cell transcriptomics and spatial coordinates to organize differentiation trajectories remains a challenge. Here we introduce spaTrack, a trajectory inference method using optimal transport to incorporate both transcriptomics and distance of spatial transcriptomics sequencing data into transition costs. spaTrack could construct fine spatial trajectories reflecting the true differentiation topology, as well as trace cell dynamics across multiple samples with temporal intervals. To capture the dynamic drivers, spaTrack models the cell fate as a function of expression profile along temporal intervals driven by transcription factors. Applying spaTrack, we successfully disentangle spatiotemporal trajectories of axolotl telencephalon regeneration and mouse midbrain development. Furthermore, we uncover diverse malignant lineages expanding in a primary tumor. One of the lineages with upregulated extracellular matrix organization implants to the metastatic site and subsequently colonizes to a secondary tumor. Overall, spaTrack greatly facilitates trajectory inference from spatial transcriptomics, providing insights in cell differentiation of broad areas.

bioinformatics↗

bioRxiv2023

Building explainable graph neural network by sparse learning for the drug-protein binding prediction

Explainable Graph Neural Networks (GNNs) have been developed and applied to drug-protein binding prediction to identify the key chemical structures in a drug that have active interactions with the target proteins. However, the key structures identified by the current explainable GNN models are typically chemically invalid. Furthermore, a threshold needs to be manually selected to pinpoint the key structures from the rest. To overcome the limitations of the current explainable GNN models, we propose our SLGNN, which stands for using Sparse Learning to Graph Neural Networks. Our SLGNN relies on using a chemical-substructure-based graph (where nodes are chemical substructures) to represent a drug molecule. Furthermore, SLGNN incorporates generalized fussed lasso with message-passing algorithms to identify connected subgraphs that are critical for the drug-protein binding prediction. Due to the use of the chemical-substructure-based graph, it is guaranteed that any subgraphs in a drug identified by our SLGNN are chemically valid structures. These structures can be further interpreted as the key chemical structures for the drug to bind to the target protein. We demonstrate the explanatory power of our SLGNN by first showing all the key structures identified by our SLGNN are chemically valid. In addition, we illustrate that the key structures identified by our SLGNN have more predictive power than the key structures identified by the competing methods. At last, we use known drug-protein binding data to show the key structures identified by our SLGNN contain most of the binding sites. CCS CONCEPTS* Computing methodologies [->] Neural networks; Supervised learning by classification; Neural networks. ACM Reference FormatYang Wang, Zanyu Shi, Timothy Richardson, Kun Huang, Pathum Weerawarna, and Yijie Wang. 2023. Building explainable graph neural network by sparse learning for the drug-protein binding prediction. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (CNB MAC 2023). ACM, New York, NY, USA, 10 pages. https://doi.org/XXXXXXX.XXXXXXX

bioinformatics↗

bioRxiv2023

Lung-innervating neurons expressing Tmc3 can induce broncho-constriction and dilation with direct consequences for the respiratory cycle

Sensory neurons of the vagal ganglia (VG) innervate lungs and play a critical role in maintaining airway homeostasis. However, the specific VG neurons that innervate lungs, and the mechanisms by which these neurons sense and respond to airway insults, are not well understood. Here, we identify a subpopulation of lung-innervating VG neurons defined by their expression of Tmc3. Single cell transcriptomics illuminated several subpopulations of Tmc3+ sensory neurons, revealing distinct Piezo2- and Trpv1-expressing subclusters. Furthermore, Tmc3 deficiency in VG neurons leads to global and subcluster specific transcriptional changes related to metabolic and ion channel function. Importantly, we show that broncho-constriction and dilation can be modulated through inhibition or activation of Tmc3+ VG neurons resulting in a decrease or increase of end-expiratory lung volume, respectively. Together, our data show that Tmc3 is a marker of lung-innervating neurons and may play a pivotal role in maintaining fundamental inspiratory and expiratory processes. SignificanceHarnessing the neuronal mechanisms that regulate lung function offers potential alternatives to existing corticosteroid treatment regimens for respiratory illness associated with acute bronchoconstriction including asthma, COPD, and emphysema. Our findings define Transmembrane channel-like 3, Tmc3, as a marker of lung-innervating sensory neurons, identify distinct subpopulations of Tmc3+ neurons with unique transcriptional profiles, and show that activation or inhibition of these neurons has a significant impact on airway function. Our work highlights potential avenues of novel targeted intervention in respiratory conditions driven by dysfunctional neuronal reflexes.

neuroscience↗

bioRxiv2023

A cryo-EM structure of KTF1-bound polymerase V transcription elongation complex

De novo DNA methylation in plants relies on transcription elongation of RNA polymerase V (Pol V) along with KTF1 and SPT4, which produces long non-coding RNAs for recruitment and assembly of DNA methylation machinery. Here, we report a cryo-EM structure of the KTF1-bound Pol V transcription elongation complex. The structure reveals conformation of the structure motifs in the active site of Pol V that accounts for its inferior RNA-extension ability. The structure further reveals structural features of Pol V that prevent it from interacting with the general transcription factors of Pol II and Pol IV. The KOW5 domain of KTF1 binds near the RNA exit channel of Pol V, where it provides a scaffold for the proposed recruitment of Argonaute proteins to initiate the assembly of DNA methylation machinery. The structure provides insight into the Pol V transcription elongation and the role of KTF1 during Pol V transcription-coupled DNA methylation.

biochemistry↗