Pharmacological Targeting of STK19 Inhibits Oncogenic NRAS-Driven Melanomagenesis.

Activating mutations in NRAS account for 20%-30% of melanoma, but despite decades of research and in contrast to BRAF, no effective anti-NRAS therapies have been forthcoming. Here, we identify a previously uncharacterized serine/threonine kinase STK19 as a novel NRAS activator. STK19 phosphorylates NRAS to enhance its binding to its downstream effectors and promotes oncogenic NRAS-mediated melanocyte malignant transformation. A recurrent D89N substitution in STK19 whose alterations were identified in 25% of human melanomas represents a gain-of-function mutation that interacts better with NRAS to enhance melanocyte transformation. STK19D89N knockin leads to skin hyperpigmentation and promotes NRASQ61R-driven melanomagenesis in vivo. Finally, we developed ZT-12-037-01 (1a) as a specific STK19-targeted inhibitor and showed that it effectively blocks oncogenic NRAS-driven melanocyte malignant transformation and melanoma growth in vitro and in vivo. Together, our findings provide a new and viable therapeutic strategy for melanomas harboring NRAS mutations.

Crystal Structure of the Human Cannabinoid Receptor CB1.

Cannabinoid receptor 1 (CB1) is the principal target of Δ9-tetrahydrocannabinol (THC), a psychoactive chemical from Cannabis sativa with a wide range of therapeutic applications and a long history of recreational use. CB1 is activated by endocannabinoids and is a promising therapeutic target for pain management, inflammation, obesity, and substance abuse disorders. Here, we present the 2.8 Å crystal structure of human CB1 in complex with AM6538, a stabilizing antagonist, synthesized and characterized for this structural study. The structure of the CB1-AM6538 complex reveals key features of the receptor and critical interactions for antagonist binding. In combination with functional studies and molecular modeling, the structure provides insight into the binding mode of naturally occurring CB1 ligands, such as THC, and synthetic cannabinoids. This enhances our understanding of the molecular basis for the physiological functions of CB1 and provides new opportunities for the design of next-generation CB1-targeting pharmaceuticals.

Global population exposure to landscape fire air pollution from 2000 to 2019.

Wildfires are thought to be increasing in severity and frequency as a result of climate change1-5. Air pollution from landscape fires can negatively affect human health4-6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale7-23. Here, we estimate global daily LFS outdoor fine particulate matter (PM2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the period 2000-2019 with the help of machine learning and chemical transport models. We found that overall population-weighted average LFS PM2.5 and ozone concentrations were 2.5 µg m-3 (6.1% of all-source PM2.5) and 3.2 µg m-3 (3.6% of all-source ozone), respectively, in 2010-2019, with a slight increase for PM2.5, but not for ozone, compared with 2000-2009. Central Africa, Southeast Asia, South America and Siberia experienced the highest LFS PM2.5 and ozone concentrations. The concentrations of LFS PM2.5 and ozone were about four times higher in low-income countries than in high-income countries. During the period 2010-2019, 2.18 billion people were exposed to at least 1 day of substantial LFS air pollution per year, with each person in the world having, on average, 9.9 days of exposure per year. These two metrics increased by 6.8% and 2.1%, respectively, compared with 2000-2009. Overall, we find that the global population is increasingly exposed to LFS air pollution, with socioeconomic disparities.

Cryo-EM Structures of Human Drosha and DGCR8 in Complex with Primary MicroRNA.

Metazoan microRNAs require specific maturation steps initiated by Microprocessor, comprising Drosha and DGCR8. Lack of structural information for the assembled complex has hindered an understanding of how Microprocessor recognizes primary microRNA transcripts (pri-miRNAs). Here we present a cryoelectron microscopy structure of human Microprocessor with a pri-miRNA docked in the active site, poised for cleavage. The basal junction is recognized by a four-way intramolecular junction in Drosha, triggered by the Belt and Wedge regions that clamp over the ssRNA. The belt is important for efficiency and accuracy of pri-miRNA processing. Two dsRBDs form a molecular ruler to measure the stem length between the two dsRNA-ssRNA junctions. The specific organization of the dsRBDs near the apical junction is independent of Drosha core domains, as observed in a second structure in the partially docked state. Collectively, we derive a molecular model to explain how Microprocessor recognizes a pri-miRNA and accurately identifies the cleavage site.

The transcription factors ZAT5 and BLH2/4 regulate homogalacturonan demethylesterification in Arabidopsis seed coat mucilage.

The level of methylesterification alters the functional properties of pectin, which is believed to influence plant growth and development. However, the mechanisms that regulate demethylesterification remain largely unexplored. Pectin with a high degree of methylesterification is produced in the Golgi apparatus and then transferred to the primary cell wall where it is partially demethylesterified by pectin methylesterases (PMEs). Here, we show that in Arabidopsis (Arabidopsis thaliana) seed mucilage, pectin demethylesterification is negatively regulated by the transcription factor ZINC FINGER FAMILY PROTEIN5 (ZAT5). Plants carrying null mutations in ZAT5 had increased PME activity, decreased pectin methylesterification, and produced seeds with a thinner mucilage layer. We provide evidence that ZAT5 binds to a TGATCA motif and thereby negatively regulates methylesterification by reducing the expression of PME5, HIGHLY METHYL ESTERIFIED SEEDS (HMS)/PME6, PME12, and PME16. We also demonstrate that ZAT5 physically interacts with BEL1-LIKE HOMEODOMAIN2 (BLH2) and BLH4 transcription factors. BLH2 and BLH4 are known to modulate pectin demethylesterification by directly regulating PME58 expression. The ZAT5-BLH2/4 interaction provides a mechanism to control the degree of pectin methylesterification in seed coat mucilage by modifying each transcription factor's ability to regulate the expression of target genes encoding PMEs. Taken together, these findings reveal a transcriptional regulatory module comprising ZAT5, BLH2, and BLH4, that functions in modulating the demethylesterification of homogalacturonan in seed coat mucilage.

Cryo-EM structures of full-length Tetrahymena ribozyme at 3.1 Å resolution.

Single-particle cryogenic electron microscopy (cryo-EM) has become a standard technique for determining protein structures at atomic resolution1-3. However, cryo-EM studies of protein-free RNA are in their early days. The Tetrahymena thermophila group I self-splicing intron was the first ribozyme to be discovered and has been a prominent model system for the study of RNA catalysis and structure-function relationships4, but its full structure remains unknown. Here we report cryo-EM structures of the full-length Tetrahymena ribozyme in substrate-free and bound states at a resolution of 3.1 Å. Newly resolved peripheral regions form two coaxially stacked helices; these are interconnected by two kissing loop pseudoknots that wrap around the catalytic core and include two previously unforeseen (to our knowledge) tertiary interactions. The global architecture is nearly identical in both states; only the internal guide sequence and guanosine binding site undergo a large conformational change and a localized shift, respectively, upon binding of RNA substrates. These results provide a long-sought structural view of a paradigmatic RNA enzyme and signal a new era for the cryo-EM-based study of structure-function relationships in ribozymes.

Tertiary folds of the SL5 RNA from the 5' proximal region of SARS-CoV-2 and related coronaviruses.

Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2), resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4 to 6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across these human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9 to 8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4 to 9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities and notable differences, with implications for potential protein-binding modes and therapeutic targets.

MLSNet: a deep learning model for predicting transcription factor binding sites.

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

Endogenous cell membrane interactome mapping for the GLP-1 receptor in different cell types.

The GLP-1 receptor, one of the most successful drug targets for the treatment of type 2 diabetes and obesity, is known to engage multiple intracellular signaling proteins. However, it remains less explored how the receptor interacts with proteins on the cell membrane. Here, we present a ligand-based proximity labeling approach to interrogate the native cell membrane interactome for the GLP-1 receptor after agonist simulation. Our study identified several unreported putative cell membrane interactors for the endogenous receptor in either a pancreatic ß cell line or a neuronal cell line. We further uncovered new regulators of GLP-1 receptor-mediated signaling and insulinotropic responses in ß cells. Additionally, we obtained a time-resolved cell membrane interactome map for the receptor in ß cells. Therefore, our study provides a new approach that is generalizable to map endogenous cell membrane interactomes for G-protein-coupled receptors to decipher the molecular basis of their cell-type-specific functional regulation.

Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models.

Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.

Molecular basis for C-degron recognition by CRL2APPBP2 ubiquitin ligase.

E3 ubiquitin ligases determine the specificity of eukaryotic protein degradation by selective binding to destabilizing protein motifs, termed degrons, in substrates for ubiquitin-mediated proteolysis. The exposed C-terminal residues of proteins can act as C-degrons that are recognized by distinct substrate receptors (SRs) as part of dedicated cullin-RING E3 ubiquitin ligase (CRL) complexes. APPBP2, an SR of Cullin 2-RING ligase (CRL2), has been shown to recognize R-x-x-G/C-degron; however, the molecular mechanism of recognition remains elusive. By solving several cryogenic electron microscopy structures of active CRL2APPBP2 bound with different R-x-x-G/C-degrons, we unveiled the molecular mechanisms underlying the assembly of the CRL2APPBP2 dimer and tetramer, as well as C-degron recognition. The structural study, complemented by binding experiments and cell-based assays, demonstrates that APPBP2 specifically recognizes the R-x-x-G/C-degron via a bipartite mechanism; arginine and glycine, which play critical roles in C-degron recognition, accommodate distinct pockets that are spaced by two residues. In addition, the binding pocket is deep enough to enable the interaction of APPBP2 with the motif placed at or up to three residues upstream of the C-end. Overall, our study not only provides structural insight into CRL2APPBP2-mediated protein turnover but also serves as the basis for future structure-based chemical probe design.

Trying Harder: How Cognitive Effort Sculpts Neural Representations during Working Memory.

While the exertion of mental effort improves performance on cognitive tasks, the neural mechanisms by which motivational factors impact cognition remain unknown. Here, we used fMRI to test how changes in cognitive effort, induced by changes in task difficulty, impact neural representations of working memory (WM). Participants (both sexes) were precued whether WM difficulty would be hard or easy. We hypothesized that hard trials demanded more effort as a later decision required finer mnemonic precision. Behaviorally, pupil size was larger and response times were slower on hard compared with easy trials suggesting our manipulation of effort succeeded. Neurally, we observed robust persistent activity during delay periods in the prefrontal cortex (PFC), especially during hard trials. Yet, details of the memoranda could not be decoded from patterns in prefrontal activity. In the patterns of activity in the visual cortex, however, we found strong decoding of memorized targets, where accuracy was higher on hard trials. To potentially link these across-region effects, we hypothesized that effort, carried by persistent activity in the PFC, impacts the quality of WM representations encoded in the visual cortex. Indeed, we found that the amplitude of delay period activity in the frontal cortex predicted decoded accuracy in the visual cortex on a trial-wise basis. These results indicate that effort-related feedback signals sculpt population activity in the visual cortex, improving mnemonic fidelity.

SMG: self-supervised masked graph learning for cancer gene identification.

Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG reconstruction (pretext task) and task-specific fine-tuning (downstream task). In the pretext task, the nodes of multi-omic featured protein-protein interaction (PPI) networks are randomly substituted with a defined mask token. The PPI networks are then reconstructed using the graph neural network (GNN)-based autoencoder, which explores the node correlations in a self-prediction manner. In the downstream tasks, the pre-trained GNN encoder embeds the input networks into feature graphs, whereas a task-specific layer proceeds with the final prediction. To assess the performance of the proposed SMG method, benchmarking experiments are performed on three node-level tasks (identification of CGs, essential genes and healthy driver genes) and one graph-level task (identification of disease subnetwork) across eight PPI networks. Benchmarking experiments and performance comparison with existing state-of-the-art methods demonstrate the superiority of SMG on multi-omic feature engineering.

Enhanced mitophagy driven by ADAR1-GLI1 editing supports the self-renewal of cancer stem cells in HCC.

BACKGROUND AND AIMS: Deregulation of adenosine-to-inosine editing by adenosine deaminase acting on RNA 1 (ADAR1) leads to tumor-specific transcriptome diversity with prognostic values for HCC. However, ADAR1 editase-dependent mechanisms governing liver cancer stem cell (LCSC) generation and maintenance have remained elusive. APPROACH AND RESULTS: RNA-seq profiling identified ADAR1-responsive recoding editing events in HCC and showed editing frequency of GLI1 , rather than transcript abundance was clinically relevant. Functional differences in LCSC self-renewal and tumor aggressiveness between wild-type (GLI1 wt ) and edited GLI1 (GLI1 edit ) were elucidated. We showed that overediting of GLI1 induced an arginine-to-glycine (R701G) substitution, augmenting tumor-initiating potential and exhibiting a more aggressive phenotype. GLI1 R701G harbored weak affinity to SUFU, which in turn, promoted its cytoplasmic-to-nuclear translocation to support LCSC self-renewal by increased pluripotency gene expression. Moreover, editing predisposed to stabilize GLI1 by abrogating ß-TrCP-GLI1 interaction. Integrative analysis of single-cell transcriptome further revealed hyperactivated mitophagy in ADAR1-enriched LCSCs. GLI1 editing promoted a metabolic switch to oxidative phosphorylation to control stress and stem-like state through PINK1-Parkin-mediated mitophagy in HCC, thereby conferring exclusive metastatic and sorafenib-resistant capacities. CONCLUSIONS: Our findings demonstrate a novel role of ADAR1 as an active regulator for LCSCs properties through editing GLI1 in the highly heterogeneous HCC.

Molecular and phylogenetic evidence of parallel expansion of anion channels in plants.

Aluminum-activated malate transporters (ALMTs) and slow anion channels (SLACs) are important in various physiological processes in plants, including stomatal regulation, nutrient uptake, and in response to abiotic stress such as aluminum toxicity. To understand their evolutionary history and functional divergence, we conducted phylogenetic and expression analyses of ALMTs and SLACs in green plants. Our findings from phylogenetic studies indicate that ALMTs and SLACs may have originated from green algae and red algae, respectively. The ALMTs of early land plants and charophytes formed a monophyletic clade consisting of three subgroups. A single duplication event of ALMTs was identified in vascular plants and subsequent duplications into six clades occurred in angiosperms, including an identified clade, 1-1. The ALMTs experienced gene number losses in clades 1-1 and 2-1 and expansions in clades 1-2 and 2-2b. Interestingly, the expansion of clade 1-2 was also associated with higher expression levels compared to genes in clades that experienced apparent loss. SLACs first diversified in bryophytes, followed by duplication in vascular plants, giving rise to three distinct clades (I, II, and III), and clade II potentially associated with stomatal control in seed plants. SLACs show losses in clades II and III without substantial expansion in clade I. Additionally, ALMT clade 2-2 and SLAC clade III contain genes specifically expressed in reproductive organs and roots in angiosperms, lycophytes, and mosses, indicating neofunctionalization. In summary, our study demonstrates the evolutionary complexity of ALMTs and SLACs, highlighting their crucial role in the adaptation and diversification of vascular plants.

Snapshots of the first-step self-splicing of Tetrahymena ribozyme revealed by cryo-EM.

Tetrahymena ribozyme is a group I intron, whose self-splicing is the result of two sequential ester-transfer reactions. To understand how it facilitates catalysis in the first self-splicing reaction, we used cryogenic electron microscopy (cryo-EM) to resolve the structures of L-16 Tetrahymena ribozyme complexed with a 11-nucleotide 5'-splice site analog substrate. Four conformations were achieved to 4.14, 3.18, 3.09 and 2.98 Å resolutions, respectively, corresponding to different splicing intermediates during the first enzymatic reaction. Comparison of these structures reveals structural alterations, including large conformational changes in IGS/IGSext (P1-P1ext duplex) and J5/4, as well as subtle local rearrangements in the G-binding site. These structural changes are required for the enzymatic activity of the Tetrahymena ribozyme. Our study demonstrates the ability of cryo-EM to capture dynamic RNA structural changes, ushering in a new era in the analysis of RNA structure-function by cryo-EM.

Topological crossing in the misfolded Tetrahymena ribozyme resolved by cryo-EM.

The Tetrahymena group I intron has been a key system in the understanding of RNA folding and misfolding. The molecule folds into a long-lived misfolded intermediate (M) in vitro, which has been known to form extensive native-like secondary and tertiary structures but is separated by an unknown kinetic barrier from the native state (N). Here, we used cryogenic electron microscopy (cryo-EM) to resolve misfolded structures of the Tetrahymena L-21 ScaI ribozyme. Maps of three M substates (M1, M2, M3) and one N state were achieved from a single specimen with overall resolutions of 3.5 Å, 3.8 Å, 4.0 Å, and 3.0 Å, respectively. Comparisons of the structures reveal that all the M substates are highly similar to N, except for rotation of a core helix P7 that harbors the ribozyme's guanosine binding site and the crossing of the strands J7/3 and J8/7 that connect P7 to the other elements in the ribozyme core. This topological difference between the M substates and N state explains the failure of 5'-splice site substrate docking in M, supports a topological isomer model for the slow refolding of M to N due to a trapped strand crossing, and suggests pathways for M-to-N refolding.

A prototype protein nanocage minimized from carboxysomes with gated oxygen permeability.

Protein nanocages (PNCs) in cells and viruses have inspired the development of self-assembling protein nanomaterials for various purposes. Despite the successful creation of artificial PNCs, the de novo design of PNCs with defined permeability remains challenging. Here, we report a prototype oxygen-impermeable PNC (OIPNC) assembled from the vertex protein of the ß-carboxysome shell, CcmL, with quantum dots as the template via interfacial engineering. The structure of the cage was solved at the atomic scale by combined solid-state NMR spectroscopy and cryoelectron microscopy, showing icosahedral assembly of CcmL pentamers with highly conserved interpentamer interfaces. Moreover, a gating mechanism was established by reversibly blocking the pores of the cage with molecular patches. Thus, the oxygen permeability, which was probed by an oxygen sensor inside the cage, can be completely controlled. The CcmL OIPNC represents a PNC platform for oxygen-sensitive or oxygen-responsive storage, catalysis, delivery, sensing, etc.

Biomimetic Design of 3D Fe3O4/V-EVOH Fiber-Based Self-Floating Composite Aerogel to Enhance Solar Steam Generation Performance.

An interfacial solar steam generation evaporator for seawater desalination has attracted extensive interest in recent years. Nevertheless, challenges still remain in relatively low evaporation rate, unsatisfactory energy conversion efficiency, and salt accumulation. Herein, we have demonstrated a biomimetic bilayer composite aerogel consisting of bottom hydrophilic and vertically aligned EVOH channels and an upper hydrophobic conical Fe3O4 array. Thanks to the design merits, the 3D Fe3O4/V-EVOH evaporator exhibits a high evaporation rate of ∼2.446 kg m-2 h-1 and an impressive solar energy conversion efficiency of ∼165.5% under 1 sun illumination, which is superior to those of state-of-the-art evaporators reported so far. Moreover, the asymmetrical wettability not only allows the evaporator to self-float on the water but also facilitates the salt ion diffusion in the channels; thus, the evaporator shows no salt crystals on its surface and only a 6% decrease in evaporation performance even after the salt concentration increases from 0 to 10.0 wt %.