CPEB2-activated axonal translation of VGLUT2 mRNA promotes glutamatergic transmission and presynaptic plasticity.

BACKGROUND: Local translation at synapses is important for rapidly remodeling the synaptic proteome to sustain long-term plasticity and memory. While the regulatory mechanisms underlying memory-associated local translation have been widely elucidated in the postsynaptic/dendritic region, there is no direct evidence for which RNA-binding protein (RBP) in axons controls target-specific mRNA translation to promote long-term potentiation (LTP) and memory. We previously reported that translation controlled by cytoplasmic polyadenylation element binding protein 2 (CPEB2) is important for postsynaptic plasticity and memory. Here, we investigated whether CPEB2 regulates axonal translation to support presynaptic plasticity. METHODS: Behavioral and electrophysiological assessments were conducted in mice with pan neuron/glia- or glutamatergic neuron-specific knockout of CPEB2. Hippocampal Schaffer collateral (SC)-CA1 and temporoammonic (TA)-CA1 pathways were electro-recorded to monitor synaptic transmission and LTP evoked by 4 trains of high-frequency stimulation. RNA immunoprecipitation, coupled with bioinformatics analysis, were used to unveil CPEB2-binding axonal RNA candidates associated with learning, which were further validated by Western blotting and luciferase reporter assays. Adeno-associated viruses expressing Cre recombinase were stereotaxically delivered to the pre- or post-synaptic region of the TA circuit to ablate Cpeb2 for further electrophysiological investigation. Biochemically isolated synaptosomes and axotomized neurons cultured on a microfluidic platform were applied to measure axonal protein synthesis and FM4-64FX-loaded synaptic vesicles. RESULTS: Electrophysiological analysis of hippocampal CA1 neurons detected abnormal excitability and vesicle release probability in CPEB2-depleted SC and TA afferents, so we cross-compared the CPEB2-immunoprecipitated transcriptome with a learning-induced axonal translatome in the adult cortex to identify axonal targets possibly regulated by CPEB2. We validated that Slc17a6, encoding vesicular glutamate transporter 2 (VGLUT2), is translationally upregulated by CPEB2. Conditional knockout of CPEB2 in VGLUT2-expressing glutamatergic neurons impaired consolidation of hippocampus-dependent memory in mice. Presynaptic-specific ablation of Cpeb2 in VGLUT2-dominated TA afferents was sufficient to attenuate protein synthesis-dependent LTP. Moreover, blocking activity-induced axonal Slc17a6 translation by CPEB2 deficiency or cycloheximide diminished the releasable pool of VGLUT2-containing synaptic vesicles. CONCLUSIONS: We identified 272 CPEB2-binding transcripts with altered axonal translation post-learning and established a causal link between CPEB2-driven axonal synthesis of VGLUT2 and presynaptic translation-dependent LTP. These findings extend our understanding of memory-related translational control mechanisms in the presynaptic compartment.

Identification of a damage-associated molecular pattern (DAMP) receptor and its cognate peptide ligand in sweet potato (Ipomoea batatas).

Sweet potato (Ipomoea batatas) is an important tuber crop, but also target of numerous insect pests. Intriguingly, the abundant storage protein in tubers, sporamin, has intrinsic trypsin protease inhibitory activity. In leaves, sporamin is induced by wounding or a volatile homoterpene and enhances insect resistance. While the signalling pathway leading to sporamin synthesis is partially established, the initial event, perception of a stress-related signal is still unknown. Here, we identified an IbLRR-RK1 that is induced upon wounding and herbivory, and related to peptide-elicitor receptors (PEPRs) from tomato and Arabidopsis. We also identified a gene encoding a precursor protein comprising a peptide ligand (IbPep1) for IbLRR-RK1. IbPep1 represents a distinct signal in sweet potato, which might work in a complementary and/or parallel pathway to the previously described hydroxyproline-rich systemin (HypSys) peptides to strengthen insect resistance. Notably, an interfamily compatibility in the Pep/PEPR system from Convolvulaceae and Solanaceae was identified.

PRAP1 is a novel lipid-binding protein that promotes lipid absorption by facilitating MTTP-mediated lipid transport.

Microsomal triglyceride transfer protein (MTTP) is an endoplasmic reticulum resident protein that is essential for the assembly and secretion of triglyceride (TG)-rich, apoB-containing lipoproteins. Although the function and structure of mammalian MTTP have been extensively studied, how exactly MTTP transfers lipids to lipid acceptors and whether there are other biomolecules involved in MTTP-mediated lipid transport remain elusive. Here we identify a role in this process for the poorly characterized protein PRAP1. We report that PRAP1 and MTTP are partially colocalized in the endoplasmic reticulum. We observe that PRAP1 directly binds to TG and facilitates MTTP-mediated lipid transfer. A single amino acid mutation at position 85 (E85V) impairs PRAP1's ability to form a ternary complex with TG and MTTP, as well as impairs its ability to facilitate MTTP-mediated apoB-containing lipoprotein assembly and secretion, suggesting that the ternary complex formation is required for PRAP1 to facilitate MTTP-mediated lipid transport. PRAP1 is detectable in chylomicron/VLDL-rich plasma fractions, suggesting that MTTP recognizes PRAP1-bound TG as a cargo and transfers TG along with PRAP1 to lipid acceptors. Both PRAP1-deficient and E85V knock-in mutant mice fed a chow diet manifested an increase in the length of their small intestines, likely to compensate for challenges in absorbing lipid. Interestingly, both genetically modified mice gained significantly less body weight and fat mass when on high-fat diets compared with littermate controls and were prevented from hepatosteatosis. Together, this study provides evidence that PRAP1 plays an important role in MTTP-mediated lipid transport and lipid absorption.

Identification of Entry Factors Involved in Hepatitis C Virus Infection Based on Host-Mimicking Short Linear Motifs.

Host factors that facilitate viral entry into cells can, in principle, be identified from a virus-host protein interaction network, but for most viruses information for such a network is limited. To help fill this void, we developed a bioinformatics approach and applied it to hepatitis C virus (HCV) infection, which is a current concern for global health. Using this approach, we identified short linear sequence motifs, conserved in the envelope proteins of HCV (E1/E2), that potentially can bind human proteins present on the surface of hepatocytes so as to construct an HCV (envelope)-host protein interaction network. Gene Ontology functional and KEGG pathway analyses showed that the identified host proteins are enriched in cell entry and carcinogenesis functionalities. The validity of our results is supported by much published experimental data. Our general approach should be useful when developing antiviral agents, particularly those that target virus-host interactions.

Proteins with Highly Evolvable Domain Architectures Are Nonessential but Highly Retained.

The functions of proteins are usually determined by domains, and the sequential order in which domains are connected to make up a protein chain is known as the domain architecture. Here, we constructed evolutionary networks of protein domain architectures in species from three major life lineages (bacteria, fungi, and metazoans) by connecting any two architectures between which an evolutionary event could be inferred by a model that assumes maximum parsimony. We found that proteins with domain architectures with a higher level of evolvability, indicated by a greater number of connections in the evolutionary network, are present in a wider range of species. However, these proteins tend to be less essential to the organism, are duplicated more often during evolution, have more isoforms, and, intriguingly, tend to be associated with functional categories important for organismal adaptation. These results reveal the presence, in many genomes, of genes coding for a core set of nonessential proteins that have a highly evolvable domain architecture and thus a repertoire of genetic materials accessible for organismal adaptation.

A critical assessment of information-guided protein-protein docking predictions.

The structures of protein complexes are increasingly predicted via protein-protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.

LISE: a server using ligand-interacting and site-enriched protein triangles for prediction of ligand-binding sites.

LISE is a web server for a novel method for predicting small molecule binding sites on proteins. It differs from a number of servers currently available for such predictions in two aspects. First, rather than relying on knowledge of similar protein structures, identification of surface cavities or estimation of binding energy, LISE computes a score by counting geometric motifs extracted from sub-structures of interaction networks connecting protein and ligand atoms. These network motifs take into account spatial and physicochemical properties of ligand-interacting protein surface atoms. Second, LISE has now been more thoroughly tested, as, in addition to the evaluation we previously reported using two commonly used small benchmark test sets and targets of two community-based experiments on ligand-binding site predictions, we now report an evaluation using a large non-redundant data set containing >2000 protein-ligand complexes. This unprecedented test, the largest ever reported to our knowledge, demonstrates LISE's overall accuracy and robustness. Furthermore, we have identified some hard to predict protein classes and provided an estimate of the performance that can be expected from a state-of-the-art binding site prediction server, such as LISE, on a proteome scale. The server is freely available at http://lise.ibms.sinica.edu.tw.

A computational pipeline for identifying kinetic motifs to aid in the design and improvement of synthetic gene circuits.

BACKGROUND: An increasing number of genetic components are available in several depositories of such components to facilitate synthetic biology research, but picking out those that will allow a designed circuit to achieve the specified function still requires multiple cycles of testing. Here, we addressed this problem by developing a computational pipeline to mathematically simulate a gene circuit for a comprehensive range and combination of the kinetic parameters of the biological components that constitute the gene circuit. RESULTS: We showed that, using a well-studied transcriptional repression cascade as an example, the sets of kinetic parameters that could produce the specified system dynamics of the gene circuit formed clusters of recurrent combinations, referred to as kinetic motifs, which appear to be associated with both the specific topology and specified dynamics of the circuit. Furthermore, the use of the resulting "handbook" of performance-ranked kinetic motifs in finding suitable circuit components was illustrated in two application scenarios. CONCLUSIONS: These results show that the computational pipeline developed here can provide a rational-based guide to aid in the design and improvement of synthetic gene circuits.

Ligand-binding site prediction using ligand-interacting and binding site-enriched protein triangles.

MOTIVATION: Knowledge about the site at which a ligand binds provides an important clue for predicting the function of a protein and is also often a prerequisite for performing docking computations in virtual drug design and screening. We have previously shown that certain ligand-interacting triangles of protein atoms, called protein triangles, tend to occur more frequently at ligand-binding sites than at other parts of the protein. RESULTS: In this work, we describe a new ligand-binding site prediction method that was developed based on binding site-enriched protein triangles. The new method was tested on 2 benchmark datasets and on 19 targets from two recent community-based studies of such predictions, and excellent results were obtained. Where comparisons were made, the success rates for the new method for the first predicted site were significantly better than methods that are not a meta-predictor. Further examination showed that, for most of the unsuccessful predictions, the pocket of the ligand-binding site was identified, but not the site itself, whereas for some others, the failure was not due to the method itself but due to the use of an incorrect biological unit in the structure examined, although using correct biological units would not necessarily improve the prediction success rates. These results suggest that the new method is a valuable new addition to a suite of existing structure-based bioinformatics tools for studies of molecular recognition and related functions of proteins in post-genomics research. AVAILABILITY: The executable binaries and a web server for our method are available from http://sourceforge.net/projects/msdock/ and http://lise.ibms.sinica.edu.tw, respectively, free for academic users.

The architectural design of networks of protein domain architectures.

Protein domain architectures (PDAs), in which single domains are linked to form multiple-domain proteins, are a major molecular form used by evolution for the diversification of protein functions. However, the design principles of PDAs remain largely uninvestigated. In this study, we constructed networks to connect domain architectures that had grown out from the same single domain for every single domain in the Pfam-A database and found that there are three main distinctive types of these networks, which suggests that evolution can exploit PDAs in three different ways. Further analysis showed that these three different types of PDA networks are each adopted by different types of protein domains, although many networks exhibit the characteristics of more than one of the three types. Our results shed light on nature's blueprint for protein architecture and provide a framework for understanding architectural design from a network perspective.

A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.

In cancer genomics research, gene expressions provide clues to gene regulations implicating patients' risk of survival. Gene expressions, however, fluctuate due to noises arising internally and externally, making their use to infer gene associations, hence regulation mechanisms, problematic. Here, we develop a new regression approach to model gene association networks while considering uncertain biological noises. In a series of simulation experiments accounting for varying levels of biological noises, the new method was shown to be robust and perform better than conventional regression methods, as judged by a number of statistical measures on unbiasedness, consistency and accuracy. Application to infer gene associations in germinal-center B cells led to the discovery of a three-by-two regulatory motif gene expression and a three-gene prognostic signature for diffuse large B-cell lymphoma.

Gut butyrate-producers confer post-infarction cardiac protection.

The gut microbiome and its metabolites are increasingly implicated in several cardiovascular diseases, but their role in human myocardial infarction (MI) injury responses have yet to be established. To address this, we examined stool samples from 77 ST-elevation MI (STEMI) patients using 16 S V3-V4 next-generation sequencing, metagenomics and machine learning. Our analysis identified an enriched population of butyrate-producing bacteria. These findings were then validated using a controlled ischemia/reperfusion model using eight nonhuman primates. To elucidate mechanisms, we inoculated gnotobiotic mice with these bacteria and found that they can produce beta-hydroxybutyrate, supporting cardiac function post-MI. This was further confirmed using HMGCS2-deficient mice which lack endogenous ketogenesis and have poor outcomes after MI. Inoculation increased plasma ketone levels and provided significant improvements in cardiac function post-MI. Together, this demonstrates a previously unknown role of gut butyrate-producers in the post-MI response.

On the use of distance constraints in protein-protein docking computations.

Protein-protein docking (PPD) is a computational process that predicts the structure of a complex of two interacting proteins from their unbound structures. The accuracy of PPD predictions is low, but can be greatly enhanced if experimentally determined distance data are available for incorporation into the prediction. However, the specific effects of distance constraints on PPD predictions are largely uncharacterized. In this study, we systematically simulated the effects of using distance constraints both on a new distance constraint-driven PPD approach, called DPPD, and also, by re-ranking, on a well-established grid-based global search approach. Our results for a PPD benchmark dataset of 84 protein complexes of known structures showed that near 100% docking success rates could be obtained when the number of distance constraints exceeded six, the degrees of freedom of the system, but the success rate was significantly reduced by long distance constraints, large binding-induced conformational changes, and large errors in the distance data. Our results also showed that, under most conditions simulated, even two or three distance constraints were sufficient to achieve a much better success rate than those using a sophisticated physicochemical function to re-rank the results of the global search. Our study provides guidelines for the practical incorporation of experimental distance data to aid PPD predictions.

Glucose-6-phosphate dehydrogenase (G6PD) mutations database: review of the "old" and update of the new mutations.

In the present paper we have updated the G6PD mutations database, including all the last discovered G6PD genetic variants. We underline that the last database has been published by Vulliamy et al. [1] who analytically reported 140 G6PD mutations: along with Vulliamy's database, there are two main sites, such as http://202.120.189.88/mutdb/ and www.LOVD.nl/MR, where almost all G6PD mutations can be found. Compared to the previous mutation reports, in our paper we have included for each mutation some additional information, such as: the secondary structure and the enzyme 3D position involving by mutation, the creation or abolition of a restriction site (with the enzyme involved) and the conservation score associated with each amino acid position. The mutations reported in the present tab have been divided according to the gene's region involved (coding and non-coding) and mutations affecting the coding region in: single, multiple (at least with two bases involved) and deletion. We underline that for the listed mutations, reported in italic, literature doesn't provide all the biochemical or bio-molecular information or the research data. Finally, for the "old" mutations, we tried to verify features previously reported and, when subsequently modified, we updated the specific information using the latest literature data.

JAK2-CHK2 signaling safeguards the integrity of the mitotic spindle assembly checkpoint and genome stability.

Checkpoint kinase 2 (CHK2) plays an important role in safeguarding the mitotic progression, specifically the spindle assembly, though the mechanism of regulation remains poorly understood. Here, we identified a novel mitotic phosphorylation site on CHK2 Tyr156, and its responsible kinase JAK2. Expression of a phospho-deficient mutant CHK2 Y156F or treatment with JAK2 inhibitor IV compromised mitotic spindle assembly, leading to genome instability. In contrast, a phospho-mimicking mutant CHK2 Y156E restored mitotic normalcy in JAK2-inhibited cells. Mechanistically, we show that this phosphorylation is required for CHK2 interaction with and phosphorylation of the spindle assembly checkpoint (SAC) kinase Mps1, and failure of which results in impaired Mps1 kinetochore localization and defective SAC. Concordantly, analysis of clinical cancer datasets revealed that deletion of JAK2 is associated with increased genome alteration; and alteration in CHEK2 and JAK2 is linked to preferential deletion or amplification of cancer-related genes. Thus, our findings not only reveal a novel JAK2-CHK2 signaling axis that maintains genome integrity through SAC but also highlight the potential impact on genomic stability with clinical JAK2 inhibition.

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension.

Background: Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension. Objectives: This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications. Methods: From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan. Results: Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort. Conclusions: The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

Implementation of an All-Day Artificial Intelligence-Based Triage System to Accelerate Door-to-Balloon Times.

OBJECTIVE: To implement an all-day artificial intelligence (AI)-based system to facilitate chest pain triage in the emergency department. METHODS: The AI-based triage system encompasses an AI model combining a convolutional neural network and long short-term memory to detect ST-elevation myocardial infarction (STEMI) on electrocardiography (ECG) and a clinical risk score (ASAP) to prioritize patients for ECG examination. The AI model was developed on 2907 twelve-lead ECGs: 882 STEMI and 2025 non-STEMI ECGs. RESULTS: Between November 1, 2019, and October 31, 2020, we enrolled 154 consecutive patients with STEMI: 68 during the AI-based triage period and 86 during the conventional triage period. The mean ± SD door-to-balloon (D2B) time was significantly shortened from 64.5±35.3 minutes to 53.2±12.7 minutes (P=.007), with 98.5% vs 87.2% (P=.009) of D2B times being less than 90 minutes in the AI group vs the conventional group. Among patients with an ASAP score of 3 or higher, the median door-to-ECG time decreased from 30 minutes (interquartile range [IQR], 7-59 minutes) to 6 minutes (IQR, 4-30 minutes) (P<.001). The overall performances of the AI model in identifying STEMI from 21,035 ECGs assessed by accuracy, precision, recall, area under the receiver operating characteristic curve, F1 score, and specificity were 0.997, 0.802, 0.977, 0.999, 0.881, and 0.998, respectively. CONCLUSION: Implementation of an all-day AI-based triage system significantly reduced the D2B time, with a corresponding increase in the percentage of D2B times less than 90 minutes in the emergency department. This system may help minimize preventable delays in D2B times for patients with STEMI undergoing primary percutaneous coronary intervention.

CRM1-spike-mediated nuclear export of hepatitis B virus encapsidated viral RNA.

Hepatitis B virus (HBV) is a global pathogen. We report here that the cellular CRM1 machinery can mediate nuclear export of entire HBV core (HBc) particles containing encapsidated viral RNAs. Two CRM1-mediated nuclear export signals (NESCRM1) cluster at the conformationally flexible spike tips of HBc particles. Mutant NESCRM1 capsids exhibit strongly reduced associations with CRM1 and nucleoporin358 in vivo. CRM1 and NXF1 machineries mediate nuclear export of HBc particles independently. Inhibition of nuclear export has pleiotropic consequences, including nuclear accumulation of HBc particles, a significant reduction of encapsidated viral RNAs in the cytoplasm but not in the nucleus, and barely detectable viral DNA. We hypothesize an HBV life cycle where encapsidation of the RNA pregenome can initiate early in the nucleus, whereas DNA genome maturation occurs mainly in the cytoplasm. We identified a druggable target for HBV by blocking its intracellular trafficking.

Corrigendum: Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

[This corrects the article DOI: 10.3389/fcvm.2022.1001982.].

Patient-Derived Organoid Serves as a Platform for Personalized Chemotherapy in Advanced Colorectal Cancer Patients.

Background: Addition of oxaliplatin to adjuvant 5-FU has significantly improved the disease-free survival and served as the first line adjuvant chemotherapy in advanced colorectal cancer (CRC) patients. However, a fraction of patients remains refractory to oxaliplatin-based treatment. It is urgent to establish a preclinical platform to predict the responsiveness toward oxaliplatin in CRC patients as well as to improve the efficacy in the resistant patients. Methods: A living biobank of organoid lines were established from advanced CRC patients. Oxaliplatin sensitivity was assessed in patient-derived tumor organoids (PDOs) in vitro and in PDO-xenografted tumors in mice. Based on in vitro oxaliplatin IC50 values, PDOs were classified into either oxaliplatin-resistant (OR) or oxaliplatin-sensitive (OS) PDOs. The outcomes of patients undergone oxaliplatin-based treatment was followed. RNA-sequencing and bioinformatics tools were performed for molecular profiling of OR and OS PDOs. Oxaliplatin response signatures were submitted to Connectivity Map algorithm to identify perturbagens that may antagonize oxaliplatin resistance. Results: Oxaliplatin sensitivity in PDOs was shown to correlate to oxaliplatin-mediated inhibition on PDO xenograft tumors in mice, and parallelled clinical outcomes of CRC patients who received FOLFOX treatment. Molecular profiling of transcriptomes revealed oxaliplatin-resistant and -sensitive PDOs as two separate entities, each being characterized with distinct hallmarks and gene sets. Using Leave-One-Out Cross Validation algorithm and Logistic Regression model, 18 gene signatures were identified as predictive biomarkers for oxaliplatin response. Candidate drugs identified by oxaliplatin response signature-based strategies, including inhibitors targeting c-ABL and Notch pathway, DNA/RNA synthesis inhibitors, and HDAC inhibitors, were demonstrated to potently and effectively increase oxaliplatin sensitivity in the resistant PDOs. Conclusions: PDOs are useful in informing decision-making on oxaliplatin-based chemotherapy and in designing personalized chemotherapy in CRC patients.