Progress in the application of artificial intelligence in molecular generation models based on protein structure.

The molecular generation models based on protein structures represent a cutting-edge research direction in artificial intelligence-assisted drug discovery. This article aims to comprehensively summarize the research methods and developments by analyzing a series of novel molecular generation models predicated on protein structures. Initially, we categorize the molecular generation models based on protein structures and highlight the architectural frameworks utilized in these models. Subsequently, we detail the design and implementation of protein structure-based molecular generation models by introducing different specific examples. Lastly, we outline the current opportunities and challenges encountered in this field, intending to offer guidance and a referential framework for developing and studying new models in related fields in the future.

Fecal virome transplantation: A promising strategy for the treatment of metabolic diseases.

Metabolic diseases are a group of disorders caused by metabolic abnormalities, including obesity, diabetes, non-alcoholic fatty liver disease, and more. Increasing research indicates that, beyond inherent metabolic irregularities, the onset and progression of metabolic diseases are closely linked to alterations in the gut microbiota, particularly gut bacteria. Additionally, fecal microbiota transplantation (FMT) has demonstrated effectiveness in clinically treating metabolic diseases, notably diabetes. Recent attention has also focused on the role of gut viruses in disease onset. This review first introduces the characteristics and influencing factors of gut viruses, then summarizes their potential mechanisms in disease development, highlighting their impact on gut bacteria and regulation of host immunity. We also compare FMT, fecal filtrate transplantation (FFT), washed microbiota transplantation (WMT), and fecal virome transplantation (FVT). Finally, we review the current understanding of gut viruses in metabolic diseases and the application of FVT in treating these conditions. In conclusion, FVT may provide a novel and promising treatment approach for metabolic diseases, warranting further validation through basic and clinical research.

Understanding the mechanism of gully erosion in the alpine region through an interpretable machine learning approach.

In the alpine region, climate warming has led to the retreat of glaciers, snow cover, and permafrost. This has intensified water cycling, soil erosion, and increased the occurrence of natural disasters in the alpine region. This study investigated the Lhasa River Basin in the southern Tibetan Plateau, serving as a representative case study of a typical alpine basin, with a specific focus on gully erosion. Based on field investigations and interpretation using high-resolution satellite remote sensing images, the Random Forest (RF) algorithm was applied to evaluate gully erosion susceptibility on watershed level. The Shapley Additive Interpretation method was then used to interpret the RF model and gain deeper insights into the influencing variables of gully erosion. The results showed that the RF model achieved an area under the receiver operating characteristic (AUC) accuracy of 0.99 and 0.98 for the training and testing datasets, respectively, indicating an outstanding performance of the model. The resulting susceptibility map based on the RF model shows that areas with moderate and higher levels of gully erosion susceptibility are covering 50 % of the basin. The model interpretation results indicated that elevation, slope, permafrost, rainstorm, silt loam topsoil, human activity, stream power, and vegetation were the explaining variables with the highest importance for gully erosion occurrence. Different variables are characterized by specific thresholds promoting gully erosion such as: i) elevations higher than 4950 m, ii) slopes steeper than 13.5°, iii) extreme rainstorms longer than 11 days per year, iv) silt loam topsoil, v) presence of permafrost, vi) stream power index higher than 1.2, and vii) normalized difference vegetation index (NDVI) lower than 0.25. Our findings provide the scientific basis to improve soil erosion control in such highly vulnerable alpine area.

Sulforaphane reverses the enhanced NSCLC metastasis by regulating the miR-7-5p/c-Myc/LDHA axis in the acidic tumor microenvironment.

BACKGROUND: The presence of distant metastasis at the time of initial diagnosis is a prevalent issue in non-small cell lung cancer (NSCLC), affecting around 30-40 % of the patients. Acidic tumor microenvironment (TME) provides favorable conditions that increase the invasiveness and aggressiveness of NSCLC. The activity of the glycolytic enzyme lactate dehydrogenase (LDHA) increases intracellular lactate accumulation, which creates an acidic TME. However, it is not yet known whether LDHA is involved in enhancing the metastatic potential of NSCLC and if LDHA is a druggable therapeutic target for NSCLC. PURPOSE: We aimed to investigate the molecular mechanisms underlying the enhanced NSCLC metastasis in acidic TME, and to explore whether sulforaphane (SFN), an active compound in Raphani Semen, can serve as a LDHA inhibitor to inhibit NSCLC metastasis in the acidic TME. METHODS: To mimic the acidic TME, NSCLC cells were cultured in acidic medium (pH 6.6), normal medium (pH 7.4) served as control. Western blotting, bioinformatic analysis, luciferase assay and rescue experiments were used to explore the mechanism and investigate the anti-metastatic effect of SFN both in vitro and in vivo. RESULTS: Acidic environment increases the expression of LDHA which in turn increases the production of lactic acid that contributes to the acidity of TME. Interestingly, elevated LDHA expression results from increased c-Myc expression, which transactivates LDHA. c-Myc expression is directly regulated by miR-7-5p. In vitro study shows that overexpression of miR-7-5p reverses the acidic pH-enhanced c-Myc and LDHA expressions and also abolishes the enhanced NSCLC cell migration. More importantly, SFN significantly inhibits NSCLC growth and metastasis by reducing lactate production via the miR-7-5p/c-Myc/LDHA axis. Besides, it also regulates the expressions of monocarboxylate transporter 1 (MCT1) and MCT4 that transport lactate across cell membrane. CONCLUSIONS: The miR-7-5p/c-Myc/LDHA axis is involved in the enhanced NSCLC metastasis in the acidic TME. SFN, a novel LDHA inhibitor, reduces lactate production by targeting the miR-7-5p/c-Myc/LDHA axis, and hence inhibits NSCLC metastasis. Our findings not only delineate a novel mechanism, but also support the clinical translation of SFN as a novel therapeutic agent for treating metastatic NSCLC.

Soft, Tough, Antifatigue Fracture Elastomer Composites with Low Thermal Resistance through Synergistic Crack Pinning and Interfacial Slippage.

Soft elastomer composites are promising functional materials for engineer interfaces, where the miniaturized electronic devices have triggered increasing demand for effective heat dissipation, high fracture energy, and antifatigue fracture. However, such a combination of these properties can be rarely met in the same elastomer composites simultaneously. Here a strategy is presented to fabricate a soft, extreme fracture tough (3316 J m-2) and antifatigue fracture (1052.56 J m⁻2) polydimethylsiloxane/aluminum elastomer composite. These outstanding properties are achieved by optimizing the dangling chains and spherical aluminum fillers, resulting in the combined effects of crack pinning and interfacial slippage. The dangling chains that lengthen the polymer chains between cross-linked points pin the cracks and the rigid fillers obstruct the cracks, enhancing the energy per unit area needed for fatigue failure. The dangling chains also promote polymer/filler interfacial slippage, enabling effective deflection and blunting of an advancing crack tip, thus enhancing mechanical energy dissipation. Moreover, the elastomer composite exhibits low thermal resistance (≈0.12 K cm2 W-1), due to the formation of a thermally conductive network. These remarkable characteristics render this elastomer composite promising for application as a thermal interface material in electronic devices.

Different emotional states engage distinct descending pathways from the prefrontal cortex.

Emotion regulation, essential for adaptive behavior, depends on the brain's capacity to process a range of emotions. Current research has largely focused on individual emotional circuits without fully exploring how their interaction influences physiological responses or understanding the neural mechanisms that differentiate emotional valence. Using in vivo calcium imaging, electrophysiology, and optogenetics, we examined neural circuit dynamics in the medial prefrontal cortex (mPFC), targeting two key areas: the basal lateral amygdala (BLA) and nucleus accumbens (NAc). Our results demonstrate distinct activation patterns in the mPFC→BLA and mPFC→NAc pathways in response to social stimuli, indicating a mechanism for discriminating emotions: increased mPFC→BLA activity signals anxiety, while heightened mPFC→NAc responses are linked to exploration. Additionally, chronic emotional states amplify activity in these pathways-positivity enhances mPFC→NAc, while negativity boosts mPFC→BLA. This study sheds light on the nuanced neural circuitry involved in emotion regulation, revealing the pivotal roles of mPFC projections in emotional processing. Identifying these specific circuits engaged by varied emotional states advances our understanding of emotional regulation's biological underpinnings and highlights potential targets for addressing emotional dysregulation in psychiatric conditions. Significance statement: While existing circuitry studies have underscored the significance of emotional circuits, the majority of research has concentrated on individual circuits. The assessment of whether and how the balance among multiple circuits influences overall physiological outcomes is often overlooked. This study delves into the neural underpinnings of emotion regulation, focusing on how positive and negative valences are discriminated and managed. By examining the specific pathways from the medial prefrontal cortex (mPFC) to key emotional centers-the basal lateral amygdala (BLA) for negative valence and the nucleus accumbens (NAc) for positive one-we uncovered a novel dual-balanced neural circuit mechanism that enables this essential aspect of human cognition.

Trade-offs between grain supply and soil conservation in the Grain for Green Program under changing climate: A case study in the Three Gorges Reservoir region.

Understanding the trade-offs between ecological benefits and cost of grain supply caused by ecosystem restoration is essential for decision-making. Nevertheless, due to climate change, the benefits of ecosystem restoration and cost of grain supply change across various spatial locations, thereby complicating the trade-offs. Taking one of China's largest scale ecosystem restorations, the Grain for Green Program (GGP), as an example, this study used the Three Gorges Reservoir (TGR) region as the case study area and combined the crop environment resource synthesis (CERES)-Crop model, future land-use simulation (FLUS), and the revised universal soil loss equation (RUSLE) to simulate future grain supply and soil erosion during 2021-2050 under three climate change and socioeconomic development scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) in the TGR region. The results showed that: (1) Until 2050, the implementation of GGP would bring a large soil conservation benefit by reducing soil erosion of 2.47-5.68 million tons, at the cost of 130,277-660,279 tons decrease in grain production in the TGR region. (2) Under SSP5-8.5 climate change scenario with the highest rainfall in the future, the GGP would maintain the greatest soil conservation benefits, resulting in a total amount of soil erosion decrease by 2.55 to 5.68 million tons. (3) Trade-offs between benefit of reducing soil erosion and cost of grain supply vary considerably across income. Specifically, GGP benefits are greater under low-income and higher-emission scenarios, with significant gains in soil erosion control and less impact on grain supply. In contrast, in high-income and low-emission scenarios, the GGP results in less soil erosion control and greater impact on grain supply.

SLKIR: A framework for extracting key information from air traffic control instructions Using small sample learning.

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR .

LectoScape: A Highly Multiplexed Imaging Platform for Glycome Analysis and Biomedical Diagnosis.

Glycosylation, a fundamental biological process, involves the attachment of glycans to proteins, lipids, and RNA, and it plays a crucial role in various biological pathways. It is of great significance to obtain the precise spatial distribution of glycosylation modifications at the cellular and tissue levels. Here, we introduce LectoScape, an innovative method enabling detailed imaging of tissue glycomes with up to 1 µm resolution through image mass cytometry (IMC). This method utilizes 12 distinct, nonoverlapping lectins selected via microarray technology, enabling the multiplexed detection of a wide array of glycans. Furthermore, we developed an efficient labeling strategy for these lectins. Crucially, our approach facilitates the concurrent imaging of diverse glycan motifs, including N-glycan and O-glycan, surpassing the capabilities of existing technologies. Using LectoScape, we have successfully delineated unique glycan structures in various cell types, enhancing our understanding of the glycan distribution across human tissues. Our method has identified specific glycan markers, such as α2,3-sialylated Galß1, 3GalNAc in O-glycan, and terminal GalNAc, as diagnostic indicators for cervical intraepithelial neoplasia. This highlights the potential of LectoScape in cancer diagnostics through the detection of abnormal glycosylation patterns.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

OBJECTIVE: Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). METHODS: We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. RESULTS: The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. INTERPRETATION: Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Inflammation-related molecular signatures involved in the anticancer activities of brigatinib as well as the prognosis of EML4-ALK lung adenocarcinoma patient.

Although ALK tyrosine kinase inhibitors (ALK-TKIs) have shown remarkable benefits in EML4-ALK positive NSCLC patients compared to conventional chemotherapy, the optimal sequence of ALK-TKIs treatment remains unclear due to the emergence of primary and acquired resistance and the lack of potential prognostic biomarkers. In this study, we systematically explored the validity of sequential ALK inhibitors (alectinib, lorlatinib, crizotinib, ceritinib and brigatinib) for a heavy-treated patient with EML4-ALK fusion via developing an in vitro and in vivo drug testing system based on patient-derived models. Based on the patient-derived models and clinical responses of the patient, we found that crizotinib might inhibit proliferation of EML4-ALK positive tumors resistant to alectinib and lorlatinib. In addition, NSCLC patients harboring the G1269A mutation, which was identified in alectinib, lorlatinib and crizotinib-resistant NSCLC, showed responsiveness to brigatinib and ceritinib. Transcriptomic analysis revealed that brigatinib suppressed the activation of multiple inflammatory signaling pathways, potentially contributing to its anti-tumor activity. Moreover, we constructed a prognostic model based on the expression of IL6, CXCL1, and CXCL5, providing novel perspectives for predicting prognosis in EML4-ALK positive NSCLC patients. In summary, our results delineate clinical responses of sequential ALK-TKIs treatments and provide insights into the mechanisms underlying the superior effects of brigatinib in patients harboring ALKG1269A mutation and resistant towards alectinib, lorlatinib and crizotinib. The molecular signatures model based on the combination of IL6, CXCL1 and CXCL5 has the potential to predict prognosis of EML4-ALK positive NSCLC patients.

Advancing Glucose Conjugated Gibberellins Discovery: A Structure-Oriented Screening and Identification Method for Unraveling Gibberellin Metabolites in Plants.

Gibberellins (GAs) play a pivotal role in modulating plant growth and development. Glucose-conjugated gibberellins (Glc-GAs), a prevalent conjugated form of GAs, regulate intracellular GA levels by the coupling and decoupling of glucose groups. However, the diversity of Glc-GAs identified within individual species remains limited, hinting at a multitude of yet undiscovered gibberellin metabolites. This lacuna poses considerable impediments to research efforts dedicated to comprehensively delineating the GA metabolic pathway. In this study, we developed a structure-oriented screening and identification method for Glc-GAs in plant species by employing LC-MS/MS coupled with chemical derivatization. Through the application of chemical derivatization technique, carboxyl groups on Glc-GAs were labeled which effectively enhanced the sensitivity and selectivity of mass spectrometry detection for these compounds. Concurrently, the integration of mass spectrometry fragmentation and chromatographic retention behavior facilitated the efficient screening and identification of potential Glc-GAs. With this strategy, we screened and identified 12 potential Glc-GAs from six plant species. These findings expand the Glc-GA diversity in plants and contribute to understanding GA metabolic pathways.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Exosomes from adipose-derived stem cells restore fibroblast function and accelerate diabetic wound healing.

Background: Diabetes is common yet challenging chronic disease, that affects a wide range of people around the world. Complex cellular environments around diabetic wounds tend to damage the function of effector cells, including vascular endothelial cells (VECs), fibroblasts and epithelial cells. This study aims to analyze the differences between diabetic wounds and normal skin as well as whether adipose-derived stem cell (ADSC) exosome could promote healing of diabetic wound. Methods: Human diabetic wounds and normal skin were collected and stained with HE, Masson, CD31 and 8-hydroxy-2 deoxyguanosine immunohistochemical staining. RNA-seq data were collected for further bioinformatics analysis. ADSC exosomes were isolated and identified by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and western blotting. The effect of ADSC exosomes on diabetic wound healing was assessed on full thickness wounds in mice. To further verify the regulative impact of ADSCs exosomes in high glucose treated fibroblasts, we isolated fibroblasts from normal skin tissue and measured the cell viability, apoptosis rate, proliferation and migration of fibroblasts. In addition, collagen formation and fibrosis-related molecules were also detected. To further disclose the mechanism of ADSC exosomes on the function of high glucose treated fibroblasts, we detected the expression of apoptosis related molecules including BCL2, Bax, and cleaved caspase-3. Results: Histological observation indicated that perilesional skin tissues from diabetic patients showed structural disorder, less collagen disposition and increased injury compared with normal skin. Bioinformatics analysis showed that the levels of inflammatory and collagen synthesis related molecules, as well as oxidative stress and apoptosis related molecules, were significantly changed. Furthermore, we found that ADSC exosomes could not only speed up diabetic wound healing, but could also improve healing quality. ADSC exosomes restored high glucose induced damage to cell viability, migration and proliferation activity, as well as fibrosis-related molecules such as SMA, collagen 1 and collagen 3. In addition, we verified that ADSC exosomes downregulated high glucose induced increased apoptosis rate in fibroblast and the protein expression of Bax as well as cleaved caspases 3. Conclusions: This study indicated that ADSC exosomes alleviated high glucose induced damage to fibroblasts and accelerate diabetic wound healing by inhibiting Bax/caspase 3.

Water hammer in pipelines based on different friction models.

Water hammer in pipelines is a difficult problem in fluid transmission field. Especially, there exists some friction items of pipeline transient model such that the simulation model is not consistent to the experimental results. By using the friction model proposed by Kagawa and the model of impulse response function, the pressure transients are calculated with and without cavitation. The corresponding simulation results involving pressure, velocity, steady and dynamic frictions, cavitation volume are analyzed to reveal the effect of friction item on pressure transients. Moreover, the features of steady and dynamic frictions are captured in pipelines with upstream and downstream valves. The comparative simulation results of two friction models have verified that the friction model using an impulse response function has higher consistency between simulation and experimental results of pipeline transients.

Investigation of the spatial effects on PM2.5 in relation to land use and ecological restoration in urban agglomerations.

Heavy pollution of particulate matter with an aerodynamic diameter of <2.5 µm (PM2.5) poses increasing threats to the living environment worldwide. Urban agglomerations often lead to regional rather than local air pollution problems. This study explored the underlying global and local spatial driving mechanisms of PM2.5 variations of the 195 county-level administrative units in the urban agglomeration in the middle reaches of the Yangtze River, China, in 2020, using the global spatial regression and geographically weighted regression methods. Results showed that (1) at the county level, there were spatial variations of PM2.5, fluctuating from 20.1263 µg/m3 to 44.8416 µg/m3. (2) The concentrations of PM2.5 presented a positive spatial autocorrelation with a remarkable direct spatial spillover effect. (3) Forestland, grassland, elevation and ecological restoration were negatively correlated with PM2.5 concentrations, the indirect spatial spillover effect of elevation was noticeable. (4) The indirect reduction effects of ecological restoration on PM2.5 concentrations were substantial in the Wuhan urban agglomeration. (5) The reduction effect of forestland, grassland, ecological restoration and elevation on PM2.5 showed a noticeable spatial heterogeneity. In the future, it is suggested regional variability and the spatial spillover effect of air pollution be taken into account in environmental governance. Simultaneously, utilization of the mitigation effect of ecological restoration on PM2.5 is anticipated for the concerted effort in air pollution governance.

RNet: a network strategy to predict RNA binding preferences.

Determining the RNA binding preferences remains challenging because of the bottleneck of the binding interactions accompanied by subtle RNA flexibility. Typically, designing RNA inhibitors involves screening thousands of potential candidates for binding. Accurate binding site information can increase the number of successful hits even with few candidates. There are two main issues regarding RNA binding preference: binding site prediction and binding dynamical behavior prediction. Here, we propose one interpretable network-based approach, RNet, to acquire precise binding site and binding dynamical behavior information. RNetsite employs a machine learning-based network decomposition algorithm to predict RNA binding sites by analyzing the local and global network properties. Our research focuses on large RNAs with 3D structures without considering smaller regulatory RNAs, which are too small and dynamic. Our study shows that RNetsite outperforms existing methods, achieving precision values as high as 0.701 on TE18 and 0.788 on RB9 tests. In addition, RNetsite demonstrates remarkable robustness regarding perturbations in RNA structures. We also developed RNetdyn, a distance-based dynamical graph algorithm, to characterize the interface dynamical behavior consequences upon inhibitor binding. The simulation testing of competitive inhibitors indicates that RNetdyn outperforms the traditional method by 30%. The benchmark testing results demonstrate that RNet is highly accurate and robust. Our interpretable network algorithms can assist in predicting RNA binding preferences and accelerating RNA inhibitor design, providing valuable insights to the RNA research community.

HIV-1 Transcription Inhibition Using Small RNA-Binding Molecules.

The HIV-1 transactivator protein Tat interacts with the transactivation response element (TAR) at the three-nucleotide UCU bulge to facilitate the recruitment of transcription elongation factor-b (P-TEFb) and induce the transcription of the integrated proviral genome. Therefore, the Tat-TAR interaction, unique to the virus, is a promising target for developing antiviral therapeutics. Currently, there are no FDA-approved drugs against HIV-1 transcription, suggesting the need to develop novel inhibitors that specifically target HIV-1 transcription. We have identified potential candidates that effectively inhibit viral transcription in myeloid and T cells without apparent toxicity. Among these candidates, two molecules showed inhibition of viral protein expression. A molecular docking and simulation approach was used to determine the binding dynamics of these small molecules on TAR RNA in the presence of the P-TEFb complex, which was further validated by a biotinylated RNA pulldown assay. Furthermore, we examined the effect of these molecules on transcription factors, including the SWI/SNF complex (BAF or PBAF), which plays an important role in chromatin remodeling near the transcription start site and hence regulates virus transcription. The top candidates showed significant viral transcription inhibition in primary cells infected with HIV-1 (98.6). Collectively, our study identified potential transcription inhibitors that can potentially complement existing cART drugs to address the current therapeutic gap in current regimens. Additionally, shifting of the TAR RNA loop towards Cyclin T1 upon molecule binding during molecular simulation studies suggested that targeting the TAR loop and Tat-binding UCU bulge together should be an essential feature of TAR-binding molecules/inhibitors to achieve complete viral transcription inhibition.

A Relational Identity-Based Solution to Group Polarization: Can Priming Parental Identity Reduce the Partisan Gap in Attitudes Toward the COVID-19 Pandemic.

This study explores the influence of both group identity (e.g., partisan identity) and relational identity (e.g., parental identity) on beliefs and attitudes toward the coronavirus disease 2019 (COVID-19) pandemic. Results from a between-subject randomized survey experiment suggest that partisans are motivated to process factual information about COVID-19 through a partisan lens. However, priming parental identity can reduce partisan polarization over risk perceptions, policy support, and precautious behaviors. These findings demonstrate the need to incorporate relational identity into identity-based science communication research and offer a relational identity-based strategic communication solution to partisan gaps in responses to COVID-19.