Mechanistic insights into the interactions of TAX1BP1 with RB1CC1 and mammalian ATG8 family proteins.

TAX1BP1, a multifunctional autophagy adaptor, plays critical roles in different autophagy processes. As an autophagy receptor, TAX1BP1 can interact with RB1CC1, NAP1, and mammalian ATG8 family proteins to drive selective autophagy for relevant substrates. However, the mechanistic bases underpinning the specific interactions of TAX1BP1 with RB1CC1 and mammalian ATG8 family proteins remain elusive. Here, we find that there are two distinct binding sites between TAX1BP1 and RB1CC1. In addition to the previously reported TAX1BP1 SKICH (skeletal muscle and kidney enriched inositol phosphatase (SKIP) carboxyl homology)/RB1CC1 coiled-coil interaction, the first coiled-coil domain of TAX1BP1 can directly bind to the extreme C-terminal coiled-coil and Claw region of RB1CC1. We determine the crystal structure of the TAX1BP1 SKICH/RB1CC1 coiled-coil complex and unravel the detailed binding mechanism of TAX1BP1 SKICH with RB1CC1. Moreover, we demonstrate that RB1CC1 and NAP1 are competitive in binding to the TAX1BP1 SKICH domain, but the presence of NAP1's FIP200-interacting region (FIR) motif can stabilize the ternary TAX1BP1/NAP1/RB1CC1 complex formation. Finally, we elucidate the molecular mechanism governing the selective interactions of TAX1BP1 with ATG8 family members by solving the structure of GABARAP in complex with the non-canonical LIR (LC3-interacting region) motif of TAX1BP1, which unveils a unique binding mode between LIR and ATG8 family protein. Collectively, our findings provide mechanistic insights into the interactions of TAX1BP1 with RB1CC1 and mammalian ATG8 family proteins and are valuable for further understanding the working mode and function of TAX1BP1 in autophagy.

Neural basis of reward expectancy inducing proactive aggression.

Proactive aggression refers to deliberate and unprovoked behavior, typically motivated by personal gain or expected reward. Reward expectancy is generally recognized as a critical factor that may influence proactive aggression, but its neural mechanisms remain unknown. We conducted a task-based functional magnetic resonance imaging (fMRI) experiment to investigate the relationship between reward expectancy and proactive aggression. 37 participants (20 females, mean age = 20.8 ± 1.42, age range = 18-23 years) completed a reward-harm task. In the experiment, reward valence expectancy and reward possibility expectancy were manipulated respectively by varying amounts (low: 0.5-1.5 yuan; high: 10.5-11.5 yuan) and possibilities (low: 10%-30%; high: 70%-90%) of money that participants could obtain by choosing to aggress. Participants received fMRI scans throughout the experiment. Brain activation regions associated with reward expectancy mainly involve the middle frontal gyrus, lingual gyrus, inferior temporal gyrus, anterior cuneus, caudate nucleus, inferior frontal gyrus, cingulate gyrus, anterior central gyrus, and posterior central gyrus. Associations between brain activation and reward expectancy in the left insula, left middle frontal gyrus, left thalamus, and right middle frontal gyrus were found to be related to proactive aggression. Furthermore, the brain activation regions primarily involved in proactive aggression induced by reward expectancy were the insula, inferior frontal gyrus, inferior temporal gyrus, pallidum, and caudate nucleus. Under conditions of high reward expectancy, participants engage in more proactive aggressive behavior. Reward expectancy involves the activation of reward- and social-cognition-related brain regions, and these associations are instrumental in proactive aggressive decisions.

A self-regulated network for dynamically balancing multiple precursors in complex biosynthetic pathways.

Microbial synthesis has emerged as a promising and sustainable alternative to traditional chemical synthesis and plant extraction. However, the competition between synthetic pathways and central metabolic pathways for cellular resources may impair final production efficiency. Moreover, when the synthesis of target product requires multiple precursors from the same node, the conflicts of carbon flux have further negative impacts on yields. In this study, a self-regulated network was developed to relieve the competition of precursors in complex synthetic pathways. Using 4-hydroxycoumarin (4-HC) synthetic pathway as a proof of concept, we employed an intermediate as a trigger to dynamically rewire the metabolic flux of pyruvate and control the expression levels of genes in 4-HC synthetic pathway, achieving self-regulation of multiple precursors and enhanced titer. Transcriptomic analysis results additionally demonstrated that the gene transcriptional levels of both pyruvate kinase PykF and synthetic pathway enzyme SdgA dynamically changed according to the intermediate concentrations. Overall, our work established a self-regulated network to dynamically balance the metabolic flux of two precursors in 4-HC biosynthesis, providing insight into balancing biosynthetic pathways where multiple precursors compete and interfere with each other.

Recent progresses in analytical method development for 210Pb in environmental and biological samples.

As a decay product of uranium series, 210Pb spreads widely in the nature and imposes strong radiological and chemical toxicity. It is vital to establish reliable and efficient radioanalytical methods for 210Pb determination to support environment and food radioactivity monitoring programs. This article critically reviews analytical methods developed for determining 210Pb in environmental and biological samples, especially new development in recent years. Techniques applied throughout different analytical steps including sample pretreatment, separation, purification, and detection are summarized and their pros and cons are discussed to provide a holistic overview for 210Pb environmental and biological assay.

Investigating and Engineering an 1,2-Propanediol-Responsive Transcription Factor-Based Biosensor.

Transcription factor (TF)-based biosensors have arisen as powerful tools in the advancement of metabolic engineering. However, with the emergence of numerous bioproduction targets, the variety of applicable TF-based biosensors remains severely limited. In this study, we investigated and engineered an 1,2-propanediol (1,2-PD)-responsive transcription activator, PocR, from Salmonella typhimurium to enrich the current biosensor repertoire. Heterologous characterization of PocR in E. coli revealed a significantly limited operational range and dynamic range, primarily attributed to the leaky binding between PocR and its corresponding promoters in the absence of the 1,2-PD inducer. Promiscuity characterization uncovered the minor responsiveness of PocR toward glycerol and 1,2-butanediol (1,2-BD). Using AlphaFold-predicted structure and protein mutagenesis, we preliminarily explored the underlying mechanism of PocR. Based on the investigated mechanism, we engineered a PcoR-F46R/G105D variant with an altered inducer specificity to glycerol, as well as a PocR-ARE (Q107A/S192R/A203E) variant with nearly a 4-fold higher dynamic range (6.7-fold activation) and a 20-fold wider operational range (0-20 mM 1,2-PD). Finally, we successfully converted PocR to a repressor through promoter engineering. Integrating the activation and repression functions established a versatile 1,2-PD-induced bifunctional regulation system based on PocR-ARE. Our work showcases the exploration and exploitation of an underexplored type of transcriptional activator capable of recruiting RNA polymerase. It also expands the biosensor toolbox by providing a 1,2-PD-responsive bifunctional regulator and glycerol-responsive activator.

Determination and human health risk assessment of TFWT, OBT and carbon-14 in seafood around Qinshan Nuclear Power Plant.

This work aims to evaluate the effects of the operation of Qinshan nuclear Power Plant (QNPP) on tritium (3H) and carbon-14 (14C) levels in seafood and assess the health risks caused by seafood consumption. Five kinds of seafood, including marine fish, prawn, razor clam, crabs, and seaweed, were collected from QNPP and the sea around Hangzhou Bay. The activity concentrations of tissue free water tritium (TFWT), organically bound tritium (OBT) and 14C were determined, respectively, and the annual intake and annual effective dose (AED) were calculated. The results showed that the TFWT, OBT, and 14C activity concentrations of the seafood in the surrounding area of QNPP ranged from 2.00 to 74.75 Bq/L, <1.04 to 19.68 Bq/L and 0.09 to 0.17 Bq/g·C, respectively. The TFWT, OBT, and 14C activity concentrations of the seafood in Hangzhou Bay ranged from 1.36 to 10.55 Bq/L, 1.08 to 6.78 Bq/L and 0.07 to 0.13 Bq/g·C, respectively. The differences were not statistically significant. The total AED from 3H and 14C due to the seafood consumption for the residents in the surrounding of QNPP and Hangzhou Bay were 1.96 × 10-4 and 1.61 × 10-4 mSv/year, respectively. The results showed that the operation of QNPP had no obvious effect on 3H and 14C accumulation in seafood, and the dose burden of population was low.

Neural correlates of aggression outcome expectation and their association with aggression: A voxel-based morphometry study.

BACKGROUND: Aggression outcome expectation is an important cognitive factor of aggression. Discovering the neural mechanism of aggression outcome expectation is conducive to developing aggression research. However, the neural correlates underlying aggression outcome expectation and its effect remain elusive. METHODS: We utilized voxel-based morphometry (VBM) to unravel the neural architecture of aggression outcome expectation measured by the Social Emotional Information Processing Assessment for Adults and its relationship with aggression measured by the Buss Perry Aggression Questionnaire in a sample of 185 university students (114 female; mean age = 19.94 ± 1.62 years; age range: 17-32 years). RESULTS: We found a significantly positive correlation between aggression outcome expectation and the regional gray matter volume (GMV) in the right middle temporal gyrus (MTG) (x = 55.5, y = -58.5, z = 1.5; t = 3.35; cluster sizes = 352, p < 0.05, GRF corrected). Moreover, aggression outcome expectation acted as a mediator underlying the association between the right MTG volume and aggression. CONCLUSIONS: These results revealed the neural correlates of aggression outcome expectation and its effect on aggression for the first time, which may contribute to our understanding of the cognitive neural mechanism of aggression and potentially identifying neurobiological markers for aggression.

Structure-based development of potent and selective type-II kinase inhibitors of RIPK1.

Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) functions as a key regulator in inflammation and cell death and is involved in mediating a variety of inflammatory or degenerative diseases. A number of allosteric RIPK1 inhibitors (RIPK1i) have been developed, and some of them have already advanced into clinical evaluation. Recently, selective RIPK1i that interact with both the allosteric pocket and the ATP-binding site of RIPK1 have started to emerge. Here, we report the rational development of a new series of type-II RIPK1i based on the rediscovery of a reported but mechanistically atypical RIPK3i. We also describe the structure-guided lead optimization of a potent, selective, and orally bioavailable RIPK1i, 62, which exhibits extraordinary efficacies in mouse models of acute or chronic inflammatory diseases. Collectively, 62 provides a useful tool for evaluating RIPK1 in animal disease models and a promising lead for further drug development.

Inhibition of Nogo-B reduces the progression of pancreatic cancer by regulation NF-κB/GLUT1 and SREBP1 pathways.

Pancreatic cancer (PC) is a lethal disease and associated with metabolism dysregulation. Nogo-B is related to multiple metabolic related diseases and types of cancers. However, the role of Nogo-B in PC remains unknown. In vitro, we showed that cell viability and migration was largely reduced in Nogo-B knockout or knockdown cells, while enhanced by Nogo-B overexpression. Consistently, orthotopic tumor and metastasis was reduced in global Nogo knockout mice. Furthermore, we indicated that glucose enhanced cell proliferation was associated to the elevation expression of Nogo-B and nuclear factor κB (NF-κB). While, NF-κB, glucose transporter type 1 (GLUT1) and sterol regulatory element-binding protein 1 (SREBP1) expression was reduced in Nogo-B deficiency cells. In addition, we showed that GLUT1 and SREBP1 was downstream target of NF-κB. Therefore, we demonstrated that Nogo deficiency inhibited PC progression is regulated by the NF-κB/GLUT1 and SREBP1 pathways, and suggested that Nogo-B may be a target for PC therapy.

Advancing microbial production through artificial intelligence-aided biology.

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.