Programmable Lanthanide Metal-Organic Framework for Ultra-Efficient Nucleic Acids Extraction and Interaction Analysis.

Efficient, mild, and reversible adsorption of nucleic acids onto nanomaterials represents a promising analytical approach for medical diagnosis. However, there is a scarcity of efficient and reversible nucleic acid adsorption nanomaterials. Additionally, the lack of comprehension of the molecular mechanisms governing their interactions poses significant challenges. These issues hinder the rational design and analytical applications of the nanomaterials. Herein, we propose an ultra-efficient nucleic acid affinity nanomaterial based on programmable lanthanide metal-organic frameworks (Ln-MOFs). Through experiments and density functional theory calculations, a rational design guideline for nucleic acid affinity of Ln-MOF was proposed, and a modular and flexible preparation scheme was provided. Then, Er-TPA (terephthalic acid) MOF emerged as the optimal candidate due to its pore size-independent adsorption and desorption capabilities for nucleic acids, enabling ultra-efficient adsorption (about 150% mass ratio) within 1 min. Furthermore, we elucidate the molecular-level mechanisms underlying the Ln-MOF adsorption of single- and double-stranded DNA and G4 structures. The affinity nanomaterial based on Ln-MOF exhibits robust nucleic acid extraction capability (4-fold higher than commercial reagent kits) and enables mild and reversible CRISPR/Cas9 functional regulation. This method holds significant promise for broad application in DNA/RNA liquid biopsy and gene editing, facilitating breakthroughs in analytical chemistry, pharmacy, and medical research.

Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins.

Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.

REME: an integrated platform for reaction enzyme mining and evaluation.

A key challenge in pathway design is finding proper enzymes that can be engineered to catalyze a non-natural reaction. Although existing tools can identify potential enzymes based on similar reactions, these tools encounter several issues. Firstly, the calculated similar reactions may not even have the same reaction type. Secondly, the associated enzymes are often numerous and identifying the most promising candidate enzymes is difficult due to the lack of data for evaluation. Thirdly, existing web tools do not provide interactive functions that enable users to fine-tune results based on their expertise. Here, we present REME (https://reme.biodesign.ac.cn/), the first integrated web platform for reaction enzyme mining and evaluation. Combining atom-to-atom mapping, atom type change identification, and reaction similarity calculation enables quick ranking and visualization of reactions similar to an objective non-natural reaction. Additional functionality enables users to filter similar reactions by their specified functional groups and candidate enzymes can be further filtered (e.g. by organisms) or expanded by Enzyme Commission number (EC) or sequence homology. Afterward, enzyme attributes (such as kcat, Km, optimal temperature and pH) can be assessed with deep learning-based methods, facilitating the swift identification of potential enzymes that can catalyze the non-natural reaction.

Historical trends of breast cancer burden attributable to metabolic factors among Chinese women, 1990-2019: A population-based epidemiological study.

BACKGROUND:  This study aims to analyze breast cancer burden attributable to high body mass index (BMI) and high fasting plasma glucose (FPG) in China from 1990 to 2019. METHODS: Data were obtained from the Global Burden of Disease (GBD) study 2019. Deaths and disability-adjusted life years (DALYs) were used for attributable burden, and age-period-cohort (APC) model was used to evaluate the independent effects of age, period and birth cohort. RESULTS: In 2019, the age-standardized mortality and DALY rates of breast cancer attributable to high BMI were 1.107 (95% UI: 0.311, 2.327) and 29.990 (8.384, 60.713) per 100 000, and mortality and DALY rates attributable to high FPG were 0.519 (0.095, 1.226) and 13.662 (2.482, 32.425) per 100 000. From 1990 to 2019, the age-standardized mortality and DALY rates of breast cancer attributable to high BMI increased by 1.192% and 1.180%, and the trends of high FPG were not statistically significant. The APC results showed that the age effects of high BMI and high FPG-mortality and DALY rates increased, with the highest rates in the age group over 80 years. The birth cohort effects of high BMI showed "inverted V" shapes, while high FPG showed downward trends. CONCLUSIONS: Age was the main reason for the increase of attributable burden, and postmenopausal women were the high-risk groups. Therefore, targeted prevention measures should be developed to improve postmenopausal women's awareness and effectively reduce the prevalence of obesity and diabetes, thereby reducing the breast cancer burden caused by metabolic factors in China.

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models.

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

Microwave ablation combined with percutaneous vertebroplasty for treating painful non-small cell lung cancer with spinal metastases under real-time temperature monitoring.

PURPOSE: To retrospectively study the therapeutic effect and safety performance of the combination strategies of the computed tomography (CT)-guided microwave ablation (MWA) and percutaneous vertebroplasty (PVP) as a treatment for painful non-small cell lung cancer (NSCLC) with spinal metastases. MATERIALS AND METHODS: A retrospective review included 71 patients with 109 vertebral metastases who underwent microwave ablation combined with percutaneous vertebroplasty by the image-guided and real-time temperature monitoring. Treatment efficacy was determined by comparing visual analog scale (VAS) scores, daily morphine equivalent opioid consumption, and Oswestry Disability Index (ODI) scores before treatment and during the follow-up period. RESULTS: Technical success was achieved in all patients. The mean pre-procedure VAS score and morphine doses were 6.6 ± 1.8 (4-10) and 137.2 ± 38.7 (40-200) mg, respectively. The mean VAS scores and daily morphine doses at 24 h and 1, 4, 12, and 24 weeks postoperatively were 3.3 ± 1.9 and 73.5 ± 39.4 mg; 2.2 ± 1.5 and 40.2 ± 29.8 mg; 1.7 ± 1.2 and 31.3 ± 23.6 mg; 1.4 ± 1.1 and 27.3 ± 21.4 mg; and 1.3 ± 1.1 and 24.8 ± 21.0 mg, respectively (all P < 0.001). ODI scores significantly decreased (P < 0.05). Minor cement leakage occurred in 51 cases (46.8%), with one patient having a grade 3 neural injury. No local tumor progression was observed by follow-up imaging. CONCLUSIONS: MWA combined with PVP can significantly relieve pain and improve patients' quality of life, which implied this is an effective treatment option for painful NSCLC with spinal metastases. Additionally, its efficacy should be further verified through the mid- and long-term studies.

Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment.

Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.

Shared and Specific Changes of Cortico-Striatal Functional Connectivity in Stable Mild Cognitive Impairment and Progressive Mild Cognitive Impairment.

Background: Mild cognitive impairment (MCI), the prodromal stage of Alzheimer's disease, has two distinct subtypes: stable MCI (sMCI) and progressive MCI (pMCI). Early identification of the two subtypes has important clinical significance. Objective: We aimed to compare the cortico-striatal functional connectivity (FC) differences between the two subtypes of MCI and enhance the accuracy of differential diagnosis between sMCI and pMCI. Methods: We collected resting-state fMRI data from 31 pMCI patients, 41 sMCI patients, and 81 healthy controls. We chose six pairs of seed regions, including the ventral striatum inferior, ventral striatum superior, dorsal-caudal putamen, dorsal-rostral putamen, dorsal caudate, and ventral-rostral putamen and analyzed the differences in cortico-striatal FC among the three groups, additionally, the relationship between the altered FC within the MCI subtypes and cognitive function was examined. Results: Compared to sMCI, the pMCI patients exhibited decreased FC between the left dorsal-rostral putamen and right middle temporal gyrus, the right dorsal caudate and right inferior temporal gyrus, and the left dorsal-rostral putamen and left superior frontal gyrus. Additionally, the altered FC between the right inferior temporal gyrus and right putamen was significantly associated with episodic memory and executive function. Conclusions: Our study revealed common and distinct cortico-striatal FC changes in sMCIs and pMCI across different seeds; these changes were associated with cognitive function. These findings can help us understand the underlying pathophysiological mechanisms of MCI and distinguish pMCI and sMCI in the early stage potentially.

Knockdown of ACOT4 alleviates gluconeogenesis and lipid accumulation in hepatocytes.

Acyl-CoA thioesterase 4 (ACOT4) has been reported to be related to acetyl-CoA carboxylase activity regulation; However, its exact functions in liver lipid and glucose metabolism are still unclear. Here, we discovered explored the regulatory roles of ACOT4 in hepatic lipid and glucose metabolism in vitro. We found that the expression level of ACOT4 was significantly increased in the hepatic of db/db and ob/ob mice as well as obese mice fed a high fat diet. Adenovirus-mediated overexpression of ACOT4 promoted gluconeogenesis and high-glucose/high-insulin-induced lipid accumulation and impaired insulin sensitivity in primary mouse hepatocytes, whereas ACOT4 knockdown notably suppressed gluconeogenesis and decreased the triglycerides accumulation in hepatocytes. Furthermore, ACOT4 knockdown increased insulin-induced phosphorylation of AKT and GSK-3ß in primary mouse hepatocytes. Mechanistically, we found that upregulation of ACOT4 expression inhibited AMP-activated protein kinase (AMPK) activity, and its knockdown had the opposite effect. However, activator A769662 and inhibitor compound C of AMPK suppressed the impact of the change in ACOT4 expression on AMPK activity. Our data indicated that ACOT4 is related to hepatic glucose and lipid metabolism, primarily via the regulation of AMPK activity. In conclusion, ACOT4 is a potential target for the therapy of non-alcoholic fatty liver (NAFLD) and type 2 diabetes.

Repetitive transcranial magnetic stimulation regulates effective connectivity patterns of brain networks in the spectrum of preclinical Alzheimer's disease.

Objectives: Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are considered as the spectrum of preclinical Alzheimer's disease (AD), with abnormal brain network connectivity as the main neuroimaging feature. Repetitive transcranial magnetic stimulation (rTMS) has been proven to be an effective non-invasive technique for addressing neuropsychiatric disorders. This study aims to explore the potential of targeted rTMS to regulate effective connectivity within the default mode network (DMN) and the executive control network (CEN), thereby improving cognitive function. Methods: This study included 86 healthy controls (HCs), 72 SCDs, and 86 aMCIs. Among them, 10 SCDs and 11 aMCIs received a 2-week rTMS course of 5-day, once-daily. Cross-sectional analysis with the spectral dynamic causal model (spDCM) was used to analyze the DMN and CEN effective connectivity patterns of the three groups. Afterwards, longitudinal analysis was conducted on the changes in effective connectivity patterns and cognitive function before and after rTMS for SCD and aMCI, and the correlation between them was analyzed. Results: Cross-sectional analysis showed different effective connectivity patterns in the DMN and CEN among the three groups. Longitudinal analysis showed that the effective connectivity pattern of the SCD had changed, accompanied by improvements in episodic memory. Correlation analysis indicated a negative relationship between effective connectivity from the left angular gyrus (ANG) to the anterior cingulate gyrus and the ANG.R to the right middle frontal gyrus, with visuospatial and executive function, respectively. In patients with aMCI, episodic memory and executive function improved, while the effective connectivity pattern remained unchanged. Conclusion: This study demonstrates that PCUN-targeted rTMS in SCD regulates the abnormal effective connectivity patterns in DMN and CEN, thereby improving cognition function. Conversely, in aMCI, the mechanism of improvement may differ. Our findings further suggest that rTMS is more effective in preventing or delaying disease progression in the earlier stages of the AD spectrum. Clinical Trial Registration: http://www.chictr.org.cn, ChiCTR2000034533.

Tetrahydropiperine, a natural alkaloid with neuroprotective effects in ischemic stroke.

BACKGROUND: Ischemic stroke (IS) is a life-threatening neurological disease with various pathological mechanisms. Tetrahydropiperine (THP) is a natural alkaloid with protective effects against multiple diseases, such as seizure, and pain. This study was to examine the impact of THP on IS and investigate its potential mechanism. MATERIAL AND METHODS: We employed network pharmacology and molecular docking techniques to identify the target proteins of THP for intervention in IS. Adult male Sprague-Dawley rats were used to create a permanent middle cerebral artery occlusion model. PC-12 cells were chosen to establish an oxygen-glucose deprivation (OGD) cell model. Disease modeling followed by nimodipine (NIMO); 3-methyladenine (3-MA) and rapamycin (RAP) interventions. Open field test, Longa score, balance beam test, and forelimb grip test were used to measure motor and neurological functions. The degree of neurological damage recovery was assessed through behavioral analysis, and cerebral infarction volume was determined using TTC staining. Morphological changes were examined through HE and Nissl staining, and ultrastructural changes in neurons were observed using transmission electron microscopy. The protein expression of autophagy and related pathways was analyzed through Western blot (WB). The appropriate hypoxia time and drug concentration were determined using CCK-8 assay, which also measured cell survival rate. RESULTS: The network pharmacology findings indicated that the impact of THP on IS was enhanced in the PI3K/Akt signaling pathway. THP demonstrated robust docking capability with proteins associated with the autophagy and PI3K/Akt/mTOR, as indicated by the molecular docking outcomes. THP significantly improved behavioral damage, reduced the area of cerebral infarction, ameliorated histopathological damage from ischemia, increase neuronal survival, and alleviated ultrastructural damage in neurons (P < 0.05). THP enhanced the survival of PC-12 cells induced by OGD and ameliorated the morphological harm to the cells (P < 0.05). THP was found to elevate the quantities of P62, LC3-Ⅰ, PI3K, P-AKt/Akt, and P-mTOR/mTOR proteins while reducing the levels of Atg7 and Beclin1 proteins. The results of transmission electron microscopy showed no autophagosomes in the THP, 3-MA, and 3-MA + THP groups. CONCLUSION: The activation of the PI3K/Akt/mTOR signaling pathway by THP inhibits autophagy and provides relief from neurological damage in IS.

Co-Cultivated Enzyme Constraint Metabolic Network Model for Rational Guidance in Constructing Synthetic Consortia to Achieve Optimal Pathway Allocation Prediction.

Synthetic consortia have emerged as a promising biosynthetic platform that offers new opportunities for biosynthesis. Genome-scale metabolic network models (GEMs) with complex constraints are extensively utilized to guide the synthesis in monocultures. However, few methods are currently available to guide the rational construction of synthetic consortia for predicting the optimal allocation strategy of synthetic pathways aimed at enhancing product synthesis. A standardized method to construct the co-cultivated Enzyme Constraint metabolic network model (CulECpy) is proposed, which integrates enzyme constraints and modular interaction scale constraints based on the research concept of "independent + global". This method is applied to construct several synthetic consortia models, which encompassed different target products, strains, synthetic pathways, and compositional structures. Analyzing the model, the optimal pathway allocation and initial inoculum ratio that enhance the synthesis of target products by synthetic consortia are predicted and verified. When comparing with the constructed co-culture synthesis system, the normalized root mean square error of all optimal theoretical yield simulations is found to be less than or equal to 0.25. The analyses and verifications demonstrate that the method CulECpy can guide the rational construction of synthetic consortia systems to facilitate biochemical synthesis.

Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan.

Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the Acidithiobacillus genus, along with the process of energy production, remain areas that necessitate further research and elucidation. In this study, a novel strain of sulfur-oxidizing bacterium, Acidithiobacillus Ameehan, was isolated. Several physiological characteristics of the strain Ameehan were verified and its complete genome sequence was presented in the study. Besides, the first genome-scale metabolic network model (AMEE_WP1377) was reconstructed for Acidithiobacillus Ameehan to gain a comprehensive understanding of the metabolic capacity of the strain.The characteristics of Acidithiobacillus Ameehan included morphological size and an optimal growth temperature range of 37-45°C, as well as an optimal growth pH range of pH 2.0-8.0. The microbe was found to be capable of growth when sulfur and K2O6S4 were supplied as the energy source and electron donor for CO2 fixation. Conversely, it could not utilize Na2S2O3, FeS2, and FeSO4·7H2O as the energy source or electron donor for CO2 fixation, nor could it grow using glucose or yeast extract as a carbon source. Genome annotation revealed that the strain Ameehan possessed a series of sulfur oxidizing genes that enabled it to oxidize elemental sulfur or various reduced inorganic sulfur compounds (RISCs). In addition, the bacterium also possessed carbon fixing genes involved in the incomplete Calvin-Benson-Bassham (CBB) cycle. However, the bacterium lacked the ability to oxidize iron and fix nitrogen. By implementing a constraint-based flux analysis to predict cellular growth in the presence of 71 carbon sources, 88.7% agreement with experimental Biolog data was observed. Five sulfur oxidation pathways were discovered through model simulations. The optimal sulfur oxidation pathway had the highest ATP production rate of 14.81 mmol/gDW/h, NADH/NADPH production rate of 5.76 mmol/gDW/h, consumed 1.575 mmol/gDW/h of CO2, and 1.5 mmol/gDW/h of sulfur. Our findings provide a comprehensive outlook on the most effective cellular metabolic pathways implicated in sulfur oxidation within Acidithiobacillus Ameehan. It suggests that the OMP (outer-membrane proteins) and SQR enzymes (sulfide: quinone oxidoreductase) have a significant impact on the energy production efficiency of sulfur oxidation, which could have potential biotechnological applications.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501.

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Deciphering cell wall sensors enabling the construction of robust P. pastoris for single-cell protein production.

Single-cell protein (SCP) production in the methylotrophic yeast Pichia pastoris has the potential to achieve a sustainable protein supply. However, improving the methanol fermentation efficiency and reducing carbon loss has been a long-standing challenge with far-reaching scientific and practical implications. Here, comparative transcriptomics revealed that PAS_0305, a gene directly associated with cell wall thickness under methanol stress, can be used as a target for unlocking cell wall sensors. Intracellular trehalose accumulation confirmed that cell wall sensors were activated after knocking out PAS_0305, which resulted in increased cell wall permeability. Genome-wide signal perturbations were transduced through the HOG module and the CWI pathway, which was confirmed to connected by Pbs2-Mkk. As a consequence of CWI pathway activation, ΔPAS_0305 elicited a rescue response of cell wall remodeling by increasing the ß-1,3-glucan content and decreasing the chitin/mannose content. Remarkably, perturbations in global stress signals led to a fine-tuning of the metabolic network of ΔPAS_0305, resulting in a superior phenotype with highest crude protein and methanol conversion rate of 67.21% and 0.46 gDCW/g. Further genome-scale metabolic models were constructed to validate the experimental results, confirming that unlocking cell wall sensors resulted in maximized flux from methanol towards SCP and effectively addressing the issue of carbon loss in methanol fermentation. This work sheds new light on the potential of manipulating cellular signaling pathways to optimize metabolic networks and achieve exceptional phenotypic characteristics, providing new strategies for constructing versatile cell factories in P. pastoris.

Elucidating the impact of in vitro cultivation on Nicotiana tabacum metabolism through combined in silico modeling and multiomics analysis.

The systematical characterization and understanding of the metabolic behaviors are the basis of the efficient plant metabolic engineering and synthetic biology. Genome-scale metabolic networks (GSMNs) are indispensable tools for the comprehensive characterization of overall metabolic profile. Here we first constructed a GSMN of tobacco, which is one of the most widely used plant chassis, and then combined the tobacco GSMN and multiomics analysis to systematically elucidate the impact of in-vitro cultivation on the tobacco metabolic network. In-vitro cultivation is a widely used technique for plant cultivation, not only in the field of basic research but also for the rapid propagation of valuable horticultural and pharmaceutical plants. However, the systemic effects of in-vitro cultivation on overall plant metabolism could easily be overlooked and are still poorly understood. We found that in-vitro tobacco showed slower growth, less biomass and suppressed photosynthesis than soil-grown tobacco. Many changes of metabolites and metabolic pathways between in-vitro and soil-grown tobacco plants were identified, which notably revealed a significant increase of the amino acids content under in-vitro condition. The in silico investigation showed that in-vitro tobacco downregulated photosynthesis and primary carbon metabolism, while significantly upregulated the GS/GOGAT cycle, as well as producing more energy and less NADH/NADPH to acclimate in-vitro growth demands. Altogether, the combination of experimental and in silico analyses offers an unprecedented view of tobacco metabolism, with valuable insights into the impact of in-vitro cultivation, enabling more efficient utilization of in-vitro techniques for plant propagation and metabolic engineering.

Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in Escherichia coli.

Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process. We previously reported a biosynthetic route that produces IPDO via extending the leucine catabolism (route A), the efficiency of which, however, is not satisfactory. To address this issue, we computationally designed a novel non-natural IPDO synthesis pathway (route B) using RetroPath RL, the state-of-the-art tool for bioretrosynthesis based on artificial intelligence methods. We compared this new pathway with route A and two other intuitively designed routes for IPDO biosynthesis from various perspectives. Route B, which exhibits the highest thermodynamic driving force, least non-native reaction steps, and lowest energy requirements, appeared to hold the greatest potential for IPDO production. All three newly designed routes were then implemented in the Escherichia coli BL21(DE3) strain. Results show that the computationally designed route B can produce 2.2 mg/L IPDO from glucose but no IPDO production from routes C and D. These results highlight the importance and usefulness of in silico design and comprehensive evaluation of the potential efficiencies of candidate pathways in constructing novel non-natural pathways for the production of biochemicals.

Aberrant spontaneous static and dynamic amplitude of low-frequency fluctuations in cerebral small vessel disease with or without mild cognitive impairment.

BACKGROUND: Cerebral small vessel disease (CSVD) is considered an age-related degenerative neurological disorder and the most common risk factor for vascular cognitive impairment (VCI). The amplitude of fluctuation of low frequency (ALFF) can detect altered intrinsic brain activity in CSVD. This study explored the static and dynamic ALFFs in the early stage of CSVD with (CSVD-M) or without (CSVD-W) mild cognitive impairment (MCI) in these patients and how these changes contribute to cognitive deterioration. METHODS: Thirty consecutive CSVD cases and 18 healthy controls (HC) were included in this study. All the participants underwent a 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence to obtain structural T1-weighted images. Simultaneous multislice imaging 5(SMS5) was used for resting-state functional MRI (rs-fMRI), and Data Processing and Analysis of Brain Imaging software helped determine static ALFF (sALFF). The dynamic ALFF (dALFF) was calculated using the sliding window method of DynamicBC software. Analysis of Covariance (ANCOVA) and two-sample t-test were used to evaluate the sALFF and temporal variability of dALFF among the three groups. The subjects were rated on a broad standard neuropsychological scale. Partial correlation analysis was used to evaluate the correlation between sALFF and dALFF variability and cognition (Bonferroni correction, statistical threshold set at p < .05). RESULTS: Compared with HCs, the CSVD-M group indicated decreased sALFF values in the bilateral cerebellum posterior lobe (CPL) and the left inferior Parietal Lobule (IPL), with increased sALFF values in the right SFG. For dALFF analysis, the CSVD-W group had significant dALFF variability in the right fusiform gyrus compared with HC. Moreover, the postcentral gyrus (PoCG) was significantly high in the CSVD-W group. While in the CSVD-M group, the bilateral paracentral lobules (PL) revealed significantly elevated dALFF variability and low dALFF variability in the left CPL and right IPL compared with HCs. The CSVD-M group had high dALFF variability in the bilateral PL but low dALFF variability in the left middle temporal gyrus (MTG) and right PoCG compared with the CSVD-W group. The partial correlation analysis indicated that dALFF variability in the left MTG was positively associated with EM (r = 0.713, p = .002) in CSVD-W and CSVD-M groups. In the groups with CSVD-M and HC, altered dALFF variability in the bilateral PL was negatively correlated with EM (r = -0.560, p = .002). CONCLUSION: There were significant changes in sALFF and dALFF variability in CSVD patients. Abnormal spontaneous static and dynamic ALFFs may provide new insights into cognitive dysfunction in CSVD with MCI and may be valuable biomarkers for early diagnosis.

Transmissive Pancharatnam-Berry metasurfaces with stable amplitude and precise phase modulations using dartboard discretization configuration.

Metasurfaces are ultra-thin artificial structures capable of flexibly manipulating electromagnetic (EM) waves. Among various applications, phase modulation of electromagnetic (EM) waves using metasurfaces holds great significance. The Pancharatnam-Berry (P-B) metasurfaces provides a complete 2π phase modulation by simply rotating the meta-atom. However, the fixed lattice in rotation employed by traditional P-B metasurfaces often results in unstable amplitude and imprecise P-B phase, leading to performance degradation. In this work, we demonstrate transmissive P-B metasurfaces with stable amplitude and precise phase modulation. To ensure stable amplitude and precise P-B phase, we adopt a dartboard discretization configuration with a hexagonal lattice for the meta-atom design. By applying topology optimization to the encoding sequence formed by surface pixels and dimensions, we significantly enhancing the high transmissive bandwidth of the optimized meta-atom. Furthermore, the optimized meta-atom exhibits a stable amplitude and precise P-B phase for each rotation angle. As proof-of-concept demonstrations, two metasurfaces for single and multiplexed vortex beams generating are designed utilizing the optimized meta-atom. Both the simulated and measured results indicate high mode purity of generated vortex beams. The design method can also be readily extended to other high performance metasurfaces with stable amplitude and precise phase manipulations, which can enhance the efficiency and capacity of metasurface-assisted holographic imaging and 6 G wireless communication systems.

A mitochondrial EglN1-AMPKα axis drives breast cancer progression by enhancing metabolic adaptation to hypoxic stress.

Mitochondria play essential roles in cancer cell adaptation to hypoxia, but the underlying mechanisms remain elusive. Through mitochondrial proteomic profiling, we here find that the prolyl hydroxylase EglN1 (PHD2) accumulates on mitochondria under hypoxia. EglN1 substrate-binding region in the ß2ß3 loop is responsible for its mitochondrial translocation and contributes to breast tumor growth. Furthermore, we identify AMP-activated protein kinase alpha (AMPKα) as an EglN1 substrate on mitochondria. The EglN1-AMPKα interaction is essential for their mutual mitochondrial translocation. After EglN1 prolyl-hydroxylates AMPKα under normoxia, they rapidly dissociate following prolyl-hydroxylation, leading to their immediate release from mitochondria. In contrast, hypoxia results in constant EglN1-AMPKα interaction and their accumulation on mitochondria, leading to the formation of a Ca2+ /calmodulin-dependent protein kinase 2 (CaMKK2)-EglN1-AMPKα complex to activate AMPKα phosphorylation, ensuring metabolic homeostasis and breast tumor growth. Our findings identify EglN1 as an oxygen-sensitive metabolic checkpoint signaling hypoxic stress to mitochondria through its ß2ß3 loop region, suggesting a potential therapeutic target for breast cancer.