Pyrrolic Nitrogen Boosted H2 Generation from an Aqueous Solution of HCHO at Room Temperature by Metal-Free Carbon Catalysts.

Hydrogen production from organic hydrides represents a promising strategy for the development of safe and sustainable technologies for H2 storage and transportation. Nonetheless, the majority of existing procedures rely on noble metal catalysts and emit greenhouse gases such as CO2/CO. Herein, we demonstrated an alternative N-doped carbon (CN) catalyst for highly efficient and robust H2 production from an aqueous solution of formaldehyde (HCHO). Importantly, this process generated formic acid as a valuable byproduct instead of CO2/CO, enabling a clean H2 generation process with 100% atom economy. Mechanism investigations revealed that the pyrrolic N in the CN catalysts played a critical role in promoting H2 generation via enhancing the transformation of O2 to generate •OO- free radicals. Consequently, the optimized CN catalysts achieved a remarkable H2 generation rate of 13.6 mmol g-1 h-1 at 30 °C. This finding is anticipated to facilitate the development of liquid H2 storage and its large-scale utilization.

A Comparative Study of Removal of Acid Red 27 by Adsorption on Four Different Chitosan Morphologies.

To investigate the relationship between structures and adsorption properties, four different morphologies of chitosan, with hydrogel (CSH), aerogel (CSA), powder (CSP), and electrospinning nanofiber (CSEN) characteristics, were employed as adsorbents for the removal of Acid Red 27. The structures and morphologies of the four chitosan adsorbents were characterized with SEM, XRD, ATR-FTIR, and BET methods. The adsorption behaviors and mechanisms of the four chitosan adsorbents were comparatively studied. All adsorption behaviors exhibited a good fit with the pseudo-second-order kinetic model (R2 > 0.99) and Langmuir isotherm model (R2 > 0.99). Comparing the adsorption rates and the maximum adsorption capacities, the order was CSH > CSA > CSP > CSEN. The maximum adsorption capacities of CSH, CSA, CSP, and CSEN were 2732.2 (4.523), 676.7 (1.119), 534.8 (0.885), and 215.5 (0.357) mg/g (mmol/g) at 20 °C, respectively. The crystallinities of CSH, CSA, CSP, and CSEN were calculated as 0.41%, 6.97%, 8.76%, and 39.77%, respectively. The crystallinity of the four chitosan adsorbents was the main factor impacting the adsorption rates and adsorption capacities, compared with the specific surface area. With the decrease in crystallinity, the adsorption rates and capacities of the four chitosan adsorbents increased gradually under the same experimental conditions. CSH with a low crystallinity and large specific surface area resulted in the highest adsorption rate and capacity.

Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS.

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.

New multiple-layered 3D polymers showing aggregation-induced emission and polarization.

An exceptional achiral and chiral multilayer 3D polymer has been created and controlled by uniform and distinct aromatic chromophore units that are multiply sandwiched by naphthyl berths. In order to put together this assembly, it was necessary to search for new catalytic Suzuki-Miyaura polycouplings among various catalytic systems, monomers, and catalysts. Gel Permeation Chromatography (GPC) was able to verify the presence of many framework layers. The resulting achiral and chiral polymers displayed notable optical characteristic.

Relation-Aware Heterogeneous Graph Network for Learning Intermodal Semantics in Textbook Question Answering.

Textbook question answering (TQA) task aims to infer answers for given questions from a multimodal context, including text and diagrams. The existing studies have aggregated intramodal semantics extracted from a single modality but have yet to capture the intermodal semantics between different modalities. A major challenge in learning intermodal semantics is maintaining lossless intramodal semantics while bridging the gap of semantics caused by heterogeneity. In this article, we propose an intermodal relation-aware heterogeneous graph network (IMR-HGN) to extract the intermodal semantics for TQA, which aggregates different modalities while learning features rather than representing them independently. First, we design a multidomain consistent representation (MDCR) to eliminate semantic gaps by capturing intermodal features while maintaining lossless intramodal semantics in multidomains. Furthermore, we present neighbor-based relation inpainting (NRI) to reduce semantic ambiguity via repairing fuzzy relations with correlations of relations. Finally, we propose hierarchical multisemantics aggregation (HMSA) to guarantee the completeness of semantics by aggregating features of nodes and relations with a reconstruction network (RN). Experimental results show that IMR-HGN could extract the intermodal semantics of answers, achieving a 2.16% improvement on the validation set of the TQA dataset and a 3.04% increase on the test set of the AI2D dataset.

Finite-Time Consensus Adaptive Neural Network Control for Nonlinear Multiagent Systems Under PDE Models.

In this article, a novel adaptive control method based on neural networks is proposed for a class of multiagent systems (MASs) with nonlinear functions and external disturbances. First, the approximation properties of neural networks are used to approximate the MAS partial differential equation (PDE) model with nonlinear terms containing two variables, time t, and spatial variable x. Second, an adaptive controller is constructed to actuate the parabolic MAS to reach consensus under external disturbances. Based on this, the finite-time theorem and special inequalities are applied to prove the stability of the closed-loop system. Thus, MAS that have nonlinear functions and external disturbances are enabled with finite-time consensus. Finally, the effectiveness of the proposed control method is demonstrated by numerical simulations.

Dopamine promotes osteogenic differentiation of PDLSCs by activating DRD1 and DRD2 during orthodontic tooth movement via ERK1/2 signaling pathway.

Introduction: Orthodontic tooth movement (OTM) involves complex interactions between mechanical forces and periodontal tissue adaptation, mainly mediated by periodontal ligament cells, including periodontal ligament stem cells (PDLSCs), osteoblasts, and osteoclasts. Dopamine (DA), a neurotransmitter known for its critical role in bone metabolism, is investigated in this study for its potential to enhance osteogenic differentiation in PDLSCs, which are pivotal in OTM. This study examined the potential of DA to facilitate OTM by binding to DA receptors (D1R and D2R) and activating the ERK1/2 signaling pathway. We propose that DA's interaction with these receptors on PDLSCs could enhance osteogenic differentiation, thereby accelerating bone remodeling and reducing the duration of orthodontic treatments, which offering a novel approach to improve clinical outcomes in orthodontic care. Methods: This study utilized a rat OTM model, micro-CT, histological analyses, and in vitro assays to investigate dopamine's effect on osteogenesis. PDLSCs were cultured and treated with DA, and cytotoxicity, osteogenic differentiation, gene and protein expression assessed. Results: Dopamine administration significantly increased trabecular bone density and osteogenic marker expression in an OTM rat model. In vitro, DA at 10 nM optimally promoted human PDLSCs osteogenesis without affecting proliferation. Blocking DA receptors or inhibiting the ERK1/2 pathway attenuated these effects, underscoring the importance of dopaminergic signaling in tension-induced osteogenesis during OTM. Conclusion: Taken together, our study reveals that local dopamine administration at a concentration of 10 nM not only enhances tension-induced osteogenesis in vivo but also significantly promotes osteogenic differentiation of PDLSCs in vitro through D1 and D2 receptor-mediated ERK1/2 signaling pathway activation.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

Dissolution and regeneration of starch in hydroxyl-functionalized ionic liquid aqueous solution.

There have been continuous quests for suitable solvents for starch, given the importance of effective starch dissolution in its modification and subsequent materials production. In light of this, the potential of hydroxyl-functionalized ionic liquid (IL) as a promising solvent for starch was investigated. Within this study, a hydroxyl-functionalized IL 1-(2,3-dihydroxypropyl)-3-methylimidazole chloride ([Dhpmim][Cl]) was synthesized, and the dissolution of starch in this IL and its aqueous solutions was examined. Starch (5.35 wt%) was completely dissolved in [Dhpmim][Cl] within 2 h at 100 °C. The solubility of starch in [Dhpmim][Cl]-water mixtures initially increased and then decreased with rising water content. The optimal ratio was found to be 1:9 (wt/wt) water:[Dhpmim][Cl], achieving the highest solubility at 9.28 wt%. Density functional theory (DFT) simulations elucidated the possible interactions between starch and solvents. After dissolution and regeneration in the 1:9 water:[Dhpmim][Cl] mixture, starch showed no discernible change in the molecular structure, with no derivatization reaction observed. Regenerated starch exhibited a transformation in crystalline structure from A-type to V-type, and its relative crystallinity (12.4 %) was lower than that of native starch (25.2 %), resulting in decreased thermal stability. This study suggests that the hydroxyl-functionalized IL, [Dhpmim][Cl], and its aqueous solutions serve as effective solvents for starch dissolution.

Design and Asymmetric Control of Orientational Chirality by Using the Combination of C(sp2)-C(sp) Levers and Achiral N-Protecting Group.

New chiral targets of orientational chirality have been designed and asymmetrically synthesized by taking advantage of N-sulfinyl imine-directed nucleophilic addition/oxidation, Suzuki-Miyaura, and Sonogashira cross-coupling reactions. Orientation of single isomers has been selectively controlled by using aryl/alkynyl levers [C(sp2)-C(sp) axis] and tBuSO2- protecting group on nitrogen as proven by X-ray diffraction analysis. The key structural characteristic of resulting orientational products is shown by remote through-space blocking manner. Seventeen examples of multi-step synthesis were obtained with modest to good chemical yields and complete orientational selectivity.

Inhalation bioaccessibility of imidacloprid in particulate matter: Implications for risk assessment during spraying.

Adverse health outcomes due to the inhalation of pesticide residues in atmospheric particulate matter (PM) are gaining global attention. Quantitative health risk assessments of pesticide inhalation exposure highlight the need to understand the bioaccessibility of pesticide residues. Herein, the inhalation bioaccessibility of imidacloprid in PM was determined using three commonly used in vitro lung modeling methods (Artificial Lysosomal Fluid, Gamble Solution, and Simulated Lung Fluid). To validate its feasibility and effectiveness, we evaluated the bioavailability of imidacloprid using a mouse nasal instillation assay. The in vitro inhalation bioaccessibility of imidacloprid was extracted using Gamble Solution with a solid-liquid ratio of 1/1000, an oscillation rate of 150 r/min, and an extraction time of 24 h, showed a strong linear correlation with its in vivo liver-based bioavailability (R2 =0.8928). Moreover, the margin of exposure was incorporated into the inhalation exposure risk assessment, considering both formulations and nozzles. The inhalation unit exposure of imidacloprid for residents was 0.95-4.09 ng/m3. The margin of exposure for imidacloprid was determined to be acceptable when considering inhalation bioaccessibility. Taken together, these results indicate that the inhalation bioaccessibility of pesticides should be incorporated into assessments of human health risks posed by PM particles.

Preclinical evaluation of the SARS-CoV-2 Mpro inhibitor RAY1216 shows improved pharmacokinetics compared with nirmatrelvir.

Although vaccines are available for SARS-CoV-2, antiviral drugs such as nirmatrelvir are still needed, particularly for individuals in whom vaccines are less effective, such as the immunocompromised, to prevent severe COVID-19. Here we report an α-ketoamide-based peptidomimetic inhibitor of the SARS-CoV-2 main protease (Mpro), designated RAY1216. Enzyme inhibition kinetic analysis shows that RAY1216 has an inhibition constant of 8.4 nM and suggests that it dissociates about 12 times slower from Mpro compared with nirmatrelvir. The crystal structure of the SARS-CoV-2 Mpro:RAY1216 complex shows that RAY1216 covalently binds to the catalytic Cys145 through the α-ketoamide group. In vitro and using human ACE2 transgenic mouse models, RAY1216 shows antiviral activities against SARS-CoV-2 variants comparable to those of nirmatrelvir. It also shows improved pharmacokinetics in mice and rats, suggesting that RAY1216 could be used without ritonavir, which is co-administered with nirmatrelvir. RAY1216 has been approved as a single-component drug named 'leritrelvir' for COVID-19 treatment in China.

An odorant receptor mediates the avoidance of Plutella xylostella against parasitoid.

BACKGROUND: Ecosystems are brimming with myriad compounds, including some at very low concentrations that are indispensable for insect survival and reproduction. Screening strategies for identifying active compounds are typically based on bioassay-guided approaches. RESULTS: Here, we selected two candidate odorant receptors from a major pest of cruciferous plants-the diamondback moth Plutella xylostella-as targets to screen for active semiochemicals. One of these ORs, PxylOR16, exhibited a specific, sensitive response to heptanal, with both larvae and adult P. xylostella displaying heptanal avoidance behavior. Gene knockout studies based on CRISPR/Cas9 experimentally confirmed that PxylOR16 mediates this avoidance. Intriguingly, rather than being involved in P. xylostella-host plant interaction, we discovered that P. xylostella recognizes heptanal from the cuticular volatiles of the parasitoid wasp Cotesia vestalis, possibly to avoid parasitization. CONCLUSIONS: Our study thus showcases how the deorphanization of odorant receptors can drive discoveries about their complex functions in mediating insect survival. We also demonstrate that the use of odorant receptors as a screening platform could be efficient in identifying new behavioral regulators for application in pest management.

Metagenomic insights into the response of soil microbial communities to pathogenic Ralstonia solanacearum.

Understanding the response of soil microbial communities to pathogenic Ralstonia solanacearum is crucial for preventing bacterial wilt outbreaks. In this study, we investigated the soil physicochemical and microbial community to assess their impact on the pathogenic R.solanacearum through metagenomics. Our results revealed that certain archaeal taxa were the main contributors influencing the health of plants. Additionally, the presence of the pathogen showed a strong negative correlation with soil phosphorus levels, while soil phosphorus was significantly correlated with bacterial and archaeal communities. We found that the network of microbial interactions in healthy plant rhizosphere soils was more complex compared to diseased soils. The diseased soil network had more linkages, particularly related to the pathogen occurrence. Within the network, the family Comamonadaceae, specifically Ramlibacter_tataouinensis, was enriched in healthy samples and showed a significantly negative correlation with the pathogen. In terms of archaea, Halorubrum, Halorussus_halophilus (family: Halobacteriaceae), and Natronomonas_pharaonis (family: Haloarculaceae) were enriched in healthy plant rhizosphere soils and showed negative correlations with R.solanacearum. These findings suggested that the presence of these archaea may potentially reduce the occurrence of bacterial wilt disease. On the other hand, Halostagnicola_larseniia and Haloterrigena_sp._BND6 (family: Natrialbaceae) had higher relative abundance in diseased plants and exhibited significantly positive correlations with R.solanacearum, indicating their potential contribution to the pathogen's occurrence. Moreover, we explored the possibility of functional gene sharing among the correlating bacterial pairs within the Molecular Ecological Network. Our analysis revealed 468 entries of horizontal gene transfer (HGT) events, emphasizing the significance of HGT in shaping the adaptive traits of plant-associated bacteria, particularly in relation to host colonization and pathogenicity. Overall, this work revealed key factors, patterns and response mechanisms underlying the rhizosphere soil microbial populations. The findings offer valuable guidance for effectively controlling soil-borne bacterial diseases and developing sustainable agriculture practices.

Identification of a lncRNA/circRNA-miRNA-mRNA network in Nasopharyngeal Carcinoma by deep sequencing and bioinformatics analysis.

Background: Accumulating evidence indicates that non-coding RNAs (ncRNA), including long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), can function as competitive endogenous RNAs (ceRNAs) by binding to microRNAs (miRNAs) and regulating host gene expression at the transcriptional or post-transcriptional level. Dysregulation in ceRNA network regulation has been implicated in the occurrence and development of cancer. However, the lncRNA/circRNA-miRNA-mRNA regulatory network is still lacking in nasopharyngeal carcinoma (NPC). Methods: Differentially expressed genes (DEGs) were obtained from our previous sequencing data and Gene Expression Omnibus (GEO). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were used to explore the biological functions of these common DEGs. Through a series of bioinformatic analyses, the lncRNA/circRNA-miRNA-mRNA network was established. In additional, the external data GSE102349 was used to test the prognostic value of the hub mRNAs through the Kaplan-Meier method. Results: We successfully constructed a lncRNA/circRNA-miRNA-mRNA network in NPC, consisting of 16 lncRNAs, 6 miRNAs, 3 circRNAs and 10 mRNAs and found that three genes (TOP2A, ZWINT, TTK) were significantly associated with overall survival time (OS) in patients. Conclusion: The regulatory network revealed in this study may help comprehensively elucidate the ceRNA mechanisms driving NPC, and provide novel candidate biomarkers for evaluating the prognosis of NPC.

Research progress on the multi-omics and survival status of circulating tumor cells.

In the dynamic process of metastasis, circulating tumor cells (CTCs) emanate from the primary solid tumor and subsequently acquire the capacity to disengage from the basement membrane, facilitating their infiltration into the vascular system via the interstitial tissue. Given the pivotal role of CTCs in the intricate hematogenous metastasis, they have emerged as an essential resource for a deeper comprehension of cancer metastasis while also serving as a cornerstone for the development of new indicators for early cancer screening and new therapeutic targets. In the epoch of precision medicine, as CTC enrichment and separation technologies continually advance and reach full fruition, the domain of CTC research has transcended the mere straightforward detection and quantification. The rapid advancement of CTC analysis platforms has presented a compelling opportunity for in-depth exploration of CTCs within the bloodstream. Here, we provide an overview of the current status and research significance of multi-omics studies on CTCs, including genomics, transcriptomics, proteomics, and metabolomics. These studies have contributed to uncovering the unique heterogeneity of CTCs and identifying potential metastatic targets as well as specific recognition sites. We also review the impact of various states of CTCs in the bloodstream on their metastatic potential, such as clustered CTCs, interactions with other blood components, and the phenotypic states of CTCs after undergoing epithelial-mesenchymal transition (EMT). Within this context, we also discuss the therapeutic implications and potential of CTCs.

Prediction models for lymph node metastasis in cervical cancer based on preoperative heart rate variability.

Background: The occurrence of lymph node metastasis (LNM) is one of the critical factors in determining the staging, treatment and prognosis of cervical cancer (CC). Heart rate variability (HRV) is associated with LNM in patients with CC. The purpose of this study was to validate the feasibility of machine learning (ML) models constructed with preoperative HRV as a feature of CC patients in predicting CC LNM. Methods: A total of 292 patients with pathologically confirmed CC admitted to the Department of Gynecological Oncology of the First Affiliated Hospital of Bengbu Medical University from November 2020 to September 2023 were included in the study. The patient' preoperative 5-min electrocardiogram data were collected, and HRV time-domain, frequency-domain and non-linear analyses were subsequently performed, and six ML models were constructed based on 32 parameters. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: Among the 6 ML models, the random forest (RF) model showed the best predictive performance, as specified by the following metrics on the test set: AUC (0.852), accuracy (0.744), sensitivity (0.783), and specificity (0.785). Conclusion: The RF model built with preoperative HRV parameters showed superior performance in CC LNM prediction, but multicenter studies with larger datasets are needed to validate our findings, and the physiopathological mechanisms between HRV and CC LNM need to be further explored.

A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer.

OBJECTIVE: This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional cone beam computed tomography (4D-CBCT) for dose calculation in lung cancer patients. METHODS: 4D-CBCT and 4D computed tomography (CT) of 20 patients with locally advanced non-small cell lung cancer were used to paired train the deep-learning model. The lung tumors were located in the right upper lobe, right lower lobe, left upper lobe, and left lower lobe, or in the mediastinum. Additionally, five patients to create 4D synthetic computed tomography (sCT) for test. Using the 4D-CT as the ground truth, the quality of the 4D-sCT images was evaluated by quantitative and qualitative assessment methods. The correction of CT values was evaluated holistically and locally. To further validate the accuracy of the dose calculations, we compared the dose distributions and calculations of 4D-CBCT and 4D-sCT with those of 4D-CT. RESULTS: The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the 4D-sCT increased from 87% and 22.31 dB to 98% and 29.15 dB, respectively. Compared with cycle consistent generative adversarial networks, CLCGAN enhanced SSIM and PSNR by 1.1% (p < 0.01) and 0.42% (p < 0.01). Furthermore, CLCGAN significantly decreased the absolute mean differences of CT value in lungs, bones, and soft tissues. The dose calculation results revealed a significant improvement in 4D-sCT compared to 4D-CBCT. CLCGAN was the most accurate in dose calculations for left lung (V5Gy), right lung (V5Gy), right lung (V20Gy), PTV (D98%), and spinal cord (D2%), with the relative dose difference were reduced by 6.84%, 3.84%, 1.46%, 0.86%, 3.32% compared to 4D-CBCT. CONCLUSIONS: Based on the satisfactory results obtained in terms of image quality, CT value measurement, it can be concluded that CLCGAN-based corrected 4D-CBCT can be utilized for dose calculation in lung cancer.

Rare and common genetic determinants of mitochondrial function determine severity but not risk of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease involving selective vulnerability of energy-intensive motor neurons (MNs). It has been unclear whether mitochondrial function is an upstream driver or a downstream modifier of neurotoxicity. We separated upstream genetic determinants of mitochondrial function, including genetic variation within the mitochondrial genome or autosomes; from downstream changeable factors including mitochondrial DNA copy number (mtCN). Across three cohorts including 6,437 ALS patients, we discovered that a set of mitochondrial haplotypes, chosen because they are linked to measurements of mitochondrial function, are a determinant of ALS survival following disease onset, but do not modify ALS risk. One particular haplotype appeared to be neuroprotective and was significantly over-represented in two cohorts of long-surviving ALS patients. Causal inference for mitochondrial function was achievable using mitochondrial haplotypes, but not autosomal SNPs in traditional Mendelian randomization (MR). Furthermore, rare loss-of-function genetic variants within, and reduced MN expression of, ACADM and DNA2 lead to ∼50 % shorter ALS survival; both proteins are implicated in mitochondrial function. Both mtCN and cellular vulnerability are linked to DNA2 function in ALS patient-derived neurons. Finally, MtCN responds dynamically to the onset of ALS independently of mitochondrial haplotype, and is correlated with disease severity. We conclude that, based on the genetic measures we have employed, mitochondrial function is a therapeutic target for amelioration of disease severity but not prevention of ALS.

Transcriptomic and physiological analysis of atractylodes chinensis in response to drought stress reveals the putative genes related to sesquiterpenoid biosynthesis.

BACKGROUND: Atractylodes chinensis (DC) Koidz., a dicotyledonous and hypogeal germination species, is an important medicinal plant because its rhizome is enriched in sesquiterpenes. The development and production of A. chinensis are negatively affected by drought stress, especially at the seedling stage. Understanding the molecular mechanism of A. chinensis drought stress response plays an important role in ensuring medicinal plant production and quality. In this study, A. chinensis seedlings were subjected to drought stress treatment for 0 (control), 3 (D3), and 9 days (D9). For the control, the sample was watered every two days and collected on the second morning after watering. The integration of physiological and transcriptomic analyses was carried out to investigate the effects of drought stress on A. chinensis seedlings and to reveal the molecular mechanism of its drought stress response. RESULTS: The malondialdehyde, proline, soluble sugar, and crude protein contents and antioxidative enzyme (superoxide dismutase, peroxidase, and catalase) activity were significantly increased under drought stress compared with the control. Transcriptomic analysis indicated a total of 215,665 unigenes with an average length of 759.09 bp and an N50 of 1140 bp. A total of 29,449 differentially expressed genes (DEGs) were detected between the control and D3, and 14,538 DEGs were detected between the control and D9. Under drought stress, terpenoid backbone biosynthesis had the highest number of unigenes in the metabolism of terpenoids and polyketides. To identify candidate genes involved in the sesquiterpenoid and triterpenoid biosynthetic pathways, we observed 22 unigene-encoding enzymes in the terpenoid backbone biosynthetic pathway and 15 unigene-encoding enzymes in the sesquiterpenoid and triterpenoid biosynthetic pathways under drought stress. CONCLUSION: Our study provides transcriptome profiles and candidate genes involved in sesquiterpenoid and triterpenoid biosynthesis in A. chinensis in response to drought stress. Our results improve our understanding of how drought stress might affect sesquiterpenoid and triterpenoid biosynthetic pathways in A. chinensis.