Search

Abstract Callosobruchus maculatus is one of the most competitive stored grain pests, which causes a great loss to agricultural economy. However, due to an inadequacy of high-quality reference genome, the molecular mechanisms for olfactory and hypoxic adaptations to stored environments are unknown and require to be revealed urgently, which will contribute to the detection and prevention of the invasive pests C. maculatus. Here, we presented a high-quality chromosome-level genome of C. maculatus based on Illumina, Nanopore and Hi-C sequencing data. The total size was 1.2 Gb, and 65.17% (797.47 Mb) of it was identified to be repeat sequences. Among assembled chromosomes, chromosome 10 was considered the X chromosome according to the evidence of reads coverage and homologous genes among species. The current version of high-quality genome provides preferable data resources for the adaptive evolution research of C. maculatus.

The latest review published in Nature Reviews Drug Discovery by Michael W. Mullowney and co-authors focuses on the use of artificial intelligence techniques, specifically machine learning, in natural product drug discovery. The authors discussed various applications of AI in this field, such as genome and metabolome mining, structural characterization of natural products, and predicting targets and biological activities of these compounds. They also highlighted the challenges associated with creating and managing large datasets for training algorithms, as well as strategies to address these obstacles. Additionally, the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.

Abstract Diabetic retinopathy (DR) is an eye disease that causes blindness and vision loss in diabetic. Risk factors for DR include high blood glucose levels and some environmental factors. The pathogenesis is based on inflammation caused by interferon and other nuclear proteins. This review article provides an overview of DR and discusses the role of nuclear proteins in the pathogenesis of the disease. Some core proteins such as MAPK, transcription co-factors, transcription co-activators, and others are part of this review. In addition, some current advanced treatment resulting from the role of nuclear proteins will be analyzes, including epigenetic modifications, the use of methylation, acetylation, and histone modifications. Stem cell technology and the use of nanobiotechnology are proposed as promising approaches for a more effective treatment of DR.

Abstract Given the insidious and high-fatality nature of cardiovascular diseases (CVDs), the emergence of fluoride as a newly identified risk factor demands serious consideration alongside traditional risk factors. While vascular smooth muscle cells (VSMCs) play a pivotal role in the progression of CVDs, the toxicological impact of fluoride on VSMCs remains largely uncharted. In this study, we constructed fluorosis model in SD rats and A7R5 aortic smooth muscle cell lines to confirm fluoride impaired VSMCs. Fluoride aggravated the pathological damage of rat aorta in vivo. Then A7R5 were exposed to fluoride with concentration ranging from 0 to 1200 μmol/L over a 24-h period, revealing a dose-dependent inhibition of cell proliferation and migration. The further metabolomic analysis showed alterations in metabolite profiles induced by fluoride exposure, notably decreasing organic acids and lipid molecules level. Additionally, gene network analysis underscored the frequency of fluoride's interference with amino acids metabolism, potentially impacting the tricarboxylic acid (TCA) cycle. Our results also highlighted the ATP-binding cassette (ABC) transporters pathway as a central element in VSMC impairment. Moreover, we observed a dose-dependent increase in osteopontin (OPN) and α-smooth muscle actin (α-SMA) mRNA level and a dose-dependent decrease in ABC subfamily C member 1 (ABCC1) and bestrophin 1 (BEST1) mRNA level. These findings advance our understanding of fluoride as a CVD risk factor and its influence on VSMCs and metabolic pathways, warranting further investigation into this emerging risk factor.

Abstract Background Migraine is a highly prevalent neurological disease with a substantial societal burden due to lost productivity. From a societal perspective, we assessed the cost-effectiveness of eptinezumab for the preventive treatment of migraine. Methods An individual patient simulation of discrete competing events was developed to evaluate eptinezumab cost-effectiveness compared to best supportive care for adults in the United Kingdom with ≥ 4 migraine days per month and prior failure of ≥ 3 preventive migraine treatments. Individuals with sampled baseline characteristics were created to represent this population, which comprised dedicated episodic and chronic migraine subpopulations. Clinical efficacy, utility, and work productivity inputs were based on results from the DELIVER randomised controlled trial (NCT04418765). Timing of natural history events and treatment holidays—informed by the literature—were simulated to unmask any natural improvement of the disease unrelated to treatment. The primary outcomes were monthly migraine days, migraine-associated costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratio, and net monetary benefit, each evaluated over a 5-year time horizon from 2020. Secondary analyses explored a lifetime horizon and an alternative treatment stopping rule. Results Treatment with eptinezumab resulted in an average of 0.231 QALYs gained at a saving of £4,894 over 5 years, making eptinezumab dominant over best supportive care (i.e., better health outcomes and less costly). This result was confirmed by the probabilistic analysis and all alternative assumption scenarios under the same societal perspective. Univariate testing of inputs showed net monetary benefit was most sensitive to the number of days of productivity loss, and monthly salary. Conclusions This economic evaluation shows that from a societal perspective, eptinezumab is a cost-effective treatment in patients with ≥ 4 migraine days per month and for whom ≥ 3 other preventive migraine treatments have failed. Trial registration N/A. Graphical Abstract

BackgroundThe gut microbiota significantly influences the onset and progression of juvenile idiopathic arthritis (JIA) and associated uveitis (JIAU); however, the causality remains unclear. This study aims to establish a causal link between gut microbiota and JIA or JIAU.MethodsUsing publicly available genome-wide association studies (GAWS) summary data, we conducted a two-sample Mendelian randomisation (MR) analysis employing various methods, namely inverse variance weighted (IVW), simple mode, weighted mode, weighted median and MR-Egger regression methods, to assess the causal association between JIA or JIAU and gut microbiota. Sensitivity analyses, including Cochrane’s Q test, MR-Egger intercept test, leave-one-out analysis and MR-PRESSO, were performed to evaluate the robustness of the MR results. Subsequently, reverse MR analysis was conducted to determine causality between gene-predicted gut microbiota abundance and JIA or JIAU.ResultsThe MR analysis revealed a causal association between gut microbiota abundance variations and JIA or JIAU risk. Specifically, the increased abundance of genus Ruminococcaceae UCG013 (OR: 0.055, 95%CI: 0.006–0.103, p = 0.026) and genus Ruminococcaceae UCG003 (β: 0.06, 95%CI: 0.003–0.117, p = 0.041) correlated with an increased risk of JIA, while genus Lachnospiraceae UCG001 (OR: 0.833, 95%CI: 0.699~0.993, p = 0.042) was associated with a reduced risk of JIA, among others. Sensitivity analysis confirmed MR analysis robustness.ConclusionsThis study provides substantial evidence supporting a causal association between genetically predicted gut microbiota and JIA or JIAU. It highlights the significant role of intestinal flora in JIA or JIAU development, suggesting their potential as novel biomarkers for diagnosis and prevention. These findings offer valuable insights to mitigate the impact of JIA or JIAU.

Abstract Background As new migraine therapies emerge, it is crucial for measures to capture the complexities of health-related quality of life (HRQoL) improvement beyond improvements in monthly migraine day (MMD) reduction. Investigations into the correlations between MMD reduction, symptom management, and HRQoL are lacking, particularly those that focus on improvements in canonical symptoms and improvement in patient-identified most-bothersome symptoms (PI-MBS), in patients treated with eptinezumab. This exploratory analysis identified efficacy measures mediating the effect of eptinezumab on HRQoL improvements in patients with migraine. Methods Data from the DELIVER study of patients with 2–4 prior preventive migraine treatment failures (NCT04418765) were inputted to two structural equation models describing sources of HRQoL improvement via Migraine-Specific Quality-of-Life Questionnaire (MSQ) scores. A single latent variable was defined to represent HRQoL and describe the sources of HRQoL in DELIVER. One model included all migraine symptoms while the second model included the PI-MBS as the only migraine symptom. Mediating variables capturing different aspects of efficacy included MMDs, other canonical symptoms, and PI-MBS. Results In the first model, reductions in MMDs and other canonical symptoms accounted for 35% (standardized effect size [SES] − 0.11) and 25% (SES − 0.08) of HRQoL improvement, respectively, with 41% (SES − 0.13) of improvement comprising “direct treatment effect,” i.e., unexplained by mediators. In the second model, substantial HRQoL improvement with eptinezumab (86%; SES − 0.26) is due to MMD reduction (17%; SES − 0.05) and change in PI-MBS (69%; SES − 0.21). Conclusions Improvements in HRQoL experienced by patients treated with eptinezumab can be substantially explained by its effect on migraine frequency and PI-MBS. Therefore, in addition to MMD reduction, healthcare providers should discuss PI-MBS improvements, since this may impact HRQoL. Health technology policymakers should consider implications of these findings in economic evaluation, as they point to alternative measurement of quality-adjusted life years to capture fully treatment benefits in cost-utility analyses. Trial registration ClinicalTrials.gov (Identifier: NCT04418765 ; EudraCT (Identifier: 2019–004497-25; URL: https://www.clinicaltrialsregister.eu/ctr-search/search?query=2019-004497-25 ). Graphical Abstract

Abstract Objective To analyze the risk factors affecting psychiatric behavior and study the psychobehavioral conditions of children with epilepsy. Method We randomly selected and enrolled 294 children with epilepsy who visited and were hospitalized in the pediatric clinic of Hebei General Hospital between January 2017 and January 2022, as the study participants. We comprehensively assessed their cognitive functions using the Gesell development schedule or Wechsler Intelligence Scales. The participants were divided into the study group (n = 123) with cognitive impairment and the control group (n = 171) with normal cognitive functions, for analysis. Results There were statistically significant differences between the two groups in disease course, frequency of epilepsy, status epilepticus, and the number of antiseizure medications (ASMs) used (P 0.05). Based on multivariate logistic regression analysis, the course of disease, frequency of onset, status epilepticus and number of ASMs used were identified as high-risk factors for cognitive impairment in children with epilepsy. Similarly, early onset, long course of disease, known etiology, and combination of multiple drugs have a negative impact on behavioral problems, school education, and social adaptability. Conclusion The course of disease, the frequency of onset, status epilepticus, and the number of ASMs used are high-risk factors for cognitive impairment in children with epilepsy, which can be prevented and controlled early. When selecting ASMs, their advantages and disadvantages should be weighed. Moreover, the availability of alternative treatment options must be considered. With the help of genomic technology, the causes of epilepsy should be identified as early as possible, and precision medicine and gene therapy for children with epilepsy should be actively developed.

Abstract For a long time, hydrogen sulfide (H2S) has been considered a toxic compound, but recent studies have found that H2S is the third gaseous signaling molecule which plays a vital role in physiological and pathological conditions. Currently, a large number of studies have shown that H2S mediates apoptosis through multiple signaling pathways to participate in cancer occurrence and development, for example, PI3K/Akt/mTOR and MAPK signaling pathways. Therefore, the regulation of the production and metabolism of H2S to mediate the apoptotic process of cancer cells may improve the effectiveness of cancer treatment. In this review, the role and mechanism of H2S in cancer cell apoptosis in mammals are summarized.

Abstract The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449–3500, 2278–3257 and 1621–2904 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

Abstract Background Single-nucleotide variants (SNVs) within gene coding sequences can significantly impact pre-mRNA splicing, bearing profound implications for pathogenic mechanisms and precision medicine. In this study, we aim to harness the well-established full-length gene splicing assay (FLGSA) in conjunction with SpliceAI to prospectively interpret the splicing effects of all potential coding SNVs within the four-exon SPINK1 gene, a gene associated with chronic pancreatitis. Results Our study began with a retrospective analysis of 27 SPINK1 coding SNVs previously assessed using FLGSA, proceeded with a prospective analysis of 35 new FLGSA-tested SPINK1 coding SNVs, followed by data extrapolation, and ended with further validation. In total, we analyzed 67 SPINK1 coding SNVs, which account for 9.3% of the 720 possible coding SNVs. Among these 67 FLGSA-analyzed SNVs, 12 were found to impact splicing. Through detailed comparison of FLGSA results and SpliceAI predictions, we inferred that the remaining 653 untested coding SNVs in the SPINK1 gene are unlikely to significantly affect splicing. Of the 12 splice-altering events, nine produced both normally spliced and aberrantly spliced transcripts, while the remaining three only generated aberrantly spliced transcripts. These splice-impacting SNVs were found solely in exons 1 and 2, notably at the first and/or last coding nucleotides of these exons. Among the 12 splice-altering events, 11 were missense variants (2.17% of 506 potential missense variants), and one was synonymous (0.61% of 164 potential synonymous variants). Notably, adjusting the SpliceAI cut-off to 0.30 instead of the conventional 0.20 would improve specificity without reducing sensitivity. Conclusions By integrating FLGSA with SpliceAI, we have determined that less than 2% (1.67%) of all possible coding SNVs in SPINK1 significantly influence splicing outcomes. Our findings emphasize the critical importance of conducting splicing analysis within the broader genomic sequence context of the study gene and highlight the inherent uncertainties associated with intermediate SpliceAI scores (0.20 to 0.80). This study contributes to the field by being the first to prospectively interpret all potential coding SNVs in a disease-associated gene with a high degree of accuracy, representing a meaningful attempt at shifting from retrospective to prospective variant analysis in the era of exome and genome sequencing.

Although advancements in n- and p-doping of gallium oxide (Ga2O3) are underway, the realization of functional pn diodes remains elusive. Here, we present the successful fabrication of a Ga2O3 pn diode utilizing ion implantation technology. The Ga2O3 epilayers were grown on c-plane sapphire substrates by metalorganic chemical vapor deposition (MOCVD). P-type conductivity Ga2O3 epilayer, confirmed by Hall effect analysis, was achieved by phosphorus (P) ion implantation followed with a rapid thermal annealing (RTA) process. This p-Ga2O3 epilayer reveals a significant reduction in resistivity ( 3kT/q) and remained very low at 2✕10−8 A, as the diode operated at 150oC. The behavior could be due to Ga2O3 being a wide bandgap material.

Autophagy is an evolutionarily conserved cellular recycling process that maintains cellular homeostasis. Despite extensive research in endocrine contexts, the role of autophagy in ovarian and testicular steroidogenesis remains elusive. The significant role of autophagy in testosterone production suggests potential treatments for conditions like oligospermia and azoospermia. Further, influence of autophagy in folliculogenesis, ovulation, and luteal development emphasizes its importance for improved fertility and reproductive health. Thus, investigating autophagy in gonadal cells is clinically significant. Understanding these processes could transform treatments for endocrine disorders, enhancing reproductive health and longevity. Herein, we provide the functional role of autophagy in testicular and ovarian steroidogenesis to date, highlighting its modulation in testicular steroidogenesis and its impact on hormone synthesis, follicle development, and fertility therapies.

Epigenomic imbalance drives abnormal transcriptional processes, promoting the onset and progression of cancer. Although defective gene regulation generally affects carcinogenesis and tumor suppression networks, tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes, which may have significant implications for the development and application of epigenetic therapy, cancer immunotherapy, and their combinations. Herein, we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes, DNA methylation, histone post-translational modification, and chromatin structure in tumor immunogenicity, and introduce these epigenetic research methods. We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immunotherapy through the complex interaction between cancer epigenetics and cancer immunology.

Summary: Upcycling plastics presents an opportunity not only to reduce plastic waste, but also to provide an alternative carbon source to fossil fuels. Herein, we present a protocol to upcycle plastics with resin codes 2–7 using a commercially available base-metal photocatalyst. We first conducted batch reactions, followed by a continuous, segmented flow system for gram-scale upcycling into value-added platform chemicals. This protocol, employing tandem carbon-hydrogen bond oxidation/carbon-carbon bond cleavage reactions, can be useful for photocatalytically transforming plastics at ambient conditions.For complete details on the use and execution of this protocol, please refer to Li et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.