A dual RNA-seq analyses revealed dynamic arms race during the invasion of walnut by Colletotrichum gloeosporioides.

BACKGROUND: Walnut anthracnose caused by Colletotrichum gloeosporioides seriously endangers the yield and quality of walnut, and has now become a catastrophic disease in the walnut industry. Therefore, understanding both pathogen invasion mechanisms and host response processes is crucial to defense against C. gloeosporioides infection. RESULTS: Here, we investigated the mechanisms of interaction between walnut fruits (anthracnose-resistant F26 fruit bracts and anthracnose-susceptible F423 fruit bracts) and C. gloeosporioides at three infection time points (24hpi, 48hpi, and 72hpi) using a high-resolution time series dual transcriptomic analysis, characterizing the arms race between walnut and C. gloeosporioides. A total of 20,780 and 6670 differentially expressed genes (DEGs) were identified in walnut and C. gloeosporioides against 24hpi, respectively. Generous DEGs in walnut exhibited opposite expression patterns between F26 and F423, which indicated that different resistant materials exhibited different transcriptional responses to C. gloeosporioides during the infection process. KEGG functional enrichment analysis indicated that F26 displayed a broader response to C. gloeosporioides than F423. Meanwhile, the functional analysis of the C. gloeosporioides transcriptome was conducted and found that PHI, SignalP, CAZy, TCDB genes, the Fungal Zn (2)-Cys (6) binuclear cluster domain (PF00172.19) and the Cytochrome P450 (PF00067.23) were largely prominent in F26 fruit. These results suggested that C. gloeosporioides secreted some type of effector proteins in walnut fruit and appeared a different behavior based on the developmental stage of the walnut. CONCLUSIONS: Our present results shed light on the arms race process by which C. gloeosporioides attacked host and walnut against pathogen infection, laying the foundation for the green prevention of walnut anthracnose.

Heterocyclic Modulated Electronic States of Alkynyl-Containing Conjugated Microporous Polymers for Efficient Oxygen Reduction.

The oxygen reduction reaction (ORR) is a key process in green energy conversion technology. Heteroatom doping has been proven to be a prospective strategy to prepare metal-free carbon-based electrocatalysts, but such methods often suffer from uncontrollable catalyst frameworks and imprecise active sites. Herein, an organic heterocyclic strategy is adopted to modulate the charge redistribution of alkynyl-containing conjugated microporous polymers (CMPs) by introducing varied five-membered heterocyclic structures. Among these CMPs, the S, 2N-containing thiadiazole heterocyclic molecule (CMP-Tdz) with carbonized alginate materials (CCA ) displays a remarkable quasi-four-electron-transfer ORR pathway, exhibiting an excellent half-wave potential (E1/2 ) of 0.77 V, coupled with superior methanol tolerance and electrochemical stability, which are among the highest performance in the metal-free organic catalytic material systems. Density functional theory calculations prove that the high catalytic performance of these catalysts originates from the sp-hybridized C atom (site-2) which is activated by their adjacent heterocyclic structures. Importantly, the five-membered heterocyclic structures can also modulate the local charge distribution, and increase dipole moment, with significantly improved catalytic kinetics. This incorporation of chemically designed heterocyclic-containing alkynyl-CMPs provides a new approach to developing efficient metal-free carbon-based electrocatalysts for fuel cells.

Locus-level antagonistic selection shaped the polygenic architecture of human complex diseases.

BACKGROUND: We aimed to evaluate the potential role of antagonistic selection in polygenic diseases: if one variant increases the risk of one disease and decreases the risk of another disease, the signals of genetic risk elimination by natural selection will be distorted, which leads to a higher frequency of risk alleles. METHODS: We applied local genetic correlations and transcriptome-wide association studies to identify genomic loci and genes adversely associated with at least two diseases. Then, we used different population genetic metrics to measure the signals of natural selection for these loci and genes. RESULTS: First, we identified 2120 cases of antagonistic pleiotropy (negative local genetic correlation) among 87 diseases in 716 genomic loci (antagonistic loci). Next, by comparing with non-antagonistic loci, we observed that antagonistic loci explained an excess proportion of disease heritability (median 6%), showed enhanced signals of balancing selection, and reduced signals of directional polygenic adaptation. Then, at the gene expression level, we identified 31,991 cases of antagonistic pleiotropy among 98 diseases at 4368 genes. However, evidence of altered signals of selection pressure and heritability distribution at the gene expression level is limited. CONCLUSION: We conclude that antagonistic pleiotropy is widespread among human polygenic diseases, and it has distorted the evolutionary signal and genetic architecture of diseases at the locus level.

Oxygen Reduction Activity of B←N-Containing Organic Molecule Affected by Asymmetric Regulation.

Organic molecular catalysts have received great attention as they have the merits of well-controlled molecular structures for the development of catalytic chemistry. Herein, the electronic distribution of active sites is regulated by asymmetrically introducing S-heterocycle on one side of the molecular core. As a result, the asymmetric as-PYT and as-BNT show higher oxygen reduction performance than their symmetric counterparts without (s-PY, s-PY2T) or with two S-heterocycle units (s-BN, s-BN2T). Density functional theory calculations reveal that the carbon atoms (site-12) at symmetric s-BN and s-BN2T are the catalytic active sites, while for asymmetric as-BNT, it has changed to amino-N atom (site-14). Due to the non-uniform charge distribution and increased dipole moment of as-BNT caused by asymmetric molecular configuration, the kinetics of catalytic reaction has changed significantly. The catalytically active sites of specific N atoms are further verified experimentally and theoretically by using sterically hindered phenyl groups. This work provides a simple but efficient method to design metal-free oxygen reduction electrocatalysts.

Single-cell spatial transcriptomic analysis reveals common and divergent features of developing postnatal granule cerebellar cells and medulloblastoma.

BACKGROUND: Cerebellar neurogenesis involves the generation of large numbers of cerebellar granule neurons (GNs) throughout development of the cerebellum, a process that involves tight regulation of proliferation and differentiation of granule neuron progenitors (GNPs). A number of transcriptional regulators, including Math1, and the signaling molecules Wnt and Shh have been shown to have important roles in GNP proliferation and differentiation, and deregulation of granule cell development has been reported to be associated with the pathogenesis of medulloblastoma. While the progenitor/differentiation states of cerebellar granule cells have been broadly investigated, a more detailed association between developmental differentiation programs and spatial gene expression patterns, and how these lead to differential generation of distinct types of medulloblastoma remains poorly understood. Here, we provide a comparative single-cell spatial transcriptomics analysis to better understand the similarities and differences between developing granule and medulloblastoma cells. RESULTS: To acquire an enhanced understanding of the precise cellular states of developing cerebellar granule cells, we performed single-cell RNA sequencing of 24,919 murine cerebellar cells from granule neuron-specific reporter mice (Math1-GFP; Dcx-DsRed mice). Our single-cell analysis revealed that there are four major states of developing cerebellar granule cells, including two subsets of granule progenitors and two subsets of differentiating/differentiated granule neurons. Further spatial transcriptomics technology enabled visualization of their spatial locations in cerebellum. In addition, we performed single-cell RNA sequencing of 18,372 cells from Patched+/- mutant mice and found that the transformed granule cells in medulloblastoma closely resembled developing granule neurons of varying differentiation states. However, transformed granule neuron progenitors in medulloblastoma exhibit noticeably less tendency to differentiate compared with cells in normal development. CONCLUSION: In sum, our study revealed the cellular and spatial organization of the detailed states of cerebellar granule cells and provided direct evidence for the similarities and discrepancies between normal cerebellar development and tumorigenesis.

Loss of Setd2 associates with aberrant microRNA expression and contributes to inflammatory bowel disease progression in mice.

Both SETD2-mediated H3K36me3 and miRNAs play critical epigenetic roles in inflammatory bowel disease (IBD) and involve in the dysfunctional intestinal barrier. However, little is known about cross-talk between these two types of regulators in IBD progression. We performed small RNA sequencing of Setd2 epithelium-specific knockout mice (Setd2Vil-KO) and wild-type controls, both with DSS-induced colitis, and designed a framework for integrative analysis. Firstly, we integrated the downloaded ChIP-seq data with miRNA expression profiles and identified a significant intersection of pre-miRNA expression and H3K36me3 modification. A significant inverse correlation was detected between changes of H3K36me3 modification and expression of the 171 peak-covered miRNAs. We further integrated RNA-seq data with predicted miRNA targets to screen negatively regulated miRNA-mRNA pairs and found the H3K36me3-associated differentially expressed microRNAs significantly enriched in cell-cell junction and signaling pathways. Using network analysis, we identified ten hub miRNAs, among which six are H3K36me3-associated, suggesting therapeutic targets for IBD patients with SETD2-deficiency.

An Integrative Analysis of Identified Schizophrenia-Associated Brain Cell Types and Gene Expression Changes.

Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes. Firstly, we observed that the proportions of brain cell types in SCZ differed from normal samples. Among these cell types, astrocyte, pericyte, and PAX6 cells were found to have a higher proportion in SCZ patients (astrocyte: SCZ = 0.163, control = 0.145, P.adj = 4.9 × 10-4, effect size = 0.478; pericyte: SCZ = 0.057, control = 0.066, P.adj = 1.1 × 10-4, effect size = 0.519; PAX6: SCZ = 0.014, control = 0.011, P.adj = 0.014, effect size = 0.377), while the L5/6_IT_CAR3 cells and LAMP5 cells are the exact opposite (L5/6_IT_Car3: SCZ = 0.102, control = 0.108, P.adj = 0.016, effect size = 0.369; LAMP5: SCZ = 0.057, control = 0.066, P.adj = 2.2 × 10-6, effect size = 0.617). Next, we investigated gene expression in cell types and functional pathways in SCZ. We observed chemical synaptic transmission dysregulation in two types of GABAergic neurons (PVALB and LAMP5), and immune reaction involvement in GABAergic neurons (SST) and non-neuronal cell types (endothelial and oligodendrocyte). Furthermore, we observed that some differential expression genes from bulk RNA-seq displayed cell-type-specific abnormalities in the expression of molecules in SCZ. Finally, the cell types with the SCZ-related transcriptomic changes could be considered to belong to the same module since we observed two major similar coordinated transcriptomic changes across these cell types. Together, our results offer novel insights into cellular heterogeneity and the molecular mechanisms underlying SCZ.

Mendelian randomization studies of brain MRI yield insights into the pathogenesis of neuropsychiatric disorders.

BACKGROUND: Observational studies have identified various associations between neuroimaging alterations and neuropsychiatric disorders. However, whether such associations could truly reflect causal relations remains still unknown. RESULTS: Here, we leveraged genome-wide association studies (GWAS) summary statistics for (1) 11 psychiatric disorders (sample sizes varied from n = 9,725 to 1,331,010); (2) 110 diffusion tensor imaging (DTI) measurement (sample size n = 17,706); (3) 101 region-of-interest (ROI) volumes, and investigate the causal relationship between brain structures and neuropsychiatric disorders by two-sample Mendelian randomization. Among all DTI-Disorder combinations, we observed a significant causal association between the superior longitudinal fasciculus (SLF) and the risk of Anorexia nervosa (AN) (Odds Ratio [OR] = 0.62, 95 % confidence interval: 0.50 ~ 0.76, P = 6.4 × 10- 6). Similar significant associations were also observed between the body of the corpus callosum (fractional anisotropy) and Alzheimer's disease (OR = 1.07, 95 % CI: 1.03 ~ 1.11, P = 4.1 × 10- 5). By combining all observations, we found that the overall p-value for DTI - Disorder associations was significantly elevated compared to the null distribution (Kolmogorov-Smirnov P = 0.009, inflation factor λ = 1.37), especially for DTI - Bipolar disorder (BP) (λ = 2.64) and DTI - AN (λ = 1.82). In contrast, for ROI-Disorder combinations, we only found a significant association between the brain region of pars triangularis and Schizophrenia (OR = 0.48, 95 % CI: 0.34 ~ 0.69, P = 5.9 × 10- 5) and no overall p-value elevation for ROI-Disorder analysis compared to the null expectation. CONCLUSIONS: As a whole, we show that SLF degeneration may be a risk factor for AN, while DTI variations could be causally related to some neuropsychiatric disorders, such as BP and AN. Also, the white matter structure might have a larger impact on neuropsychiatric disorders than subregion volumes.

Single-cell RNA sequencing reveals the epithelial cell heterogeneity and invasive subpopulation in human bladder cancer.

Bladder cancer represents a highly heterogeneous disease characterized by distinct histological, molecular and clinical phenotypes, and a detailed analysis of tumor cell invasion and crosstalks within bladder tumor cells has not been determined. Here, we applied droplet-based single-cell RNA sequencing (scRNA-seq) to acquire transcriptional profiles of 36 619 single cells isolated from seven patients. Single cell transcriptional profiles matched well with the pathological basal/luminal subtypes. Notably, in T1 tumors diagnosed as luminal subtype, basal cells displayed characteristics of epithelial-mesenchymal transition (EMT) and mainly located at the tumor-stromal interface as well as micrometastases in the lamina propria. In one T3 tumor, muscle-invasive tumor showed significantly higher expression of cancer stem cell markers SOX9 and SOX2 than the primary tumor. We additionally analyzed communications between tumor cells and demonstrated its relevance to basal/luminal phenotypes. Overall, our single-cell study provides a deeper insight into the tumor cell heterogeneity associated with bladder cancer progression.

Expression alteration of microRNAs in Nucleus Accumbens is associated with chronic stress and antidepressant treatment in rats.

BACKGROUND: Nucleus Accumbens (NAc) is a vital brain region for the process of reward and stress, whereas microRNA plays a crucial role in depression pathology. However, the abnormality of NAc miRNA expression during the stress-induced depression and antidepressant treatment, as well as its biological significance, are still unknown. METHODS: We performed the small RNA-sequencing in NAc of rats from three groups: control, chronic unpredictable mild stress (CUMS), and CUMS with an antidepressant, Escitalopram. We applied an integrative pipeline for analyzing the miRNA expression alternation in different model groups, including differential expression analysis, co-expression analysis, as well as a subsequent pathway/network analysis to discover both miRNA alteration pattern and its biological significance. RESULT: A total of 423 miRNAs were included in analysis.18/8 differential expressing (DE) miRNA (adjusted p < 0.05, |log2FC| > 1) were observed in controls Vs. depression/depression Vs. treatment, 2 of which are overlapping. 78% (14/18) of these miRNAs showed opposite trends of alteration in stress and treatment. Two micro RNA, miR-10b-5p and miR-214-3p, appeared to be hubs in the regulation networks and also among the top findings in both differential analyses. Using co-expression analysis, we found a functional module that strongly correlated with stress (R = 0.96, P = 0.003), and another functional module with a moderate correlation with anhedonia (R = 0.89, P = 0.02). We also found that predicted targets of these miRNAs were significantly enriched in the Ras signaling pathway, which is associated with both depression, anhedonia, and antidepressant treatment. CONCLUSION: Escitalopram treatment can significantly reverse NAc miRNA abnormality induced by chronic stress. However, the novel miRNA alteration that is absent in stress pathology also emerges, which means that antidepressant treatment is unlikely to bring miRNA expression back to the same level as the controls. Also, the Ras-signaling pathway may be involved in explaining the depression disease etiology, the clinical symptom, and treatment response of stress-induced depression.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits.

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Scattered throughout the human genome are variations in the genetic code that make individuals more or less likely to develop certain traits. To identify these variants, scientists carry out Genome-wide association studies (GWAS) which compare the DNA variants of large groups of people with and without the trait of interest. This method has been able to find the underlying genes for many human diseases, but it has limitations. For instance, some variations are linked together due to where they are positioned within DNA, which can result in GWAS falsely reporting associations between genetic variants and traits. This phenomenon, known as linkage equilibrium, can be avoided by analyzing functional genomics which looks at the multiple ways a gene's activity can be influenced by a variation. For instance, how the gene is copied and decoded in to proteins and RNA molecules, and the rate at which these products are generated. Researchers can now use an artificial intelligence technique called deep learning to generate functional genomic data from a particular DNA sequence. Here, Song et al. used one of these deep learning models to calculate the functional genomics of haplotypes, groups of genetic variants inherited from one parent. The approach was applied to DNA samples from over 350 thousand individuals included in the UK BioBank. An activity score, defined as the haplotype function score (or HFS for short), was calculated for at least two haplotypes per individual, and then compared to various complex traits like height or bone density. Song et al. found that the HFS framework was better at finding links between genes and specific traits than existing methods. It also provided more information on the biology that may be underpinning these outcomes. Although more work is needed to reduce the computer processing times required to calculate the HFS, Song et al. believe that their new method has the potential to improve the way researchers identify links between genes and human traits.

Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution.

Background: The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear. Aims: To investigate the contribution of HLMs to human evolution through post-transcriptional modification. Methods: We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity. Results: We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. These HLMs enriched in a set of conserved genes highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and were involved in synapse organisation and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia and schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, suggesting their essential roles in cognition and bipedalism. Conclusions: Our findings suggest that a small subset of human-specific mutations have contributed to human speciation through impacts on post-transcriptional modification of critical brain-related genes.

Causality Investigation between Gut Microbiome and Sleep-Related Traits: A Bidirectional Two-Sample Mendelian Randomization Study.

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a bidirectional two-sample Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386,577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method, alongside a Bonferroni correction ((p < 2.4 × 10-4) to determine significant causal associations. We further applied Cochran's Q statistical analysis, MR-Egger intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) for heterogeneity and pleiotropy assessment. IVW estimates revealed 79 potential causal effects of microbial taxa and pathways on sleep-related traits and 45 inverse causal relationships, with over half related to pathways, emphasizing their significance. The results revealed two significant causal associations: genetically determined relative abundance of pentose phosphate decreased sleep duration (p = 9.00 × 10-5), and genetically determined increase in fatty acid level increased the ease of getting up in the morning (p = 8.06 × 10-5). Sensitivity analyses, including heterogeneity and pleiotropy tests, as well as a leave-one-out analysis of single nucleotide polymorphisms, confirmed the robustness of these relationships. This study explores the potential causal relationships between sleep and microbial taxa and pathways, offering novel insights into their complex interplay.

n-Type boron ß-diketone-containing conjugated polymers for high-performance room temperature ammonia sensors.

Organic semiconductor (OSC) gas sensors with good mechanical flexibility have received considerable attention as commercial and wearable devices. However, due to poor resistance to moisture and low conductivity, the improvement in the sensing capability of individual OSCs is limited. Reported here is a promising pathway to construct a series of conjugated organic polymers (COPs) with well-defined pyrimidine (Py-COP) or boron ß-diketone (BF-COP) units. Unlike traditional metal- or carbon-based hybrid materials, the developed COPs can provide abundant absorption sites for gaseous analytes. As a result, the as-prepared BF-COP results in an excellent sensing response of over 1500 (Ra/Rg) toward 40 ppm of NH3 at room temperature, which is the highest value among those of pristine COPs as n-type sensing materials. Notably, they can maintain their initial sensing responses for two months and 90% relative humidity resistance. Combining the results of in situ Fourier transform infrared spectroscopy and theoretical calculations, the ß-diketone skeleton is found to activate the surface electronic environment, verifying that the electron-deficient B ← O groups are adsorption centers. The B/N-heterocyclic decoration effectively modulates the redox properties and electronic interactions, as well as perturbs charge transfer in typical π-conjugated COPs. These results offer insight into developing highly efficient OSC gas sensors, which potentially have broadened sensing applications in the areas of organoboron chemistry.

Metal-Free Carbon-Based Covalent Organic Frameworks with Heteroatom-Free Units Boost Efficient Oxygen Reduction.

Accurate identification of carbon-based metal-free electrocatalyst (CMFE) activity and enhancing their catalytic efficiency for O2 conversion is an urgent and challenging task. This study reports a promising strategy to simultaneously develop a series of covalent organic frameworks (COFs) with well-defined heterocyclic-free biphenyl or fluorenyl units. Unlike heteroatom doping, the developed method not only supplies methyl-induced molecular configuration to promote activity, but also provides a direct opportunity to identify heteroatom-free carbon active centers. The introduction of methyl groups (MGs) with reversible valence bonds into a pristine biphenyl-based COF results in an excellent performance with a half-wave potential of 0.74 V versus the reversible hydrogen electrode (RHE), which is among the highest values for CMFE-COFs as oxygen reduction reaction (ORR) electrocatalysts. Combined with in situ Raman spectra and theoretical calculations, the MG-bound skeleton (DAF-COF) is found to produce ortho activation, confirming the ortho carbon (site-5) adjacent to MGs as active centers. This may be attributed to the opening and binding of MGs, which effectively regulate the molecular configuration and charge redistribution, as well as improve charge transfer and reduce the energy barrier. This study provides insight into the design of highly efficient metal-free organic electrocatalysts via the regulation of valence bonds.

Inferring the Effects of Protein Variants on Protein-Protein Interactions with Interpretable Transformer Representations.

Identifying pathogenetic variants and inferring their impact on protein-protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein-protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein-protein interactions and facilitating the discovery of clinically actionable variants.

Scoparone from Artemisia capillaris Thunb. induces apoptosis in HepG2 cells via activation of both intracellular and extracellular pathways.

Six separated compounds were identified from Artemisia capillaris Thunb., and they were 7-methoxycoumarin (1), 6,7-dimethoxycoumarin (2), 7-hydroxy-6-methoxycoumarin (3), quercetin (4), chlorogenic acid (5) and caffeic acid (6). Among them, 6,7-dimethoxycoumarin, as known as scoparone, was the most effective on scavenging ABTS free radicals (IC50 = 0.97 µΜ) and was then tested by cytotoxic activity and pro-apoptotic activity against HepG2 cells. Scoparone dose-dependently and time-dependently inhibited the cell proliferation. Furthermore, scoparone induced the expression of Bax, concurrently suppressing the expression of Bcl-2, resulting in a noteworthy elevation in the Bax/Bcl-2 ratio to up-regulate Caspase-3 activity, thus inducing cell apoptosis via the intracellular pathway. Meanwhile, scoparone promoted the expression of Fas, FasL, FADD, Caspase-8 and Caspase-3, indicating that scoparone also triggered apoptosis via the extracellular pathway. In a word, scoparone demonstrated remarkable antitumor capability to induce apoptosis of HepG2 cells through both intracellular and extracellular pathways.

Repeated methamphetamine exposure decreases plasma brain-derived neurotrophic factor levels in rhesus monkeys.

Background: Brain-derived neurotrophic factor (BDNF) is known to prevent methamphetamine (METH)-induced neurotoxicity and plays a role in various stages of METH addiction. However, there is a lack of research with longitudinal design on changes in plasma BDNF levels in active METH-dependent individuals. Aims: The aim of the study was to investigate changes in BDNF levels during METH self-administration in monkeys. Methods: This study measured plasma BDNF levels in three male rhesus monkeys with continuous METH exposure and four male control rhesus monkeys without METH exposure. Changes in plasma BDNF levels were then assessed longitudinally during 40 sessions of METH self-administration in the three monkeys. Results: Repeated METH exposure decreased plasma BDNF levels. Additionally, plasma BDNF decreased with long-term rather than short-term accumulation of METH during METH self-administration. Conclusions: These findings may indicate that the changes in peripheral BDNF may reflect the quantity of accumulative METH intake during a frequent drug use period.

Rhesus monkeys exhibiting spontaneous ritualistic behaviors resembling obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is a chronic and debilitating psychiatric disorder that affects ∼2%-3% of the population globally. Studying spontaneous OCD-like behaviors in non-human primates may improve our understanding of the disorder. In large rhesus monkey colonies, we found 10 monkeys spontaneously exhibiting persistent sequential motor behaviors (SMBs) in individual-specific sequences that were repetitive, time-consuming and stable over prolonged periods. Genetic analysis revealed severely damaging mutations in genes associated with OCD risk in humans. Brain imaging showed that monkeys with SMBs had larger gray matter (GM) volumes in the left caudate nucleus and lower fractional anisotropy of the corpus callosum. The GM volume of the left caudate nucleus correlated positively with the daily duration of SMBs. Notably, exposure to a stressor (human presence) significantly increased SMBs. In addition, fluoxetine, a serotonergic medication commonly used for OCD, decreased SMBs in these monkeys. These findings provide a novel foundation for developing better understanding and treatment of OCD.