Interaction of tau with HNRNPA2B1 and N6-methyladenosine RNA mediates the progression of tauopathy.

The microtubule-associated protein tau oligomerizes, but the actions of oligomeric tau (oTau) are unknown. We have used Cry2-based optogenetics to induce tau oligomers (oTau-c). Optical induction of oTau-c elicits tau phosphorylation, aggregation, and a translational stress response that includes stress granules and reduced protein synthesis. Proteomic analysis identifies HNRNPA2B1 as a principle target of oTau-c. The association of HNRNPA2B1 with endogenous oTau was verified in neurons, animal models, and human Alzheimer brain tissues. Mechanistic studies demonstrate that HNRNPA2B1 functions as a linker, connecting oTau with N6-methyladenosine (m6A) modified RNA transcripts. Knockdown of HNRNPA2B1 prevents oTau or oTau-c from associating with m6A or from reducing protein synthesis and reduces oTau-induced neurodegeneration. Levels of m6A and the m6A-oTau-HNRNPA2B1 complex are increased up to 5-fold in the brains of Alzheimer subjects and P301S tau mice. These results reveal a complex containing oTau, HNRNPA2B1, and m6A that contributes to the integrated stress response of oTau.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Vancomycin trough concentration in adult patients with periprosthetic joint infection: A machine learning-based covariate model.

AIMS: Although there are various model-based approaches to individualized vancomycin (VCM) administration, few have been reported for adult patients with periprosthetic joint infection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients. METHODS: The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an independent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). RESULTS: The SHAP plots visualized the weight of the covariates in the models, with estimated glomerular filtration rate and VCM daily dose as the 2 most important factors, which were adopted for the model construction. Random forest regression was the optimal ML algorithm with a relative accuracy of 82.8% and absolute accuracy of 67.2% (R2 =.61, mean absolute error = 2.4, mean square error = 10.1), and its prediction performance was verified in the validation set. CONCLUSION: The proposed ML-based model can satisfactorily predict the VCM trough concentration in adult PJI patients. Its construction can be facilitated with only 2 clinical parameters (estimated glomerular filtration rate and VCM daily dose), and prediction accuracy can be rationalized by SHAP values, which highlights a profound practical value for clinical dosing guidance and timely treatment.

High-Resolution Spatiotemporal Modeling for PM2.5 Major Components in the Pearl River Delta and Its Implications for Epidemiological Studies.

Distinguishing the effects of different fine particulate matter components (PMCs) is crucial for mitigating their effects on human health. However, the sparse distribution of locations where PM is collected for component analysis makes it challenging to investigate the relevant health effects. This study aimed to investigate the agreement between data-fusion-enhanced exposure assessment and site monitoring data in estimating the effects of PMCs on gestational diabetes mellitus (GDM). We first improved the spatial resolution and accuracy of exposure assessment for five major PMCs (EC, OM, NO3-, NH4+, and SO42-) in the Pearl River Delta region by a data fusion model that combined inputs from multiple sources using a random forest model (10-fold cross-validation R2: 0.52 to 0.61; root mean square error: 0.55 to 2.26 µg/m3). Next, we compared the associations between exposures to PMCs during pregnancy and GDM in a hospital-based cohort of 1148 pregnant women in Heshan, China, using both site monitoring data and data-fusion model estimates. The comparative analysis showed that the data-fusion-based exposure generated stronger estimates of identifying statistical disparities. This study suggests that data-fusion-enhanced estimates can improve exposure assessment and potentially mitigate the misclassification of population exposure arising from the utilization of site monitoring data.

The association between PM2.5 components and blood pressure changes in late pregnancy: A combined analysis of traditional and machine learning models.

BACKGROUND: PM2.5 is a harmful mixture of various chemical components that pose a challenge in determining their individual and combined health effects due to multicollinearity issues with traditional linear regression models. This study aimed to develop an analytical methodology combining traditional and novel machine learning models to evaluate PM2.5's combined effects on blood pressure (BP) and identify the most toxic components. METHODS: We measured late-pregnancy BP of 1138 women from the Heshan cohort while simultaneously analyzing 31 PM2.5 components. We utilized multiple linear regression modeling to establish the relationship between PM2.5 components and late-pregnancy BP and applied Random Forest (RF) and generalized Weighted Quantile Sum (gWQS) regression to identify the most toxic components contributing to elevated BP and to quantitatively evaluate the cumulative effect of the PM2.5 component mixtures. RESULTS: The results revealed that 16 PM2.5 components, such as EC, OC, Ti, Fe, Mn, Cu, Cd, Mg, K, Pb, Se, Na+, K+, Cl-, NO3-, and F-, contributed to elevated systolic blood pressure (SBP), while 26 components, including two carbon components (EC, OC), fourteen metallics (Ti, Fe, Mn, Cr, Mo, Co, Cu, Zn, Cd, Na, Mg, Al, K, Pb), one metalloid (Se), and nine water-soluble ions (Na+, K+, Mg2+, Ca2+, NH4+, Cl-, NO3-, SO42-, F-), contributed to elevated diastolic blood pressure (DBP). Mn and Cr were the most toxic components for elevated SBP and DBP, respectively, as analyzed by RF and gWQS models and verified against each other. Exposure to PM2.5 component mixtures increased SBP by 1.04 mmHg (95% CI: 0.33-1.76) and DBP by 1.13 mmHg (95% CI: 0.47-1.78). CONCLUSIONS: Our study highlights the effectiveness of combining traditional and novel models as an analytical strategy to quantify the health effects of PM2.5 constituent mixtures.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite-Protein Physical Interaction Subnetworks Altered in Cancer.

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

MCCS, a novel characterization method for protein-ligand complex.

Delineating the fingerprint or feature vector of a receptor/protein will facilitate the structural and biological studies, as well as the rational design and development of drugs with high affinities and selectivity. However, protein is complicated by its different functional regions that can bind to some of its protein partner(s), substrate(s), orthosteric ligand(s) or allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate a scoring-function-based computing protocol Molecular Complex Characterizing System to help characterize the binding feature of protein-ligand complexes. Based on the reported receptor-ligand interactions, we first quantitate the energy contribution of each individual residue which may be an alternative of MD-based energy decomposition. We then construct a vector for the energy contribution to represent the pattern of the ligand recognition at a receptor and qualitatively analyze the matching level with other receptors. Finally, the energy contribution vector is explored for extensive use in similarity and clustering. The present work provides a new approach to cluster proteins, a perspective counterpart for determining the protein characteristics in the binding, and an advanced screening technique where molecular docking is applicable.

The effects of prenatal PM2.5 oxidative potential exposure on feto-placental vascular resistance and fetal weight: A repeated-measures study.

BACKGROUND: Feto-placental hemodynamic deterioration is a critical contributing factor to fetal growth restriction. Whether PM2.5 oxidative potential (OP) affects feto-placental hemodynamics and what impact is on estimated fetal weight (EFW) have not been fully elucidated. We sought to evaluate the association of PM2.5 OP with EFW and to explore whether feto-placental vascular impedance hemodynamic change is a possible mediator in this association. METHODS: A repeated-measures study was conducted involving sixty pregnant women with at least 26 weeks of follow-up during pregnancy in Guangzhou, China, from September 2017 to October 2018. Daily filter-based PM2.5 samples were prospectively collected from ground monitors, and estimates of OP for PM2.5 and its metallic (OPv-metal) and non-metallic constituents (OPv-nonmental) were determined by dithiothreitol assay. Ultrasound data of fetal growth and umbilical arterial resistance, including estimated fetal weight (EFW), pulsatility index, resistance index, and systolic-to-diastolic ratio, were also obtained during gestation. Generalized estimating equations and polynomial distribution lag models were applied to analyze the associations of maternal exposure to PM2.5 OP with EFW and umbilical artery indices. Causal mediation analysis was used to evaluate the mediating role of umbilical arterial resistance. RESULTS: Prenatal exposure to ambient PM2.5 OP was significantly inversely associated with EFW. The magnitudes of effects of OPv-nonmetal on EFW were larger than those of OPv-metal. Significant mediation for the relationship between PM2.5-related OP and EFW by increased impedance in the umbilical artery was observed, with the estimated percent mediated ranging from 31% to 61%. The estimated percent mediated for OPv-nonmetal was higher than those for OPv-metal. CONCLUSIONS: Findings suggest that increased impedance in the umbilical artery may be one of the potential mediators of the relationship between PM2.5 oxidative potential exposure and low fetal weight.

Gestational age modifies the association between exposure to fine particles and fetal death: findings from a nationwide epidemiological study in the contiguous United States.

BACKGROUNDS: The vulnerability of fetuses differs at different developmental stages, in response to environmental stressors such as fine particulate matter (PM2.5), a ubiquitous air pollutant. Whether gestational age (GA) modifies the association between prenatal fine particulate matter (PM2.5) exposure and fetal death remains unclear. METHODS: We selected approximately 47.8 million eligible United States (US) livebirth and fetal death (defined as a termination at a GA of 20-43 weeks) records from 1989 to 2004. For each record, we took the level of prenatal exposure to PM2.5 as the average concentration in the mother's residential county during the entire gestational period, or a specific trimester (i.e., GA-specific exposure), according to well-established estimates of monthly levels across the contiguous US. First, we evaluated the associations between PM2.5 exposure and fetal death at a specific GA (i.e., GA-specific outcome) using five different logit models (unadjusted, covariate-adjusted, propensity-score, double robust, and diagnostic-score models). Double robust model was selected as the main model due to its advantages in causal inference. Then, we conducted meta-analyses to pool the estimated GA-specific associations, and explored how the pooled estimates varied with GA. RESULTS: According to the meta-analysis, all models suggested gestational PM2.5 exposure was associated with fetal death. However, there was slight heterogeneity in the estimated effects, as different models revealed a range of 3.6-10.7% increase in the odds of fetal death per 5-µg/m3 increment of PM2.5. Each 5-µg/m3 increase in PM2.5 exposure during the entire gestation period significantly increased the odds of fetal death, by 8.1% (95% confidence interval [CI]: 5.1-11.2%). In terms of GA-specific outcomes, the odds of fetal death at a GA of 20-27, 28-36, or ≥ 37 weeks increased by 11.0% (5.9-16.4%), 5.2% (0.4-10.1%), and 8.3% (2.5-14.5%), respectively. In terms of GA-specific exposure, the odds of fetal death increased by 6.0% (3.9-8.2%), 4.1% (3.9-8.2%), and 4.3% (0.5-8.2%) with 5-µg/m3 increases in PM2.5 exposure during the first, second, and third trimester, respectively. The association had the largest effect size (odds ratio = 1.098, 95% CI: 1.061-1.137) between PM2.5 exposure during early gestation (i.e., first trimester) and early fetal death (i.e., 20-27 weeks). CONCLUSIONS: Prenatal exposure to PM2.5 was significantly associated with an increased risk of fetal death. The association was varied by gestational-age-specific exposures or outcomes, suggesting gestation age as a potential modifier on the effect of PM2.5. The fetus was most vulnerable during the early stage of development to death associated with PM2.5 exposure.

Multi-Omics Profiling of Hypertrophic Cardiomyopathy Reveals Altered Mechanisms in Mitochondrial Dynamics and Excitation-Contraction Coupling.

Hypertrophic cardiomyopathy is one of the most common inherited cardiomyopathies and a leading cause of sudden cardiac death in young adults. Despite profound insights into the genetics, there is imperfect correlation between mutation and clinical prognosis, suggesting complex molecular cascades driving pathogenesis. To investigate this, we performed an integrated quantitative multi-omics (proteomic, phosphoproteomic, and metabolomic) analysis to illuminate the early and direct consequences of mutations in myosin heavy chain in engineered human induced pluripotent stem-cell-derived cardiomyocytes relative to late-stage disease using patient myectomies. We captured hundreds of differential features, which map to distinct molecular mechanisms modulating mitochondrial homeostasis at the earliest stages of pathobiology, as well as stage-specific metabolic and excitation-coupling maladaptation. Collectively, this study fills in gaps from previous studies by expanding knowledge of the initial responses to mutations that protect cells against the early stress prior to contractile dysfunction and overt disease.

Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance.

BACKGROUND: Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas. METHODS: A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan-Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics. RESULTS: In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group. CONCLUSIONS: We developed a TNF family-based signature to predict the prognosis of patients with glioma. Video abstract.

Trends and Challenges Regarding the Source-Specific Health Risk of PM2.5-Bound Metals in a Chinese Megacity from 2014 to 2020.

Identifying the health risk of PM2.5 is essential for urban air pollution control. In 2013, China announced the ever-strict national Air Pollution Prevention and Control Action Plan, and its health benefit should be evaluated to provide reference for future policymaking. In this study, we conducted a seven-year (2014-2020) continuous observation of PM2.5 in Shenzhen, the third largest city in China, which has relatively good air quality. The results showed that the annual mean PM2.5 and total concentration of 21 associated metals dropped from 37.7 to 18.5 µg/m3 and from 2.4 to 1.1 µg/m3, respectively. Combining methods for source apportionment and health risk assessment, we found that the total carcinogenic risk (CR) of five hazardous metals (Cd, Cr, Ni, Co, and Pb) showed a clear decreasing trend. However, the total CR (1.8 × 10-6) in 2020 still exceeded the widely acceptable risk level (i.e., 1 × 10-6), with the primary contributor changing from industrial emissions (61%) to vehicle emissions (63%). Further analysis indicated that the CR of vehicles mainly came from Cr and Ni released by braking and tire wearing and has fluctuated in recent years, highlighting a great challenge of controlling nonexhaust emissions of vehicles (including electric cars) in the future.

A Novel Neutralizing Antibody Targeting a Unique Cross-Reactive Epitope on the hi Loop of Domain II of the Envelope Protein Protects Mice against Duck Tembusu Virus.

The Flavivirus E protein induces protective immunity, and its Abs cause serious problems for serodiagnosis because of the difficulty in differentiating cross-reactive Abs. Moreover, cross-reactive Abs may increase disease severity after secondary Flavivirus infections via Ab-dependent enhancement. Cross-reactive epitopes are therefore critical for understanding serodiagnosis and improving the general knowledge of Flavivirus infections. A minimal epitope, 227GSSAGTWQN235, was identified by a neutralizing mAb 1G2 against duck Tembusu virus (DTMUV), which recognized only monomer E protein under nonreducing conditions. It was unexpectedly found that mutations in the epitope residues G231 or W233 completely abolished reactivity to 1G2 and sera from mice infected with Japanese encephalitis virus, West Nile virus, and Zika virus. An immunofluorescence assay confirmed that mAb 1G2 could cross-react with the E proteins from Japanese encephalitis virus, West Nile virus, and Zika virus. Protein and virus modeling revealed that the epitope was surface accessible in the mature virus and located in the hi loop of domain II. The neutralization of DTMUV by 1G2 played a clear therapeutic role in mouse models. The passive transfer of 1G2 resulted in 100% survival, reduced weight loss, and the complete clearance of DTMUV from the blood of BALB/c mice. Our findings document, for the first time to our knowledge, that mAb 1G2 targets the cross-reactive epitope on the hi loop of domain II in the E protein and might be of potential therapeutic value in treating DTMUV infection and improve the understanding of the issues related to serodiagnosis.

Modulation of Nogo receptor 1 expression orchestrates myelin-associated infiltration of glioblastoma.

As the clinical failure of glioblastoma treatment is attributed by multiple components, including myelin-associated infiltration, assessment of the molecular mechanisms underlying such process and identification of the infiltrating cells have been the primary objectives in glioblastoma research. Here, we adopted radiogenomic analysis to screen for functionally relevant genes that orchestrate the process of glioma cell infiltration through myelin and promote glioblastoma aggressiveness. The receptor of the Nogo ligand (NgR1) was selected as the top candidate through Differentially Expressed Genes (DEG) and Gene Ontology (GO) enrichment analysis. Gain and loss of function studies on NgR1 elucidated its underlying molecular importance in suppressing myelin-associated infiltration in vitro and in vivo. The migratory ability of glioblastoma cells on myelin is reversibly modulated by NgR1 during differentiation and dedifferentiation process through deubiquitinating activity of USP1, which inhibits the degradation of ID1 to downregulate NgR1 expression. Furthermore, pimozide, a well-known antipsychotic drug, upregulates NgR1 by post-translational targeting of USP1, which sensitizes glioma stem cells to myelin inhibition and suppresses myelin-associated infiltration in vivo. In primary human glioblastoma, downregulation of NgR1 expression is associated with highly infiltrative characteristics and poor survival. Together, our findings reveal that loss of NgR1 drives myelin-associated infiltration of glioblastoma and suggest that novel therapeutic strategies aimed at reactivating expression of NgR1 will improve the clinical outcome of glioblastoma patients.

Ipriflavone suppresses NLRP3 inflammasome activation in host response to biomaterials and promotes early bone healing.

AIM: Emerging studies have shown that immune response to biomaterial implants plays a central role in bone healing. Ipriflavone is clinically used for osteoporosis. However, the mechanism of ipriflavone in immune response to implants in early stages of osseointegration remains unclear. In this study, we aimed to investigate the potential role of ipriflavone in early bone healing process and uncover the underlying mechanism. MATERIALS AND METHODS: We carried out histological examination as well as analysis of proinflammatory cytokines and NLRP3 inflammasome activation in a tibial implantation mouse model with intra-peritoneal injection of ipriflavone. In addition, we explored the mechanism of ipriflavone in the regulation of NLRP3 inflammasome activation in macrophages. RESULTS: In vivo, ipriflavone ameliorated host inflammatory response related to NLRP3 inflammasome activation at implantation sites, characterized by reductions of inflammatory cell infiltration and proinflammatory cytokine interleukin-1ß levels. Ipriflavone treatment also showed beneficial effects on early osseointegration. Further investigations of the molecular mechanism showed that the suppression of NLRP3 inflammasome acts upstream of NLRP3 oligomerization through abrogating the production of reactive oxygen species. CONCLUSIONS: These results revealed an anti-inflammatory role of ipriflavone in NLRP3 inflammasome activation through improving mitochondrial function. This study provides a new strategy for the development of immune-regulated biomaterials and treatment options for NLRP3-related diseases.

Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results.

BACKGROUND: Current autoverification, which is only knowledge-based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge-based system. METHODS: New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. RESULTS: Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%-51% among hospitals. Further customization using individualized data increased this rate. CONCLUSIONS: The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital.

Genome-wide identification and molecular evolution analysis of the heat shock transcription factor (HSF) gene family in four diploid and two allopolyploid Gossypium species.

Heat shock transcription factors (HSFs) can regulate plant development and stress response. The comprehensive evolutionary history of the HSF family remains elusive in cotton. In this study, each cotton species had 78 members in Gossypium barbadense and Gossypium hirsutum. The diploid species had 39 GaHSFs in Gossypium arboreum, 31 GrHSFs in Gossypium raimondii, 34 GtHSFs in Gossypium turneri, and 34 GlHSFs in Gossypium longicalyx. The HSF family in cotton can be classified into three subfamilies, with seven groups in subfamily A and five groups in subfamily B. Different groups exhibited distinct gene proportions, conserved motifs, gene structures, expansion rates, gene loss rates, and cis-regulatory elements. The paleohexaploidization event led to the expansion of the HSF family in cotton, and the gene duplication events in six Gossypium species were inherited from their common ancestor. The HSF family in diploid species had a divergent evolutionary history, whereas two cultivated tetraploids presented a highly conserved evolution of the HSF family. The HSF members in At and Dt subgenomes of the cultivated tetraploids showed a different evolution from their corresponding diploid donors. Some HSF members were regarded as key candidates for regulating cotton development and stress response. This study provided the comprehensive information on the evolutionary history of the HSF family in cotton.

A Mutation in Hnrnph1 That Decreases Methamphetamine-Induced Reinforcement, Reward, and Dopamine Release and Increases Synaptosomal hnRNP H and Mitochondrial Proteins.

Individual variation in the addiction liability of amphetamines has a heritable genetic component. We previously identified Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1) as a quantitative trait gene underlying decreased methamphetamine-induced locomotor activity in mice. Here, we showed that mice (both females and males) with a heterozygous mutation in the first coding exon of Hnrnph1 (H1+/-) showed reduced methamphetamine reinforcement and intake and dose-dependent changes in methamphetamine reward as measured via conditioned place preference. Furthermore, H1+/- mice showed a robust decrease in methamphetamine-induced dopamine release in the NAc with no change in baseline extracellular dopamine, striatal whole-tissue dopamine, dopamine transporter protein, dopamine uptake, or striatal methamphetamine and amphetamine metabolite levels. Immunohistochemical and immunoblot staining of midbrain dopaminergic neurons and their forebrain projections for TH did not reveal any major changes in staining intensity, cell number, or forebrain puncta counts. Surprisingly, there was a twofold increase in hnRNP H protein in the striatal synaptosome of H1+/- mice with no change in whole-tissue levels. To gain insight into the mechanisms linking increased synaptic hnRNP H with decreased methamphetamine-induced dopamine release and behaviors, synaptosomal proteomic analysis identified an increased baseline abundance of several mitochondrial complex I and V proteins that rapidly decreased at 30 min after methamphetamine administration in H1+/- mice. In contrast, the much lower level of basal synaptosomal mitochondrial proteins in WT mice showed a rapid increase. We conclude that H1+/- decreases methamphetamine-induced dopamine release, reward, and reinforcement and induces dynamic changes in basal and methamphetamine-induced synaptic mitochondrial function.SIGNIFICANCE STATEMENT Methamphetamine dependence is a significant public health concern with no FDA-approved treatment. We discovered a role for the RNA binding protein hnRNP H in methamphetamine reward and reinforcement. Hnrnph1 mutation also blunted methamphetamine-induced dopamine release in the NAc, a key neurochemical event contributing to methamphetamine addiction liability. Finally, Hnrnph1 mutants showed a marked increase in basal level of synaptosomal hnRNP H and mitochondrial proteins that decreased in response to methamphetamine, whereas WT mice showed a methamphetamine-induced increase in synaptosomal mitochondrial proteins. Thus, we identified a potential role for hnRNP H in basal and dynamic mitochondrial function that informs methamphetamine-induced cellular adaptations associated with reduced addiction liability.

Alkaline phosphatase downregulation promotes lung adenocarcinoma metastasis via the c-Myc/RhoA axis.

BACKGROUND: Lung adenocarcinoma (LUAD) metastasis significantly reduces patient survival; hence inhibiting the metastatic ability of lung cancer cells will greatly prolong patient survival. Alkaline phosphatase (ALPL), a homodimeric cell surface phosphohydrolase, is reported to play a controversial role in prostate cancer and ovarian cancer cell migration; however, the function of ALPL in LUAD and the related mechanisms remain unclear. METHODS: TCGA database was used to analysis the expression of ALPL, and further verification was performed in a cohort of 36 LUAD samples by qPCR and western blot. Soft-agar assay, transwell assay and lung metastasis assay were employed to detect the function of ALPL in LUAD progression. The qPCR, luciferase promoter reporter assay and western blot were used to clarify the molecular mechanisms of ALPL in promoting metastasis in LUAD. RESULTS: ALPL was downregulated in LUAD, and the disease-free survival rate of patients with low ALPL was significantly reduced. Further studies showed that overexpression of ALPL in LUAD cell lines did not significantly affect cell proliferation, but it did significantly attenuate lung metastasis in a mouse model. ALPL downregulation in LUAD led to a decrease in the amount of phosphorylated (p)-ERK. Because p-ERK promotes the classical c-Myc degradation pathway, the decrease in p-ERK led to the accumulation of c-Myc and therefore to an increase in RhoA transcription, which enhanced LUAD cell metastasis. CONCLUSION: ALPL specially inhibits the metastasis of LUAD cells by affecting the p-ERK/c-Myc/RhoA axis, providing a theoretical basis for the targeted therapy of clinical LUAD.

SlPHL1, a MYB-CC transcription factor identified from tomato, positively regulates the phosphate starvation response.

Inorganic phosphate (Pi) deficiency is a major limiting factor for plant growth and development. Previous reports have demonstrated that PHOSPHATE STARVATION RESPONSE 1 (PHR1) and OsPHR2 play central roles in Pi-starvation signaling in Arabidopsis and rice, respectively. However, the Pi-starvation signaling network in tomato (Solanum lycopersicum) is still not fully understood. In this work, SlPHL1, a homolog of AtPHR1 and OsPHR2, was identified from tomato. It was found that SlPHL1 contains the MYB and coiled-coil (CC) domains, localizes in the nucleus, and has transcriptional activity, indicating that it is a typical MYB-CC transcription factor (TF). Overexpression of SlPHL1 enhanced Pi-starvation responses both in Arabidopsis Col-0 and in tomato Micro-Tom, including elevated root hair growth, promoted APase activity, favored Pi uptake, and increased transcription of Pi starvation-inducing (PSI) genes. Besides, overexpressing SlPHL1 was able to compensate for the Pi-starvation response weakened by the AtPHR1 mutation. Notably, electrophoretic mobility shift assay (EMSA) showed that SlPHL1 could bind to the PHR1-binding sequence (P1BS, GNATATNC)-containing DNA fragments. Furthermore, SlPHL1 specifically interacted with the promoters of the tomato PSI genes SlPht1;2 and SlPht1;8 through the P1BS cis-elements. Taken these results together, SlPHL1 is a newly identified MYB-CC TF from tomato, which participates in Pi-starvation signaling by directly upregulating the PSI genes. These findings might contribute to the understanding of the Pi-starvation signaling in tomato.