J-family genes redundantly regulate flowering time and increase yield in soybean

Soybean(Glycine max)is a short-day crop whose flowering time is regulated by photoperiod.The longjuvenile trait extends its vegetative phase and increases yield under short-day conditions.Natural variation in J,the major locus controlling this trait,modulates flowering time.We report that the three J-family genes influence soybean flowering time,with the triple mutant Guangzhou Mammoth-2 flowering late under short days by inhibiting transcription of E1-family genes.J-family genes offer promising allelic combinations for breeding.

Peripheral blood RNA biomarkers can predict lesion severity in degenerative cervical myelopathy

Degenerative cervical myelopathy is a common cause of spinal cord injury,with longer symptom duration and higher myelopathy severity indicating a worse prognosis.While numerous studies have investigated serological biomarkers for acute spinal cord injury,few studies have explored such biomarkers for diagnosing degenerative cervical myelopathy.This study involved 30 patients with degenerative cervical myelopathy(51.3±7.3 years old,12 women and 18 men),seven healthy controls(25.7±1.7 years old,one woman and six men),and nine patients with cervical spondylotic radiculopathy(51.9±8.6 years old,three women and six men).Analysis of blood samples from the three groups showed clear differences in transcriptomic characteristics.Enrichment analysis identified 128 differentially expressed genes that were enriched in patients with neurological disabilities.Using least absolute shrinkage and selection operator analysis,we constructed a five-gene model(TBCD,TPM2,PNKD,EIF4G2,and AP5Z1)to diagnose degenerative cervical myelopathy with an accuracy of 93.5%.One-gene models(TCAP and SDHA)identified mild and severe degenerative cervical myelopathy with accuracies of 83.3%and 76.7%,respectively.Signatures of two immune cell types(memory B cells and memory-activated CD4^(+)T cells)predicted levels of lesions in degenerative cervical myelopathy with 80%accuracy.Our results suggest that peripheral blood RNA biomarkers could be used to predict lesion severity in degenerative cervical myelopathy.

Genome-wide identification,characterization and functional prediction of the SRS gene family in sesame(Sesamum indicum L.)

Sesame(Sesamum indicum L.)is an ancient oilseed crop of the Pedaliaceae family with high oil content and potential health benefits.SHI RELATED SEQUENCE(SRS)proteins are the transcription factors(TFs)specific to plants that contain RING-like zinc finger domain and are associated with the regulation of several physiological and biochemical processes.They also play vital roles in plant growth and development such as root formation,leaf development,floral development,hormone biosynthesis,signal transduction,and biotic and abiotic stress responses.Nevertheless,the SRS gene family was not reported in sesame yet.In this study,identification,molecular characterization,phylogenetic relationship,cis-acting regulatory elements,protein-protein interaction,syntenic relationship,duplication events and expression pattern of SRS genes were analyzed in S.indicum.We identified total six SiSRS genes on seven different linkage groups in the S.indicum genome by comparing with the other species,including the model plant Arabidopsis thaliana.The SiSRS genes showed variation in their structure like2–5 exons and 1–4 introns.Like other species,SiSRS proteins also contained‘RING-like zinc finger'and‘LRP1'domains.Then,the SiSRS genes were clustered into subclasses via phylogenetic analysis with proteins of S.indicum,A.thaliana,and some other plant species.The cis-acting regulatory elements analysis revealed that the promoter region of SiSRS4(SIN_1011561)showed the highest 13 and 16 elements for light-and phytohormone-responses whereas,SiSRS1(SIN_1015187)showed the highest 15 elements for stress-response.The ABREs,or ABA-responsive elements,were found in a maximum of 8 copies in the SiSRS3(SIN 1009100).Moreover,the available RNA-seq based expression of SiSRS genes revealed variation in expression patterns between stress-treated and non-treated samples,especially in drought and salinity conditions in.S.indicum.Two SiSRS genes like SiSRS1(SIN_1015187)and SiSRS5(SIN_1021065),also exhibited variable expression patterns between control vs PEG-treated sesame root samples and three SiSRS genes,including SiSRS1(SIN_1015187),SiSRS2(SIN_1003328)and SiSRS5(SIN_1021065)were responsive to salinity treatments.The present outcomes will encourage more research into the gene expression and functionality analysis of SiSRS genes in S.indicum and other related species.

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication

BACKGROUND Liver cancer is one of the deadliest malignant tumors worldwide.Immunotherapy has provided hope to patients with advanced liver cancer,but only a small fraction of patients benefit from this treatment due to individual differences.Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies,thereby improving patient survival rates.Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer,the impact of cell-cell interactions in the tumor microenvir-onment has not been adequately considered.AIM To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy.METHODS Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways.Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells.The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features,and a least absolute shrinkage and selection operator(LASSO)regression model was constructed to screen for diagnostic-related features.Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model.Finally,3 genes(stathmin 1,cofilin 1,and C-C chemokine ligand 5)significantly associated with survival were identified and used to construct an immune-related gene signature.RESULTS The immune-related gene signature composed of stathmin 1,cofilin 1,and C-C chemokine ligand 5 was identified through cell-cell communication.The effectiveness of the identified gene signature was validated based on experi-mental results of predictive immunotherapy response,tumor mutation burden analysis,immune cell infiltration analysis,survival analysis,and expression analysis.CONCLUSION The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment,providing insights for personalized treatment strategies.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer

BACKGROUND Treatment options for patients with gastric cancer(GC)continue to improve,but the overall prognosis is poor.The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present,and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups.AIM To determined the effects of differentially expressed immune-related genes(DEIRGs)on the development,prognosis,tumor microenvironment(TME),and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations.METHODS Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas(TCGA),and immune-related genes(IRGs)were searched from ImmPort.DEIRGs were extracted from the intersection of the differentially-expressed genes(DEGs)and IRGs lists.The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes(KEGGs)and Gene Ontology(GO)databases.To identify genes associated with prognosis,a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression.The tumor risk score was divided into high-and lowrisk groups.The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model.The infiltration of immune cells was obtained using‘CIBERSORT,’and the infiltration of immune subgroups in high-and low-risk groups was analyzed.The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed.RESULTS We collected 412 GC and 36 adjacent tissue samples,and identified 3627 DEGs and 1311 IRGs.A total of 482 DEIRGs were obtained.GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes,receptor ligand activity,and signaling receptor activators.KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors,neuroactive ligand receptor interactions,and viral protein interactions.We ultimately obtained an immune-related signature based on 10 genes,including 9 risk genes(LCN1,LEAP2,TMSB15A mRNA,DEFB126,PI15,IGHD3-16,IGLV3-22,CGB5,and GLP2R)and 1 protective gene(LGR6).Kaplan-Meier survival analysis,receiver operating characteristic curve analysis,and risk curves confirmed that the risk model had good predictive ability.Multivariate COX analysis showed that age,stage,and risk score were independent prognostic factors for patients with GC.Meanwhile,patients in the low-risk group had higher tumor mutation burden and immunophenotype,which can be used to predict the immune checkpoint inhibitor response.Both cytotoxic T lymphocyte antigen4+and programmed death 1+patients with lower risk scores were more sensitive to immunotherapy.CONCLUSION In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME.By providing risk factor analysis and prognostic information,our risk model can provide new directions for immunotherapy in GC patients.

Establishment of a prognosis predictive model for liver cancer based on expression of genes involved in the ubiquitin-proteasome pathway

BACKGROUND The ubiquitin-proteasome pathway(UPP)has been proven to play important roles in cancer.AIM To investigate the prognostic significance of genes involved in the UPP and develop a predictive model for liver cancer based on the expression of these genes.METHODS In this study,UPP-related E1,E2,E3,deubiquitylating enzyme,and proteasome gene sets were obtained from the Kyoto Encyclopedia of Genes and Genomes(KEGG)database,aiming to screen the prognostic genes using univariate and multivariate regression analysis and develop a prognosis predictive model based RESULTS Five genes(including autophagy related 10,proteasome 20S subunit alpha 8,proteasome 20S subunit beta 2,ubiquitin specific peptidase 17 like family member 2,and ubiquitin specific peptidase 8)were proven significantly correlated with prognosis and used to develop a prognosis predictive model for liver cancer.Among training,validation,and Gene Expression Omnibus sets,the overall survival differed significantly between the high-risk and low-risk groups.The expression of the five genes was significantly associated with immunocyte infiltration,tumor stage,and postoperative recurrence.A total of 111 differentially expressed genes(DEGs)were identified between the high-risk and low-risk groups and they were enriched in 20 and 5 gene ontology and KEGG pathways.Cell division cycle 20,Kelch repeat and BTB domain containing 11,and DDB1 and CUL4 associated factor 4 like 2 were the DEGs in the E3 gene set that correlated with survival.CONCLUSION We have constructed a prognosis predictive model in patients with liver cancer,which contains five genes that associate with immunocyte infiltration,tumor stage,and postoperative recurrence.

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell lines and tissues and were found to be primarily localized at the cell membrane.The ferroptosis inducer FIN56 inhibited GC cell proliferation in a dose-dependent manner.CONCLUSION In this study,we developed a predictive model based on four FRGs that can accurately predict the prognosis of GC patients and the efficacy of immunotherapy in this population.

Development of Non-ABO Red Blood Cell Alloantibodies in Patient Undergoing Allogeneic Haematopoietic Stem Cell Transplant

Alloantibodies that are non ABO Alloimmunization to protein antigens happens only after exposure, in contrast to ABO isohaemagglutinins, which are present naturally, even in the absence of prior exposure. It is recognized that while non-ABO RBC antibodies are less common than ABO antibodies, they generate essentially the same issues that lead to unfavorable clinical results. If non-ABO alloantibodies are identified early on, these issues related complications may be avoided This call for an in-depth understanding of the recipient and donor’s ABO-Rh grouping, antibody screening, and the phenotype of certain antigens. Equally important, the temporal association time between transplantation and hemolysis can help identify the underlying mechanism of hemolysis and direct appropriate management. Therefore, for that, it is crucial to identify the etiology of post-HSCT anemia for prevention and therapy, in addition to a thorough grasp of the mechanism of anemia in non-ABO-incompatible HSCT and the temporal link between HSCT and anemia. Finding the cause of post-HSCT anemia is essential for prevention and therapy, in addition to a thorough grasp of the mechanism of anemia in non-ABO-incompatible HSCT and the temporal link between HSCT and anemia. Therefore, for that, it is crucial to identify the etiology of post-HSCT anemia. In this case report review, we would like to highlight the vital role of transfusion medicine services and stem cell clinical teams in paying particular attention to the clinical significance of non-ABO alloantibodies involved to avoid causing overt hemolysis of incompatible donor RBCs or delayed erythropoiesis. Considering the fact that some of the Haematopoietic stem cell transplant centers do not give an attention to the other non-ABO RBC antigens.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Nanotechnology-based gene therapy as a credible tool in the treatment of Alzheimer’s disease

Toxic aggregated amyloid-βaccumulation is a key pathogenic event in Alzheimer’s disease.Treatment approaches have focused on the suppression,deferral,or dispersion of amyloid-βfibers and plaques.Gene therapy has evolved as a potential therapeutic option for treating Alzheimer’s disease,owing to its rapid advancement over the recent decade.Small interfering ribonucleic acid has recently garnered considerable attention in gene therapy owing to its ability to down-regulate genes with high sequence specificity and an almost limitless number of therapeutic targets,including those that were once considered undruggable.However,lackluster cellular uptake and the destabilization of small interfering ribonucleic acid in its biological environment restrict its therapeutic application,necessitating the development of a vector that can safeguard the genetic material from early destruction within the bloodstream while effectively delivering therapeutic genes across the bloodbrain barrier.Nanotechnology has emerged as a possible solution,and several delivery systems utilizing nanoparticles have been shown to bypass key challenges regarding small interfering ribonucleic acid delivery.By reducing the enzymatic breakdown of genetic components,nanomaterials as gene carriers have considerably enhanced the efficiency of gene therapy.Liposomes,polymeric nanoparticles,magnetic nanoparticles,dendrimers,and micelles are examples of nanocarriers that have been designed,and each has its own set of features.Furthermore,recent advances in the specific delivery of neurotrophic compounds via gene therapy have provided promising results in relation to augmenting cognitive abilities.In this paper,we highlight the use of different nanocarriers in targeted gene delivery and small interfering ribonucleic acid-mediated gene silencing as a potential platform for treating Alzheimer’s disease.

Genetic variability predicting breeding potential of upland cotton(Gossypium hirsutum L.)for high temperature tolerance

Background High temperature stress at peak flowering stage of cotton is a major hindrance for crop potential.This study aimed to increase genetic divergence regarding heat tolerance in newly developed cultivars and hybrids.Fifty cotton genotypes and 40 F1(hybrids)were tested under field conditions following the treatments,viz.,high temperature stress and control at peak flowering stage in August and October under April and June sowing,respectively.Results The mean squares revealed significant differences among genotypes,treatments,genotype×treatment for relative cell injury,chlorophyll contents,canopy temperature,boll retention and seed cotton yield per plant.The genetic diversity among 50 genotypes was analyzed through cluster analysis and heat susceptibility index(HSI).The heat tolerant genotypes including FH-Noor,NIAB-545,FH-466,FH-Lalazar,FH-458,NIAB-878,IR-NIBGE-8,Weal-AGShahkar,and heat sensitive,i.e.,CIM-602,Silky-3,FH-326,SLH-12 and FH-442 were hybridized in line×tester fashion to produce F1 populations.The breeding materials’populations(40 F1)revealed higher specific combining ability variances along with dominance variances,decided the non-additive type gene action for all the traits.The best general combining ability effects for most of the traits were displayed by the lines,i.e.,FH-Lalazar,NIAB-878 along with testers FH-326 and Silky-3.Specific combining ability effects and better-parent heterosis were showed by the crosses,viz.,FH-Lalazar×Silky-3,FH-Lalazar×FH-326,NIAB-878×Silky-3,and NIAB-878×FH-326 for seed cotton yield and yield contributing traits under high temperature stress.Conclusion Heterosis breeding should be carried out in the presence of non-additive type gene action for all the studied traits.The best combiner parents with better-parent heterosis may be used in crossing program to develop high yielding cultivars,and hybrids for high temperature stress tolerance.

Characterization of Wnt genes in Argopecten scallops and their involvement in response to different temperature stresses in“Bohai Red”scallops

As“Bohai Red”scallops were originated from the hybrids between the Peruvian scallop(Argopecten purpuratus)and the bay scallop(Argopecten irradians)northern subspecies(Argopecten irradians irradians).Twelve Wnt members were identified from the two subspecies of bay scallop,and 13 Wnt genes were found in the genome of the Peruvian scallop.Protein structure analyses showed that most Wnt genes poses all 5 conserved motifs except Wnt1,Wnt2,Wnt6,and Wnt9 in the bay scallops and Wnt2 and Wnt9 in the Peruvian scallop.Unexpectedly,Wnt8 gene was present while Wnt3 was absent in both the bay scallops and the Peruvian scallop.Phylogenetic analysis revealed that Wnt3 might have disappeared in the early evolution of mollusks.The expression profile of Wnt genes in the“Bohai Red”exposed to different temperatures was examined by qRT-PCR.Results show that expression of Wnt genes responded differentially to temperature changes.The Wnt genes such as Wnt1,Wnt6,Wnt7,Wnt11,and WntA that responded slowly to low and high temperature stresses may be related to the maintenance of basic homeostasis.Other Wnt genes such as Wnt4,Wnt9,Wnt5,and Wnt2 that responded rapidly to low temperature may play an important role in organismal protection against low temperature stress.And yet some Wnt genes including Wnt10,Wnt16,and Wnt8 that responded quickly to high temperature stress may play key roles in response to high temperature stress.The results provide new insights into the evolution and function of Wnt genes in bivalves and eventually benefit culture of“Bohai Red”scallops.

A review of the literature on the use of CRISPR/Cas9 gene therapy to treat hepatocellular carcinoma

Noncoding RNAs instruct the Cas9 nuclease to site speifillyl cleave DNA in the CRISPR/Cas9 system.Despite the high incidence of hepatocellular carcinoma(HCC),the patient's outcome is poor.As a result of the emergence of therapeutic resistance in HCC patients,dlinicians have faced difficulties in treating such tumor.In addition,CRISPR/Cas9 screens were used to identify genes that improve the dlinical response of HCC patients.It is the objective of this article to summarize the current understanding of the use of the CRISPR/Cas9 system for the treatment of cancer,with a particular emphasis on HCC as part of the current state of knowledge.Thus,in order to locate recent developments in oncology research,we examined both the Scopus database and the PubMed database.The ability to selectively interfere with gene expression in combinatorial CRISPR/Cas9 screening can lead to the discovery of new effective HCC treatment regimens by combining clinically approved drugs.Drug resistance can be overcome with the help of the CRISPR/Cas9 system.HCC signature genes and resistance to treatment have been uncovered by genome-scale CRISPR activation screening although this method is not without limitations.It has been extensively examined whether CRISPR can be used as a tool for disease research and gene therapy.CRISPR and its applications to tumor research,particularly in HCC,are examined in this study through a review of the literature.

Wilm′s tumor gene1肽疫苗Galinpepimut-S在肿瘤免疫治疗中的应用

目的为Wilm′s tumor gene1(WT1)肽疫苗Galinpepimut-S(GPS)用于肿瘤免疫治疗的后续研究提供参考。方法采用计算机检索中国知网、PubMed等数据库自建库起至2022年12月的肿瘤免疫治疗相关文献,总结GPS在肿瘤免疫治疗中的应用现状。结果GPS能激发自身免疫系统,对WT1抗原产生强烈免疫反应而发挥抗肿瘤作用,在卵巢癌、恶性胸膜间皮瘤、急性髓系白血病、多发性骨髓瘤的治疗中均显示出较好的疗效。结论以GPS为代表的肿瘤疫苗是未来肿瘤治疗的重要方向,需进一步进行临床研究,以获取更多数据。

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.

Altered Liver Gene Expression Due to Hypertension and Age in Rats

Hypertension and metabolic syndrome, both of which increase with age, are multifactorial disorders. Their etiology is complex, making it challenging to isolate involved genes. This study aimed to characterize the hepatic gene expression in spontaneously hypertensive rats (SHR) at different ages. Blood pressure in SHR was determined by tail-cuff method at one and three months of age. Hepatic RNA was isolated and gene expression was compared using microarrays. Comparison between SHR and normotensive rats revealed significant variation in gene expression: 98 genes were upregulated and 122 were downregulated in SHR;while 88 genes were upregulated and 139 genes were downregulated in age-matched normotensive rats. Furthermore, within the SHR group, 110 genes were found to be upregulated and 168 genes downregulated across different ages. Analyses via the Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways revealed that several genes are potentially implicated in both, hypertension and metabolic syndrome. The results suggest that SHR display variations in gene expression due to aging, and when compared to normotensive rats. These variations could contribute to the development of hypertension and metabolic syndrome. Microarray studies involving older rats are necessary to further validate these findings.

AMME chromosomal region gene 1基因变异矮小相关综合征一例及文献复习

目的探讨1例身材矮小、面中部发育不全患儿的病因,以提高临床医师对特殊矮小综合征的认识。方法收集1例身材矮小、面中部发育不全患儿的临床资料,对患儿及父母行基因检测,并给予患儿常规治疗、随访。结果结合患儿特殊面容及基因检测,诊断为AMMECR1基因变异矮小相关综合征,结合文献复习总结AMMECR1基因变异矮小相关综合征特点。结论AMMECR1基因变异矮小相关综合征是一种罕见的X连锁遗传性疾病,临床主要表现为身材矮小、运动语言落后、肌张力减低、听力损失、面中部发育不全,部分存在心脏改变、腭裂、骨骼改变及椭圆形红细胞增多症、智力落后和肾钙质沉着症。该文报道1例AMMECR1基因新变异引起身材矮小、面中部发育不全患儿的病例资料,结合特殊面容及基因检测,诊断为AMMECR1基因变异矮小相关综合征。AMMECR1基因变异矮小相关综合征是一种罕见的X连锁遗传性疾病,本文初步概括其特点,并结合文献进行分析,以提高临床医师对AMMECR1基因变异矮小相关综合征的诊治。

Genetic and epigenetic targets of natural dietary compounds as anti-Alzheimer's agents

Alzheimer’s disease is a progressive neurodegenerative disorder and the most common cause of dementia that principally affects older adults.Pathogenic factors,such as oxidative stress,an increase in acetylcholinesterase activity,mitochondrial dysfunction,genotoxicity,and neuroinflammation are present in this syndrome,which leads to neurodegeneration.Neurodegenerative pathologies such as Alzheimer’s disease are considered late-onset diseases caused by the complex combination of genetic,epigenetic,and environmental factors.There are two main types of Alzheimer’s disease,known as familial Alzheimer’s disease(onset<65 years)and late-onset or sporadic Alzheimer’s disease(onset≥65 years).Patients with familial Alzheimer’s disease inherit the disease due to rare mutations on the amyloid precursor protein(APP),presenilin 1 and 2(PSEN1 and PSEN2)genes in an autosomaldominantly fashion with closely 100%penetrance.In contrast,a different picture seems to emerge for sporadic Alzheimer’s disease,which exhibits numerous non-Mendelian anomalies suggesting an epigenetic component in its etiology.Importantly,the fundamental pathophysiological mechanisms driving Alzheimer’s disease are interfaced with epigenetic dysregulation.However,the dynamic nature of epigenetics seems to open up new avenues and hope in regenerative neurogenesis to improve brain repair in Alzheimer’s disease or following injury or stroke in humans.In recent years,there has been an increase in interest in using natural products for the treatment of neurodegenerative illnesses such as Alzheimer’s disease.Through epigenetic mechanisms,such as DNA methylation,non-coding RNAs,histone modification,and chromatin conformation regulation,natural compounds appear to exert neuroprotective effects.While we do not purport to cover every in this work,we do attempt to illustrate how various phytochemical compounds regulate the epigenetic effects of a few Alzheimer’s disease-related genes.

Genetically modified non-human primate models for research on neurodegenerative diseases

Neurodegenerative diseases(NDs)are a group of debilitating neurological disorders that primarily affect elderly populations and include Alzheimer's disease(AD),Parkinson's disease(PD),Huntington's disease(HD),and amyotrophic lateral sclerosis(ALS).Currently,there are no therapies available that can delay,stop,or reverse the pathological progression of NDs in clinical settings.As the population ages,NDs are imposing a huge burden on public health systems and affected families.Animal models are important tools for preclinical investigations to understand disease pathogenesis and test potential treatments.While numerous rodent models of NDs have been developed to enhance our understanding of disease mechanisms,the limited success of translating findings from animal models to clinical practice suggests that there is still a need to bridge this translation gap.Old World nonhuman primates(NHPs),such as rhesus,cynomolgus,and vervet monkeys,are phylogenetically,physiologically,biochemically,and behaviorally most relevant to humans.This is particularly evident in the similarity of the structure and function of their central nervous systems,rendering such species uniquely valuable for neuroscience research.Recently,the development of several genetically modified NHP models of NDs has successfully recapitulated key pathologies and revealed novel mechanisms.This review focuses on the efficacy of NHPs in modeling NDs and the novel pathological insights gained,as well as the challenges associated with the generation of such models and the complexities involved in their subsequent analysis.

外源6-BA对小苍兰‘Red Lion’生长及花期调控的影响

本试验以小苍兰品种‘Red Lion’为材料,研究喷施不同浓度6-BA(25、50、100 mg/L和200 mg/L)对小苍兰生长、生理生化特性及花期的影响,以期为小苍兰花期调控和切花栽培提供技术支持。结果表明:喷施适当浓度的6-BA有利于延长小苍兰‘Red Lion’花期;6-BA浓度为200 mg/L时显著提高小苍兰的花朵直径、花葶长度、小花数、株高、叶片数,较对照花期延长11 d、花朵直径增加50.45%、小花数增加114.89%,同时叶片的丙二醛含量较CK降低34.65%,叶片抗氧化酶(POD、CAT)活性升高,分别比CK提高97.26%和393.24%。综之,喷施适当浓度的6-BA能够显著提高小苍兰‘Red Lion’花朵质量及数量,延长小苍兰花期,其中200 mg/L 6-BA处理效果最佳。