HDAC activity is dispensable for repression of cell-cycle genes by DREAM and E2F:RB complexes.

Histone deacetylases (HDACs) play a crucial role in transcriptional regulation and are implicated in various diseases, including cancer. They are involved in histone tail deacetylation and canonically linked to transcriptional repression. Previous studies suggested that HDAC recruitment to cell-cycle gene promoters via the retinoblastoma (RB) protein or the DREAM complex through SIN3B is essential for G1/S and G2/M gene repression during cell-cycle arrest and exit. Here we investigate the interplay among DREAM, RB, SIN3 proteins, and HDACs in the context of cell-cycle gene repression. Knockout of SIN3B does not globally derepress cell-cycle genes in non-proliferating HCT116 and C2C12 cells. Loss of SIN3A/B moderately upregulates several cell-cycle genes in HCT116 cells but does so independently of DREAM/RB. HDAC inhibition does not induce general upregulation of RB/DREAM target genes in arrested transformed or non-transformed cells. Our findings suggest that E2F:RB and DREAM complexes can repress cell-cycle genes without relying on HDAC activity.

A-MYB substitutes for B-MYB in activating cell cycle genes and in stimulating proliferation.

A-MYB (MYBL1) is a transcription factor with a role in meiosis in spermatocytes. The related B-MYB protein is a key oncogene and a master regulator activating late cell cycle genes. To activate genes, B-MYB forms a complex with MuvB and is recruited indirectly to cell cycle genes homology region (CHR) promoter sites of target genes. Activation through the B-MYB-MuvB (MMB) complex is essential for successful mitosis. Here, we discover that A-MYB has a function in transcriptional regulation of the mitotic cell cycle and can substitute for B-MYB. Knockdown experiments in cells not related to spermatogenesis show that B-MYB loss alone merely delays cell cycle progression. Only dual knockdown of B-MYB and A-MYB causes G2/M cell cycle arrest, endoreduplication, and apoptosis. A-MYB can substitute for B-MYB in binding to MuvB. The resulting A-MYB-MuvB complex activates genes through CHR sites. We find that A-MYB activates the same target genes as B-MYB. Many of the corresponding proteins are central regulators of the cell division cycle. In summary, we demonstrate that A-MYB is an activator of the mitotic cell cycle by activating late cell cycle genes.

Integrative analysis identifies oxidative stress biomarkers in non-alcoholic fatty liver disease via machine learning and weighted gene co-expression network analysis.

Background: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease globally, with the potential to progress to non-alcoholic steatohepatitis (NASH), cirrhosis, and even hepatocellular carcinoma. Given the absence of effective treatments to halt its progression, novel molecular approaches to the NAFLD diagnosis and treatment are of paramount importance. Methods: Firstly, we downloaded oxidative stress-related genes from the GeneCards database and retrieved NAFLD-related datasets from the GEO database. Using the Limma R package and WGCNA, we identified differentially expressed genes closely associated with NAFLD. In our study, we identified 31 intersection genes by analyzing the intersection among oxidative stress-related genes, NAFLD-related genes, and genes closely associated with NAFLD as identified through Weighted Gene Co-expression Network Analysis (WGCNA). In a study of 31 intersection genes between NAFLD and Oxidative Stress (OS), we identified three hub genes using three machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine - Recursive Feature Elimination (SVM-RFE), and RandomForest. Subsequently, a nomogram was utilized to predict the incidence of NAFLD. The CIBERSORT algorithm was employed for immune infiltration analysis, single sample Gene Set Enrichment Analysis (ssGSEA) for functional enrichment analysis, and Protein-Protein Interaction (PPI) networks to explore the relationships between the three hub genes and other intersecting genes of NAFLD and OS. The distribution of these three hub genes across six cell clusters was determined using single-cell RNA sequencing. Finally, utilizing relevant data from the Attie Lab Diabetes Database, and liver tissues from NASH mouse model, Western Blot (WB) and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) assays were conducted, this further validated the significant roles of CDKN1B and TFAM in NAFLD. Results: In the course of this research, we identified 31 genes with a strong association with oxidative stress in NAFLD. Subsequent machine learning analysis and external validation pinpointed two genes: CDKN1B and TFAM, as demonstrating the closest correlation to oxidative stress in NAFLD. Conclusion: This investigation found two hub genes that hold potential as novel targets for the diagnosis and treatment of NAFLD, thereby offering innovative perspectives for its clinical management.

TRPS1 modulates chromatin accessibility to regulate estrogen receptor alpha (ER) binding and ER target gene expression in luminal breast cancer cells.

Common genetic variants in the repressive GATA-family transcription factor (TF) TRPS1 locus are associated with breast cancer risk, and luminal breast cancer cell lines are particularly sensitive to TRPS1 knockout. We introduced an inducible degron tag into the native TRPS1 locus within a luminal breast cancer cell line to identify the direct targets of TRPS1 and determine how TRPS1 mechanistically regulates gene expression. We acutely deplete over 80 percent of TRPS1 from chromatin within 30 minutes of inducing degradation. We find that TRPS1 regulates transcription of hundreds of genes, including those related to estrogen signaling. TRPS1 directly regulates chromatin structure, which causes estrogen receptor alpha (ER) to redistribute in the genome. ER redistribution leads to both repression and activation of dozens of ER target genes. Downstream from these primary effects, TRPS1 depletion represses cell cycle-related gene sets and reduces cell doubling rate. Finally, we show that high TRPS1 activity, calculated using a gene expression signature defined by primary TRPS1-regulated genes, is associated with worse breast cancer patient prognosis. Taken together, these data suggest a model in which TRPS1 modulates the genomic distribution of ER, both activating and repressing transcription of genes related to cancer cell fitness.

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma.

Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs (EIF4H, CDK2, TCEA1, SPTLC2, TMEM209, RGP1, EIF3D, and CAPZA3) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.

Cell cycle gene alterations associate with a redistribution of mutation risk across chromosomal domains in human cancers.

Mutations in human cells exhibit increased burden in heterochromatic, late DNA replication time (RT) chromosomal domains, with variation in mutation rates between tissues mirroring variation in heterochromatin and RT. We observed that regional mutation risk further varies between individual tumors in a manner independent of cell type, identifying three signatures of domain-scale mutagenesis in >4,000 tumor genomes. The major signature reflects remodeling of heterochromatin and of the RT program domains seen across tumors, tissues and cultured cells, and is robustly linked with higher expression of cell proliferation genes. Regional mutagenesis is associated with loss of activity of the tumor-suppressor genes RB1 and TP53, consistent with their roles in cell cycle control, with distinct mutational patterns generated by the two genes. Loss of regional heterogeneity in mutagenesis is associated with deficiencies in various DNA repair pathways. These mutation risk redistribution processes modify the mutation supply towards important genes, diverting the course of somatic evolution.

Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data.

The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets.

CDK16 as a potential prognostic biomarker correlated with an immunosuppressive tumor microenvironment and benefits in enhancing the effectiveness of immunotherapy in human cancers.

BACKGROUND: Cyclin-Dependent Kinase 16 (CDK16) plays significant biological roles in various diseases. Nonetheless, its function in different cancer types and its relationship with the Tumor Immune Microenvironment (TIME) are still not well-understood. METHODS: We analyzed the expression profile, genetic alterations, clinical features, and prognostic value of CDK16 in pan-cancer using data from The Cancer Genome Atlas, Genotype-Tissue Expression databases, and in vitro experiments. Additionally, the TIMER2 and ImmuCellAI databases were utilized to assess the correlation between CDK16 expression and immune cell infiltration levels. Finally, we examined the correlation between CDK16 and the response to immunotherapy using collected immunotherapy data. RESULTS: CDK16 is notably overexpressed in pan-cancer and is a risk factor for poor prognosis in various cancers. Our findings reveal that CDK16 regulates not only cell cycle-related functions to promote cell proliferation but also the autoimmunity-related functions of the innate and adaptive immune systems, along with other immune-related signaling pathways. Moreover, CDK16 overexpression contributes to an immunosuppressive tumor microenvironment, extensively suppressing immune-related features such as the expression of immune-related genes and pathways, as well as the count of immune-infiltrating cells. Our analysis indicated that individuals with low CDK16 expression showed higher response rates to immune checkpoint inhibitors and longer overall survival compared to those with high CDK16 expression. CONCLUSIONS: This study establishes CDK16 as a potential biomarker for predicting poor prognosis in a wide range of cancers. Its role in shaping the immunosuppressive tumor microenvironment and influencing the efficacy of immunotherapy highlights the urgent need for developing targeted therapies against CDK16, offering new avenues for cancer treatment and management.

An update of clinical value of circulating tumor DNA in esophageal cancer: a systematic review and meta-analysis.

BACKGROUND: Esophageal cancer (EC) is a deadly disease with limited therapeutic options. Although circulating tumor DNA (ctDNA) could be a promising tool in this regard, the availiable evidence is limited. We performed a systematic review and meta-analysis to summarize the clinical applicability of the next-generation sequencing (NGS) and droplet digital polymerase chain reaction (ddPCR) technology on the ctDNA detection of the EC and listed the current challenges. METHODS: We systematically searched MEDLINE (via PubMed), Embase (via OVID), ISI Web of Science database and Cochrane Library from January, 2000 to April, 2023. Progression-free survival (PFS) and overall survival (OS) were set as primary outcome endpoints. Pathologic response was evaluated by tumor regression grade (TRG), according to the eighth edition of the American Joint Committee on Cancer (AJCC). Major pathologic regression (MPR) was defined as TRG 1 and 2. The MPR was set as secondary endpoint. Hazard rate (HR) and associated 95% CI were used as the effect indicators the association between ctDNA and prognosis of EC. MPR rates were also calculated. Fixed-effect model (Inverse Variance) or random-effect model (Mantel-Haenszel method) was performed depending on the statistically heterogeneity. RESULTS: Twenty-two studies, containing 1144 patients with EC, were included in this meta-analysis. The results showed that OS (HR = 3.87; 95% CI, 2.86-5.23) and PFS (HR = 4.28; 95% CI, 3.34-5.48) were shorter in ctDNA-positive patients. In the neoadjuvant therapy, the sensitivity analysis showed the clarified HR of ctDNA-positive was 1.13(95% CI, 1.01-1.28). We also found that TP53, NOTCH1, CCND1 and CNKN2A are the most frequent mutation genes. CONCLUSIONS: Positive ctDNA is associated with poor prognosis, which demonstrated clinical value of ctDNA. Longitudinal ctDNA monitoring showed potential prognostic value in the neoadjuvant therapy. In an era of precision medicine, ctDNA could be a promising tool to individualize treatment planning and to improve outcomes in EC. PROSPERO REGISTRATION NUMBER: CRD42023412465.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy.

Objective: This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing. Methods: Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis. Results: Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes. Conclusion: This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses.

Background: Ovarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies. Methods: Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance. Results: A total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results. Conclusion: This study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

Identification of novel potential homologous repair deficiency-associated genes in pancreatic adenocarcinoma via WGCNA coexpression network analysis and machine learning.

Homologous repair deficiency (HRD) impedes double-strand break repair, which is a common driver of carcinogenesis. Positive HRD status can be used as theranostic markers of response to platinum- and PARP inhibitor-based chemotherapies. Here, we aimed to fully investigate the therapeutic and prognostic potential of HRD in pancreatic adenocarcinoma (PAAD) and identify effective biomarkers related to HRD using comprehensive bioinformatics analysis. The HRD score was defined as the unweighted sum of the LOH, TAI, and LST scores, and it was obtained based on the previous literature. To characterize PAAD immune infiltration subtypes, the "ConsensusClusterPlus" package in R was used to conduct unsupervised clustering. A WGCNA was conducted to elucidate the gene coexpression modules and hub genes in the HRD-related gene module of PAAD. The functional enrichment study was performed using Metascape. LASSO analysis was performed using the "glmnet" package in R, while the random forest algorithm was realized using the "randomForest" package in R. The prognostic variables were evaluated using univariate Cox analysis. The prognostic risk model was built using the LASSO approach. ROC curve and KM survival analyses were performed to assess the prognostic potential of the risk model. The half-maximal inhibitory concentration (IC50) of the PARP inhibitors was estimated using the "pRRophetic" package in R and the Genomics of Drug Sensitivity in Cancer database. The "rms" package in R was used to create the nomogram. A high HRD score indicated a poor prognosis and an advanced clinical process in PAAD patients. PAAD tumors with high HRD levels revealed significant T helper lymphocyte depletion, upregulated levels of cancer stem cells, and increased sensitivity to rucaparib, Olaparib, and veliparib. Using WGCNA, 11 coexpression modules were obtained. The red module and 122 hub genes were identified as the most correlated with HRD in PAAD. Functional enrichment analysis revealed that the 122 hub genes were mainly concentrated in cell cycle pathways. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were screened via LASSO analysis and a random forest algorithm, and they were validated using independent validation sets. No direct association between HRD and CKS1B, HJURP, or TPX2 has not been reported in the literature so far. Thus, these findings indicated that CKS1B, HJURP, and TPX2 have potential as diagnostic and prognostic biomarkers for PAAD. We constructed a novel HRD-related prognostic model that provides new insights into PAAD prognosis and immunotherapy. Based on bioinformatics analysis, we comprehensively explored the therapeutic and prognostic potential of HRD in PAAD. One novel HRD-related gene signature consisting of CKS1B, HJURP, and TPX2 were identified through the combination of WGCNA, LASSO analysis and a random forest algorithm. A novel HRD-related risk model that can predict clinical prognosis and immunotherapeutic response in PAAD patients was constructed.

A Machine Learning Method for Predicting Biomarkers Associated with Prostate Cancer.

BACKGROUND: Prostate cancer (PCa) is a prevalent form of malignant tumors affecting the prostate gland and is frequently diagnosed in males in Western countries. Identifying diagnostic and prognostic biomarkers is not only important for screening drug targets but also for understanding their pathways and reducing the cost of experimental verification of PCa. The objective of this study was to identify and validate promising diagnostic and prognostic biomarkers for PCa. METHODS: This study implemented a machine learning technique to evaluate the diagnostic and prognostic biomarkers of PCa using protein-protein interaction (PPI) networks. In addition, multi-database validation and literature review were performed to verify the diagnostic biomarkers. To optimize the prognosis of our results, univariate Cox regression analysis was utilized to screen survival-related genes. This study employed stepwise multivariate Cox regression analysis to develop a prognostic risk model. Finally, receiver operating characteristic analysis confirmed that these predictive biomarkers demonstrated a substantial level of sensitivity and specificity when predicting the prognostic survival of patients. RESULTS: The hub genes were UBE2C (Ubiquitin Conjugating Enzyme E2 C), CCNB1 (Cyclin B1), TOP2A (DNA Topoisomerase II Alpha), TPX2 (TPX2 Microtubule Nucleation Factor), CENPM (Centromere Protein M), F5 (Coagulation Factor V), APOE (Apolipoprotein E), NPY (Neuropeptide Y), and TRIM36 (Tripartite Motif Containing 36). All of these hub genes were validated by multiple databases. By validation in these databases, these 10 hub genes were significantly involved in significant pathways. The risk model was constructed by a four-gene-based prognostic factor that included TOP2A, UBE2C, MYL9, and FLNA. CONCLUSIONS: The machine learning algorithm combined with PPI networks identified hub genes that can serve as diagnostic and prognostic biomarkers for PCa. This risk model will enable patients with PCa to be more accurately diagnosed and predict new drugs in clinical trials.

Establishment of the Myeloid TBX-Code Reveals Aberrant Expression of T-Box Gene TBX1 in Chronic Myeloid Leukemia.

T-box genes encode transcription factors, which control developmental processes and promote cancer if deregulated. Recently, we described the lymphoid TBX-code, which collates T-box gene activities in normal lymphopoiesis, enabling identification of members deregulated in lymphoid malignancies. Here, we have extended this analysis to cover myelopoiesis, compiling the myeloid TBX-code and, thus, highlighting which of these genes might be deregulated in myeloid tumor types. We analyzed public T-box gene expression datasets bioinformatically for normal and malignant cells. Candidate T-box-gene-expressing model cell lines were identified and examined by RQ-PCR, Western Blotting, genomic profiling, and siRNA-mediated knockdown combined with RNA-seq analysis and live-cell imaging. The established myeloid TBX-code comprised 10 T-box genes, including progenitor-cell-restricted TBX1. Accordingly, we detected aberrant expression of TBX1 in 10% of stem/progenitor-cell-derived chronic myeloid leukemia (CML) patients. The classic CML cell line K-562 expressed TBX1 at high levels and served as a model to identify TBX1 activators, including transcription factor GATA1 and genomic amplification of the TBX1 locus at 22q11; inhibitors, including BCR::ABL1 fusion and downregulated GNAI2, as well as BMP, FGF2, and WNT signaling; and the target genes CDKN1A, MIR17HG, NAV1, and TMEM38A. The establishment of the myeloid TBX-code permitted identification of aberrant TBX1 expression in subsets of CML patients and cell lines. TBX1 forms an integral part of an oncogenic regulatory network impacting proliferation, survival, and differentiation. Thus, the data spotlight novel diagnostic markers and potential therapeutic targets for this malignancy.

Sheep gastrointestinal helminthiasis in the Sertão region of Paraíba State, Northeastern Brazil: prevalence and risk factors / Helmintoses gastrintestinais de ovinos no Sertão do Estado da Paraíba, Nordeste do Brasil: prevalência e fatores de riscos

In this study, we aimed to establish the prevalence and risk factors relating to gastrointestinal helminthiasis, and to characterize the sanitary management practiced among sheep herds in the Sertão region of the state of Paraíba, northeastern Brazil, based on factors that condition the ways of controlling these parasites in these herds. The research was carried out between April and July 2012. We visited 54 farms, where fecal and blood samples were individually collected from 465 animals. On each farm, a questionnaire was applied to gather information on variables relating to potential risk factors. The prevalence of sheep gastrointestinal helminthiasis in the region was 75.9%. At least one animal tested positive for this helminthiasis on 53 (98.1%) of the 54 farms evaluated. The eggs per gram of feces (EPG) analysis showed the following infection burdens: 51.8% with mild infection, 27.1% moderate infection, 9.9% heavy infection and 11.2% fatal infection. Among the sheep farms visited, anthelmintics were used on 81.5% (p <0.05). The most relevant risk factor in this study was the farm area, because it defines the area available for grazing animals. Properties with many animals and little pasture area, which are the most abundant type in the Sertão region of Paraíba, tend to have high prevalence of gastrointestinal helminthiasis, because the animals are more prone to reinfection. The Sertão region of Paraíba presents high prevalence of gastrointestinal helminthiasis among sheep, and the farm area is the most relevant risk factor for the development of these parasites.

Objetivou-se determinar a prevalência e os fatores de risco para as helmintoses gastrintestinais, caracterizando o manejo sanitário sob fatores condicionantes das formas de controle dessas parasitoses em rebanhos de ovinos da região do Sertão da Paraíba. A pesquisa foi desenvolvida no período de abril a julho de 2012. Foram visitadas propriedades, utilizando-se 465 animais, sendo coletadas individualmente amostras de fezes e sangue durante as visitas. Em cada propriedade, foi aplicado questionário para a coleta de informações acerca de variáveis que atuariam como possíveis fatores de risco. Observou-se que a prevalência das helmintoses gastrintestinais de ovinos na região do Sertão da Paraíba foi de 75,9%. Pelo menos um animal foi positivo para essas helmintoses, em 53 (98,1%) das 54 propriedades avaliadas. A análise de OPG (Ovos Por Gramas de Fezes) demonstrou que 51,8% dos animais apresentaram infecção leve, 27,1% infecção moderada, 9,9% infecção pesada e 11,2% infecção fatal. A utilização de anti-helmínticos ocorreu em 81,5% das propriedades (p <0,05). O fator de risco mais relevante neste estudo foi a área da propriedade, porque delimita a área de pastejo do animal. Propriedades com muitos animais e pouca área de pastejo, que são as mais abundantes no Sertão da Paraíba, tendem a apresentar alta prevalência de helmintoses gastrintestinais, pois os animais estão mais propensos à reinfecção. A região do Sertão da Paraíba apresenta uma elevada prevalência de helmintoses gastrintestinais em ovinos, e a área das propriedades é o fator de risco mais relevante para o desenvolvimento dessas parasitoses.

p53-independent checkpoint controls in a plant cell model

Allium cepa L. meristems were used as a plant model to study the p53-independent control of S and G2 phases by checkpoint pathways, in eukaryotic cells. Checkpoint blocks were induced at early and mid S by hydroxyurea. After their spontaneous override, cells became accumulated in G2-prophase, giving rise later on to a delayed mitotic wave. Cell growth was maintained during the checkpoint blocks, as the delayed mitoses were larger in size than the control ones. Under continuous hydroxyurea treatment, the delayed mitotic was formed by two subpopulations: normal mitoses corresponding to cells having properly recovered from the checkpoint block, and abnormal ones resulting from checkpoint adaptation. These latter cells displayed broken chromatids as they had unduly overriden the G2 checkpoint block, without completing DNA repair. The frequency of the checkpoint-adapted mitoses increased with the hydroxyurea concentration from 0.25 to 1.0 mM. However, from 1 mM hydroxyurea upwards, some of the cells lost their competence for checkpoint adaptation. Therefore, the dose of a genotoxic agent that still allows G2 checkpoint adaptation should always be applied in order to get rid of uncontrolled proliferating cells. This is specially suitable for cells lacking a functional p53 protein.

Expressão dos genes CDC25A e p27 em linfomas não-Hodgkin de células B / CDC25A abd o27 gene expression in b-cell non-Hodgkin's lymphomas

A progressão da divisão celular é regulada pela interação de ciclinas, quinases ciclino-dependentes (CDKS) e inibidores de CDKS (CDKIs).A fosfatase CDC25A pertence a um grupo de fosfatases de dupla especificidade que ativam as CDKs por meio da remoção de fosfatos inibitórios presentes nos resíduos de treonina e tirosina das posições 14 e 15, respectivamente. Diferentemente das fosfatases CDC25B e CDC25C, a CDC25A é predominantemente expressa na fase Gl, e sofre fosforilação durante a transição Gl/S do cicio celular, resultando em aumento de sua atividade enzimática. A fosforilação da enzima CDC25A ocorre na sua extremidade regulatória N-terminai, a qual contém alta densidade de motivos de Ser/ThrPro. Esta fosforiiação é dependente de complexos ciclina E-CDK2, gerando um mecanismo de auto-amplificação essencial para que a célula entre na fase S. A cooperação entre o gene CDC25A e versões mutantes de H-ras ou deleção de Rb foi vista na transformação de fibrobiastos de ratos, e o gene CDC25A é um alvo transcricional relevante do proto-oncogene c-myc, exercendo um importante papel tanto como mediador de proliferação celular bem como de apoptose induzida por c-myc. O gene p27 é um inibidor de CDK e um potencial gene supressor de tumor implicado em bloqueio na fase Gl mediado por TGF-0 e contato celular. A proteína p27 inibe fortemente os complexos ciclina-CDKs, e sua perda de função tem sido implicada na oncogênese. Objetivando investigar a importância dos genes CDC25A e p27 no desenvolvimento e progressão de doenças linfoproliferativas, no presente estudo 93 casos de linfoma não-Hodgkin de células B (LNH-B) foram estudados através de técnica de hibddizacao in situ com sondas de RNA marcadas com digoxigenina em tecido fixado em formalina, a fim de se avaliar a expressão de RNA mensageiro do gene CDC25A. A expressão da proteína p27 também foi avaliada nos mesmos casos através de técnica de imuno-histoquímica. Os pacientes com LNH-B foram divididos em dois grandes grupos: o primeiro constituído por 42 linfomas de baixo grau de malignidade (30 linfomas de pequenas células, 9 linfomas foliculares de pequenas células clivadas, e 3 linfomas foliculares mistos); e o outro grupo constituído por 51 linfomas de grau intermediário e de alto grau de malignidade (3 linfomas foliculares de grandes células, 4 linfomas difusos de pequenas células crivadas, 2 linfomas difusos mistos, 31 linfomas difusos de grandes célula...(truncado..)

Introducción a la genética médica / Introduccion to the medical genetics

La genética médica, se la puede considerar ya como una rama de la genética humana. Los avances científicos que a nivel cromosómico se han desarrollado, en base al estudio de los principios genéticos y cromosómicos, determinan que muchos de los fundamentos médicos de las enfermedades, no se limitan a los trastornos cromosónicos o genéticos, sino que abarcan los aspectos de la hetereogenidad génetica de las emfermedades. Establece los conceptos genéticos indispensables en el proceso de formación de los profesionales de la salud, en particular de los médicos