Identification of core pathogenic genes and pathways in elderly osteoporosis based on bioinformatics analysis / 中华预防医学杂志

Objective: Using bioinformatics methods to analyze the core pathogenic genes and related pathways in elderly osteoporosis. Methods: From November 2020 and August 2021, eight elderly osteoporosis patients who received treatment and five healthy participants who underwent physical examinations in Beijing Jishuitan Hospital were selected as subjects. The expression level of RNA in the peripheral blood of eight elderly osteoporosis patients and five healthy participants was collected for high-throughput transcriptome sequencing and analysis. The gene ontology (GO) analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network was constructed using the STRING website and Cytoscape software, and the most significant modules and hub genes were screened out. Results: Among the eight elderly osteoporosis patients, there were seven females and one male, with an average age of 72.4 years (SD=4.2). Among the five healthy participants, there were four females and one male, with an average age of 68.2 years (SD=5.7). A total of 1 635 DEGs (847 up-regulated and 788 down-regulated) were identified. GO analysis revealed that the molecular functions of DEGs were mainly enriched in structural constituents of the ribosome, protein dimerization activity, and cellular components were mainly enriched in the nucleosome, DNA packaging complex, cytosolic part, protein-DNA complex and the cytosolic ribosome. KEGG pathway analysis showed that DEGs were mainly enriched in systemic lupus erythematosus and ribosome. Gene UBA52, UBB, RPS27A, RPS15, RPS12, RPL13A, RPL23A, RPL10A, RPS25 and RPS6 were selected and seven of them could encode ribosome proteins. Conclusion: The pathogenesis of elderly osteoporosis may be associated with ribosome-related genes and pathways.

A multi-stage and multi-epitope vaccine against Mycobacterium tuberculosis based on an immunoinformatics approach / 细胞与分子免疫学杂志

Objectives To develop a multi-stage and multi-epitope vaccine, which consists of epitopes from the early secretory and latency-associated antigens of Mycobacterium tuberculosis (MTB). Methods The B-cell, cytotoxic T-lymphocyte (CTL) and helper T-lymphocyte (HTL) epitopes of 12 proteins were predicted using an immunoinformatics. The epitopes with antigenicity, without cytotoxicity and sensitization, were further screened to construct the multi-epitope vaccine. Furthermore, the proposed vaccine underwent physicochemical properties analysis and secondary structure prediction as well as 3D structure modeling, refinement and validation. Then the refined model was docked with TLR4. Finally, an immune simulation of the vaccine was carried out. Results The proposed vaccine, which consists of 12 B-cell, 11 CTL and 12 HTL epitopes, had a flexible and stable globular conformation as well as a thermostable and hydrophilic structure. A stable interaction of the vaccine with TLR4 was confirmed by molecular docking. The efficiency of the candidate vaccine to trigger effective cellular and humoral immune responses was assessed by immune simulation. Conclusion A multi-stage multi-epitope MTB vaccine construction strategy based on immunoinformatics is proposed, which is expected to prevent both active and latent MTB infection.

Advances in machine learning for predicting protein functions / 生物工程学报

Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in green synthesis has been of great interest, but the high cost of obtaining specific functional enzymes as well as the variety of enzyme types and functions hamper their application. At present, the specific functions of proteins are mainly determined through tedious and time-consuming experimental characterization. With the rapid development of bioinformatics and sequencing technologies, the number of protein sequences that have been sequenced is much larger than those can be annotated, thus developing efficient methods for predicting protein functions becomes crucial. With the rapid development of computer technology, data-driven machine learning methods have become a promising solution to these challenges. This review provides an overview of protein function and its annotation methods as well as the development history and operation process of machine learning. In combination with the application of machine learning in the field of enzyme function prediction, we present an outlook on the future direction of efficient artificial intelligence-assisted protein function research.

Deep learning analysis on the resulting impacts of weekly load training on students' biological system / Análise do aprendizado profundo nos impactos resultantes dos treinos semanais de carga sobre o sistema biológico dos estudantes / Análisis de aprendizaje profundo sobre los impactos resultantes de los entrenamientos semanales de carga en el sistema biológico de los estudiantes

ABSTRACT Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students' biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes' training load in the annual preparation period. The characteristics of athletes' training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of "teaching" and "training" in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução O recente desenvolvimento do algoritmo de aprendizado profundo como um novo algoritmo de aprendizado de máquina de rede multicamadas reduziu o problema dos algoritmos de treinamento tradicionais, que facilmente caiam em locais mínimos, tornando-se uma direção recente no campo do aprendizado. Objetivo Desenvolver e validar um modelo de inteligência artificial para aprendizado profundo dos impactos resultantes dos treinos semanais de carga sobre o sistema biológico dos estudantes. Métodos De acordo com os índices fisiológicos e bioquímicos dos atletas no processo de treinamento, este artigo analisa os dados reais da carga de treinamento dos atletas no período anual de preparação. As características da carga de treinamento dos atletas no período de preparação foram discutidas. O valor, significância, fatores de composição, princípio de arranjo e método de cálculo e determinação da densidade de carga semanal usando o algoritmo de aprendizado profundo são discutidos. Resultados Os resultados mostraram que a carga diária de 24 horas de amostragem aleatória foi de intensidade moderada, treinamento de baixa densidade e alta intensidade, e o sistema físico-motor e a reatividade neural foram aprimorados. Conclusão A pesquisa mostra que pode haver duas atividades de "ensino" e "treinamento" na área de educação física e no treinamento esportivo. A pesquisa de monitoramento da biologia esportiva revela-se um ponto de crescimento da pesquisa de treinamento esportivo com grande potencial de expansão para pesquisas futuras. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción El reciente desarrollo del algoritmo de aprendizaje profundo como un nuevo algoritmo de aprendizaje automático de red multicapa ha reducido el problema de los algoritmos de entrenamiento tradicionales, que caen fácilmente en lugares mínimos, convirtiéndose en una dirección reciente en el campo del aprendizaje. Objetivo Desarrollar y validar un modelo de inteligencia artificial para el aprendizaje profundo de los impactos resultantes del entrenamiento de la carga semanal en el sistema biológico de los estudiantes. Métodos De acuerdo con los índices fisiológicos y bioquímicos de los atletas en el proceso de entrenamiento, este artículo analiza los datos reales de la carga de entrenamiento de los atletas en el período de preparación anual. Se analizaron las características de la carga de entrenamiento de los atletas en el periodo de preparación. Se analizan el valor, el significado, los factores de composición, el principio de disposición y el método de cálculo y determinación de la densidad de carga semanal mediante el algoritmo de aprendizaje profundo. Resultados Los resultados mostraron que la carga diaria de 24 horas de muestreo aleatorio era de intensidad moderada, de baja densidad y de alta intensidad de entrenamiento, y que el sistema físico-motor y la reactividad neural mejoraban. Conclusión La investigación muestra que puede haber dos actividades de "enseñanza" y "formación" en la educación física y el entrenamiento deportivo. La investigación sobre el seguimiento de la biología del deporte demuestra ser un punto de crecimiento de la investigación sobre el entrenamiento deportivo con un gran potencial de expansión para futuras investigaciones. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Gene Expression Profiles at Different Time Points after Acute Myocardial Infarction in Mice / 法医学杂志

OBJECTIVES@#To explore the mRNA differential expressions and the sequential change pattern in acute myocardial infarction (AMI) mice.@*METHODS@#The AMI mice relevant dataset GSE4648 was downloaded from Gene Expression Omnibus (GEO). In the dataset, 6 left ventricular myocardial tissue samples were selected at 0.25, 1, 4, 12, 24 and 48 h after operation in AMI group and sham control group, and 6 left ventricular myocardial tissue samples were selected in blank control group, a total of 78 samples were analyzed. Differentially expressed genes (DEGs) were analyzed by R/Bioconductor package limma, functional pathway enrichment analysis was performed by clusterProfiler, protein-protein interaction (PPI) network was constructed by STRING database and Cytoscape software, the key genes were identified by Degree topological algorithm, cluster sequential changes on DEGs were analyzed by Mfuzz.@*RESULTS@#A total of 1 320 DEGs were associated with the development of AMI. Functional enrichment results included cellular catabolic process, regulation of inflammatory response, development of muscle system and vasculature system, cell adhesion and signaling pathways mainly enriched in mitogen-activated protein kinase (MAPK) signaling pathway. The key genes of AMI included MYL7, TSC22D2, HSPA1A, BTG2, NR4A1, RYR2 were up-regulated or down-regulated at 0.25-48 h after the occurrence of AMI.@*CONCLUSIONS@#The functional signaling pathway of DEGs and the sequential expression of key genes in AMI may provide a reference for the forensic identification of AMI.

Construction of endogenous RNA regulatory network for colorectal cancer based on bioinformatics / 中南大学学报(医学版)

OBJECTIVES@#The high morbidity and mortality of colorectal cancer (CRC) have posed great threats to human health. Circular RNA (circRNA) and microRNA (miRNA), acting as competing endogenous RNAs (ceRNAs), have been found to play vital roles in carcinogenesis. This paper aims to construct a circRNA/miRNA/mRNA regulatory network so as to explore the molecular mechanism of CRC.@*METHODS@#The sequencing data of circRNA from CRC were obtained from Gene Expression Omnibus (GEO). The differential circRNA was screened and its structure was identified by Cancer-specific CircRNA Database (CSCD); the sequencing data of miRNA and messenger RNA (mRNAs) were downloaded from The Cancer Genome Atlas (TCGA) database and the differentially expressed genes were screened; the corresponding miRNA of differential circRNAs were predicted by CircInteractome database; DIANA, Miranda, PicTar, and TargetScan databases were used to predict the target genes of different miRNAs; the target genes from Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched by R language; String database combined with Cytoscape 3.7.2 software was used to construct protein-protein interaction (PPI) network and hub genes were screened; the expressions of mRNAs in the Top10 hub genes were verified in CRC. The network diagrams of circRNAs/miRNAs/mRNAs and circRNAs/miRNAs/Top10 hub mRNAs were constructed by Cytoscape3.7.2. Real-time PCR was used to examine the expression levels of hsa_circRNA_0065173, hsa-mir-450b, hsa-mir-582, adenylate cyclase 5 (ADCY5), muscarinic acetylcholine receptor M2 (CHRM2), cannabinoid receptor 1 (CNR1), and lysophosphatidic acid receptor 1 (LPAR1) in the CRC tissues and the adjacent normal tissues.@*RESULTS@#A total of 14 differential circRNAs were identified, and 8 were found in CSCD; 34 miRNAs targeted by circRNAs were obtained. The PPI network was constructed, and the Top10 hub genes were identified, which were CHRM2, melanin concentrating hormone receptor 2 (MCHR2), G-protein gamma 3 subunit (GNG3), neuropeptide Y receptor Y1 (NPY1R), CNR1, LPAR1, ADCY5, adenylate cyclase 2 (ADCY2), gamma 7 (GNG7) and chemokine 12 (CXCL12), respectively. The expressions of Top 10 hub genes were also verified, and the results showed that the Top 10 hub genes were down-regulated in CRC; the constructed network diagram showed that hsa_circRNA_0065173 may regulate ADCY5, CHRM2, and Hsa-mir-450b by modulating hsa-mir-450b and hsa-mir-582. CNR1 and LPAR1 genes might serve as potentially relevant targets for the treatment of CRC. Real-time PCR results showed that the expression levels of hsa_circRNA_0065173, ADCY5, CHRM2, CNR1 and LPAR1 in the CRC tissues were significantly reduced compared with the adjacent normal tissues (all P<0.05); the expression levels of hsa-mir-450b and hsa-miR-582 were significantly increased (both P<0.05).@*CONCLUSIONS@#In this study, a potential circRNAs/miRNAs/mRNAs network is successfully constructed, which provides a new insight for CRC development mechanism through ceRNA mediated by circRNAs.

Bioinformatics screening and analysis of key differentially expressed genes characteristics in nonalcoholic fatty liver disease / 中华肝脏病杂志

Objective: To screen and analyze the key differentially expressed genes characteristics in nonalcoholic fatty liver disease (NAFLD) with bioinformatics method. Methods: NAFLD-related expression matrix GSE89632 was downloaded from the GEO database. Limma package was used to screen differentially expressed genes (DEGs) in healthy, steatosis (SS), and nonalcoholic steatohepatitis (NASH) samples. WGCNA was used to analyze the output gene module. The intersection of module genes and differential genes was used to determine the differential genes characteristic, and then GO function and KEGG signaling pathway enrichment analysis were performed. The protein-protein interaction network (PPI) was constructed using the online website STRING and Cytoscape software, and the key (Hub) genes were screened. Finally, R software was used to analyze the receiver operating characteristic curve (ROC) of the Hub gene. Results: 92 differentially expressed genes characteristic were obtained through screening, which were mainly enriched in inflammatory response-related functions of "lipopolysaccharide response and molecular response of bacterial origin", as well as cancer signaling pathways of "proteoglycan in cancer" and "T-cell leukemia virus infection-related". 10 hub genes (FOS, CXCL8, SERPINE1, CYR61, THBS1, FOSL1, CCL2, MYC, SOCS3 and ATF3) had good diagnostic value. Conclusion: The differentially expressed hub genes among the 10 NAFLD disease-related characteristics obtained with bioinformatics analysis may become a diagnostic and prognostic marker and potential therapeutic target for NAFLD. However, further basic and clinical studies are needed to validate.

Bioinformatics Analysis on Key Genes and Immune Infiltration of Osteosarcoma / 中国医学科学院学报

Objective To screen the potential key genes of osteosarcoma by bioinformatics methods and analyze their immune infiltration patterns. Methods The gene expression profiles GSE16088 and GSE12865 associated with osteosarcoma were obtained from the Gene Expression Omnibus(GEO),and the differentially expressed genes(DEGs)related to osteosarcoma were screened by bioinformatics tools.Gene Ontology(GO)annotation,Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment,and analysis of immune cell infiltration were then carried out for the DEGs.The potential Hub genes of osteosarcoma were identified by protein-protein interaction network,and the expression of Hub genes in osteosarcoma and normal tissue samples was verified via the Cancer Genome Atlas(TCGA). Results A total of 108 DEGs were screened out.GO annotation and KEGG pathway enrichment revealed that the DEGs were mainly involved in integrin binding,extracellular matrix (ECM) structural components,ECM receptor interactions,and phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt)signaling pathway.Macrophages were the predominant infiltrating immune cells in osteosarcoma.Secreted phosphoprotein 1(SPP1),matrix metallopeptidase 2(MMP2),lysyl oxidase(LOX),collagen type V alpha(II)chain(COL5A2),and melanoma cell adhesion molecule(MCAM)presented differential expression between osteosarcoma and normal tissue samples(all P<0.05). Conclusions SPP1,MMP2,LOX,COL5A2,and MCAM are all up-regulated in osteosarcoma,which may serve as potential biomarkers of osteosarcoma.Macrophages are the key infiltrating immune cells in osteosarcoma,which may provide new perspectives for the treatment of osteosarcoma.

Application of CRISPR-Cas9 to interrogate novel gene functions in cutaneous melanoma

Melanoma accounts for 3% of skin neoplasms and is the leading cause of death from skin disorders worldwide. The high mortality rate associated with this disease stems from the high capacity of melanoma patients to develop metastases and treatment relapse with inhibitors of the MAPK signaling pathway (such as BRAF inhibitors), commonly used in melanoma therapy. Thus, the investigation of genes involved in the mechanisms of melanoma development is essential for new and more effective therapeutic strategies. Hence, we describe in this thesis two projects involving the genes SIN3B and IRF4 as possible biomarkers for cutaneous melanoma. Initially, through bioinformatics analyses performed by our group, an upregulation of SIN3B was found in metastatic melanomas. This result together with the understanding of SIN3B role in regulating gene expression and oncogenic transformation, prompted us to describe in this thesis some mechanisms by which SIN3B may influence melanoma development. We then sought to characterize the gene function using SIN3B-deleted cells, generated by the CRISPR-Cas9 methodology. Initially, we observed increased SIN3B expression in BRAF-mutant metastatic melanomas, where we noted that the long splicing variant of the gene (NM_001297595.1) was effectively prevalent in melanomas. Subsequently, we designed gRNAs between the exons 2 and 3 of the human SIN3B gene and engineered three knockout clones and three control clones (containing empty lentiCRISPRv2 plasmid) from different melanoma cell lines (SKMEL28, A2058, and A375). Through functional analyses, it was observed that the absence of the gene did not interfere in the proliferation of tumor cells; however, it led to a decrease in invasive properties. These results were verified by Boyden chamber assays and transcriptome analysis (total RNA sequencing of deleted cells), where a decrease in migration and motility pathways was observed. Additionally, a screening of synthetically lethal genes with SIN3B was performed with a genome wide CRISPR library. These results showed that USP7 and STK11 genes, which belong to the FoxO signaling pathway, were essential in SIN3B-depleted melanoma cells. Finally, through a collaborative project with the Wellcome Trust Sanger Institute, previous large-scale sequencing analyses demonstrated that deletion of the IRF4 gene was lethal for melanoma cells. Accordingly, we performed IRF4 silencing in vitro and noticed that the lack of IRF4 promotes cell death and apoptosis, independently of MYC and MITF, known in the literature to be downstream targets of this gene. Therefore, these data suggest that IRF4 plays a vital role in melanoma cell survival. Taken together, both works herein described in this thesis demonstrate how CRISPR-Cas9 can be applied to study the functions and mechanisms of genes involved in melanoma progression, collectively helping in the development of more effective therapeutic strategies for this tumor

O melanoma representa 3% dos tipos de neoplasias cutâneas e é a maior causa das mortes por distúrbios de pele no mundo. A alta taxa de mortalidade associada à essa doença advém da alta capacidade de pacientes com melanoma desenvolverem metástases, e apresentarem recidiva após tratamento com inibidores da via de sinalização MAPK (como da proteína BRAF), comumente utilizados no tratamento de pacientes metastáticos. Assim, a investigação de genes envolvidos nos mecanismos de desenvolvimento do melanoma é primordial para novas estratégias terapêuticas mais efetivas. Dessa forma, descrevemos no presente trabalho dois projetos envolvendo os genes SIN3B e IRF4 como possíveis biomarcadores para melanoma cutâneo. Em análises prévias de bioinformática realizados pelo nosso grupo, SIN3B foi identificado tendo maior expressão em melanomas metastáticos. Além disso, diversos estudos mostraram que o gene está envolvido na regulação da expressão gênica e transformação oncogênica. Dessa forma, descrevemos nessa tese alguns mecanismos pelos quais SIN3B pode influenciar no desenvolvimento do melanoma, através da caracterização funcional de células SIN3B-deletadas pela metodologia CRISPR-Cas9. Inicialmente, observamos aumento na expressão de SIN3B em melanomas metastáticos BRAF-mutados, onde notamos que a variante de splicing longa do gene (NM_001297595.1), era efetivamente prevalente em melanomas. Assim, desenhamos sequências de RNA guias entre os éxons 2 e 3 do gene SIN3B humano e, obtivemos três clones knockout e outros três clones controle (contendo plasmídeo vazio) em diferentes linhagens de melanoma (SKMEL28, A2058 e A375), para caracterização funcional. Observou-se que a ausência do gene não interferiu na proliferação das células tumorais, contudo, acarretou na diminuição de processos invasivos. Esses resultados foram averiguados através de ensaios em câmara de Boyden e análises de transcriptoma (sequenciamento de RNA total das células deletadas), onde notou-se diminuição das vias de migração e motilidade. Adicionalmente, um rastreamento de genes sinteticamente letais com SIN3B foi realizado com uma biblioteca de CRISPR capaz de silenciar todo o genoma. Esses resultados mostraram que os genes USP7 e STK11, ambos pertencentes à via de sinalização de FoxO, são essenciais nas células SIN3B deletadas. Por fim, através de um projeto colaborativo com o Wellcome Trust Sanger Institute, análises prévias de sequenciamento de larga escala demonstraram que a deleção do gene IRF4 era letal para células de melanoma. Dessa forma, realizamos o silenciamento de IRF4 in vitro e notamos que a ausência do gene promove morte celular e apoptose, independentemente de MYC e MITF, conhecidos na literatura por serem alvos downstream do gene. Portanto, esses dados sugerem que IRF4 tem um papel importante na sobrevivência de células de melanoma. Em conjunto, ambos trabalhos descritos nessa tese, demonstram como a metodologia CRISPR-Cas9 pode auxiliar no entendimento de processos importantes para a malignidade do melanoma e contribuir para estratégias terapêuticas mais efetivas para esse tumor

Bioinformatics analysis of differently expressed genes in osteoblastic sarcoma and screening of key genes / 中华肿瘤杂志

Objective: To screen the different expressed genes between osteosarcoma and normal osteoblasts, and find the key genes for the occurrence and development of osteosarcoma. Methods: The gene expression dataset GSE33382 of normal osteoblasts and osteosarcoma was obtained from Gene Expression Omnibus (GEO) database. The different expressed genes between normal osteoblasts and osteosarcoma were screened by limma package of R language, and the different expressed genes were analyzed by Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The protein interaction network was constructed by the String database, and the network modules in the interaction network were screened by the molecular complex detection (MCODE) plug-in of Cytoscape software. The different expressed genes contained in the first three main modules screened by MCODE were analyzed by gene ontology (GO) using the BiNGO module of Cytoscape software. The MCC algorithm was used to screen the top 10 key genes in the protein interaction network. The gene expression and survival dataset GSE39055 of osteosarcoma was obtained from GEO database, and the survival analysis was performed by Kaplan-Meier method. The data of 48 patients with osteosarcoma treated in the First Affiliated Hospital of Fujian Medical University from January 2005 to December 2015 were selected for verification. The expression of STC2 protein in osteosarcoma was detected by immunohistochemical method, and the survival analysis was carried out combined with the clinical data of the patients. Results: A total of 874 different expressed genes were identified from GSE33382 dataset, including 402 down-regulated genes and 472 up-regulated genes. KEGG enrichment analysis showed that different expressed genes were mainly related to p53 signal pathway, glutathione metabolism, extracellular matrix receptor interaction, cell adhesion molecules, folate tolerance, and cell senescence. The top 10 key genes in the interaction network were GAS6, IL6, RCN1, MXRA8, STC2, EVA1A, PNPLA2, CYR61, SPARCL1 and FSTL3. STC2 was related to the survival rate of patients with osteosarcoma (P<0.05). The results showed that the expression of STC2 protein was related to tumor size and Enneking stage in 48 cases of osteosarcoma. The median survival time of 25 cases with STC2 high expression was 21.4 months, and that of 23 cases with STC2 low expression was 65.4 months. The survival rate of patients with high expression of STC2 was lower than that of patients with low expression of STC2 (P<0.05). Conclusions: Bioinformatics analysis can effectively screen the different expressed genes between osteosarcoma and normal osteoblasts. STC2 is one of the important predictors for the prognosis of osteosarcoma.

Screening of differentially expressed genes for colorectal cancer and prediction of potential traditional Chinese medicine: based on bioinformatics / 中国中药杂志

This study screened and analyzed the differentially expressed genes(DEGs) between colorectal cancer(CRC) tissues and normal tissues with bioinformatics techniques to predict biomarkers and Chinese medicinals for the diagnosis and treatment of CRC. The microarray data sets GSE21815, GSE106582, and GSE41657 were downloaded from the Gene Expression Omnibus(GEO), and the DEGs were screened by GEO2 R, followed by the Gene Ontology(GO) tern enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis of the DEGs based on DAVID. The protein-protein interaction network was constructed by STRING, and MCODE and Cytohubba plug-ins were used to screen the significant modules and hub genes in the network. UCSC, cBioPortal, and Oncomine were employed for hierarchical clustering, survival analysis, Oncomine analysis, and correlation analysis of clinical data. Coremine Medical was applied to predict the Chinese medicinals acting on hub genes. A total of 284 DEGs were screened out, with 146 up-regulated and 138 down-regulated. The up-regulated genes were mainly involved in cell cycle, NLRs pathway, and TNF signaling pathway, and the down-regulated genes were related to mineral absorption, nitrogen metabolism, and bicarbonate reabsorption in proximal tubules. The 15 hub genes were CDK1, CDC20, AURKA, MELK, TOP2 A, PTTG1, BUB1, CDCA5, CDC45, TPX2, NEK2, CEP55, CENPN, TRIP13, and GINS2, among which CDK1 and CDC20 were regarded as core genes. The high expression of CDK1 and CDC20 suggested poor prognosis, and they significantly expressed in many cancers, especially breast cancer, lung cancer, and CRC. The expression of CDK1 and CDC20 was correlated with gender, tumor type, TNM stage, and KRAS gene mutation. The potential effective medicinals against CRC were Scutellariae Radix, Scutellariae Barbatae Herba, Arnebiae Radix, etc. The significant expression of CDK1 and CDC20 can help distinguish tumor tissues from normal tissues, and is related to survival prognosis. Thus, the two can be used as biomarkers for the diagnosis and treatment of CRC. This study provides a reference for related drug development.

Implementación del aula invertida en la carrera Ingeniería en Bioinformática: Estudio de caso / Implementation of the flipped classroom in the Engineering in Bioinformatics programs: Case study

El presente trabajo aborda una experiencia en la implementación del aula invertida. Se emplea como estrategia de investigación un estudio de caso efectuado en la Universidad de las Ciencias Informáticas (UCI), de 16 estudiantes de Ingeniería en Bioinformática. En los resultados obtenidos, se confirma la relación entre la interactividad, motivación, trabajo y aprendizaje colaborativo y la evaluación formativa; además, que el diseño de actividades de aprendizaje y su evaluación en el modelo de aula invertida con el desarrollo de estrategias de estudiantes prosumidores de videos contribuye a que estos mejoren sus habilidades comunicativas e informáticas. Se concluye que la evaluación debe estimular el aprendizaje colaborativo, la interactividad, la tolerancia, la motivación y la responsabilidad en los entornos virtuales(AU)

This paper presents an experience in the flipped classroom teaching. A case study conducted at the University of Informatics Science (UCI, acronym in Spanish) with 16 Bioinformatics Engineering students. In the results obtained, the relationship between interactivity, motivation, work and collaborative learning and formative evaluation is confirmed; in addition, the design of learning activities and assessment in the flipped classroom model with the development of strategies for video prosumers students helps them to improve their communication and computer skills. It is concluded that evaluation should stimulate collaborative learning, interactivity, tolerance, motivation and responsibility in virtual environments(AU)

3-State Protein Secondary Structure Prediction based on SCOPe Classes

Abstract Improving the accuracy of protein secondary structure prediction has been an important task in bioinformatics since it is not only the starting point in obtaining tertiary structure in hierarchical modeling but also enhances sequence analysis and sequence-structure threading to help determine structure and function. Herein we present a model based on DSPRED classifier, a hybrid method composed of dynamic Bayesian networks and a support vector machine to predict 3-state secondary structure information of proteins. We used the SCOPe (Structural Classification of Proteins-extended) database to train and test the model. The results show that DSPRED reached a Q3 accuracy rate of 82.36% when trained and tested using proteins from all SCOPe classes. We compared our method with the popular PSIPRED on the SCOPe test datasets and found that our method outperformed PSIPRED.

Computational prediction and characterisation of miRNAs and their pathway genes in human schistosomiasis caused by Schistosoma haematobium

BACKGROUND Key genes control the infectivity of the Schistosoma haematobium causing schistosomiasis. A method for understanding the regulation of these genes might help in developing new disease strategies to control schistosomiasis, such as the silencing mediated by microRNAs (miRNAs). The miRNAs have been studied in schistosome species and they play important roles in the post-transcriptional regulation of genes, and in parasite-host interactions. However, genome-wide identification and characterisation of novel miRNAs and their pathway genes and their gene expression have not been explored deeply in the genome and transcriptome of S. haematobium. OBJECTIVES Identify and characterise mature and precursor miRNAs and their pathway genes in the S. haematobium genome. METHODS Computational prediction and characterisation of miRNAs and genes involved in miRNA pathway from S. haematobium genome on SchistoDB. Conserved domain analysis was performed using PFAM and CDD databases. A robust algorithm was applied to identify mature miRNAs and their precursors. The characterisation of the precursor miRNAs was performed using RNAfold, RNAalifold and Perl scripts. FINDINGS We identified and characterised 14 putative proteins involved in miRNA pathway including ARGONAUTE and DICER in S. haematobium. Besides that, 149 mature miRNAs and 131 precursor miRNAs were identified in the genome including novel miRNAs. MAIN CONCLUSIONS miRNA pathway occurs in the S. haematobium, including endogenous miRNAs and miRNA pathway components, suggesting a role of this type of non-coding RNAs in gene regulation in the parasite. The results found in this work will open up a new avenue for studying miRNAs in the S. haematobium biology in helping to understand the mechanism of gene silencing in the human parasite Schistosome.

A evolução do diagnóstico da doença mista do tecido conjuntivo / Evolution of the diagnosis of mixed connective tissue disease

A Doença Mista do Tecido Conjuntivo (DMTC) é uma doença autoimune crônica composta por um misto de quatro doenças: Lúpus Eritematoso Sistêmico, Esclerose Sistêmica, Dermatomiosite/Polimiosite e Artrite Reumatoide. Por se tratar de uma combinação de doenças autoimunes o diagnóstico é bastante complexo. Atualmente existem quatro combinações sugeridas por diferentes autores para a realização de um diagnóstico preciso, são eles: Kasukawa, Alarcón-Segovia e Villareal, Kahn e Appeboom e Sharp. Desde a sua descoberta em 1972 por Sharp, passaram-se 46 anos e desta forma o objetivo desta revisão foi verificar a evolução do diagnóstico da DMTC desde a sua descoberta até a atualidade. Para isso utilizou-se sites de busca PUBMED e SCIELO. Por se tratar de uma doença autoimune que leva ao desenvolvimento de um quadro inflamatório crônico utilizou-se a ferramenta STRING que permite a análise da interação de proteínas. Até a presente data, não existe um consenso de qual critério deve ser usado para o diagnóstico correto e eficiente desta doença. A baixa relação de interações observadas a partir da ferramenta STRING demonstra que ainda não existem dados suficientes na literatura para que a ligação entre proteínas marcadoras e a DTMC possa ser estabelecida. (AU)

Mixed connective tissue disease (MCTD) is a chronic autoimmune disorder consisting of a mixture of four diseases: systemic lupus erythematosus, systemic sclerosis, dermatomyositis/polymyositis, and rheumatoid arthritis. Because it is a combination of different autoimmune disorders its diagnosis is quite complex. Currently there are four combinations suggested by the following authors to establish an accurate diagnosis: Kasukawa, Alarcón-Segovia & Villareal, Kahn, and Appeboom & Sharp. It has been 46 years since Sharp reported the disease in 1972 and thus the purpose of this review was to investigate the evolution of the diagnosis of MCTD since then. PubMed and SciELO databases were used for this investigation. Because MCTD is an autoimmune disease that leads to the development of a chronic inflammatory condition, the STRING tool was used to allow the analysis of protein interaction. To date, there is no consensus as to what criterion should be used for a correct and efficient diagnosis of this disease. The low ratio of interactions observed from the STRING tool demonstrates that there is not yet enough data in the literature for establishing the binding between marker proteins and MCTD. (AU)

In Silico Characterization of Meloidogyne Genus Nematode Cellulose Binding Proteins

Abstract Root-knot nematodes are a group of endoparasites species that induce the formation of giant cells in the hosts, by which they guarantee their feeding and development. Meloidogyne species infect over 2000 plant species, and are highly destructive, causing damage to many crops around the world. M. enterolobii is considered the most aggressive species in tropical regions, such as Africa and South America. Phytonematodes are able to penetrate and migrate within plant tissues, establishing a sophisticated interaction with their hosts through parasitism factors, which include a series of cell wall degradation enzymes and plant cell modification. Among the parasitism factors documented in the M. enterolobii species, cellulose binding protein (CBP), a nematode excretion protein that appears to be associated with the breakdown of cellulose present in the plant cell wall. In silico analysis can be of great importance for the identification, structural and functional characterization of genomic sequences, besides making possible the prediction of structures and functions of proteins. The present work characterized 12 sequences of the CBP protein of nematodes of the genus Meloidogyne present in genomic databases. The results showed that all CBP sequences had signal peptide and that, after their removal, they had an isoelectric point that characterized them as unstable in an acid medium. The values of the average hydrophilicity demonstrated the hydrophilic character of the analyzed sequences. Phylogenetic analyzes were also consistent with the taxonomic classification of the nematode species of this study. Five motifs were identified, which are present in all sequences analyzed. These results may provide theoretical grounds for future studies of plant resistance to nematode infection.

Comparative Analysis of C-repeat Binding Factors (CBFs) in Tomato and Arabidopsis

Abstract Cold stress is one of the limiting factors of plant production that plants use different mechanisms for cold tolerance. CBF genes play critical role to regulate the cold responsive genes. To better understand of CBF gene functions, the tomato-CBFs and Arabidopsis-CBFs were evaluated using bioinformatics tools, and finally the expression patterns of SlCBF1 gene were analyzed under 10 and 4˚C in two contrasting tomato species (Solanum lycopersicum and S. habrochaites). The different cis regulatory elements were observed in promoter region of SlCBF1 and AtCBF1 genes, and ICE1, COR and HOS1 proteins exhibited high interaction with CBFs. The results of Real time PCR of SlCBF1 exhibited that under 10 and 4 ˚C, SlCBF1 was down regulated in cold sensitive tomato genotype while it was slightly up-regulated in cold tolerant genotype at 4 ˚C. The results showed that the SlCBF1 and AtCBF1 genes have differential expression in cold stress.

Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle

Gene networks have been broadly used to predict gene functions based on guilt by association (GBA) principle. Thus, in order to better understand the molecular mechanisms of esophageal squamous cell carcinoma (ESCC), our study was designed to use a network-based GBA method to identify the optimal gene functions for ESCC. To identify genomic bio-signatures for ESCC, microarray data of GSE20347 were first downloaded from a public functional genomics data repository of Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) between ESCC patients and controls were identified using the LIMMA method. Afterwards, construction of differential co-expression network (DCN) was performed relying on DEGs, followed by gene ontology (GO) enrichment analysis based on a known confirmed database and DEGs. Eventually, the optimal gene functions were predicted using GBA algorithm based on the area under the curve (AUC) for each GO term. Overall, 43 DEGs and 67 GO terms were gained for subsequent analysis. GBA predictions demonstrated that 13 GO functions with AUC>0.7 had a good classification ability. Significantly, 6 out of 13 GO terms yielded AUC>0.8, which were determined as the optimal gene functions. Interestingly, there were two GO categories with AUC>0.9, which included cell cycle checkpoint (AUC=0.91648), and mitotic sister chromatid segregation (AUC=0.91597). Our findings highlight the clinical implications of cell cycle checkpoint and mitotic sister chromatid segregation in ESCC progression and provide the molecular foundation for developing therapeutic targets.

Bioinformatics analysis of microarray data to reveal the pathogenesis of diffuse intrinsic pontine glioma

BACKGROUND: Diffuse intrinsic pontine glioma (DIPG) is the main cause of pediatric brain tumor death. This study was designed to identify key genes associated with DIPG. METHODS: The gene expression profile GSE50021, which consisted of 35 pediatric DIPG samples and 10 normal brain samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by limma package. Functional and pathway enrichment analyses were performed by the DAVID tool. Protein-protein interaction (PPI) network, and transcription factor (TF)-microRNA (miRNA)-target gene network were constructed using Cytoscape. Moreover, the expression levels of several genes were validated in human glioma cell line U251 and normal glia HEB cells through real-time polymerase chain reaction (PCR). RESULTS: A total of 378 DEGs were screened (74 up-regulated and 304 down-regulated genes). In the PPI network, GRM1, HTR2A, GRM7 and GRM2 had higher degrees. Besides, GRM1 and HTR2A were significantly enriched in the neuroactive ligand-receptor interaction pathway, and calcium signaling pathway. In addition, TFAP2C was a significant down-regulated functional gene and hsa-miR-26b-5p had a higher degree in the TF-miRNA-target gene network. PCR analysis revealed that GRM7 and HTR2A were significantly downregulated while TFAP2C was upregulated in U251 cells compared with that in HEB cells (p < 0.001). GRM2 was not detected in cells. CONCLUSIONS: GRM1 and HTR2A might function in DIPG through the neuroactive ligand-receptor interaction pathway and the calcium signaling pathway. Furthermore, the TFAP2C and hsa-miR-26b-5p might play important roles in the development and progression mechanisms of DIPG.

Improved reverse transcription-polymerase chain reaction assay for the detection of flaviviruses with semi-nested primers for discrimination between dengue virus serotypes and Zika virus

BACKGROUND The genus Flavivirus includes a variety of medically important viruses, including dengue virus (DENV) and Zika virus (ZIKV), which are most prevalent in Brazil. Because the clinical profile of patients affected by different DENV serotypes or ZIKV may be similar, the development of new methods that facilitate a rapid and accurate diagnosis is crucial. OBJECTIVES The current study aimed to develop an improved reverse transcription-polymerase chain reaction (RT-PCR) protocol for universal detection of flaviviruses by using semi-nested primers that discriminate between DENV serotypes and ZIKV. METHODS The bioinformatics workflow adopted for primer design included: (1) alignment of 1,442 flavivirus genome sequences, (2) characterisation of 27 conserved regions, (3) generation of a primer set comprising 77 universal primers, and (4) selection of primer pairs with greatest coverage and specificity. Following primer design, the reaction was validated in vitro. The same approach was applied to the design of primers specific for DENV and ZIKV, using a species-specific sequence database. FINDINGS The new assay amplified an 800-806 nt variable region of the NS5 gene and allowed discrimination of virtually all flavivirus species using reference-sequence comparison. The 800-806 nt fragment was validated as a template for a semi-nested multiplex PCR using five additional primers for the detection of DENV and ZIKV. These primers were designed to generate amplicons of different sizes, allowing differentiation of the four serotypes of DENV, and ZIKV using agarose gel electrophoresis. MAIN CONCLUSIONS The bioinformatics pipeline allowed efficient primer design, making it possible to identify the best targets within the coding region of the NS5 protein. The multiplex system proved effective in differentiation of DENV1-4 and ZIKV on a 2% agarose gel. The possibility of discriminating DENV serotypes and ZIKV in the same reaction provided a faster result consuming less sample. In addition, this simplified approach ensured the reduction of the cost per analysis.