Biological Ontology Research Articles

Study on the Prognostic Values of Dynactin Genes in Low-Grade Glioma.

Objective:This present study aims to investigate the potential prognostic values of dynactin genes (DCTN) for predicting the overall survival (OS) in low-grade glioma (LGG) patients.Methods:The DCTN mRNA expression data were downloaded from The Cancer Genome Atlas database containing 518 patients with LGG. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses for DCTN genes were performed by using Database for Annotation, Visualization, and Integrated Discovery platform, and their enrichment results were verified by using the Biological Networks Gene Ontology tool. Next, the correlations between DCTN genes and LGG were identified by Pearson correlation coefficient analysis. The OS was estimated by Kaplan-Meier survival analysis. The cBio Cancer Genomics Portal was used to analyze the mutations of DCTN genes and their effects on the prognosis of LGG. The correlation between the abundance of immune infiltration and tumor purity of DCTN genes were predicted by The Tumor Immune Estimation Resource.Results:Our research showed that the mRNA expression of DCTN4 in tumor tissues was much higher (P < 0.01) than that in normal tissues. Meanwhile, there was a certain correlation between the DCTN genes. Survival analysis showed that the high expression of DCTN1, DCTN3, DCTN4, DCTN6, and their co-expression were significantly correlated with favorable OS in LGG patients (P < 0.05). In DCTN2, a high mutation rate was observed. Further research showed that the genetic alteration in DCTN genes was related to a poor OS and progression-free survival of LGG patients. The expression of DCTN genes had a certain correlation with immune infiltrating cells.Conclusion:Our study showed that the high expressions of DCTN1, DCTN3, DCTN4, and DCTN6 were associated with a favorable OS of LGG patients, indicating that these DCTN genes are potential biomarkers for evaluating the prognosis of LGG patients.

TWO PHASES IN THE DEVELOPMENT OF HEDWIG CONRAD-MARTIUS’ ONTOLOGICAL INVESTIGATION ON HUMAN BEING AND LIVING THINGS

Interest in the ontological constitution of living beings (with particular reference to the human being) characterizes the whole development of Hedwig Conrad-Martius’ philosophy. Several works written by the philosopher over the years deal with both the phenomenological description and the ontological foundation of the difference between plants, animals, and humans. Specifically, the ontological structure of the human being is investigated as a layered structure which presupposes those of plants and animals while overcoming them in a more complex and spiritual unity, on which human freedom and human knowledge depend. Although this topic maintains a crucial role in Conrad-Martius’ thought, the way the philosopher addresses it and the theoretical results of her phenomenological-ontological inquiry about it do not remain unchanged. Indeed, the ontological structure at the basis of phenomenal differences as well as the metaphysical foundations of this structure change over the decades. This paper aims at distinguishing between two phases, characterized by different ontological categories, through which Conrad-Martius’ anthropology and biological ontology develop. In the first phase, at the beginning of the twenties (precisely in her work Metaphysical Dialogues), the essential differences between plants, animals, and humans are explained with reference to a vital origin preceding the constitution of reality; to describe it Conrad-Martius employs terms such as “abyss” and “under-earthly realm.” In the second phase, exemplified by some writings published in the forties and the fifties, the reference to such a dimension disappears and the eidetic variety within the living world, including human specificity, is exclusively traced back to the finalistic substantiation of essences in the natural beings.

High levels of glucose alter Physcomitrella patens metabolism and trigger a differential proteomic response.

Sugars act not only as substrates for plant metabolism, but also have a pivotal role in signaling pathways. Glucose signaling has been widely studied in the vascular plant Arabidopsis thaliana, but it has remained unexplored in non-vascular species such as Physcomitrella patens. To investigate P. patens response to high glucose treatment, we explored the dynamic changes in metabolism and protein population by applying a metabolomic fingerprint analysis (DIESI-MS), carbohydrate and chlorophyll quantification, Fv/Fm determination and label-free untargeted proteomics. Glucose feeding causes specific changes in P. patens metabolomic fingerprint, carbohydrate contents and protein accumulation, which is clearly different from those of osmotically induced responses. The maximal rate of PSII was not affected although chlorophyll decreased in both treatments. The biological process, cellular component, and molecular function gene ontology (GO) classifications of the differentially expressed proteins indicate the translation process is the most represented category in response to glucose, followed by photosynthesis, cellular response to oxidative stress and protein refolding. Importantly, although several proteins have high fold changes, these proteins have no predicted identity. The most significant discovery of our study at the proteome level is that high glucose increase abundance of proteins related to the translation process, which was not previously evidenced in non-vascular plants, indicating that regulation by glucose at the translational level is a partially conserved response in both plant lineages. To our knowledge, this is the first time that metabolome fingerprint and proteomic analyses are performed after a high sugar treatment in non-vascular plants. These findings unravel evolutionarily shared and differential responses between vascular and non-vascular plants.

A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins.

Physical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extent these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.

Transcriptome analysis for molecular landscaping of genes controlling diterpene andrographolide biosynthesis in Andrographis paniculata (Burm . f.) Nees.

Kalmegh [Andrographis paniculata (Burm. f.) Nees.] is one of the essential medicinal plants due to an important terpenoid, i.e. andrographolide possesses immense therapeutic and pharmacological uses. The experiment was performed to elucidate the expression of candidate genes associated with andrographolide biosynthesis. Based on results obtained in chromatography for andrographolide content analysis of six genotypes, two contrast genotypes, i.e. IC-520361 (maximum andrographolide content—2.33%) and Anand Local (lowest andrographolide content—1.01%) were selected for the transcriptome analysis. A total of 1.04 Gb of raw data were produced using MiSeq Illumina platform, in which IC 520361 generated 645 million base pairs sequence along with 4,524,251 raw reads and Anand Local produced 419 million base pairs sequence along with 3,021,316 raw reads. The combined assembly of high quality reads generated for both the samples had 33,247,454 bp of total assembled bases and 38,292 of transcripts. The GC percent of assembled transcripts was 44.79%, an average read length was 800 bp and N50 value was 1186 bp. Species-specific distribution using BLAST X (Nr), showed the highest Blast hits with Sesamum indicum. Out of 23,346 transcripts, 87% of transcripts annotated in UniProt KB (Universal Protein Resource KnowledgeBase) database and only 0.21% of transcripts were annotated in TAIR (The Arabidopsis Information Resources). Biological processes gene ontology classified based on Blast2GO showed, out of 6853 transcripts, 1370 of transcripts were represented by terpenoid biosynthetic pathway, which involved in secondary metabolite andrographolide biosynthesis. The heat map showed 1016 transcripts were differentially expressed between two kalmegh genotypes, in which nine important differentially expressed transcripts related to MEP (2C methyl-d-erythritol 4-phosphate) and MVA (Mevalonic acid) andrographolide biosynthesis pathways such as, geranyl diphosphate synthase small subunit, Isopentenyl-diphosphate delta-isomerase i-like, 4, 13-hydroxy-3-methylglutaryl-coenzyme a reductase etc. were upregulated in IC 520361 as compared to Anand Local, which were validated through RT-qPCR. The highest expression of gene 13-hydroxy-3-methylglutaryl-coenzyme a reductase (HMGR) was reported, which is responsible for accumulation of andrographolide in leaf. This comparative transcriptome analysis confirmed the expression level of genes were higher in accession IC 520361 as compare to Anand Local related to andrographolide biosynthesis pathways i.e. MEP and MVA. These up-regulated genes could be over-expressed to enhance the andrographolide content using genetic engineering of these metabolic pathways. It will also give an idea to the breeder for development of molecular markers for direct screening of the genotypes.Electronic supplementary materialThe online version of this article (10.1007/s13205-020-02511-y) contains supplementary material, which is available to authorized users.

PK-DB: pharmacokinetics database for individualized and stratified computational modeling.

A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.

Correlations Between Phenotypes and Biological Process Ontologies in Monogenic Human Diseases.

A substantial body of research is focused to improve the understanding of the relationship between genotypes and phenotypes. Genotype-phenotype studies have shown promise in improving disease diagnosis in humans and identification of specific clinical phenotypes may be helpful in developing more effective therapeutic and diagnostic strategies. To expand on the existing paradigm of evaluating genotypes and phenotypes, we present an investigation of the correlation between biological processes as represented by genomic information and phenotypes in human disease. We focus on monogenic diseases and link biological process and phenotype utilizing information from the Online Mendelian Inheritance in Man, the Gene Ontology, and the Human Phenotype Ontology comprehensive genomic, phenotypic, and disease information resources. Our study uncovers 4661 statistically significant associations and identifies novel correlations between biological processes and phenotypes. We find new relationships between unique phenotype-genotype pairs related to cardiovascular diseases and hypertelorism, which suggests that differences between certain phenotype-genotype association may be the key to the divergence of corresponding phenotypes. Although the application of correlating genotype, phenotype, and biological processes may help to guide diagnosis and treatment of diseases, further investigation and more specific gene ontology descriptions are still required to elucidate mechanisms of action.

A representation model for biological entities by fusing structured axioms with unstructured texts.

Structured semantic resources, for example, biological knowledge bases and ontologies, formally define biological concepts, entities and their semantic relationships, manifested as structured axioms and unstructured texts (e.g. textual definitions). The resources contain accurate expressions of biological reality and have been used by machine-learning models to assist intelligent applications like knowledge discovery. The current methods use both the axioms and definitions as plain texts in representation learning (RL). However, since the axioms are machine-readable while the natural language is human-understandable, difference in meaning of token and structure impedes the representations to encode desirable biological knowledge. We propose ERBK, a RL model of bio-entities. Instead of using the axioms and definitions as a textual corpus, our method uses knowledge graph embedding method and deep convolutional neural models to encode the axioms and definitions respectively. The representations could not only encode more underlying biological knowledge but also be further applied to zero-shot circumstance where existing approaches fall short. Experimental evaluations show that ERBK outperforms the existing methods for predicting protein-protein interactions and gene-disease associations. Moreover, it shows that ERBK still maintains promising performance under the zero-shot circumstance. We believe the representations and the method have certain generality and could extend to other types of bio-relation. The source code is available at the gitlab repository https://gitlab.com/BioAI/erbk. Supplementary data are available at Bioinformatics online.

The Standards behind the Scenes: Explaining data from the Plazi workflow

As part of the CETAF COVID19 task force, Plazi liberated taxonomic treatments, figures, observation records, biotic interactions, taxonomic names, and collection and specimen codes involving bats and viruses from scholarly publications with the intention to create open access, findable, accessible, interoperable and reusable data (FAIR). The data is accessible via TreatmentBank and the Biodiversity Literature Repository (BLR) and it is continually harvested and reused by the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI). This data was processed, enhanced and liberated by the Plazi workflow, which involves a dedicated infrastructure including a desktop application (GoldenGate Imagine) that converts portable document format files (PDF) to a dedicated open compressed file format (Image Markup File (IMF)) that is responsible for the data enhancement. To enhance the data contained in the publications, including the biological interactions, a series of standards and vocabularies are used. To the exception of TaxPub, which is a taxonomic specific extension of the U.S. National Center for Biotechnology Information's (NCBI) Journal Article Tag Suite (JATS), all other used vocabulary were previously proposed. This goes along with Plazi’s mission to reuse standards unless they are not available. The following standards of vocabularies are used: Metadata Object Description Schema (MODS) to model article metadata information on Plazi’s XMLs; Darwin Core for taxonomic ranks and materials citation related data; Open Biological and Biomedical Ontology (OBO); Relations Ontology for biological interactions between organisms. The latter two are also used in the custom metadata in the Biodiversity Literature Repository at Zenodo. In this presentation we will provide an overview of the different types of data followed by the standards or vocabularies applied for every and each one of them and their parts. The goal is to provide the context on how the data liberated by Plazi is described, which is extensively reused by third-party applications such as GBIF or GloBI. The use of the standards allows fully automated, daily data ingests by GBIF.

Bio-activating ultrafine grain titanium: RNA sequencing reveals enhanced mechano-activation of osteoconduction on nanostructured substrates.

Titanium is essentially absent from biological systems yet reliably integrates into bone. To achieve osseointegration, titanium must activate biological processes without entering cells, defining it as a bio-activating material. Nanostructuring bulk titanium reduces grain size, increases strength, and improves other quantifiable physical properties, including cytocompatibility. The biological processes activated by increasing grain boundary availability were detected with total RNA-sequencing in mouse pre-osteoblasts grown for 72 hours on nanometrically smooth substrates of either coarse grain or nanostructured ultrafine grain titanium. The average grain boundary length under cells on the conventional coarse grain substrates is 273.0 μm, compared to 70,881.5 μm for cells adhered to the nanostructured ultrafine grain substrates; a 260-fold difference. Cells on both substrates exhibit similar expression profiles for genes whose products are critical for mechanosensation and transduction of cues that trigger osteoconduction. Biological process Gene Ontology term enrichment analysis of differentially expressed genes reveals that cell cycle, chromatin modification, telomere maintenance, and RNA metabolism processes are upregulated on ultrafine grain titanium. Processes related to immune response, including apoptosis, are downregulated. Tumor-suppressor genes are upregulated while tumor-promoting genes are downregulated. Upregulation of genes involved in chromatin remodeling and downregulation of genes under the control of the peripheral circadian clock implicate both processes in the transduction of mechanosensory information. Non-coding RNAs may also play a role in the response. Merging transcriptomics with well-established mechanobiology principles generates a unified model to explain the bio-activating properties of titanium. The modulation of processes is accomplished through chromatin remodeling in which the nucleus responds like a rheostat to grain boundary concentration. This convergence of biological and materials science reveals a pathway toward understanding the biotic-abiotic interface and will inform the development of effective bio-activating and bio-inactivating materials.

Bioinformatics analysis reveals novel hub gene pathways associated with IgA nephropathy

BackgroundImmunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism.MethodsBased on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein–protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN.ResultsIn total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Ontology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN.ConclusionsWe identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

Genome-wide association study of nephrolithiasis in an Eastern European population.

Nephrolithiasis is a urological pathology that occurs at high rates and carries a great burden in terms of costs. The probability of recurrence is significant, necessitating improvements in prophylaxis and understanding of the disease mechanism. Despite the high heritability of this disease, only five genome-wide association studies (GWAS) of nephrolithiasis have been published. We selected 335 unrelated confirmed nephrolithiasis cases from two major sample collection projects (blood and buccal swabs) in Romania. DNA was extracted from whole blood and buccal swabs at deCODE Genetics (Reykjavik, Iceland) and genotyped. Single-nucleotide polymorphisms identified from this GWAS implicated biological pathways and gene ontologies involving solute transport, renal physiology, and calcium homeostasis. Three loci especially emerged as candidates with a highly significant association with nephrolithiasis: RS10917682 in Regulator of G protein signaling 5, which has crucial roles in mRNA regulation and has been linked to renal cell carcinoma; RS1118528 in Solute carrier family 25 member 24, which encodes a mitochondrial ATP-Mg/phosphate carrier protein that likely influences a variety of important cellular pathways; and the TOX2-associated locus rs4437026, because TOX2 is upregulated in several tumor types and linked to tumor progression. This study is the largest kidney stone-related GWAS reported in an Eastern European population and the first GWAS performed in a Romanian population to investigate the genetic risk factors for nephrolithiasis. We identified several loci that warrant further investigation for a better understanding of this highly heritable condition.

Identification of potential prognostic small nucleolar RNA biomarkers for predicting overall survival in patients with sarcoma.

ObjectiveThe main purpose of the present study is to screen prognostic small nucleolar RNA (snoRNA) markers using the RNA‐sequencing (RNA‐seq) dataset of The Cancer Genome Atlas (TCGA) sarcoma cohort.MethodsThe sarcoma RNA‐seq dataset comes from the TCGA cohort. A total of 257 sarcoma patients were included into the prognostic analysis. Multiple bioinformatics analysis methods for functional annotation of snoRNAs and screening of targeted drugs, including biological network gene ontology tool, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and connectivity map (CMap) are used.ResultsWe had identified 15 snoRNAs that were significantly related to the prognosis of sarcoma and constructed a prognostic signature based on four prognostic snoRNA (U3, SNORA73B, SNORD46, and SNORA26) expression values. Functional annotation of these four snoRNAs by their co‐expression genes suggests that some of them were closely related to cell cycle‐related biological processes and tumor‐related signaling pathways, such as Wnt, mitogen‐activated protein kinase, target of rapamycin, and nuclear factor‐kappa B signaling pathway. GSEA of the risk score suggests that high risk score phenotype was significantly enriched in cell cycle‐related biological processes, protein SUMOylation, DNA replication, p53 binding, regulation of DNA repair, and DNA methylation, as well as Myc, Wnt, RB1, E2F, and TEL pathways. Then we also used the CMap online tool to screen five targeted drugs (rilmenidine, pizotifen, amiprilose, quipazine, and cinchonidine) for this risk score model in sarcoma.ConclusionOur study have identified 15 snoRNAs that may be serve as novel prognostic biomarkers for sarcoma, and constructed a prognostic signature based on four prognostic snoRNA expression values.

Protein Expression Analysis of an In Vitro Murine Model of Prostate Cancer Progression: Towards Identification of High-Potential Therapeutic Targets.

Background: Prostate cancer (PC) is the most frequently diagnosed cancer among men worldwide. The poor prognosis of PC is largely due to late diagnosis of the disease when it has progressed to advanced stages marked by androgen-independence. We interrogated proteomic signatures that embody the transition of PC from an androgen-dependent (AD) to an androgen-independent (AI) state. Methods: We have previously established AD and AI murine PC cell lines, PLum-AD and PLum-AI, respectively, which recapitulate primary and progressive PC at phenotypic and subcellular levels. We statistically surveyed global protein expression profiles in these cell lines. Differential profiles were functionally interrogated by pathways and protein–protein interaction network analyses. Results: Protein expression pattern analysis revealed a total of 683 proteins, among which 99 were significantly differentially altered in PLum-AI cells as compared to PLum-AD cells (45 increased and 54 decreased). Principal component analysis (PCA) revealed that the two different cell lines clearly separated apart, indicating a significant proteome expression difference between them. Four of the proteins (vimentin, catalase, EpCAM, and caspase 3) that were differentially expressed in PLum-AI cells compared to PLum-AD cells were subjected to biochemical validation by Western blotting. Biological process gene ontology (GO) analysis of the differentially expressed proteins demonstrated enrichment of biological functions and pathways in PLum-AI cells that are central to PI3 kinase and androgen receptor pathways. Besides, other relevant biological processes that are enriched in PLum-AI cells included cell adhesion and cell migration processes, cell and DNA damage, apoptosis, and cell cycle regulation. Conclusions: Our protein expression analysis of a murine in vitro model of PC progression identified differential protein spots that denote this progression and that comprise high-potential targets for early treatment of PC with a personalized patient-specific approach. Efforts are underway to functionally assess the potential roles of these proteins as therapeutic targets for PC progression.

Molecular Mechanisms Involved in Neural Substructure Development during Phosphodiesterase Inhibitor Treatment of Mesenchymal Stem Cells.

Stem cells are highly important in biology due to their unique innate ability to self-renew and differentiate into other specialised cells. In a neurological context, treating major injuries such as traumatic brain injury, spinal cord injury and stroke is a strong basis for research in this area. Mesenchymal stem cells (MSC) are a strong candidate because of their accessibility, compatibility if autologous, high yield and multipotency with a potential to generate neural cells. With the use of small-molecule chemicals, the neural induction of stem cells may occur within minutes or hours. Isobutylmethyl xanthine (IBMX) has been widely used in cocktails to induce neural differentiation. However, the key molecular mechanisms it instigates in the process are largely unknown. In this study we showed that IBMX-treated mesenchymal stem cells induced differentiation within 24 h with the unique expression of several key proteins such as Adapter protein crk, hypoxanthine-guanine phosphoribosyltransferase, DNA topoisomerase 2-beta and Cell division protein kinase 5 (CDK5), vital in linking signalling pathways. Furthermore, the increased expression of basic fibroblast growth factor in treated cells promotes phosphatidylinositol 3-kinase (PI3K), mitogen-activated protein kinase (MAPK) cascades and GTPase–Hras interactions. Bioinformatic and pathway analyses revealed upregulation in expression and an increase in the number of proteins with biological ontologies related to neural development and substructure formation. These findings enhance the understanding of the utility of IBMX in MSC neural differentiation and its involvement in neurite substructure development.

Genomewide analysis of microsatellite markers based on sequenced database in two anuran species

Eukaryotic and prokaryotic cell genomes exhibit multiple microsatellites. In this study, we characterized microsatellites in genomes and genes of Nanorana parkeri and Xenopus laevis. This characterization was used for gene ontology (GO) analysis of coding sequences (CDS). Compared to the genome of N. parkeri, the genome of X. laevis is larger and contains more number of microsatellites, but the diversity of both species are similar. Trinucleotide repeats in the genome of N. parkeri and dinucleotide and tetranucleotide repeats inthe genome of X. laevis were the most diverse. In both the species, diversity of microsatellites was highest in intergenic regions, followed by intron and exon regions, and lowest in coding regions. Microsatellites in CDS are thus subject to higher selective pressure. Many microsatellites are concentrated upstream and downstream of genes in both species, suggesting suppression of repeats in the middle of protein-CDS. Repeats are enriched in regions near gene termini purely due to the biophysical constraints of protein structure. In GO analysis, two and five unique GO terms, only found in N. parkeri and X. laevis, respectively, indicate advantageous mutations during species evolution. Biological process, cellular component and molecular function ontology reflected in the GO analysis predicted that the microsatellites located in CDS can alter protein function and may provide a molecular basis for species adaptation to new and changing environments.

Evaluation of the role of kras gene in colon cancer pathway using string and Cytoscape software

Introduction: cancer is one of the top three most commonly occurring cancer worldwide with more than 1.8 million cases in 2018. In Malaysia, cancer is the most common cancer in males and the second most common cancer in females. Albeit being the second most common form of cancer in Malaysia, there is a lack of informal or structured national cancer screening program in Malaysia and it remains a low priority in healthcare planning and expenditure. The risk of developing colon cancer is greatly influenced by factors such as lifestyle habits, genetic inheritance, diet, weight, and exercise. KRAS, the most frequently mutated oncogene in cancer, occurs in about 50 percent of cancers. This study maps the KRAS gene involved in colon cancer pathway using applications such as STRING version 11.0 and version 3.7.0 to provide a clear visualization of all the related and involved proteins and genes that interact with KRAS gene in the pathway.&#x0D; Methods: Using KRAS as a seed, a protein-protein interaction network was constructed with 3391 interactions which were retrieved from the STRING version 11.0 database. The protein network interaction was further grouped into 6 clusters using the MCODE application. Molecular function and biological processes of the genes involved in the KRAS protein network were determined using Biological Networks Gene Ontology (BiNGO).&#x0D; Results: According to the resulting protein-protein network interaction map, it revealed that KRAS mechanism and co-expressed genes interconnected with protein or enzyme binding, receptor signaling protein activity and vascular endothelial growth factor (VEGF) receptor 2 binding.&#x0D; Conclusion: Understanding these protein-protein interactions provide insight into cellular activities and thus aid in the understanding of the cause of disease.&#x0D;

Synthetic biology open language (SBOL) version 3.0.0.

Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both wet bench scientists and dry scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.0.0, which condenses and simplifies previous versions of SBOL based on experiences in deployment across a variety of scientific and industrial settings. In particular, SBOL 3.0.0, (1) separates sequence features from part/sub-part relationships, (2) renames Component Definition/Component to Component/Sub-Component, (3) merges Component and Module classes, (4) ensures consistency between data model and ontology terms, (5) extends the means to define and reference Sub-Components, (6) refines requirements on object URIs, (7) enables graph-based serialization, (8) moves Systems Biology Ontology (SBO) for Component types, (9) makes all sequence associations explicit, (10) makes interfaces explicit, (11) generalizes Sequence Constraints into a general structural Constraint class, and (12) expands the set of allowed constraints.

LncPRYP4-3 serves as a novel diagnostic biomarker for dissecting subtypes of metabolic associated fatty liver disease by targeting RPS4Y2.

Longitudinal studies have improved current diagnostics and management of metabolic associated fatty liver disease (MAFLD) patients by liver biopsy and therapeutic intervention, yet the deficiency of biomarker spectrum for dissecting subtypes largely hinders the symptomatic treatment. We originally enriched serum from peripheral blood of 618 healthy donors (HD) and 580 MAFLD (400 NAFL, 180 NASH) patients according to multiple clinicopathological indicators. Microarray profiling and qRT-PCR were conducted to identify lncRNAs as candidate biomarkers of MAFLD. Then, we analyzed the matching score of the indicated lncRNA with CAP or MAFLD-associated pathological parameters as well. Additionally, we took advantage of interaction network together with gene expression profiling analysis to further explore the underlying target genes of the identified lncRNA. Herein, we found CAP in nearly all of the NAFL (399/400) and NASH (179/180) patients was higher than that in the HDs (611/618). The differentially expressed lncRNAs were involved in multiple metabolic or immunologic processes by regulating MAFLD-associated pathways. Of them, serum lncPRYP4-3 was identified as a novel candidate biomarker of MAFLD, which was further confirmed by correlation analysis with clinical indicators. Thereafter, we deduced PRS4Y2 was a candidate target of lncPRYP4-3 and mediated the dysfunction in NAFL and NASH patients. Serum lncPRYP4-3 served as a novel biomarker of MAFLD and helped distinguish the subtypes and benefit precise intervention therapy. Our findings also provided overwhelming new evidence for the alteration in biological processes and gene ontology in MAFLD patients.

Distinct diagnostic and prognostic values of γ-aminobutyric acid type A receptor family genes in patients with colon adenocarcinoma.

In the present study, the significance of GABAA genes in colon adenocarcinoma (COAD) were investigated from the view of diagnosis and prognosis. All data were achieved from The Cancer Genome Atlas. Overall survival was analyzed by the Kaplan-Meier analyses and Cox regression model and the hazard ratios and 95% confidence interval were calculated for computation. The Database for Annotation, Visualization and Integrated Discovery, and the Biological Networks Gene Ontology (BiNGO) softwares were applied to assess the biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway analysis to predict the biological function of GABAA genes. The associated Gene Ontology and KEGG pathways were conducted by Gene Set Enrichment Analysis (GSEA). From receiver operating characteristics curves analysis, it was found that the expression of GABR, γ-aminobutyric acid type A receptor GABRA2, GABRA3, GABRB2, GABRB3, GABRG2, GABRG3, GABRD, GABRE were correlated with COAD occurrence [P<0.0001, area under the curve (AUC)>0.7]. The low expression of the GABRB1, GABRD, GABRP and GABRQ in genes after tumor staging adjustment were positively correlated with the overall survival rate [P=0.049, hazard ratio (HR)=1.517, 95% confidence interval (CI)=1.001–2.297; P=0.006, HR=1.807, 95% CI=1.180–2.765; P=0.005, HR=1.833, 95% CI=1.196–2.810; P=0.034, HR=1.578, 95% CI=1.036–2.405). GSEA showed enrichment of cell matrix adhesion, integrin binding, angiogenesis, endothelial growth factor and endothelial migration regulation in patients with COAD with GABRD overexpression. GABRB1, GABRD, GABRP and GABRQ were associated with the prognostic factors of COAD. The expression levels of GABRA2, GABRA3, GABRB2, GABRB3, GABRG2, GABRD and GABRE may allow differentiation between tumor tissues and adjacent normal tissues.