Gene Function Information Research Articles

Transcriptome reveals the important role of metabolic imbalances, immune disorders and apoptosis in the treatment of Procambarus clarkii at super high temperature

Temperature is an important environmental factor in the living environment of crustaceans. Changes in temperature can affect their normal growth and metabolism and even cause bacterial disease. Currently, the potential anti-reverse molecular reaction mechanism of crustaceans during high-temperature conditions has not yet been fully understood. Therefore, in this study, we characterised the transcriptome of Procambarus clarkii using RNA sequencing and performed a comparison between super-high-temperature treated samples and controls. After assembly and annotation, 81,097 unigenes with an average length of 069 bp and 358 differentially expressed genes (DEGs) were identified. Among these DEGs, 264 were differentially upregulated and 94 were differentially downregulated. To obtain comprehensive gene function information, we queried seven databases, namely, Nr, Nt, Pfam, KOG, Swiss-Prot, KEGG, and GO to annotate gene functions. Transcriptome analysis revealed that the identified DEGs have significant effects on immune-related pathways, including lysosomal and phagosomal pathways, and that super-high-temperature conditions can cause disease in P. clarkii. Some significantly downregulated genes are involved in oxidative phosphorylation and the PPAR signalling pathway; this suggests a metabolic imbalance in P. clarkia during extreme temperature conditions. In addition, elevated temperature changed the expression patterns of key apoptosis genes XIAP, CASP2, CASP2, CASP8, and CYTC, thereby confirming that high-temperature conditions caused immune disorders, metabolic imbalance, and, finally, triggered apoptosis. Our results provide a useful foundation for understanding the molecular mechanisms underlying the responses of P. clarkii during high-temperature conditions.

Gramene 2021: harnessing the power of comparative genomics and pathways for plant research.

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.

Earthworms and rice straw enhanced soil bacterial diversity and promoted the degradation of phenanthrene

BackgroundSince the industrial revolution, the contamination of agricultural soils by polycyclic aromatic hydrocarbons (PAHs) has increasingly become of serious global environmental concern and poses a huge threat to human beings and natural ecosystems. Microbial degradation is a proved technology mostly used to depollute polycyclic aromatic hydrocarbon (PAH) in the environment. However, very limited information is available regarding the interaction of earthworms with rice straw on the soil microbial community and the degradation of phenanthrene. This study was performed to enlighten the rice straw and earthworms’ interaction on soil bacterial abundance and structure and phenanthrene removal.ResultsResult about functional gene information revealed that both rice straw and earthworm enhanced phenanthrene degradation. Subsequently, both Shannon diversity index (r2 = − 0.8807, p < 0.001) and bacterial 16S rRNA genes (r2 = − 0.7795, p < 0.001) negatively correlated with the remaining phenanthrene concentration in soil. The application of both rice straw and earthworms in soil had the lowest ratio of soil remaining phenanthrene concentration (0.16 ± 0.02), the highest Shannon diversity index (6.45 ± 0.2) and the highest bacterial 16S rRNA genes. This implied that both earthworms and rice straw might improve the phenanthrene metabolism by increasing soil bacteria diversity. The abundance of genera Pseudomonas, Luteimonas, Rhodanobacter, Sphingomonas, Gemmatimonas, Flavobacterium, and Leifsonia was significantly increased in the presence of both earthworms and rice straw and was found to negatively correlate with the remaining phenanthrene concentration in soil.ConclusionBased on these results, this study offers clear and strong evidences that the positive interaction between earthworms and rice straw could promote phenanthrene degradation in soil. These finding will improve our understanding on the importance of the natural resources forsaken and how they can interact with the soil macro- and microorganisms to change soil structure and enhance PAH degradation in soil.

Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits.

Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program.

Phytoplankton distribution characteristics and its relationship with bacterioplankton in Dianchi Lake.

Phytoplankton and bacterioplankton perform important ecological functions in lake ecosystem. In this paper, the abundance and composition of phytoplankton and bacterioplankton at 13 sites of Dianchi lake during the wet and dry seasons were monitored, and the relationship between phytoplankton and bacterioplankton in this plateau lake was studied. Phytoplankton community structure analysis was carried out by ocular method, and bacterioplankton was investigated by high-throughput 16S rRNA gene Illumina sequencing. The relationship between phytoplankton and bacterioplankton was observed using redundancy analysis. The results showed that 87 species of phytoplankton belonging to 5 phyla and 29 genera were identified in Dianchi lake. Phytoplankton diversity and richness were higher in the wet season than those in the dry season. In the wet season, Cyanophyta was the dominant phylum whose density was 2.01 × 108 cells/L, accounting for more than 90% of the total algae, then followed by Chlorophyta, Bacillariophyta, Chrysophyta, and Cryptophyta. The spatial distribution of phytoplankton in the wet season and dry seasons showed significant differences. In the dry season, the north-central part of Dianchi lake was dominated by Limnothrix redekei and Microcystis minutissima of Cyanophyta, while Pseudanabaena moniliformis and Coelosphaerium nagelianum of Cyanophyta mainly was dominated in the south of Dianchi lake. In the wet season, Microcystis minutissima of Cyanophyta was the dominant species all the area, while Limnothrix redekei of Cyanophyta was second dominant. Proteobacteria and Bacteroidetes were the dominant phyla among bacterioplankton. The community structure of bacterioplankton was influenced by Cyanophyta and Bacillariophyta. Cyanophyta had a major influence on Pseudomonas, Acinetobacter of Proteobacteria, and Flavobacterium of Bacteroidetes. Bacillariophyta showed a strong correlation with Gemmobacter, Stenotrophomonas, and Aeromonas of Proteobacteria. Cyanophyta and Bacillariophyta produced the most significant impact on predicted functional genes of bacterioplankton, and the predicted functional genes of the samples were different in different seasons. Cell densities of Cyanophyta were positively related to metabolism-predicted functional genes of bacterioplankton. Bacillariophyta and Cryptophyta had an impact on most of the cellular processes and signaling predicted functional genes. Bacterioplankton-predicted functional gene information storage and processing were significantly affected by cell densities of Chlorophyta. Therefore, the analysis of the phytoplankton community and the bacterioplankton-predicted functional gene in Dianchi lake exerts a great significance in revealing the ecosystem function of plateau lakes and harmful algal bloom control.

Culture-dependent to culture-independent approaches for the bioremediation of paints: a review

Environmental pollution by paint effluents is a public health threat, distressing total biodiversity and environment. Environmental treatment options like bioremediation mediated by microorganisms is still developing and is considered as a modern approach for the environmentalists worldwide. Thus present review was sought to explore the culture-dependent (isolation) to culture-independent (multi-omics) approaches for the bioremediation of paints. Bioremediation is once considered as safe, reliable and eco-friendly yet facing challenges. Microbes exhibiting bioremediation potential were screened and characterized but, in most of the studies, cannot completely utilize the targeted pollutant or were unsuccessful against pollutant at in situ settings. Thereafter, consortia studies were conducted and found that microbial performance remained excellent in synergistic while negligible in antagonistic association. Consortia studies were most frequently carried out using bacteria while few studies were conducted on the fungi alone and their association with bacteria. Moreover, the combined strategies of consortia, genes and enzymes were also elucidated and later the genetically modified microbes were also tested for in situ cleanup. Nowadays, metagenomics approaches which reveal the functional gene information of microbes derived from the DNA of microbiome with different physiological states were studied for environmental cleanup. At present from the literature insight, it is predicted that the bioremediation future lies in the understanding of metaphenome (product of metagenome and available resources).

In Situ Hybridization and Immunostaining of Xenopus Brain.

The dynamic expression pattern analysis provides the primary information of gene function. Differences of the RNA and/or protein location will provide valuable information for gene expression regulation. Generally, in situ hybridization (ISH) and immunohistochemistry (IHC) are two main techniques to visualize the locations of gene transcripts and protein products in situ, respectively. Here we describe the protocol for the whole brain dissection, the in situ hybridization, and the immunostaining of the developing Xenopus brain sections. Additionally, we point out the modification of in situ hybridization for microRNA expression detection.

Metabolic Functional Analysis of Dominant Microbial Communities in the Rapid Sand Filters for Drinking Water

Rapid sand filter (RSF) is widely used in drinking water treatment plants. Rapid filtration is always considered a physicochemical process, but the effect of the microorganisms that attach to the filter media remain inadequately investigated. In order to understand the composition and functional characteristics of microbial communities in RSFs, influent water, effluent water, and filter materials from eleven RSFs in eight Chinese cities were sampled and analyzed. After filtration, dissolved organic carbon (DOC) showed a slight but significant removal due to the growth of heterotrophic microbes. The activity of ammonia-oxidizing microbes and nitrite-oxidizing microbes promoted a significant decrease in ammonia nitrogen (NH4+-N) and a significant increase in nitrate nitrogen (NO3--N) in water. No significant changes in total nitrogen (TN) were observed, indicating that denitrification and anammox were weak in the RSFs. The composition and function of the microbial communities of RSFs were assessed using metagenomic methods. Genera in the top 10% with respect to relative abundance (14 genera in total) were identified as the dominant genera, including the two ammonia-oxidizing bacteria Nitrospira and Nitrosomonas. Functional gene information for the dominant genera was also extracted for analysis. The dominant genera exhibited higher relative abundances of carbohydrate, nitrogen, sulfur, and xenobiotic metabolic pathways. Aeromonas had the highest relative abundance of carbohydrate metabolic genes, and Bradyrhizobium had the highest relative abundance of nitrogen, sulfur, and xenobiotics metabolic genes, indicating that these two genera play an important role in the transformation of substances in drinking water. Finally, the metabolic potential of the dominant genera on xenobiotics was evaluated, and the results showed that Bradyrhizobium, Sphingomonas, Methyloglobulus, Sphingopyxis, and Klebsiella were the key bacterial genera for the removal of micropollutants in RSFs.

SMRT sequencing analysis reveals the full-length transcripts and alternative splicing patterns in Ananas comosus var. bracteatus

BackgroundAnanas comosus var. bracteatus is an herbaceous perennial monocot cultivated as an ornamental plant for its chimeric leaves. Because of its genomic complexity, and because no genomic information is available in the public GenBank database, the complete structure of the mRNA transcript is unclear and there are limited molecular mechanism studies for Ananas comosus var. bracteatus.MethodsThree size fractionated full-length cDNA libraries (1–2 kb, 2–3 kb, and 3–6 kb) were constructed and subsequently sequenced in five single-molecule real-time (SMRT) cells (2 cells, 2 cells, and 1 cell, respectively).ResultsIn total, 19,838 transcripts were identified for alternative splicing (AS) analysis. Among them, 19,185 (96.7%) transcripts were functionally annotated. A total of 9,921 genes were identified by mapping the non-redundant isoforms to the reference genome. A total of 10,649 AS events were identified, the majority of which were intron retention events. The alternatively spliced genes had functions in the basic metabolism processes of the plant such as carbon metabolism, amino acid biosynthesis, and glycolysis. Fourteen genes related to chlorophyll biosynthesis were identified as having AS events. The distribution of the splicing sites and the percentage of conventional and non-canonical AS sites of the genes categorized in pathways related to the albino leaf phenotype (ko00860, ko00195, ko00196, and ko00710) varied greatly. The present results showed that there were 8,316 genes carrying at least one poly (A) site, which generated 21,873 poly (A) sites. These findings indicated that the quality of the gene structure and functional information of the obtained genome was greatly improved, which may facilitate further genetic study of Ananas comosus var. bracteatus.

IProEP: A Computational Predictor for Predicting Promoter

Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).

Genome-wide association study identifies energy metabolism genes for resistance to ketosis in Chinese Holstein cattle.

Ketosis (KET) is one common metabolic disorder that occurs mainly in early lactation and affects the dairy industry with significant economic losses. Cows with ketosis have lower milk yield and reproductive performance and greater risk of other periparturient diseases. As a metabolic disease, the pathogenesis of KET is multifactorial. To better understand the genetic background of KET, a genome-wide association study was performed using the Illumina BovineSNP50 BeadChip. Single-step genomic BLUP methodology was used to incorporate genomic data into a threshold-liability model. Results of the GWAS are reported as the proportion of variance explained by 20-SNP windows. Six genomic regions on Bos taurus autosomes 10, 13, 14 and 25 showed association with KET. Most interestingly, several candidate genes, including previously reported genes (BMP4, HNF4A and APOBR) and newly identified genes (SOCS4, GCH1, ATG14, RGS6, CYP7A1 and MAPK3), are involved in insulin metabolism or lipid metabolism, implicating the contribution of energy-metabolism-associated genes to the genetic basis of KET. Our results provide new information about the underlying biology and molecular mechanisms associated with KET. Future studies that combine genomic variation analysis and functional gene information may help elucidate the biology of KET.

Integrating Multiple Interaction Networks for Gene Function Inference.

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

Single-Molecule Long-Read Sequencing of Zanthoxylum bungeanum Maxim. Transcriptome: Identification of Aroma-Related Genes

Zanthoxylum bungeanum Maxim. is an economically important tree species that is resistant to drought and infertility, and has potential medicinal and edible value. However, comprehensive genomic data are not yet available for this species, limiting its potential utility for medicinal use, breeding programs, and cultivation. Transcriptome sequencing provides an effective approach to remedying this shortcoming. Herein, single-molecule long-read sequencing and next-generation sequencing approaches were used in parallel to obtain transcript isoform structure and gene functional information in Z. bungeanum. In total, 282,101 reads of inserts (ROIs) were identified, including 134,074 full-length non-chimeric reads, among which 65,711 open reading frames (ORFs), 50,135 simple sequence repeats (SSRs), and 1492 long non-coding RNAs (lncRNAs) were detected. Functional annotation revealed metabolic pathways related to aroma components and color characteristics in Z. bungeanum. Unexpectedly, 30 transcripts were annotated as genes involved in regulating the pathogenesis of breast and colorectal cancers. This work provides a comprehensive transcriptome resource for Z. bungeanum, and lays a foundation for the further investigation and utilization of Zanthoxylum resources.

Automatic gene annotation using GO terms from cellular component domain

BackgroundThe Gene Ontology (GO) is a resource that supplies information about gene product function using ontologies to represent biological knowledge. These ontologies cover three domains: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP). GO annotation is a process which assigns gene functional information using GO terms to relevant genes in the literature. It is a common task among the Model Organism Database (MOD) groups. Manual GO annotation relies on human curators assigning gene functional information using GO terms by reading the biomedical literature. This process is very time-consuming and labor-intensive. As a result, many MODs can afford to curate only a fraction of relevant articles.MethodsGO terms from the CC domain can be essentially divided into two sub-hierarchies: subcellular location terms, and protein complex terms. We cast the task of gene annotation using GO terms from the CC domain as relation extraction between gene and other entities: (1) extract cases where a protein is found to be in a subcellular location, and (2) extract cases where a protein is a subunit of a protein complex. For each relation extraction task, we use an approach based on triggers and syntactic dependencies to extract the desired relations among entities.ResultsWe tested our approach on the BC4GO test set, a publicly available corpus for GO annotation. Our approach obtains a F1-score of 71%, a precision of 91% and a recall of 58% for predicting GO terms from CC Domain for given genes.ConclusionsWe have described a novel approach of treating gene annotation with GO terms from CC domain as two relation extraction subtasks. Evaluation results show that our approach achieves a F1-score of 71% for predicting GO terms for given genes. Thereby our approach can be used to accelerate the process of GO annotation for the bio-annotators.

SNP markers associated with body size and pelt length in American mink (Neovison vison)

BackgroundIdentification of genes underlying production traits is a key aim of the mink research community. Recent availability of genomic tools have opened the possibility for faster genetic progress in mink breeding. Availability of mink genome assembly allows genome-wide association studies in mink.ResultsIn this study, we used genotyping-by-sequencing to obtain single nucleotide polymorphism (SNP) genotypes of 2496 mink. After multiple rounds of filtering, we retained 28,336 high quality SNPs and 2352 individuals for a genome-wide association study (GWAS). We performed the first GWAS for body weight, behavior, along with 10 traits related to fur quality in mink.ConclusionsCombining association results with existing functional information of genes and mammalian phenotype databases, we proposed WWC3, MAP2K4, SLC7A1 and USP22 as candidate genes for body weight and pelt length in mink.

An Updated Functional Annotation of Protein-Coding Genes in the Cucumber Genome.

Background: Although the cucumber reference genome and its annotation were published several years ago, the functional annotation of predicted genes, particularly protein-coding genes, still requires further improvement. In general, accurately determining orthologous relationships between genes allows for better and more robust functional assignments of predicted genes. As one of the most reliable strategies, the determination of collinearity information may facilitate reliable orthology inferences among genes from multiple related genomes. Currently, the identification of collinear segments has mainly been based on conservation of gene order and orientation. Over the course of plant genome evolution, various evolutionary events have disrupted or distorted the order of genes along chromosomes, making it difficult to use those genes as genome-wide markers for plant genome comparisons.Results: Using the localized LASTZ/MULTIZ analysis pipeline, we aligned 15 genomes, including cucumber and other related angiosperm plants, and identified a set of genomic segments that are short in length, stable in structure, uniform in distribution and highly conserved across all 15 plants. Compared with protein-coding genes, these conserved segments were more suitable for use as genomic markers for detecting collinear segments among distantly divergent plants. Guided by this set of identified collinear genomic segments, we inferred 94,486 orthologous protein-coding gene pairs (OPPs) between cucumber and 14 other angiosperm species, which were used as proxies for transferring functional terms to cucumber genes from the annotations of the other 14 genomes. In total, 10,885 protein-coding genes were assigned Gene Ontology (GO) terms which was nearly 1,300 more than results collected in Uniprot-proteomic database. Our results showed that annotation accuracy would been improved compared with other existing approaches.Conclusions: In this study, we provided an alternative resource for the functional annotation of predicted cucumber protein-coding genes, which we expect will be beneficial for the cucumber's biological study, accessible from http://cmb.bnu.edu.cn/functional_annotation. Meanwhile, using the cucumber reference genome as a case study, we presented an efficient strategy for transferring gene functional information from previously well-characterized protein-coding genes in model species to newly sequenced or “non-model” plant species.

Human gene expression microarray analysis of the HPV 6bE7-HaCaT stable cell line

ObjectiveHuman papillomavirus (HPV) is the most common sexually transmitted agent in the world. HPV6b is a low-risk type of HPVs that causes benign verrucous hyperplastic lesions of the skin and anal genital mucosa. Previous research has indicated that HPV genotype 6 is sometimes associated with high-grade lesions and anal cancer. The pathogenesis of low-risk HPV infection and its relationship to high-risk HPV is not clear at present. The E7 protein, which is encoded by HPV early –expressing genes, plays an important role in HPV infection. The aim of this study is to investigate the human gene expression signature of the HPV6b E7-transfected HaCaT stable cell line. The identification of differentially expressed genes might provide a more comprehensive understanding of HPV6b infection and will allow us to explore the specific role of E7 protein. MethodsWe established a stable cell line transfected with the HPV6b E7 gene and analyzed the line's genome-wide expression profile by microarray. Quantitative real-time PCR (qRT-PCR) was used to verify the differentially expressed genes. GO enrichment analysis was applied for gene annotation according to functions. KEGG analysis, a system for analyzing gene function and genome information, was used to help us integrate differentially expressed genes into pathways. ResultsA total of 3519 genes were identified to be significantly differentially expressed between the HPV 6bE7-HaCaT stable cell line and a control cell line, among which 1884 genes were up-regulated and 1635 genes were down-regulated with a fold-change > 2.0 between the two groups. The expression profiles of the top 20 up-regulated and the top 20 down-regulated genes in the HPV 6bE7-HaCaT stable cell line as analyzed by qRT-PCR were consistent with the microarray data. The most significantly enhanced genes HPV 6bE7-HaCaT cells were SIMC1, S100A8 and S100A9, whereas PXDN expression was markedly down-regulated. GO analysis showed that HPV 6bE7 primarily affected biological processes and that the most significant difference was in heart induction (GO:0003129). Many differentially expressed genes were linked to histone H4-K20 demethylation (GO:0035574). KEGG analysis showed that the most significant changes in gene expression were related to primary bile acid biosynthesis, and the most diverse biological processes were related to systemic lupus erythematosus pathogenesis. ConclusionsThe global gene expression profile of the HPV 6bE7-HaCaT stable cell line was analyzed, revealing the genes regulated by E7 protein and providing insight into the pathogenesis of HPV6b infection.

Identification of a histone family gene signature for predicting the prognosis of cervical cancer patients

Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer patients is an unsolved issue. Here, we systematically analyzed the aberrant expression patterns of cervical cancer using RNA-Seq data from The Cancer Genome Atlas (TCGA). We incorporated gene profiling, molecular signatures, functional and pathway information with gene set enrichment and protein-protein interaction (PPI) network analysis, to identify sub-networks of genes. Those identified genes relating to DNA replication and DNA repair-mediated signaling pathways associated with systemic lupus erythematosus (SLE). Next, we combined cross-validated prognostic scores to build an integrated prognostic model for survival prediction. The combined approach revealed that the DNA repair-mediated including the functional interaction module of 18 histone genes (Histone cluster 1 H2A, B and H4), were significantly correlated with the survival rate. Furthermore, five of these histone genes were highly expressed in three cervical cancer cohorts from the Oncomine database. Comparison of high and low histone variant-expressing human cervical cancer cell lines revealed different responses to DNA damage, suggesting protective functions of histone genes against DNA damage. Collectively, we provide evidence that two SLE-associated gene sets (HIST1H2BD and HIST1H2BJ; and HIST1H2BD, HIST1H2BJ, HIST1H2BH, HIST1H2AM and HIST1H4K) can be used as prognostic factors for survival prediction among cervical cancer patients.

Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network.

Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of the underlying pathogenesis remains poor. Here, we constructed a T2D phenotypic-linkage network (T2D-PLN), by integrating diverse gene functional information that highlight genes, which when disrupted in mice, elicit similar T2D-relevant phenotypes. Sensitising the network to T2D-relevant phenotypes enabled significant functional convergence to be detected between genes implicated in monogenic or syndromic diabetes and genes lying within genomic regions associated with T2D common risk. We extended these analyses to a recent multiethnic T2D case-control exome of 12,940 individuals that found no evidence of T2D risk association for rare frequency variants outside of previously known T2D risk loci. Examining associations involving protein-truncating variants (PTV), most at low population frequencies, the T2D-PLN was able to identify a convergent set of biological pathways that were perturbed within four of five independent T2D case/control ethnic sets of 2000 to 5000 exomes each. These same pathways were found to be over-represented among both known monogenic or syndromic diabetes genes and genes within T2D-associated common risk loci. Our study demonstrates convergent biology amongst variants representing different classes of T2D genetic risk. Although convergence was observed at the pathway level, few of the contributing genes were found in common between different cohorts or variant classes, most notably between the exome variant sets which suggests that future rare variant studies may be better focusing their power onto a single population of recent common ancestry.

A new genome-scale metabolic model of Corynebacterium glutamicum and its application

BackgroundCorynebacterium glutamicum is an important platform organism for industrial biotechnology to produce amino acids, organic acids, bioplastic monomers, and biofuels. The metabolic flexibility, broad substrate spectrum, and fermentative robustness of C. glutamicum make this organism an ideal cell factory to manufacture desired products. With increases in gene function, transport system, and metabolic profile information under certain conditions, developing a comprehensive genome-scale metabolic model (GEM) of C. glutamicum ATCC13032 is desired to improve prediction accuracy, elucidate cellular metabolism, and guide metabolic engineering.ResultsHere, we constructed a new GEM for ATCC13032, iCW773, consisting of 773 genes, 950 metabolites, and 1207 reactions. Compared to the previous model, iCW773 supplemented 496 gene–protein-reaction associations, refined five lumped reactions, balanced the mass and charge, and constrained the directionality of reactions. The simulated growth rates of C. glutamicum cultivated on seven different carbon sources using iCW773 were consistent with experimental values. Pearson’s correlation coefficient between the iCW773-simulated and experimental fluxes was 0.99, suggesting that iCW773 provided an accurate intracellular flux distribution of the wild-type strain growing on glucose. Furthermore, genetic interventions for overproducing l-lysine, 1,2-propanediol and isobutanol simulated using OptForceMUST were in accordance with reported experimental results, indicating the practicability of iCW773 for the design of metabolic networks to overproduce desired products. In vivo genetic modifications of iCW773-predicted targets resulted in the de novo generation of an l-proline-overproducing strain. In fed-batch culture, the engineered C. glutamicum strain produced 66.43 g/L l-proline in 60 h with a yield of 0.26 g/g (l-proline/glucose) and a productivity of 1.11 g/L/h. To our knowledge, this is the highest titer and productivity reported for l-proline production using glucose as the carbon resource in a minimal medium.ConclusionsOur developed iCW773 serves as a high-quality platform for model-guided strain design to produce industrial bioproducts of interest. This new GEM will be a successful multidisciplinary tool and will make valuable contributions to metabolic engineering in academia and industry.