Understanding Of Gene Function Research Articles

Indel locations are determined by template polarity in highly efficient in vivo CRISPR/Cas9-mediated HDR in Atlantic salmon

Precise gene editing such as CRISPR/Cas9-mediated homology directed repair (HDR) can increase our understanding of gene function and improve traits of importance for aquaculture. This fine-tuned technology has not been developed for farmed fish including Atlantic salmon. We performed knock-in (KI) of a FLAG element in the slc45a2 gene in salmon using sense (S), anti-sense (AS) and double-stranded (ds) oligodeoxynucleotide (ODN) templates with short (24/48/84 bp) homology arms. We show in vivo ODN integration in almost all the gene edited animals, and demonstrate perfect HDR rates up to 27% in individual F0 embryos, much higher than reported previously in any fish. HDR efficiency was dependent on template concentration, but not homology arm length. Analysis of imperfect HDR variants suggest that repair occurs by synthesis-dependent strand annealing (SDSA), as we show for the first time in any species that indel location is dependent on template polarity. Correct ODN polarity can be used to avoid 5′-indels interrupting the reading frame of an inserted sequence and be of importance for HDR template design in general.

Bayesian differential analysis of gene regulatory networks exploiting genetic perturbations

BackgroundGene regulatory networks (GRNs) can be inferred from both gene expression data and genetic perturbations. Under different conditions, the gene data of the same gene set may be different from each other, which results in different GRNs. Detecting structural difference between GRNs under different conditions is of great significance for understanding gene functions and biological mechanisms.ResultsIn this paper, we propose a Bayesian Fused algorithm to jointly infer differential structures of GRNs under two different conditions. The algorithm is developed for GRNs modeled with structural equation models (SEMs), which makes it possible to incorporate genetic perturbations into models to improve the inference accuracy, so we name it BFDSEM. Different from the naive approaches that separately infer pair-wise GRNs and identify the difference from the inferred GRNs, we first re-parameterize the two SEMs to form an integrated model that takes full advantage of the two groups of gene data, and then solve the re-parameterized model by developing a novel Bayesian fused prior following the criterion that separate GRNs and differential GRN are both sparse.ConclusionsComputer simulations are run on synthetic data to compare BFDSEM to two state-of-the-art joint inference algorithms: FSSEM and ReDNet. The results demonstrate that the performance of BFDSEM is comparable to FSSEM, and is generally better than ReDNet. The BFDSEM algorithm is also applied to a real data set of lung cancer and adjacent normal tissues, the yielded normal GRN and differential GRN are consistent with the reported results in previous literatures. An open-source program implementing BFDSEM is freely available in Additional file 1.

Whole-Genome Shotgun Sequence of Halomonas sp. Strain SBS 10, Isolated from a Hypersaline Lake in India.

The whole-genome shotgun sequence of a moderately halophilic bacterium, Halomonas sp. strain SBS 10, was assembled and studied. The assembled genome size was 1.5 Mb, with a G+C content of 63.6%. The genome sequence of this Halomonas sp. SBS 10 isolate will be valuable in understanding gene clusters and functions involved in the adaptability of this bacterium to hypersaline conditions.

Using a Lentivirus-Based Inducible RNAi Vector to Silence a Gene.

RNA interference (RNAi) is a powerful approach for inhibiting gene expression and its wide applications have expanded our understanding of gene functions. Short hairpin RNAs (shRNAs) are artificially synthesized RNA molecules used to mediate RNAi. The expression of shRNA in cells can be achieved by using plasmids or viral/bacterial vectors. The use of viral vectors to carry and deliver shRNA shows many advantages, including the ability to overcome the difficulty of transfecting certain cell types, the capability to establish stable cell lines via antibiotics selection or fluorescence-activated cell sorting, and the option to control temporally shRNA expression using inducible promoters. In this chapter, we introduce a gene silencing method utilizing a lentivirus-based inducible shRNA system. Using the human topoisomerase (TOP) gene as an example, we describe a procedure to generate stable HepG2 cells showing inducible suppression of TOP1. In addition, a procedure for assessing the efficiency of gene silencing is described in detail.

Fine mapping of a major locus representing the lack of prickles in eggplant revealed the availability of a 0.5-kb insertion/deletion for marker-assisted selection.

As prickles cause labour inefficiency during cultivation and scratches on the skin of fruits during transportation, they are considered undesirable traits of eggplant (Solanum melongena L.). Because the molecular basis of prickle emergence has not been entirely revealed in plants, we mapped an eggplant semi-dominant Prickle (Pl) gene locus, which causes the absence of prickles, on chromosome 6 of a linkage map of the F2 population derived from crossing the no-prickly cultivar ‘Togenashi-senryo-nigo’ and the prickly line LS1934. By performing synteny mapping with tomato, the genomic region corresponding to the eggplant Pl locus was identified. Through bacterial artificial chromosome (BAC) screening, positive BAC clones and the contig sequence that harbour the Pl locus in the prickly eggplant genome were revealed. The BAC contig length was 133 kb, and it contained 16 predicted genes. Among them, a characteristic 0.5-kb insertion/deletion was detected. As the 0.5-kb insertion was commonly identified with the prickly phenotype worldwide, a primer pair that amplifies the insertion/deletion could be used for marker-assisted selection of the no-prickly phenotype. Such findings contribute to map-based-cloning of the Pl gene and the understanding of gene function, ultimately providing new insights into the regulatory molecular mechanisms underlying prickle emergence in plants.

From stinkweed to oilseed

Up to now, creativity, ingenuity, time and more than a little luck have been essential for transforming a wild plant into a new food crop. Building on the understanding of gene function in Arabidopsis, the process of domestication can be rapidly accelerated.

Impact of Fuzzy Normalization on Clustering Microarray Temporal Datasets Using Cuckoo Search

Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and molecular functions. Clustering techniques are used to examine gene expression data to extract groups of genes from the tested samples based on a similarity criterion. Subspace clustering broadens the traditional clustering by extracting the groups of genes that are highly correlated in different subspace within the dataset. Mining the temporal patterns in high dimensional data is done with computational effort and thus normalization is needed. In this work, normalization using fuzzy logic is applied to the data before clustering. The multi-objective cuckoo search optimization is implemented to extract co-expressed genes over different subspaces. The proposed methods are applied to the real life temporal gene expression datasets in which it extracts the genes that are responsible for the disease grouped in a same cluster. The experiment results prove that the impact of fuzzy normalization on the dataset improves the clustering.

From Stained Plant Tissues to Quantitative Cell Segmentation Analysis with MorphoGraphX.

Development and growth of plant organs is determined by a myriad of molecular processes that occur in each individual cell. As a direct consequence of these processes, cells alter in size and shape. They therefore serve as excellent parameters to thoroughly understand gene function. However, conventional single-plane analyses fail to accurately capture cell metrics. Here, we present a comprehensive illustrated guide that demonstrates how SCRI Renaissance 2200 staining of Arabidopsis thaliana embryos and roots can be combined with the open-source application MorphoGraphX to quantify cell parameters in 3D. We compare this staining method with other common staining techniques and provide examples of embryo and root tissue segmentation. With our novel approach, subtle single-cell phenotypes can be identified in their native context, providing new possibilities to dissect gene networks.

A Sparse Bayesian Learning Method for Structural Equation Model-Based Gene Regulatory Network Inference

Gene regulatory networks (GRNs) are underlying networks identified by interactive relationships between genes. Reconstructing GRNs from massive genetic data is important for understanding gene functions and biological mechanism, and can provide effective service for medical treatment and genetic research. A series of artificial intelligence based methods have been proposed to infer GRNs from both gene expression data and genetic perturbations. The accuracy of such algorithms can be better than those models that just consider gene expression data. A structural equation model (SEM), which provides a systematic framework integrating both types of gene data conveniently, is a commonly used model for GRN inference. Considering the sparsity of GRNs, in this paper, we develop a novel sparse Bayesian inference algorithm based on Normal-Equation-Gamma (NEG) type hierarchical prior (BaNEG) to infer GRNs modeled with SEMs more accurately. First, we reparameterize an SEM as a linear type model by integrating the endogenous and exogenous variables; Then, a Bayesian adaptive lasso with a three-level NEG prior is applied to deduce the corresponding posterior mode and estimate the parameters. Simulations on synthetic data are run to compare the performance of BaNEG to some state-of-the-art algorithms, the results demonstrate that the proposed algorithm visibly outperforms the others. What’s more, BaNEG is applied to infer underlying GRNs from a real data set composed of 47 yeast genes from Saccharomyces cerevisiae to discover potential relationships between genes.

Reference Data Based Insights Expand Understanding of Human Metabolomes

The human metabolome has remained largely unknown, with most studies annotating ~10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic “dark matter.”

In vivo and in vitro knockout system labelled using fluorescent protein via microhomology-mediated end joining.

Gene knockout is important for understanding gene function and genetic disorders. The CRISPR/Cas9 system has great potential to achieve this purpose. However, we cannot distinguish visually whether a gene is knocked out and in how many cells it is knocked out among a population of cells. Here, we developed a new system that enables the labelling of knockout cells with fluorescent protein through microhomology-mediated end joining-based knock-in. Using a combination with recombinant adeno-associated virus, we delivered our system into the retina, where the expression of Staphylococcus aureus Cas9 was driven by a retina ganglion cell (RGC)-specific promoter, and knocked out carnitine acetyltransferase (CAT). We evaluated RGCs and revealed that CAT is required for RGC survival. Furthermore, we applied our system to Keap1 and confirmed that Keap1 is not expressed in fluorescently labelled cells. Our system provides a promising framework for cell type-specific genome editing and fluorescent labelling of gene knockout based on knock-in.

Detection of a Frameshift Deletion in the SPTBN4 Gene Leads to Prevention of Severe Myopathy and Postnatal Mortality in Pigs.

Piglet mortality is a complex phenotype that depends on the environment, selection on piglet health, but also on the interaction between the piglet and sow. However, also monogenic recessive defects contribute to piglet mortality. Selective breeding has decreased overall piglet mortality by improving both mothering abilities and piglet viability. However, variants underlying recessive monogenic defects are usually not well captured within the breeding values, potentially drifting to higher frequency as a result of intense selection or genetic drift. This study describes the identification by whole-genome sequencing of a recessive 16-bp deletion in the SPTBN4 gene causing postnatal mortality in a pig breeding line. The deletion induces a frameshift and a premature stop codon, producing an impaired and truncated spectrin beta non-erythrocytic 4 protein (SPTBN4). Applying medium density single nucleotide polymorphism (SNP) data available for all breeding animals, a pregnant carrier sow sired by a carrier boar was identified. Of the resulting piglets, two confirmed homozygous piglets suffered from severe myopathy, hind-limb paralysis, and tremors. Histopathological examination showed dispersed degeneration and decrease of cross-striations in the dorsal and hind-limb muscle fibers of the affected piglets. Hence, the affected piglets are unable to walk or drink, usually resulting in death within a few hours after birth. This study demonstrates how growing genomic resources in pig breeding can be applied to identify rare syndromes in breeding populations, that are usually poorly documented and often are not even known to have a genetic basis. The study allows to prevent carrier-by-carrier matings, thereby gradually decreasing the frequency of the detrimental allele and avoiding the birth of affected piglets, improving animal welfare. Finally, these “natural knockouts” increase our understanding of gene function within the mammalian clade, and provide a potential model for human disease.

Inferring biosynthetic and gene regulatory networks from Artemisia annua RNA sequencing data on a credit card-sized ARM computer

Prediction of gene function and gene regulatory networks is one of the most active topics in bioinformatics. The accumulation of publicly available gene expression data for hundreds of plant species, together with advances in bioinformatical methods and affordable computing, sets ingenuity as one of the major bottlenecks in understanding gene function and regulation. Here, we show how a credit card-sized computer retailing for <50 USD can be used to rapidly predict gene function and infer regulatory networks from RNA sequencing data. To achieve this, we constructed a bioinformatical pipeline that downloads and allows quality-control of RNA sequencing data; and generates a gene co-expression network that can reveal enzymes and transcription factors participating and controlling a given biosynthetic pathway. We exemplify this by first identifying genes and transcription factors involved in the biosynthesis of secondary cell wall in the plant Artemisia annua, the main natural source of the anti-malarial drug artemisinin. Networks were then used to dissect the artemisinin biosynthesis pathway, which suggest potential transcription factors regulating artemisinin biosynthesis. We provide the source code of our pipeline (https://github.com/mutwil/LSTrAP-Lite) and envision that the ubiquity of affordable computing, availability of biological data and increased bioinformatical training of biologists will transform the field of bioinformatics. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Visualization Technique for Mutation Functional Analysis

A great body of research have been devoted in the past two decades to understanding gene functions, gene mutations, and mutation-disease relationships. In this paper, we present a method for exploring and understanding mutation functions using visualization and graphics representation. Functional annotation of human gene mutations is an important step in mutation pathogenicity prediction and understanding diseases progress and mechanisms. The presented visualization method is based on identifying the most significant and most specific sets of functions for a given set of mutations under a target disease or medical condition. The presented visualization allows for easy and effective understanding of the details and differences among the various sets of functions of the given mutations under certain disease or medical condition.

The History of Transgenesis.

A transgenic mouse carries within its genome an artificial DNA construct (transgene) that is deliberately introduced by an experimentalist. These animals are widely used to understand gene function and protein function. When addressing the history of transgenic mouse technology, it is apparent that a number of basic science research areas laid the groundwork for success. These include reproductive science, genetics and molecular biology, and micromanipulation and microscopy equipment. From reproductive physiology came applications on how to optimize mouse breeding, how to superovulate mice to produce zygotes for DNA microinjection or preimplantation embryos for combination with embryonic stem (ES) cells, and how to return zygotes and embryos to a pseudopregnant surrogate dam for gestation and birth. From developmental biology, it was learned how to micromanipulate embryos for morula aggregation and blastocyst microinjection and how to establish germline competent ES cells. From genetics came the foundational principles governing the inheritance of genes, the interactions of gene products, and an understanding of the phenotypic consequences of genetic mutations. From molecular biology came a panoply of tools and reagents that are used to clone DNA transgenes, to detect the presence of transgenes, to assess gene expression by measuring transcription, and to detect proteins in cells and tissues. Technical advances in light microscopes, micromanipulators, micropipette pullers, and ancillary equipment made it possible for experimentalists to insert thin glass needles into zygotes or embryos under controlled conditions to inject DNA solutions or ES cells. To fully discuss the breadth of contributions of these numerous scientific disciplines to a comprehensive history of transgenic science is beyond the scope of this work. Examples will be used to illustrate scientific developments central to the foundation of transgenic technology and that are in use today.

A CRISPR/Cas9 method to generate heterozygous alleles in Saccharomyces cerevisiae.

Saccharomyces cerevisiae is a genetically facile organism, yet multiple CRISPR/Cas9 techniques are widely used to edit its genome more efficiently and cost effectively than conventional methods. The absence of selective markers makes CRISPR/Cas9 editing particularly useful when making mutations within genes or regulatory sequences. Heterozygous mutations within genes frequently arise in the winners of evolution experiments. The genetic dissection of heterozygous alleles can be important to understanding gene structure and function. Unfortunately, the high efficiency of genome cutting and repair makes the introduction of heterozygous alleles by standard CRISPR/Cas9 technique impossible. To be able to quickly and reliably determine the individual phenotypes of the thousands of heterozygous mutations that can occur during directed evolutions is of particular interest to industrial strain improvement research. In this report, we describe a CRISPR/Cas9 method that introduces specific heterozygous mutations into the S.cerevisiae genome. This method relies upon creating silent point mutations in the protospacer adjacent motif site or removing the protospacer adjacent motif site entirely to stop the multiple rounds of genome editing that prevent heterozygous alleles from being generated. This technique should be able to create heterozygous alleles in other diploid yeasts and different allelic copy numbers in polyploid cells.

Comparison between Lipofectamine RNAiMAX and GenMute transfection agents in two cellular models of human hepatoma.

RNA interference is a powerful approach to understand gene function both for therapeutic and experimental purposes. Since the lack of knowledge in the gene silencing of various hepatic cell lines, this work was aimed to compare two transfection agents, the liposome-based Lipofectamine™ RNAiMAX and the HepG2-specific, polymer-based GenMute™, in two cellular models of human hepatoma, HepG2 and Huh7.5. In the first part, we assessed transfection efficiency of a fluorescent Cy3-labeled negative control siRNA by cell imaging analysis; we found that cells treated with GenMute present a higher uptake of the fluorescent negative control siRNA when compared to Lipofectamine RNAiMAX-trasfected cells, both in HepG2 and in Huh7.5 cells. In the second part, we evaluated GAPDH silencing with the two transfection reagents by RT-PCR finding similar GAPDH mRNA expression after each transfection treatment. Finally, we measured cell viability by the MTT assay, observing that cells transfected with GenMute have a higher viability with respect to Lipofectamine RNAiMAXadministered cells. These results suggest that GenMute reagent might be considered the most suitable transfection agent for hepatic gene silencing.

First person – Francesco Chiani and Tiziana Orsini

ABSTRACTFirst Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms (DMM), helping early-career researchers promote themselves alongside their papers. Francesco Chiani and Tiziana Orsini are co-first authors on ‘Functional loss of Ccdc151 leads to hydrocephalus in a mouse model of primary ciliary dyskinesia’, published in DMM. Francesco is a researcher (PhD) in the lab of Fabio Mammano (Italian IMPC/INFRANFRONTIER delegate) at the Institute of Biochemistry and Cell Biology – Italian National Research Council, Monterotondo, Rome, Italy, where he is interested in engineering and studying novel mouse models for human disease with the aim to understand gene function in normal physiology and pathological conditions. Tiziana is a CNR Technologist in the lab of Fabio Mammano, studying the function of genes in normal conditions and during disease development in mouse models by applying the micro-computed tomography (micro-CT) imaging technique.

ISuRe-Cre is a genetic tool to reliably induce and report Cre-dependent genetic modifications

Most biomedical research aimed at understanding gene function uses the Cre-Lox system, which consists of the Cre recombinase-dependent deletion of genes containing LoxP sites. This system enables conditional genetic modifications because the expression and activity of the recombinase Cre/CreERT2 can be regulated in space by tissue-specific promoters and in time by the ligand tamoxifen. Since the precise Cre-Lox recombination event is invisible, methods were developed to report Cre activity and are widely used. However, numerous studies have shown that expression of a given Cre activity reporter cannot be assumed to indicate deletion of other LoxP-flanked genes of interest. Here, we report the generation of an inducible dual reporter-Cre mouse allele, iSuRe-Cre. By significantly increasing Cre activity in reporter-expressing cells, iSuRe-Cre provides certainty that these cells have completely recombined floxed alleles. This genetic tool increases the ease, efficiency, and reliability of conditional mutagenesis and gene function analysis.

Gcorn Plant: A Database for Retrieving Functional and Evolutionary Traits of Plant Genes.

Gene homology helps us understand gene function and speciation. The number of plant genes and species registered in public databanks is continuously increasing. It is useful to associate homologous genes of various plants to better understand plant speciation. We designed the Gcorn plant database for the retrieval of information on homology and evolution of a plant gene of interest. Amino acid sequences of 73 species (62 land plants and 11 green algae), containing 2,682,261 sequences, were obtained from the National Center for Biotechnology Information (NCBI) Reference Sequence database. Based on NCBI BLAST searches between these sequences, homologous genes were grouped at various thresholds of homology indices devised by the authors. To show functional and evolutionary traits of a gene of interest, a phylogenetic tree, connecting genes with high homology indices, and line charts of the numbers of genes with various homology indices, are depicted. In addition, such indices are projected on a network graph in which species studied are connected based on the ratios of homologous genes, and on a phylogenetic tree for species based on NCBI Taxonomy. Gcorn plant provides information on homologous genes at various virtual time points along with speciation in plants.