系统生物学

Genetic interaction screens are a powerful methodology to establish novel roles for genes and elucidate functional connections between genes. Such studies have been performed to great effect in single-cell organisms such as yeast and E. coli (Schuldiner et al., 2005; Butland et al., 2008; Costanzo et al., 2010), but similar large-scale interaction studies using targeted reverse-genetic deletions in multi-cellular organisms have not been feasible. We developed a CRISPR/Cas9-based method for deleting genes in C. elegans and replacing them with a heterologous fluorescent reporter (Norris et al., 2015). Recently we took advantage of that system to perform a large-scale, reverse genetic screen using null alleles in animals for the first time, focusing on RNA binding protein genes (Norris et al., 2017). This type of approach should be similarly applicable to many other gene classes in C. elegans. Here we detail the protocols involved in generating a library of double mutants and performing medium-throughput competitive fitness assays to test for genetic interactions resulting in fitness changes.

Plant genomes harbor dozens to hundreds of nucleotide-binding site-leucine-rich repeat (NBS-LRR, NBS for short) type disease resistance genes (Shao et al., 2014; Zhang et al., 2015). Proper regulation of these genes is important for normal growth of plants by reducing unnecessary fitness costs in the absence of pathogen infection. Recent studies have revealed that microRNAs are involved in regulation of NBS genes in plants (Zhai et al., 2011; Shivaprasad et al., 2012). This protocol describes computational methods for the genome-wide identification of plant NBS genes potentially regulated by microRNAs.

Hypothetical proteins (HP) are those that are not characterized in the laboratory and so remain “orphaned” in genomic databases. In recent times there has been a lot of progress in characterizing HPs in the laboratory. Various methods, such as sequence capture and Next Generation Sequencing (NGS), have been used to rapidly identify HP functions and their encoded genes. Applications and methods, such as the isolation of single genes, are greatly facilitated by pull-down assays to characterize proteins. Furthermore, there are methods to extract proteins from either the whole cell or a subcellular fraction. But the weakness is that some assays are fairly expensive and laborious, and characterizing HP function is always imperfect. In the recent past, statistical interpretations of the in silico selection strategies have improved the identification of the most promising candidates, including those from various annotation methods, such as protein interaction networks (PIN). Given the improvements in technology that have permitted a substantial increase in computational annotation, we ask if the prediction of HP function in silico (validation of models through algorithms and data subsets) could likewise be improved. In this work, we apply a bioinformatics analogy to each step of a wet lab experiment performed to predict aspects confirming protein function. Although it may be a less bona fide approach, assigning a putative function from conservation observed in homologous protein sequences might be worthwhile to consider prior to a wet lab experiment.

We developed an in vivo method to assay plant transcription factor (TF)–promoter interactions using the transient expression system in Nicotiana benthamiana (N. benthamiana) plants. The system uses the Arabidopsis stay green (SGR) gene as a reporter. Induction of SGR expression in N. benthamiana causes chlorophyll degradation and causes leaves to turn yellow.

This protocol describes how to build a gene network based on the graphical Gaussian model (GGM) from large scale microarray data. GGM uses partial correlation coefficient (pcor) to infer co-expression relationship between genes. Compared to the traditional Pearson’ correlation coefficient, partial correlation is a better measurement of direct dependency between genes. However, to calculate pcor requires a large number of observations (microarray slides) greatly exceeding the number of variables (genes). This protocol uses a regularized method to circumvent this obstacle, and is capable of building a network for ~20,000 genes from ~2,000 microarray slides. For more details, see Ma et al. (2007). For help regarding the script, please contact the author.