系统生物学

Genome-wide screens using yeast or phage displays are powerful tools for identifying protein–ligand interactions, including drug or vaccine targets, ligand receptors, or protein–protein interactions. However, assembling libraries for genome-wide screens can be challenging and often requires unbiased cloning of 10⁵–10⁷ DNA fragments for a complete representation of a eukaryote genome. A sub-optimal genomic library can miss key genomic sequences and thus result in biased screens. Here, we describe an efficient method to generate genome-wide libraries for yeast surface display using Gibson assembly. The protocol entails genome fragmentation, ligation of adapters, library cloning using Gibson assembly, library transformation, library DNA recovery, and a streamlined Oxford nanopore library sequencing procedure that covers the length of the cloned DNA fragments. We also describe a computational pipeline to analyze the library coverage of the genome and predict the proportion of expressed proteins. The method allows seamless library transfer among multiple vectors and can be easily adapted to any expression system.

Agrobacterium-mediated transient expression has greatly contributed to research in molecular plant biology but has low efficiency and inconsistency in Arabidopsis thaliana (Arabidopsis). Here, we describe a simple, efficient and fast protocol to make transient gene expression in NahG Arabidopsis plants using Agrobacterium tumefaciens. This protocol has been successfully used to assess protein sub-cellular localization and accumulation, enzyme activity, and protein-protein interaction. In addition, this assay overcomes the use of Nicotiana benthamiana plants as a surrogate system for transient gene expression assays. Finally, the use of this protocol does not require complex inoculation methods or specific growth conditions, and can be used with different Agrobacterium strains with similar results.

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.

Molecular dynamics (MD) simulations have become one of the most important tools in understanding the behavior of bio-molecules on nanosecond to microsecond time scales. In this protocol, we provide a general approach and standard setup protocol for MD simulations by using the Gromacs MD suite.

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.

Protein-protein interactions are at the core of a plethora of developmental, physiological and biochemical processes. Consequently, insights into the origin and evolutionary dynamics of protein-protein interactions may provide information on the constraints and dynamics of specific biomolecular circuits and their impact on the organismal phenotype.

This protocol describes how ancestral protein-protein interaction patterns can be inferred using a set of known protein interactions from phylogenetically informative species. Although this protocol focuses on protein-protein interaction data, character-state reconstructions can in general be performed with other kinds of binary data in the same way.

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.