Next-Generation Sequencing for Binary Protein-Protein Interactions

Powdery mildew effectors AVRA1 and BEC1016 target the ER J-domain protein HvERdj3B required for immunity in barley

The barley powdery mildew fungus, Blumeria hordei (Bh), secretes hundreds of candidate secreted effector proteins (CSEPs) to facilitate pathogen infection and colonization. One of these, CSEP0008, is directly recognized by the barley nucleotide-binding leucine-rich-repeat (NLR) receptor MLA1 and therefore is designated AVRA1. Here, we show that AVRA1 and the sequence-unrelated Bh effector BEC1016 (CSEP0491) suppress immunity in barley. We used yeast two-hybrid next-generation interaction screens (Y2H-NGIS), followed by binary Y2H and in planta protein-protein interactions studies, and identified a common barley target of AVRA1 and BEC1016, the endoplasmic reticulum (ER)-localized J-domain protein HvERdj3B. Silencing of this ER quality control (ERQC) protein increased Bh penetration. HvERdj3B is ER luminal, and we showed using split GFP that AVRA1 and BEC1016 translocate into the ER signal peptide-independently. Overexpression of the two effectors impeded trafficking of a vacuolar marker through the ER; silencing of HvERdj3B also exhibited this same cellular phenotype, coinciding with the effectors targeting this ERQC component. Together, these results suggest that the barley innate immunity, preventing Bh entry into epidermal cells, requires ERQC. Here, the J-domain protein HvERdj3B appears to be essential and can be regulated by AVRA1 and BEC1016. Plant disease resistance often occurs upon direct or indirect recognition of pathogen effectors by host NLR receptors. Previous work has shown that AVRA1 is directly recognized in the cytosol by the immune receptor MLA1. We speculate that the AVRA1 J-domain target being inside the ER, where it is inapproachable by NLRs, has forced the plant to evolve this challenging direct recognition.

Bioinformatic Analysis of Yeast Two-Hybrid Next-Generation Interaction Screen Data

Yeast two-hybrid next-generation interaction screening (Y2H-NGIS) uses the output of next-generation sequencing to mine for novel protein-protein interactions. Here, we outline the analytics underlying Y2H-NGIS datasets. Different systems, libraries, and experimental designs comprise Y2H-NGIS methodologies. We summarize the analysis in several layers that comprise the characterization of baits and preys, quantification, and identification of true interactions for subsequent secondary validation. We present two software designed for this purpose, NGPINT and Y2H-SCORES, which are used as front-end and back-end tools in the analysis. Y2H-SCORES software can be used and adapted to analyze different datasets not only from Y2H-NGIS but from other techniques ruled by similar biological principles.

Deciphering Spatial Protein-Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein-protein, protein-RNA, and protein-DNA interactions regulate and execute most biological functions. In particular in brain, protein-protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell-cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte-neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.

Unraveling Protein Interactions between the Temperate Virus Bam35 and Its Bacillus Host Using an Integrative Yeast Two Hybrid-High Throughput Sequencing Approach

Bacillus virus Bam35 is the model Betatectivirus and member of the family Tectiviridae, which is composed of tailless, icosahedral, and membrane-containing bacteriophages. Interest in these viruses has greatly increased in recent years as they are thought to be an evolutionary link between diverse groups of prokaryotic and eukaryotic viruses. Additionally, betatectiviruses infect bacteria of the Bacillus cereus group, which are known for their applications in industry and notorious since it contains many pathogens. Here, we present the first protein–protein interactions (PPIs) network for a tectivirus–host system by studying the Bam35–Bacillus thuringiensis model using a novel approach that integrates the traditional yeast two-hybrid system and high-throughput sequencing (Y2H-HTS). We generated and thoroughly analyzed a genomic library of Bam35′s host B. thuringiensis HER1410 and screened interactions with all the viral proteins using different combinations of bait–prey couples. Initial analysis of the raw data enabled the identification of over 4000 candidate interactions, which were sequentially filtered to produce 182 high-confidence interactions that were defined as part of the core virus–host interactome. Overall, host metabolism proteins and peptidases were particularly enriched within the detected interactions, distinguishing this host–phage system from the other reported host–phage PPIs. Our approach also suggested biological roles for several Bam35 proteins of unknown function, including the membrane structural protein P25, which may be a viral hub with a role in host membrane modification during viral particle morphogenesis. This work resulted in a better understanding of the Bam35–B. thuringiensis interaction at the molecular level and holds great potential for the generalization of the Y2H-HTS approach for other virus–host models.

C2orf69 mutations disrupt mitochondrial function and cause a multisystem human disorder with recurring autoinflammation

BACKGROUNDDeciphering the function of the many genes previously classified as uncharacterized open reading frame (ORF) would complete our understanding of a cell's function and its pathophysiology.METHODSWhole-exome sequencing, yeast 2-hybrid and transcriptome analyses, and molecular characterization were performed in this study to uncover the function of the C2orf69 gene.RESULTSWe identified loss-of-function mutations in the uncharacterized C2orf69 gene in 8 individuals with brain abnormalities involving hypomyelination and microcephaly, liver dysfunction, and recurrent autoinflammation. C2orf69 contains an N-terminal signal peptide that is required and sufficient for mitochondrial localization. Consistent with mitochondrial dysfunction, the patients showed signs of respiratory chain defects, and a CRISPR/Cas9-KO cell model of C2orf69 had similar respiratory chain defects. Patient-derived cells revealed alterations in immunological signaling pathways. Deposits of periodic acid-Schiff-positive (PAS-positive) material in tissues from affected individuals, together with decreased glycogen branching enzyme 1 (GBE1) activity, indicated an additional impact of C2orf69 on glycogen metabolism.CONCLUSIONSOur study identifies C2orf69 as an important regulator of human mitochondrial function and suggests that this gene has additional influence on other metabolic pathways.

Identification of host factors binding to dengue and Zika virus subgenomic RNA by efficient yeast three-hybrid screens of the human ORFeome

Flaviviruses such as the dengue (DENV) and the Zika virus (ZIKV) are important human pathogens causing around 100 million symptomatic infections each year. During infection, small subgenomic flavivirus RNAs (sfRNAs) are formed inside the infected host cell as a result of incomplete degradation of the viral RNA genome by cellular exoribonuclease XRN1. Although the full extent of sfRNA functions is to be revealed, these non-coding RNAs are key virulence factors and their detrimental effects on multiple cellular processes seem to consistently involve molecular interactions with RNA-binding proteins (RBPs). Discovery of such sfRNA-binding host-factors has followed established biochemical pull-down approaches skewed towards highly abundant proteins hampering proteome-wide coverage. Yeast three-hybrid (Y3H) systems represent an attractive alternative approach. To facilitate proteome-wide screens for RBP, we revisited and improved existing RNA-Y3H methodology by (1) implementing full-length ORF libraries in combination with (2) efficient yeast mating to increase screening depth and sensitivity, and (3) stringent negative controls to eliminate over-representation of non-specific RNA-binders. These improvements were validated employing the well-characterized interaction between DDX6 (DEAD-box helicase 6) and sfRNA of DENV as paradigm. Our advanced Y3H system was used to screen for human proteins binding to DENV and ZIKV sfRNA, resulting in a list of 69 putative sfRNA-binders, including several previously reported as well as numerous novel RBP host factors. Our methodology requiring no sophisticated infrastructure or analytic pipeline may be employed for the discovery of meaningful RNA-protein interactions at large scale in other fields.

Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor

Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

DoMY-Seq: A yeast two-hybrid-based technique for precision mapping of protein-protein interaction motifs

Interactions between proteins are fundamental for every biological process and especially important in cell signaling pathways. Biochemical techniques that evaluate these protein-protein interactions (PPIs), such as in vitro pull downs and coimmunoprecipitations, have become popular in most laboratories and are essential to identify and validate novel protein binding partners. Most PPIs occur through small domains or motifs, which are challenging and laborious to map by using standard biochemical approaches because they generally require the cloning of several truncation mutants. Moreover, these classical methodologies provide limited resolution of the interacting interface. Here, we describe the development of an alternative technique to overcome these limitations termed "Protein Domain mapping using Yeast 2 Hybrid-Next Generation Sequencing" (DoMY-Seq), which leverages both yeast two-hybrid and next-generation sequencing techniques. In brief, our approach involves creating a library of fragments derived from an open reading frame of interest and enriching for the interacting fragments using a yeast two-hybrid reporter system. Next-generation sequencing is then subsequently employed to read and map the sequence of the interacting fragment, yielding a high-resolution plot of the binding interface. We optimized DoMY-Seq by taking advantage of the well-described and high-affinity interaction between KRAS and CRAF, and we provide high-resolution domain mapping on this and other protein-interacting pairs, including CRAF-MEK1, RIT1-RGL3, and p53-MDM2. Thus, DoMY-Seq provides an unbiased alternative method to rapidly identify the domains involved in PPIs by advancing the use of yeast two-hybrid technology.

NGPINT: a next-generation protein-protein interaction software

Mapping protein-protein interactions at a proteome scale is critical to understanding how cellular signaling networks respond to stimuli. Since eukaryotic genomes encode thousands of proteins, testing their interactions one-by-one is a challenging prospect. High-throughput yeast-two hybrid (Y2H) assays that employ next-generation sequencing to interrogate complementary DNA (cDNA) libraries represent an alternative approach that optimizes scale, cost and effort. We present NGPINT, a robust and scalable software to identify all putative interactors of a protein using Y2H in batch culture. NGPINT combines diverse tools to align sequence reads to target genomes, reconstruct prey fragments and compute gene enrichment under reporter selection. Central to this pipeline is the identification of fusion reads containing sequences derived from both the Y2H expression plasmid and the cDNA of interest. To reduce false positives, these fusion reads are evaluated as to whether the cDNA fragment forms an in-frame translational fusion with the Y2H transcription factor. NGPINT successfully recognized 95% of interactions in simulated test runs. As proof of concept, NGPINT was tested using published data sets and it recognized all validated interactions. NGPINT can process interaction data from any biosystem with an available genome or transcriptome reference, thus facilitating the discovery of protein-protein interactions in model and non-model organisms.

High-resolution protein-protein interaction mapping using all-versus-all sequencing (AVA-Seq)

Two-hybrid systems can be used for investigating protein-protein interactions and may provide important information about gene products with unknown function. Despite their success in mapping protein interactions, two-hybrid systems have remained mostly untouched by improvements in next-generation DNA sequencing. The two-hybrid systems rely on one-versus-all methods in which each bait is sequentially screened against an entire library. Here, we developed a screening method that joins both bait and prey as a convergent fusion into one bacterial plasmid vector that can then be amplified and paired-end sequencing by next-generation sequencing (NGS). Our method enables all-versus-all sequencing (AVA-Seq) and utilizes NGS to remove multiple bottlenecks of the two-hybrid system. AVA-Seq allows for high-resolution protein-protein interaction mapping of a small set of proteins and has the potential for lower-resolution mapping of entire proteomes. Features of the system include ORF selection to improve efficiency, high bacterial transformation efficiency, a convergent fusion vector to allow paired-end sequencing of interactors, and the use of protein fragments rather than full-length proteins to better resolve specific protein contact points. We demonstrate the system's strengths and limitations on a set of proteins known to interact in humans and provide a framework for future large-scale projects.

2HybridTools, a handy software to facilitate clone identification and mutation mapping from yeast two-hybrid screening

Yeast Two-Hybrid (Y2H) and reverse Two-Hybrid (RY2H) are powerful protein–protein interaction screening methods that rely on the interaction of bait and prey proteins fused to DNA binding (DB) and activation domains (AD), respectively. Y2H allows identification of protein interaction partners using screening libraries, while RY2H is used to determine residues critical to a given protein–protein interaction by exploiting site-directed mutagenesis. Currently, both these techniques still rely on sequencing of positive clones using conventional Sanger sequencing. For Y2H, a screen can yield several positives; the identification of such clones is further complicated by the fact that sequencing products usually contain vector sequence. For RY2H, obtaining a complete sequence is required to identify the full range of residues involved in protein–protein interactions. However, with Sanger sequencing limited to 500–800 nucleotides, sequencing is usually carried from both ends for clones greater than this length. Analysis of such RY2H data thus requires assembly of sequencing products combined with trimming of vector sequences and of low-quality bases at the beginning and ends of sequencing products. Further, RY2H analysis requires collation of mutations that abrogate a DB/AD interaction. Here, we present 2HybridTools, a Java program with a user-friendly interface that allows addressing all these issues inherent to both Y2H and RY2H. Specifically, for Y2H, 2HybridTools enables automated identification of positive clones, while for RY2H, 2HybridTools provides detailed mutation reports as a basis for further investigation of given protein–protein interactions.

El dominio STK de la proteína de resistencia a la bacteriosis vascular de yuca RXAM1 interactúa con una E3 Ubiquitin Ligasa

La yuca (Manihot esculenta) representa el pilar de la seguridad alimentaria para cerca de mil millones de personas, principalmente en las zonas tropicales. Uno de los factores limitantes de la producción de yuca es la bacteriosis vascular causada por la bacteria Xanthomonas axonopodis pv. manihotis (Xam). Recientemente se identificó el gen RXam1 el cual confiere resistencia parcial de yuca a cepas de Xam. RXam1 codifica una proteína con un dominio LRR (Leucine Rich Repeats) extracelular y un dominio STK (Serina Treonina Kinasa) citoplasmático; estas proteínas son conocidas como RLKs (Receptor Like Kinases). En este estudio se realizó el tamizaje de una librería de ADNc de yuca mediante doble híbrido de levadura para identificar las posibles proteínas que interactúan con el dominio STK de RXam1. El tamizaje de 3x108 clones permitió identificar y confirmar cinco clones de ellos los cuales corresponden al mismo gen, el cual codifica para una proteína que presenta un dominio central de dedos de zinc CHY, seguido por un dominio C-terminal “RING finger” y un “Zinc ribbon” el cual fue denominado CRFE3-1 (Cassava RING Finger E3 ligase). La interacción entre STK y CRFE3-1 fue altamente especifica ya que se demostró también por doble híbrido que STK no interactúa con una E3 ligasa de Arabidopsis, altamente similar a CRFE3-1, así como tampoco CRFE3-1 interactúa con el dominio STK de un RLK de lechuga similar a RXam1. La identificación de CRFE3-1 sugiere que mecanismos de degradación proteica son importantes para regular la actividad de RXam1.

Data integration and predictive modeling methods for multi-omics datasets

We provide an overview of opportunities and challenges in multi-omics predictive analytics with particular emphasis on data integration and machine learning methods.

Protein-protein interactions: detection, reliability assessment and applications

Protein-protein interactions (PPIs) participate in all important biological processes in living organisms, such as catalyzing metabolic reactions, DNA replication, DNA transcription, responding to stimuli and transporting molecules from one location to another. To reveal the function mechanisms in cells, it is important to identify PPIs that take place in the living organism. A large number of PPIs have been discovered by high-throughput experiments and computational methods. However, false-positive PPIs have been introduced too. Therefore, to obtain reliable PPIs, many computational methods have been proposed. Generally, these methods can be classified into two categories. One category includes the methods that are designed to determine new reliable PPIs. The other one is designed to assess the reliability of existing PPIs and filter out the unreliable ones. In this article, we review the two kinds of methods for detecting reliable PPIs, and then focus on evaluating the performance of some of these typical methods. Later on, we also enumerate several PPI network-based applications with taking a reliability assessment of the PPI data into consideration. Finally, we will discuss the challenges for obtaining reliable PPIs and future directions of the construction of reliable PPI networks. Our research will provide readers some guidance for choosing appropriate methods and features for obtaining reliable PPIs.

DEEPN as an Approach for Batch Processing of Yeast 2-Hybrid Interactions

We adapted the yeast 2-hybrid assay to simultaneously uncover multiple transient protein interactions within a single screen by using a strategy termed DEEPN (dynamic enrichment for evaluation of protein networks). This approach incorporates high-throughput DNA sequencing and computation to follow competition among a plasmid population encoding interacting partners. To demonstrate the capacity of DEEPN, we identify a wide range of ubiquitin-binding proteins, including interactors that we verify biochemically. To demonstrate the specificity of DEEPN, we show that DEEPN allows simultaneous comparison of candidate interactors across multiple bait proteins, allowing differential interactions to be identified. This feature was used to identify interactors that distinguish between GTP- and GDP-bound conformations of Rab5.

Genome-Wide Transcriptional Excavation of Dipsacus asperoides Unmasked both Cryptic Asperosaponin Biosynthetic Genes and SSR Markers

BACKGROUND

Dipsacus asperoides is a traditional Chinese medicinal crop. The root is generally used as a medicine and is frequently prescribed by Chinese doctors for the treatment of back pain, limb paralysis, flutter trauma, tendon injuries, and fractures. With the rapid development of bioinformatics, research has been focused on this species at the gene or molecular level. For purpose of fleshing out genome information about D. asperoides, in this paper we conducted transcriptome analysis of this species.

PRINCIPAL FINDINGS

To date, many genes encoding enzymes involved in the biosynthesis of triterpenoid saponins in D.asperoides have not been elucidated. Illumina paired-end sequencing was employed to probe D. asperoides's various enzymes associated with the relevant mesostate. A total of 30, 832,805 clean reads and de novo spliced 43,243 unigenes were obtained. Of all unigenes, only 8.27% (3578) were successfully annotated in total of seven public databases: Nr, Nt, Swiss-Prot, GO, KOG, KEGG, and Pfam, which might be attributed to the poor studies on D. asperoides. The candidate genes encoding enzymes involved in triterpenoid saponin biosynthesis were identified and experimentally verified by reverse transcription qPCR, encompassing nine cytochrome P450s and 17 UDP-glucosyltransferases. Specifically, unearthly putative genes involved in the glycosylation of hederagenin were acquired. Simultaneously, 4490 SSRs from 43,243 examined sequences were determined via bioinformatics analysis.

CONCLUSION

This study represents the first report on the use of the Illumina sequence platform on this crop at the transcriptome level. Our findings of candidate genes encoding enzymes involved in Dipsacus saponin VI biosynthes is provide novel information in efforts to further understand the triterpenoid metabolic pathway on this species. The initial genetics resources in this study will contribute significantly to the genetic breeding program of D. asperoides, and are beneficial for clinical diagnosis and treatment.