Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations

Research progress and application strategies of sugar transport mechanisms in rice

In plants, carbohydrates are central products of photosynthesis. Rice is a staple that contributes to the daily calorie intake for over half of the world's population. Hence, the primary objective of rice cultivation is to maximize carbohydrate production. The "source-sink" theory is proposed as a valuable principle for guiding crop breeding. However, the "flow" research lag, especially in sugar transport, has hindered high-yield rice breeding progress. This review concentrates on the genetic and molecular foundations of sugar transport and its regulation, enhancing the fundamental understanding of sugar transport processes in plants. We illustrate that the apoplastic pathway is predominant over the symplastic pathway during phloem loading in rice. Sugar transport proteins, such as SUTs and SWEETs, are essential carriers for sugar transportation in the apoplastic pathway. Additionally, we have summarized a regulatory pathway for sugar transport genes in rice, highlighting the roles of transcription factors (OsDOF11, OsNF-YB1, OsNF-YC12, OsbZIP72, Nhd1), OsRRM (RNA Recognition Motif containing protein), and GFD1 (Grain Filling Duration 1). Recognizing that the research shortfall in this area stems from a lack of advanced research methods, we discuss cutting-edge analytical techniques such as Mass Spectrometry Imaging and single-cell RNA sequencing, which could provide profound insights into the dynamics of sugar distribution and the associated regulatory mechanisms. In summary, this comprehensive review serves as a valuable guide, directing researchers toward a deep understanding and future study of the intricate mechanisms governing sugar transport.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

DNA Repair Protein XRCC1 Stimulates Activity of DNA Polymerase λ under Conditions of Microphase Separation

Non-membrane compartments or biomolecular condensates play an important role in the regulation of cellular processes including DNA repair. Here, an ability of XRCC1, a scaffold protein involved in DNA base excision repair (BER) and single-strand break repair, to form protein-rich microphases in the presence of DNA duplexes was discovered. We also showed that the gap-filling activity of BER-related DNA polymerase λ (Pol λ) is significantly increased by the presence of XRCC1. The stimulation of the Pol λ activity was observed only at micromolar XRCC1 concentrations, which were well above the nanomolar dissociation constant determined for the XRCC1-Pol λ complex and pointed to the presence of an auxiliary stimulatory factor in addition to protein-protein interactions. Indeed, according to dynamic light scattering measurements, the stimulation of the Pol λ activity by XRCC1 was coupled with microphase separation in a protein-DNA mixture. Fluorescence microscopy revealed colocalization of Pol λ, XRCC1, and gapped DNA within the microphases. Thus, stimulation of Pol λ activity is caused both by its interaction with XRCC1 and by specific conditions of microphase separation; this phenomenon is shown for the first time.

ReLo is a simple and rapid colocalization assay to identify and characterize direct protein-protein interactions

The characterization of protein-protein interactions (PPIs) is fundamental to the understanding of biochemical processes. Many methods have been established to identify and study direct PPIs; however, screening and investigating PPIs involving large or poorly soluble proteins remains challenging. Here, we introduce ReLo, a simple, rapid, and versatile cell culture-based method for detecting and investigating interactions in a cellular context. Our experiments demonstrate that ReLo specifically detects direct binary PPIs. Furthermore, we show that ReLo bridging experiments can also be used to determine the binding topology of subunits within multiprotein complexes. In addition, ReLo facilitates the identification of protein domains that mediate complex formation, allows screening for interfering point mutations, and it is sensitive to drugs that mediate or disrupt an interaction. In summary, ReLo is a simple and rapid alternative for the study of PPIs, especially when studying structurally complex proteins or when established methods fail.

Interplay of EXO70 and MLO proteins modulates trichome cell wall composition and susceptibility to powdery mildew

Exocyst component of 70-kDa (EXO70) proteins are constituents of the exocyst complex implicated in vesicle tethering during exocytosis. MILDEW RESISTANCE LOCUS O (MLO) proteins are plant-specific calcium channels and some MLO isoforms enable fungal powdery mildew pathogenesis. We here detected an unexpected phenotypic overlap of Arabidopsis thaliana exo70H4 and mlo2 mlo6 mlo12 triple mutant plants regarding the biogenesis of leaf trichome secondary cell walls. Biochemical and Fourier transform infrared spectroscopic analyses corroborated deficiencies in the composition of trichome cell walls in these mutants. Transgenic lines expressing fluorophore-tagged EXO70H4 and MLO exhibited extensive colocalization of these proteins. Furthermore, mCherry-EXO70H4 mislocalized in trichomes of the mlo triple mutant and, vice versa, MLO6-GFP mislocalized in trichomes of the exo70H4 mutant. Expression of GFP-marked PMR4 callose synthase, a known cargo of EXO70H4-dependent exocytosis, revealed reduced cell wall delivery of GFP-PMR4 in trichomes of mlo triple mutant plants. In vivo protein-protein interaction assays in plant and yeast cells uncovered isoform-preferential interactions between EXO70.2 subfamily members and MLO proteins. Finally, exo70H4 and mlo6 mutants, when combined, showed synergistically enhanced resistance to powdery mildew attack. Taken together, our data point to an isoform-specific interplay of EXO70 and MLO proteins in the modulation of trichome cell wall biogenesis and powdery mildew susceptibility.

Dual-transgenic BiFC vector systems for protein-protein interaction analysis in plants

Protein-protein interaction (PPI) play a pivotal role in cellular signal transduction. The bimolecular fluorescence complementation (BiFC) assay offers a rapid and intuitive means to ascertain the localization and interactions of target proteins within living cells. BiFC is based on fluorescence complementation by reconstitution of a functional fluorescent protein by co-expression of N- and C-terminal fragments of this protein. When fusion proteins interact, the N- and C-terminal fragments come into close proximity, leading to the reconstitution of the fluorescent protein. In the conventional approach, the N-terminal and C-terminal fragments of the fluorescent protein are typically expressed using two separate vectors, which largely relies on the efficiency of the transformation of the two vectors in the same cells. Furthermore, issues of vector incompatibility can often result in loss of one plasmid. To address these challenges, we have developed novel dual-transgenic BiFC vectors, designed as pDTQs, derived from the previously published pDT1 vector. This set of BiFC vectors offers the following advantages: 1) Both fluorescent fusion proteins are expressed sequentially within a single vector, enhancing expression efficiency; 2) Independent promoters and terminators regulate the expression of the two proteins potentially mitigating vector compatibility issues; 3) A long linker is inserted between the fluorescent protein fragment and the gene of interest, facilitating the recombination of the fused fluorescent protein into an active form; 4) Four distinct types of fluorescent proteins, namely, EYFP, mVenus, mRFP1Q66T and mCherry are available for BiFC analysis. We assessed the efficiency of the pDTQs system by investigating the oligomerization of Arabidopsis CRY2 and CRY2-BIC2 interactions in N. benthamiana. Notably, the pDTQs were found to be applicable in rice, underscoring their potential utility across various plant species.

Cell Cycle-Specific Protein Phosphatase 1 (PP1) Substrates Identification Using Genetically Modified Cell Lines

The identification of protein phosphatase 1 (PP1) holoenzyme substrates has proven to be a challenging task. PP1 can form different holoenzyme complexes with a variety of regulatory subunits, and many of those are cell cycle regulated. Although several methods have been used to identify PP1 substrates, their cell cycle specificity is still an unmet need. Here, we present a new strategy to investigate PP1 substrates throughout the cell cycle using clustered regularly interspersed short palindromic repeats (CRISPR)-Cas9 genome editing and generate cell lines with endogenously tagged PP1 regulatory subunit (regulatory interactor of protein phosphatase one, RIPPO). RIPPOs are tagged with the auxin-inducible degron (AID) or ascorbate peroxidase 2 (APEX2) modules, and PP1 substrate identification is conducted by SILAC proteomic-based approaches. Proteins in close proximity to RIPPOs are first identified through mass spectrometry (MS) analyses using the APEX2 system; then a list of differentially phosphorylated proteins upon RIPPOs rapid degradation (achieved via the AID system) is compiled via SILAC phospho-mass spectrometry. The "in silico" overlap between the two proteomes will be enriched for PP1 putative substrates. Several methods including fluorescence resonance energy transfer (FRET), proximity ligation assays (PLA), and in vitro assays can be used as substrate validations approaches.

Characterization of allosteric modulators that disrupt androgen receptor co-activator protein-protein interactions to alter transactivation-Drug leads for metastatic castration resistant prostate cancer

Three series of compounds were prioritized from a high content screening campaign that identified molecules that blocked dihydrotestosterone (DHT) induced formation of Androgen Receptor (AR) protein-protein interactions (PPIs) with the Transcriptional Intermediary Factor 2 (TIF2) coactivator and also disrupted preformed AR-TIF2 PPI complexes; the hydrobenzo-oxazepins (S1), thiadiazol-5-piperidine-carboxamides (S2), and phenyl-methyl-indoles (S3). Compounds from these series inhibited AR PPIs with TIF2 and SRC-1, another p160 coactivator, in mammalian 2-hybrid assays and blocked transcriptional activation in reporter assays driven by full length AR or AR-V7 splice variants. Compounds inhibited the growth of five prostate cancer cell lines, with many exhibiting differential cytotoxicity towards AR positive cell lines. Representative compounds from the 3 series substantially reduced both endogenous and DHT-enhanced expression and secretion of the prostate specific antigen (PSA) cancer biomarker in the C4-2 castration resistant prostate cancer (CRPC) cell line. The comparatively weak activities of series compounds in the H3-DHT and/or TIF2 box 3 LXXLL-peptide binding assays to the recombinant ligand binding domain of AR suggest that direct antagonism at the orthosteric ligand binding site or AF-2 surface respectively are unlikely mechanisms of action. Cellular enhanced thermal stability assays (CETSA) indicated that compounds engaged AR and reduced the maximum efficacy and right shifted the EC50 of DHT-enhanced AR thermal stabilization consistent with the effects of negative allosteric modulators. Molecular docking of potent representative hits from each series to AR structures suggest that S1-1 and S2-6 engage a novel binding pocket (BP-1) adjacent to the orthosteric ligand binding site, while S3-11 occupies the AR binding function 3 (BF-3) allosteric pocket. Hit binding poses indicate spaces and residues adjacent to the BP-1 and BF-3 pockets that will be exploited in future medicinal chemistry optimization studies. Small molecule allosteric modulators that prevent/disrupt AR PPIs with coactivators like TIF2 to alter transcriptional activation in the presence of orthosteric agonists might evade the resistance mechanisms to existing prostate cancer drugs and provide novel starting points for medicinal chemistry lead optimization and future development into therapies for metastatic CRPC.

Programming Super DNA-Enzyme Molecules for On-Demand Enzyme Activity Modulation

Dynamic interactions of enzymes, including programmable configuration and cycling of enzymes, play important roles in the regulation of cellular metabolism. Here, we constructed a super DNA-enzymes molecule (SDEM) that comprises at least two cascade enzymes and multiple linked DNA strands to control and detect metabolism. We found that the programmable SDEM, which comprises glucose oxidase (GOx) and horseradish peroxidase (HRP), has a 20-fold lower detection limit and a 1.6-fold higher reaction rate than free enzymes. An SDEM can be assembled and disassembled using a hairpin structure and a displacement DNA strand to complete multiple cycles. An entropically driven catalytic assembly (catassembly) enables different SDEMs to switch from an SDEM with GOx and HRP cascades to an SDEM with sarcosine oxidase (SOX) and HRP cascades in over six orders of magnitude less time than without the catassembly to detect different metabolisms (GO and sarcosine) on demand.

MATE transporter GFD1 cooperates with sugar transporters, mediates carbohydrate partitioning and controls grain-filling duration, grain size and number in rice

More than half of the world's food is provided by cereals, as humans obtain >60% of daily calories from grains. Producing more carbohydrates is always the final target of crop cultivation. The carbohydrate partitioning pathway directly affects grain yield, but the molecular mechanisms and biological functions are poorly understood, including rice (Oryza sativa L.), one of the most important food sources. Here, we reported a prolonged grain filling duration mutant 1 (gfd1), exhibiting a long grain-filling duration, less grain number per panicle and bigger grain size without changing grain weight. Map-based cloning and molecular biological analyses revealed that GFD1 encoded a MATE transporter and expressed high in vascular tissues of the stem, spikelet hulls and rachilla, but low in the leaf, controlling carbohydrate partitioning in the stem and grain but not in the leaf. GFD1 protein was partially localized on the plasma membrane and in the Golgi apparatus, and was finally verified to interact with two sugar transporters, OsSWEET4 and OsSUT2. Genetic analyses showed that GFD1 might control grain-filling duration through OsSWEET4, adjust grain size with OsSUT2 and synergistically modulate grain number per panicle with both OsSUT2 and OsSWEET4. Together, our work proved that the three transporters, which are all initially classified in the major facilitator superfamily family, could control starch storage in both the primary sink (grain) and temporary sink (stem), and affect carbohydrate partitioning in the whole plant through physical interaction, giving a new vision of sugar transporter interactome and providing a tool for rice yield improvement.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings

Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Potential application of TurboID-based proximity labeling in studying the protein interaction network in plant response to abiotic stress

Abiotic stresses are major environmental conditions that reduce plant growth, productivity and quality. Protein-protein interaction (PPI) approaches can be used to screen stress-responsive proteins and reveal the mechanisms of protein response to various abiotic stresses. Biotin-based proximity labeling (PL) is a recently developed technique to label proximal proteins of a target protein. TurboID, a biotin ligase produced by directed evolution, has the advantages of non-toxicity, time-saving and high catalytic efficiency compared to other classic protein-labeling enzymes. TurboID-based PL has been successfully applied in animal, microorganism and plant systems, particularly to screen transient or weak protein interactions, and detect spatially or temporally restricted local proteomes in living cells. This review concludes classic PPI approaches in plant response to abiotic stresses and their limitations for identifying complex network of regulatory proteins of plant abiotic stresses, and introduces the working mechanism of TurboID-based PL, as well as its feasibility and advantages in plant abiotic stress research. We hope the information summarized in this article can serve as technical references for further understanding the regulation of plant adaptation to abiotic stress at the protein level.

Transcriptome Sequence Analysis of the Defense Responses of Resistant and Susceptible Cucumber Strains to Podosphaera xanthii

Powdery mildew (PM) caused by Podosphaera xanthii poses a continuous threat to the performance and yield of the cucumber (Cucumis sativus L.). Control in the initial stages of infection is particularly important. Here, we studied the differential physiological and transcriptomic changes between PM-resistant strain B21-a-2-1-2 and PM-susceptible strain B21-a-2-2-2 at the early stage of P. xanthii attack. When challenged with P. xanthii, the tolerant line can postpone the formation of the pathogen primary germ. Comparative transcriptomic analysis suggested that DEGs related to the cell wall and to pathogen and hormone responses were similar enriched in both cucumber lines under P. xanthii infection. Notably, the number of DEGs triggered by P. xanthii in B21-a-2-1-2 was quintuple that in B21-a-2-2-2, revealing that the success of defense of resistant cucumber is due to rapidly mobilizing multiple responses. The unique responses detected were genes related to SA signaling, MAPK signaling, and Dof and WRKY transcription factors. Furthermore, 5 P. xanthii -inducible hub genes were identified, including GLPK, ILK1, EIN2, BCDHβ1, and RGGA, which are considered to be key candidate genes for disease control. This study combined multiple analytical approaches to capture potential molecular players and will provide key resources for developing cucumber cultivars resistant to pathogen stress.

Plant Metabolic Network 15: A resource of genome-wide metabolism databases for 126 plants and algae

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.

Mapping the SARS-CoV-2-Host Protein-Protein Interactome by Affinity Purification Mass Spectrometry and Proximity-Dependent Biotin Labeling: A Rational and Straightforward Route to Discover Host-Directed Anti-SARS-CoV-2 Therapeutics

Protein-protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a "high resolution" view of virus-host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.

Analysis of Protein-Protein Interaction Networks through Computational Approaches

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein-protein interaction prediction.

Optimized Detection of Protein-Protein and Protein-DNA Interactions, with Particular Application to Plant Telomeres

Characterization of protein-protein and protein-DNA interactions is critical to understand mechanisms governing the biology of cells. Here we describe optimized methods and their mutual combinations for this purpose: bimolecular fluorescence complementation (BiFC), co-immunoprecipitation (Co-IP), yeast two-hybrid systems (Y2H), and chromatin immunoprecipitation (ChIP). These improved protocols  detect trimeric complexes in which two proteins of interest interact indirectly via a protein sandwiched between them. They also allow isolation of low-abundance chromatin proteins and confirmation that proteins of interest are associated with specific DNA sequences, for example telomeric tracts. Here we describe these methods and their application to map interactions of several telomere- and telomerase-associated proteins and to purify a sufficient amount of chromatin from Arabidopsis thaliana for further investigations (e.g., next-generation sequencing, hybridization).

Functional Role of N-Terminal Extension of Human AP Endonuclease 1 In Coordination of Base Excision DNA Repair via Protein-Protein Interactions

Human apurinic/apyrimidinic endonuclease 1 (APE1) has multiple functions in base excision DNA repair (BER) and other cellular processes. Its eukaryote-specific N-terminal extension plays diverse regulatory roles in interaction with different partners. Here, we explored its involvement in interaction with canonical BER proteins. Using fluorescence based-techniques, we compared binding affinities of the full-length and N-terminally truncated forms of APE1 (APE1NΔ35 and APE1NΔ61) for functionally and structurally different DNA polymerase β (Polβ), X-ray repair cross-complementing protein 1 (XRCC1), and poly(adenosine diphosphate (ADP)-ribose) polymerase 1 (PARP1), in the absence and presence of model DNA intermediates. Influence of the N-terminal truncation on binding the AP site-containing DNA was additionally explored. These data suggest that the interaction domain for proteins is basically formed by the conserved catalytic core of APE1. The N-terminal extension being capable of dynamically interacting with the protein and DNA partners is mostly responsible for DNA-dependent modulation of protein–protein interactions. Polβ, XRCC1, and PARP1 were shown to more efficiently regulate the endonuclease activity of the full-length protein than that of APE1NΔ61, further suggesting contribution of the N-terminal extension to BER coordination. Our results advance the understanding of functional roles of eukaryote-specific protein extensions in highly coordinated BER processes.

Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets

Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

The Arabidopsis heterotrimeric G-protein β subunit, AGB1, is required for guard cell calcium sensing and calcium-induced calcium release

Cytosolic calcium concentration ([Ca²⁺ ]_cyt ) and heterotrimeric G-proteins are universal eukaryotic signaling elements. In plant guard cells, extracellular calcium (Ca_o ) is as strong a stimulus for stomatal closure as the phytohormone abscisic acid (ABA), but underlying mechanisms remain elusive. Here, we report that the sole Arabidopsis heterotrimeric Gβ subunit, AGB1, is required for four guard cell Ca_o responses: induction of stomatal closure; inhibition of stomatal opening; [Ca²⁺ ]_cyt oscillation; and inositol 1,4,5-trisphosphate (InsP3) production. Stomata in wild-type Arabidopsis (Col) and in mutants of the canonical Gα subunit, GPA1, showed inhibition of stomatal opening and promotion of stomatal closure by Ca_o . By contrast, stomatal movements of agb1 mutants and agb1/gpa1 double-mutants, as well as those of the agg1agg2 Gγ double-mutant, were insensitive to Ca_o . These behaviors contrast with ABA-regulated stomatal movements, which involve GPA1 and AGB1/AGG3 dimers, illustrating differential partitioning of G-protein subunits among stimuli with similar ultimate impacts, which may facilitate stimulus-specific encoding. AGB1 knockouts retained reactive oxygen species and NO production, but lost YC3.6-detected [Ca²⁺ ]_cyt oscillations in response to Ca_o , initiating only a single [Ca²⁺ ]_cyt spike. Experimentally imposed [Ca²⁺ ]_cyt oscillations restored stomatal closure in agb1. Yeast two-hybrid and bimolecular complementation fluorescence experiments revealed that AGB1 interacts with phospholipase Cs (PLCs), and Ca_o induced InsP3 production in Col but not in agb1. In sum, G-protein signaling via AGB1/AGG1/AGG2 is essential for Ca_o -regulation of stomatal apertures, and stomatal movements in response to Ca_o apparently require Ca²⁺ -induced Ca²⁺ release that is likely dependent on Gβγ interaction with PLCs leading to InsP3 production.

Look Closely, the Beautiful May Be Small: Precursor-Derived Peptides in Plants

During the past decade, a flurry of research focusing on the role of peptides as short- and long-distance signaling molecules in plant cell communication has been undertaken. Here, we focus on peptides derived from nonfunctional precursors, and we address several key questions regarding peptide signaling. We provide an overview of the regulatory steps involved in producing a biologically active peptide ligand that can bind its corresponding receptor(s) and discuss how this binding and subsequent activation lead to specific cellular outputs. We discuss different experimental approaches that can be used to match peptide ligands with their receptors. Lastly, we explore how peptides evolved from basic signaling units regulating essential processes in plants to more complex signaling systems as new adaptive traits developed and how nonplant organisms exploit this signaling machinery by producing peptide mimics.

Exploring the protein-protein interaction landscape in plants

Protein-protein interactions (PPIs) represent an essential aspect of plant systems biology. Identification of key protein players and their interaction networks provide crucial insights into the regulation of plant developmental processes and into interactions of plants with their environment. Despite the great advance in the methods for the discovery and validation of PPIs, still several challenges remain. First, the PPI networks are usually highly dynamic, and the in vivo interactions are often transient and difficult to detect. Therefore, the properties of the PPIs under study need to be considered to select the most suitable technique, because each has its own advantages and limitations. Second, besides knowledge on the interacting partners of a protein of interest, characteristics of the interaction, such as the spatial or temporal dynamics, are highly important. Hence, multiple approaches have to be combined to obtain a comprehensive view on the PPI network present in a cell. Here, we present the progress in commonly used methods to detect and validate PPIs in plants with a special emphasis on the PPI features assessed in each approach and how they were or can be used for the study of plant interactions with their environment.

High Content Positional Biosensor Assay to Screen for Compounds that Prevent or Disrupt Androgen Receptor and Transcription Intermediary Factor 2 Protein-Protein Interactions

Transcriptional Intermediary Factor 2 (TIF2) is a key Androgen receptor (AR) coactivator that has been implicated in the development and progression of castration resistant prostate cancer (CRPC). This chapter describes the implementation of an AR-TIF2 protein-protein interaction (PPI) biosensor assay to screen for small molecules that can induce AR-TIF2 PPIs, inhibit the DHT-induced formation of AR-TIF2 PPIs, or disrupt pre-existing AR-TIF2 PPIs. The biosensor assay employs high content imaging and analysis to quantify AR-TIF2 PPIs and integrates physiologically relevant cell-based assays with the specificity of binding assays by incorporating structural information from AR and TIF2 functional domains along with intracellular targeting sequences using fluorescent protein reporters. Expression of the AR-Red Fluorescent Protein (RFP) "prey" and TIF2-Green Fluorescent Protein (GFP) "bait" components of the biosensor is directed by recombinant adenovirus (rAV) expression constructs that facilitated a simple co-infection protocol to produce homogeneous expression of both biosensors that is scalable for screening. In untreated cells, AR-RFP expression is localized predominantly to the cytoplasm and TIF2-GFP expression is localized only in the nucleoli of the nucleus. Exposure to DHT induces the co-localization of AR-RFP within the TIF2-GFP positive nucleoli of the nucleus. The AR-TIF2 biosensor assay therefore recapitulates the ligand-induced translocation of latent AR from the cytoplasm to the nucleus, and the PPIs between AR and TIF2 result in the colocalization of AR-RFP within TIF2-GFP expressing nucleoli. The AR-TIF2 PPI biosensor approach offers significant promise for identifying molecules with potential to modulate AR transcriptional activity in a cell-specific manner that may overcome the development of resistance and progression to CRPC.

Live Cell Imaging and Confocal Microscopy

The availability of more specific dyes for a subset of endomembrane compartments, combined with the development of genetically encoded probes and advanced microscopy technologies, makes live cell imaging an approach that goes beyond the microscopically observation of cell structure. Here we describe the latest improved techniques to investigate protein-protein interaction, protein topology, and protein dynamics.Furthermore, we depict new technical approaches to identify mutants for chloroplast morphology and distribution through the tracking of chlorophyll fluorescence, as well as mutants for chloroplast movement.

How to prove the existence of metabolons?

Sequential enzymes in biosynthetic pathways are organized in metabolons. It is challenging to provide experimental evidence for the existence of metabolons as biosynthetic pathways are composed of highly dynamic protein-protein interactions. Many different methods are being applied, each with strengths and weaknesses. We will present and evaluate several techniques that have been applied in providing evidence for the orchestration of the biosynthetic pathways of cyanogenic glucosides and glucosinolates in metabolons. These evolutionarily related pathways have ER-localized cytochromes P450 that are proposed to function as anchoring site for assembly of the enzymes into metabolons. Additionally, we have included commonly used techniques, even though they have not been used (yet) on these two pathways. In the review, special attention will be given to less-exploited fluorescence-based methods such as FCS and FLIM. Ultimately, understanding the orchestration of biosynthetic pathways may contribute to successful engineering in heterologous hosts.

Optimizing FRET-FLIM Labeling Conditions to Detect Nuclear Protein Interactions at Native Expression Levels in Living Arabidopsis Roots

Protein complex formation has been extensively studied using Förster resonance energy transfer (FRET) measured by Fluorescence Lifetime Imaging Microscopy (FLIM). However, implementing this technology to detect protein interactions in living multicellular organism at single-cell resolution and under native condition is still difficult to achieve. Here we describe the optimization of the labeling conditions to detect FRET-FLIM in living plants. This study exemplifies optimization procedure involving the identification of the optimal position for the labels either at the N or C terminal region and the selection of the bright and suitable, fluorescent proteins as donor and acceptor labels for the FRET study. With an effective optimization strategy, we were able to detect the interaction between the stem cell regulators SHORT-ROOT and SCARECROW at endogenous expression levels in the root pole of living Arabidopsis embryos and developing lateral roots by FRET-FLIM. Using this approach we show that the spatial profile of interaction between two transcription factors can be highly modulated in reoccurring and structurally resembling organs, thus providing new information on the dynamic redistribution of nuclear protein complex configurations in different developmental stages. In principle, our optimization procedure for transcription factor complexes is applicable to any biological system.

A photo-responsive F-box protein FOF2 regulates floral initiation by promoting FLC expression in Arabidopsis

Floral initiation is regulated by various genetic pathways in response to light, temperature, hormones and developmental status; however, the molecular mechanisms underlying the interactions between different genetic pathways are not fully understood. Here, we show that the photoresponsive gene FOF2 (F-box of flowering 2) negatively regulates flowering. FOF2 encodes a putative F-box protein that interacts specifically with ASK14, and its overexpression results in later flowering under both long-day and short-day photoperiods. Conversely, transgenic plants expressing the F-box domain deletion mutant of FOF2 (FOF2ΔF), or double loss of function mutant of FOF2 and FOL1 (FOF2-LIKE 1) present early flowering phenotypes. The late flowering phenotype of the FOF2 overexpression lines is suppressed by the flc-3 loss-of-function mutation. Furthermore, FOF2 mRNA expression is regulated by autonomous pathway gene FCA, and the repressive effect of FOF2 in flowering can be overcome by vernalization. Interestingly, FOF2 expression is regulated by light. The protein level of FOF2 accumulates in response to light, whereas it is degraded under dark conditions via the 26S proteasome pathway. Our findings suggest a possible mechanistic link between light conditions and the autonomous floral promotion pathway in Arabidopsis.

Dual-targeting of Arabidopsis DMP1 isoforms to the tonoplast and the plasma membrane

The reports of dual-targeted proteins in plants have steadily increased over the past years. The vast majority of these proteins are soluble proteins distributed between compartments of the non-secretory pathway, predominantly chloroplasts and mitochondria. In contrast, dual-targeted transmembrane proteins, especially of the secretory pathway, are rare and the mechanisms leading to their differential targeting remain largely unknown. Here, we report dual-targeting of the Arabidopsis DUF679 Membrane Protein 1 (DMP1) to the tonoplast (TP) and the plasma membrane (PM). In Arabidopsis and tobacco two equally abundant DMP1 isoforms are synthesized by alternative translation initiation: a full length protein, DMP1.1, and a truncated one, DMP1.2, which lacks the N-terminal 19 amino acids including a TP-targeting dileucine motif. Accumulation of DMP1.1 and DMP1.2 in the TP and the PM, respectively, is Brefeldin A-sensitive, indicating transit via the Golgi. However, DMP1.2 interacts with DMP1.1, leading to extensive rerouting of DMP1.2 to the TP and “eclipsed” localization of DMP1.2 in the PM where it is barely visible by confocal laser scanning microscopy but clearly detectable by membrane fractionation. It is demonstrated that eGFP fusion to either DMP1 terminus can cause mistargeting artifacts: C-terminal fusion to DMP1.1 or DMP1.2 results in altered ER export and N-terminal fusion to DMP1.1 causes mistargeting to the PM, presumably by masking of the TP targeting signal. These results illustrate how the interplay of alternative translation initiation, presence or absence of targeting information and rerouting due to protein-protein interaction determines the ultimate distribution of a transmembrane protein between two membranes.

Prediction of Protein-Protein Interactions by Evidence Combining Methods

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Reconfiguring the AR-TIF2 Protein-Protein Interaction HCS Assay in Prostate Cancer Cells and Characterizing the Hits from a LOPAC Screen

The continued activation of androgen receptor (AR) transcription and elevated expression of AR and transcriptional intermediary factor 2 (TIF2) coactivator observed in prostate cancer (CaP) recurrence and the development of castration-resistant CaP (CRPC) support a screening strategy for small-molecule inhibitors of AR-TIF2 protein-protein interactions (PPIs) to find new drug candidates. Small molecules can elicit tissue selective effects, because the cells of distinct tissues express different levels and cohorts of coregulatory proteins. We reconfigured the AR-TIF2 PPI biosensor (PPIB) assay in the PC-3 CaP cell line to determine whether AR modulators and hits from an AR-TIF2 PPIB screen conducted in U-2 OS cells would behave differently in the CaP cell background. Although we did not observe any significant differences in the compound responses between the assay performed in osteosarcoma and CaP cells, the U-2 OS AR-TIF2 PPIB assay would be more amenable to screening, because both the virus and cell culture demands are lower. We implemented a testing paradigm of counter-screens and secondary hit characterization assays that allowed us to identify and deprioritize hits that inhibited/disrupted AR-TIF2 PPIs and AR transcriptional activation (AR-TA) through antagonism of AR ligand binding or by non-specifically blocking nuclear receptor trafficking. Since AR-TIF2 PPI inhibitor/disruptor molecules act distally to AR ligand binding, they have the potential to modulate AR-TA in a cell-specific manner that is distinct from existing anti-androgen drugs, and to overcome the development of resistance to AR antagonism. We anticipate that the application of this testing paradigm to characterize the hits from an AR-TIF2 PPI high-content screening campaign will enable us to prioritize the AR-TIF2 PPI inhibitor/disruptor leads that have potential to be developed into novel therapeutics for CaP and CRPC.

Improving analytical methods for protein-protein interaction through implementation of chemically inducible dimerization

When investigating interactions between two proteins with complementary reporter tags in yeast two-hybrid or split GFP assays, it remains troublesome to discriminate true- from false-negative results and challenging to compare the level of interaction across experiments. This leads to decreased sensitivity and renders analysis of weak or transient interactions difficult to perform. In this work, we describe the development of reporters that can be chemically induced to dimerize independently of the investigated interactions and thus alleviate these issues. We incorporated our reporters into the widely used split ubiquitin-, bimolecular fluorescence complementation (BiFC)- and Förster resonance energy transfer (FRET)- based methods and investigated different protein-protein interactions in yeast and plants. We demonstrate the functionality of this concept by the analysis of weakly interacting proteins from specialized metabolism in the model plant Arabidopsis thaliana. Our results illustrate that chemically induced dimerization can function as a built-in control for split-based systems that is easily implemented and allows for direct evaluation of functionality.

PTIR: Predicted Tomato Interactome Resource

Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.

Protein-protein interaction analysis for functional characterization of helicases

Helicases are enzymes involved in nucleic acid metabolism, playing major roles in replication, transcription, and repair. Defining helicases oligomerization state and transient and persistent protein interactions is essential for understanding of their function. In this article we review current methods for the protein-protein interaction analysis, and discuss examples of its application to the study of helicases: Pif1 and DDX3. Proteomics methods are our main focus - affinity pull-downs and chemical cross-linking followed by mass spectrometry. We review advantages and limitations of these methods and provide general guidelines for their implementation in the functional analysis of helicases.

NRPB3, the third largest subunit of RNA polymerase II, is essential for stomatal patterning and differentiation in Arabidopsis

Stomata are highly specialized epidermal structures that control transpiration and gas exchange between plants and the environment. Signal networks underlying stomatal development have been previously uncovered but much less is known about how signals involved in stomatal development are transmitted to RNA polymerase II (Pol II or RPB), which plays a central role in the transcription of mRNA coding genes. Here, we identify a partial loss-of-function mutation of the third largest subunit of nuclear DNA-dependent Pol II (NRPB3) that exhibits an increased number of stomatal lineage cells and paired stomata. Phenotypic and genetic analyses indicated that NRPB3 is not only required for correct stomatal patterning, but is also essential for stomatal differentiation. Protein-protein interaction assays showed that NRPB3 directly interacts with two basic helix-loop-helix (bHLH) transcription factors, FAMA and INDUCER OF CBF EXPRESSION1 (ICE1), indicating that NRPB3 serves as an acceptor for signals from transcription factors involved in stomatal development. Our findings highlight the surprisingly conserved activating mechanisms mediated by the third largest subunit of Pol II in eukaryotes.

Summary: RNA polymerase II subunit NRPB3 interacts with stomatal bHLH transcription factors FAMA and ICE1, connecting the stomatal development pathway to the general transcription machinery.

Preparation of Gap Junctions in Membrane Microdomains for Immunoprecipitation and Mass Spectrometry Interactome Analysis

Protein interaction networks at gap junction plaques are increasingly implicated in a variety of intracellular signaling cascades. Identifying protein interactions of integral membrane proteins is a valuable tool for determining channel function. However, several technical challenges exist. Subcellular fractionation of the bait protein matrix is usually required to identify less abundant proteins in complex homogenates. Sufficient solvation of the lipid environment without perturbation of the protein interactome must also be achieved. The present chapter describes the flotation of light and heavy liver tissue membrane microdomains to facilitate the identification and analysis of endogenous gap junction proteins and includes technical notes for translation to other integral membrane proteins, tissues, or cell culture models. These procedures are valuable tools for the enrichment of gap junction membrane compartments and for the identification of gap junction signaling interactomes.

Gγ recruitment systems specifically select PPI and affinity-enhanced candidate proteins that interact with membrane protein targets

Protein-protein interactions (PPIs) are crucial for the vast majority of biological processes. We previously constructed a Gγ recruitment system to screen PPI candidate proteins and desirable affinity-altered (affinity-enhanced and affinity-attenuated) protein variants. The methods utilized a target protein fused to a mutated G-protein γ subunit (Gγ_cyto) lacking the ability to localize to the inner leaflet of the plasma membrane. However, the previous systems were adapted to use only soluble cytosolic proteins as targets. Recently, membrane proteins have been found to form the principal nodes of signaling involved in diseases and have attracted a great deal of interest as primary drug targets. Here, we describe new protocols for the Gγ recruitment systems that are specifically designed to use membrane proteins as targets to overcome previous limitations. These systems represent an attractive approach to exploring novel interacting candidates and affinity-altered protein variants and their interactions with proteins on the inner side of the plasma membrane, with high specificity and selectivity.

Visualization and translocation of ternary Calcineurin-A/Calcineurin-B/Calmodulin-2 protein complexes by dual-color trimolecular fluorescence complementation

Fluorescence complementation (FC) techniques are expedient for analyzing bimolecular protein-protein interactions. Here we aimed to develop a method for visualization of ternary protein complexes using dual-color trimolecular fluorescence complementation (TriFC). Dual-color TriFC combines protein fragments of mCherry and mVenus, in which a scaffold protein is bilaterally fused to C-terminal fragments of both fluorescent proteins and combined with potential interacting proteins fused to an N-terminal fluorescent protein fragment. For efficient visual verification of ternary complex formation, TriFC was combined with a cytoplasm to plasma membrane translocation assay. Modular vector sets were designed which are fully compatible with previously reported bimolecular fluorescence complementation (BiFC) vectors. As a proof-of-principle, the ternary complex formation of the PP2B protein phosphatase Calcineurin-A/Calcineurin-B with Calmodulin-2 was investigated in transiently transformed Nicotiana benthamiana leaf epidermal cells. The results indicate a Calcineurin-B-induced interaction of Calmodulin-2 with Calcineurin-A. TriFC and the translocation of TriFC complexes provide a novel tool to investigate ternary complex formations with the simplicity of a BiFC approach. The robustness of FC applications and the opportunity to quantify fluorescence complementation render this assay suitable for a broad range of interaction analyses.

Small-angle X-ray scattering method to characterize molecular interactions: Proof of concept

Characterizing biomolecular interactions is crucial to the understanding of biological processes. Existing characterization methods have low spatial resolution, poor specificity, and some lack the capability for deep tissue imaging. We describe a novel technique that relies on small-angle X-ray scattering signatures from high-contrast molecular probes that correlate with the presence of biomolecular interactions. We describe a proof-of-concept study that uses a model system consisting of mixtures of monomer solutions of gold nanoparticles (GNPs) as the non-interacting species and solutions of GNP dimers linked with an organic molecule (dimethyl suberimidate) as the interacting species. We report estimates of the interaction fraction obtained with the proposed small-angle X-ray scattering characterization method exhibiting strong correlation with the known relative concentration of interacting and non-interacting species.

High-content positional biosensor screening assay for compounds to prevent or disrupt androgen receptor and transcriptional intermediary factor 2 protein-protein interactions

The androgen receptor-transcriptional intermediary factor 2 (AR-TIF2) positional protein-protein interaction (PPI) biosensor assay described herein combines physiologically relevant cell-based assays with the specificity of binding assays by incorporating structural information of AR and TIF2 functional domains along with intracellular targeting sequences and fluorescent reporters. Expression of the AR-red fluorescent protein (RFP) "prey" and TIF2-green fluorescent protein (GFP) "bait" components of the biosensor was directed by recombinant adenovirus constructs that expressed the ligand binding and activation function 2 surface domains of AR fused to RFP with nuclear localization and nuclear export sequences, and three α-helical LXXLL motifs from TIF2 fused to GFP and an HIV Rev nucleolar targeting sequence. In unstimulated cells, AR-RFP was localized predominantly to the cytoplasm and TIF2-GFP was localized to nucleoli. Dihydrotestosterone (DHT) treatment induced AR-RFP translocation into the nucleus where the PPIs between AR and TIF2 resulted in the colocalization of both biosensors within the nucleolus. We adapted the translocation enhanced image analysis module to quantify the colocalization of the AR-RFP and TIF2-GFP biosensors in images acquired on the ImageXpress platform. DHT induced a concentration-dependent AR-TIF2 colocalization and produced a characteristic condensed punctate AR-RFP PPI nucleolar distribution pattern. The heat-shock protein 90 inhibitor 17-N-allylamino-17-demethoxygeldanamycin (17-AAG) and antiandrogens flutamide and bicalutamide inhibited DHT-induced AR-TIF2 PPI formation with 50% inhibition concentrations (IC50s) of 88.5±12.5 nM, 7.6±2.4 μM, and 1.6±0.4 μM, respectively. Images of the AR-RFP distribution phenotype allowed us to distinguish between 17-AAG and flutamide, which prevented AR translocation, and bicalutamide, which blocked AR-TIF2 PPIs. We screened the Library of Pharmacologically Active Compounds (LOPAC) set for compounds that inhibited AR-TIF2 PPI formation or disrupted preexisting complexes. Eleven modulators of steroid family nuclear receptors (NRs) and 6 non-NR ligands inhibited AR-TIF2 PPI formation, and 10 disrupted preexisting complexes. The hits appear to be either AR antagonists or nonspecific inhibitors of NR activation and trafficking. Given that the LOPAC set represents such a small and restricted biological and chemical diversity, it is anticipated that screening a much larger and more diverse compound library will be required to find AR-TIF2 PPI inhibitors/disruptors. The AR-TIF2 protein-protein interaction biosensor (PPIB) approach offers significant promise for identifying molecules with potential to modulate AR transcriptional activity in a cell-specific manner that is distinct from the existing antiandrogen drugs that target AR binding or production. Small molecules that disrupt AR signaling at the level of AR-TIF2 PPIs may also overcome the development of resistance and progression to castration-resistant prostate cancer.

Quantitative characterization of protein-protein complexes involved in base excision DNA repair

Base Excision Repair (BER) efficiently corrects the most common types of DNA damage in mammalian cells. Step-by-step coordination of BER is facilitated by multiple interactions between enzymes and accessory proteins involved. Here we characterize quantitatively a number of complexes formed by DNA polymerase β (Polβ), apurinic/apyrimidinic endonuclease 1 (APE1), poly(ADP-ribose) polymerase 1 (PARP1), X-ray repair cross-complementing protein 1 (XRCC1) and tyrosyl-DNA phosphodiesterase 1 (TDP1), using fluorescence- and light scattering-based techniques. Direct physical interactions between the APE1-Polβ, APE1-TDP1, APE1-PARP1 and Polβ-TDP1 pairs have been detected and characterized for the first time. The combined results provide strong evidence that the most stable complex is formed between XRCC1 and Polβ. Model DNA intermediates of BER are shown to induce significant rearrangement of the Polβ complexes with XRCC1 and PARP1, while having no detectable influence on the protein–protein binding affinities. The strength of APE1 interaction with Polβ, XRCC1 and PARP1 is revealed to be modulated by BER intermediates to different extents, depending on the type of DNA damage. The affinity of APE1 for Polβ is higher in the complex with abasic site-containing DNA than after the APE1-catalyzed incision. Our findings advance understanding of the molecular mechanisms underlying coordination and regulation of the BER process.

Fast and continuous-flow detection and separation of DNA complexes and DNA in nanofluidic chip format

Fast separation of DNA and detection of protein/DNA complexes are important in many state-of-the-art molecular medicine technologies, like the production of gene vaccines or medical diagnostics. Here, we describe a nanofluidic chip-based technique for fast, efficient, and virtually label-free detection and separation of protein/DNA and drug/DNA complexes and topological DNA variants. The mechanism is based on a continuous-flow dielectrophoresis at a nanoslit and allows efficient separation of small DNA fragments (<7,000 base pairs) and fast detection of DNA complexes within 1 min.

Significant reduction of BiFC non-specific assembly facilitates in planta assessment of heterotrimeric G-protein interactors

Protein networks and signaling cascades are key mechanisms for intra- and intercellular signal transduction. Identifying the interacting partners of a protein can provide vital clues regarding its physiological role. The bimolecular fluorescence complementation (BiFC) assay has become a routine tool for in vivo analysis of protein-protein interactions and their subcellular location. Although the BiFC system has improved since its inception, the available options for in planta analysis are still subject to very low signal-to-noise ratios, and a systematic comparison of BiFC confounding background signals has been lacking. Background signals can obscure weak interactions, provide false positives, and decrease confidence in true positives. To overcome these problems, we performed an extensive in planta analysis of published BiFC fragments used in metazoa and plants, and then developed an optimized single vector BiFC system which utilizes monomeric Venus (mVenus) split at residue 210, and contains an integrated mTurquoise2 marker to precisely identify transformed cells in order to distinguish true negatives. Here we provide our streamlined double ORF expression (pDOE) BiFC system, and show that our advance in BiFC methodology functions even with an internally fused mVenus210 fragment. We illustrate the efficacy of the system by providing direct visualization of Arabidopsis MLO1 interacting with a calmodulin-like (CML) protein, and by showing that heterotrimeric G-protein subunits Gα (GPA1) and Gβ (AGB1) interact in plant cells. We further demonstrate that GPA1 and AGB1 each physically interact with PLDα1 in planta, and that mutation of the so-called PLDα1 'DRY' motif abolishes both of these interactions.

In vivo proximity labeling for the detection of protein-protein and protein-RNA interactions

Accurate and sensitive detection of protein–protein and protein–RNA interactions is key to understanding their biological functions. Traditional methods to identify these interactions require cell lysis and biochemical manipulations that exclude cellular compartments that cannot be solubilized under mild conditions. Here, we introduce an in vivo proximity labeling (IPL) technology that employs an affinity tag combined with a photoactivatable probe to label polypeptides and RNAs in the vicinity of a protein of interest in vivo. Using quantitative mass spectrometry and deep sequencing, we show that IPL correctly identifies known protein–protein and protein–RNA interactions in the nucleus of mammalian cells. Thus, IPL provides additional temporal and spatial information for the characterization of biological interactions in vivo.

Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes

An increasing number of experiments have been designed to detect intracellular and intercellular molecular interactions. Based on these molecular interactions (especially protein interactions), molecular networks have been built for using in several typical applications, such as the discovery of new disease genes and the identification of drug targets and molecular complexes. Because the data are incomplete and a considerable number of false-positive interactions exist, protein interactions from different sources are commonly integrated in network analyses to build a stable molecular network. Although various types of integration strategies are being applied in current studies, the topological properties of the networks from these different integration strategies, especially typical applications based on these network integration strategies, have not been rigorously evaluated. In this paper, systematic analyses were performed to evaluate 11 frequently used methods using two types of integration strategies: empirical and machine learning methods. The topological properties of the networks of these different integration strategies were found to significantly differ. Moreover, these networks were found to dramatically affect the outcomes of typical applications, such as disease gene predictions, drug target detections, and molecular complex identifications. The analysis presented in this paper could provide an important basis for future network-based biological researches.

Quantifying interactions of a membrane protein embedded in a lipid nanodisc using fluorescence correlation spectroscopy

Using fluorescence correlation spectroscopy, we measured a dissociation constant of 20 nM between EGFP-labeled LcrV from Yersinia pestis and its cognate membrane-bound protein YopB inserted into a lipid nanodisc. The combination of fluorescence correlation spectroscopy and nanodisc technologies provides a powerful approach to accurately measure binding constants of interactions between membrane bound and soluble proteins in solution. Straightforward sample preparation, acquisition, and analysis procedures make this combined technology attractive for accurately measuring binding kinetics for this important class of protein-protein interactions.

Protein interactome analysis of 12 mitogen-activated protein kinase kinase kinase in rice using a yeast two-hybrid system

The mitogen-activated protein kinase (MAPK) cascade is composed at least of MAP3K (for MAPK kinase kinase), MAP2K, and MAPK family modules. These components together play a central role in mediating extracellular signals to the cell and vice versa by interacting with their partner proteins. However, the MAP3K-interacting proteins remain poorly investigated in plants. Here, we utilized a yeast two-hybrid system and bimolecular fluorescence complementation in the model crop rice (Oryza sativa) to map MAP3K-interacting proteins. We identified 12 novel nonredundant interacting protein pairs (IPPs) representing 11 nonredundant interactors using 12 rice MAP3Ks (available as full-length cDNA in the rice KOME (http://cdna01.dna.affrc.go.jp/cDNA/) at the time of experimental design and execution) as bait and a rice seedling cDNA library as prey. Of the 12 MAP3Ks, only six had interacting protein partners. The established MAP3K interactome consisted of two kinases, three proteases, two forkhead-associated domain-containing proteins, two expressed proteins, one E3 ligase, one regulatory protein, and one retrotransposon protein. Notably, no MAP3K showed physical interaction with either MAP2K or MAPK. Seven IPPs (58.3%) were confirmed in vivo by bimolecular fluorescence complementation. Subcellular localization of 14 interactors, together involved in nine IPPs (75%) further provide prerequisite for biological significance of the IPPs. Furthermore, GO of identified interactors predicted their involvement in diverse physiological responses, which were supported by a literature survey. These findings increase our knowledge of the MAP3K-interacting proteins, help in proposing a model of MAPK modules, provide a valuable resource for developing a complete map of the rice MAPK interactome, and allow discussion for translating the interactome knowledge to rice crop improvement against environmental factors.