Development and application of a recombination-based library versus library high- throughput yeast two-hybrid (RLL-Y2H) screening system

Late embryogenesis abundant gene GhLEA-5 of semi-wild cotton positively regulates salinity tolerance in upland cotton

The productivity and quality of cotton are significantly compromised by salt stress. In this study, the full length of encoding region and genomic DNA sequences of GhLEA_5A/D (Gh_A10G166600 and Gh_D10G188300), which belong to the late embryogenesis abundant gene family in allotetraploid upland cotton (Gossypium hirsutum L.) and semi-wild cotton (Gossypium purpurascens), were isolated and their salt tolerance was experimentally confirmed. Analysis of sequence alignments and phylogenetic trees indicated a significant level of homology between GhLEA-5A and GhLEA-5D. Additionally, a conserved protein motif was consistently identified across these sequences. The transcriptome data analysis showed that the expression level of GhLEA-5A/D was substantially enhanced in the leaves of salt-tolerant G. purpurascens accessions compared to salt-sensitive materials. In the real-time quantitative reverse transcription PCR (qRT-PCR) assays, notable expression levels of the GhLEA-5D gene were detected in salt-tolerant upland cotton materials following exposure to salt stress at 3 and 12-hour time points. The suppression of GhLEA-5A/D transcription via Virus-induced Gene Silencing (VIGS) technology significantly exacerbates salt sensitivity in cotton. This is evidenced by the nearly 50 % increase in malondialdehyde (MDA) content alongside a 60 % reduction in peroxidase (POD) levels in salt-treated plants when compared to the control group. The overexpression of the GhLEA-5A/D gene conferred enhanced salt tolerance in Arabidopsis, resulting in a 25 % increase in root length, a 30 % improvement in survival rate, a 15 % increase in water retention, and a 15 % boost in photosynthetic efficiency. The chlorophyll fluorescence parameters, enzyme activities, diaminobenzine, and nitroblue tetrazolium staining suggested that GhLEA-5A/D likely exhibited a positive regulatory role for cotton responding to salt stress. Furthermore, we identified 76 candidate proteins that potentially interact with GhLEA-5 in the yeast two-hybrid screening library. These results provide a theoretical basis for studying the mechanism of cotton salt tolerance and offer new resources for improving cotton salt tolerance genes.

Global Protein Interactome Mapping in Rice Using Barcode-Indexed PCR Coupled with HiFi Long-Read Sequencing

Establishing the protein-protein interaction network sheds light on functional genomics studies by providing insights from known counterparts. However, the rice interactome has barely been studied due to the lack of massive, reliable, and cost-effective methodologies. Here, the development of a barcode-indexed PCR coupled with HiFi long-read sequencing pipeline (BIP-seq) is reported for high throughput Protein Protein Interaction (PPI)identification. BIP-seq is essentially built on the integration of library versus library Y2H mating strategy to facilitate the efficient acquisition of random PPI colonies, semi-mechanized dual barcode-indexed yeast colony PCR for the large-scale indexed amplification of bait and prey cDNAs, and massive pac-bio sequencing of PCR amplicon pools. It is demonstrated that BIP-seq could map over 15 000 high-confidence (≈62.5% could be verified by Bimolecular fluorescence Complementation (BiFC)) rice PPIs within 2 months, outperforming the other reported methods. In addition, the obtained 23 032 rice PPIs, including 22,665 newly identified PPIs, greatly expanded the current rice PPI dataset, provided a comprehensive overview of the rice PPIs networks, and could be a valuable asset in facilitating functional genomics research in rice.

Structure-Based Modeling of the Gut Bacteria-Host Interactome Through Statistical Analysis of Domain-Domain Associations Using Machine Learning

The gut microbiome, a complex ecosystem of microorganisms, plays a pivotal role in human health and disease. The gut microbiome's influence extends beyond the digestive system to various organs, and its imbalance is linked to a wide range of diseases, including cancer and neurodevelopmental, inflammatory, metabolic, cardiovascular, autoimmune, and psychiatric diseases. Despite its significance, the interactions between gut bacteria and human proteins remain understudied, with less than 20,000 experimentally validated protein interactions between the host and any bacteria species. This study addresses this knowledge gap by predicting a protein-protein interaction network between gut bacterial and human proteins. Using statistical associations between Pfam domains, a comprehensive dataset of over one million experimentally validated pan-bacterial-human protein interactions, as well as inter- and intra-species protein interactions from various organisms, were used for the development of a machine learning-based prediction method to uncover key regulatory molecules in this dynamic system. This study's findings contribute to the understanding of the intricate gut microbiome-host relationship and pave the way for future experimental validation and therapeutic strategies targeting the gut microbiome interplay.

Structure-Based Deep Learning Framework for Modeling Human-Gut Bacterial Protein Interactions

Background: The interaction network between the human host proteins and the proteins of the gut bacteria is essential for the establishment of human health, and its dysregulation directly contributes to disease development. Despite its great importance, experimental data on protein-protein interactions (PPIs) between these species are sparse due to experimental limitations. Methods: This study presents a deep learning-based framework for predicting PPIs between human and gut bacterial proteins using structural data. The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. The model addresses common challenges in PPI datasets, such as class imbalance, using focal loss to emphasize harder-to-classify samples. Results: The results demonstrated that this framework exhibits robust performance, with high precision and recall across validation and test datasets, underscoring its generalizability. By incorporating proteoforms in the analysis, the model accounts for the structural complexity within proteomes, making predictions biologically relevant. Conclusions: These findings offer a scalable tool for investigating the interactions between the host and the gut microbiota, potentially yielding new treatment targets and diagnostics for disorders linked to the microbiome.

Massively parallel measurement of protein-protein interactions by sequencing using MP3-seq

Protein-protein interactions (PPIs) regulate many cellular processes and engineered PPIs have cell and gene therapy applications. Here, we introduce massively parallel PPI measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Lastly, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics-based energy terms to predict MP3-seq values. We find that AF-M-based models could be valuable for prescreening interactions but experimentally measuring interactions remains necessary to rank their strengths quantitatively.

Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein-protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Interactome between ASFV and host immune pathway proteins

IMPORTANCE

African swine fever (ASF), caused by African swine fever virus (ASFV), has become a major crisis for the pork industry in recent years. The mechanism for ASFV pathology and the clinical symptoms difference of ASF between domestic pigs and reservoir hosts remain to be elucidated. We deciphered the comprehensive protein-protein interaction (PPI) network between ASFV and host immune pathways. The intensive PPI network contained both ASFV-host immune pathway PPI and ASFV-ASFV PPI information, providing a comprehensive ASFV-host interaction landscape. Furthermore, the ASFV-host PPI difference between domestic pigs and warthogs was explored, which will be instructive for exploring essential candidates involved in ASFV pathology. Moreover, we screened the inhibitory effect of ASFV proteins in the PPI with cGAS-STING pathway on IFN-I and NF-κB, further providing possible functions of ASFV-host PPI network in innate immune regulation.

Massively parallel protein-protein interaction measurement by sequencing (MP3-seq) enables rapid screening of protein heterodimers

Protein-protein interactions (PPIs) regulate many cellular processes, and engineered PPIs have cell and gene therapy applications. Here we introduce massively parallel protein-protein interaction measurement by sequencing (MP3-seq), an easy-to-use and highly scalable yeast-two-hybrid approach for measuring PPIs. In MP3-seq, DNA barcodes are associated with specific protein pairs, and barcode enrichment can be read by sequencing to provide a direct measure of interaction strength. We show that MP3-seq is highly quantitative and scales to over 100,000 interactions. We apply MP3-seq to characterize interactions between families of rationally designed heterodimers and to investigate elements conferring specificity to coiled-coil interactions. Finally, we predict coiled heterodimer structures using AlphaFold-Multimer (AF-M) and train linear models on physics simulation energy terms to predict MP3-seq values. We find that AF-M and AF-M complex prediction-based models could be valuable for pre-screening interactions, but that measuring interactions experimentally remains necessary to rank their strengths quantitatively.

A multiplexed bacterial two-hybrid for rapid characterization of protein-protein interactions and iterative protein design

Protein-protein interactions (PPIs) are crucial for biological functions and have applications ranging from drug design to synthetic cell circuits. Coiled-coils have been used as a model to study the sequence determinants of specificity. However, building well-behaved sets of orthogonal pairs of coiled-coils remains challenging due to inaccurate predictions of orthogonality and difficulties in testing at scale. To address this, we develop the next-generation bacterial two-hybrid (NGB2H) method, which allows for the rapid exploration of interactions of programmed protein libraries in a quantitative and scalable way using next-generation sequencing readout. We design, build, and test large sets of orthogonal synthetic coiled-coils, assayed over 8,000 PPIs, and used the dataset to train a more accurate coiled-coil scoring algorithm (iCipa). After characterizing nearly 18,000 new PPIs, we identify to the best of our knowledge the largest set of orthogonal coiled-coils to date, with fifteen on-target interactions. Our approach provides a powerful tool for the design of orthogonal PPIs.

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

ZmHOX32 is related to photosynthesis and likely functions in plant architecture of maize

HOX32, a member of the HD-ZIP III family, functions in the leaf morphogenesis and plant photosynthesis. However, the regulatory mechanism of HOX32 in maize has not been studied and the regulatory relationship in photosynthesis is unclear. We conducted a comprehensive study, including phylogenetic analysis, expression profiling at both transcriptome and translatome levels, subcellular localization, tsCUT&Tag, co-expression analysis, and association analysis with agronomic traits on HOX32 for the dissection of the functional roles of HOX32. ZmHOX32 shows conservation in plants. As expected, maize HOX32 protein is specifically expressed in the nucleus. ZmHOX32 showed constitutively expression at both transcriptome and translatome levels. We uncovered the downstream target genes of ZmHOX32 by tsCUT&Tag and constructed a cascaded regulatory network combining the co-expression networks. Both direct and indirect targets of ZmHOX32 showed significant gene ontology enrichment in terms of photosynthesis in maize. The association study suggested that ZmHOX32 plays an important role in regulation of plant architecture. Our results illustrate a complex regulatory network of HOX32 involving in photosynthesis and plant architecture, which deepens our understanding of the phenotypic variation in plants.

Involvement of 2′-5′ oligoadenylate synthetase-like protein in the survival of Mycobacterium tuberculosis avirulent strain in macrophages

Mycobacterium tuberculosis (M. tuberculosis) can replicate in the macrophage by interfering with many host protein functions. While it is far from known these host proteins for controlling M. tuberculosis infection. Herein, we infected macrophages including THP-1 and Raw264.7 cells with M. tuberculosis and identified the differentially expressed genes (DEGs) in the interferon signaling pathway. Among them, 2′-5′ oligoadenylate synthetase-like (OASL) underwent the greatest upregulation in M. tuberculosis-infected macrophages. Knockdown of the expression of OASL attenuated M. tuberculosis survival in macrophages. Further, bioinformatics analysis revealed the potential interaction axis of OASL-TAB3- Rv0127, which was further validated by the yeast-two-hybrid (Y2H) assay and Co-IP. This interaction axis might regulate the M. tuberculosis survival and proliferation in macrophages. The study reveals a possible role of OASL during M. tuberculosis infection as a target to control its propagation.

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.

A multi-omics integrative network map of maize

Networks are powerful tools to uncover functional roles of genes in phenotypic variation at a system-wide scale. Here, we constructed a maize network map that contains the genomic, transcriptomic, translatomic and proteomic networks across maize development. This map comprises over 2.8 million edges in more than 1,400 functional subnetworks, demonstrating an extensive network divergence of duplicated genes. We applied this map to identify factors regulating flowering time and identified 2,651 genes enriched in eight subnetworks. We validated the functions of 20 genes, including 18 with previously unknown connections to flowering time in maize. Furthermore, we uncovered a flowering pathway involving histone modification. The multi-omics integrative network map illustrates the principles of how molecular networks connect different types of genes and potential pathways to map a genome-wide functional landscape in maize, which should be applicable in a wide range of species.

ERV14 receptor impacts mycelial growth via its interactions with cell wall synthase and transporters in Aspergillus niger

Efficient protein secretion is closely correlated with vesicle sorting and packaging, especially with cargo receptor-mediated selective transport for ER exit. Even though Aspergillus niger is considered an industrially natural host for protein production due to its exceptional secretion capacity, the trafficking mechanism in the early secretory pathway remains a black box for us to explore. Here, we identified and characterized all putative ER cargo receptors of the three families in A. niger. We successfully constructed overexpression and deletion strains of each receptor and compared the colony morphology and protein secretion status of each strain. Among them, the deletion of Erv14 severely inhibited mycelial growth and secretion of extracellular proteins such as glucoamylase. To gain a comprehensive understanding of the proteins associated with Erv14, we developed a high-throughput method by combining yeast two-hybrid (Y2H) with next-generation sequencing (NGS) technology. We found Erv14 specifically interacted with transporters. Following further validation of the quantitative membrane proteome, we determined that Erv14 was associated with the transport of proteins involved in processes such as cell wall synthesis, lipid metabolism, and organic substrate metabolism.

Next-Generation Yeast Two-Hybrid Screening to Discover Protein-Protein Interactions

Yeast two-hybrid is a powerful approach to discover new protein-protein interactions. Traditional methods involve screening a target protein against a cDNA expression library and assaying individual positive colonies to identify interacting partners. Here we describe a simple approach to perform yeast two-hybrid screens of a cDNA expression library in batch liquid culture. Positive yeast cell populations are enriched under selection and then harvested en masse. Prey cDNAs are amplified and used as input for next-generation sequencing libraries for identification, quantification, and ranking.

Screening of genes encoding proteins that interact with ISG15: Probing a cDNA library from a snakehead fish cell line using a yeast two-hybrid system

Interferon-stimulated gene 15 (ISG15) regulates cellular life processes, including defense responses against infection by a variety of viral pathogens, by binding to target proteins. At present, various fish ISG15s have been identified, but the biological function of ISG15 in snakehead fish is still unclear. In this study, total RNA was extracted from snakehead fish cell line E11, ds cDNA was synthesized and purified using SMART technology, and the resulting cDNA library was screened by co-transforming yeast cells. The library titer was 4.28 × 10⁹ CFU/mL. Using snakehead ISG15 as the bait protein, the recombinant bait vector pGBKT7-ISG15 was constructed and transformed into the yeast strain Y2HGold. The toxicity and self-activation activity of the bait vector were detected on the deficient medium, and the prey proteins interacting with ISG15 were screened. In total, 19 interacting proteins of ISG15 were identified, including mitotic checkpoint protein BUB3, hypothetical protein SnRVgp6, elongation factor 1-beta, 60S ribosomal protein L9, dual specificity protein phosphatase 5-like, eukaryotic translation initiation factor 3 subunit I and ferritin. A yeast spotting assay further probed the interaction between ISG15 and DUSP5. These results increase our understanding of the interaction network of snakehead ISG15 and will aid in exploring the underlying mechanisms of snakehead ISG15 functions in the future.

Structural insight into protein-protein interactions between intestinal microbiome and host

Protein-protein interactions between the microbiome and host organism play an important role in shaping host health. These host-modulating proteins have therapeutic potential in treating microbiome-linked disorders such as inflammatory bowel disease and obesity. Structural analysis of interacting proteins provides highly mechanistic insight into the domains driving these interactions and the resulting influence on host cell processes. Here, we briefly review recent publication of microbiome protein structures involved in host binding interactions, the effects of these interactions on host physiology, and the need for further study to increase the ability to detect proteins with therapeutic potential.

Clp protease and antisense RNA jointly regulate the global regulator CarD to mediate mycobacterial starvation response

Under starvation conditions, bacteria tend to slow down their translation rate by reducing rRNA synthesis, but the way they accomplish that may vary in different bacteria. In Mycobacterium species, transcription of rRNA is activated by the RNA polymerase (RNAP) accessory transcription factor CarD, which interacts directly with RNAP to stabilize the RNAP-promoter open complex formed on rRNA genes. The functions of CarD have been extensively studied, but the mechanisms that control its expression remain obscure. Here, we report that the level of CarD was tightly regulated when mycobacterial cells switched from nutrient-rich to nutrient-deprived conditions. At the translational level, an antisense RNA of carD (AscarD) was induced in a SigF-dependent manner to bind with carD mRNA and inhibit CarD translation, while at the post-translational level, the residual intracellular CarD was quickly degraded by the Clp protease. AscarD thus worked synergistically with Clp protease to decrease the CarD level to help mycobacterial cells cope with the nutritional stress. Altogether, our work elucidates the regulation mode of CarD and delineates a new mechanism for the mycobacterial starvation response, which is important for the adaptation and persistence of mycobacterial pathogens in the host environment.

OUP accepted manuscript

Barcode fusion genetics (BFG) utilizes deep sequencing to improve the throughput of protein–protein interaction (PPI) screening in pools. BFG has been implemented in Yeast two-hybrid (Y2H) screens (BFG-Y2H). While Y2H requires test protein pairs to localize in the nucleus for reporter reconstruction, dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA) allows proteins to localize in broader subcellular contexts and proves to be largely orthogonal to Y2H. Here, we implemented BFG to DHFR-PCA (BFG-PCA). This plasmid-based system can leverage ORF collections across model organisms to perform comparative analysis, unlike the original DHFR-PCA that requires yeast genomic integration. The scalability and quality of BFG-PCA were demonstrated by screening human and yeast interactions for >11 000 bait-prey pairs. BFG-PCA showed high-sensitivity and high-specificity for capturing known interactions for both species. BFG-Y2H and BFG-PCA capture distinct sets of PPIs, which can partially be explained based on the domain orientation of the reporter tags. BFG-PCA is a high-throughput protein interaction technology to interrogate binary PPIs that exploits clone collections from any species of interest, expanding the scope of PPI assays.

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.

Revealing protein-protein interactions at the transcriptome scale by sequencing

We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.

Distinct Persistence Fate of Mycobacterium tuberculosis in Various Types of Cells

Mycobacterium tuberculosis can invade different cells with distinct persistence fates because cells are equipped with different host restriction factors. However, the underlying mechanisms remain elusive. Here, we infected THP1 and Raw264.7 macrophages cell lines, A549 epithelial cell line, and hBMEC and bEnd.3 endothelial cell lines with M. tuberculosis and demonstrated that M. tuberculosis significantly inhibited lysosome acidification in THP1, hBMEC, A549, and Raw264.7 cells, while, in bEnd.3 cells, M. tuberculosis was mainly delivered into acidified phagolysosomes and auto-lysosomes. The systematic gene profile analysis of different cells and intracellular M. tuberculosis showed that the phagosome autophagy-pathway-related genes itgb3 and atg3 were highly expressed in bEnd.3 cells. Knockdown of these genes significantly increased the number of viable intracellular M. tuberculosis bacilli by altering phagosomal trafficking in bEnd.3 cells. Treatment with itgb3 agonist significantly decreased M. tuberculosis survival in vivo. These findings could facilitate the identification of anti-M. tuberculosis host genes and guide M. tuberculosis-resistant livestock breeding.

IMPORTANCE As an intracellular pathogen, Mycobacterium tuberculosis could avoid host cell immune clearance using multiple strategies for its long-term survival. Understanding these processes could facilitate the development of new approaches to restrict intracellular M. tuberculosis survival. Here, we characterized the detailed molecular events occurring during intracellular trafficking of M. tuberculosis in macrophage, epithelial, and endothelial cell lines and found that ITGB3 facilitates M. tuberculosis clearance in endothelial cells through altering phagosomal trafficking. Meanwhile, the treatment with ITGB3 agonist could reduce bacterial load in vivo. Our results identified new anti-M. tuberculosis restriction factors and illuminated a new anti-M. tuberculosis defense mechanism.

Visualizing protein-protein interactions in plants by rapamycin-dependent delocalization

Identifying protein-protein interactions (PPIs) is crucial for understanding biological processes. Many PPI tools are available, yet only some function within the context of a plant cell. Narrowing down even further, only a few tools allow complex multi-protein interactions to be visualized. Here, we present a conditional in vivo PPI tool for plant research that meets these criteria. Knocksideways in plants (KSP) is based on the ability of rapamycin to alter the localization of a bait protein and its interactors via the heterodimerization of FKBP and FRB domains. KSP is inherently free from many limitations of other PPI systems. This in vivo tool does not require spatial proximity of the bait and prey fluorophores and it is compatible with a broad range of fluorophores. KSP is also a conditional tool and therefore the visualization of the proteins in the absence of rapamycin acts as an internal control. We used KSP to confirm previously identified interactions in Nicotiana benthamiana leaf epidermal cells. Furthermore, the scripts that we generated allow the interactions to be quantified at high throughput. Finally, we demonstrate that KSP can easily be used to visualize complex multi-protein interactions. KSP is therefore a versatile tool with unique characteristics and applications that complements other plant PPI methods.

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.

Rv3722c Promotes Mycobacterium tuberculosis Survival in Macrophages by Interacting With TRAF3

Mycobacterium tuberculosis (M.tb) secretes numerous proteins to interfere with host immune response for its long-term survival. As one of the top abundant M.tb secreted proteins, Rv3722c was found to be essential for bacilli growth. However, it remains elusive how this protein interferes with the host immune response and regulates M.tb survival. Here, we confirmed that Rv3722c interacted with host TRAF3 to promote M.tb replication in macrophages. Knock-down of TRAF3 attenuated the effect of Rv3722c on the intracellular M.tb survival. The interaction between Rv3722c and TRAF3 hampered MAPK and NF-κB pathways, resulting in a significant increase of IFN-β expression and decrease of IL-1β, IL-6, IL-12p40, and TNF-α expression. Our study revealed that Rv3722c interacted with TRAF3 and interrupted its downstream pathways to promote M.tb survival in macrophages. These findings facilitate further understanding of the mechanism of M.tb secreted proteins in regulating the host cell immune response and promoting its intracellular survival.

In vivo interactome profiling by enzyme-catalyzed proximity labeling

Enzyme-catalyzed proximity labeling (PL) combined with mass spectrometry (MS) has emerged as a revolutionary approach to reveal the protein-protein interaction networks, dissect complex biological processes, and characterize the subcellular proteome in a more physiological setting than before. The enzymatic tags are being upgraded to improve temporal and spatial resolution and obtain faster catalytic dynamics and higher catalytic efficiency. In vivo application of PL integrated with other state of the art techniques has recently been adapted in live animals and plants, allowing questions to be addressed that were previously inaccessible. It is timely to summarize the current state of PL-dependent interactome studies and their potential applications. We will focus on in vivo uses of newer versions of PL and highlight critical considerations for successful in vivo PL experiments that will provide novel insights into the protein interactome in the context of human diseases.

Directed co-evolution of interacting protein-peptide pairs by compartmentalized two-hybrid replication (C2HR)

Directed evolution methodologies benefit from read-outs quantitatively linking genotype to phenotype. We therefore devised a method that couples protein–peptide interactions to the dynamic read-out provided by an engineered DNA polymerase. Fusion of a processivity clamp protein to a thermostable nucleic acid polymerase enables polymerase activity and DNA amplification in otherwise prohibitive high-salt buffers. Here, we recapitulate this phenotype by indirectly coupling the Sso7d processivity clamp to Taq DNA polymerase via respective fusion to a high affinity and thermostable interacting protein–peptide pair. Escherichia coli cells co-expressing protein–peptide pairs can directly be used in polymerase chain reactions to determine relative interaction strengths by the measurement of amplicon yields. Conditional polymerase activity is further used to link genotype to phenotype of interacting protein–peptide pairs co-expressed in E. coli using the compartmentalized self-replication directed evolution platform. We validate this approach, termed compartmentalized two-hybrid replication, by selecting for high-affinity peptides that bind two model protein partners: SpyCatcher and the large fragment of NanoLuc luciferase. We further demonstrate directed co-evolution by randomizing both protein and peptide components of the SpyCatcher–SpyTag pair and co-selecting for functionally interacting variants.

Construction and characterization of a high-quality cDNA library of Cymbidium faberi suitable for yeast one- and two-hybrid assays

Background

Cymbidium faberi is one of the oldest cultivars of oriental orchids, with an elegant flower fragrance. In order to investigate the molecular mechanism and the functions of related proteins in the methyl jasmonate (MeJA) signaling pathway, one of the main components of flower fragrance in C. faberi, yeast one- and two-hybrid three-frame cDNA libraries were constructed.

Results

In this study, a modified cDNA library used for yeast one- and two-hybrid screening was successfully constructed, with a recombinant efficiency of 95%. The lengths of inserted fragments ranged from 750~3000 bp, and the library capacity reached 6 × 10⁹ CFU/ μg of cDNA insert, which was suitable for the requirements of subsequent screening. Finally, a homologous protein related with pathogenesis was screened out by the bait vector of CfbHLH36, which may participate in the MeJA signaling pathway.

Conclusion

The yeast one- and two-hybrid library of C. faberi provides large amounts of useful information for the functional genomics research in C. faberi, and this method could also be applied to other plants to screen DNA-protein and protein-protein interactions.