Antagonistic roles of canonical and Alternative-RPA in disease-associated tandem CAG repeat instability.

Expansions of repeat DNA tracts cause >70 diseases, and ongoing expansions in brains exacerbate disease. During expansion mutations, single-stranded DNAs (ssDNAs) form slipped-DNAs. We find the ssDNA-binding complexes canonical replication protein A (RPA1, RPA2, and RPA3) and Alternative-RPA (RPA1, RPA3, and primate-specific RPA4) are upregulated in Huntington disease and spinocerebellar ataxia type 1 (SCA1) patient brains. Protein interactomes of RPA and Alt-RPA reveal unique and shared partners, including modifiers of CAG instability and disease presentation. RPA enhances in vitro melting, FAN1 excision, and repair of slipped-CAGs and protects against CAG expansions in human cells. RPA overexpression in SCA1 mouse brains ablates expansions, coincident with decreased ATXN1 aggregation, reduced brain DNA damage, improved neuron morphology, and rescued motor phenotypes. In contrast, Alt-RPA inhibits melting, FAN1 excision, and repair of slipped-CAGs and promotes CAG expansions. These findings suggest a functional interplay between the two RPAs where Alt-RPA may antagonistically offset RPA's suppression of disease-associated repeat expansions, which may extend to other DNA processes.

Transcription factor protein interactomes reveal genetic determinants in heart disease.

Congenital heart disease (CHD) is present in 1% of live births, yet identification of causal mutations remains challenging. We hypothesized that genetic determinants for CHDs may lie in the protein interactomes of transcription factors whose mutations cause CHDs. Defining the interactomes of two transcription factors haplo-insufficient in CHD, GATA4 and TBX5, within human cardiac progenitors, and integrating the results with nearly 9,000 exomes from proband-parent trios revealed an enrichment of de novo missense variants associated with CHD within the interactomes. Scoring variants of interactome members based on residue, gene, and proband features identified likely CHD-causing genes, including the epigenetic reader GLYR1. GLYR1 and GATA4 widely co-occupied and co-activated cardiac developmental genes, and the identified GLYR1 missense variant disrupted interaction with GATA4, impairing in vitro and in vivo function in mice. This integrative proteomic and genetic approach provides a framework for prioritizing and interrogating genetic variants in heart disease.

A Map of Protein-Metabolite Interactions Reveals Principles of Chemical Communication.

Metabolite-protein interactions control a variety of cellular processes, thereby playing a major role in maintaining cellular homeostasis. Metabolites comprise the largest fraction of molecules in cells, but our knowledge of the metabolite-protein interactome lags behind our understanding of protein-protein or protein-DNA interactomes. Here, we present a chemoproteomic workflow for the systematic identification of metabolite-protein interactions directly in their native environment. The approach identified a network of known and novel interactions and binding sites in Escherichia coli, and we demonstrated the functional relevance of a number of newly identified interactions. Our data enabled identification of new enzyme-substrate relationships and cases of metabolite-induced remodeling of protein complexes. Our metabolite-protein interactome consists of 1,678 interactions and 7,345 putative binding sites. Our data reveal functional and structural principles of chemical communication, shed light on the prevalence and mechanisms of enzyme promiscuity, and enable extraction of quantitative parameters of metabolite binding on a proteome-wide scale.

Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames.

All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids-all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.

Dosage sensitivity to Pumilio1 variants in the mouse brain reflects distinct molecular mechanisms.

Different mutations in the RNA-binding protein Pumilio1 (PUM1) cause divergent phenotypes whose severity tracks with dosage: a mutation that reduces PUM1 levels by 25% causes late-onset ataxia, whereas haploinsufficiency causes developmental delay and seizures. Yet PUM1 targets are derepressed to equal degrees in both cases, and the more severe mutation does not hinder PUM1's RNA-binding ability. We therefore considered the possibility that the severe mutation might disrupt PUM1 interactions, and identified PUM1 interactors in the murine brain. We find that mild PUM1 loss derepresses PUM1-specific targets, but the severe mutation disrupts interactions with several RNA-binding proteins and the regulation of their targets. In patient-derived cell lines, restoring PUM1 levels restores these interactors and their targets to normal levels. Our results demonstrate that dosage sensitivity does not always signify a linear relationship with protein abundance but can involve distinct mechanisms. We propose that to understand the functions of RNA-binding proteins in a physiological context will require studying their interactions as well as their targets.

TasA Fibre Interactions Are Necessary for Bacillus subtilis Biofilm Structure.

The extracellular matrix of biofilms provides crucial structural support to the community and protection from environmental perturbations. TasA, a key Bacillus subtilis biofilm matrix protein, forms both amyloid and non-amyloid fibrils. Non-amyloid TasA fibrils are formed via a strand-exchange mechanism, whereas the amyloid-like form involves non-specific self-assembly. We performed mutagenesis of the N-terminus to assess the role of non-amyloid fibrils in biofilm development. We find that the N-terminal tail is essential for the formation of structured biofilms, providing evidence that the strand-exchange fibrils are the active form in the biofilm matrix. Furthermore, we demonstrate that fibre formation alone is not sufficient to give structure to the biofilm. We build an interactome of TasA with other extracellular protein components, and identify important interaction sites. Our results provide insight into how protein-matrix interactions modulate biofilm development.

A feature extraction free approach for protein interactome inference from co-elution data.

Protein complexes are key functional units in cellular processes. High-throughput techniques, such as co-fractionation coupled with mass spectrometry (CF-MS), have advanced protein complex studies by enabling global interactome inference. However, dealing with complex fractionation characteristics to define true interactions is not a simple task, since CF-MS is prone to false positives due to the co-elution of non-interacting proteins by chance. Several computational methods have been designed to analyze CF-MS data and construct probabilistic protein-protein interaction (PPI) networks. Current methods usually first infer PPIs based on handcrafted CF-MS features, and then use clustering algorithms to form potential protein complexes. While powerful, these methods suffer from the potential bias of handcrafted features and severely imbalanced data distribution. However, the handcrafted features based on domain knowledge might introduce bias, and current methods also tend to overfit due to the severely imbalanced PPI data. To address these issues, we present a balanced end-to-end learning architecture, Software for Prediction of Interactome with Feature-extraction Free Elution Data (SPIFFED), to integrate feature representation from raw CF-MS data and interactome prediction by convolutional neural network. SPIFFED outperforms the state-of-the-art methods in predicting PPIs under the conventional imbalanced training. When trained with balanced data, SPIFFED had greatly improved sensitivity for true PPIs. Moreover, the ensemble SPIFFED model provides different voting schemes to integrate predicted PPIs from multiple CF-MS data. Using the clustering software (i.e. ClusterONE), SPIFFED allows users to infer high-confidence protein complexes depending on the CF-MS experimental designs. The source code of SPIFFED is freely available at: https://github.com/bio-it-station/SPIFFED.

trans-Interacting Plasma Membrane Proteins and Binding Partner Identification.

Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.

Homologous mapping yielded a comprehensive predicted protein-protein interaction network for peanut (Arachis hypogaea L.).

BACKGROUND: Protein-protein interactions are the primary means through which proteins carry out their functions. These interactions thus have crucial roles in life activities. The wide availability of fully sequenced animal and plant genomes has facilitated establishment of relatively complete global protein interaction networks for some model species. The genomes of cultivated and wild peanut (Arachis hypogaea L.) have also been sequenced, but the functions of most of the encoded proteins remain unclear. RESULTS: We here used homologous mapping of validated protein interaction data from model species to generate complete peanut protein interaction networks for A. hypogaea cv. 'Tifrunner' (282,619 pairs), A. hypogaea cv. 'Shitouqi' (256,441 pairs), A. monticola (440,470 pairs), A. duranensis (136,363 pairs), and A. ipaensis (172,813 pairs). A detailed analysis was conducted for a putative disease-resistance subnetwork in the Tifrunner network to identify candidate genes and validate functional interactions. The network suggested that DX2UEH and its interacting partners may participate in peanut resistance to bacterial wilt; this was preliminarily validated with overexpression experiments in peanut. CONCLUSION: Our results provide valuable new information for future analyses of gene and protein functions and regulatory networks in peanut.

Combination of in vivo proximity labeling and co-immunoprecipitation identifies the host target network of a tumor-inducing effector in the fungal maize pathogen Ustilago maydis.

Plant pathogens secrete effectors, which target host proteins to facilitate infection. The Ustilago maydis effector UmSee1 is required for tumor formation in the leaf during infection of maize. UmSee1 interacts with maize SGT1 (suppressor of G2 allele of skp1) and blocks its phosphorylation in vivo. In the absence of UmSee1, U. maydis cannot trigger tumor formation in the bundle sheath. However, it remains unclear which host processes are manipulated by UmSee1 and the UmSee1-SGT1 interaction to cause the observed phenotype. Proximity-dependent protein labeling involving the turbo biotin ligase tag (TurboID) for proximal labeling of proteins is a powerful tool for identifying the protein interactome. We have generated transgenic U. maydis that secretes biotin ligase-fused See1 effector (UmSee1-TurboID-3HA) directly into maize cells. This approach, in combination with conventional co-immunoprecipitation, allowed the identification of additional UmSee1 interactors in maize cells. Collectively, our data identified three ubiquitin-proteasome pathway-related proteins (ZmSIP1, ZmSIP2, and ZmSIP3) that either interact with or are close to UmSee1 during host infection of maize with U. maydis. ZmSIP3 represents a cell cycle regulator whose degradation appears to be promoted in the presence of UmSee1. Our data provide a possible explanation of the requirement for UmSee1 in tumor formation during U. maydis-Zea mays interaction.

Identification of mutant p53-specific proteins interaction network using TurboID-based proximity labeling.

BACKGROUNDS: Although several studies on mutant p53 reported cancer-promoting activities via "gain-of-function", the mechanism underlying these differences in function between p53 R175H, R175P, and p53 wild-type (WT) remains unclear. METHODS: Linking miniTurbo with p53 WT, R175H, and R175P, the expression of fusion and biotinylated proteins were assessed by Western blotting. The function and subcellular localization of fusion proteins were detected by apoptosis assay and immunofluorescence, respectively. Biotinylated proteins were analyzed by liquid chromatography-tandem mass spectrometry, followed by bioinformatics analysis. Small-scale pull-downs and Co-Immunoprecipitation were performed to validate the interaction between mutant or p53 WT and biotinylated proteins. RESULTS: The fusion protein's cellular localization and function were consistent with those of previous studies on the corresponding p53. Comparative profiles of R175H versus WT showed that most of the interacting proteins belonged to the intracellular organelle lumen, and the pathways involved were metabolism and genetic information processing. Comparative profiles of R175P versus WT suggested that the majority of the interacting proteins belonged to the intracellular organelle lumen and the extracellular membrane-bounded organelle, and the pathways involved were metabolism and genetic information processing pathways. The comparison between R175H and R175P revealed that most interacting proteins belonged to the organelle lumen, and pathways involved were genetic information processing pathways. Finally, the mutation of p53 significantly altered the interaction with the target proteins were confirmed. CONCLUSION: We verified the reliability of the miniTurbo system and obtained candidate targets of mutant p53, which provided new thoughts on the mechanism of mutant p53 gain-of-function and new potential targets for cancer therapy.

Multiple-genotypes transcriptional analysis revealed candidates genes and nucleotide variants for improvement of quality characteristics in tea (Camellia sinensis (L.) O. Kuntze).

Tea quality is a polygenic trait that exhibits tremendous genetic variability due to accumulation of array of secondary metabolites. To elucidate global molecular insights controlling quality attributes, metabolite profiling and transcriptome sequencing of twelve diverse tea cultivars was performed in tea shoots harvested during quality season. RP-HPLC-DAD analysis of quality parameters revealed significant difference in catechins, theanine and caffeine contents. Transcriptome sequencing resulted into 50,107 non-redundant transcripts with functional annotations of 81.6% (40,847) of the transcripts. Interestingly, 2872 differentially expressed transcripts exhibited significant enrichment in 38 pathways (FDR ≤ 0.05) including secondary metabolism, amino acid and carbon metabolism. Thirty-eight key candidates reportedly involved in biosynthesis of fatty acid derived volatiles, volatile terpenes, glycoside hydrolysis and key quality related pathways (flavonoid, caffeine and theanine-biosynthesis) were highly expressed in catechins-rich tea cultivars. Furthermore, enrichment of candidates involved in flavonoid biosynthesis, transcriptional regulation, volatile terpene and biosynthesis of fatty acid derived volatile in Protein-Protein Interactome network revealed well-coordinated regulation of quality characteristics in tea. Additionally, ascertainment of 23,649 non-synonymous SNPs and validation of candidate SNPs present in quality related genes suggests their potential utility in genome-wide mapping and marker development for expediting breeding of elite compound-rich tea cultivars.

Photocatalytic Chemical Crosslinking for Profiling RNA-Protein Interactions in Living Cells.

The dynamic interactions between RNAs and proteins play crucial roles in regulating diverse cellular processes. Proteome-wide characterization of these interactions in their native cellular context remains desirable but challenging. Herein, we developed a photocatalytic crosslinking (PhotoCAX) strategy coupled with mass spectrometry (PhotoCAX-MS) and RNA sequencing (PhotoCAX-seq) for the study of the composition and dynamics of protein-RNA interactions. By integrating the blue light-triggered photocatalyst with a dual-functional RNA-protein crosslinker (RP-linker) and the phase separation-based enrichment strategy, PhotoCAX-MS revealed a total of 2044 RBPs in human HEK293 cells. We further employed PhotoCAX to investigate the dynamic change of RBPome in macrophage cells upon LPS-stimulation, as well as the identification of RBPs interacting directly with the 5' untranslated regions of SARS-CoV-2 RNA.

The hybrid protein interactome contributes to rice heterosis as epistatic effects.

Heterosis is the phenomenon in which hybrid progeny exhibits superior traits in comparison with those of their parents. Genomic variations between the two parental genomes may generate epistasis interactions, which is one of the genetic hypotheses explaining heterosis. We postulate that protein-protein interactions specific to F1 hybrids (F1 -specific PPIs) may occur when two parental genomes combine, as the proteome of each parent may supply novel interacting partners. To test our assumption, an inter-subspecies hybrid interactome was simulated by in silico PPI prediction between rice japonica (cultivar Nipponbare) and indica (cultivar 9311). Four-thousand, six-hundred and twelve F1 -specific PPIs accounting for 20.5% of total PPIs in the hybrid interactome were found. Genes participating in F1 -specific PPIs tend to encode metabolic enzymes and are generally localized in genomic regions harboring metabolic gene clusters. To test the genetic effect of F1 -specific PPIs in heterosis, genomic selection analysis was performed for trait prediction with additive, dominant and epistatic effects separately considered in the model. We found that the removal of single nucleotide polymorphisms associated with F1 -specific PPIs reduced prediction accuracy when epistatic effects were considered in the model, but no significant changes were observed when additive or dominant effects were considered. In summary, genomic divergence widely dispersed between japonica and indica rice may generate F1 -specific PPIs, part of which may accumulatively contribute to heterosis according to our computational analysis. These candidate F1 -specific PPIs, especially for those involved in metabolic biosynthesis pathways, are worthy of experimental validation when large-scale protein interactome datasets are generated in hybrid rice in the future.

Proximity labeling and other novel mass spectrometric approaches for spatiotemporal protein dynamics.

BACKGROUND: Proteins are highly dynamic and their biological function is controlled by not only temporal abundance changes but also via regulated protein-protein interaction networks, which respond to internal and external perturbations. A wealth of novel analytical reagents and workflows allow studying spatiotemporal protein environments with great granularity while maintaining high throughput and ease of analysis. AREAS COVERED: We review technology advances for measuring protein-protein proximity interactions with an emphasis on proximity labeling, and briefly summarize other spatiotemporal approaches including protein localization, and their dynamic changes over time, specifically in human cells and mammalian tissues. We focus especially on novel technologies and workflows emerging within the past 5 years. This includes enrichment-based techniques (proximity labeling and crosslinking), separation-based techniques (organelle fractionation and size exclusion chromatography), and finally sorting-based techniques (laser capture microdissection and mass spectrometry imaging). EXPERT OPINION: Spatiotemporal proteomics is a key step in assessing biological complexity, understanding refined regulatory mechanisms, and forming protein complexes and networks. Studying protein dynamics across space and time holds promise for gaining deep insights into how protein networks may be perturbed during disease and aging processes, and offer potential avenues for therapeutic interventions, drug discovery, and biomarker development.

Specificity landscapes unmask submaximal binding site preferences of transcription factors.

We have developed Differential Specificity and Energy Landscape (DiSEL) analysis to comprehensively compare DNA-protein interactomes (DPIs) obtained by high-throughput experimental platforms and cutting edge computational methods. While high-affinity DNA binding sites are identified by most methods, DiSEL uncovered nuanced sequence preferences displayed by homologous transcription factors. Pairwise analysis of 726 DPIs uncovered homolog-specific differences at moderate- to low-affinity binding sites (submaximal sites). DiSEL analysis of variants of 41 transcription factors revealed that many disease-causing mutations result in allele-specific changes in binding site preferences. We focused on a set of highly homologous factors that have different biological roles but "read" DNA using identical amino acid side chains. Rather than direct readout, our results indicate that DNA noncontacting side chains allosterically contribute to sculpt distinct sequence preferences among closely related members of transcription factor families.

Rho GTPase ROP1 Interactome Analysis Reveals Novel ROP1-Associated Pathways for Pollen Tube Polar Growth in Arabidopsis.

ROP (Rho-like GTPases from plants) GTPases are polarly localized key regulators of polar growth in pollen tubes and other cells in plants. However, how ROP GTPases are regulated and how they control polar growth remains to be fully understood. To gain new insights into ROP-dependent mechanisms underlying polar cell growth, we characterized the interactome of ROP1 GTPase that controls Arabidopsis pollen tube (PT) tip growth, an extreme form of polar cell growth. We established an efficient method for culturing Arabidopsis pollen tubes in liquid medium, which was used for immunoprecipitation/mass spectrometry-based identification of ROP1-associated proteins. A total of 654 candidates were isolated from the ROP1 interactome in Arabidopsis pollen tubes, and GO (Gene Ontology) classification and pathway analysis revealed multiple uncharacterized ROP1-dependent processes including translation, cell wall modification, post transcriptional modification, and ion homeostasis, in addition to known ROP1-dependent pathways. The ROP1-interactome data was further supported by the co-expression of the candidate interactors in highly mature pollen with PT germination and growth defects being discovered in 25% (8/32) of the candidate mutant genes. Taken together, our work uncovers valuable information for the identification and functional elucidation of ROP-associated proteins in the regulation of polar growth, and provides a reliable reference to identify critical regulators of polar cell growth in the future.

RNA-binding proteins and heat-shock protein 90 are constituents of the cytoplasmic capping enzyme interactome.

The N7-methylguanosine cap is added in the nucleus early in gene transcription and is a defining feature of eukaryotic mRNAs. Mammalian cells also possess cytoplasmic machinery for restoring the cap at uncapped or partially degraded RNA 5' ends. Central to both pathways is capping enzyme (CE) (RNA guanylyltransferase and 5'-phosphatase (RNGTT)), a bifunctional, nuclear and cytoplasmic enzyme. CE is recruited to the cytoplasmic capping complex by binding of a C-terminal proline-rich sequence to the third Src homology 3 (SH3) domain of NCK adapter protein 1 (NCK1). To gain broader insight into the cellular context of cytoplasmic recapping, here we identified the protein interactome of cytoplasmic CE in human U2OS cells through two complementary approaches: chemical cross-linking and recovery with cytoplasmic CE and protein screening with proximity-dependent biotin identification (BioID). This strategy unexpectedly identified 66 proteins, 52 of which are RNA-binding proteins. We found that CE interacts with several of these proteins independently of RNA, mediated by sequences within its N-terminal triphosphatase domain, and we present a model describing how CE-binding proteins may function in defining recapping targets. This analysis also revealed that CE is a client protein of heat shock protein 90 (HSP90). Nuclear and cytoplasmic CEs were exquisitely sensitive to inhibition of HSP90, with both forms declining significantly following treatment with each of several HSP90 inhibitors. Importantly, steady-state levels of capped mRNAs decreased in cells treated with the HSP90 inhibitor geldanamycin, raising the possibility that the cytotoxic effect of these drugs may partially be due to a general reduction in translatable mRNAs.

Unphosphorylated STAT1 represses apoptosis in macrophages during Mycobacteriumtuberculosis infection.

In murine macrophages infected with Mycobacterium tuberculosis (Mtb), the level of phosphorylated STAT1 (P-STAT1), which drives the expression of many pro-apoptosis genes, increases quickly but then declines over a period of hours. By contrast, infection induces a continued increase in the level of unphosphorylated STAT1 that persists for several days. Here, we found that the level of unphosphorylated STAT1 correlated with the intracellular bacterial burden during the later stages of infection. To investigate the significance of a high level of unphosphorylated STAT1, we increased its concentration exogenously, and found that the apoptosis rate induced by Mtb was sufficiently decreased. Further experiments confirmed that unphosphorylated STAT1 affects the expression of several immune-associated genes and lessens the sensitivity of macrophages to CD95 (FAS)-mediated apoptosis during Mtb infection. Furthermore, we characterized 149 proteins that interacted with unphosphorylated STAT1 and the interactome network. The cooperation between unphosphorylated STAT1 and STAT3 results in downregulation of CD95 expression. Additionally, we verified that unphosphorylated STAT1 and IFIT1 competed for binding to eEF1A. Taken together, our data show that the role of unphosphorylated STAT1 differs from that of P-STAT1, and represses apoptosis in macrophages to promote immune evasion during Mtb infection.

Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474.

The investigational compound BIA 10-2474, designed as a long-acting and reversible inhibitor of fatty acid amide hydrolase for the treatment of neuropathic pain, led to the death of one participant and hospitalization of five others due to intracranial hemorrhage in a Phase I clinical trial. Putative off-target activities of BIA 10-2474 have been suggested to be major contributing factors to the observed neurotoxicity in humans, motivating our study's proteome-wide screening approach to investigate its polypharmacology. Accordingly, we performed an in silico screen against 80,923 protein structures reported in the Protein Data Bank. The resulting list of 284 unique human interactors was further refined using target-disease association analyses to a subset of proteins previously linked to neurological, intracranial, inflammatory, hemorrhagic or clotting processes and/or diseases. Eleven proteins were identified as potential targets of BIA 10-2474, and the two highest-scoring proteins, Factor VII and thrombin, both essential blood-clotting factors, were predicted to be inhibited by BIA 10-2474 and suggest a plausible mechanism of toxicity. Once this small molecule becomes commercially available, future studies will be conducted to evaluate the predicted inhibitory effect of BIA 10-2474 on blood clot formation specifically in the brain.