Putative Protein-protein Interactions Research Articles

Macrocephaly and Digital Anomalies Expand the Phenotypic Spectrum of PGAP2 Variants in Hyperphosphatasia with Impaired Intellectual Development Syndrome 3 (HPMRS3)

Glycosylphosphatidylinositols (GPIs) anchor over 150 proteins as GPI-anchored proteins (GPI-APs) with crucial roles in diverse biological processes. The highly conserved biosynthesis of GPI-APs involves precise steps with at least 21 genes, categorized as PIG and PGAP genes. Pathogenic variants in these genes are linked to human diseases, highlighting the importance of each biosynthesis step. PGAP2 stands out among these genes due to its association with an expanded clinical spectrum of neurodevelopmental disorder (NDD) phenotypes with biallelic pathogenic variants. We present four patients from two families, one consanguineous and the other nonconsanguineous, each displaying distinct clinical presentations, including intellectual disability, hyperphosphatasia, hearing impairment, and epilepsy, as well as craniofacial and digital anomalies. Genetic analyses revealed homozygous and novel compound heterozygous missense variants in PGAP2 in four affected individuals, confirming the molecular diagnosis of hyperphosphatasia with impaired intellectual development syndrome 3 (HPMRS3). Importantly, the three amino acids affected by missense variants exhibit complete conservation in 10 vertebrate species, illuminating their crucial role in the gene’s functionality. Protein modeling provided additional evidence for the pathogenicity of the three substitutions, demonstrating their detrimental impact on protein folding and putative protein-protein interactions, ultimately leading to impaired protein function. The four patients in our study displayed common phenotypic features, such as brachydactyly, camptodactyly, and syndactyly, which have not been previously documented in individuals with PGAP2 variants. Notably, the occurrence of macrocephaly in two affected brothers from a consanguineous Pakistani family represents a novel finding. These previously unreported digital anomalies, along with macrocephaly and the identification of novel compound heterozygous variants, contribute to the expansion of the phenotypic and genotypic spectrum of HPMRS3 associated with PGAP2 variants.

Abstract A145: CBFβ supports global protein translation in osteosarcoma and may provide a new therapeutic target

Abstract Background: Core binding factor beta (CBFβ) is a binding partner to the RUNX family of DNA binding transcription factors (RUNX1-3) and acts as a transcriptional co-activator by allosterically increasing their affinity for DNA. RUNX2 is the master regulator of bone growth and differentiation, and its binding to CBFβ is of particular interest to osteosarcoma (OS) research. Notably, increased expression of each is correlated with poor disease-free and overall survival in numerous sarcomas. CBFβ’s transcriptional role is well-understood but recent research in breast cancer cells proposes a non-canonical role in the regulation of protein translation, influencing translation of hundreds of mRNA transcripts via binding with heterogenous nuclear ribonucleoprotein K (hnRNPK). The binding of CBFβ to hnRNPK or RUNX1 is reportedly mutually exclusive, suggesting possible interplay between the two roles. We hypothesize that CBFβ regulates protein translation in OS, participating in the translation of RUNX2 and other oncoproteins, promoting a malignant phenotype. Furthermore, we propose that disrupting its translational role may result in an antitumor effect. While CBFβ’s transcriptional activities are well-characterized, we endeavored to isolate its translational role and investigate its contribution to OS. Methods: CRISPR/Cas9 was used to generate a CBFβ knockout (KO) U2OS cell line, while site-directed mutagenesis of CBFβ and lentiviral transduction of U2OS CBFβ KO cells was used to generate cell lines expressing FLAG-tagged wild type (WT) mutant CBFβ. Western Blotting (WB) was used to assess changes in protein expression and subcellular localization while RUNX2 mRNA expression was measured via qPCR and RNAseq. CBFβ’s interacting partners were identified by Immunoprecipitation Mass Spectrometry (IP-MS). Cellular electro-thermal shift assay was used to evaluate influences of CBFβ mutations on RUNX2 and hnRNPK binding. The Click-IT system followed by WB and IP-MS were used to assess global protein translation and synthesis rates of specific proteins, respectively. Results: CBFβ and hnRNPK interact in U2OS cells and loss of CBFβ leads to a decrease in global protein translation. This results in decreased RUNX2 protein, but not mRNA, expression. Re-introduction of WT CBFβ caused RUNX2 protein levels to recover while expression of CBFβ with key mutations in the binding interface abrogated this effect. Importantly, we observed this was not due to direct protection of RUNX2 from proteasomal degradation by WT CBFβ. We identified a cohort of proteins that appear to be translationally regulated by CBFβ and investigated the impact of CBFβ mutations on its translational activities and RUNX2 binding. We also identified a list of putative CBFβ protein-protein interactions in human OS cells that are altered by these mutations. Conclusions: CBFβ appears to participate in protein translation in OS, regulating the expression of numerous oncoproteins including RUNX2. Key mutations in CBFβ designed to interrupt RUNX2 binding also affect this non-canonical activity of CBFβ. Citation Format: Nicholas A Oldberg, Dayn R Godinez, Luke A Wittenburg. CBFβ supports global protein translation in osteosarcoma and may provide a new therapeutic target [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A145.

Dissection of Developmental Programs and Regulatory Modules Directing Endosperm Transfer Cell and Aleurone Identity in the Syncytial Endosperm of Barley.

Endosperm development in barley starts with the formation of a multinucleate syncytium, followed by cellularization in the ventral part of the syncytium generating endosperm transfer cells (ETCs) as first differentiating subdomain, whereas aleurone (AL) cells will originate from the periphery of the enclosing syncytium. Positional signaling in the syncytial stage determines cell identity in the cereal endosperm. Here, we performed a morphological analysis and employed laser capture microdissection (LCM)-based RNA-seq of the ETC region and the peripheral syncytium at the onset of cellularization to dissect developmental and regulatory programs directing cell specification in the early endosperm. Transcriptome data revealed domain-specific characteristics and identified two-component signaling (TCS) and hormone activities (auxin, ABA, ethylene) with associated transcription factors (TFs) as the main regulatory links for ETC specification. On the contrary, differential hormone signaling (canonical auxin, gibberellins, cytokinin) and interacting TFs control the duration of the syncytial phase and timing of cellularization of AL initials. Domain-specific expression of candidate genes was validated by in situ hybridization and putative protein-protein interactions were confirmed by split-YFP assays. This is the first transcriptome analysis dissecting syncytial subdomains of cereal seeds and provides an essential framework for initial endosperm differentiation in barley, which is likely also valuable for comparative studies with other cereal crops.

Protein-Protein Interactions in Abiotic Stress Signaling: An Overview of Biochemical and Biophysical Methods of Characterization.

The identification and characterization of bona fide abiotic stress signaling proteins can occur at different levels of the complete in vivo signaling cascade or network. Knowledge of a particular abiotic stress signaling protein could theoretically lead to the characterization of complete networks through the analysis of unknown proteins that interact with the previously known protein. Such signaling proteins of interest can indeed be experimentally used as bait proteins to catch interacting prey proteins, provided that the association of bait proteins and prey proteins should yield a biochemical or biophysical signal that can be detected. To this end, several biochemical and biophysical techniques are available to provide experimental evidence for specific protein-protein interactions, such as co-immunoprecipitation, bimolecular fluorescence complementation, tandem affinity purification coupled to mass spectrometry, yeast two hybrid, protein microarrays, Förster resonance energy transfer, or fluorescence correlation spectroscopy. This array of methods can be implemented to establish the biochemical reality of putative protein-protein interactions between two proteins of interest or to identify previously unknown partners related to an initially known protein of interest. The ultimate validity of these methods however depends on the in vitro/in vivo nature of the approach and on the heterologous/homologous context of the analysis. This chapter will review the application and success of some classical methods of protein-protein interaction analysis in the field of plant abiotic stress signaling.

Computational Systems Biology of Alfalfa - Bacterial Blight Host-Pathogen Interactions: Uncovering the Complex Molecular Networks for Developing Durable Disease Resistant Crop.

Medicago sativa (also known as alfalfa), a forage legume, is widely cultivated due to its high yield and high-value hay crop production. Infectious diseases are a major threat to the crops, owing to huge economic losses to the agriculture industry, worldwide. The protein-protein interactions (PPIs) between the pathogens and their hosts play a critical role in understanding the molecular basis of pathogenesis. Pseudomonas syringae pv. syringae ALF3 suppresses the plant’s innate immune response by secreting type III effector proteins into the host cell, causing bacterial stem blight in alfalfa. The alfalfa-P. syringae system has little information available for PPIs. Thus, to understand the infection mechanism, we elucidated the genome-scale host-pathogen interactions (HPIs) between alfalfa and P. syringae using two computational approaches: interolog-based and domain-based method. A total of ∼14 M putative PPIs were predicted between 50,629 alfalfa proteins and 2,932 P. syringae proteins by combining these approaches. Additionally, ∼0.7 M consensus PPIs were also predicted. The functional analysis revealed that P. syringae proteins are highly involved in nucleotide binding activity (GO:0000166), intracellular organelle (GO:0043229), and translation (GO:0006412) while alfalfa proteins are involved in cellular response to chemical stimulus (GO:0070887), oxidoreductase activity (GO:0016614), and Golgi apparatus (GO:0005794). According to subcellular localization predictions, most of the pathogen proteins targeted host proteins within the cytoplasm and nucleus. In addition, we discovered a slew of new virulence effectors in the predicted HPIs. The current research describes an integrated approach for deciphering genome-scale host-pathogen PPIs between alfalfa and P. syringae, allowing the researchers to better understand the pathogen’s infection mechanism and develop pathogen-resistant lines.

Extrinsic Complex Structure Developed through Novel Simulation Methodology: Key Putative Atomic-Level Interactions Identified

The ternary extrinsic complex of the coagulation pathway (Tissue Factor (TF): Factor VIIa (FVIIa): Factor X (FX)) is critically important to the regulation of blood clotting. Activation of FX in this membrane-bound complex is vital to both the major pathways which produce the explosive burst of thrombin required for clot formation to proceed. Despite high interest in this complex as a potential drug target for hemophilia and thrombosis, little has been determined experimentally concerning its structure due to the lipid-dependent nature of its formation. As the complex will only form on the surface of a membrane with a specific composition of anionic phospholipids, experimental structure determination through NMR or X-ray crystallography is prohibitively difficult.We will present the first atomic-level resolution structure of the ternary complex to include membrane interactions, developed using a novel methodology which combines protein-protein docking and extensive non-equilibrium molecular dynamics simulations. Over 560,000 protein-only structures predicting interactions between key domains of the extrinsic complex were first developed using the DOT2 protein docking program. Top docked structures were selected based on agreement with experimental information, and used to identify residues of FX and TF:FVIIa likely to interact. Protein-protein contact information from docking was then used to developed biased non-equilibrium simulations in which the complex was induced to form along a particular pathway. Using five non-equilibrium simulations of 200 ns, binding between membrane-bound structures of FX and TF:FVIIa was slowly induced. The highly mobile membrane mimetic (HMMM) model, an advanced membrane model which replaces slow-moving lipid tails with detergent molecules, was used to allow increased sampling of protein-lipid interacts during this deliberate complex formation. Each of the five resulting complexes was simulated for an additional 200 ns without biases, and putative key protein-protein and protein-lipid interactions involved in complex formation were identified. Residues on the membrane-binding domain of FX where found in the simulations to bind residues of TF previously identified experimentally as critical for complex formation. This structure provides not only the best available information on protein binding for the extrinsic complex but the only atomic-level information on the protein-lipid interactions which are crucial for its formation. DisclosuresNo relevant conflicts of interest to declare.

Mechanism of electron transfers mediated by cytochromes c and b 5 in mitochondria and endoplasmic reticulum: classical and murburn perspectives

We explore the mechanism of electron transfers mediated by cytochrome c, a soluble protein involved in mitochondrial oxidative phosphorylation and cytochrome b 5, a microsomal membrane protein acting as a redox aide in xenobiotic metabolism. We found minimal conservation in the sequence and surface amino acid residues of cytochrome c/b 5 proteins among divergent species. Therefore, we question the evolutionary logic for electron transfer (ET) occurring through affinity binding via recognition of specific surface residues/topography. Also, analysis of putative protein-protein interactions in the crystal structures of these proteins and their redox partners did not point to any specific interaction logic. A comparison of the kinetic and thermodynamic constants of wildtype vs. mutants did not provide strong evidence to support the binding-based ET paradigm, but indicated support for diffusible reactive species (DRS)-mediated process. Topographically divergent cytochromes from one species have been substituted for reaction with proteins from other species, implying the involvement of non-specific interactions. We provide a viable alternative (murburn concept) to classical protein-protein binding-based long range ET mechanism. To account for the promiscuity of interactions and solvent-accessible hemes, we propose that the two proteins act as non- specific redox capacitors, mediating one-electron redox equilibriums involving DRS and unbound ions. Communicated by Ramaswamy H. Sarma

Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome.

BackgroundUnderstanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 (“coronavirus disease 2019”), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics.MethodsWe leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments.ResultsThe all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions.ConclusionWe publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.

Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions.

Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

HOPPI-NMR: Hot-Peptide-Based Screening Assay for Inhibitors of Protein-Protein Interactions by NMR.

Protein-protein interactions (PPIs) contribute to the onset and/or progression of several diseases, especially cancer, and this discovery has paved the way for considering disruption of the PPIs as an attractive anti-tumor strategy. In this regard, simple and efficient biophysical methods for detecting the interaction of the inhibitors with the protein counterpart are still in high demand. Herein, we describe a convenient NMR method for the screening of putative PPI inhibitors based on the use of "hot peptides" (HOPPI-NMR). As a case study, HOPPI-NMR was successful applied to the well-known p53/MDM2 system. Our outcomes highlight the main advantages of the method, including the use of a small amount of unlabeled proteins, the minimization of the risk of protein aggregation, and the ability to identify weak binders. The last leaves open the possibility for application of HOPPI-NMR in tandem with fragment-based drug discovery as a valid strategy for the identification of novel chemotypes acting as PPI inhibitors.

Misregulation of ELK1, AP1, and E12 Transcription Factor Networks Is Associated with Melanoma Progression.

Melanoma is among the most malignant cutaneous cancers and when metastasized results in dramatically high mortality. Despite advances in high-throughput gene expression profiling in cancer transcriptomic studies, our understanding of mechanisms driving melanoma progression is still limited. We present here an in-depth bioinformatic analysis of the melanoma RNAseq, chromatin immunoprecipitation (ChIP)seq, and single-cell (sc)RNA seq data to understand cancer progression. Specifically, we have performed a consensus network analysis of RNA-seq data from clinically re-grouped melanoma samples to identify gene co-expression networks that are conserved in early (stage 1) and late (stage 4/invasive) stage melanoma. Overlaying the fold-change information on co-expression networks revealed several coordinately up or down-regulated subnetworks that may play a critical role in melanoma progression. Furthermore, by incorporating histone lysine-27 acetylation information and highly expressed genes identified from the single-cell RNA data from melanoma patient samples, we present a comprehensive list of pathways, putative protein-protein interactions (PPIs) and transcription factor (TF) networks that are driving cancer progression. From this analysis, we have identified Elk1, AP1 and E12 TF networks that coordinately change expression in late melanoma when compared to early melanoma, implicating these TFs in melanoma progression. Additionally, the sumoylation-associated interactome is upregulated in invasive melanoma. Together, this bioinformatic analysis potentially implicates a combination of TF networks and PPIs in melanoma progression, which if confirmed in the experimental systems, could be used as targets for drug intervention in melanoma.

In silico evidence of de novo interactions between ribosomal and Epstein - Barr virus proteins

BackgroundAssociation of Epstein-Barr virus (EBV) encoded latent gene products with host ribosomal proteins (RPs) has not been fully explored, despite their involvement in the aetiology of several human cancers. To gain an insight into their plausible interactions, we employed a computational approach that encompasses structural alignment, gene ontology analysis, pathway analysis, and molecular docking.ResultsIn this study, the alignment analysis based on structural similarity allows the prediction of 48 potential interactions between 27 human RPs and the EBV proteins EBNA1, LMP1, LMP2A, and LMP2B. Gene ontology analysis of the putative protein-protein interactions (PPIs) reveals their probable involvement in RNA binding, ribosome biogenesis, metabolic and biosynthetic processes, and gene regulation. Pathway analysis shows their possible participation in viral infection strategies (viral translation), as well as oncogenesis (Wnt and EGFR signalling pathways). Finally, our molecular docking assay predicts the functional interactions of EBNA1 with four RPs individually: EBNA1-eS10, EBNA1-eS25, EBNA1-uL10 and EBNA1-uL11.ConclusionThese interactions have never been revealed previously via either experimental or in silico approach. We envisage that the calculated interactions between the ribosomal and EBV proteins herein would provide a hypothetical model for future experimental studies on the functional relationship between ribosomal proteins and EBV infection.

Biotin Identification Proteomics in Three-Dimensional Organotypic Human Skin Cultures.

Biotin identification (BioID) proteomics facilitates the unbiased detection of protein interaction neighborhoods in live cells. The BioID technique relies on the covalent biotin alteration of vicinal proteins by a modified bacterial biotin ligase. The biotin ligase is fused to a protein of interest to identify putative protein-protein interactions. Here, we describe the adaptation of this technique for use in three-dimensional epidermal cultures. Due to the covalent biotin modification of proteins, our protocol allows for the complete solubilization of the total cellular protein content in differentiated keratinocytes. Thus, a comprehensive network of potential interactors of a protein of interest can be mapped.

Functional signaling and gene regulatory networks between the oocyte and the surrounding cumulus cells

BackgroundThe maturation and successful acquisition of developmental competence by an oocyte, the female gamete, during folliculogenesis is highly dependent on molecular interactions with somatic cells. Most of the cellular interactions identified, thus far, are modulated by growth factors, ions or metabolites. We hypothesized that this interaction is also modulated at the transcriptional level, which leads to the formation of gene regulatory networks between the oocyte and cumulus cells. We tested this hypothesis by analyzing transcriptome data from single oocytes and the surrounding cumulus cells collected from antral follicles employing an analytical framework to determine interdependencies at the transcript level.ResultsWe overlapped our transcriptome data with putative protein-protein interactions and identified hundreds of ligand-receptor pairs that can transduce paracrine signaling between an oocyte and cumulus cells. We determined that 499 ligand-encoding genes expressed in oocytes and cumulus cells are functionally associated with transcription regulation (FDR < 0.05). Ligand-encoding genes with specific expression in oocytes or cumulus cells were enriched for biological functions that are likely associated with the coordinated formation of transzonal projections from cumulus cells that reach the oocyte’s membrane. Thousands of gene pairs exhibit significant linear co-expression (absolute correlation > 0.85, FDR < 1.8 × 10− 5) patterns between oocytes and cumulus cells. Hundreds of co-expressing genes showed clustering patterns associated with biological functions (FDR < 0.5) necessary for a coordinated function between the oocyte and cumulus cells during folliculogenesis (i.e. regulation of transcription, translation, apoptosis, cell differentiation and transport).ConclusionOur analyses revealed a complex and functional gene regulatory circuit between the oocyte and surrounding cumulus cells. The regulatory profile of each cumulus-oocyte complex is likely associated with the oocytes’ developmental potential to derive an embryo.

Protein-Protein Interactions in the Molecular Chaperone Network.

Molecular chaperones play a central role in protein homeostasis (a.k.a. proteostasis) by balancing protein folding, quality control, and turnover. To perform these diverse tasks, chaperones need the malleability to bind nearly any "client" protein and the fidelity to detect when it is misfolded. Remarkably, these activities are carried out by only ∼180 dedicated chaperones in humans. How do a relatively small number of chaperones maintain cellular and organismal proteostasis for an entire proteome? Furthermore, once a chaperone binds a client, how does it "decide" what to do with it? One clue comes from observations that individual chaperones engage in protein-protein interactions (PPIs)-both with each other and with their clients. These physical links coordinate multiple chaperones into organized, functional complexes and facilitate the "handoff" of clients between them. PPIs also link chaperones and their clients to other cellular pathways, such as those that mediate trafficking (e.g., cytoskeleton) and degradation (e.g., proteasome). The PPIs of the chaperone network have a wide range of affinity values (nanomolar to micromolar) and involve many distinct types of domain modules, such as J domains, zinc fingers, and tetratricopeptide repeats. Many of these motifs have the same binding surfaces on shared partners, such that members of one chaperone class often compete for the same interactions. Somehow, this collection of PPIs draws together chaperone families and creates multiprotein subnetworks that are able to make the "decisions" of protein quality control. The key to understanding chaperone-mediated proteostasis might be to understand how PPIs are regulated. This Account will discuss the efforts of our group and others to map, measure, and chemically perturb the PPIs within the molecular chaperone network. Structural biology methods, including X-ray crystallography, NMR spectroscopy, and electron microscopy, have all played important roles in visualizing the chaperone PPIs. Guided by these efforts and -omics approaches to measure PPIs, new advances in high-throughput chemical screening that are specially designed to account for the challenges of this system have emerged. Indeed, chemical biology has played a particularly important role in this effort, as molecules that either promote or inhibit specific PPIs have proven to be invaluable research probes in cells and animals. In addition, these molecules have provided leads for the potential treatment of protein misfolding diseases. One of the major products of this research field has been the identification of putative PPI drug targets within the chaperone network, which might be used to change chaperone "decisions" and rebalance proteostasis.

Circadian clock genes are differentially modulated during the daily cycles and chronological age in the social honeybee (Apis mellifera)

The circadian clock is an advantageous adaptive system that enables organisms to predict and anticipate the daily environmental changes. The circadian rhythms are generated molecularly through the expression of clock genes, based on autoregulatory feedback loops. Honeybees are an excellent model to investigate how the circadian rhythms are modulated accordingly to the social context, behavioral plasticity, and task-related activities. Here, we show how the clock genes behave during the daily cycles in adult worker heads of Apis mellifera. Our results point to the clock genes period and cryptochrome as essential regulators of the circadian rhythms associated to the behavioral maturation in this social insect. We also identified putative miRNA-target and protein-protein interactions involving honeybee clock genes, indicating regulatory networks behind the adjustment of the molecular clock.

Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.

Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes the functional understanding of molecular plant stress response compromising the design of breeding strategies and biotechnological processes. Thus, defining a molecular network to enable the prediction of a plant's stress mode will improve the understanding of stress responsive biochemical regulation and will yield novel molecular targets for technological application. Arabidopsis wild type plants and two mutant lines with deficiency in sucrose or starch metabolism were grown under ambient and combined cold/high light stress conditions. Stress-induced dynamics of the primary metabolome and the proteome were quantified by mass spectrometry. Wild type data were used to train a machine learning algorithm to classify mutant lines under control and stress conditions. Multivariate analysis and classification identified a module consisting of 23 proteins enabling the reliable prediction of combined temperature/high light stress conditions. 18 of these 23 proteins displayed putative protein-protein interactions connecting transcriptional regulation with regulation of primary and secondary metabolism. The identified stress-responsive core module supports prediction of complex biochemical regulation under changing environmental conditions.

Bayesian Analysis of MicroScale Thermophoresis Data to Quantify Affinity of Protein:Protein Interactions with Human Survivin

A biomolecular ensemble exhibits different responses to a temperature gradient depending on its diffusion properties. MicroScale Thermophoresis technique exploits this effect and is becoming a popular technique for analyzing interactions of biomolecules in solution. When comparing affinities of related compounds, the reliability of the determined thermodynamic parameters often comes into question. The thermophoresis binding curves can be assessed by Bayesian inference, which provides a probability distribution for the dissociation constant of the interacting partners. By applying Bayesian machine learning principles, binding curves can be autonomously analyzed without manual intervention and without introducing subjective bias by outlier rejection. We demonstrate the Bayesian inference protocol on the known survivin:borealin interaction and on the putative protein-protein interactions between human survivin and two members of the human Shugoshin-like family (hSgol1 and hSgol2). These interactions were identified in a protein microarray binding assay against survivin and confirmed by MicroScale Thermophoresis.

Accounting for Protein Subcellular Localization: A Compartmental Map of the Rat Liver Proteome

Accurate knowledge of the intracellular location of proteins is important for numerous areas of biomedical research including assessing fidelity of putative protein-protein interactions, modeling cellular processes at a system-wide level and investigating metabolic and disease pathways. Many proteins have not been localized, or have been incompletely localized, partly because most studies do not account for entire subcellular distribution. Thus, proteins are frequently assigned to one organelle whereas a significant fraction may reside elsewhere. As a step toward a comprehensive cellular map, we used subcellular fractionation with classic balance sheet analysis and isobaric labeling/quantitative mass spectrometry to assign locations to >6000 rat liver proteins. We provide quantitative data and error estimates describing the distribution of each protein among the eight major cellular compartments: nucleus, mitochondria, lysosomes, peroxisomes, endoplasmic reticulum, Golgi, plasma membrane and cytosol. Accounting for total intracellular distribution improves quality of organelle assignments and assigns proteins with multiple locations. Protein assignments and supporting data are available online through the Prolocate website (http://prolocate.cabm.rutgers.edu). As an example of the utility of this data set, we have used organelle assignments to help analyze whole exome sequencing data from an infant dying at 6 months of age from a suspected neurodegenerative lysosomal storage disorder of unknown etiology. Sequencing data was prioritized using lists of lysosomal proteins comprising well-established residents of this organelle as well as novel candidates identified in this study. The latter included copper transporter 1, encoded by SLC31A1, which we localized to both the plasma membrane and lysosome. The patient harbors two predicted loss of function mutations in SLC31A1, suggesting that this may represent a heretofore undescribed recessive lysosomal storage disease gene.

Persistent Alterations in Microglial Enhancers in a Model of Chronic Pain.

Chronic pain is a common and devastating condition that induces well-characterized changes in neurons and microglia. One major unanswered question is why these changes should persist long after the precipitating injury has healed. Here, we suggest that some of the longer-lasting consequences of nerve injury may be hidden in the epigenome. Cell sorting and sequencing techniques were used to characterize the spinal cord immune response in a mouse model of chronic neuropathic pain. Infiltration of peripheral myeloid cells was found to be absent, and RNA sequencing (RNA-seq) of central microglia revealed transient gene expression changes in response to nerve ligation. Conversely, examination of microglial enhancers revealed persistent, post-injury alterations in close proximity to transcriptionally regulated genes. Enhancers are regions of open chromatin that define a cell's transcription factor binding profile. We hypothesize that changes at enhancers may constitute a mechanism by which painful experiences are recorded at a molecular level.