Global analysis of protein activities using proteome chips

Exploring lipid-protein interactions in plant membranes

Once regarded as mere membrane building blocks, lipids are now recognized as diverse and intricate players that mold the functions, identities, and responses of cellular membranes. Although the interactions of lipids with integral and peripheral membrane proteins are crucial for their localization, activity, and function, how proteins bind lipids is still far from being thoroughly explored. Describing and characterizing these dynamic protein-lipid interactions is thus essential to understanding the membrane-associated processes. Here we review the current range of experimental techniques employed to study plant protein-lipid interactions, integrating various methods. We summarize the principles, advantages, and limitations of classical in vitro biochemical approaches, including protein-lipid overlays and various liposome binding assays, and complement them with in vivo microscopic techniques centered around the use of genetically encoded lipid sensors and pharmacological or genetic membrane lipid manipulation tools. We also highlight several emerging techniques still awaiting their advancement into plant membrane research and emphasize the need to use complementary experimental strategies as key for elucidating the mechanistic roles of protein-lipid interactions in plant cell biology.

Exploring the Knowledge of an Outstanding Protein to Protein Interaction Transformer

Protein-to-protein interaction (PPI) prediction aims to predict whether two given proteins interact or not. Compared with traditional experimental methods of high cost and low efficiency, the current deep learning based approach makes it possible to discover massive potential PPIs from large-scale databases. However, deep PPI prediction models perform poorly on unseen species, as their proteins are not in the training set. Targetting on this issue, the paper first proposes PPITrans, a Transformer based PPI prediction model that exploits a language model pre-trained on proteins to conduct binary PPI prediction. To validate the effectiveness on unseen species, PPITrans is trained with Human PPIs and tested on PPIs of other species. Experimental results show that PPITrans significantly outperforms the previous state-of-the-art on various metrics, especially on PPIs of unseen species. For example, the AUPR improves 0.339 absolutely on Fly PPIs. Aiming to explore the knowledge learned by PPITrans from PPI data, this paper also designs a series of probes belonging to three categories. Their results reveal several interesting findings, like that although PPITrans cannot capture the spatial structure of proteins, it can obtain knowledge of PPI type and binding affinity, learning more than binary PPI.

Methods to Investigate the Secretome of Senescent Cells

The word "secretome" was first used to describe the proteins that cells secrete under different circumstances; however, recent studies have proven the existence of other molecules such as RNA and chemical compounds in the secretome. The study of secretome has significance for the diagnosis and treatment of disease as it provides insight into cellular functions, including immune responses, development, and homeostasis. By halting cell division, cellular senescence plays a role in both cancer defense and aging by secreting substances known as senescence-associated secretory phenotypes (SASP). A variety of techniques could be used to analyze the secretome: protein-based approaches like mass spectrometry and protein microarrays, nucleic acid-based methods like RNA sequencing, microarrays, and in silico prediction. Each method offers unique advantages and limitations in characterizing secreted molecules. Top-down and bottom-up strategies for thorough secretome analysis are became possible by mass spectrometry. Understanding cellular function, disease causes, and proper treatment targets is aided by these methodologies. Their approaches, benefits, and drawbacks will all be discussed in this review.

Bifunctional glycosphingolipid (GSL) probes to investigate GSL-interacting proteins in cell membranes

Glycosphingolipids (GSLs) are abundant glycolipids on cells and essential for cell recognition, adhesion, signal transduction, and so on. However, their lipid anchors are not long enough to cross the membrane bilayer. To transduce transmembrane signals, GSLs must interact with other membrane components, whereas such interactions are difficult to investigate. To overcome this difficulty, bifunctional derivatives of II3-β-N-acetyl-D-galactosamine-GA2 (GalNAc-GA2) and β-N-acetyl-D-glucosamine-ceramide (GlcNAc-Cer) were synthesized as probes to explore GSL-interacting membrane proteins in live cells. Both probes contain photoreactive diazirine in the lipid moiety, which can crosslink with proximal membrane proteins upon photoactivation, and clickable alkyne in the glycan to facilitate affinity tag addition for crosslinked protein pull-down and characterization. The synthesis is highlighted by the efficient assembly of simple glycolipid precursors followed by on-site lipid remodeling. These probes were employed to profile GSL-interacting membrane proteins in HEK293 cells. The GalNAc-GA2 probe revealed 312 distinct proteins, with GlcNAc-Cer probe-crosslinked proteins as controls, suggesting the potential influence of the glycan on GSL functions. Many of the proteins identified with the GalNAc-GA2 probe are associated with GSLs, and some have been validated as being specific to this probe. The versatile probe design and experimental protocols are anticipated to be widely applicable to GSL research.

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

Revisiting the antigen markers of vector-borne parasitic diseases identified by immunomics: identification and application to disease control

INTRODUCTION

Protein microarray is a promising immunomic approach for identifying biomarkers. Based on our previous study that reviewed parasite antigens and recent parasitic omics research, this article expands to include information on vector-borne parasitic diseases (VBPDs), namely, malaria, schistosomiasis, leishmaniasis, babesiosis, trypanosomiasis, lymphatic filariasis, and onchocerciasis.

AREAS COVERED

We revisit and systematically summarize antigen markers of vector-borne parasites identified by the immunomic approach and discuss the latest advances in identifying antigens for the rational development of diagnostics and vaccines. The applications and challenges of this approach for VBPD control are also discussed.

EXPERT OPINION

The immunomic approach has enabled the identification and/or validation of antigen markers for vaccine development, diagnosis, disease surveillance, and treatment. However, this approach presents several challenges, including limited sample size, variability in antigen expression, false-positive results, complexity of omics data, validation and reproducibility, and heterogeneity of diseases. In addition, antigen involvement in host immune evasion and antigen sensitivity/specificity are major issues in its application. Despite these limitations, this approach remains promising for controlling VBPD. Advances in technology and data analysis methods should continue to improve candidate antigen identification, as well as the use of a multiantigen approach in diagnostic and vaccine development for VBPD control.

Systems Approaches to Cell Culture-Derived Extracellular Vesicles for Acute Kidney Injury Therapy: Prospects and Challenges

Acute kidney injury (AKI) is a heterogeneous syndrome, comprising diverse etiologies of kidney insults that result in high mortality and morbidity if not well managed. Although great efforts have been made to investigate underlying pathogenic mechanisms of AKI, there are limited therapeutic strategies available. Extracellular vesicles (EV) are membrane-bound vesicles secreted by various cell types, which can serve as cell-free therapy through transfer of bioactive molecules. In this review, we first overview the AKI syndrome and EV biology, with a particular focus on the technical aspects and therapeutic application of cell culture-derived EVs. Second, we illustrate how multi-omic approaches to EV miRNA, protein, and genomic cargo analysis can yield new insights into their mechanisms of action and address unresolved questions in the field. We then summarize major experimental evidence regarding the therapeutic potential of EVs in AKI, which we subdivide into stem cell and non-stem cell-derived EVs. Finally, we highlight the challenges and opportunities related to the clinical translation of animal studies into human patients.

A multi-source molecular network representation model for protein-protein interactions prediction

The prediction of potential protein-protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein-protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein-protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein-protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs .

Uncertain Facts or Uncertain Values? Testing the Distinction Between Empirical and Normative Uncertainty in Moral Judgments

People can be uncertain in their moral judgments. Philosophers have argued that such uncertainty can either refer to the underlying empirical facts (empirical uncertainty) or to the normative evaluation of these facts itself (normative uncertainty). Psychological investigations of this distinction, however, are rare. In this paper, we combined factor-analytical and experimental approaches to show that empirical and normative uncertainty describe two related but different psychological states. In Study 1, we asked N = 265 participants to describe a case of moral uncertainty and to rate different aspects of their uncertainty about this case. Across this wide range of moral scenarios, our items loaded onto three reliable factors: lack of information, unclear consequences, and normative uncertainty. In Study 2, we confirmed this factor structure using predefined stimulus material. N = 402 participants each rated eight scenarios that systematically varied in their degree of uncertainty regarding the consequences of the described actions and in the value conflict that was inherent to them. The empirical uncertainty factors were mainly affected by the introduction of uncertainty regarding consequences, and the normative uncertainty factor was mainly affected by the introduction of value conflict. Our studies provide evidence that the distinction between empirical and normative uncertainty accurately describes a psychological reality. We discuss the relevance of our findings for research on moral judgments and decision-making, and folk metaethics.

Recent advances in liposome development for studying protein-lipid interactions

Protein-lipid interactions are crucial for various cellular biological processes like intracellular signaling, membrane transport, and cytoskeletal dynamics. Therefore, studying these interactions is essential to understand and unravel their specific functions. Nevertheless, the interacting proteins of many lipids are poorly understood and still require systematic study. Liposomes are the most well-known and familiar biomimetic systems used to study protein-lipid interactions. Although liposomes have been widely used for studying protein-lipid interactions in classical methods such as the co-flotation assay (CFA), co-sedimentation assay (CSA), and flow cytometric assay (FCA), an overview of their current applications and developments in high-throughput methods is not yet available. Here, we summarize the liposome development in low and high-throughput methods to study protein-lipid interactions. Besides, a constructive comment for each platform is presented to stimulate the advancement of these technologies in the future.

Quantitative assessment of multiple pathogen exposure and immune dynamics at scale

IMPORTANCE

Serology reveals exposure to pathogens, as well as the state of autoimmune and other clinical conditions. It is used to evaluate individuals and their histories and as a public health tool to track epidemics. Employing a variety of formats, studies nearly always perform serology by testing response to only one or a few antigens. However, clinical outcomes of new infections also depend on which previous infections may have occurred. We developed a high-throughput serology method that evaluates responses to hundreds of antigens simultaneously. It can be used to evaluate thousands of samples at a time and provide a quantitative readout. This tool will enable doctors to monitor which pathogens an individual has been exposed to and how that changes in the future. Moreover, public health officials could track populations and look for infectious trends among large populations. Testing many potential antigens at a time may also aid in vaccine development.

From beer to breadboards: yeast as a force for biological innovation

The history of yeast Saccharomyces cerevisiae, aka brewer's or baker's yeast, is intertwined with our own. Initially domesticated 8,000 years ago to provide sustenance to our ancestors, for the past 150 years, yeast has served as a model research subject and a platform for technology. In this review, we highlight many ways in which yeast has served to catalyze the fields of functional genomics, genome editing, gene-environment interaction investigation, proteomics, and bioinformatics-emphasizing how yeast has served as a catalyst for innovation. Several possible futures for this model organism in synthetic biology, drug personalization, and multi-omics research are also presented.

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

A low-temperature digital microfluidic system used for protein-protein interaction detection

The occurrence, development and prediction of various biological processes and diseases are inseparable from the protein-protein interaction (PPI), so it is extremely meaningful to perfect PPI networks. However, shortcomings of traditional detection methods, such as protein degradation, long detection time, complex operation, poor automation and high cost, restrict the rapid development of PPI networks. Here, a low-temperature digital microfluidic (LTDMF) system-based PPI detection box (LTDMF-PPI-Box) was developed to achieve rapid, lossless and efficient PPI detection. It consists of a PMMA shell, LTDMF-PPI and an integrated temperature control system. LTDMF reduces the PPI detection time from tens of hours to 1.5 hours by programmatically controlling the movement of droplets. Moreover, an integrated thermoelectric cooler (TEC) ensures an operating temperature of 4 °C, resulting in a protein protection up to 90%. The interaction between RILP protein and Rab26 protein which has a close connection to insulin secretion was demonstrated as a prototype to illustrate the feasibility of the LTDMF-PPI-Box. LTDMF with automation characteristics is capable of meeting the requirement of high-throughput screening of interacting proteins; therefore, the LTDMF-PPI-Box is expected to accelerate the establishment of the PPI network in the future.

Contribution of Proteomics in Transplantation: Identification of Injury and Rejection Markers

Solid organ transplantation saves thousands of lives suffering from end-stage diseases. Although early transplants experienced acute organ injury, medical breakthroughs, such as tissue typing, and use of immunosuppressive agents have considerably improved graft survival. However, the overall incidence of allograft injury and chronic rejection remains high. Often the clinical manifestations of organ injury or rejection are nonspecific and late. Current requirement for successful organ transplantation is the identification of reliable, accurate, disease-specific, noninvasive methods for the early diagnosis of graft injury or rejection. Development of noninvasive techniques is important to allow routine follow-ups without the discomfort and risks associated with a graft biopsy. Multiple biofluids have been successfully tested for the presence of potential proteomic biomarkers; these include serum, plasma, urine, and whole blood. Kidney transplant research has provided significant evidence to the potential of proteomics-based biomarkers for acute and chronic kidney rejection, delayed graft function, early detection of declining allograft health. Multiple proteins have been implicated as biomarkers; however, recent observations implicate the use of similar canonical pathways and biofunctions associated with graft injury/rejection with altered proteins as potential biomarkers. Unfortunately, the current biomarker studies lack high sensitivity and specificity, adding to the complexity of their utility in the clinical space. In this review, we first describe the high-throughput proteomics technologies and then discuss the outcomes of proteomics profiling studies in the transplantation of several organs. Existing literature provides hope that novel biomarkers will emerge from ongoing efforts and guide physicians in delivering specific therapies to prolong graft survival.

Saccharomyces cerevisiae as a research tool for RNA-mediated human disease

The budding yeast, Saccharomyces cerevisiae, has been used for decades as a powerful genetic tool to study a broad spectrum of biological topics. With its ease of use, economic utility, well-studied genome, and a highly conserved proteome across eukaryotes, it has become one of the most used model organisms. Due to these advantages, it has been used to study an array of complex human diseases. From broad, complex pathological conditions such as aging and neurodegenerative disease to newer uses such as SARS-CoV-2, yeast continues to offer new insights into how cellular processes are affected by disease and how affected pathways might be targeted in therapeutic settings. At the same time, the roles of RNA and RNA-based processes have become increasingly prominent in the pathology of many of these same human diseases, and yeast has been utilized to investigate these mechanisms, from aberrant RNA-binding proteins in amyotrophic lateral sclerosis to translation regulation in cancer. Here we review some of the important insights that yeast models have yielded into the molecular pathology of complex, RNA-based human diseases. This article is categorized under: RNA in Disease and Development > RNA in Disease.

Identification and immunogenic potential of glycosylphosphatidylinositol-anchored proteins in Paracoccidioides brasiliensis

In fungal pathogens the cell wall plays an important role in host-pathogen interactions because its molecular components (e.g., polysaccharides and proteins) may trigger immune responses during infection. GPI-anchored proteins represent the main protein class in the fungal cell wall where they can perform several functions, such as cell wall remodeling and adhesion to host tissues. Genomic analysis has identified the complement of GPI-anchored proteins in many fungal pathogens, but the function has remained unknown for most of them. Here, we conducted an RNA expression analysis of GPI-anchored proteins of Paracoccidioides brasiliensis which causes paracoccidioidomycosis (PCM), an important human systemic mycosis endemic in Latin America. The expression of the GPI-anchored proteins was analyzed by quantitative PCR in both the mycelium and yeast forms. qPCR analysis revealed that the transcript levels of 22 of them were increased in hyphae and 10 in yeasts, respectively, while 14 did not show any significant difference in either form. Furthermore, we cloned 46 open reading frames and purified their corresponding GPI-anchored proteins in the budding yeast. Immunoblot and ELISA analysis of four purified GPI-anchored proteins revealed immune reactivity of these proteins against sera obtained from PCM patients. The information obtained in this study provides valuable information about the expression of many GPI-anchored proteins of unknown function. In addition, based on our immune analysis, some GPI-anchored proteins are expressed during infection and therefore, they might serve as good candidates for the development of new diagnostic methods.

Specific pupylation as IDEntity reporter (SPIDER) for the identification of protein-biomolecule interactions

Protein-biomolecule interactions play pivotal roles in almost all biological processes. For a biomolecule of interest, the identification of the interacting protein(s) is essential. For this need, although many assays are available, highly robust and reliable methods are always desired. By combining a substrate-based proximity labeling activity from the pupylation pathway of Mycobacterium tuberculosis and the streptavidin (SA)-biotin system, we developed the Specific Pupylation as IDEntity Reporter (SPIDER) method for identifying protein-biomolecule interactions. Using SPIDER, we validated the interactions between the known binding proteins of protein, DNA, RNA, and small molecule. We successfully applied SPIDER to construct the global protein interactome for m6A and mRNA, identified a variety of uncharacterized m6A binding proteins, and validated SRSF7 as a potential m6A reader. We globally identified the binding proteins for lenalidomide and CobB. Moreover, we identified SARS-CoV-2-specific receptors on the cell membrane. Overall, SPIDER is powerful and highly accessible for the study of protein-biomolecule interactions.

CMGC Kinases in Health and Cancer

CMGC kinases, encompassing cyclin-dependent kinases (CDKs), mitogen-activated protein kinases (MAPKs), glycogen synthase kinases (GSKs), and CDC-like kinases (CLKs), play pivotal roles in cellular signaling pathways, including cell cycle regulation, proliferation, differentiation, apoptosis, and gene expression regulation. The dysregulation and aberrant activation of these kinases have been implicated in cancer development and progression, making them attractive therapeutic targets. In recent years, kinase inhibitors targeting CMGC kinases, such as CDK4/6 inhibitors and BRAF/MEK inhibitors, have demonstrated clinical success in treating specific cancer types. However, challenges remain, including resistance to kinase inhibitors, off-target effects, and the need for better patient stratification. This review provides a comprehensive overview of the importance of CMGC kinases in cancer biology, their involvement in cellular signaling pathways, protein-protein interactions, and the current state of kinase inhibitors targeting these kinases. Furthermore, we discuss the challenges and future perspectives in targeting CMGC kinases for cancer therapy, including potential strategies to overcome resistance, the development of more selective inhibitors, and novel therapeutic approaches, such as targeting protein-protein interactions, exploiting synthetic lethality, and the evolution of omics in the study of the human kinome. As our understanding of the molecular mechanisms and protein-protein interactions involving CMGC kinases expands, so too will the opportunities for the development of more selective and effective therapeutic strategies for cancer treatment.

GATS tag system is compatible with biotin labelling methods for protein analysis

Polypeptide tags and biotin labelling technologies are widely used for protein analyses in biochemistry and cell biology. However, many peptide tag epitopes contain lysine residues (or amino acids) that are masked after biotinylation. Here, we propose the GATS tag system without a lysine residue and with high sensitivity and low non-specific binding using a rabbit monoclonal antibody against Plasmodium falciparum glycosylphosphatidylinositol (GPI)-anchored micronemal antigen (PfGAMA). From 14 monoclonal clones, an Ra3 clone was selected as it recognized an epitope-TLSVGVQNTF-without a lysine residue; this antibody and epitope tag set was called the GATS tag system. Surface plasmon resonance analysis showed that the tag system had a high affinity of 8.71 × 10-9 M. GATS tag indicated a very low background with remarkably high sensitivity and specificity in immunoblotting using the lysates of mammalian cells. It also showed a high sensitivity for immunoprecipitation and immunostaining of cultured human cells. The tag system was highly sensitive in both biotin labelling methods for proteins using NHS-Sulfo-biotin and BioID (proximity-dependent biotin identification) in the human cells, as opposed to a commercially available tag system having lysine residues, which showed reduced sensitivity. These results showed that the GATS tag system is suitable for methods such as BioID involving labelling lysine residues.

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein-protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein-protein interaction predictors, the Protein-protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a "guilt by association" in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean.

Metal organic frameworks-in-nanochannels: A tailorable chromatography membrane for isolation of target protein

Metal-organic frameworks (MOFs) demonstrate strong potential in biosample separation. However, the obtained MOFs powders are unsuitable for recovery techniques in an aqueous solution, especially the challenges of withdrawing MOFs particles and expanding their functions for specific applications. Herein, a general strategy is designed utilizing metal oxide-nanochannel arrays as precursors and templates for in-situ selective growth of MOFs structures. The exemplary MOFs (Ni-bipy) with tailored composition are selectively grown in NiO/TiO₂ nanochannel membrane (NM) using NiO as the sacrificial precursor, which enables one to achieve a ∼262 times concentration of histidine-tagged proteins within 100 min. The significantly improved adsorption efficiency in a wide pH range and the effective enrichment from a complex matrix as a nanofilter illustrate the great potential of MOFs in nanochannels membranes for the high-efficiency recovery of essential proteins in complex biological samples. The porous self-aligned Ni-MOFs/TiO₂ NM exhibits biocompatibility and flexible functionalities, which is desirable for the generation of multifunctional nanofilter devices and developing biomacromolecule delivery vehicles.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

Associations of adenovirus-reactive immunoglobulins with atrial fibrillation and body mass index

Adenovirus (AdV) has been suggested to be involved in pathogenesis of atrial fibrillation (AF). We aimed to evaluate an association between AdV-specific immunoglobulins G in the serum (AdV-IgG) and AF. The present case-control study comprised two cohorts, including cohort 1 of patients with AF and cohort 2 of asymptomatic subjects. Initially, two groups, MA and MB, were selected from the cohorts 1 and 2, respectively, for serum proteome profiling using an antibody microarray to identify possible relevant protein targets. The data of microarray analysis indicated a possible overall increase in the total adenovirus signals in the group MA vs. group MB, suggesting potential relevance of adenoviral infection to AF. Then, the groups A (with AF) and B (control) were selected from the cohorts 1 and 2, respectively, to assay the presence and levels of AdV-IgG- by ELSA. The prevalence of AdV-IgG-positive status demonstrated a 2-fold increase in the group A (AF) compared with that in the group B (asymptomatic subjects); odds ratio 2.06 (95%CI: 1.11-3.84; P = 0.02). The prevalence of obesity demonstrated an approximately 3-fold increase in AdV-IgG-positive patients of the group A compared with that in AdV-IgG-negative patients of the same group A (odds ratio 2.7; 95% CI: 1.02-7.1; P = 0.04). Thus, AdV-IgG-positive reactivity was independently associated with AF, and AF was independently associated with BMI, indicating that adenoviral infection may be a possible etiological factor for AF.

To Explore the Binding Affinity of Human γ-Secretase Activating Protein (GSAP) Isoform 4 with APP-C99 Peptides

γ-Secretase activating protein (GSAP) is known to play an important role in the β-amyloid pathway. It acts as a modulator and accentuates the truncation of the amyloid precursor protein C-99 fragment through the γ-secretase complex. GSAP has four isoforms, out of which canonical isoform 1, a 16 kDa C-terminal portion, has been extensively studied, whereas the function of other three isoforms remains unknown. Here, we explore the GSAP isoform 4 (GSAP_I4) expression and purification from inclusion bodies followed by the refolding of the protein. The secondary structure of GSAP_I4 is predicted using circular dichroism. The protein is further characterized by western blotting and mass spectroscopy analysis. Additionally, biochemical assays and in silico molecular docking and molecular simulation are performed to investigate the binding of GSAP_I4 and APP-C99 peptide fragments. The results reflect that although GSAP_I1 and GSAP_I4 share high sequence similarity, the isoform 4 does not show any affinity toward APP-C99 peptide fragments. This hints toward the fact that GSAP_I4 might have a different role in the living system that is yet unexplored.

Research progress in protein microarrays: Focussing on cancer research

Although several effective treatment modalities have been developed for cancers, the morbidity and mortality associated with cancer continues to increase every year. As one of the most exciting emerging technologies, protein microarrays represent a powerful tool in the field of cancer research because of their advantages such as high throughput, small sample usage, more flexibility, high sensitivity and direct readout of results. In this review, we focus on the research progress in four types of protein microarrays (proteome microarray, antibody microarray, lectin microarray and reversed protein array) with emphasis on their application in cancer research. Finally, we discuss the current challenges faced by protein microarrays and directions for future developments. We firmly believe that this novel systems biology research tool holds immense potential in cancer research and will become an irreplaceable tool in this field.

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology.

Active-site cysteine 215 sulfonation targets protein tyrosine phosphatase PTP1B for Cullin1 E3 ligase-mediated degradation

Reactive oxygen species (ROS), released as byproducts of mitochondrial metabolism or as products of NADPH oxidases and other processes, can directly oxidize the active-site cysteine (Cys) residue of protein tyrosine phosphatases (PTPs) in a mammalian cell. Robust degradation of irreversibly oxidized PTPs is essential for preventing accumulation of these permanently inactive enzymes. However, the mechanism underlying the degradation of these proteins was unknown. In this study, we found that the active-site Cys215 of endogenous PTP1B is sulfonated in H9c2 cardiomyocytes under physiological conditions. The sulfonation of Cys215 led PTP1B to exhibit a conformational change, and drive the subsequent ubiquitination and degradation of this protein. We then discovered that Cullin1, an E3 ligase, interacts with the Cys215-sulfonated PTP1B. The functional impairment of Cullin1 prevented PTP1B from oxidation-dependent ubiquitination and degradation in H9c2 cells. Moreover, delivery of the terminally oxidized PTP1B resulted in proteotoxicity-caused injury in the affected cells. In conclusion, we elucidate how sulfonation of the active-site Cys215 can direct turnover of endogenous PTP1B through the engagement of ubiquitin-proteasome system. These data highlight a novel mechanism that maintains PTP homeostasis in cardiomyocytes with constitutive ROS production.

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo amino acid compositions, which were then carried out at two levels: categories and features. In brief, extra-tree was initially used for feature selection while SVM was then used to build the classification model. After that, the most suitable models for each category and each feature were selected by comparing with the three ensemble learning algorithms, that is, Random Forest, Adaboost, and Xgboost. According to our results, profile-based features were more suitable for building predictive models among the four categories. AUC value of the model constructed by Xgboost algorithm on independent data set could reach 92.66%. Moreover, Distance-based Residue, Physicochemical Distance Transformation and Profile-based Physicochemical Distance Transformation performed much better among the 17 features. AUC value of the Adaboost classifier constructed by Profile-based Physicochemical Distance Transformation on the independent dataset achieved 93.74%. Taken together, we proposed a better model with improved prediction capacity for protein-protein interactions between human and HCV in this study, which could provide practical reference for further experimental investigation into HCV-related diseases in future.Communicated by Ramaswamy H. Sarma.

A portable analog front-end system for label-free sensing of proteins using nanowell array impedance sensors

Proteins are useful biomarkers for a wide range of applications such as cancer detection, discovery of vaccines, and determining exposure to viruses and pathogens. Here, we present a low-noise front-end analog circuit interface towards development of a portable readout system for the label-free sensing of proteins using Nanowell array impedance sensing with a form factor of approximately 35cm2. The electronic interface consists of a low-noise lock-in amplifier enabling reliable detection of changes in impedance as low as 0.1% and thus detection of proteins down to the picoMolar level. The sensitivity of our system is comparable to that of a commercial bench-top impedance spectroscope when using the same sensors. The aim of this work is to demonstrate the potential of using impedance sensing as a portable, low-cost, and reliable method of detecting proteins, thus inching us closer to a Point-of-Care (POC) personalized health monitoring system. We have demonstrated the utility of our system to detect antibodies at various concentrations and protein (45 pM IL-6) in PBS, however, our system has the capability to be used for assaying various biomarkers including proteins, cytokines, virus molecules and antibodies in a portable setting.

Current advances in antibody-based serum biomarker studies: From protein microarray to phage display

PURPOSE

This review aims to summarize the technological advances in the field of antibody-based biomarker studies by proteome microarray and phage display. In addition, the possible development directions of this field are also discussed.

EXPERIMENTAL DESIGN

We have focused on the antibody profiling by proteome microarray and phage display, including the technological advances, the tools/resources constructed, and the characteristics of both platforms.

RESULTS

With the help of tools/resources and technological advances in proteome microarray and phage display, the efficiency of profiling antibody-based biomarkers in serum samples has been greatly improved.

CONCLUSIONS

In the past few years, proteome microarray and phage display, especially the latter one, have already demonstrated their capacity and efficiency for biomarker identification. In the near future, we believe that more antibody-based biomarkers could be identified, and some of them could eventually be developed into real clinical applications.

Autoantibody discovery across monogenic, acquired, and COVID-19-associated autoimmunity with scalable PhIP-seq

Phage immunoprecipitation sequencing (PhIP-seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-seq for autoantigen discovery, including our previous work (Vazquez et al., 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), and finally, mild and severe forms of COVID-19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as prodynorphin (PDYN) in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in two patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID-19, including the endosomal protein EEA1. Together, scaled PhIP-seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.

Saccharomyces cerevisiae as a Model System for Eukaryotic Cell Biology, from Cell Cycle Control to DNA Damage Response

The yeast Saccharomyces cerevisiae has been used for bread making and beer brewing for thousands of years. In addition, its ease of manipulation, well-annotated genome, expansive molecular toolbox, and its strong conservation of basic eukaryotic biology also make it a prime model for eukaryotic cell biology and genetics. In this review, we discuss the characteristics that made yeast such an extensively used model organism and specifically focus on the DNA damage response pathway as a prime example of how research in S. cerevisiae helped elucidate a highly conserved biological process. In addition, we also highlight differences in the DNA damage response of S. cerevisiae and humans and discuss the challenges of using S. cerevisiae as a model system.

Chaperone requirements for de novo folding of Saccharomyces cerevisiae septins

Polymers of septin protein complexes play cytoskeletal roles in eukaryotic cells. The specific subunit composition within complexes controls functions and higher-order structural properties. All septins have globular GTPase domains. The other eukaryotic cytoskeletal NTPases strictly require assistance from molecular chaperones of the cytosol, particularly the cage-like chaperonins, to fold into oligomerization-competent conformations. We previously identified cytosolic chaperones that bind septins and influence the oligomerization ability of septins carrying mutations linked to human disease, but it was unknown to what extent wild-type septins require chaperone assistance for their native folding. Here we use a combination of in vivo and in vitro approaches to demonstrate chaperone requirements for de novo folding and complex assembly by budding yeast septins. Individually purified septins adopted nonnative conformations and formed nonnative homodimers. In chaperonin- or Hsp70-deficient cells, septins folded slower and were unable to assemble posttranslationally into native complexes. One septin, Cdc12, was so dependent on cotranslational chaperonin assistance that translation failed without it. Our findings point to distinct translation elongation rates for different septins as a possible mechanism to direct a stepwise, cotranslational assembly pathway in which general cytosolic chaperones act as key intermediaries.

Selective C-Terminal Conjugation of Protease-Derived Native Peptides for Proteomic Measurements

Bottom-up proteomic experiments often require selective conjugation or labeling of the N- and/or C-termini of peptides resulting from proteolytic digestion. For example, techniques based on surface fluorescence imaging are emerging as a promising route to high-throughput protein sequencing but require the generation of peptide surface arrays immobilized through single C-terminal point attachment while leaving the N-terminus free. While several robust approaches are available for selective N-terminal conjugation, it has proven to be much more challenging to implement methods for selective labeling or conjugation of the C-termini that can discriminate between the C-terminal carboxyl group and other carboxyl groups on aspartate and glutamate residues. Further, many approaches based on conjugation through amide bond formation require protection of the N-terminus to avoid unwanted cross-linking reactions. To overcome these challenges, herein, we describe a new strategy for single-point selective immobilization of peptides generated by protease digestion via the C-terminus. The method involves immobilization of peptides via lysine amino acids which are found naturally at the C-terminal end of cleaved peptides from digestions of certain serine endoproteinases, like LysC. This lysine and the N-terminus, the sole two primary amines in the peptide fragments, are chemically reacted with a custom phenyl isothiocyanate (EPITC) that contains an alkyne handle. Subsequent exposure of the double-modified peptides to acid selectively cleaves the N-terminal amino acid, while the modified C-terminus lysine remains unchanged. The alkyne-modified peptides with free N-termini can then be immobilized on an azide surface through standard click chemistry. Using this general approach, surface functionalization is demonstrated using a combination of X-ray photoelectron spectroscopy (XPS), ellipsometry, and atomic force microscopy (AFM).

Research Advances and Prospects of Orphan Genes in Plants

Orphan genes (OGs) are defined as genes having no sequence similarity with genes present in other lineages. OGs have been regarded to play a key role in the development of lineage-specific adaptations and can also serve as a constant source of evolutionary novelty. These genes have often been found related to various stress responses, species-specific traits, special expression regulation, and also participate in primary substance metabolism. The advancement in sequencing tools and genome analysis methods has made the identification and characterization of OGs comparatively easier. In the study of OG functions in plants, significant progress has been made. We review recent advances in the fast evolving characteristics, expression modulation, and functional analysis of OGs with a focus on their role in plant biology. We also emphasize current challenges, adoptable strategies and discuss possible future directions of functional study of OGs.

Chip-DSF: A rapid screening strategy for drug protein targets

Identification of the drug target of lead compounds is an important means for rapid and efficient drug discovery. Protein chips are a high-throughput protein function analysis technology that has been widely used in screening drug protein targets in recent years. However, the verification of the results after high-throughput protein chip screening is still cumbersome. Based on our mature protein chip preparation platform, we prepared a protein chip containing 150 important high-frequency protein targets and used antibodies to prove the availability of the protein chip. To improve the accuracy of target screening, we combined the label-free differential scanning fluorimetry (DSF) with the protein chip, proposing the Chip-DSF strategy. Subsequently, we tested the method with small molecular ginsenoside-Rg2 (Rg2). The Chip-DSF strategy was used to successfully screen the potential target protein KRAS(G12C) of Rg2. Consistently, we found that Rg2 could inhibit NCI-H23 cell proliferation by inducing cell cycle arrest. Also, we found that Rg2 could reduce the amount of KRAS protein and inhibit the phosphorylation of KRAS downstream key signaling protein ERK1, RPS6, and P70S6K in NCI-H23 cells. Collectively, our Chip-DSF strategy could achieve rapid target verification which improved the accuracy and efficiency of target screening of protein chips.

A Survey on Deep Networks Approaches in Prediction of Sequence-Based Protein–Protein Interactions

The prominence of protein–protein interactions (PPIs) in system biology with diverse biological procedures has become the topic to discuss because it acts as a fundamental part in predicting the protein function of the target protein and drug ability of molecules. Numerous researches have been published to predict PPIs computationally because they provide an alternative solution to laboratory trials and a cost-effective way of predicting the most likely set of interactions at the entire proteome scale. In recent computational methods, deep learning has become a buzzword with numerous scientific researches. This paper presents, for the first time, a comprehensive survey of sequence-based PPI prediction by three popular deep learning architectures i.e. deep neural networks, convolutional neural networks and recurrent neural networks and its variants. The thorough survey discussed herein carefully mined every possible information, can help the researchers to further explore the success in this area.

CF-PPiD technology based on cell-free protein array and proximity biotinylation enzyme for in vitro direct interactome analysis

Protein–protein interaction (PPI) analysis is a key process to understand protein functions. Recently, we constructed a human protein array (20 K human protein beads array) consisting of 19,712 recombinant human proteins produced by a wheat cell-free protein production system. Here, we developed a cell-free protein array technology for proximity biotinylation-based PPI identification (CF-PPiD). The proximity biotinylation enzyme AirID-fused TP53 and -IκBα proteins each biotinylated specific interacting proteins on a 1536-well magnetic plate. In addition, AirID-fused cereblon was shown to have drug-inducible PPIs using CF-PPiD. Using the human protein beads array with AirID-IκBα, 132 proteins were biotinylated, and then selected clones showed these biological interactions in cells. Although ZBTB9 was not immunoprecipitated, it was highly biotinylated by AirID-IκBα, suggesting that this system detected weak interactions. These results indicated that CF-PPiD is useful for the biochemical identification of directly interacting proteins.

Mechanochemical Lithography

Surfaces with patterned biomolecules have wide applications in biochips and biomedical diagnostics. However, most patterning methods are inapplicable to physiological conditions and incapable of creating complex structures. Here, we develop a mechanochemical lithography (MCL) method based on compressive force-triggered reactions. In this method, biomolecules containing a bioaffinity ligand and a mechanoactive group are used as mechanochemical inks (MCIs). The bioaffinity ligand facilitates concentrating MCIs from surrounding solutions to a molded surface, enabling direct and continuous printing in an aqueous environment. The mechanoactive group facilitates covalent immobilization of MCIs through force-triggered reactions, thus avoiding the broadening of printed features due to the diffusion of inks. We discovered that the ubiquitously presented amino groups in biomolecules can react with maleimide through a force-triggered Michael addition. The resulting covalent linkage is mechanically and chemically stable. As a proof-of-concept, we fabricate patterned surfaces of biotin and His-tagged proteins at nanoscale spatial resolution by MCL and verify the resulting patterns by fluorescence imaging. We further demonstrated the creation of multiplex protein patterns using this technique.

Navigate Flying Molecular Elephants Safely to the Ground: Mass-Selective Soft Landing up to the Mega-Dalton Range by Electrospray Controlled Ion-Beam Deposition

The prototype of a highly versatile and efficient preparative mass spectrometry system used for the deposition of molecules in ultrahigh vacuum (UHV) is presented, along with encouraging performance data obtained using four model species that are thermolabile or not sublimable. The test panel comprises two small organic compounds, a small and very large protein, and a large DNA species covering a 4-log mass range up to 1.7 MDa as part of a broad spectrum of analyte species evaluated to date. Three designs of innovative ion guides, a novel digital mass-selective quadrupole (dQMF), and a standard electrospray ionization (ESI) source are combined to an integrated device, abbreviated electrospray controlled ion-beam deposition (ES-CIBD). Full control is achieved by (i) the square-wave-driven radiofrequency (RF) ion guides with steadily tunable frequencies, including a dQMF allowing for investigation, purification, and deposition of a virtually unlimited m/z range, (ii) the adjustable landing energy of ions down to ∼2 eV/z enabling integrity-preserving soft landing, (iii) the deposition in UHV with high ion beam intensity (up to 3 nA) limiting contaminations and deposition time, and (iv) direct coverage control via the deposited charge. The maximum resolution of R = 650 and overall efficiency up to T_total = 4.4% calculated from the solution to UHV deposition are advantageous, whereby the latter can be further enhanced by optimizing ionization performance. In the setup presented, a scanning tunneling microscope (STM) is attached for in situ UHV investigations of deposited species, demonstrating a selective, structure-preserving process and atomically clean layers.

Enzymatic analysis of WWP2 E3 ubiquitin ligase using protein microarrays identifies autophagy-related substrates

WWP2 is a HECT E3 ligase that targets protein Lys residues for ubiquitination and is comprised of an N-terminal C2 domain, four central WW domains, and a C-terminal catalytic HECT domain. The peptide segment between the middle WW domains, the 2,3-linker, is known to autoinhibit the catalytic domain, and this autoinhibition can be relieved by phosphorylation at Tyr369. Several protein substrates of WWP2 have been identified, including the tumor suppressor lipid phosphatase PTEN, but the full substrate landscape and biological functions of WWP2 remain to be elucidated. Here, we used protein microarray technology and the activated enzyme phosphomimetic mutant WWP2Y369E to identify potential WWP2 substrates. We identified 31 substrate hits for WWP2Y369E using protein microarrays, of which three were known autophagy receptors (NDP52, OPTN, and SQSTM1). These three hits were validated with in vitro and cell-based transfection assays and the Lys ubiquitination sites on these proteins were mapped by mass spectrometry. Among the mapped ubiquitin sites on these autophagy receptors, many had been previously identified in the endogenous proteins. Finally, we observed that WWP2 KO SH-SH5Y neuroblastoma cells using CRISPR-Cas9 showed a defect in mitophagy, which could be rescued by WWP2Y369E transfection. These studies suggest that WWP2-mediated ubiquitination of the autophagy receptors NDP52, OPTN, and SQSTM1 may positively contribute to the regulation of autophagy.

Mass Spectrometry-Based Proteomics for Biomarker Discovery

Proteomics plays a pivotal role in systems medicine, in which pharmacoproteomics and toxicoproteomics have been developed to address questions related to efficacy and toxicity of drugs. Mass spectrometry is the core technology for quantitative proteomics, providing the capabilities of identification and quantitation of thousands of proteins. The technology has been applied to biomarker discovery and understanding the mechanisms of drug action. Both stable isotope labeling of proteins or peptides and label-free approaches have been incorporated with multidimensional LC separation and tandem mass spectrometry (LC-MS/MS) to increase the coverage and depth of proteome analysis. A protocol of such an approach exemplified by dimethyl labeling in combination with 2D-LC-MS/MS is described. With further development of novel proteomic tools and increase in sample throughput, the full spectrum of mass spectrometry-based proteomic research will greatly advance systems medicine.

Autoantibody discovery across monogenic, acquired, and COVID19-associated autoimmunity with scalable PhIP-Seq

Phage Immunoprecipitation-Sequencing (PhIP-Seq) allows for unbiased, proteome-wide autoantibody discovery across a variety of disease settings, with identification of disease-specific autoantigens providing new insight into previously poorly understood forms of immune dysregulation. Despite several successful implementations of PhIP-Seq for autoantigen discovery, including our previous work (Vazquez et al. 2020), current protocols are inherently difficult to scale to accommodate large cohorts of cases and importantly, healthy controls. Here, we develop and validate a high throughput extension of PhIP-seq in various etiologies of autoimmune and inflammatory diseases, including APS1, IPEX, RAG1/2 deficiency, Kawasaki Disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), and finally, mild and severe forms of COVID19. We demonstrate that these scaled datasets enable machine-learning approaches that result in robust prediction of disease status, as well as the ability to detect both known and novel autoantigens, such as PDYN in APS1 patients, and intestinally expressed proteins BEST4 and BTNL8 in IPEX patients. Remarkably, BEST4 antibodies were also found in 2 patients with RAG1/2 deficiency, one of whom had very early onset IBD. Scaled PhIP-Seq examination of both MIS-C and KD demonstrated rare, overlapping antigens, including CGNL1, as well as several strongly enriched putative pneumonia-associated antigens in severe COVID19, including the endosomal protein EEA1. Together, scaled PhIP-Seq provides a valuable tool for broadly assessing both rare and common autoantigen overlap between autoimmune diseases of varying origins and etiologies.

The microprotein Nrs1 rewires the G1/S transcriptional machinery during nitrogen limitation in budding yeast

Commitment to cell division at the end of G1 phase, termed Start in the budding yeast Saccharomyces cerevisiae, is strongly influenced by nutrient availability. To identify new dominant activators of Start that might operate under different nutrient conditions, we screened a genome-wide ORF overexpression library for genes that bypass a Start arrest caused by absence of the G1 cyclin Cln3 and the transcriptional activator Bck2. We recovered a hypothetical gene YLR053c, renamed NRS1 for Nitrogen-Responsive Start regulator 1, which encodes a poorly characterized 108 amino acid microprotein. Endogenous Nrs1 was nuclear-localized, restricted to poor nitrogen conditions, induced upon TORC1 inhibition, and cell cycle-regulated with a peak at Start. NRS1 interacted genetically with SWI4 and SWI6, which encode subunits of the main G1/S transcription factor complex SBF. Correspondingly, Nrs1 physically interacted with Swi4 and Swi6 and was localized to G1/S promoter DNA. Nrs1 exhibited inherent transactivation activity, and fusion of Nrs1 to the SBF inhibitor Whi5 was sufficient to suppress other Start defects. Nrs1 appears to be a recently evolved microprotein that rewires the G1/S transcriptional machinery under poor nitrogen conditions.

Commitment to cell division at the end of G1 phase in the budding yeast Saccharomyces cerevisiae is strongly influenced by nutrient availability. This study identifies a micro-protein that promotes G1/S transcription activation and cell cycle entry in yeast under nitrogen-limited conditions.

SpAD Biofunctionalized Cellulose Acetate Scaffolds Inhibit Staphylococcus aureus Adherence in a Coordinating Function with the von Willebrand A1 Domain (vWF A1)

Staphylococcus aureus is one of the major pathogens causing and spreading hospital acquired infections. Since it is highly resistant to new generation antibiotics, novel strategies have to be developed such as the construction of biofunctionalized non-adherent surfaces that will prevent its tethering and subsequent spread in the hospital environment. In this frame, the domain D of protein A (SpAD) of S. aureus has been immobilized onto cellulose acetate scaffolds by using the streptavidin/biotin interaction, in order to study its interaction with the A1 domain of von Willebrand factor (vWF A1), a protein essential for hemostasis, found in human plasma. Subsequently, the biofunctionalized cellulose acetate scaffolds were incubated with S. aureus in the presence and absence of vWF A1 at different time periods and their potential to inhibit S. aureus growth was studied with scanning electron microscopy (SEM). The SpAD biofunctionalized scaffolds perceptibly ameliorated the non-adherent properties of the material, and in particular, the interaction between SpAD and vWF A1 effectively inhibited the growth of S. aureus. Thus, the exhibition of significant non-adherent properties of scaffolds addresses their potential use for covering medical equipment, implants, and stents.

Interrogating Kinase-Substrate Relationships with Proximity Labeling and Phosphorylation Enrichment

Kinases govern many cellular responses through the reversible transfer of a phosphate moiety to their substrates. However, pairing a substrate with a kinase is challenging. In proximity labeling experiments, proteins proximal to a target protein are marked by biotinylation, and mass spectrometry can be used for their identification. Here, we combine ascorbate peroxidase (APEX) proximity labeling and a phosphorylation enrichment-based workflow, Phospho-APEX (pAPEX), to rapidly identify phosphorylated and biotinylated neighbor proteins which can be considered for candidate substrates. The pAPEX strategy enriches and quantifies differences in proximity for proteins and phosphorylation sites proximal to an APEX2-tagged kinase under the kinase "ON" and kinase "OFF" conditions. As a proof of concept, we identified candidate substrates of MAPK1 in HEK293T and HCT116 cells and candidate substrates of PKA in HEK293T cells. In addition to many known substrates, C15orf39 was identified and confirmed as a novel MAPK1 substrate. In all, we adapted the proximity labeling-based platform to accommodate phosphorylation analysis for kinase substrate identification.

Systematic Screening of Penetratin's Protein Targets by Yeast Proteome Microarrays

Cell-penetrating peptides (CPPs) have distinct properties to translocate across cell envelope. The key property of CPPs to translocation with attached molecules has been utilized as vehicles for the delivery of several potential drug candidates that illustrate the significant effect in in-vitro experiment but fail in in-vivo experiment due to selectively permeable nature of cell envelop. Penetratin, a well-known CPP identified from the third α-helix of Antennapedia homeodomain of Drosophila, has been widely used and studied for the delivery of bioactive molecules to treat cancers, stroke, and infections caused by pathogenic organisms. Few studies have demonstrated that penetratin directly possesses antimicrobial activities against bacterial and fungal pathogens; however, the mechanism is unknown. In this study, we have utilized the power of high-throughput Saccharomyces cerevisiae proteome microarrays to screen all the potential protein targets of penetratin. Saccharomyces cerevisiae proteome microarrays assays of penetratin followed by statistical analysis depicted 123 Saccharomyces cerevisiae proteins as the protein targets of penetratin out of ~5800 Saccharomyces cerevisiae proteins. To understand the target patterns of penetratin, enrichment analyses were conducted using 123 protein targets. In biological process: ribonucleoprotein complex biogenesis, nucleic acid metabolic process, actin filament-based process, transcription, DNA-templated, and negative regulation of gene expression are a few significantly enriched terms. Cytoplasm, nucleus, and cell-organelles are enriched terms for cellular component. Protein-protein interactions network depicted ribonucleoprotein complex biogenesis, cortical cytoskeleton, and histone binding, which represent the major enriched terms for the 123 protein targets of penetratin. We also compared the protein targets of penetratin and intracellular protein targets of antifungal AMPs (Lfcin B, Histatin-5, and Sub-5). The comparison results showed few unique proteins between penetratin and AMPs. Nucleic acid metabolic process and cellular component disassembly were the common enrichment terms for penetratin and three AMPs. Penetratin shows unique enrichment items that are related to DNA biological process. Moreover, motif enrichment analysis depicted different enriched motifs in the protein targets of penetratin, LfcinB, Histatin-5, and Sub-5.