Protein-protein interaction detection: methods and analysis

Multi-Omics Analysis of the virulence factors and designing of next-generation multi-epitopes Vaccines against Rickettsia prowazekii: a computer-aided vaccine designing approach

Rickettsia is a genus of bacteria that are obligate intracellular parasites and are responsible for the febrile diseases known collectively as Rickettsioses. The emergence of antibiotic resistance is an escalating concern and thus developing a vaccine against Rickettsia is of paramount importance due to the significant public health threat posed by these bacteria. Thus, we employed structural vaccinology guided by machine learning algorithms to explore the virulence landscape of Rickettsia prowazekii to design a multi-epitopes-based vaccine (MEVC) that is immunogenic and safe. From a pool of virulence factors, we shortlisted five targets including sca0, sca1, sca4, sca5 and tlyA that were classified as non-allergenic as well as antigenic. The immune epitopes mapping results shortlisted five CTL epitopes, five HTL (IFN+) epitopes and five B cell epitopes as the best choice to design a vaccine construct of 475 amino acids. Various parameters were used to validate the designed MEVC which involved prediction of physiochemical properties, modeling and validation of the 3D structure, interaction with the immune receptors such as TLR2 (Toll-like receptor) and TLR4. Moreover, all-atoms simulation and binding free energy (BFE) results revealed a stable and favorable dynamic properties determined by these complexes. Jcat revealed that the improved sequence exhibits a GC content of 48.14% and a CAI (Codon Adaptation Index) value of 1.0. We used a multi-dose criterion at different time intervals i.e., 1st, 84th and 170th day to understand the immune potential of our constructed vaccine. The results provide a comprehensive overview of immune factors that ensure effective antigen memory cells generation after each injection, as predicted by the in silico pipeline. However, limitations in current algorithms particularly their inability to fully account for HLA polymorphism and the lack of experimental and clinical validation remain major shortcomings of the study. These issues should be addressed in future research to support the development of a robust immune response against Rickettsia infections.

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction

MOTIVATION

Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcity of quality hard negative samples, shortcut learning, and limited generalizability to novel proteins.

RESULTS

In this study, we introduce a novel approach for strategic sampling of protein-protein noninteractions (PPNIs) by leveraging higher-order network characteristics that capture the inherent complementarity-driven mechanisms of PPIs. Next, we introduce Unsupervised Pre-training of Node Attributes tuned for PPI (UPNA-PPI), a high throughput sequence-to-function ML pipeline, integrating unsupervised pre-training in protein representation learning with Topological PPNI (TPPNI) samples, capable of efficiently screening billions of interactions. By using our TPPNI in training the UPNA-PPI model, we improve PPI prediction generalizability and interpretability, particularly in identifying potential binding sites locations on amino acid sequences, strengthening the prioritization of screening assays and facilitating the transferability of ML predictions across protein families and homodimers. UPNA-PPI establishes the foundation for a fundamental negative sampling methodology in graph machine learning by integrating insights from network topology.

AVAILABILITY AND IMPLEMENTATION

Code and UPNA-PPI predictions are freely available at https://github.com/alxndgb/UPNA-PPI.

Identification, characterization, and CADD analysis of Plasmodium DMAP1 reveals it as a potential molecular target for new anti-malarial discovery

Developing drug resistance in the malaria parasite is a reason for apprehension compelling the scientific community to focus on identifying new molecular targets that can be exploited for developing new anti-malarial compounds. Despite the availability of the Plasmodium genome, many protein-coding genes in Plasmodium are still not characterized or very less information is available about their functions. DMAP1 protein is known to be essential for growth and plays an important role in maintaining genomic integrity and transcriptional repression in vertebrate organisms. In this study, we have identified a homolog of DMAP1 in P. falciparum. Our sequence and structural analysis showed that although PfDMAP1 possesses a conserved SANT domain, parasite protein displays significant structural dissimilarities from human homolog at full-length protein level as well as within its SANT domain. PPIN analysis of PfDMAP1 revealed it to be vital for parasite and virtual High-throughput screening of various pharmacophore libraries using BIOVIA platform-identified compounds that pass ADMET profiling and showed specific binding with PfDMAP1. Based on MD simulations and protein-ligand interaction studies two best hits were identified that could be novel potent inhibitors of PfDMAP1 protein.

Integrated bioinformatics approach for the identification and validation of novel biomarkers in ACC progression and prognosis

CONCLUSION

In conclusion, the identified novel biomarkers and associated pathways, provides a comprehensive insight into the molecular mechanisms, prognosis, and potential clinical applications for the diagnosis and therapeutic interventions of ACC.

Deciphering bacterial protein functions with innovative computational methods

Bacteria colonize every niche on Earth and play key roles in many environmental and host-associated processes. The sequencing revolution revealed the remarkable bacterial genetic and proteomic diversity and the genomic content of cultured and uncultured bacteria. However, deciphering functions of novel proteins remains a high barrier, often preventing the deep understanding of microbial life and its interaction with the surrounding environment. In recent years, exciting new bioinformatic tools, many of which are based on machine learning, facilitate the challenging task of gene and protein function discovery in the era of big genomics data, leading to the generation of testable hypotheses for bacterial protein functions. The new tools allow prediction of protein structures and interactions and allow sensitive and efficient sequence- and structure-based searching and clustering. Here, we summarize some of these recent tools which revolutionize modern microbiology research, along with examples for their usage, emphasizing the user-friendly, web-based ones. Adoption of these capabilities by experimentalists and computational biologists could save resources and accelerate microbiology research.

Computational insights into host-pathogen protein interactions: unveiling penaeid shrimp and white spot syndrome virus interplay

White spot syndrome virus (WSSV) has been a major threat in shrimp farming system especially for penaeid shrimps. The lack of effective control measures for WSSV makes this disease a significant threat to aquaculture. This study seeks to explore the mechanisms of WSSV infection and its impact on shrimp by examining host-pathogen interactions (HPI) through in silico approach, which can offer valuable insights into the processes of infection and disease progression. The investigation focused on five Penaeus species, including Penaeus vannamei, Penaeus chinensis, Penaeus monodon, Penaeus japonicus, and Penaeus indicus, studying their interaction with the WSSV. This study employed orthology-based and domain-driven analyses to reveal protein-protein interactions (PPIs) between the host and the pathogen. The combined strategies were found to be effective in detecting shared molecular mechanisms in pathogenesis, unveiling intricate PPI networks critical for virulence and host response. Most interacting proteins in WSSV are immediate early proteins involved in DNA replication and proliferation, and are crucial for ubiquitination, transcription regulation, and nucleotide metabolism. A large number of host proteins interact with WSSV across species (2360-11,704 interactions), with P. chinensis (11,704) and P. japonicus (11,458) exhibiting the highest counts, suggesting greater susceptibility or response. Host hub proteins are crucial in signaling, cellular processes, and metabolism, interacting across the cytoplasm, nucleus, and membrane, highlighting their role in WSSV pathogenesis. This study provides essential insights into host-pathogen interactions, offering a foundation for future research aimed at improving WSSV control in shrimp aquaculture.

Mechanistic Insights into the Anticancer Potential of Asparagus racemosus Willd. Against Triple-Negative Breast Cancer: A Network Pharmacology and Experimental Validation Study

Background/Objectives: The present study investigated the anticancer potential of Asparagus racemosus Willd. against triple-negative breast cancer (TNBC) using a combined in silico and in vitro approach. Methods: Network pharmacology identified 115 potential targets shared between A. racemosus phytochemicals and TNBC, highlighting key cancer-related pathways. Molecular docking predicted strong binding affinities between specific phytochemicals (beta-sitosterol, quercetin, and others) and crucial TNBC targets, including AKT1 and ERBB2. Results: Molecular dynamics simulations validated these interactions, demonstrating stable complex formation. In vitro, A. racemosus crude extracts exhibited potent anticancer activity against MDA-MB-231 TNBC cells, showing a dose-dependent reduction in viability (IC50 = 90.44 μg/mL), induction of G1 phase cell cycle arrest, and significant early apoptosis. Conclusions: These integrated findings provide compelling evidence for the anticancer potential of A. racemosus against TNBC, suggesting its promise for further development as a therapeutic strategy.

Leveraging Artificial Intelligence and Machine Learning for Characterizing Protein Corona, Nanobiological Interactions, and Advancing Drug Discovery

Proteins are essential for all living organisms, playing key roles in biochemical reactions, structural support, signal transduction, and gene regulation. Their importance in biomedical research is highlighted by their role as drug targets in various diseases. The interactions between proteins and nanoparticles (NPs), including the protein corona's formation, significantly affect NP behavior, biodistribution, cellular uptake, and toxicity. Comprehending these interactions is pivotal for advancing the design of NPs to augment their efficacy and safety in biomedical applications. While traditional nanomedicine design relies heavily on experimental work, the use of data science and machine learning (ML) is on the rise to predict the synthesis and behavior of nanomaterials (NMs). Nanoinformatics combines computational simulations with laboratory studies, assessing risks and revealing complex nanobio interactions. Recent advancements in artificial intelligence (AI) and ML are enhancing the characterization of the protein corona and improving drug discovery. This review discusses the advantages and limitations of these approaches and stresses the importance of comprehensive datasets for better model accuracy. Future developments may include advanced deep-learning models and multimodal data integration to enhance protein function prediction. Overall, systematic research and advanced computational tools are vital for improving therapeutic outcomes and ensuring the safe use of NMs in medicine.

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Targeting cell cycle arrest in breast cancer by phytochemicals from Caryto urens L. fruit ethyl acetate fraction: in silico and in vitro validation

BACKGROUND

Caryota urens, also known as Shivjata, has been documented in ancient Indian texts for its therapeutic benefits, addressing conditions from seminal weakness to gastric ulcers. This study aims to investigate its contemporary medicinal potential in treating breast cancer.

OBJECTIVES

The study focuses on exploring the therapeutic potential of Caryota urens fruit against breast cancer, specifically targeting cell cycle genes CDK1, CDC25A, and PLK1 through bioinformatics, network pharmacology, and in vitro validation.

MATERIALS AND METHODS

Using mass spectrometry and nuclear magnetic resonance (NMR), 60 key phytoconstituents from Caryota urens fruit were identified. Bioinformatics analysis, integrating Gene Cards and GEO databases, 15,474 breast cancer-associated genes focusing on the HR+/HER2-subtype were identified. Molecular docking and qPCR validated the interactions of key phytoconstituents, particularly Episesamin, with CDK1, CDC25A, and PLK1. In vitro studies were conducted on the MCF7 cell line, supplemented by ROC and survival analyses to evaluate diagnostic and therapeutic potential.

RESULTS

The bioinformatics analysis identified CDK1, CDC25A, and PLK1 as pivotal genes regulating cell cycle progression and breast cancer tumorigenesis. Network pharmacology and in vitro studies indicated that phytoconstituents, especially Episesamin, downregulated these genes in breast cancer cells. Molecular docking and qPCR confirmed these interactions, and ROC and survival analyses underscored their diagnostic and therapeutic significance.

CONCLUSIONS

This study suggests that Caryota urens fruit extract, particularly Episesamin, may inhibit breast cancer metastasis by downregulating CDK1, CDC25A, and PLK1, offering promising new strategies for targeting the cell cycle in breast cancer and emphasizing the value of integrating bioinformatics with experimental methods in cancer research.

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues

Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.

Spastin and alsin protein interactome analyses begin to reveal key canonical pathways and suggest novel druggable targets

Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein-protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as "causative" for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration-approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.

Functional analysis and interaction networks of Rboh in poplar under abiotic stress

Introduction

Plant respiratory burst oxidase homologs (Rbohs) are essential in the generation of reactive oxygen species (ROS) and play critical roles in plant stress responses. Despite their importance, Rbohs in poplar species remain under-explored, especially in terms of their characteristics and functional diversity across different species within the same genus.

Methods

In this study, we employed bioinformatics methods to identify 62 Rboh genes across five poplar species. We analyzed the gene structure, physical properties, chromosomal distribution, and cis-elements. Additionally, we used qRT-PCR to examine the expression of PyRbohs (Populus yunnanensis Rbohs) under various stress treatments and yeast two-hybrid (Y2H) assays to confirm interactions with calcium-dependent protein kinases (CPKs).

Results

All identified Rboh genes consistently contained six conserved functional domains and were classified into four distinct groups (I-IV). The number of Rboh members across poplar species was consistent with evolutionary patterns. These Rbohs exhibited relatively conserved amino acid lengths (832-989) and shared basic protein characteristics, including cell membrane localization. Chromosomal distribution analysis revealed an uneven distribution of PyRbohs across chromosomes, with abundant collinearity pairs among different plant species, indicating tandem segment duplications and a shared evolutionary origin within group members. Cis-element analysis identified stress-responsive and hormone signaling-related elements. qRT-PCR demonstrated the upregulation of PyRbohs under salt, drought, PEG, and ABA treatments. Protein interaction predictions using the STRING database identified potential functional mechanisms of PyRbohs, including interactions with CPKs. Y2H assays confirmed the interaction between PyRbohs and CPKs, suggesting that CPK binding might regulate PyRboh activity and ROS production.

Discussion

Overall, these findings provide a comprehensive understanding of the evolutionary, structural, and functional diversity of poplar Rbohs. They highlight promising candidate genes for enhancing stress tolerance in poplar species and lay a foundation for future research on the molecular mechanisms underlying Rboh-mediated stress responses in poplar.

Potential promising of synthetic lethality in cancer research and treatment

Cancer is a complex disease driven by multiple genetic changes, including mutations in oncogenes, tumor suppressor genes, DNA repair genes, and genes involved in cancer metabolism. Synthetic lethality (SL) is a promising approach in cancer research and treatment, where the simultaneous dysfunction of specific genes or pathways causes cell death. By targeting vulnerabilities created by these dysfunctions, SL therapies selectively kill cancer cells while sparing normal cells. SL therapies, such as PARP inhibitors, WEE1 inhibitors, ATR and ATM inhibitors, and DNA-PK inhibitors, offer a distinct approach to cancer treatment compared to conventional targeted therapies. Instead of directly inhibiting specific molecules or pathways, SL therapies exploit genetic or molecular vulnerabilities in cancer cells to induce selective cell death, offering benefits such as targeted therapy, enhanced treatment efficacy, and minimized harm to healthy tissues. SL therapies can be personalized based on each patient's unique genetic profile and combined with other treatment modalities to potentially achieve synergistic effects. They also broaden the effectiveness of treatment across different cancer types, potentially overcoming drug resistance and improving patient outcomes. This review offers an overview of the current understanding of SL mechanisms, advancements, and challenges, as well as the preclinical and clinical development of SL. It also discusses new directions and opportunities for utilizing SL in targeted therapy for anticancer treatment.

Focal Molography Allows for Affinity and Concentration Measurements of Proteins in Complex Matrices with High Accuracy

Characterizing biomolecular receptor-ligand interactions is critical for research and development. However, performing analyses in complex, biologically relevant matrices, such as serum, remains challenging due to non-specific binding that often impairs measurements. Here, we evaluated Focal Molography (FM) for determining KD and kinetic constants in comparison to gold-standard methods using single-domain heavy-chain antibodies in various systems. FM provided kinetic constants highly comparable to SPR and BLI in standard buffers containing blocking proteins, with KDs of soluble CD4 (sCD4) interactions within a 2.4-fold range across technologies. In buffers lacking blocking proteins, FM demonstrated greater robustness against non-specific binding and rebinding effects. In serum, FM exhibited stable baseline signals, unlike SPR and BLI, and yielded KDs of sCD4 interaction in 50% Bovine Serum within a 1.8-fold range of those obtained in standard buffers. For challenging molecules prone to non-specific binding (Granzyme B), FM successfully determined kinetic constants without external referencing. Finally, FM enabled direct analyte quantification in complex matrices. sCD4 quantification in cell culture media and 50% FBS showed recovery rates of 97.8-100.3% with an inter-assay CV below 1.3%. This study demonstrates the high potential of FM for kinetic affinity determination and biomarker quantification in complex matrices, enabling reliable measurements under biologically relevant conditions.

Proteomic analysis reveals the roles of silicon in mitigating glyphosate-induced toxicity in Brassica napus L

Glyphosate (Gly) is a widely used herbicide for weed control in agriculture, but it can also adversely affect crops by impairing growth, reducing yield, and disrupting nutrient uptake, while inducing toxicity. Therefore, adopting integrated eco-friendly approaches and understanding the mechanisms of glyphosate tolerance in plants is crucial, as these areas remain underexplored. This study provides proteome insights into Si-mediated improvement of Gly-toxicity tolerance in Brassica napus. The proteome analysis identified a total of 4,407 proteins, of which 594 were differentially abundant, including 208 up-regulated and 386 down-regulated proteins. These proteins are associated with diverse biological processes in B. napus, including energy metabolism, antioxidant activity, signal transduction, photosynthesis, sulfur assimilation, cell wall functions, herbicide tolerance, and plant development. Protein-protein interactome analyses confirmed the involvement of six key proteins, including L-ascorbate peroxidase, superoxide dismutase, glutaredoxin-C2, peroxidase, glutathione peroxidase (GPX) 2, and peptide methionine sulfoxide reductase A3 which involved in antioxidant activity, sulfur assimilation, and herbicide tolerance, contributing to the resilience of B. napus against Gly toxicity. The proteomics insights into Si-mediated Gly-toxicity mitigation is an eco-friendly approach, and alteration of key molecular processes opens a new perspective of multi-omics-assisted B. napus breeding for enhancing herbicide resistant oilseed crop production.

Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning

Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques. These methods not only improve predictive accuracy but also provide insights into functional sites, such as binding and catalytic residues. However, challenges such as limited high-resolution structural data and the need for effective negative sampling persist. Through the integration of experimental and computational tools, structure-based prediction paves the way for comprehensive proteomic network analysis, holding promise for advancements in drug discovery, biomarker identification, and personalized medicine. Future directions include enhancing scalability and dataset reliability to expand these approaches across diverse proteomes.

Interaction of lysozyme with solid supports cryogels containing imidazole functional group

This paper details the preparation of acrylamide-based supermacroporous cryogels and their application in removing lysozyme from aqueous solutions. N-Vinyl imidazole was copolymerized with acrylamide as a comonomer to impart pseudo-specificity to the cryogels, forming poly(AAm-VIM) cryogel. Characterization studies to assess the physical and chemical properties of the synthesized cryogels involved swelling tests, Fourier Transform Infrared Spectroscopy (FTIR), elemental analysis, Field Emission Scanning Electron Microscopy (FESEM), and Thermogravimetric Analysis (TGA-DTA). To ascertain the optimal conditions for the adsorption process, pH 9.0 (TRIS buffer) was selected for lysozyme adsorption, using the parametres such as initial concentration screening, ionic strength, temperature, and column flow rate. The Langmuir and Freundlich isotherm models were analyzed to assess the adsorption parameters mathematically. The regression coefficient results indicated that lysozyme adsorption aligned more closely with the Langmuir isotherm model. The adsorption process is considered to be thermodynamically physical and spontaneous. SDS-PAGE analysis assessed the purity of lysozyme isolated from an aqueous solution using a poly(AAm-VIM) cryogel column. The inertness and regeneration capacity of poly(AAm-VIM) cryogel affinity columns were assessed using reusability studies conducted during the adsorption-desorption cycle.

The members of zinc finger-homeodomain (ZF-HD) transcription factors are associated with abiotic stresses in soybean: insights from genomics and expression analysis

BACKGROUND

Zinc finger homeodomain (ZF-HD) belongs to the plant-specific transcription factor (TF) family and is widely involved in plant growth, development and stress responses. Despite their importance, a comprehensive identification and analysis of ZF-HD genes in the soybean (Glycine max) genome and their possible roles under abiotic stress remain unexplored.

RESULTS

In this study, 51 ZF-HD genes were identified in the soybean genome that were unevenly distributed on 17 chromosomes. All GmZF-HD genes contained a conserved ZF-HD_dimer domain and had diverse physicochemical features. Furthermore, the GmZF-HD gene structures exhibited 3 to 10 conserved motifs, and most of them showed intronless gene structures. Phylogenetic analysis categorized them into eight major groups with the highest closeness to dicots including Brassica rapa and Malus domestica. The cis-element analysis recognized plant growth and development (10%), phytohormones (31%) and stress-responsive (59%) elements. Synteny analysis identified 73 segmental and 1 tandem duplicated genes that underwent purifying selection. The collinearity analysis revealed that GmZF-HD genes showed higher homology with dicot species, indicating common ancestors with close evolutionary relationships. A total of 94 gma-miRNAs from 41 diverse miRNA families were identified, targeting 40 GmZF-HD genes, with GmZF-HD6 being most targeted by 7 miRNAs, and gma-miR4993 emerging as the dominant miRNA family. Different TFs including ERF, LBD, BBR-BPC and MYB, etc., were predicted in all 51 GmZF-HD genes upstream regions and visualized in the network. Expression profiling through RNA-Seq showed diverse expressions of GmZF-HD genes in different tissues including seeds, roots, shoots and leaves under diverse conditions. Further, the qRT-PCR analysis demonstrated that all tested GmZF-HD genes were significantly induced in soybean leaves, mainly the GmZF-HD5/6/13/39 and GmZF-HD45 genes were significantly upregulated (2.5 to 8.8 folds) under the tested stress treatments compared to control, highlighting their potential roles in response to stresses in soybean.

CONCLUSION

Overall, this study reveals comprehensive insights into the ZF-HD genes in soybeans and provides a valuable contribution towards functional studies for soybean improvement under stress conditions.

Investigating Protein-Protein Interactions of Autophagy-Involved TNIP1

Myriad proteins are involved in the process of autophagy, which they participate in via their protein-protein interactions (PPI). Herein we outline a methodology for examining such interactions utilizing the case of intrinsically disordered protein (IDP) TNIP1 and its interaction with linear M1-linked polyubiquitin. This includes methods for recombinant production, purification, immuno-identification, and analysis of an IDP associated with autophagy, its ordered binding partner, and means of quantitatively analyzing their interaction.

Quest for space: Tenacity of DNA, Protein, and Lipid macromolecules in intracellular crowded environment

The biochemical processes in the cellular milieu involving biomacromolecular interaction usually occur in crowded and heterogeneous environments, impacting their structure, stability, and reactivity. The crowded environment in vivo is typically ignored for experimental investigations since the studies get complex due to intracellular biophysical interactions between nucleic acids, proteins, cellular membranes, and various cations/anions present in the cell. Thus, being a ubiquitous property of all cells, studying those biophysical aspects affecting biochemical processes under realistically crowded conditions is of prime importance. Crowders or crowding agents are usually exploited to mimic the in vivo conditions on interacting with such genomic species, revealing structural and functional changes resulting from excluded volume and soft interactions. In the last few years, studies including crowders of varied sizes have gained attention concerning the consequences of crowding agents on biomolecular structural transitions and stability. This review comprehensively summarizes macromolecular crowding, emphasizing the biophysical effects and contribution of soft interactions in the heterogeneous cellular environment.

Big Data and Artificial Intelligence in Drug Discovery for Gastric Cancer: Current Applications and Future Perspectives

Gastric cancer (GC) represents a significant global health burden, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related death worldwide. Despite recent advancements in GC treatment, the five-year survival rate for advanced-stage GC patients remains low. Consequently, there is an urgent need to identify novel drug targets and develop effective therapies. However, traditional drug discovery approaches are associated with high costs, time-consuming processes, and a high failure rate, posing challenges in meeting this critical need. In recent years, there has been a rapid increase in the utilization of artificial intelligence (AI) algorithms and big data in drug discovery, particularly in cancer research. AI has the potential to improve the drug discovery process by analyzing vast and complex datasets from multiple sources, enabling the prediction of compound efficacy and toxicity, as well as the optimization of drug candidates. This review provides an overview of the latest AI algorithms and big data employed in drug discovery for GC. Additionally, we examine the various applications of AI in this field, with a specific focus on therapeutic discovery. Moreover, we discuss the challenges, limitations, and prospects of emerging AI methods, which hold significant promise for advancing GC research in the future.

Mitochondria Localized Anticancer Iridium(III) Prodrugs for Targeted Delivery of Myeloid Cell Leukemia-1 (Mcl-1) Inhibitors and Cytotoxic Iridium(III) Complex

Myeloid cell leukemia-1 (Mcl-1) is an antiapoptotic oncoprotein overexpressed in several malignancies and acts as one of the promising therapeutic targets for cancer. Even though there are several small molecule based Mcl-1 inhibitors reported, the delivery of Mcl-1 inhibitor at the target site is quite challenging. In this regard, we developed a series of mitochondria targeting luminescent cyclometalated iridium(III) prodrugs bearing Mcl-1 inhibitors via ester linkage due to the presence of Mcl-1 protein in the outer mitochondrial membrane. Among the synthesized prodrugs, IrThpy@L2 was found to exhibit the potent cytotoxicity (IC50 = 30.93 nM) against HCT116 cell line when compared with bare Mcl-1 inhibitors (IC50 > 100 μM). Mechanistic studies further revealed that IrThpy@L2 quickly gets internalized inside the mitochondria of HCT116 cells and undergoes activation in the presence of overexpressed esterase which leads to the release of two cytotoxic species i.e. Mcl-1 inhibitors (I-2) and cytotoxic iridium(III) complex (IrThpy@OH). The improved cytotoxicity of IrThpy@L2 is due to the mitochondria targeting ability of iridium(III) prodrug, subsequent esterase activated release of I-2 to inhibit Mcl-1 protein and IrThpy@OH to generate reactive oxygen species (ROS). After prodrug activation, the released cytotoxic species cause mitochondrial membrane depolarization, activate a cascade of mitochondria-mediated cell death events, and arrest the cell cycle in S-phase which leads to apoptosis. The potent anticancer activity of IrThpy@L2 was further evident from the drastic morphological changes, size reduction in the solid tumor mimicking 3D multicellular tumor spheroids (MCTS) of HCT116.

Heterogeneous network approaches to protein pathway prediction

Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.

A versatile attention-based neural network for chemical perturbation analysis and its potential to aid surgical treatment: an experimental study

Deep learning models have emerged as rapid, accurate, and effective approaches for clinical decisions. Through a combination of drug screening and deep learning models, drugs that may benefit patients before and after surgery can be discovered to reduce the risk of complications or speed recovery. However, most existing drug prediction methods have high data requirements and lack interpretability, which has a limited role in adjuvant surgical treatment. To address these limitations, the authors propose the attention-based convolution transpositional interfusion network (ACTIN) for flexible and efficient drug discovery. ACTIN leverages the graph convolution and the transformer mechanism, utilizing drug and transcriptome data to assess the impact of chemical pharmacophores containing certain elements on gene expression. Remarkably, just with only 393 training instances, only one-tenth of the other models, ACTIN achieves state-of-the-art performance, demonstrating its effectiveness even with limited data. By incorporating chemical element embedding disparity and attention mechanism-based parameter analysis, it identifies the possible pharmacophore containing certain elements that could interfere with specific cell lines, which is particularly valuable for screening useful pharmacophores for new drugs tailored to adjuvant surgical treatment. To validate its reliability, the authors conducted comprehensive examinations by utilizing transcriptome data from the lung tissue of fatal COVID-19 patients as additional input for ACTIN, the authors generated novel lead chemicals that align with clinical evidence. In summary, ACTIN offers insights into the perturbation biases of elements within pharmacophore on gene expression, which holds the potential for guiding the development of new drugs that benefit surgical treatment.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

The Future of Pharmacogenomics: Integrating Epigenetics, Nutrigenomics, and Beyond

Pharmacogenomics (PGx) has revolutionized personalized medicine by empowering the tailoring of drug treatments based on individual genetic profiles. However, the complexity of drug response mechanisms necessitates the integration of additional biological and environmental factors. This article explores integrating epigenetics, nutrigenomics, microbiomes, protein interactions, exosomes, and metabolomics with PGx to enhance personalized medicine. In addition to discussing these scientific advancements, we examine the regulatory and ethical challenges of translating multi-omics into clinical practice, including considerations of data privacy, regulatory oversight, and equitable access. By framing these factors within the context of Medication Adherence, Medication Appropriateness, and Medication Adverse Events (MA3), we aim to refine therapeutic strategies, improve drug efficacy, and minimize adverse effects, with the goal of improving personalized medicine. This approach has the potential to benefit patients, healthcare providers, payers, and the healthcare system as a whole by enabling more precise and effective treatments.

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.

Identification of Host Factors Interacting with Movement Proteins of the 30K Family in Nicotiana tabacum

The interaction of viral proteins with host factors represents a crucial aspect of the infection process in plants. In this work, we developed a strategy to identify host factors in Nicotiana tabacum that interact with movement proteins (MPs) of the 30K family, a group of viral proteins around 30 kDa related to the MP of tobacco mosaic virus, which enables virus movement between plant cells. Using the alfalfa mosaic virus (AMV) MP as a model, we incorporated tags into its coding sequence, without affecting its functionality, enabling the identification of 121 potential interactors through in vivo immunoprecipitation of the tagged MP. Further analysis of five selected candidates (histone 2B (H2B), actin, 14-3-3A protein, eukaryotic initiation factor 4A (elF4A), and a peroxidase-POX-) were conducted using bimolecular fluorescence complementation (BiFC). The interactions between these factors were also studied, revealing that some form part of protein complexes associated with AMV MP. Moreover, H2B, actin, 14-3-3, and eIF4A interacted with other MPs of the 30K family. This observation suggests that, beyond functional and structural features, 30K family MPs may share common interactors. Our results demonstrate that tagging 30K family MPs is an effective strategy to identify host factors associated with these proteins during viral infection.

Tau Protein and Tauopathies: Exploring Tau Protein-Protein and Microtubule Interactions, Cross-Interactions and Therapeutic Strategies

Tau, a microtubule-associated protein (MAP), is essential to maintaining neuronal stability and function in the healthy brain. However, aberrant modifications and pathological aggregations of Tau are implicated in various neurodegenerative disorders, collectively known as tauopathies. The most common Tauopathy is Alzheimer's Disease (AD) counting nowadays more than 60 million patients worldwide. This comprehensive review delves into the multifaceted realm of Tau protein, puzzling out its intricate involvement in both physiological and pathological roles. Emphasis is put on Tau Protein-Protein Interactions (PPIs), depicting its interaction with tubulin, microtubules and its cross-interaction with other proteins such as Aβ1-42, α-synuclein, and the chaperone machinery. In the realm of therapeutic strategies, an overview of diverse possibilities is presented with their relative clinical progresses. The focus is mostly addressed to Tau protein aggregation inhibitors including recent small molecules, short peptides and peptidomimetics with specific focus on compounds that showed a double anti aggregative activity on both Tau protein and Aβ amyloid peptide. This review amalgamates current knowledge on Tau protein and evolving therapeutic strategies, providing a comprehensive resource for researchers seeking to deepen their understanding of the Tau protein and for scientists involved in the development of new peptide-based anti-aggregative Tau compounds.

Time-course characterization of whole-transcriptome dynamics of HepG2/C3A spheroids and its toxicological implications

Physiologically relevant in vitro models are a priority in predictive toxicology to replace and/or reduce animal experiments. The compromised toxicant metabolism of many immortalized human liver cell lines grown as monolayers as compared to in vivo metabolism limits their physiological relevance. However, recent efforts to culture liver cells in a 3D environment, such as spheroids, to better mimic the in vivo conditions, may enhance the toxicant metabolism of human liver cell lines. In this study, we characterized the dynamic changes in the transcriptome of HepG2/C3A hepatocarcinoma cell spheroids maintained in a clinostat system (CelVivo) to gain insight into the metabolic capacity of this model as a function of spheroid size and culture time. We assessed morphological changes (size, necrotic core), cell health, and proliferation rate from initial spheroid seeding to 35 days of continuous culture in conjunction with a time-course (0, 3, 7, 10, 14, 21, 28 days) of the transcriptome (TempO-Seq, BioSpyder). The phenotypic characteristics of HepG2/C3A growing in spheroids were comparable to monolayer growth until ∼Day 12 (Day 10-14) when a significant decrease in cell doubling rate was noted which was concurrent with down-regulation of cell proliferation and cell cycle pathways over this time period. Principal component analysis of the transcriptome data suggests that the Day 3, 7, and 10 spheroids are pronouncedly different from the Day 14, 21, and 28 spheroids in support of a biological transition time point during the long-term 3D spheroid cultures. The expression of genes encoding cellular components involved in toxicant metabolism and transport rapidly increased during the early time points of spheroids to peak at Day 7 or Day 10 as compared to monolayer cultures with a gradual decrease in expression with further culture, suggesting the most metabolically responsive time window for exposure studies. Overall, we provide baseline information on the cellular and molecular characterization, with a particular focus on toxicant metabolic capacity dynamics and cell growth, of HepG2/C3A 3D spheroid cultures over time.

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

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

Investigating the Activities of CAF20 and ECM32 in the Regulation of PGM2 mRNA Translation

Translation is a fundamental process in biology, and understanding its mechanisms is crucial to comprehending cellular functions and diseases. The regulation of this process is closely linked to the structure of mRNA, as these regions prove vital to modulating translation efficiency and control. Thus, identifying and investigating these fundamental factors that influence the processing and unwinding of structured mRNAs would be of interest due to the widespread impact in various fields of biology. To this end, we employed a computational approach and identified genes that may be involved in the translation of structured mRNAs. The approach is based on the enrichment of interactions and co-expression of genes with those that are known to influence translation and helicase activity. The in silico prediction found CAF20 and ECM32 to be highly ranked candidates that may play a role in unwinding mRNA. The activities of neither CAF20 nor ECM32 have previously been linked to the translation of PGM2 mRNA or other structured mRNAs. Our follow-up investigations with these two genes provided evidence of their participation in the translation of PGM2 mRNA and several other synthetic structured mRNAs.

Single-cell and spatial detection of senescent cells using DeepScence

Accurately identifying senescent cells is essential for studying their spatial and molecular features. We developed DeepScence, a method based on deep neural networks, to identify senescent cells in single-cell and spatial transcriptomics data. DeepScence is based on CoreScence, a senescence-associated gene set we curated that incorporates information from multiple published gene sets. We demonstrate that DeepScence can accurately identify senescent cells in single-cell gene expression data collected both in vitro and in vivo, as well as in spatial transcriptomics data generated by different platforms, substantially outperforming existing methods.

YkuR functions as a protein deacetylase in Streptococcus mutans

Protein acetylation is a common and reversible posttranslational modification tightly governed by protein acetyltransferases and deacetylases crucial for various biological processes in both eukaryotes and prokaryotes. Although recent studies have characterized many acetyltransferases in diverse bacterial species, only a few protein deacetylases have been identified in prokaryotes, perhaps in part due to their limited sequence homology. In this study, we identified YkuR, encoded by smu_318, as a unique protein deacetylase in Streptococcus mutans. Through protein acetylome analysis, we demonstrated that the deletion of ykuR significantly upregulated protein acetylation levels, affecting key enzymes in translation processes and metabolic pathways, including starch and sucrose metabolism, glycolysis/gluconeogenesis, and biofilm formation. In particular, YkuR modulated extracellular polysaccharide synthesis and biofilm formation through the direct deacetylation of glucosyltransferases (Gtfs) in the presence of NAD+. Intriguingly, YkuR can be acetylated in a nonenzymatic manner, which then negatively regulated its deacetylase activity, suggesting the presence of a self-regulatory mechanism. Moreover, in vivo studies further demonstrated that the deletion of ykuR attenuated the cariogenicity of S. mutans in the rat caries model, substantiating its involvement in the pathogenesis of dental caries. Therefore, our study revealed a unique regulatory mechanism mediated by YkuR through protein deacetylation that regulates the physiology and pathogenicity of S. mutans.

Computational tools to predict context-specific protein complexes

Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.

Knowledge Graphs for drug repurposing: a review of databases and methods

Drug repurposing has emerged as a effective and efficient strategy to identify new treatments for a variety of diseases. One of the most effective approaches for discovering potential new drug candidates involves the utilization of Knowledge Graphs (KGs). This review comprehensively explores some of the most prominent KGs, detailing their structure, data sources, and how they facilitate the repurposing of drugs. In addition to KGs, this paper delves into various artificial intelligence techniques that enhance the process of drug repurposing. These methods not only accelerate the identification of viable drug candidates but also improve the precision of predictions by leveraging complex datasets and advanced algorithms. Furthermore, the importance of explainability in drug repurposing is emphasized. Explainability methods are crucial as they provide insights into the reasoning behind AI-generated predictions, thereby increasing the trustworthiness and transparency of the repurposing process. We will discuss several techniques that can be employed to validate these predictions, ensuring that they are both reliable and understandable.

Micro-arc driven porous ZrO2 coating for tailoring surface properties of titanium for dental implants application

Titanium (Ti) is an ideal material for dental implants due to its excellent properties. However, corrosion and mechanical wear lead to Ti ions and particles release, triggering inflammatory responses and bone resorption. To overcome these challenges, surface modification techniques are used, including micro-arc oxidation (MAO). MAO creates adherent, porous coatings on Ti implants with diverse chemical compositions. In this context, zirconia element stands out in its wear and corrosion properties associated with low friction and chemical stability. Therefore, we investigated the impact of adding zirconium oxide (ZrO2) to Ti surfaces through MAO, aiming for improved electrochemical and mechanical properties. Additionally, the antimicrobial and modulatory potentials, cytocompatibility, and proteomic profile of surfaces were investigated. Ti discs were divided into four groups: machined - control (cpTi), treated by MAO with 0.04 M KOH - control (KOH), and two experimental groups incorporating ZrO2 at concentrations of 0.04 M and 0.08 M, composing the KOH@Zr4 and KOH@Zr8 groups. KOH@Zr8 showed higher surface porosity and roughness, even distribution of zirconia, formation of crystalline phases like ZrTiO4, and hydrophilicity. ZrO2 groups showed better mechanical performance including higher hardness values, lower wear area and mass loss, and higher friction coefficient under tribological conditions. The formation of a more compact oxide layer was observed, which favors the electrochemical stability of ZrO2 surfaces. Besides not inducing greater biofilm formation, ZrO2 surfaces reduced the load of pathogenic bacteria evidenced by the DNA-DNA checkerboard analysis. ZrO2 surfaces were cytocompatible with pre-osteoblastic cells. The saliva proteomic profile, evaluated by liquid chromatography coupled with tandem mass spectrometry, was slightly changed by zirconia, with more proteins adsorbed. KOH@Zr8 group notably absorbed proteins crucial for implant biological responses, like albumin and fibronectin. Incorporating ZrO2 improved the mechanical and electrochemical behavior of Ti surfaces, as well as modulated biofilm composition and provided suitable biological responses.

Exploring the promise of regulator of G Protein Signaling 20: insights into potential mechanisms and prospects across solid cancers and hematological malignancies

RGS (Regulator of G protein signaling) proteins have long captured the fascination of researchers due to their intricate involvement across a wide array of signaling pathways within cellular systems. Their diverse and nuanced functions have positioned them as continual subjects of scientific inquiry, especially given the implications of certain family members in various cancer types. Of particular note in this context is RGS20, whose clinical relevance and molecular significance in hepatocellular carcinoma we have recently investigated. These investigations have prompted questions into the prevalence of pathogenic mutations within the RGS20 gene and the intricate network of interacting proteins that could contribute to the complex landscape of cancer biology. In our study, we aim to unravel the mutations within the RGS20 gene and the multifaceted interplay between RGS20 and other proteins within the context of cancer. Expanding on this line of inquiry, our research is dedicated to uncovering the intricate mechanisms of RGS20 in various cancers. In particular, we have redirected our attention to examining the role of RGS20 within hematological malignancies, with a specific focus on multiple myeloma and follicular lymphoma. These hematological cancers hold significant promise for further investigation, as understanding the involvement of RGS20 in their pathogenesis could unveil novel therapeutic strategies and treatment avenues. Furthermore, our exploration has extended to encompass the latest discoveries concerning the potential involvement of RGS20 in diseases affecting the central nervous system, thereby broadening the scope of its implications beyond oncology to encompass neurobiology and related fields.

Intricate Crosstalk Between Food Allergens, Phages, Bacteria, and Eukaryotic Host Cells of the Gut-skin Axis

Bacterial and food allergens are associated with immune-mediated food allergies via the gut-skin axis. However, there has been no data on the potential use of phages to rescue this pathological process. A human triple cell co-culture model incorporating colonocytes (T84 cells), macrophages (THP-1 cells), and hepatocytes (Huh7 cells) was established and infected with Pseudomonas aeruginosa PAO1 (P.a PAO1) in the absence or presence of its KPP22 phage in Dulbecco's Modified Eagle's Medium (DMEM), DMEM+ ovalbumin (OVA), or DMEM+β-casein media. The physiological health of cells was verified by assessing cell viability and Transepithelial electrical resistance (TEER) across the T84 monolayer. The immune response of cells was investigated by determining the secretions of IL-1β, IL-8, IL-22, and IL-25. The ability of P.a PAO1 to adhere to and invade T84 cells was evaluated. The addition of either OVA or β-casein potentiated the P.a PAO1-elicited secretion of cytokines. The viability and TEER of the T84 monolayer were lower in the P.a PAO1+OVA group compared to the P.a PAO1 alone and PAO1+β-casein groups. OVA and β-casein significantly increased the adherence and invasion of P.a PAO1 to T84 cells. In the presence of the KPP22 phage, these disruptive effects were abolished. These results imply that: (1) food allergens and bacterial toxic effector molecules exacerbate each other's disruptive effects; (2) food allergen and bacterial signaling at the gut-skin mucosal surface axis depend on a network of bacteria-phage-eukaryotic host interactions; and (3) phages are complementary for the evaluation of pathobiological processes that occur at the interface between bacteria, host cellular milieu, and food antigens because phages intervene in P.a PAO1-, OVA-, and β-casein-derived inflammation.

The Eyes Absent family: At the intersection of DNA repair, mitosis, and replication

The Eyes Absent family (EYA1-4) are a group of dual function proteins that act as both tyrosine phosphatases and transcriptional co-activators. EYA proteins play a vital role in development, but are also aberrantly overexpressed in cancers, where they often confer an oncogenic effect. Precisely how the EYAs impact cell biology is of growing interest, fuelled by the therapeutic potential of an expanding repertoire of EYA inhibitors. Recent functional studies suggest that the EYAs are important players in the regulation of genome maintenance pathways including DNA repair, mitosis, and DNA replication. While the characterized molecular mechanisms have predominantly been ascribed to EYA phosphatase activities, EYA co-transcriptional activity has also been found to impact the expression of genes that support these pathways. This indicates functional convergence of EYA phosphatase and co-transcriptional activities, highlighting the emerging importance of the EYA protein family at the intersection of genome maintenance mechanisms. In this review, we discuss recent progress in defining EYA protein substrates and transcriptional effects, specifically in the context of genome maintenance. We then outline future directions relevant to the field and discuss the clinical utility of EYA inhibitors.

Structural proteomics guided annotation of vaccine targets and designing of multi-epitopes vaccine to instigate adaptive immune response against Francisella tularensis

Francisella tularensis can cause severe disease in humans via the respiratory or cutaneous routes and a case fatality ratio of up to 10 % is reported due to lack of proper antibiotic treatment, while F. novicida causes disease in severely immunocompromised individuals. Efforts are needed to develop effective vaccine candidates against Francisella species. Thus, in this study, a systematic computational work frame was used to deeply investigate the whole proteome of Francisella novicida containing 1728 proteins to develop vaccine against F. tularensis and related species. Whole-proteome analysis revealed that four proteins including (A0Q492) (A0Q7Y4), (A0Q4N4), and (A0Q5D9) are the suitable vaccine targets after the removal of homologous, paralogous and prediction of subcellular localization. These proteins were used to predict the T cell, B cell, and HTL epitopes which were joined together through suitable linkers to construct a multi-epitopes vaccine (MEVC). The MEVC was found to be highly immunogenic and non-allergenic while the physiochemical properties revealed the feasible expression and purification. Moreover, the molecular interaction of MEVC with TLR2, molecular simulation, and binding free energy analyses further validated the immune potential of the construct. According to Jcat analysis, the refined sequence demonstrates GC contents of 41.48 % and a CAI value of 1. The in-silico cloning and optimization process ensured compatibility with host codon usage, thereby facilitating efficient expression. Computational immune simulation studies underscored the capacity of MEVC to induce both primary and secondary immune responses. The conservation analysis further revealed that the selected epitopes exhibit 100 % conservation across different species and thus provides wider protection against Francisella.

An F-box Kelch repeat protein, PmFBK2, from Persicaria minor interacts with GID1b to modulate gibberellin signalling

The F-box protein (FBP) family plays diverse functions in the plant kingdom, with the function of many members still unrevealed. In this study, a specific FBP called PmFBK2, containing Kelch repeats from Persicaria minor, was functionally investigated. Employing the yeast two-hybrid (Y2H) assay, PmFBK2 was found to interact with Skp1-like proteins from P. minor, suggesting its potential to form an E3 ubiquitin ligase, known as the SCF complex. Y2H and co-immunoprecipitation tests revealed that PmFBK2 interacts with full-length PmGID1b. The interaction marks the first documented binding between these two protein types, which have never been reported in other plants before, and they exhibited a negative effect on gibberellin (GA) signal transduction. The overexpression of PmFBK2 in the kmd3 mutant, a homolog from Arabidopsis, demonstrated the ability of PmFBK2 to restore the function of the mutated KMD3 gene. The function restoration was supported by morphophysiological and gene expression analyses, which exhibited patterns similar to the wild type (WT) compared to the kmd3 mutant. Interestingly, the overexpression of PmFBK2 or PmGID1b in Arabidopsis had opposite effects on rosette diameter, seed weight, and plant height. This study provides new insights into the complex GA signalling. It highlights the crucial roles of the interaction between FBP and the GA receptor (GID1b) in regulating GA responses. These findings have implications for developing strategies to enhance plant growth and yield by modulating GA signalling in crops.

The identification, evolutionary analysis, and immune roles of Rab family members in red swamp crayfish, Procambarus clarkii

The Rab GTPase constitutes the largest family of small GTPases that regulate intracellular trafficking. Different eukaryotes possess varying numbers of Rab paralogs. However, limited knowledge exists regarding the evolutionary pattern of Rab family in most major eukaryotic supergroups. This study cloned 24 Rab genes from transcriptome data of Procambarus clarkii haemocytes. The multiple sequence alignment and phylogenetic tree analysis revealed a relatively high degree of conservation for PcRab. Furthermore, PcRab exhibited similarities in motif composition with all members showing presence of G, PM, RabF, and RabSF motifs. The tertiary structure indicated that PcRab proteins mainly consisted of α-helices and β-strands, and most PcRab proteins shared similar tertiary structures, and it was indicated that they have similar protein characteristics. Protein-protein interaction prediction identified a total of 20 interacting proteins involved in vesicle trafficking, phagocytosis, and signal transduction with 193 interactions. Expression analysis showed wide expression patterns for PcRab in P. clarkii organs. Upon infection by white spot syndrome virus and Aeromonas veronii, significant induction was observed for PcRab gene expression levels, indicating their involvement in pathogen response mechanisms. The present study represents the pioneering effort in comprehensively identifying and cloning the Rab family genes in crustacean, followed by a systematic investigation into their evolutionary patterns and immune response upon pathogen infection. The results provided valuable insights for further investigation into the molecular mechanism underlying the response of P. clarkii to pathogen infection.

Molecular docking and antimicrobial activities of photoexcited inhibitors in antimicrobial photodynamic therapy against Enterococcus faecalis biofilms in endodontic infections

Antimicrobial photodynamic therapy (aPDT) is a promising approach to combat antibiotic resistance in endodontic infections. It eliminates residual bacteria from the root canal space and reduces the need for antibiotics. To enhance its effectiveness, an in silico and in vitro study was performed to investigate the potential of targeted aPDT using natural photosensitizers, Kojic acid and Parietin. This approach aims to inhibit the biofilm formation of Enterococcus faecalis, a frequent cause of endodontic infections, by targeting the Ace and Esp proteins. After determining the physicochemical characteristics of Ace and Esp proteins and model quality assessment, the molecular dynamic simulation was performed to recognize the structural variations. The stability and physical movement of the protein-ligand complexes were evaluated. In silico molecular docking was conducted, followed by ADME/Tox profiling, pharmacokinetics characteristics, and assessment of drug-likeness properties of the natural photosensitizers. The study also investigated the changes in the expression of genes (esp and ace) involved in E. faecalis biofilm formation. The results showed that both Kojic acid and Parietin complied with Lipinski's rule of five and exhibited drug-like properties. In silico analysis indicated stable complexes between Ace and Esp proteins and the natural photosensitizers. The molecular docking studies demonstrated good binding affinity. Additionally, the expression of the ace and esp genes was significantly downregulated in aPDT using Kojic acid and Parietin with blue light compared to the control group. This investigation concluded that Kojic acid and Parietin with drug-likeness could efficiently interact with Ace and Esp proteins with a strong binding affinity. Hence, natural photosensitizers-mediated aPDT can be considered a promising adjunctive treatment against endodontic infections.

Genome-wide identification and characterization of FORMIN gene family in potato (Solanum tuberosum L.) and their expression profiles in response to drought stress condition

Formin proteins, characterized by the FH2 domain, are critical in regulating actin-driven cellular processes and cytoskeletal dynamics during abiotic stress. However, no genome-wide analysis of the formin gene family has yet to be conducted in the economically significant plant potato (Solanum tuberosum L.). In this study, 26 formin genes were identified and characterized in the potato genome (named as StFH), each containing the typical FH2 domain and distributed across the ten chromosomes. The StFH was categorized into seven subgroups (A-G) and the gene structure and motif analysis demonstrated higher structural similarities within the subgroups. Besides, the StFH exhibited ancestry and functional similarities with Arabidopsis. The Ka/Ks ratio indicated that StFH gene pairs were evolving through purifying selection, with five gene pairs exhibiting segmental duplications and two pairs exhibiting tandem duplications. Subcellular localization analysis suggested that most of the StFH genes were located in the chloroplast and plasma membrane. Moreover, 54 cis-acting regulatory elements (CAREs) were identified in the promoter regions, some of which were associated with stress responses. According to gene ontology analysis, the majority of the StFH genes were involved in biological processes, with 63 out of 74 GO terms affecting actin polymerization. Six major transcription factor families, including bZIP, C2H2, ERF, GATA, LBD, NAC, and HSF, were identified that were involved in the regulation of StFH genes in various abiotic stresses, including drought. Further, the 60 unique microRNAs targeted 24 StFH by regulating gene expression in response to drought stress were identified. The expression of StFH genes in 14 different tissues, particularly in drought-responsive tissues such as root, stem, shoot apex, and leaf, underscores their significance in managing drought stress. RNA-seq analysis of the drought-resistant Qingshu No. 9 variety revealed the potential role of up-regulated genes, including StFH2, StFH10, StFH19, and StFH25, in alleviating drought stress. Overall, these findings provide crucial insights into the response to drought stress in potatoes and can be utilized in breeding programs to develop potato cultivars with enhanced drought-tolerant traits.