Basic Protein Modules Combining Abscisic Acid and Light Signaling in Arabidopsis

It is generally accepted that plants use the complex signaling system regulated by light and abscisic acid (ABA) signaling components to optimize growth and development in different situations. The role of ABA–light interactions is evident in the coupling of stress defense reactions with seed germination and root development, maintaining of stem cell identity and stem cell specification, stem elongation and leaf development, flowering and fruit formation, senescence, and shade avoidance. All these processes are regulated jointly by the ABA–light signaling system. Although a lot of work has been devoted to ABA–light signal interactions, there is still no systematic description of central signaling components and protein modules, which jointly regulate plant development. New data have emerged to promote understanding of how ABA and light signals are integrated at the molecular level, representing an extensively growing area of research. This work is intended to fill existing gaps by using literature data combined with bioinformatics analysis.

Proteomics analysis of the gut–brain axis in a gut microbiota-dysbiosis model of depression

Major depressive disorder (MDD) is a serious mental illness. Increasing evidence from both animal and human studies suggested that the gut microbiota might be involved in the onset of depression via the gut–brain axis. However, the mechanism in depression remains unclear. To explore the protein changes of the gut–brain axis modulated by gut microbiota, germ-free mice were transplanted with gut microbiota from MDD patients to induce depression-like behaviors. Behavioral tests were performed following fecal microbiota transplantation. A quantitative proteomics approach was used to examine changes in protein expression in the prefrontal cortex (PFC), liver, cecum, and serum. Then differential protein analysis and weighted gene coexpression network analysis were used to identify microbiota-related protein modules. Our results suggested that gut microbiota induced the alteration of protein expression levels in multiple tissues of the gut–brain axis in mice with depression-like phenotype, and these changes of the PFC and liver were model specific compared to chronic stress models. Gene ontology enrichment analysis revealed that the protein changes of the gut–brain axis were involved in a variety of biological functions, including metabolic process and inflammatory response, in which energy metabolism is the core change of the protein network. Our data provide clues for future studies in the gut–brain axis on protein level and deepen the understanding of how gut microbiota cause depression-like behaviors.

An Overview of the Primary Cilium and RPGRIP1L: The Signalling Hub’s Anchor for Organ Development and Homeostasis

Research on the primary cilium has been growing exponentially in the past several decades due to its functions as a cell signalling hub, which defects leads to several disorders and abnormalities collectively known as ciliopathies. Among other parts of the primary cilium structures, the transition zone is the area whose defects lead to the most severe clinical manifestations and high lethality. The ciliary transition zone consists of multiple protein modules that are hypothesized to be anchored by the RPGRIP1L protein. Despite its importance, RPGRIP1L studies remain hidden from the limelight, and our understanding of the protein remains scattered. This review summarizes the clinical manifestations and molecular mechanisms of the RPGRIP1L in the primary cilium. We then take a closer look at each RPGRIP1L’s protein domain to understand how each domain ensures proper functions and localization of RPGRIP1L. The three domains of RPGRIP1L are postulated to be involved in different roles. While the coiled coil domain is vital for scaffolding the protein to the centriolar structure, the ability of the C2 domain to interact with lipid allows the formation of ‘lipid gate’ at the transition zone. The high variability of the RPGR interaction domain enable the RPGRIP1L to interact with multiple different proteins, making it an ideal anchor for other ciliary protein modules in the transition zone.

Lung disease network reveals impact of comorbidity on SARS-CoV-2 infection and opportunities of drug repurposing

Background Higher mortality of COVID-19 patients with lung disease is a formidable challenge for the health care system. Genetic association between COVID-19 and various lung disorders must be understood to comprehend the molecular basis of comorbidity and accelerate drug development. Methods Lungs tissue-specific neighborhood network of human targets of SARS-CoV-2 was constructed. This network was integrated with lung diseases to build a disease–gene and disease-disease association network. Network-based toolset was used to identify the overlapping disease modules and drug targets. The functional protein modules were identified using community detection algorithms and biological processes, and pathway enrichment analysis. Results In total, 141 lung diseases were linked to a neighborhood network of SARS-CoV-2 targets, and 59 lung diseases were found to be topologically overlapped with the COVID-19 module. Topological overlap with various lung disorders allows repurposing of drugs used for these disorders to hit the closely associated COVID-19 module. Further analysis showed that functional protein–protein interaction modules in the lungs, substantially hijacked by SARS-CoV-2, are connected to several lung disorders. FDA-approved targets in the hijacked protein modules were identified and that can be hit by exiting drugs to rescue these modules from virus possession. Conclusion Lung diseases are clustered with COVID-19 in the same network vicinity, indicating the potential threat for patients with respiratory diseases after SARS-CoV-2 infection. Pathobiological similarities between lung diseases and COVID-19 and clinical evidence suggest that shared molecular features are the probable reason for comorbidity. Network-based drug repurposing approaches can be applied to improve the clinical conditions of COVID-19 patients.

Modular and Molecular Optimization of a LOV (Light–Oxygen–Voltage)-Based Optogenetic Switch in Yeast

Optogenetic switches allow light-controlled gene expression with reversible and spatiotemporal resolution. In Saccharomyces cerevisiae, optogenetic tools hold great potential for a variety of metabolic engineering and biotechnology applications. In this work, we report on the modular optimization of the fungal light–oxygen–voltage (FUN-LOV) system, an optogenetic switch based on photoreceptors from the fungus Neurospora crassa. We also describe new switch variants obtained by replacing the Gal4 DNA-binding domain (DBD) of FUN-LOV with nine different DBDs from yeast transcription factors of the zinc cluster family. Among the tested modules, the variant carrying the Hap1p DBD, which we call “HAP-LOV”, displayed higher levels of luciferase expression upon induction compared to FUN-LOV. Further, the combination of the Hap1p DBD with either p65 or VP16 activation domains also resulted in higher levels of reporter expression compared to the original switch. Finally, we assessed the effects of the plasmid copy number and promoter strength controlling the expression of the FUN-LOV and HAP-LOV components, and observed that when low-copy plasmids and strong promoters were used, a stronger response was achieved in both systems. Altogether, we describe a new set of blue-light optogenetic switches carrying different protein modules, which expands the available suite of optogenetic tools in yeast and can additionally be applied to other systems.

Deciphering biological evolution exploiting the topology of Protein Locality Graph

The conventional sequence comparison-based evolutionary studies ignore other evolutionary constraints like interaction among proteins, functions of proteins and genes etc. A lot of speculations exist in literature regarding the presence of species divergence at the level of the Protein Interaction Networks. Additionally, it has been conjectured that the intra-module connections stay conserved whereas the inter-module connections change during evolution. The most important components of the biological networks are the functional modules which are more conserved among the evolutionary closer species. Here, we demonstrate an alternative method to decipher biological evolution by exploiting the topology of a spatially localized Protein Interaction Network called Protein Locality Graph (PLG). Our lossless graph compression from PLG to a power graph called Protein Cluster Interaction Network (PCIN) results in a 90% size reduction and aids in improving computational time. Further, we exploit the topology of PCIN and demonstrate our capability of deriving the correct species tree by focusing on the cross-talk between the protein modules exclusively. Our results provide new evidence that traces of evolution are not only present at the level of the Protein-Protein Interactions, but are also very much present at the level of the inter-module interactions.

ProMoCell and ProModb: web services for analyzing interaction-based functionally localized protein modules in a cell

The modular organization of a cell which can be determined by its interaction network allows us to understand a mesh of cooperation among the functional modules. Therefore, cellular level identification of functional modules aids in understanding the functional and structural characteristics of the biological network of a cell and also assists in determining or comprehending the evolutionary signal. We develop ProMoCell that performs real-time web scraping for generating clusters of the cellular level functional units of an organism. ProMoCell constructs the Protein Locality Graphs and clusters the cellular level functional units of an organism by utilizing experimentally verified data from various online sources. Also, we develop ProModb, a database service that houses precomputed whole-cell protein-protein interaction network-based functional modules of an organism using ProMoCell. Our web service is entirely synchronized with the KEGG pathway database and allows users to generate spatially localized protein modules for any organism belonging to the KEGG genome using its real-time web scraping characteristics. Hence, the server will host as many organisms as is maintained by the KEGG database. Our web services provide the users a comprehensive and integrated tool for an efficient browsing and extraction of the spatial locality-based protein locality graph and the functional modules constructed by gathering experimental data from several interaction databases and pathway maps. We believe that our web services will be beneficial in pharmacological research, where a novel research domain called modular pharmacology has initiated the study on the diagnosis, prevention, and treatment of deadly diseases using functional modules.

Moonlighting of mitotic regulators in cilium disassembly

Correct timing of cellular processes is essential during embryological development and to maintain the balance between healthy proliferation and tumour formation. Assembly and disassembly of the primary cilium, the cell’s sensory signalling organelle, are linked to cell cycle timing in the same manner as spindle pole assembly and chromosome segregation. Mitotic processes, ciliary assembly, and ciliary disassembly depend on the centrioles as microtubule-organizing centres (MTOC) to regulate polymerizing and depolymerizing microtubules. Subsequently, other functional protein modules are gathered to potentiate specific protein–protein interactions. In this review, we show that a significant subset of key mitotic regulator proteins is moonlighting at the cilium, among which PLK1, AURKA, CDC20, and their regulators. Although ciliary assembly defects are linked to a variety of ciliopathies, ciliary disassembly defects are more often linked to brain development and tumour formation. Acquiring a better understanding of the overlap in regulators of ciliary disassembly and mitosis is essential in finding therapeutic targets for the different diseases and types of tumours associated with these regulators.

Lysin Motif (LysM) Proteins: Interlinking Manipulation of Plant Immunity and Fungi

The proteins with lysin motif (LysM) are carbohydrate-binding protein modules that play a critical role in the host-pathogen interactions. The plant LysM proteins mostly function as pattern recognition receptors (PRRs) that sense chitin to induce the plant’s immunity. In contrast, fungal LysM blocks chitin sensing or signaling to inhibit chitin-induced host immunity. In this review, we provide historical perspectives on plant and fungal LysMs to demonstrate how these proteins are involved in the regulation of plant’s immune response by microbes. Plants employ LysM proteins to recognize fungal chitins that are then degraded by plant chitinases to induce immunity. In contrast, fungal pathogens recruit LysM proteins to protect their cell wall from hydrolysis by plant chitinase to prevent activation of chitin-induced immunity. Uncovering this coevolutionary arms race in which LysM plays a pivotal role in manipulating facilitates a greater understanding of the mechanisms governing plant-fungus interactions.

GraphSynergy: Network Inspired Deep Learning Model for Anti–Cancer Drug Combination Prediction

Compared with monotherapy, anti-cancer drug combination can provide effective therapy with less toxicity in cancer treatment. Recent studies found that the topological positions of protein modules related to the drugs and the cancer cell lines in the protein-protein interaction (PPI) network may reveal the effects of drugs. However, due to the size of the combinatorial space, identifying synergistic combinations of drugs from PPI network is computationally difficult. To address this challenge, we propose an end-to-end deep learning framework, namely Graph Convolutional Network for Drug Synergy (GraphSynergy), to make synergistic drug combination predictions. GraphSynergy adapts a spatial-based Graph Convolutional Network component to encode the high-order structure information of protein modules targeted by a pair of drugs, as well as the protein modules associated with a specific cancer cell line in the PPI network. The pharmacological effects of drug combinations are explicitly evaluated by their therapy and toxic scores. By introducing an attention component to automatically allocate contribution weights to the proteins, we show the ability of GraphSynergy to capture the pivotal proteins that play a part in both PPI network and biomolecular interactions between drug combinations and cancer cell lines. Experiments on two latest drug combination datasets demonstrate that GraphSynergy outperforms the state-of-the-art in predicting synergistic drug combinations. This study sheds light on using machine learning to discover effective combination therapies for cancer and other complex diseases.