Signaling networks: the origins of cellular multitasking

Exploiting Host Kinases to Combat Dengue Virus Infection and Disease

The burden of dengue on human health has dramatically increased in recent years, underscoring the urgent need for effective therapeutic interventions. Despite decades of research since the discovery of the dengue virus, no specific antiviral treatments are available and strategies to reliably prevent severe disease remain limited. Direct-acting antivirals against dengue are under active investigation but have shown limited efficacy to date. An underappreciated Achille's heal of the virus is its dependence on host factors for infection and pathogenesis, each of which presents a potential avenue for therapeutic intervention. We and others have demonstrated that dengue virus relies on multiple host kinases, some of which are already targeted by clinically approved inhibitors. These offer drug repurposing opportunities for host-directed dengue treatment. Here, we summarize findings on the role of kinases in dengue infection and disease and highlight potential kinase targets for the development of innovative host-directed therapeutics.

scMINER: a mutual information-based framework for clustering and hidden driver inference from single-cell transcriptomics data

Single-cell transcriptomics data present challenges due to their inherent stochasticity and sparsity, complicating both cell clustering and cell type-specific network inference. To address these challenges, we introduce scMINER (single-cell Mutual Information-based Network Engineering Ranger), an integrative framework for unsupervised cell clustering, transcription factor and signaling protein network inference, and identification of hidden drivers from single-cell transcriptomic data. scMINER demonstrates superior accuracy in cell clustering, outperforming five state-of-the-art algorithms and excelling in distinguishing closely related cell populations. For network inference, scMINER outperforms three established methods, as validated by ATAC-seq and CROP-seq. In particular, it surpasses SCENIC in revealing key transcription factor drivers involved in T cell exhaustion and Treg tissue specification. Moreover, scMINER enables the inference of signaling protein networks and drivers with high accuracy, which presents an advantage in multimodal single cell data analysis. In addition, we establish scMINER Portal, an interactive visualization tool to facilitate exploration of scMINER results.

PI3K/AKT signaling mediates stress-inducible amyloid formation through c-Myc

In response to environmental stress, eukaryotic cells reversibly form functional amyloid aggregates called amyloid bodies (A-bodies). While these solid-like biomolecular condensates share many biophysical characteristics with pathological amyloids, A-bodies are non-toxic, and they induce a protective state of cellular dormancy. As a recently identified structure, the modulators of A-body biogenesis remain uncharacterized, with the seeding noncoding RNA being the only known regulatory factor. Here, we use an image-based high-throughput screening approach to identify candidate pathways regulating A-body biogenesis. Our data demonstrate that the phosphatidylinositol 3-kinase (PI3K)/AKT signaling axis meditates A-body formation during stress exposure, with AKT activation repressing glycogen synthase kinase-3 (GSK3)-mediated degradation of c-Myc. This enhances c-Myc binding to regulatory elements of the seeding noncoding RNA, upregulating the transcripts that nucleate A-body formation. Identifying a link between PI3K/AKT signaling, c-Myc, and physiological amyloid aggregates extends the range of activity for these well-established regulators while providing insight into cellular components whose dysregulation could underly amyloidogenic disorders.

Engineering synthetic phosphorylation signaling networks in human cells

Protein phosphorylation signaling networks have a central role in how cells sense and respond to their environment. We engineered artificial phosphorylation networks in which reversible enzymatic phosphorylation cycles were assembled from modular protein domain parts and wired together to create synthetic phosphorylation circuits in human cells. Our design scheme enabled model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, and downstream connections can regulate gene expression. We engineered cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach that allows the design of signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Tumor Cell-Specific Signal Processing Platform Controlled by ATP for Non-invasive Modulation of Cellular Behavior

Regulating the spatial distribution of membrane receptors can artificially reprogram cellular behaviors, which play a critical biological role in various physiological and pathological processes. Herein, we construct a tumor cell-specific signal processing platform (TCS-SPP) for controlled promotion/inhibition of cellular-mesenchymal epithelial transition factor (c-Met) receptor dimerization to noninvasively modulate cellular behaviors. Upon the dual-aptamer recognition in the upstream input signal circuit (UISC) to discriminate target cancer cells, the membrane-anchored DNA signal processor (DSP) is activated for signal amplification via rolling circle amplification (RCA) followed by the working of an ATP molecular switch for signal conversion, achieving receptor modulation in the downstream output signal circuit (DOSC). Benefiting from the rigid structure of DSP, the protective effect, and spatial confinement effect of RCA products, this TCS-SPP has demonstrated good performance in accurately modulating cellular behavior such as cell migration, invasion, and proliferation, showing great potential for targeted cancer therapy and biomedical engineering applications.

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

Dynamic disease pathways are a combination of complex dynamical processes among bio-molecules in a cell that leads to diseases. Network modeling of disease pathways considers disease-related bio-molecules (e.g. DNA, RNA, transcription factors, enzymes, proteins, and metabolites) and their interaction (e.g. DNA methylation, histone modification, alternative splicing, and protein modification) to study disease progression and predict therapeutic responses. These bio-molecules and their interactions are the basic elements in the study of the misregulation in the disease-related gene expression that lead to abnormal cellular responses. Gene regulatory networks, cell signaling networks, and metabolic networks are the three major types of intracellular networks for the study of the cellular responses elicited from extracellular signals. The disease-related cellular responses can be prevented or regulated by designing control strategies to manipulate these extracellular or other intracellular signals. The paper reviews the regulatory mechanisms, the dynamic models, and the control strategies for each intracellular network. The applications, limitations and the prospective for modeling and control are also discussed.

SignalingProfiler 2.0 a network-based approach to bridge multi-omics data to phenotypic hallmarks

Unraveling how cellular signaling is remodeled upon perturbation is crucial for understanding disease mechanisms and identifying potential drug targets. In this pursuit, computational tools generating mechanistic hypotheses from multi-omics data have invaluable potential. Here, we present a newly implemented version (2.0) of SignalingProfiler, a multi-step pipeline to draw mechanistic hypotheses on the signaling events impacting cellular phenotypes. SignalingProfiler 2.0 derives context-specific signaling networks by integrating proteogenomic data with the prior knowledge-causal network. This is a freely accessible and flexible tool that incorporates statistical, footprint-based, and graph algorithms to accelerate the integration and interpretation of multi-omics data. Through a benchmarking process on three proof-of-concept studies, we demonstrate the tool's ability to generate hierarchical mechanistic networks recapitulating novel and known perturbed signaling and phenotypic outcomes, in both human and mice contexts. In summary, SignalingProfiler 2.0 addresses the emergent need to derive biologically relevant information from complex multi-omics data by extracting interpretable networks.

Revisiting the evolution of bow-tie architecture in signaling networks

Bow-tie architecture is a layered network structure that has a narrow middle layer with multiple inputs and outputs. Such structures are widely seen in the molecular networks in cells, suggesting that a universal evolutionary mechanism underlies the emergence of bow-tie architecture. The previous theoretical studies have implemented evolutionary simulations of the feedforward network to satisfy a given input-output goal and proposed that the bow-tie architecture emerges when the ideal input-output relation is given as a rank-deficient matrix with mutations in network link intensities in a multiplicative manner. Here, we report that the bow-tie network inevitably appears when the link intensities representing molecular interactions are small at the initial condition of the evolutionary simulation, regardless of the rank of the goal matrix. Our dynamical system analysis clarifies the mechanisms underlying the emergence of the bow-tie structure. Further, we demonstrate that the increase in the input-output matrix reduces the width of the middle layer, resulting in the emergence of bow-tie architecture, even when evolution starts from large link intensities. Our data suggest that bow-tie architecture emerges as a side effect of evolution rather than as a result of evolutionary adaptation.

Signal execution modes emerge in biochemical reaction networks calibrated to experimental data

Mathematical models of biomolecular networks are commonly used to study cellular processes; however, their usefulness to explain and predict dynamic behaviors is often questioned due to the unclear relationship between parameter uncertainty and network dynamics. In this work, we introduce PyDyNo (Python dynamic analysis of biochemical networks), a non-equilibrium reaction-flux based analysis to identify dominant reaction paths within a biochemical reaction network calibrated to experimental data. We first show, in a simplified apoptosis execution model, that despite the thousands of parameter vectors with equally good fits to experimental data, our framework identifies the dynamic differences between these parameter sets and outputs three dominant execution modes, which exhibit varying sensitivity to perturbations. We then apply our methodology to JAK2/STAT5 network in colony-forming unit-erythroid (CFU-E) cells and provide previously unrecognized mechanistic explanation for the survival responses of CFU-E cell population that would have been impossible to deduce with traditional protein-concentration based analyses.

Exercise-Regulated Mitochondrial and Nuclear Signalling Networks in Skeletal Muscle

Exercise perturbs energy homeostasis in skeletal muscle and engages integrated cellular signalling networks to help meet the contraction-induced increases in skeletal muscle energy and oxygen demand. Investigating exercise-associated perturbations in skeletal muscle signalling networks has uncovered novel mechanisms by which exercise stimulates skeletal muscle mitochondrial biogenesis and promotes whole-body health and fitness. While acute exercise regulates a complex network of protein post-translational modifications (e.g. phosphorylation) in skeletal muscle, previous investigations of exercise signalling in human and rodent skeletal muscle have primarily focused on a select group of exercise-regulated protein kinases [i.e. 5' adenosine monophosphate-activated protein kinase (AMPK), protein kinase A (PKA), Ca2+/calmodulin-dependent protein kinase (CaMK) and mitogen-activated protein kinase (MAPK)] and only a small subset of their respective protein substrates. Recently, global mass spectrometry-based phosphoproteomic approaches have helped unravel the extensive complexity and interconnection of exercise signalling pathways and kinases beyond this select group and phosphorylation and/or translocation of exercise-regulated mitochondrial and nuclear protein substrates. This review provides an overview of recent advances in our understanding of the molecular events associated with acute endurance exercise-regulated signalling pathways and kinases in skeletal muscle with a focus on phosphorylation. We critically appraise recent evidence highlighting the involvement of mitochondrial and nuclear protein phosphorylation and/or translocation in skeletal muscle adaptive responses to an acute bout of endurance exercise that ultimately stimulate mitochondrial biogenesis and contribute to exercise's wider health and fitness benefits.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

The potential of phytochemicals against epidermal growth factor receptor tyrosine kinase (EGFRK): an insight from molecular dynamic simulations

Cancer is an umbrella term used to define various diseases with abnormal cell proliferation at the focal point. According to the WHO, cancer is the leading cause of death worldwide, with lung cancer being the second most common perpetrator after breast cancer. There are several proteins acting in harmony that lead to cancer. EGFR has been identified as one of the proteins that is linked to cell division, even when it is cancerous in nature. Cancer can be treated using therapeutic agents that target EGFR or their signaling networks. Available drugs that could inhibit EGFR have acquired resistance in most cases and multiple side effects on the human body. That is why phytochemicals are being studied for their role in this case. Around 8000 compounds were retrieved from our previously created phytochemdb database for their drug activity, and the 3D protein structure was collected from the protein data bank. The selected dataset of ligands was virtually screened through HTVS, SP, and XP to retain the top 4 hits. Molecular dynamics revealed the stability and flexibility of protein-(selected)ligand interactions. The non-bond interactions of each of the compounds with EGFR, such as Gossypetin interacting with active site MET769 and ASP831; Muxiangrine III interacting with MET769 and ASP831; Quercetagetin showing non-bonded interactions with GLU738, GLN767, and MET769 for >100% of the simulation timeframe These findings suggest further research into these compounds, which can yield a potential phytochemical drug against cancer.Communicated by Ramaswamy H. Sarma.

Insights into the neurobiology of suicidality: explicating the role of glutamatergic systems through the lens of ketamine

Suicidality is a prevalent mental health condition, and managing suicidal patients is one of the most challenging tasks for health care professionals due to the lack of rapid-acting, effective psychopharmacological treatment options. According to the literature, suicide has neurobiological underpinnings that are not fully understood, and current treatments for suicidal tendencies have considerable limitations. To treat suicidality and prevent suicide, new treatments are required; to achieve this, the neurobiological processes underlying suicidal behavior must be thoroughly investigated. Although multiple neurotransmitter systems, particularly serotonergic systems, have been studied in the past, less has been reported in relation to disruptions in glutamatergic neurotransmission, neuronal plasticity, and neurogenesis that result from stress-related abnormalities of the hypothalamic-pituitary-adrenal system. Informed by the literature, which reports robust antisuicidal and antidepressive properties of subanaesthetic doses of ketamine, this review aims to provide an examination of the neurobiology of suicidality (and relevant mood disorders) with implications of pertinent animal, clinical, and postmortem studies. We discuss dysfunctions in the glutamatergic system, which may play a role in the neuropathology of suicidality and the role of ketamine in restoring synaptic connectivity at the molecular levels.

Cellular harmonics for the morphology-invariant analysis of molecular organization at the cell surface

The spatiotemporal organization of membrane-associated molecules is central to the regulation of cellular signals. Powerful new microscopy techniques enable the three-dimensional visualization of localization and activation of these molecules; however, the quantitative interpretation and comparison of molecular organization on the three-dimensional cell surface remains challenging because cells themselves vary greatly in morphology. Here we introduce u-signal3D, a framework to assess the spatial scales of molecular organization at the cell surface in a cell-morphology-invariant manner. We validated the framework by analyzing synthetic signaling patterns painted onto observed cell morphologies, as well as measured distributions of cytoskeletal and signaling molecules. To demonstrate the framework's versatility, we further compared the spatial organization of cell surface signals both within, and between, cell populations, and powered an upstream machine-learning-based analysis of signaling motifs.

Biodegradable Poly(trehalose) Nanoparticle for Preventing Amyloid Beta Aggregation and Related Neurotoxicity

Trehalose is a disaccharide that is capable of inhibiting protein aggregation and activating cellular autophagy. It has been shown that a polymer or nanoparticle form, terminated with multiple trehalose units, can significantly enhance the anti-amyloidogenic performance and is suitable for the treatment of neurodegenerative diseases. Here, we report a trehalose-conjugated polycarbonate-co-lactide polymer and formulation of its nanoparticles having multiple numbers of trehalose exposed on the surface. The resultant poly(trehalose) nanoparticle inhibits the aggregation of amyloid beta peptides and disintegrates matured amyloid fibrils into smaller fragments. Moreover, the poly(trehalose) nanoparticle lowers extracellular amyloid β oligomer-driven cellular stress and enhances cell viability. The presence of biodegradable polycarbonate components in the poly(trehalose) nanoparticle would enhance their application potential as an anti-amyloidogenic material.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Dexamethasone-doped nanoparticles improve mineralization, crystallinity and collagen structure of human dentin

OBJECTIVES

Bioactive materials have been used for functionalization of adhesives to promote dentin remineralization. This study aims to evaluate bonding ability and both mechanical and chemical behavior of demineralized dentin infiltrated with polymeric nanoparticles doped with dexamethasone (Dex-NPs).

METHODS

Dentin conditioned surfaces were infiltrated with NPs, Dex-NPs or Dex-Zn-NPs. Bonded interfaces were also created and stored for 24 h or 21d, and then submitted to microtensile bond strength testing. Dentin remineralization was analyzed by Nanohardness, Young's modulus and Raman analysis.

RESULTS

At 21d of storage, dentin treated with undoped-NPs attained the lowest nanohardness and Young's modulus. Dex-NPs and Zn-Dex-NPs increased dentin nanohardness and Young's modulus after 21d Raman analysis showed high remineralization, crystallinity, crosslinking and better structure of collagen when functionalized Dex-NPs were present at the dentin interface.

CONCLUSIONS

Infiltration of dentin with Dex-NPs promoted functional remineralization as proved by nanomechanical and morpho-chemical evaluation tests. Dexamethasone in dentin facilitated crystallographic maturity, crystallinity and improved maturity and secondary structure of dentin collagen.

CLINICAL SIGNIFICANCE

Using dexamethasone-functionalized NPs before resin infiltration is a clear option to obtain dentin remineralization, as these NPs produce the reinforcement of the dentin structure, which will lead to the improvement of the longevity of resin restorations.

Computational drug repurposing by exploiting large-scale gene expression data: Strategy, methods and applications

De novo drug development is an extremely complex, time-consuming and costly task. Urgent needs for therapies of various diseases have greatly accelerated searches for more effective drug development methods. Luckily, drug repurposing provides a new and effective perspective on disease treatment. Rapidly increased large-scale transcriptome data paints a detailed prospect of gene expression during disease onset and thus has received wide attention in the field of computational drug repurposing. However, how to efficiently mine transcriptome data and identify new indications for old drugs remains a critical challenge. This review discussed the irreplaceable role of transcriptome data in computational drug repurposing and summarized some representative databases, tools and strategies. More importantly, it proposed a practical guideline through establishing the correspondence between three gene expression data types and five strategies, which would facilitate researchers to adopt appropriate strategies to deeply mine large-scale transcriptome data and discover more effective therapies.

Small-Molecule Modulated Affinity-Tunable Semisynthetic Protein Switches

Engineered protein switches have been widely applied in cell-based protein sensors and point-of-care diagnosis for the rapid and simple analysis of a wide variety of proteins, metabolites, nucleic acids, and enzymatic activities. Currently, these protein switches are based on two main types of switching mechanisms to transduce the target binding event to a quantitative signal, through a change in the optical properties of fluorescent molecules and the activation of enzymatic activities. In this paper, we introduce a new affinity-tunable protein switch strategy in which the binding of a small-molecule target with the protein activates the streptavidin-biotin interaction to generate a readout signal. In the absence of a target, the biotinylated protein switch forms a closed conformation where the biotin is positioned in close proximity to the protein, imposing a large steric hindrance to prevent the effective binding with streptavidin. In the presence of the target molecule, this steric hindrance is removed, thereby exposing the biotin for streptavidin binding to produce strong fluorescent signals. With this modular sensing concept, various sulfonamide, methotrexate, and trimethoprim drugs can be selectively detected on the cell surface of native and genetically engineered cells using different fluorescent dyes and detection techniques.

Wired to Connect: The Autonomic Socioemotional Reflex Arc

We have previously proposed that mothers and infants co-regulate one another’s autonomic state through an autonomic conditioning mechanism, which starts during gestation and results in the formation of autonomic socioemotional reflexes (ASRs) following birth. Theoretically, autonomic physiology associated with the ASR should correlate concomitantly with behaviors of mother and infant, although the neuronal pathway by which this phenomenon occurs has not been elucidated. In this paper, we consider the neuronal pathway by which sensory stimuli between a mother and her baby/child affect the physiology and emotional behavior of each. We divide our paper into two parts. In the first part, to gain perspective on current theories on the subject, we conduct a 500-year narrative history of scientific investigations into the human nervous system and theories that describe the neuronal pathway between sensory stimulus and emotional behavior. We then review inconsistencies between several currently accepted theories and recent data. In the second part, we lay out a new theory of emotions that describes how sensory stimuli between mother and baby unconsciously control the behavior and physiology of both. We present a theory of mother/infant emotion based on a set of assumptions fundamentally different from current theories. Briefly, we propose that mother/infant sensory stimuli trigger conditional autonomic socioemotional reflexes (ASRs), which drive cardiac function and behavior without the benefit of the thalamus, amygdala or cortex. We hold that the ASR is shaped by an evolutionarily conserved autonomic learning mechanism (i.e., functional Pavlovian conditioning) that forms between mother and fetus during gestation and continues following birth. We highlight our own and others research findings over the past 15 years that support our contention that mother/infant socioemotional behavior is driven by mutual autonomic state plasticity, as opposed to cortical trait plasticity. We review a novel assessment tool designed to measure the behaviors associated with the ASR phenomenon. Finally, we discuss the significance of our theory for the treatment of mothers and infants with socioemotional disorders.

Robust and tunable signal processing in mammalian cells via engineered covalent modification cycles

Engineered signaling networks can impart cells with new functionalities useful for directing differentiation and actuating cellular therapies. For such applications, the engineered networks must be tunable, precisely regulate target gene expression, and be robust to perturbations within the complex context of mammalian cells. Here, we use bacterial two-component signaling proteins to develop synthetic phosphoregulation devices that exhibit these properties in mammalian cells. First, we engineer a synthetic covalent modification cycle based on kinase and phosphatase proteins derived from the bifunctional histidine kinase EnvZ, enabling analog tuning of gene expression via its response regulator OmpR. By regulating phosphatase expression with endogenous miRNAs, we demonstrate cell-type specific signaling responses and a new strategy for accurate cell type classification. Finally, we implement a tunable negative feedback controller via a small molecule-stabilized phosphatase, reducing output expression variance and mitigating the context-dependent effects of off-target regulation and resource competition. Our work lays the foundation for establishing tunable, precise, and robust control over cell behavior with synthetic signaling networks.

Transcriptome-Guided Identification of Drugs for Repurposing to Treat Age-Related Hearing Loss

Age-related hearing loss (ARHL) or presbycusis is a prevalent condition associated with social isolation, cognitive impairment, and dementia. Age-related changes in the cochlea, the auditory portion of the inner ear, are the primary cause of ARHL. Unfortunately, there are currently no pharmaceutical approaches to treat ARHL. To examine the biological processes underlying age-related changes in the cochlea and identify candidate drugs for rapid repurposing to treat ARHL, we utilized bulk RNA sequencing to obtain transcriptomes from the functional substructures of the cochlea—the sensorineural structures, including the organ of Corti and spiral ganglion neurons (OC/SGN) and the stria vascularis and spiral ligament (SV/SL)—in young (6-week-old) and old (2-year-old) C57BL/6 mice. Transcriptomic analyses revealed both overlapping and unique patterns of gene expression and gene enrichment between substructures and with ageing. Based on these age-related transcriptional changes, we queried the protein products of genes differentially expressed with ageing in DrugBank and identified 27 FDA/EMA-approved drugs that are suitable to be repurposed to treat ARHL. These drugs target the protein products of genes that are differentially expressed with ageing uniquely in either the OC/SGN or SV/SL and that interrelate diverse biological processes. Further transcriptomic analyses revealed that most genes differentially expressed with ageing in both substructures encode protein products that are promising drug target candidates but are, nevertheless, not yet linked to approved drugs. Thus, with this study, we apply a novel approach to characterize the druggable genetic landscape for ARHL and propose a list of drugs to test in pre-clinical studies as potential treatment options for ARHL.

Programmable Multistimuli-Responsive and Multimodal Polymer Actuator Based on a Designed Energy Transduction Network

A polymer actuator typically responds to only one or two types of stimuli, where sensing and actuation are simultaneously exerted by the same responsive polymer. In cells, sensing and actuation are exerted separately by different biomolecules, which are integrated into nanoscale assemblies to construct the signaling network, making cells a multistimuli responsive and multimodal system. Inspired by the structure-function relationship of the signaling network in cells, we have developed a strategy to select and assemble proper functional polymers into assemblies, where sensing and actuation are exerted by different polymers, and the assemblies can present novel functions beyond that of each polymer component. Three polymers [polyaniline, PANi; poly(N-isopropylacrylamide), PNIPAm; and polydimethylsiloxane, PDMS] are integrated as nodes into a simple energy transduction network, which can be regulated by three molecular factors (pH, kosmotropic anions, and polyethylene glycol). PANi converts the light or electric stimulus into heat, which triggers the actuation of PNIPAm and PDMS. Relying on this energy transduction network, the polymer assembly can respond to six types of stimuli (light, electricity, temperature, water, ions, and organic solvents) and perform different actuation modes, serving as a powerful actuator. Programmable complex deformation upon multiple simultaneous or sequential stimuli has also been achieved by this actuator. An adaptive gripper to catch thin objects and a self-regulating switch to maintain environmental humidity illustrate the wide potential of this actuator for next-generation smart materials and soft robots.

Comparative Transcriptome Analysis of Spleen Reveals Potential Regulation of Genes and Immune Pathways Following Administration of Aeromonas salmonicida subsp. masoucida Vaccine in Atlantic Salmon (Salmo salar)

Aeromonas salmonicida is a global fish pathogen. Aeromonas salmonicida subsp. masoucida (ASM) is classified as atypical A. salmonicida and caused huge losses to salmonid industry in China. Hence, it is of great significance to develop ASM vaccine and explore its protection mechanism in salmonids. In this regard, we conducted RNA-seq analysis with spleen tissue of Atlantic salmon after ASM vaccination to reveal genes, their expression patterns, and pathways involved in immune protections. In our results, a total of 441.63 million clean reads were obtained, and 389.37 million reads were mapped onto the Atlantic salmon reference genome. In addition, 1125, 2126, 1098, 820, and 1351 genes were significantly up-regulated, and 747, 2626, 818, 254, and 908 genes were significantly down-regulated post-ASM vaccination at 12 h, 24 h, 1 month, 2 months, and 3 months, respectively. Subsequent pathway analysis revealed that many differentially expressed genes (DEGs) following ASM vaccination were involved in cytokine-cytokine receptor interaction (TNFRSF11b, IL-17RA, CCR9, and CXCL11), HTLV-I infection (MR1 and HTLV-1), MAPK signaling pathway (MAPK, IL8, and TNF-α-1), PI3K-Akt signaling pathway (PIK3R3, THBS4, and COL2A1), and TNF signaling pathway (PTGS2, TNFRSF21-l, and CXCL10). Finally, the results of qRT-PCR showed a significant correlation with RNA-seq results, suggesting the reliability of RNA-seq for gene expression analysis. This study provided insights into regulation of gene expression and their involved pathways in Atlantic salmon spleen in responses to vaccine, and set the foundation for further study on the vaccine protective mechanism in Atlantic salmon as well as other teleost species.

Gene by Environment Interactions reveal new regulatory aspects of signaling network plasticity

Phenotypes can change during exposure to different environments through the regulation of signaling pathways that operate in integrated networks. How signaling networks produce different phenotypes in different settings is not fully understood. Here, Gene by Environment Interactions (GEIs) were used to explore the regulatory network that controls filamentous/invasive growth in the yeast Saccharomyces cerevisiae. GEI analysis revealed that the regulation of invasive growth is decentralized and varies extensively across environments. Different regulatory pathways were critical or dispensable depending on the environment, microenvironment, or time point tested, and the pathway that made the strongest contribution changed depending on the environment. Some regulators even showed conditional role reversals. Ranking pathways' roles across environments revealed an under-appreciated pathway (OPI1) as the single strongest regulator among the major pathways tested (RAS, RIM101, and MAPK). One mechanism that may explain the high degree of regulatory plasticity observed was conditional pathway interactions, such as conditional redundancy and conditional cross-pathway regulation. Another mechanism was that different pathways conditionally and differentially regulated gene expression, such as target genes that control separate cell adhesion mechanisms (FLO11 and SFG1). An exception to decentralized regulation of invasive growth was that morphogenetic changes (cell elongation and budding pattern) were primarily regulated by one pathway (MAPK). GEI analysis also uncovered a round-cell invasion phenotype. Our work suggests that GEI analysis is a simple and powerful approach to define the regulatory basis of complex phenotypes and may be applicable to many systems.

Interplay between ADP-ribosyltransferases and essential cell signaling pathways controls cellular responses

Signaling cascades provide integrative and interactive frameworks that allow the cell to respond to signals from its environment and/or from within the cell itself. The dynamic regulation of mammalian cell signaling pathways is often modulated by cascades of protein post-translational modifications (PTMs). ADP-ribosylation is a PTM that is catalyzed by ADP-ribosyltransferases and manifests as mono- (MARylation) or poly- (PARylation) ADP-ribosylation depending on the addition of one or multiple ADP-ribose units to protein substrates. ADP-ribosylation has recently emerged as an important cell regulator that impacts a plethora of cellular processes, including many intracellular signaling events. Here, we provide an overview of the interplay between the intracellular diphtheria toxin-like ADP-ribosyltransferase (ARTD) family members and five selected signaling pathways (including NF-κB, JAK/STAT, Wnt-β-catenin, MAPK, PI3K/AKT), which are frequently described to control or to be controlled by ADP-ribosyltransferases and how these interactions impact the cellular responses.

Centrality of drug targets in protein networks

Background

In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses.

Results

We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets’ network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a ‘good’ target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein’s ‘fitness’ as potential drug target. Correlations between protein nodes’ network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions.

Conclusions

Despite some entanglement with knowledge bias, like structure-oriented ‘druggability’ assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04342-x.

Alveolar Regeneration in COVID-19 Patients: A Network Perspective

A viral infection involves entry and replication of viral nucleic acid in a host organism, subsequently leading to biochemical and structural alterations in the host cell. In the case of SARS-CoV-2 viral infection, over-activation of the host immune system may lead to lung damage. Albeit the regeneration and fibrotic repair processes being the two protective host responses, prolonged injury may lead to excessive fibrosis, a pathological state that can result in lung collapse. In this review, we discuss regeneration and fibrosis processes in response to SARS-CoV-2 and provide our viewpoint on the triggering of alveolar regeneration in coronavirus disease 2019 (COVID-19) patients.

Interactome Networks of FOSL1 and FOSL2 in Human Th17 Cells

Dysregulated function of Th17 cells has implications in immunodeficiencies and autoimmune disorders. Th17 cell differentiation is orchestrated by a complex network of transcription factors, including several members of the activator protein (AP-1) family. Among the latter, FOSL1 and FOSL2 modulate the effector functions of Th17 cells. However, the molecular mechanisms underlying these effects are unclear, owing to the poorly characterized protein interaction networks of FOSL factors. Here, we establish the first interactomes of FOSL1 and FOSL2 in human Th17 cells, using affinity purification-mass spectrometry analysis. In addition to the known JUN proteins, we identified several novel binding partners of FOSL1 and FOSL2. Gene ontology analysis found a significant fraction of these interactors to be associated with RNA-binding activity, which suggests new mechanistic links. Intriguingly, 29 proteins were found to share interactions with FOSL1 and FOSL2, and these included key regulators of Th17 fate. We further validated the binding partners identified in this study by using parallel reaction monitoring targeted mass spectrometry and other methods. Our study provides key insights into the interaction-based signaling mechanisms of FOSL proteins that potentially govern Th17 cell differentiation and associated pathologies.

Self-Assembled Biophotonic Lasing Network Driven by Amyloid Fibrils in Microcavities

Self-assembled biological structures have played a significant role in many living systems for its functionality and distinctiveness. Here, we experimentally demonstrate that the random dynamic behavior of strong light-matter interactions in complex biological structures can provide hidden information on optical coupling in a network. The concept of biophotonic lasing network is therefore introduced, where a self-assembled human amyloid fibril network was confined in a Fabry-Perot optical cavity. Distinctive lasing patterns were discovered from self-assembled amyloids with different structural dimensions (0D, 1D, 2D, and 3D) confined in a microcavity. Network laser emission exhibiting evidence of light coupling at different wavelengths and locations was spectrally resolved. Dynamic changes of lasing patterns can therefore be interpreted into a graph to reveal the optical correlation in biophotonic networks. Our findings indicate that each graph represents the highly unclonable features of a self-assembled network which can sensitively respond to environmental stimulus. This study offers the potential for studying dynamic biological networks through amplified interactions, shedding light on the development of biologically controlled photonic devices, biosensing, and information encryption.

Melatonin-doped polymeric nanoparticles induce high crystalline apatite formation in root dentin

OBJECTIVE

To investigate the effect of novel polymeric nanoparticles (NPs) doped with melatonin (ML) on nano-hardness, crystallinity and ultrastructure of the formed hydroxyapatite after endodontic treatment.

METHODS

Undoped-NPs and ML-doped NPs (ML-NPs) were tested at radicular dentin, after 24 h and 6 m. A control group without NPs was included. Radicular cervical and apical dentin surfaces were studied by nano-hardness measurements, X-ray diffraction and transmission electron microscopy. Mean and standard deviation were analyzed by ANOVA and Student-Newman-Keuls multiple comparisons (p < 0.05).

RESULTS

Cervical dentin treated with undoped NPs maintained its nano-hardness values after 6 m of storage being [24 h: 0.29 (0.01); 6 m: 0.30 (0.02) GPa], but it decreased at apical dentin [24 h: 0.36 (0.01); 6 m: 0.28 (0.02) GPa]. When ML-NPs were used, nano-hardness was similar over time [24h: 0.31 (0.02); 6 m: 0.28 (0.03) GPa], at apical dentin. Root dentin treated with ML-NPs produced, in general, high crystallinity of new minerals and thicker crystals than those produced in the rest of the groups. After 6 m, crystals became organized in randomly oriented polyhedral, square polygonal block-like apatite or drop-like apatite polycrystalline lattices when ML-NPs were used. Undoped NPs generated poor crystallinity, with preferred orientation of small crystallite and increased microstrain.

SIGNIFICANCE

New polycrystalline formations encountered in dentin treated with ML-NPs may produce structural dentin stability and high mechanical performance at the root. The decrease of mechanical properties over time in dentin treated without NPs indicates scarce remineralization potential, dentin demineralization and further potential degradation. The amorphous stage may provide high hydroxyapatite solubility and remineralizing activity.

Spatiotemporal control of pathway sensors and cross-pathway feedback regulate a differentiation MAPK pathway in yeast

Mitogen-activated protein kinase (MAPK) pathways control cell differentiation and the response to stress. In Saccharomyces cerevisiae, the MAPK pathway that controls filamentous growth (fMAPK) shares components with the pathway that regulates the response to osmotic stress (HOG). Here, we show that the two pathways exhibit different patterns of activity throughout the cell cycle. The different patterns resulted from different expression profiles of genes encoding mucin sensors that regulate the pathways. Cross-pathway regulation from the fMAPK pathway stimulated the HOG pathway, presumably to modulate fMAPK pathway activity. We also show that the shared tetraspan protein Sho1p, which has a dynamic localization pattern throughout the cell cycle, induced the fMAPK pathway at the mother-bud neck. A Sho1p-interacting protein, Hof1p, which also localizes to the mother-bud neck and regulates cytokinesis, also regulated the fMAPK pathway. Therefore, spatial and temporal regulation of pathway sensors, and cross-pathway regulation, control a MAPK pathway that regulates cell differentiation in yeast.

Next-Generation Kinase Inhibitors Targeting Specific Biomarkers in Non-Small Cell Lung Cancer (NSCLC): A Recent Overview

Lung cancer causes many deaths globally. Mutations in regulatory genes, irregularities in specific signal transduction events, or alterations of signalling pathways are observed in cases of non-small cell lung cancer (NSCLC). Over the past two decades, a few kinases have been identified, validated, and studied as biomarkers for NSCLC. Among them, EGFR, ALK, ROS1, MET, RET, NTRK, and BRAF are regarded as targetable biomarkers to cure and/or control the disease. In recent years, the US Food and Drug Administration (FDA) approved more than 15 kinase inhibitors targeting these NSCLC biomarkers. The kinase inhibitors significantly improved the progression-free survival (PFS) of NSCLC patients. Challenges still remain for metastatic diseases and advanced NSCLC cases. New discoveries of potent kinase inhibitors and rapid development of modern medical technologies will help to control NSCLC cases. This article provides an overview of the discoveries of various types of kinase inhibitors against NSCLC, along with medicinal chemistry aspects and related developments in next-generation kinase inhibitors that have been reported in recent years.

ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles

Determining the tissue- and disease-specific circuit of biological pathways remains a fundamental goal of molecular biology. Many components of these biological pathways still remain unknown, hindering the full and accurate characterization of biological processes of interest. Here we describe ACSNI, an algorithm that combines prior knowledge of biological processes with a deep neural network to effectively decompose gene expression profiles (GEPs) into multi-variable pathway activities and identify unknown pathway components. Experiments on public GEP data show that ACSNI predicts cogent components of mTOR, ATF2, and HOTAIRM1 signaling that recapitulate regulatory information from genetic perturbation and transcription factor binding datasets. Our framework provides a fast and easy-to-use method to identify components of signaling pathways as a tool for molecular mechanism discovery and to prioritize genes for designing future targeted experiments (https://github.com/caanene1/ACSNI).

Gain and loss of function mutations in biological chemical reaction networks: a mathematical model with application to colorectal cancer cells

This paper studies a system of Ordinary Differential Equations modeling a chemical reaction network and derives from it a simulation tool mimicking Loss of Function and Gain of Function mutations found in cancer cells. More specifically, from a theoretical perspective, our approach focuses on the determination of moiety conservation laws for the system and their relation with the corresponding stoichiometric surfaces. Then we show that Loss of Function mutations can be implemented in the model via modification of the initial conditions in the system, while Gain of Function mutations can be implemented by eliminating specific reactions. Finally, the model is utilized to examine in detail the G1-S phase of a colorectal cancer cell.

Quantifying persistence in the T-cell signaling network using an optically controllable antigen receptor

T cells discriminate between healthy and infected cells with remarkable sensitivity when mounting an immune response, which is hypothesized to depend on T cells combining stimuli from multiple antigen-presenting cell interactions into a more potent response. To quantify the capacity for T cells to accomplish this, we have developed an antigen receptor that is optically tunable within cell conjugates, providing control over the duration, and intensity of intracellular T-cell signaling. We observe limited persistence within the T-cell intracellular network on disruption of receptor input, with signals dissipating entirely in ~15 min, and directly show sustained proximal receptor signaling is required to maintain gene transcription. T cells thus primarily accumulate the outputs of gene expression rather than integrate discrete intracellular signals. Engineering optical control in a clinically relevant chimeric antigen receptor (CAR), we show that this limited signal persistence can be exploited to increase CAR-T cell activation threefold using pulsatile stimulation. Our results are likely to apply more generally to the signaling dynamics of other cellular networks.

Crosstalk between 17β-Estradiol and TGF-β Signaling Modulates Glioblastoma Progression

Epithelial–mesenchymal transition (EMT) is an essential mechanism contributing to glioblastoma multiforme (GBM) progression, the most common and malignant brain tumor. EMT is induced by signaling pathways that crosstalk and regulate an intricate regulatory network of transcription factors. It has been shown that downstream components of 17β-estradiol (E2) and transforming growth factor β (TGF-β) signaling pathways crosstalk in estrogen-sensitive tumors. However, little is known about the interaction between the E2 and TGF-β signaling components in brain tumors. We have investigated the relationship between E2 and TGF-β signaling pathways and their effects on EMT induction in human GBM-derived cells. Here, we showed that E2 and TGF-β negatively regulated the expression of estrogen receptor α (ER-α) and Smad2/3. TGF-β induced Smad2 phosphorylation and its subsequent nuclear translocation, which E2 inhibited. Both TGF-β and E2 induced cellular processes related to EMT, such as morphological changes, actin filament reorganization, and mesenchymal markers (N-cadherin and vimentin) expression. Interestingly, we found that the co-treatment of E2 and TGF-β blocked EMT activation. Our results suggest that E2 and TGF-β signaling pathways interact through ER-α and Smad2/3 mediators in cells derived from human GBM and inhibit EMT activation induced by both factors alone.

Leaf apoplastic alkalization promotes transcription of the ABA-synthesizing enzyme Vp14 and stomatal closure in Zea mays

The chloride component of NaCl salinity causes the leaf apoplast to transiently alkalinize. This transition in pH reduces stomatal aperture. However, whether this apoplastic pH (pHapo) transient initiates stomatal closure by interacting with other chloride stress-induced responses or whether the pH transient alone initiates stomatal closure is unknown. To clarify the problem, the transient alkalinization of the leaf apoplast was mimicked in intact maize (Zea mays L.) by infiltrating near-neutral pH buffers into the leaf apoplast. Effects of the pHapo transient could thus be investigated independently from other chloride stress-derived effects. Microscopy-based ratiometric live pHapo imaging was used to monitor pHapoin planta. LC-MS/MS and real-time quantitative reverse transcription-PCR leaf analyses showed that the artificially induced pHapo transient led to an increase in the concentrations of the stomata-regulating plant hormone abscisic acid (ABA) and in transcripts of the key ABA-synthesizing gene ZmVp14 in the leaf. Since stomatal aperture and stomatal conductance decreased according to pHapo, we conclude that the pHapo transient alone initiates stomatal closure. Therefore, the functionality does not depend on interactions with other compounds induced by chloride stress. Overall, our data indicate that the pH of the leaf apoplast links chloride salinity with the control of stomatal aperture via effects exerted on the transcription of ABA.

Corrected score methods for estimating Bayesian networks with error-prone nodes

Motivated by inferring cellular signaling networks using noisy flow cytometry data, we develop procedures to draw inference for Bayesian networks based on error-prone data. Two methods for inferring causal relationships between nodes in a network are proposed based on penalized estimation methods that account for measurement error and encourage sparsity. We discuss consistency of the proposed network estimators and develop an approach for selecting the tuning parameter in the penalized estimation methods. Empirical studies are carried out to compare the proposed methods with a naive method that ignores measurement error. Finally, we apply these methods to infer signaling networks using single cell flow cytometry data.

Integrated control of surface adaptation by the bacterial flagellum

Many bacteria can alternate between motile and sessile lifestyles, and wide-ranging sets of environmental stimuli regulate the transition from a free-swimming to a surface-attached state. A transenvelope machine called the flagellum, known primarily for its role in promoting cellular motility, stimulates the motile-sessile transition by detecting contact with solid substrates. Recent work has revealed a striking level of sophistication within the regulatory circuits that link flagellar function to surface colonization. I describe the current paradigm whereby the flagellum promotes the sessile state by increasing production of the second-messenger bis-(3'-5')-cyclic diguanosine monophosphate (c-di-GMP). I then highlight studies that have identified multiple routes by which the flagellum activates c-di-GMP production, calling the concept of a linear surface recognition pathway into the question. I conclude by proposing a role for the flagellum as a signaling hub that integrates environmental stimuli to coordinate a surface colonization program that occurs across a range of spatial and temporal scales.

Preparing Membrane Proteins for Simulation Using CHARMM-GUI

Molecular dynamics simulations of membrane proteins have grown dramatically in the last 20 years. Running these simulations first requires embedding the protein's three-dimensional structure in a lipid bilayer of a suitable composition, one that resembles its native environment. This step is far from trivial, especially for modeling heterogeneous mixtures of lipids. CHARMM-GUI, a webserver for simulation system preparation greatly simplifies this step, allowing for the construction of complex heterogeneous and/or asymmetric membranes. Here, we demonstrate how to use CHARMM-GUI to build the membrane for the outer-membrane protein BamA.

Hybrid Quinazoline 1,3,5-Triazines as Epidermal Growth Factor Receptor (EGFR) Inhibitors with Anticancer Activity: Design, Synthesis, and Computational Study

We report a series of hybrid quinazoline-1,3,5-triazine derivatives as EGFR inhibitors, which were synthesised and tested by using a variety of in vitro, in silico, and in vivo techniques. The derivatives were found to be active against different cancer cell lines and nontoxic against normal ones, with compounds 7 c, 7 d, 7 e, and 7 j being the most potent ones. The derivatives were also evaluated for angiogenesis inhibition potency in chicken eggs, and molecular docking and dynamics simulation studies were carried out to elucidate the fundamental substituent groups essential for their bioactivity. Additionally, a SAR study of the derivatives was performed for future compound optimisation. These studies suggested that the derivatives have a high affinity towards EGFR with favourable pharmacological properties. The most active compound (7 e) was further evaluated for in vivo anticancer activity against DMBA-induced tumours in female Sprague-Dawley rats as well as its effects on plasma antioxidant status, biotransformation enzymes, and lipid profile. The study suggested that 7 e has lead properties against breast cancer and can serve as a starting compound for further development of anti-EGFR compounds.

The Anti-Neuroinflammatory Role of Anthocyanins and Their Metabolites for the Prevention and Treatment of Brain Disorders

Anthocyanins are naturally occurring polyphenols commonly found in fruits and vegetables. Numerous studies have described that anthocyanin-rich foods may play a crucial role in the prevention and treatment of different pathological conditions, which have encouraged their consumption around the world. Anthocyanins exhibit a significant neuroprotective role, mainly due to their well-recognized antioxidant and anti-inflammatory properties. Neuroinflammation is an intricate process relevant in both homeostatic and pathological circumstances. Since the progression of several neurological disorders relies on neuroinflammatory process, targeting brain inflammation has been considered a promising strategy in those conditions. Recent data have shown the anti-neuroinflammatory abilities of many anthocyanins and of their metabolites in the onset and development of several neurological disorders. In this review, it will be discussed the importance and the applicability of these polyphenolic compounds as neuroprotective agents and it will be also scrutinized the molecular mechanisms underlying the modulation of neuroinflammation by these natural compounds in the context of several brain diseases.

Dual-memory retrieval efficiency after practice: effects of strategy manipulations

The study investigated practice effects, instruction manipulations, and the associated cognitive architecture of dual-memory retrieval from a single cue. In two experiments, we tested predictions about the presence of learned parallelism in dual-memory retrieval within the framework of the set-cue bottleneck model. Both experiments included three experimental laboratory sessions and involved computerized assessments of dual-memory retrieval performance with strategy instruction manipulations. In Experiment 1, subjects were assigned to three distinct dual-task practice instruction groups: (1) a neutral instruction group without a specific direction on how to solve the task (i.e., neutral instruction), (2) an instruction to synchronize the responses (i.e., synchronize instruction), and (3) an instruction to use a sequential response style (i.e., immediate instruction). Results indicate that strategy instructions are able to effectively influence dual retrieval during practice. Mainly, the instruction to synchronize responses led to the presence of learned retrieval parallelism. Experiment 2 provided an assessment of the cognitive processing architecture of dual-memory retrieval. The results provide support for the presence of a structural bottleneck that cannot be eliminated by extensive practice and instruction manipulations. Further results are discussed with respect to the set-cue bottleneck model.

Genetically Encodable Fluorescent and Bioluminescent Biosensors Light Up Signaling Networks

Cell signaling networks are intricately regulated in time and space to determine the responses and fates of cells to different cues. Genetically encodable fluorescent and bioluminescent biosensors enable the direct visualization of these spatiotemporal signaling dynamics within the native biological context, and have therefore become powerful molecular tools whose unique benefits are being used to address challenging biological questions. We first review the basis of biosensor design and remark on recent technologies that are accelerating biosensor development. We then discuss a few of the latest advances in the development and application of genetically encodable fluorescent and bioluminescent biosensors that have led to scientific or technological breakthroughs.

Cytokine Expression Pattern and Protein-Protein interaction network analysis of Leucocyte Rich Platelet Rich Fibrin and Injectable Form of Platelet Rich Fibrin

PURPOSE

Platelet-rich fibrin (PRF) such as leucocyte-rich PRF (L-PRF) and injectable form of PRF (i-PRF) are widely used in various surgical applications. L-PRF- and i-PRF-derived cytokine variations and functional pathways are still unexplored. The aim of the study was to evaluate the expression pattern of Th1-, Th2-, and Th17-related cytokines by L-PRF and i-PRF under in vitro.

METHODS

Cytokine levels were evaluated using multi-analyte ELISArray kit. Using elevated level of cytokines, the protein-protein interaction and pathway were predicted by computational method.

RESULTS

The expressed cytokine levels were higher in L-PRF than in i-PRF. Specifically in L-PRF, IL8, IL2, IL6, and IL1A were expressed abundantly, whereas IL4, IL10, and IL6 were significantly high in i-PRF. Furthermore, protein-protein interaction (PPI) networks (cytokine-cytokine interactions) and pathway analyses were predicted using higher-order cytokines. PPI networks and gene ontology enrichment analysis showed functional variations between L-PRF and i-PRF. Kyoto Encyclopedia of Gene and Genome pathway analysis found that L-PRF mediates NF-k B signaling, Toll-like receptor signaling (TLR), and MAPK signaling via T-cell receptor signaling pathway. i-PRF is significantly involved in JAK-STAT signaling pathway through upregulation of STAT1.

CONCLUSION

Our study concludes that L-PRF and i-PRF act via different pathways that confirm functional variations between them. Therefore, we speculate that L-PRF may be effective in acute phase of chronic wounds such as in diabetes mellitus and immunocompromised patients whereas i-PRF may have a better outcome in acute wounds.

Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data

BACKGROUND

Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent, such as a protein or gene, and every edge has a mechanistic interpretation, such as a regulatory interaction along a signaling pathway.

RESULTS

With knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights in multi-layered networks, resulting in a widely applicable approach for interpretable deep learning. We present a learning method that enhances the interpretability of trained KPNNs by stabilizing node weights in the presence of redundancy, enhancing the quantitative interpretability of node weights, and controlling for uneven connectivity in biological networks. We validate KPNNs on simulated data with known ground truth and demonstrate their practical use and utility in five biological applications with single-cell RNA-seq data for cancer and immune cells.

CONCLUSIONS

We introduce KPNNs as a method that combines the predictive power of deep learning with the interpretability of biological networks. While demonstrated here on single-cell sequencing data, this method is broadly relevant to other research areas where prior domain knowledge can be represented as networks.