Functional Interaction Research Articles

Crosstalk interactions between transcription factors ERRα and PPARα assist PPARα-mediated gene expression

ObjectiveThe peroxisome proliferator-activated receptor α (PPARα) is a transcription factor driving target genes involved in fatty acid β-oxidation. To what extent various PPARα interacting proteins may assist its function as a transcription factor is incompletely understood. An ORFeome-wide unbiased mammalian protein-protein interaction trap (MAPPIT) using PPARα as bait revealed a PPARα-ligand-dependent interaction with the orphan nuclear receptor estrogen-related receptor α (ERRα). The goal of this study was to characterize the nature of the interaction in depth and to explore whether it was of physiological relevance. MethodsWe used orthogonal protein-protein interaction assays and pharmacological inhibitors of ERRα in various systems to confirm a functional interaction and study the impact of crosstalk mechanisms. To characterize the interaction surfaces and contact points we applied a random mutagenesis screen and structural overlays. We pinpointed the extent of reciprocal ligand effects of both nuclear receptors via coregulator peptide recruitment assays. On PPARα targets revealed from a genome-wide transcriptome analysis, we performed an ERRα chromatin immunoprecipitation analysis on both fast and fed mouse livers. ResultsRandom mutagenesis scanning of PPARα’s ligand-binding domain and coregulator profiling experiments supported the involvement of (a) bridging coregulator(s), while recapitulation of the interaction in vitro indicated the possibility of a trimeric interaction with RXRα. The PPARα·ERRα interaction depends on 3 C-terminal residues within helix 12 of ERRα and is strengthened by both PGC1α and serum deprivation. Pharmacological inhibition of ERRα decreased the interaction of ERRα to ligand-activated PPARα and revealed a transcriptome in line with enhanced mRNA expression of prototypical PPARα target genes, suggesting a role for ERRα as a transcriptional repressor. Strikingly, on other PPARα targets, including the isolated PDK4 enhancer, ERRα behaved oppositely. Chromatin immunoprecipitation analyses demonstrate a PPARα ligand-dependent ERRα recruitment onto chromatin at PPARα-binding regions, which is lost following ERRα inhibition in fed mouse livers. ConclusionOur data support the coexistence of multiple layers of transcriptional crosstalk mechanisms between PPARα and ERRα, which may serve to finetune the activity of PPARα as a nutrient-sensing transcription factor.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

The interaction effect of high social support and resilience on functional connectivity using seed-based resting-state assessed by 7-Tesla ultra-high field MRI

Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p<0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain’s resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC’s connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges.

Astrocyte Calcium Signaling.

Astrocytes are predominant glial cells that tile the central nervous system and participate in well-established functional and morphological interactions with neurons, blood vessels, and other glia. These ubiquitous cells display rich intracellular Ca2+ signaling, which has now been studied for over 30 years. In this review, we provide a summary and perspective of recent progress concerning the study of astrocyte intracellular Ca2+ signaling as well as discussion of its potential functions. Progress has occurred in the areas of imaging, silencing, activating, and analyzing astrocyte Ca2+ signals. These insights have collectively permitted exploration of the relationships of astrocyte Ca2+ signals to neural circuit function and behavior in a variety of species. We summarize these aspects along with a framework for mechanistically interpreting behavioral studies to identify directly causal effects. We finish by providing a perspective on new avenues of research concerning astrocyte Ca2+ signaling.

Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations.

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.

Impact of Interactions between Su(Hw)-Dependent Insulators on the Transvection Effect in Drosophila melanogaster.

Transvection is a phenomenon of interallelic communication in which enhancers can activate a specific promoter located on a homologous chromosome. Insulators play a significant role in ensuring functional interactions between enhancers and promoters. In the presented work, we created a model where two or three copies of the insulator are located next to enhancers and promoters localized on homologous chromosomes. Using the Su(Hw) insulator as a model, we showed that the functional interaction between a pair of insulators promotes enhancer-promoter trans-interactions. The interaction between the three insulators, on the contrary, can lead to the formation of chromatin loops that sterically hinder the full enhancer-promoter interaction. The results of the work suggest the participation of insulators in the regulation of homologous chromosome pairing and in communication between distant genomic loci.

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

Peripheral 5-HT mediates GnIH-induced feeding behavior and energy metabolism disorder in chickens via the 5-HT2C receptor.

Gonadotropin-inhibitory hormone (GnIH) plays a critical role of reproduction in vertebrate since its discovery. Recently, a regulatory role of GnIH in appetite and the energy metabolism has emerged, despite its precise physiological mechanisms remain unknown. Thus, the present study evaluated the effects of a single or long-term GnIH treatments (administered via intraperitoneal injection) on the food intake, weight and glucolipid metabolism of chickens, while investigated the possible neuroendocrinology factors and its mechanism that involved in GnIH-induced obesity and glucolipid metabolism disorder. Our results showed that the intraperitoneal administration of GnIH to chickens resulted in marked body mass increased, hyperlipidemia, hyperglycemia and glucose intolerance. Subsequently, the results of metabolomics and pharmacological inhibition of 5-HT2C receptor studies revealed that blocked 5-HT2C receptor reinforced the effects of GnIH on food intake, body weight and the levels of blood glucose and lipid, resulted in GnIH-induced hyperglycaemia, hyperlipidemia and hepatic lipid deposition even worse, suggesting that peripheral 5-HT via 5-HT2C receptor may act as a negative feedback regulator to interplay with GnIH and jointly homeostatic control of energy balance in chickens. Our present study provide evidence of the cross-talk between GnIH and 5-HT in food intake and energy metabolism at the in vivo pharmacological level and to propose a molecular basis for these interactions, suggesting that functional interaction between GnIH and 5-HT may open new avenues to understand the mechanism of neuroendocrine network involved in appetite and energy metabolism as well as provide a new therapeutic strategy to prevent obesity, diabetes and metabolic disorders.

Equivalent mating system parameters in post‐mining and undisturbed native plant populations confirms restitution of bird‐pollinator function

Abstract The restoration of diverse self‐sustaining ecosystems requires re‐establishment of functional interactions among species. For plant communities, pollinators are usually essential for pollination, seed set and seed quality. A common assumption in ecological restoration for plants pollinated by animals is one of ‘build it and they will come’, which is rarely tested. Beyond seed set, there may be negative genetic consequences for seed quality if pollinators and their behaviour do not reflect those in reference populations. Here, we conduct an ecological genetic assessment of seed quality via mating system parameters in Lambertia multiflora (Proteaceae), a species dependent on nectar‐feeding birds for pollination. Four populations of L. multiflora in disturbed sites that were rehabilitated following mineral sands mining were compared with four native reference populations, near Eneabba, Western Australia. In each population, approximately 10 offspring from each of 10 maternal plants were genotyped with 11 highly polymorphic microsatellite markers. From these data, genetic diversity and mating system parameters were assessed, and found to be equivalent across all populations. Mean allelic diversity and heterozygosity across loci were very high. All populations were completely outcrossing with no bi‐parental inbreeding. Mean correlated paternity, sibship and effective population size estimates for restored and natural populations were not significantly different and reflected uniformly high paternal diversity and wide outcrossing. Equivalent genetic results for restored and natural reference populations indicate successful restitution of bird‐pollinator services for L. multiflora in these post‐mining rehabilitation sites. Synthesis and applications. Reviewing our results with other published studies to date suggests a resilience of bird‐pollinator services in restored plant communities. These findings provide some reassurance to restoration practitioners working in these global south systems where bird pollination is a feature, at least for similar landscape scenarios. Our study also highlights the global contribution of ecological genetics to the objective assessment of functional species interactions in ecological restoration, an increasingly important goal of land managers and regulators seeking to improve restoration standards.

Deciphering mycobiota and its functional dynamics in root hairs of Rhododendron campanulatum D. Don through Next-gen sequencing

The Himalayas provide unique opportunities for the extension of shrubs beyond the upper limit of the tree. However, little is known about the limitation of the biotic factors belowground of shrub growth at these cruising altitudes. To fill this gap, the present study deals with the documentation of root-associated microbiota with their predicted functional profiles and interactions in the host Rhododendron campanulatum, a krummholz species. While processing 12 root samples of R. campanulatum from the sites using Omics we could identify 134 root-associated fungal species belonging to 104 genera, 74 families, 39 orders, 17 classes, and 5 phyla. The root-associated microbiota members of Ascomycota were unambiguously dominant followed by Basidiomycota. Using FUNGuild, we reported that symbiotroph and pathotroph as abundant trophic modes. Furthermore, FUNGuild revealed the dominant prevalence of the saptroptroph guild followed by plant pathogens and wood saprotrophs. Alpha diversity was significantly different at the sites. The heatmap dendrogram showed the correlation between various soil nutrients and some fungal species. The study paves the way for a more in-depth exploration of unidentified root fungal symbionts, their interactions and their probable functional roles, which may serve as an important factor for the growth and conservation of these high-altitude ericaceous plants.

Neurophenomenological Investigation of Mindfulness Meditation "Cessation" Experiences Using EEG Network Analysis in an Intensively Sampled Adept Meditator.

Mindfulness meditation is a contemplative practice that is informed by Buddhism. It has been proven effective for improving mental and physical health in clinical and non-clinical contexts. To date, mainstream dialogue and scientific research on mindfulness has focused primarily on short-term mindfulness training and applications of mindfulness for reducing stress. Understanding advanced mindfulness practice has important implications for mental health and general wellbeing. According to Theravada Buddhist meditation, a "cessation" event is a dramatic experience of profound clarity and equanimity that involves a complete discontinuation in experience, and is evidence of mastery of mindfulness meditation. Thirty-seven cessation events were captured in a single intensively sampled advanced meditator (over 6,000h of retreat mindfulness meditation training) while recording electroencephalography (EEG) in 29 sessions between November 12, 2019 and March 11, 2020. Functional connectivity and network integration were assessed from 40s prior to cessations to 40s after cessations. From 21s prior to cessations there was a linear decrease in large-scale functional interactions at the whole-brain level in the alpha band. In the 40s following cessations these interactions linearly returned to prior levels. No modulation of network integration was observed. The decrease in whole-brain functional connectivity was underlain by frontal to left temporal and to more posterior decreases in connectivity, while the increase was underlain by wide-spread increases in connectivity. These results provide neuroscientific evidence of large-scale modulation of brain activity related to cessation events that provides a foundation for future studies of advanced meditation.

Alginate functionalized chitosan nanoparticles using multilayer co-axial electro-spraying for ovalbumin controlled release via oral delivery

The current oral vaccine delivery presents numerous challenges due to physiological barriers in the gastrointestinal tract including antigen degradation by enzymes and gastric pH conditions. Contributing to overcome these problems, this work developed non-cross-linked chitosan (CHI) nanoparticles, with/without functionalization with alginate (ALG), using multilayer co-axial electro-spraying method, to orally deliver ovalbumin (OVA), a model antigen. The prepared OVA@CHI and OVA@CHI@ALG nanoparticles possessed spherical shape, uniform size and non-agglomeration, high OVA encapsulation efficiency of above 90 %, and core-shell structure with mean diameters of 276.9 ± 70.7 nm (for OVA@CHI) and 304.3 ± 63.9 nm (for OVA@CHI@ALG), determined via SEM images. The particle hydrodynamic diameters, analyzed by the DLS method, were 313.0 ± 72.4 nm for OVA@CHI and 367.6 ± 83.2 nm for OVA@CHI@ALG, in agreement with the SEM results. Additionally, the material functional group interactions and the polyelectrolyte complex formations were confirmed by FTIR spectra and TGA/DSC thermo-grams, indicating the successful formulation. Subsequently, the OVA@CHI@ALG nanoparticles revealed good stability in acidic environment under the presence of digestive enzymes. The OVA release profiles of OVA@CHI@ALG nanoparticles exhibited limited release in simulated gastric fluid (pH 1.2), followed by sustained release in simulated intestinal fluid (pH 6.8) and simulated colonic fluid (pH 7.4) over a 48-h test period. Interestingly, the primary structure of OVA, analyzed by SDS-PAGE analysis, was maintained during the preparation process and after the release test, while the OVA secondary structure, examined by circular dichroism measurement, demonstrated a decrease of α-helix and an increase of β-sheet contents. Finally, the in vitro mucin-binding study indicated a significant adhesive efficiency of 69.21 ± 4.24 % for OVA@CHI and 75.07 ± 2.58 % for OVA@CHI@ALG at pH 7.4. Conclusively, the OVA@CHI@ALG nanoparticles fabricated by tri-axial electro-spraying method could be a promising oral vaccine delivery system for sustained release of OVA.

A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms.

Many self-motivated and goal-directed behaviours display highly flexible, approximately 4 hour ultradian (shorter than a day) oscillations. Despite lacking direct correspondence to physical cycles in the environment, these ultradian rhythms may be involved in optimizing functional interactions with the environment and reflect intrinsic neural dynamics. Current evidence supports a role of mesostriatal dopamine (DA) in the expression and propagation of ultradian rhythmicity, however, the biochemical processes underpinning these oscillations remain to be identified. Here, we use a mathematical model to investigate D2 autoreceptor-dependent DA self-regulation as the source of ultradian behavioural rhythms. DA concentration at the midbrain-striatal synapses is governed through a dual-negative feedback-loop structure, which naturally gives rise to rhythmicity. This model shows the propensity of striatal DA to produce an ultradian oscillation characterized by a flexible period that is highly sensitive to parameter variations. Circadian (approximately 24 hour) regulation consolidates the ultradian oscillations and alters their response to the phase-dependent, rapid-resetting effect of a transient excitatory stimulus. Within a circadian framework, the ultradian rhythm orchestrates behavioural activity and enhances responsiveness to an external stimulus. This suggests a role for the circadian-ultradian timekeeping hierarchy in governing organized behaviour and shaping daily experience through coordinating the motivation to engage in recurring, albeit not highly predictable events, such as social interactions.

Abstract PO3-24-09: May ESR1 mutant breast cancer cells influence fibroblast phenotype?

Abstract For almost 50 years, it has been postulated that carcinogenesis relies on the intrinsic genetic abnormalities of tumor cells, but in the last decades, it has been reported that their functional interactions with the tumor microenvironment (TME) might affect therapy response and contribute to the disease progression. Based on these observations, the aim of the present study was to investigate the functional interactions between one of the essential components of TME, the fibroblasts, and breast cancer (BC) cells, expressing mutations in the hormone-binding domain (HBD) of the gene encoding for the estrogen receptor alpha (ESR1). Thus, we employed estrogen receptor-positive MCF-7 BC cells CRISPR engineered to express the Y537S-HBD mutation. Y537S is the most commonly detected mutation harbored in HBD-ESR1 and is a major contributor to endocrine therapy resistance and poor clinical outcome in BC. We collected conditioned media (CM) from BC cells and fibroblasts isolated from primary human tissues (normal fibroblasts, NFs, and cancer-associated fibroblasts, CAFs) in order to assess in vitro co-culture systems that can mimic the complex in vivo TME. It was interesting to observe that CAFs displayed a significant increase in their proliferation and migration when exposed to CM of mutant BC cells with respect to the parental ones. These latter findings fit with the immunofluorescence analysis showing a more evident formation of actin stress fibers. However, proteomic analysis demonstrated, in the same circumstances, more significant changes in NFs than in CAFs. Among the up-regulated proteins revealed by NFs upon Y537S-CM exposure, most of them overlap the up-regulated proteins featuring the intrinsic phenotype of CAFs, mainly involved in the cellular ultrastructural organization (i.e. cytoskeleton, focal adhesion, vesicle, and organelle) as evidenced by Metacore functional analysis. On the other side, we demonstrated that mutant clones showed more aggressive behavior in terms of proliferation, growth, migration, and invasion, compared to the parental counterpart upon exposure to the CM-derived from NFs and CAFs. This led us to further investigate the intrinsic properties of the mutated clones through their genomic and proteomic profiles that evidenced, by using Metacore software, an enrichment of insulin-like growth factor 1 receptor (IGF1R) pathway. The relevance of the latter datum was confirmed by the higher content of the IGF-1 ligand found in the secretome of mutant cells. Thus, the short autocrine loop maintaining IGF1/IGF1R signaling activation in mutant clones might reasonably play an important role in mediating the autocrine and paracrine effects between mutant cells and fibroblasts. For instance, the pharmacological blockade of IGF1R signaling interfered with the reciprocal interactions between fibroblasts and BC cells that sustained tumor growth and progression as evidenced in both “in vitro” and “in vivo” experiments. In conclusion, our study addressed the IGF1R pathway as a potential druggable target helpful to disconnect mutant breast cancer cell-fibroblast crosstalk that potentiates breast tumor growth and progression. Citation Format: Luca Gelsomino, Amanda Caruso, Rocco Malivindi, Adel Elisabetta Leonetti, Emine Tasan, Salvatore Panza, Giuseppina Daniela Naimo, Ines Barone, Cinzia Giordano, Daniela Bonofiglio, Loredana Mauro, Guowei Gu, Suzanne Fuqua, Stefania Catalano, Sebastiano Andò. May ESR1 mutant breast cancer cells influence fibroblast phenotype? [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-24-09.

Surface Analysis-From Crystal Structures to Particle Properties.

Understanding the surface properties of particles is crucial for optimizing the performance of formulated products in various industries. However, acquiring this understanding often requires expensive trial-and-error studies. Here, we present advanced surface analysis tools that enable the visualization and quantification of chemical and topological information derived from crystallographic data. By employing functional group analysis, roughness calculations, and statistical interaction data, we facilitate direct comparisons of surfaces. We further demonstrate the practicality of our approach by correlating the sticking propensity of distinct ibuprofen morphologies with surface and particle descriptors calculated from a single crystal structure. Our findings support and expand upon previous work, demonstrating that the presence of a carboxylic acid group on the {011} facet leads to significant differences in particle properties and explains the higher electrostatic potential observed in the block-like morphology. While our surface analysis tools are not intended to replace the importance of chemical intuition and expertise, they provide valuable insights for formulators and particle engineers, facilitating informed, data-driven decisions to mitigate formulation risks. This research represents a significant step toward a comprehensive understanding of particle surfaces and their impact on products.

Protective Effects and Mechanisms of Luteolin against Acute Respiratory Distress Syndrome: Network Pharmacology and In vivo and In vitro Studies.

Acute Respiratory Distress Syndrome (ARDS) is an acute life-threatening disease, and luteolin has the potential to become a therapeutic agent for ARDS. However, its mechanism of action has not yet been clarified. The present study explored the potential effects and mechanisms of luteolin in the treatment of ARDS through network pharmacology analysis and verified them through biological experiments. The potential targets of luteolin and ARDS were obtained from online databases. Functional enrichment and protein-protein interaction (PPI) analyses were performed to explore the underlying molecular mechanisms and to identify hub targets. Molecular docking was used to verify the relationship between luteolin and target proteins. Finally, the effects of luteolin on key signaling pathways and biological processes were verified by in vitro and in vivo experiments. A total of 146 luteolin- and 496 ARDS-related targets were extracted from public databases. The network pharmacological analysis suggested that luteolin could inhibit ARDS through the following potential therapeutic targets: AKT1, RELA, and NFKBIA. Inflammatory and oxidative stress responses were the main biological processes involved, with the AKT/NF-κB signaling pathway being the key signaling pathway targeted by luteolin for the treatment of ARDS. Molecular docking analysis indicated that luteolin had a good binding affinity to AKT1, RELA, and NFKBIA. The in vitro and in vivo experiments revealed that luteolin could regulate the inflammatory response and oxidative stress in the treatment of ARDS by inhibiting the AKT/NF- κB signaling pathway. Luteolin could reduce the production of reactive oxygen species and inflammatory factors by inhibiting the AKT/NF-κB signaling pathway, thus reducing apoptosis and attenuating ARDS.

Simple analysis of complex system safety based on Finite State Machine Network and phase space theory

Safety analysis of complex systems, such as aero-engines, has always been a challenging problem. In particular, the risk propagation caused by system functional interactions has become increasingly prominent, leading to the difficulty of traditional safety analysis methods. Existing methods are mostly based on manual experience and deduction with difficulty to completely analyze the whole system safety regions. In this paper, a model-based framework is proposed by combining Finite State Machine Network (FSMN) and phase space theory to perform safety analysis of complex systems from function-logic perspective. We first construct the system model and system state-transition network based on FSMN. Then combining with phase space theory, we identify system safety regions of the entire operating space from macro perspective, and investigate the system risk propagation from micro perspective. An aero-engine system is taken as an example to illustrate the proposed method. Our results show that this method has the ability to effectively identify the potential risk factors and explore emergent unknown risks, which can provide guidance for formulating safety testing.

Altered effective connectivity among face-processing systems in major depressive disorder.

Neuroimaging studies have revealed abnormal functional interaction during the processing of emotional faces in patients with major depressive disorder (MDD), thereby enhancing our comprehension of the pathophysiology of MDD. However, it is unclear whether there is abnormal directional interaction among face-processing systems in patients with MDD. A group of patients with MDD and a healthy control group underwent a face-matching task during functional magnetic resonance imaging. Dynamic causal modelling (DCM) analysis was used to investigate effective connectivity between 7 regions in the face-processing systems. We used a Parametric Empirical Bayes model to compare effective connectivity between patients with MDD and controls. We included 48 patients and 44 healthy controls in our analyses. Both groups showed higher accuracy and faster reaction time in the shape-matching condition than in the face-matching condition. However, no significant behavioural or brain activation differences were found between the groups. Using DCM, we found that, compared with controls, patients with MDD showed decreased self-connection in the right dorsolateral prefrontal cortex (DLPFC), amygdala, and fusiform face area (FFA) across task conditions; increased intrinsic connectivity from the right amygdala to the bilateral DLPFC, right FFA, and left amygdala, suggesting an increased intrinsic connectivity centred in the amygdala in the right side of the face-processing systems; both increased and decreased positive intrinsic connectivity in the left side of the face-processing systems; and comparable task modulation effect on connectivity. Our study did not include longitudinal neuroimaging data, and there was limited region of interest selection in the DCM analysis. Our findings provide evidence for a complex pattern of alterations in the face-processing systems in patients with MDD, potentially involving the right amygdala to a greater extent. The results confirm some previous findings and highlight the crucial role of the regions on both sides of face-processing systems in the pathophysiology of MDD.

Microbial Metagenomes and Host Transcriptomes Reveal the Dynamic Changes of Rumen Gene Expression, Microbial Colonization and Co-Regulation of Mineral Element Metabolism in Yaks from Birth to Adulthood.

Yaks are the main pillar of plateau animal husbandry and the material basis of local herdsmen's survival. The level of mineral elements in the body is closely related to the production performance of yaks. In this study, we performed a comprehensive analysis of rumen epithelial morphology, transcriptomics and metagenomics to explore the dynamics of rumen functions, microbial colonization and functional interactions in yaks from birth to adulthood. Bacteria, eukaryotes, archaea and viruses colonized the rumen of yaks from birth to adulthood, with bacteria being the majority. Bacteroidetes and Firmicutes were the dominant phyla in five developmental stages, and the abundance of genus Lactobacillus and Fusobacterium significantly decreased with age. Glycoside hydrolase (GH) genes were the most highly represented in five different developmental stages, followed by glycosyltransferases (GTs) and carbohydrate-binding modules (CBMs), where the proportion of genes coding for CBMs increased with age. Integrating host transcriptome and microbial metagenome revealed 30 gene modules related to age, muscle layer thickness, nipple length and width of yaks. Among these, the MEmagenta and MEturquoise were positively correlated with these phenotypic traits. Twenty-two host genes involved in transcriptional regulation related to metal ion binding (including potassium, sodium, calcium, zinc, iron) were positively correlated with a rumen bacterial cluster 1 composed of Alloprevotella, Paludibacter, Arcobacter, Lactobacillus, Bilophila, etc. Therefore, these studies help us to understand the interaction between rumen host and microorganisms in yaks at different ages, and further provide a reliable theoretical basis for the development of feed and mineral element supplementation for yaks at different ages.

The Potential Antinociceptive Effect and Mechanism of Cannabis sativa L. Extract on Paclitaxel-Induced Neuropathic Pain in Rats Uncovered by Multi-Omics Analysis.

Cannabis sativa L. (hemp) is a herbaceous plant rich in cannabinoids with a long history of use in pain treatment. The most well-characterized cannabinoids, cannabidiol (CBD) and Δ9-tetrahydrocannabinol (Δ9-THC), garnered much attention in chemotherapy-induced peripheral neuropathy (CIPN) treatment. However, few studies have investigated the biological benefits and mechanism of hemp extract on CIPN. In the present study, hemp extract (JG) rich in cannabinoids was extracted by supercritical fluid carbon dioxide extraction (SFCE). The antinociceptive efficacy was evaluated using a paclitaxel-induced peripheral neuropathy (PIPN) rat model based on behavioral tests. Further omics-based approaches were applied to explore the potential mechanisms. The results showed that JG decreased mechanical allodynia, thermal hyperalgesia, and inflammatory cytokines in PIPN rats significantly. Transcriptome analysis identified seven key genes significantly regulated by JG in PIPN model rats, mainly related to the neuroactive ligand-receptor interaction pathway, PPAR signaling pathway, and cAMP signaling pathway. In metabolomic analysis, a total of 39 significantly altered metabolites were identified, mainly correlated with pentose and glucuronate interconversions and the glycerophospholipid metabolism pathway. Gut microbiota analysis suggested that increased community Lachnoclostridium and Lachnospiraceae_UCG-006 in PIPN rats can be reversed significantly by JG. In conclusion, hemp extract exhibited antinociceptive effects on PIPN. The analgesic mechanism was probably related to the regulation of inflammation, neuroactive ligand-receptor interaction pathway, sphingolipid metabolism, etc. This study provides novel insights into the functional interactions of Cannabis sativa L. extract on PIPN.