Posttraumatic stress disorder (PTSD) imposes a significant burden on patients and communities. Although the microbiome-gut-brain axis has been proposed as a mediator or moderator of PTSD risk and persistence of symptoms, clinical data directly delineating the gut microbiome's relationship to PTSD are sparse. This study investigated associations between the gut microbiome and mental health outcomes in participants with PTSD (n = 79) and trauma-exposed controls (TECs) (n = 58). Diagnoses of PTSD, major depressive disorder (MDD), and childhood trauma were made using the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5), MINI International Neuropsychiatric Interview (MINI), and Childhood Trauma Questionnaire (CTQ), respectively. Microbial communities from stool samples were profiled using 16S ribosomal RNA gene V4 amplicon sequencing and tested for associations with PTSD-related variables of interest. Random forest models identified a consortium of four genera, i.e., a combination of Mitsuokella, Odoribacter, Catenibacterium, and Olsenella, previously associated with periodontal disease, that could distinguish PTSD status with 66.4% accuracy. The relative abundance of this consortium was higher in the PTSD group and correlated positively with CAPS-5 and CTQ scores. MDD diagnosis was also associated with increased relative abundance of the Bacteroidetes phylum. Current use of psychotropics significantly impacted community composition and the relative abundances of several taxa. Early life trauma may prime the microbiome for changes in composition that facilitate a pro-inflammatory cascade and increase the risk of development of PTSD. Future studies should rigorously stratify participants into healthy controls, TECs, and PTSD (stratified by psychotropic drug use) to explore the role of the oral-gut-microbiome-brain axis in trauma-related disorders.
Numerous stages of organismal development rely on the cellular interpretation of gradients of secreted morphogens including members of the Bone Morphogenetic Protein (BMP) family through transmembrane receptors. Early gradients of BMPs drive dorsal/ventral patterning throughout the animal kingdom in both vertebrates and invertebrates. Growing evidence in Drosophila, zebrafish, murine and other systems suggests that BMP ligand heterodimers are the primary BMP signaling ligand, even in systems in which mixtures of BMP homodimers and heterodimers are present. Signaling by heterodimers occurs through a hetero-tetrameric receptor complex comprising of two distinct type one BMP receptors and two type II receptors. To understand the system dynamics and determine whether kinetic assembly of heterodimer-heterotetramer BMP complexes is favored, as compared to other plausible BMP ligand-receptor configurations, we developed a kinetic model for BMP tetramer formation based on current measurements for binding rates and affinities. We find that contrary to a common hypothesis, heterodimer-heterotetramer formation is kinetically favored over the formation of homodimer-tetramer complexes under physiological conditions of receptor and ligand concentrations and therefore other mechanisms, potentially including differential kinase activities of the formed heterotetramer complexes, must be the cause of heterodimer-heterotetramer signaling primacy. Further, although BMP complex assembly favors homodimer and homomeric complex formation over a wide range of parameters, ignoring these signals and instead relying on the heterodimer improves the range of morphogen interpretation in a broad set of conditions, suggesting a performance advantage for heterodimer signaling in patterning multiple cell types in a gradient.
Information flow within and between cells depends significantly on calcium (Ca2+) signaling dynamics. However, the biophysical mechanisms that govern emergent patterns of Ca2+ signaling dynamics at the organ level remain elusive. Recent experimental studies in developing wing imaginal discs demonstrate the emergence of four distinct patterns of Ca2+ activity: Ca2+ spikes, intercellular Ca2+ transients, tissue-level Ca2+ waves, and a global “fluttering” state. Here, we used a combination of computational modeling and experimental approaches to identify two different populations of cells within tissues that are connected by gap junction proteins. We term these two subpopulations “initiator cells,” defined by elevated levels of Phospholipase C (PLC) activity, and “standby cells,” which exhibit baseline activity. We found that the type and strength of hormonal stimulation and extent of gap junctional communication jointly determine the predominate class of Ca2+ signaling activity. Further, single-cell Ca2+ spikes are stimulated by insulin, while intercellular Ca2+ waves depend on Gαq activity. Our computational model successfully reproduces how the dynamics of Ca2+ transients varies during organ growth. Phenotypic analysis of perturbations to Gαq and insulin signaling support an integrated model of cytoplasmic Ca2+ as a dynamic reporter of overall tissue growth. Further, we show that perturbations to Ca2+ signaling tune the final size of organs. This work provides a platform to further study how organ size regulation emerges from the crosstalk between biochemical growth signals and heterogeneous cell signaling states.
Tremendous progress has been made to control the COVID-19 pandemic caused by the SARS-CoV-2 virus. However, effective therapeutic options are still rare. Drug repurposing and combination represent practical strategies to address this urgent unmet medical need. Viruses, including coronaviruses, are known to hijack host metabolism to facilitate viral proliferation, making targeting host metabolism a promising antiviral approach. Here, we describe an integrated analysis of 12 published in vitro and human patient gene expression datasets on SARS-CoV-2 infection using genome-scale metabolic modeling (GEM), revealing complicated host metabolism reprogramming during SARS-CoV-2 infection. We next applied the GEM-based metabolic transformation algorithm to predict anti-SARS-CoV-2 targets that counteract the virus-induced metabolic changes. We successfully validated these targets using published drug and genetic screen data and by performing an siRNA assay in Caco-2 cells. Further generating and analyzing RNA-sequencing data of remdesivir-treated Vero E6 cell samples, we predicted metabolic targets acting in combination with remdesivir, an approved anti-SARS-CoV-2 drug. Our study provides clinical data-supported candidate anti-SARS-CoV-2 targets for future evaluation, demonstrating host metabolism targeting as a promising antiviral strategy.
Due to the complexity of human diseases, an inclusive approach such as network biology may be an appropriate choice to study their mechanism and investigate similar or distinctive patterns among disease groups. In literary works, network-based properties within phenotypically homogenous diseases have not been considered. This issue could help us in the diagnosis of diseases at beginning stages, finding an overall solution to treat analogous diseases, and exploring disease behavior. Herein, to answer the proposed question, we analyzed the human interactome network to manifest the topological patterns of different classes of diseases. Statistical comparisons suggest that the network structure of similar diseases in a group is conserved and differs from other groups. This is compatible with the fact that similar diseases in a category comprise a range of common origins such as pathways and cellular signaling interactions and gene expression profile. From network-based discriminating features, we focused on maximal cliques and observed that there are common super cliques inside disease families, which might be a reason for their related behaviors. Results illustrate that proteins inside maximal cliques have similar activities, and take part in the same biological processes. Existence of these functional modules in diseases of a family exhibits their influential roles.
Rumen microbial environment hosts a variety of microorganisms that interact with each other to carry out the feed digestion and generation of several by-products especially methane, which plays an essential role in global warming as a greenhouse gas. However, due to its multi-factorial nature, the exact cause of methane production in the rumen has not yet been fully determined. The current study is an attempt to use system modeling to analyze the relationship between interacting components of rumen microbiome and its role in methane production. Metagenomic data of sheep rumen, with equal numbers of high methane yield (HMY) and low methane yield (LMY) samples, were used. As a well-known approach for the systematic comparative study of complex traits, the co-abundance networks were constructed in both operational taxonomic unit (OTU) and gene levels. A gene-catalog of 1,444 different rumen microbial strains was developed as a reference to measure gene abundances. The results from both types of co-abundance networks showed that methanogens, which are the main ruminal source for methanogenesis, need other microbial species to accomplish the task of methane production through producing the main precursor molecules like H2 and acetate for methanogenesis pathway as their byproducts. KEGG Orthology(KO) analysis of the current study shows that the metabolism and growth rate of methanogens will be increased due to the higher rate of the metabolism and carbohydrate/fiber digestion pathways in the hidden elements. This finding proposes that any ruminant methane yield alteration strategy should consider complex interactions of rumen microbiome components as one tightly integrated unit rather than several separate parts.
Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. In this study we did an integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein–protein interaction (PPI) networks. Then, functional clusters in PPI networks were determined. Enrichr, a gene list enrichment analysis tool, was utilized for the functional enrichment of clusters. As a result a total of 12, 2, and 4 functional clusters from 619, 52, and 119 DEGs were determined in the lung, peripheral blood mononuclear cell (PBMC), and skin tissues, respectively. Analysis revealed that the tumor necrosis factor (TNF) signaling pathway was enriched significantly in the three investigated tissues as a common pathway. In addition, clusters associated with inflammation and immunity were common in the three investigated tissues. However, clusters related to the fibrosis process were common in lung and skin tissues.
Fireflies flashing in unison is a mesmerizing manifestation of animal collective behavior and an archetype of biological synchrony. To elucidate synchronization mechanisms and inform theoretical models, we recorded the collective display of thousands of Photinus carolinus fireflies in natural swarms, and provide the first spatiotemporal description of the onset of synchronization. At low firefly density, flashes appear uncorrelated. At high density, the swarm produces synchronous flashes within periodic bursts. Using three-dimensional reconstruction, we demonstrate that flash bursts nucleate and propagate across the swarm in a relay-like process. Our results suggest that fireflies interact locally through a dynamic network of visual connections defined by visual occlusion from terrain and vegetation. This model illuminates the importance of the environment in shaping self-organization and collective behavior. Flash bursts relay around vegetation across the swarm, illuminating the role of the environment in shaping self-organization. Flash bursts relay around vegetation across the swarm, illuminating the role of the environment in shaping self-organization.
TGFβ mediated epithelial to mesenchymal transition (EMT) proceeds through hybrid "E/M" states. A deeper understanding of these states and events which regulate entry to and exit from the E/M states is needed for therapeutic exploitation. We quantified >60,000 molecules across ten time points and twelve omic layers in mammary epithelial cells. Proteomes of whole cells, phosphoproteins, nucleus, extracellular vesicles, secretome and membrane resolved major shifts, E→E/M and E/M→M during EMT, and defined state-specific signatures. Metabolomics identified early activation of arachidonic acid pathway and an enzyme-mediated switch from Cytochrome P450 to Cyclooxygenase / Lipoxygenase branches during E→E/M. Single-cell transcriptomics identified GLIS2 as an early modulator of EMT. Integrative modeling-predicted combinatorial inhibition of AURKB, PP2A and SRC exposed vulnerabilities at E→E/M juncture. Covariance analysis revealed remarkable discordance between proteins and transcripts, and between proteomic layers, implying insufficiency of current approaches. Overall, this dataset provides an unprecedented resource on TGFβ signaling, EMT and cancer.