Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein-protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients.

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

Quantifying Energy Conversion in Higher-Order Phase Space Density Moments in Plasmas.

Weakly collisional and collisionless plasmas are typically far from local thermodynamic equilibrium (LTE), and understanding energy conversion in such systems is a forefront research problem. The standard approach is to investigate changes in internal (thermal) energy and density, but this omits energy conversion that changes any higher-order moments of the phase space density. In this Letter, we calculate from first principles the energy conversion associated with all higher moments of the phase space density for systems not in LTE. Particle-in-cell simulations of collisionless magnetic reconnection reveal that energy conversion associated with higher-order moments can be locally significant. The results may be useful in numerous plasma settings, such as reconnection, turbulence, shocks, and wave-particle interactions in heliospheric, planetary, and astrophysical plasmas.

Using Direct Laboratory Measurements of Electron Temperature Anisotropy to Identify the Heating Mechanism in Electron-Only Guide Field Magnetic Reconnection.

Anisotropic electron heating during electron-only magnetic reconnection with a large guide magnetic field is directly measured in a laboratory plasma through in situ measurements of electron velocity distribution functions. Electron heating preferentially parallel to the magnetic field is localized to one separatrix, and anisotropies of 1.5 are measured. The mechanism for electron energization is identified as the parallel reconnection electric field because of the anisotropic nature of the heating and spatial localization. These characteristics are reproduced in a 2D particle-in-cell simulation and are also consistent with numerous magnetosheath observations. A measured increase in the perpendicular temperature along both separatrices is not reproduced by our 2D simulations. This work has implications for energy partition studies in magnetosheath and laboratory reconnection.

Nutrition and Aging.

This Special Issue focuses on the importance of nutritional interventions for the delay of age-related conditions [...].

Laboratory Observations of Electron Heating and Non-Maxwellian Distributions at the Kinetic Scale during Electron-Only Magnetic Reconnection.

Non-Maxwellian electron velocity distribution functions composed of a warm bulk population and a cold beam are directly measured during electron-only reconnection with a strong out-of-plane (guide) magnetic field in a laboratory plasma. Electron heating is localized to the separatrix, and the electron temperature increases continuously along the separatrix. The measured gain in enthalpy flux is 70% of the incoming Poynting flux. The electron beams are oppositely directed on either side of the X point, and their velocities are comparable to, and scale with, the electron Alfvén speed. Particle-in-cell simulations are consistent with the measurements. The experimental results are consistent with, and go beyond, recent observations in the magnetosheath.

Involvement of Intestinal Microbiota in Adult Neurogenesis and the Expression of Brain-Derived Neurotrophic Factor.

Growing evidence suggests a possible involvement of the intestinal microbiota in generating new neurons, but a detailed breakdown of the microbiota composition is lacking. In this report, we systematically reviewed preclinical rodent reports addressing the connection between the composition of the intestinal microbiota and neurogenesis and neurogenesis-affecting neurotrophins in the hippocampus. Various changes in bacterial composition from low taxonomic resolution at the phylum level to high taxonomic resolution at the species level were identified. As for neurogenesis, studies predominantly used doublecortin (DCX) as a marker of newly formed neurons or bromodeoxyuridine (BrdU) as a marker of proliferation. Brain-derived neurotrophic factor (BDNF) was the only neurotrophin found researched in relation to the intestinal microbiota. Phylum Actinobacteria, genus Bifidobacterium and genus Lactobacillus found the strongest positive. In contrast, phylum Firmicutes, phylum Bacteroidetes, and family Enterobacteriaceae, as well as germ-free status, showed the strongest negative correlation towards neurogenesis or BDNF mRNA expression. Age, short-chain fatty acids (SCFA), obesity, and chronic stress were recurring topics in all studies identified. Overall, these findings add to the existing evidence of a connection between microbiota and processes in the brain. To better understand this interaction, further investigation based on analyses of higher taxonomic resolution and clinical studies would be a gain to the matter.

Molecular Pathology of Salivary Gland Neoplasms: Diagnostic, Prognostic, and Predictive Perspective.

Salivary gland neoplasms are an uncommon and widely heterogeneous group of tumors. In recent years, there has been considerable progress in efforts to reveal the molecular landscape of these tumors, although it is still limited and appears to be only the tip of the iceberg. Genomic aberrations, especially specific chromosomal rearrangements including CRTC1-MAML2 and CRTC3-MAML2 in mucoepidermoid carcinoma, MYB-NFIB and MYBL1-NFIB fusions in adenoid cystic carcinoma, PLAG1 and HMGA2 alterations in pleomorphic adenoma and carcinoma ex pleomorphic adenoma, ETV6-NTRK3 and ETV6-RET in secretory carcinoma, EWSR1-ATF1 and EWSR1-CREM in clear cell carcinoma, provide new insights into the molecular pathogenesis of various salivary gland neoplasms and help to better classify them. These genetic aberrations primarily serve as diagnostic tools in salivary gland tumor diagnosis; however, some also have promise as prognostic or predictive biomarkers. This review summarizes the latest developments in molecular pathology of salivary gland tumors with a focus on distinctive molecular characteristics.

Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach.

Emerging evidence indicates IBD is a risk factor for the increasing incidence of colorectal cancer (CRC) development. We used a system biology approach to identify common molecular signatures and pathways that interact between IBD and CRC and the indispensable pathological mechanisms. First, we identified 177 common differentially expressed genes (DEGs) between IBD and CRC. Gene set enrichment, protein-protein, DEGs-transcription factors, DEGs-microRNAs, protein-drug interaction, gene-disease association, Gene Ontology, pathway enrichment analyses were conducted to these common genes. The inclusion of common DEGs with bimolecular networks disclosed hub proteins (LYN, PLCB1, NPSR1, WNT5A, CDC25B, CD44, RIPK2, ASAP1), transcription factors (SCD, SLC7A5, IKZF3, SLC16A1, SLC7A11) and miRNAs (mir-335-5p, mir-26b-5p, mir-124-3p, mir-16-5p, mir-192-5p, mir-548c-3p, mir-29b-3p, mir-155-5p, mir-21-5p, mir-15a-5p). Analysis of the interaction between protein and drug discovered ASAP1 interacts with cysteine sulfonic acid and double oxidized cysteine drug compounds. Gene-disease association analysis retrieved ASAP1 also associated with pulmonary and bladder neoplasm diseases.

Norepinephrine Regulation of Ventromedial Hypothalamic Nucleus Astrocyte Glycogen Metabolism.

The catecholamine norepinephrine (NE) links hindbrain metabolic-sensory neurons with key glucostatic control structures in the brain, including the ventromedial hypothalamic nucleus (VMN). In the brain, the glycogen reserve is maintained within the astrocyte cell compartment as an alternative energy source to blood-derived glucose. VMN astrocytes are direct targets for metabolic stimulus-driven noradrenergic signaling due to their adrenergic receptor expression (AR). The current review discusses recent affirmative evidence that neuro-metabolic stability in the VMN may be shaped by NE influence on astrocyte glycogen metabolism and glycogen-derived substrate fuel supply. Noradrenergic modulation of estrogen receptor (ER) control of VMN glycogen phosphorylase (GP) isoform expression supports the interaction of catecholamine and estradiol signals in shaping the physiological stimulus-specific control of astrocyte glycogen mobilization. Sex-dimorphic NE control of glycogen synthase and GP brain versus muscle type proteins may be due, in part, to the dissimilar noradrenergic governance of astrocyte AR and ER variant profiles in males versus females. Forthcoming advances in the understanding of the molecular mechanistic framework for catecholamine stimulus integration with other regulatory inputs to VMN astrocytes will undoubtedly reveal useful new molecular targets in each sex for glycogen mediated defense of neuronal metabolic equilibrium during neuro-glucopenia.

Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting.

Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/.

Norepinephrine Regulation of Ventromedial Hypothalamic Nucleus Metabolic-Sensory Neuron 5'-AMP-Activated Protein Kinase Activity: Impact of Estradiol.

The mediobasal hypothalamus (MBH) shapes the neural regulation of glucostasis by 5'-AMP-activated protein kinase (AMPK)-dependent mechanisms. Yet, the neurochemical identity and neuroanatomical distribution of MBH neurons that express glucoprivic-sensitive AMPK remain unclear. The neurotransmitters γ-aminobutyric acid (GABA) and nitric oxide (NO) act within the MBH to correspondingly inhibit or stimulate glucose counter-regulation. The current review highlights recent findings that GABA and NO, neurons located in the ventromedial hypothalamic nucleus (VMN), a distinct important element of the MBH, are direct targets of noradrenergic regulatory signaling, and thereby, likely operate under the control of hindbrain metabolic-sensory neurons. The ovarian hormone estradiol acts within the VMN to govern energy homeostasis. Discussed here is current evidence that estradiol regulates GABA and NO nerve cell receptivity to norepinephrine and moreover, controls the noradrenergic regulation of AMPK activity in each cell type. Future gains in insight on mechanisms underpinning estradiol's impact on neurotransmitter communication between the hindbrain and hypothalamic AMPKergic neurons are expected to disclose viable new molecular targets for the therapeutic simulation of hormonal enhancement of neuro-metabolic stability during circumstances of diminished endogenous estrogen secretion or glucose dysregulation.

The Possible Role of the Microbiota-Gut-Brain-Axis in Autism Spectrum Disorder.

New research points to a possible link between autism spectrum disorder (ASD) and the gut microbiota as many autistic children have co-occurring gastrointestinal problems. This review focuses on specific alterations of gut microbiota mostly observed in autistic patients. Particularly, the mechanisms through which such alterations may trigger the production of the bacterial metabolites, or leaky gut in autistic people are described. Various altered metabolite levels were observed in the blood and urine of autistic children, many of which were of bacterial origin such as short chain fatty acids (SCFAs), indoles and lipopolysaccharides (LPS). A less integrative gut-blood-barrier is abundant in autistic individuals. This explains the leakage of bacterial metabolites into the patients, triggering new body responses or an altered metabolism. Some other co-occurring symptoms such as mitochondrial dysfunction, oxidative stress in cells, altered tight junctions in the blood-brain barrier and structural changes in the cortex, hippocampus, amygdala and cerebellum were also detected. Moreover, this paper suggests that ASD is associated with an unbalanced gut microbiota (dysbiosis). Although the cause-effect relationship between ASD and gut microbiota is not yet well established, the consumption of specific probiotics may represent a side-effect free tool to re-establish gut homeostasis and promote gut health. The diagnostic and therapeutic value of bacterial-derived compounds as new possible biomarkers, associated with perturbation in the phenylalanine metabolism, as well as potential therapeutic strategies will be discussed.

The role of the microbiome for human health: from basic science to clinical applications.

The 2017 annual symposium organized by the University Medical Center Groningen in The Netherlands focused on the role of the gut microbiome in human health and disease. Experts from academia and industry examined interactions of prebiotics, probiotics, or vitamins with the gut microbiome in health and disease, the development of the microbiome in early-life and the role of the microbiome on the gut-brain axis. The gut microbiota changes dramatically during pregnancy and intrinsic factors (such as stress), in addition to extrinsic factors (such as diet, and drugs) influence the composition and activity of the gut microbiome throughout life. Microbial metabolites, e.g. short-chain fatty acids affect gut-brain signaling and the immune response. The gut microbiota has a regulatory role on anxiety, mood, cognition and pain which is exerted via the gut-brain axis. Ingestion of prebiotics or probiotics has been used to treat a range of conditions including constipation, allergic reactions and infections in infancy, and IBS. Fecal microbiota transplantation (FMT) highly effective for treating recurrent Clostridium difficile infections. The gut microbiome affects virtually all aspects of human health, but the degree of scientific evidence, the models and technologies and the understanding of mechanisms of action vary considerably from one benefit area to the other. For a clinical practice to be broadly accepted, the mode of action, the therapeutic window, and potential side effects need to thoroughly be investigated. This calls for further coordinated state-of-the art research to better understand and document the human gut microbiome's effects on human health.

Food can lift mood by affecting mood-regulating neurocircuits via a serotonergic mechanism.

It is commonly assumed that food can affect mood. One prevalent notion is that food containing tryptophan increases serotonin levels in the brain and alters neural processing in mood-regulating neurocircuits. However, tryptophan competes with other long-neutral-amino-acids (LNAA) for transport across the blood-brain-barrier, a limitation that can be mitigated by increasing the tryptophan/LNAA ratio. We therefore tested in a double-blind, placebo-controlled crossover study (N=32) whether a drink with a favourable tryptophan/LNAA ratio improves mood and modulates specific brain processes as assessed by functional magnetic resonance imaging (fMRI). We show that one serving of this drink increases the tryptophan/LNAA ratio in blood plasma, lifts mood in healthy young women and alters task-specific and resting-state processing in brain regions implicated in mood regulation. Specifically, Test-drink consumption reduced neural responses of the dorsal caudate nucleus during reward anticipation, increased neural responses in the dorsal cingulate cortex during fear processing, and increased ventromedial prefrontal-lateral prefrontal connectivity under resting-state conditions. Our results suggest that increasing tryptophan/LNAA ratios can lift mood by affecting mood-regulating neurocircuits.

Potential effects of the most prescribed drugs on the microbiota-gut-brain-axis: A review.

The link between drug-induced dysbiosis and its influence on brain diseases through gut-residing bacteria and their metabolites, named the microbiota-gut-brain axis (MGBA), remains largely unexplored. This review investigates the effects of commonly prescribed drugs (metformin, statins, proton-pump-inhibitors, NSAIDs, and anti-depressants) on the gut microbiota, comparing the findings with altered bacterial populations in major brain diseases (depression, multiple sclerosis, Parkinson's and Alzheimer's). The report aims to explore whether drugs can influence the development and progression of brain diseases via the MGBA. Central findings indicate that all explored drugs induce dysbiosis. These dysbiosis patterns were associated with brain disorders. The influence on brain diseases varied across different bacterial taxa, possibly mediated by direct effects or through bacterial metabolites. Each drug induced both positive and negative changes in the abundance of bacteria, indicating a counterbalancing effect. Moreover, the above-mentioned drugs exhibited similar effects, suggesting that they may counteract or enhance each other's effects on brain diseases when taken together by comorbid patients. In conclusion, the interplay of bacterial species and their abundances may have a greater impact on brain diseases than individual drugs or bacterial strains. Future research is needed to better understand drug-induced dysbiosis and the implications for brain disease pathogenesis, with the potential to develop more effective therapeutic options for patients with brain-related diseases.

Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features.

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.

Higher-order nonequilibrium term: Effective power density quantifying evolution towards or away from local thermodynamic equilibrium.

A common approach to assess the nature of energy conversion in a classical fluid or plasma is to compare power densities of the various possible energy conversion mechanisms. A leading research area is quantifying energy conversion for systems that are not in local thermodynamic equilibrium (LTE), as is common in a number of fluid and plasma systems. Here we introduce the "higher-order nonequilibrium term" (HORNET) effective power density, which quantifies the rate of change of departure of a phase space density from LTE. It has dimensions of power density, which allows for quantitative comparisons with standard power densities. We employ particle-in-cell simulations to calculate HORNET during two processes, magnetic reconnection and decaying kinetic turbulence in collisionless magnetized plasmas, that inherently produce non-LTE effects. We investigate the spatial variation of HORNET and the time evolution of its spatial average. By comparing HORNET with power densities describing changes to the internal energy (pressure dilatation, Pi-D, and divergence of the vector heat flux density), we find that HORNET can be a significant fraction of these other measures (8% and 35% for electrons and ions, respectively, for reconnection; up to 67% for both electrons and ions for turbulence), meaning evolution of the system towards or away from LTE can be dynamically important. Applications to numerous plasma phenomena are discussed.