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Immune cell infiltrations with lobular inflammation in the background of steatosis and deregulated gut-liver axis are the cardinal features of non-alcoholic steatohepatitis (NASH). An array of gut microbiota-derived metabolites including short-chain fatty acids (SCFA) multifariously modulates NASH pathogenesis. However, the molecular basis for the favorable impact of sodium butyrate (NaBu), a gut microbiota-derived SCFA, on the immunometabolic homeostasis in NASH remains elusive. We show that NaBu imparts a robust anti-inflammatory effect in lipopolysaccharide (LPS) stimulated or classically activated M1 polarized macrophages and in the diet-induced murine NASH model. Moreover, it impedes monocyte-derived inflammatory macrophage recruitment in liver parenchyma and induces apoptosis of proinflammatory liver macrophages (LM) in NASH livers. Mechanistically, by histone deactylase (HDAC) inhibition NaBu enhanced acetylation of canonical NF-κB subunit p65 along with its differential recruitment to the proinflammatory gene promoters independent of its nuclear translocation. NaBu-treated macrophages thus exhibit transcriptomic signatures that corroborate with a M2-like prohealing phenotype. NaBu quelled LPS-mediated catabolism and phagocytosis of macrophages, exhibited a differential secretome which consequently resulted in skewing toward prohealing phenotype and induced death of proinflammatory macrophages to abrogate metaflammation in vitro and in vivo. Thus NaBu could be a potential therapeutic as well as preventive agent in mitigating NASH.

Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth. Depth prediction from 2D images poses a problem that is both ill–posed and non–linear. Such networks are computationally and time–wise expensive as they have dense configurations. Further, the network performance depends on the trained model configuration, the loss functions used, and the dataset applied for training. We propose a moderately dense encoder–decoder network based on discrete wavelet decomposition and trainable coefficients (LL, LH, HL, HH). Our Nested Wavelet–Net (NDWTN) preserves the high–frequency information that is otherwise lost during the downsampling process in the encoder. Furthermore, we study the effect of activation functions, batch normalization, convolution layers, skip, etc., in our models. The network is trained with NYU datasets. Our network trains faster with good results.

The global incidence of lung diseases, particularly lung cancer, is increasing at an alarming rate, underscoring the urgent need for early detection, robust monitoring, and timely intervention. This study presents design aspects of an artificial intelligence (AI)-integrated microwave-based diagnostic tool for the early detection of lung tumors. The proposed method assimilates the prowess of machine learning (ML) tools with microwave imaging (MWI). A microwave unit containing eight antennas in the form of a wearable belt is employed for data collection from the CST body models. The data, collected in the form of scattering parameters, are reconstructed as 2D images. Two different ML approaches have been investigated for tumor detection and prediction of the size of the detected tumor. The first approach employs XGBoost models on raw S-parameters and the second approach uses convolutional neural networks (CNN) on the reconstructed 2-D microwave images. It is found that the XGBoost-based classifier with S-parameters outperforms the CNN-based classifier on reconstructed microwave images for tumor detection. Whereas a CNN-based model on reconstructed microwave images performs much better than an XGBoost-based regression model designed on the raw S-parameters for tumor size prediction. The performances of both of these models are evaluated on other body models to examine their generalization capacity over unknown data. This work explores the feasibility of a low-cost portable AI-integrated microwave diagnostic device for lung tumor detection, which eliminates the risk of exposure to harmful ionizing radiations of X-ray and CT scans.

Bacteria promote faster evolution of individual genes through orientation-dependent encounters between DNA replication and transcription. Head-on collisions drive gene evolution Most bacterial genes are encoded on the leading strand of replication, potentially avoiding detrimental head-on collisions that can occur between the replication and transcription machineries when genes are encoded on the lagging strand. Here the authors report that 17% of core genes in Bacillus subtilis lie on the lagging strand, and that they have a higher rate of point mutations and of convergent changes at the same amino acid position than those on the leading strand. Taking into account gene length and gene expression levels, the authors propose that head-on replication–transcription conflicts are more mutagenic than co-directional conflicts and that bacteria — and perhaps other organisms — may use head-on replication–transcription conflicts as a mechanism for targeted adaptive evolution of certain genes. Several mechanisms that increase the rate of mutagenesis across the entire genome have been identified; however, how the rate of evolution might be promoted in individual genes is unclear. Most genes in bacteria are encoded on the leading strand of replication 1 , 2 , 3 , 4 . This presumably avoids the potentially detrimental head-on collisions that occur between the replication and transcription machineries when genes are encoded on the lagging strand 1 , 2 , 3 , 4 . Here we identify the ubiquitous (core) genes in Bacillus subtilis and determine that 17% of them are on the lagging strand. We find a higher rate of point mutations in the core genes on the lagging strand compared with those on the leading strand, with this difference being primarily in the amino-acid-changing (nonsynonymous) mutations. We determine that, overall, the genes under strong negative selection against amino-acid-changing mutations tend to be on the leading strand, co-oriented with replication. In contrast, on the basis of the rate of convergent mutations, genes under positive selection for amino-acid-changing mutations are more commonly found on the lagging strand, indicating faster adaptive evolution in many genes in the head-on orientation. Increased gene length and gene expression amounts are positively correlated with the rate of accumulation of nonsynonymous mutations in the head-on genes, suggesting that the conflict between replication and transcription could be a driving force behind these mutations. Indeed, using reversion assays, we show that the difference in the rate of mutagenesis of genes in the two orientations is transcription dependent. Altogether, our findings indicate that head-on replication–transcription conflicts are more mutagenic than co-directional conflicts and that these encounters can significantly increase adaptive structural variation in the coded proteins. We propose that bacteria, and potentially other organisms, promote faster evolution of specific genes through orientation-dependent encounters between DNA replication and transcription.

The protein γ-tubulin plays an important role in centrosomal clustering and this makes it an attractive therapeutic target for treating cancers. Griseofulvin, an antifungal drug, has recently been used to inhibit proliferation of various types of cancer cells. It can also affect the microtubule dynamics by targeting the γ-tubulin protein. So far, the binding pockets of γ-tubulin protein are not properly identified and the exact mechanism by which the drug binds to it is an area of intense speculation and research. The aim of the present study is to investigate the binding mechanism and binding affinity of griseofulvin on γ-tubulin protein using classical molecular dynamics simulations. Since the drug griseofulvin is sparingly soluble in water, here we also present a promising approach for formulating and achieving delivery of hydrophobic griseofulvin drug via hydrotrope sodium cumene sulfonate (SCS) cluster. We observe that the binding pockets of γ-tubulin protein are mainly formed by the H8, H9 helices and S7, S8, S14 strands and the hydrophobic interactions between the drug and γ-tubulin protein drive the binding process. The release of the drug griseofulvin from the SCS cluster is confirmed by the coordination number analysis. We also find hydrotrope-induced alteration of the binding sites of γ-tubulin protein and the weakening of the drug-protein interactions.

Yeast is an integral part of mammalian microbiome, and like commensal bacteria, has the potential of being harnessed to influence immunity in clinical settings. However, functional specificities of yeast-derived immunoregulatory molecules remain elusive. Here we find that while under steady state, β-1,3-glucan-containing polysaccharides potentiate pro-inflammatory properties, a relatively less abundant class of cell surface polysaccharides, dubbed mannan/β-1,6-glucan-containing polysaccharides (MGCP), is capable of exerting potent anti-inflammatory effects to the immune system. MGCP, in contrast to previously identified microbial cell surface polysaccharides, through a Dectin1-Cox2 signaling axis in dendritic cells, facilitates regulatory T (Treg) cell induction from naïve T cells. Furthermore, through a TLR2-dependent mechanism, it restrains Th1 differentiation of effector T cells by suppressing IFN-γ expression. As a result, administration of MGCP display robust suppressive capacity towards experimental inflammatory disease models of colitis and experimental autoimmune encephalomyelitis (EAE) in mice, thereby highlighting its potential therapeutic utility against clinically relevant autoimmune diseases. Yeast form part of the host microbiome with known impact on host immunity. Here the authors identify and investigate the impact of commensal yeast-derived polysaccharides in modulating host inflammation, and show its potential for inhibiting inflammation in a number of models of inflammatory diseases.

Enterococcus faecalis , a leading nosocomial pathogen and yet a prominent member of gut microbiome, lacks clear demarcation between pathogenic and non-pathogenic strains at genome level. Here we present the comparative genome analysis of 36 E. faecalis strains with different pathogenic features and from different body-habitats. This study begins by addressing the genome dynamics, which shows that the pan-genome of E. faecalis is still open, though the core genome is nearly saturated. We identified eight uncharacterized strains as potential pathogens on the basis of their co-segregation with reported pathogens in gene presence-absence matrix and Pathogenicity Island (PAI) distribution. A ~7.4 kb genomic-cassette, which is itself a part of PAI, is found to exist in all reported and potential pathogens, but not in commensals and other uncharacterized strains. This region encodes four genes and among them, products of two hypothetical genes are predicted to be intrinsically disordered that may serve as novel targets for therapeutic measures. Exclusive existence of 215, 129, 4 and 1 genes in the blood, gastrointestinal tract, urogenital tract, oral cavity and lymph node derived E. faecalis genomes respectively suggests possible employment of distinct habitat-specific genetic strategies in the adaptation of E. faecalis in human host.

Among all species of Bartonella, human-restricted Bartonella bacilliformis is the most virulent but harbors one of the most reduced genomes. Carrión's disease, the infection caused by B. bacilliformis, has been afflicting poor rural populations for centuries in the high-altitude valleys of the South American Andes, where the pathogen's distribution is probably restricted by its sand fly vector's range. Importantly, Carrión's disease satisfies the criteria set by the World Health Organization for a disease amenable to elimination. However, to date, there are no genome-level studies to identify potential footprints of B. bacilliformis (patho)adaptation. Our comparative genomic approach demonstrates that the evolution of this intracellular pathogen is shaped predominantly via mutation. Analysis of strains having publicly-available genomes shows high mutational divergence of core genes leading to multiple sub-species. We infer that the sub-speciation event might have happened recently where a possible adaptive divergence was accelerated by intermediate emergence of a mutator phenotype. Also, within a sub-species the pathogen shows inter-clonal adaptive evolution evidenced by non-neutral accumulation of convergent amino acid mutations. A total of 67 non-recombinant core genes (over-representing functional categories like DNA repair, glucose metabolic process, ATP-binding and ligase) were identified as candidates evolving via adaptive mutational convergence. Such convergence, both at the level of genes and their encoded functions, indicates evolution of B. bacilliformis clones along common adaptive routes, while there was little diversity within a single clone.

Disease caused by SARS-CoV-2 coronavirus (COVID-19) led to significant morbidity and mortality worldwide. A systemic hyper-inflammation characterizes severe COVID-19 disease, often associated with acute respiratory distress syndrome (ARDS). Blood biomarkers capable of risk stratification are of great importance in effective triage and critical care of severe COVID-19 patients. Flow cytometry and next-generation sequencing were done on peripheral blood cells and urokinase-type plasminogen activator receptor (suPAR), and cytokines were measured from and mass spectrometry-based proteomics was done on plasma samples from an Indian cohort of COVID-19 patients. Publicly available single-cell RNA sequencing data were analyzed for validation of primary data. Statistical analyses were performed to validate risk stratification. We report here higher plasma abundance of suPAR, expressed by an abnormally expanded myeloid cell population, in severe COVID-19 patients with ARDS. The plasma suPAR level was found to be linked to a characteristic plasma proteome, associated with coagulation disorders and complement activation. Receiver operator characteristic curve analysis to predict mortality identified a cutoff value of suPAR at 1,996.809 pg/ml (odds ratio: 2.9286, 95% confidence interval 1.0427–8.2257). Lower-than-cutoff suPAR levels were associated with a differential expression of the immune transcriptome as well as favorable clinical outcomes, in terms of both survival benefit (hazard ratio: 0.3615, 95% confidence interval 0.1433–0.912) and faster disease remission in our patient cohort. Thus, we identified suPAR as a key pathogenic circulating molecule linking systemic hyperinflammation to the hypercoagulable state and stratifying clinical outcomes in severe COVID-19 patients with ARDS.

The protein [gamma]-tubulin plays an important role in centrosomal clustering and this makes it an attractive therapeutic target for treating cancers. Griseofulvin, an antifungal drug, has recently been used to inhibit proliferation of various types of cancer cells. It can also affect the microtubule dynamics by targeting the [gamma]-tubulin protein. So far, the binding pockets of [gamma]-tubulin protein are not properly identified and the exact mechanism by which the drug binds to it is an area of intense speculation and research. The aim of the present study is to investigate the binding mechanism and binding affinity of griseofulvin on [gamma]-tubulin protein using classical molecular dynamics simulations. Since the drug griseofulvin is sparingly soluble in water, here we also present a promising approach for formulating and achieving delivery of hydrophobic griseofulvin drug via hydrotrope sodium cumene sulfonate (SCS) cluster. We observe that the binding pockets of [gamma]-tubulin protein are mainly formed by the H8, H9 helices and S7, S8, S14 strands and the hydrophobic interactions between the drug and [gamma]-tubulin protein drive the binding process. The release of the drug griseofulvin from the SCS cluster is confirmed by the coordination number analysis. We also find hydrotrope-induced alteration of the binding sites of [gamma]-tubulin protein and the weakening of the drug-protein interactions.