Exploration and characterization of a newly isolated bacterium, Enterobacter quasihormaechei strain BDIFST24001, capable of producing rhamnolipid biosurfactant for oil remediation.

Biosurfactants are naturally occurring compounds synthesized by micro-organisms that increasingly attract attention due to both their living area and application in various industries. In this study, we explore and characterize a novel bacterium, Enterobacter quasihormaechei strain BDIFST24001, isolated for its ability to produce rhamnolipid biosurfactants, with the aim of facilitating oil remediation processes. The isolation of this bacterium was carried out using Luria-Bertani (LB) broth media from environmental samples collected from oil-contaminated sites in Dhaka City. Screening tests, including the oil spreading method and drop collapse assay, were conducted to identify potential biosurfactant-producing strains, leading to the selection of E. quasihormaechei strain BDIFST24001 based on its favourable performance. Subsequent molecular identification revealed a high similarity of the strain's 16S rRNA gene to E. quasihormaechei, which was corroborated through phylogenetic analysis. Further analysis of the biosurfactant produced by this strain indicated its rhamnolipid nature, as confirmed by FT-IR spectroscopy. The rhamnolipids exhibited promising surface-active properties, including a significant reduction in surface tension and emulsification activity, as evidenced by surface tension measurements and emulsification index assays. Optimization studies revealed that the optimal conditions for rhamnolipid production by E. quasihormaechei strain BDIFST24001 were a temperature of 37 °C, pH 10.0 and salinity of 4 %. The rhamnolipids produced by this strain demonstrated effective oil remediation capabilities, as observed through controlled experiments using petrol oil. The rhamnolipids effectively reduced the surface tension of the oil-water interface, facilitating the dispersion and emulsification of the oil phase in water. Overall, our findings highlight the potential of E. quasihormaechei strain BDIFST24001 as a promising candidate for biosurfactant-mediated oil spill cleanup and environmental remediation efforts.

Is gender a factor in socioeconomic disparities in undiagnosed, and untreated hypertension in Bangladesh?

Our objectives were to ascertain the following: (1) the prevalence and socioeconomic distribution of hypertension (HTN), undiagnosed for HTN, and untreated cases of HTN-diagnosed individuals; (2) the relationship between SES and the prevalence of HTN, undiagnosed for HTN, and untreated for HTN; and (3) whether sex moderate this association. Data from the 2017-18 Bangladesh Demographic Health Survey were used. 11,776 participants who were 18 years of age or older responded to our analysis. The age-adjusted prevalence of HTN, undiagnosed for HTN, and untreated cases was 25.1%, 57.2%, and 12.3%. Compared to females, males were less likely to have HTN but more likely to have undiagnosed HTN. People in the rich SES groups had a higher odd of (adjusted odds ratio [aoR] 1.25; 95% confidence interval [CI] 1.08-3.45) of having HTN compared to those in the poor SES group. When compared to individuals in the poor SES group, those in the rich SES group had lower odds of undiagnosed (aoR 0.57; 95% CI 0.44-0.74) and untreated (aoR 0.56; 95% CI 0.31-0.98) for HTN. Sex moderated the association between SES and HTN prevalence, which showed that men from rich SES were more likely to suffer from HTN than men from poor SES. According to this study, the government and other pertinent stakeholders should concentrate more on developing suitable policy measures to reduce the risk of HTN, particularly for men in rich socioeconomic groups. They should also concentrate on screening and diagnosing HTN in socioeconomically disadvantaged populations, regardless of sex.

Ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry-characterized extract of Aerides odorata Lour alleviates paracetamol-induced hepatotoxicity in animal model evidenced by biochemical, molecular, and computational studies.

BACKGROUND: Many kinds of orchids have significant health benefits although adequate research on their biological functions is yet to be carried out. This study investigated the paracetamol-induced liver damage-protecting effect of epiphytic Aerides odorata methanol extract (AODE). METHODS: The protective effects of AODE were studied by analyzing its effect on liver function parameters, messenger RNA (mRNA) expression, and tissue histopathological architecture. The results were confirmed by ligand-receptor interaction of molecular docking and multitarget interaction of network pharmacological analyses. RESULTS: AODE significantly (p < 0.05) minimized the dose-dependent increase in acid phosphatase, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, γ-glutamyl transferase, lactate dehydrogenase, and total bilirubin compared to the reference drug silymarin. Malondialdehyde level decreased, and the antioxidant genes catalase (CAT), superoxide dismutase (SOD), ß-actin, paraoxonase-1 (PON1), and phosphofructokinase-1 (PFK-1) were upregulated in AODE-treated paracetamol-intoxicated rats. A total of 376 compounds comprising phenols and flavonoids were identified using ultra-high-performance liquid chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-qTOF-MS). The online toxicity assessment using SwissADME and admetSAR exhibited drug-like, nontoxic, and potential pharmacological properties. Additionally, in silico analysis showed that isoacteoside, one of the identified compounds, exhibited the best docking score (-11.42) with the liver protein human pituitary adenylate cyclase-1 (Protein Data Bank ID: 3N94). Furthermore, network pharmacology analysis identified the top 10 hub genes, namely AKT1 (protein kinase B), CTNNB1 (catenin beta-1), SRC (proto-oncogene c-Src), TNF (tumor necrosis factor), EGFR (epidermal growth factor receptor), HSP90AA1 (heat shock protein 90α), MAPK3 (mitogen-activated protein kinase 3), STAT3 (signal transducer and activator of transcription 3), CASP3 (caspase protein), and ESR1 (estrogen receptor 1), which are responsible for hepatoprotective activity. CONCLUSION: The findings demonstrate that AODE could be a novel hepatoprotective target in drug-induced liver damage with a further single compound-based animal study.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

The main duct of von Ebner's glands is a source of Sox10 + taste bud progenitors and susceptible to pathogen infections.

We have recently demonstrated that Sox10 -expressing ( Sox10 + ) cells give rise to mainly type-III neuronal taste bud cells that are responsible for sour and salt taste. The two tissue compartments containing Sox10 + cells in the surrounding of taste buds include the connective tissue core of taste papillae and von Ebner's glands (vEGs) that are connected to the trench of circumvallate and foliate papillae. In this study, we used inducible Cre mouse models to map the cell lineages of connective tissue (including stromal and Schwann cells) and vEGs and performed single cell RNA-sequencing of the epithelium of Sox10-Cre/tdT mouse circumvallate/vEG complex. In vivo lineage mapping showed that the distribution of traced cells in circumvallate taste buds was closely linked with that in the vEGs, but not in the connective tissue. Sox10 , but not the known stem cells marker Lgr5 , expression was enriched in the cell clusters of main ducts of vEGs that contained abundant proliferating cells, while Sox10-Cre/tdT expression was enriched in type-III taste bud cells and excretory ductal cells. Moreover, multiple genes encoding pathogen receptors are enriched in the vEG main ducts. Our data indicate that the main duct of vEGs is a source of Sox10 + taste bud progenitors and susceptible to pathogen infections.

Can digital twin efforts shape microorganism-based alternative food?

With the continuous increment in global population growth, compounded by post-pandemic food security challenges due to labor shortages, effects of climate change, political conflicts, limited land for agriculture, and carbon emissions control, addressing food production in a sustainable manner for future generations is critical. Microorganisms are potential alternative food sources that can help close the gap in food production. For the development of more efficient and yield-enhancing products, it is necessary to have a better understanding on the underlying regulatory molecular pathways of microbial growth. Nevertheless, as microbes are regulated at multiomics scales, current research focusing on single omics (genomics, proteomics, or metabolomics) independently is inadequate for optimizing growth and product output. Here, we discuss digital twin (DT) approaches that integrate systems biology and artificial intelligence in analyzing multiomics datasets to yield a microbial replica model for in silico testing before production. DT models can thus provide a holistic understanding of microbial growth, metabolite biosynthesis mechanisms, as well as identifying crucial production bottlenecks. Our argument, therefore, is to support the development of novel DT models that can potentially revolutionize microorganism-based alternative food production efficiency.

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data.

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data. In the algorithm, we integrated multi-class sparse canonical correlation analysis (MSCCA) and molecular pathway analysis in order to derive discriminative molecular features that are correlated across different omics layers. We applied our proposed method to analyzing proteome and metabolome data of heart failure (HF), and extracted trans-omic biomarkers for HF subtypes; specifically, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). We were able to detect not only individual proteins that were previously reported from single-omics studies but also correlated protein-metabolite pairs characteristic of HF disease subtypes. For example, we identified hexokinase1(HK1)-d-fructose-6-phosphate as a paired trans-omic biomarker for DCM, which could significantly perturb amino-sugar metabolism. Our proposed method is expected to be useful for various applications in precision medicine.

Appearance of tolerance-induction and non-inflammatory SARS-CoV-2 spike-specific IgG4 antibodies after COVID-19 booster vaccinations.

Background: Understanding the characteristics of the humoral immune responses following COVID-19 vaccinations is crucial for refining vaccination strategies and predicting immune responses to emerging SARS-CoV-2 variants. Methods: A longitudinal analysis of SARS-CoV-2 spike receptor binding domain (RBD) specific IgG antibody responses, encompassing IgG subclasses IgG1, IgG2, IgG3, and IgG4 was performed. Participants received four mRNA vaccine doses (group 1; n=10) or two ChAdOx1 nCoV-19 and two mRNA booster doses (group 2; n=19) in Bangladesh over two years. Results: Findings demonstrate robust IgG responses after primary Covishield or mRNA doses; declining to baseline within six months. First mRNA booster restored and surpassed primary IgG responses but waned after six months. Surprisingly, a second mRNA booster did not increase IgG levels further. Comprehensive IgG subclass analysis showed primary Covishield/mRNA vaccination generated predominantly IgG1 responses with limited IgG2/IgG3, Remarkably, IgG4 responses exhibited a distinct pattern. IgG4 remained undetectable initially but increased extensively six months after the second mRNA dose, eventually replacing IgG1 after the 3rd/4th mRNA doses. Conversely, initial Covishield recipients lack IgG4, surged post-second mRNA booster. Notably, mRNA-vaccinated individuals displayed earlier, robust IgG4 levels post first mRNA booster versus Covishield counterparts. IgG1 to IgG4 ratios decreased with increasing doses, most pronounced with four mRNA doses. This study highlights IgG response kinetics, influenced by vaccine type and doses, impacting immunological tolerance and IgG4 induction, shaping future vaccination strategies. Conclusions: This study highlights the dynamics of IgG responses dependent on vaccine type and number of doses, leading to immunological tolerance and IgG4 induction, and shaping future vaccination strategies.

Are safe haven assets really safe during the 2008 global financial crisis and COVID-19 pandemic?

This study evaluates the safe-haven role of twelve assets against the US stock market during the 2008 global financial crisis (GFC) and the COVID-19 pandemic. Our results show that silver and the Islamic stock index were safe havens during the 2008 GFC, and the Islamic stock index and Tether have been safe havens during COVID-19. We observe that the Islamic stock index and Tether have emerged as strong new safe havens. However, our supplementary analysis reveals that gold and Bitcoin still exhibit safe-haven behavior during severe market downturns. Overall, our findings suggest that safe-haven assets may vary over time.