Fucose as a potential therapeutic molecule against the immune-mediated inflammation in IgA nepharopathy: An unrevealed link

Genetic insights into the connection between pulmonary TB and non-communicable diseases: An integrated analysis of shared genes and potential treatment targets

BACKGROUND

Pulmonary Tuberculosis (PTB) is a significant global health issue due to its high incidence, drug resistance, contagious nature, and impact on people with compromised immune systems. As mentioned by the World Health Organization (WHO), TB is responsible for more global fatalities than any other infectious illness. On the other side, WHO also claims that noncommunicable diseases (NCDs) kill 41 million people yearly worldwide. In this regard, several studies suggest that PTB and NCDs are linked in various ways and that people with PTB are more likely to acquire NCDs. At the same time, NCDs can increase susceptibility to active TB infection. Furthermore, because of potential drug interactions and therapeutic challenges, treating individuals with both PTB and NCDs can be difficult. This study focuses on seven NCDs (lung cancer (LC), diabetes mellitus (DM), Parkinson's disease (PD), silicosis (SI), chronic kidney disease (CKD), cardiovascular disease (CVD), and rheumatoid arthritis (RA)) and rigorously presents the genetic relationship with PTB regarding shared genes and outlines possible treatment plans.

OBJECTIVES

BlueThis study aims to identify the drug components that can regulate abnormal gene expression in NCDs. The study will reveal hub genes, potential biomarkers, and drug components associated with hub genes through statistical measures. This will contribute to targeted therapeutic interventions.

METHODS

Numerous investigations, including protein-protein interaction (PPI), gene regulatory network (GRN), enrichment analysis, physical interaction, and protein-chemical interaction, have been carried out to demonstrate the genetic correlation between PTB and NCDs. During the study, nine shared genes such as TNF, IL10, NLRP3, IL18, IFNG, HMGB1, CXCL8, IL17A, and NFKB1 were discovered between TB and the above-mentioned NCDs, and five hub genes (NFKB1, TNF, CXCL8, NLRP3, and IL10) were selected based on degree values.

RESULTS AND CONCLUSION

In this study, we found that all of the hub genes are linked with the 10 drug components, and it was observed that aspirin CTD 00005447 was mostly associated with all the other hub genes. This bio-informatics study may help researchers better understand the cause of PTB and its relationship with NCDs, and eventually, this can lead to exploring effective treatment plans.

Effects of Zhuang medicine compound Xiancao Granule on diabetic kidney disease: A multi-omics analysis

ETHNOPHARMACOLOGICAL RELEVANCE

Diabetic kidney disease (DKD) poses a severe threat to human health. Compound Xiancao Granule (CXCG), a classic Zhuang medicinal formula, is reported as highly effective in treating DKD. However, the mechanisms underlying the action of CXCG in DKD remain unclear.

AIM OF THE STUDY

This study aimed to investigate the mechanisms of action of CXCG against DKD using multi-omics analysis, including 16s rRNA sequencing, metabolomics, and transcriptomics.

MATERIALS AND METHODS

The chemical compounds of CXCG were identified using ultra-high- performance liquid chromatography quadrupole/electrostatic field orbital trap high-resolution mass spectrometry analysis. A rat model of DKD was established by combining nephrectomy of the left kidney, high-fat diet, and streptozotocin. The therapeutic effects of CXCG on DKD were assessed based on body weight, blood glucose level, renal function, inflammatory cytokine levels, and histological staining. Subsequently, 16s rRNA sequencing, liquid chromatography-tandem mass spectrometry untargeted metabolomic profiling, and RNA sequencing analysis were used to investigate the mechanisms of action of CXCG in DKD. Spearman's correlation analysis was performed to elucidate the correlations between efficacy indicators, gut microbiota, metabolites, and inflammation-related genes.

RESULTS

A total of 118 compounds were identified in CXCG. CXCG significantly ameliorated glucose metabolism disorders, improved renal function, attenuated inflammation, and delayed renal pathological changes in DKD rats. CXCG modulated gut microbiota dysbiosis, including Alloprevotella, Oscillibacter, Anaeroplasma, Anaerotruncus, and Faecalibacterium. In addition, metabolic disruption in DKD rats was regulated by CXCG, which is involved in the metabolism of carbohydrates and amino acids. Transcriptome analysis showed that CXCG affected DKD mainly by regulating inflammation-related genes and pathways, such as the PI3K/Akt and MAPK signaling pathways. Furthermore, there were significant correlations between efficacy indicators, gut microbiota, metabolites, and genes.

CONCLUSION

This multi-omics association study provides novel insights into the effects of CXCG on DKD by remodeling the gut microbiota structure and restoring the metabolic homeostasis through the regulation of carbohydrate metabolism, amino acid metabolism, and inflammation-related pathways, highlighting a potential therapeutic strategy for DKD.

The role of the glycome in symbiotic host-microbe interactions

Glycosylation plays a crucial role in many aspects of cell biology, including cellular and organismal integrity, structure-and-function of many glycosylated molecules in the cell, signal transduction, development, cancer, and in a number of diseases. Besides, at the inter-organismal level of interaction, a variety of glycosylated molecules are involved in the host-microbiota recognition and initiation of downstream signalling cascades depending on the outcomes of the glycome-mediated ascertainment. The role of glycosylation in host-microbe interactions is better elaborated within the context of virulence and pathogenicity in bacterial infection processes but the symbiotic host-microbe relationships also involve substantive glycome-mediated interactions. The works in the latter field have been reviewed to a much lesser extent, and the main aim of this mini-review is to compensate for this deficiency and summarise the role of glycomics in host-microbe symbiotic interactions.

L-Fucose is involved in human-gut microbiome interactions

L-Fucose is one of the key metabolites in human-gut microbiome interactions. It is continuously synthesized by humans in the form of fucosylated glycans and fucosyl-oligosaccharides and delivered into the gut throughout their lifetime. Gut microorganisms metabolize L-fucose and produce short-chain fatty acids, which are absorbed by epithelial cells and used as energy sources or signaling molecules. Recent studies have revealed that the carbon flux in L-fucose metabolism by gut microorganisms is distinct from that in other sugar metabolisms because of cofactor imbalance and low efficiencies in energy synthesis of L-fucose metabolism. The large amounts of short-chain fatty acids produced during microbial L-fucose metabolism are used by epithelial cells to recover most of the energy used up during L-fucose synthesis. In this review, we present a detailed overview of microbial L-fucose metabolism and a potential solution for disease treatment and prevention using genetically engineered probiotics that modulate fucose metabolism. Our review contributes to the understanding of human-gut microbiome interactions through L-fucose metabolism. KEY POINTS: • Fucose-metabolizing microorganisms produce large amounts of short-chain fatty acids • Fucose metabolism differs from other sugar metabolisms by cofactor imbalance • Modulating fucose metabolism is the key to control host-gut microbiome interactions.

Identification and validation of immune and oxidative stress-related diagnostic markers for diabetic nephropathy by WGCNA and machine learning

Background

Diabetic nephropathy (DN) is the primary cause of end-stage renal disease, but existing therapeutics are limited. Therefore, novel molecular pathways that contribute to DN therapy and diagnostics are urgently needed.

Methods

Based on the Gene Expression Omnibus (GEO) database and Limma R package, we identified differentially expressed genes of DN and downloaded oxidative stress-related genes based on the Genecard database. Then, immune and oxidative stress-related hub genes were screened by combined WGCNA, machine learning, and protein-protein interaction (PPI) networks and validated by external validation sets. We conducted ROC analysis to assess the diagnostic efficacy of hub genes. The correlation of hub genes with clinical characteristics was analyzed by the Nephroseq v5 database. To understand the cellular clustering of hub genes in DN, we performed single nucleus RNA sequencing through the KIT database.

Results

Ultimately, we screened three hub genes, namely CD36, ITGB2, and SLC1A3, which were all up-regulated. According to ROC analysis, all three demonstrated excellent diagnostic efficacy. Correlation analysis revealed that the expression of hub genes was significantly correlated with the deterioration of renal function, and the results of single nucleus RNA sequencing showed that hub genes were mainly clustered in endothelial cells and leukocyte clusters.

Conclusion

By combining three machine learning algorithms with WGCNA analysis, this research identified three hub genes that could serve as novel targets for the diagnosis and therapy of DN.