Black Phosphorus/MnO2 Nanocomposite Disrupting Bacterial Thermotolerance for Efficient Mild-Temperature Photothermal Therapy

The emergence of multi-drug resistant (MDR) pathogens is a major public health concern, posing a substantial global economic burden. Photothermal therapy (PTT) at mild temperature presents a promising alternative to traditional antibiotics due to its biological safety and ability to circumvent drug resistance. However, the efficacy of mild PTT is limited by bacterial thermotolerance. Herein, a nanocomposite, BP@Mn-NC, comprising black phosphorus nanosheets and a manganese-based nanozyme (Mn-NZ) is developed, which possesses both photothermal and catalytic properties. Mn-NZ imparts glucose oxidase- and peroxidase-like properties to BP@Mn-NC, generating reactive oxygen species (ROS) that induce lipid peroxidation and malondialdehyde accumulation across the bacterial cell membrane. This process disrupts unprotected respiratory chain complexes exposed on the bacterial cell membrane, leading to a reduction in the intracellular adenosine triphosphate (ATP) content. Consequently, mild PTT mediated by BP@Mn-NC effectively eliminates MDR infections by specifically impairing bacterial thermotolerance because of the dependence of bacterial heat shock proteins (HSPs) on ATP molecules for their proper functioning. This study paves the way for the development of a novel photothermal strategy to eradicate MDR pathogens, which targets bacterial HSPs through ROS-mediated inhibition of bacterial respiratory chain activity.

Integrative enrichment analysis of gene expression based on an artificial neuron

BACKGROUND

Huntington's disease is a kind of chronic progressive neurodegenerative disease with complex pathogenic mechanisms. To data, the pathogenesis of Huntington's disease is still not fully understood, and there has been no effective treatment. The rapid development of high-throughput sequencing technologies makes it possible to explore the molecular mechanisms at the transcriptome level. Our previous studies on Huntington's disease have shown that it is difficult to distinguish disease-associated genes from non-disease genes. Meanwhile, recent progress in bio-medicine shows that the molecular origin of chronic complex diseases may not exist in the diseased tissue, and differentially expressed genes between different tissues may be helpful to reveal the molecular origin of chronic diseases. Therefore, developing integrative analysis computational methods for the multi-tissues gene expression data, exploring the relationship between differentially expressed genes in different tissues and the disease, can greatly accelerate the molecular discovery process.

METHODS

For analysis of the intra- and inter- tissues' differentially expressed genes, we designed an integrative enrichment analysis method based on an artificial neuron (IEAAN). Firstly, we calculated the differential expression scores of genes which are seen as features of the corresponding gene, using fold-change approach with intra- and inter- tissues' gene expression data. Then, we weighted sum all the differential expression scores through a sigmoid function to get differential expression enrichment score. Finally, we ranked the genes according to the enrichment score. Top ranking genes are supposed to be the potential disease-associated genes.

RESULTS

In this study, we conducted large amounts of experiments to analyze the differentially expressed genes of intra- and inter- tissues. Experimental results showed that genes differentially expressed between different tissues are more likely to be Huntington's disease-associated genes. Five disease-associated genes were selected out in this study, two of which have been reported to be implicated in Huntington's disease.

CONCLUSIONS

We proposed a novel integrative enrichment analysis method based on artificial neuron (IEAAN), which displays better prediction precision of disease-associated genes in comparison with the state-of-the-art statistical-based methods. Our comprehensive evaluation suggests that genes differentially expressed between striatum and liver tissues of health individuals are more likely to be Huntington's disease-associated genes.

Integrative Analysis Identified Key Schizophrenia Risk Factors from an Abnormal Behavior Mouse Gene Set

Schizophrenia (SCZ) is a severe chronic psychiatric illness with heterogeneous symptoms. However, the pathogenesis of SCZ is unclear, and the number of well-defined SCZ risk factors is limited. We hypothesized that an abnormal behavior (AB) gene set verified by mouse model experiments can be used to better understand SCZ risks. In this work, we carried out an integrative bioinformatics analysis to study two types of risk genes that are either differentially expressed (DEGs) in the case-control study data or carry reported SCZ genetic variants (MUTs). Next, we used RNA-Seq expression data from the hippocampus (HIPPO) and dorsolateral prefrontal cortex (DLPFC) to define the key genes affected by different types (DEGs and MUTs) in different brain regions (DLPFC and HIPPO): DLPFC-kDEG, DLPFC-kMUT, HIPPO-kDEG, and HIPPO-kMUT. The four hub genes (SHANK1, SHANK2, DLG4, and NLGN3) of the biological functionally enriched terms were strongly linked to SCZ via gene co-expression network analysis. Then, we observed that specific spatial expressions of DLPFC-kMUT and HIPPO-kMUT were convergent in the early stages and divergent in the later stages of development. In addition, all four types of key genes showed significantly larger average protein-protein interaction degrees than the background. Comparing the different cell types, the expression of four types of key genes showed specificity in different dimensions. Together, our results offer new insights into potential risk factors and help us understand the complexity and regional heterogeneity of SCZ.