Integrated geochemical and geophysical monitoring for the assessment of natural pollutant mitigation in heavy metal-contaminated areas of South Korea, Deagu.

The evaluation and monitoring of sites contaminated with heavy metals are essential for pollution remediation and prevention. In this study, we conducted geophysical and geochemical investigations at a site exhibiting heavy metal contamination downstream from an abandoned mine, with the aim of analyzing the extent of contamination and its temporal variation. We employed geophysical survey methods including electrical resistivity and induced polarization surveys of areas contaminated with heavy metals. Repeated surveys were conducted over time using the electrical resistivity method. Numerical simulations were employed to mitigate and eliminate electrical noise stemming from topography on the site. Additionally, time-lapse inversion was conducted on the resistivity data sets to analyze the changes in resistivity caused by variations in heavy metal contaminants. In the geochemical survey, soil samples were collected from the same locations as the geophysical survey, and chemical properties including pH, water content, electrical conductivity, and cation exchange capacity were analyzed. Our results showed that with the reduction of major sources of As and Zn contamination by 50%, the time-lapse electrical resistivity inversion results indicated that the resistivity of the subsurface materials increased by a factor of two. This paper demonstrated the natural reduction of the heavy metal contaminants at the site due to rainfall, aiming to comprehensively analyze the resultant alteration of both geochemical and geophysical properties.

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

Predicting herb-disease associations using network-based measures in human protein interactome.

BACKGROUND: Natural herbs are frequently used to treat diseases or to relieve symptoms in many countries. Moreover, as their safety has been proven for a long time, they are considered as main sources of new drug development. However, in many cases, the herbs are still prescribed relying on ancient records and/or traditional practices without scientific evidences. More importantly, the medicinal efficacy of the herbs has to be evaluated in the perspective of MCMT (multi-compound multi-target) effects, but most efforts focus on identifying and analyzing a single compound experimentally. To overcome these hurdles, computational approaches which are based on the scientific evidences and are able to handle the MCMT effects are needed to predict the herb-disease associations. RESULTS: In this study, we proposed a network-based in silico method to predict the herb-disease associations. To this end, we devised a new network-based measure, WACP (weighted average closest path length), which not only quantifies proximity between herb-related genes and disease-related genes but also considers compound compositions of each herb. As a result, we confirmed that our method successfully predicts the herb-disease associations in the human protein interactome (AUROC = 0.777). In addition, we observed that our method is superior than the other simple network-based proximity measures (e.g. average shortest and closest path length). Additionally, we analyzed the associations between Brassica oleracea var. italica and its known associated diseases more specifically as case studies. Finally, based on the prediction results of the WACP, we suggested novel herb-disease pairs which are expected to have potential relations and their literature evidences. CONCLUSIONS: This method could be a promising solution to modernize the use of the natural herbs by providing the scientific evidences about the molecular associations between the herb-related genes targeted by multiple compounds and the disease-related genes in the human protein interactome.

Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.

The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression.

The dysadherin/FAK axis promotes individual cell migration in colon cancer.

Dysregulation of cancer cell motility is a key driver of invasion and metastasis. High dysadherin expression in cancer cells is correlated with invasion and metastasis. Here, we found the molecular mechanism by which dysadherin regulates the migration and invasion of colon cancer (CC). Comprehensive analysis using single-cell RNA sequencing data from CC patients revealed that high dysadherin expression in cells is linked to cell migration-related gene signatures. We confirmed that the deletion of dysadherin in tumor cells hindered local invasion and distant migration using in vivo tumor models. In this context, by performing cell morphological analysis, we found that aberrant cell migration resulted from impaired actin dynamics, focal adhesion turnover and protrusive structure formation upon dysadherin expression. Mechanistically, the activation of focal adhesion kinase (FAK) was observed in dysadherin-enriched cells. The dysadherin/FAK axis enhanced cell migration and invasion by activating the FAK downstream cascade, which includes the Rho family of small GTPases. Overall, this study illuminates the role of dysadherin in modulating cancer cell migration by forcing actin dynamics and protrusive structure formation via FAK signaling, indicating that targeting dysadherin may be a potential therapeutic strategy for CC patients.

STAT3 Decoy Oligodeoxynucleotides Suppress Liver Inflammation and Fibrosis in Liver Cancer Cells and a DDC-Induced Liver Injury Mouse Model.

Liver damage caused by various factors results in fibrosis and inflammation, leading to cirrhosis and cancer. Fibrosis results in the accumulation of extracellular matrix components. The role of STAT proteins in mediating liver inflammation and fibrosis has been well documented; however, approved therapies targeting STAT3 inhibition against liver disease are lacking. This study investigated the anti-fibrotic and anti-inflammatory effects of STAT3 decoy oligodeoxynucleotides (ODN) in hepatocytes and liver fibrosis mouse models. STAT3 decoy ODN were delivered into cells using liposomes and hydrodynamic tail vein injection into 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC)-fed mice in which liver injury was induced. STAT3 target gene expression changes were verified using qPCR and Western blotting. Liver tissue fibrosis and bile duct proliferation were assessed in animal experiments using staining techniques, and macrophage and inflammatory cytokine distribution was verified using immunohistochemistry. STAT3 decoy ODN reduced fibrosis and inflammatory factors in liver cancer cell lines and DDC-induced liver injury mouse model. These results suggest that STAT3 decoy ODN may effectively treat liver fibrosis and must be clinically investigated.

Slow gut transit increases the risk of Alzheimer's disease: An integrated study of the bi-national cohort in South Korea and Japan and Alzheimer's disease model mice.

INTRODUCTION: Although the association between Alzheimer's disease (AD) and constipation is controversial, its causality and underlying mechanisms remain unknown. OBJECTIVES: To investigate the potential association between slow gut transit and AD using epidemiological data and a murine model. METHODS: We conducted a bi-national cohort study in South Korea (discovery cohort, N=3,130,193) and Japan (validation cohort, N=4,379,285) during the pre-observation period to determine the previous diagnostic history (2009-2010) and the follow-up period (2011-2021). To evaluate the causality, we induced slow gut transit using loperamide in 5xFAD transgenic mice. Changes in amyloid-beta (Aß) and other markers were examined using ELISA, qRT-PCR, RNA-seq, and behavioral tests. RESULTS: Constipation was associated with an increased risk of AD in the discovery cohort (hazard ratio, 2.04; 95% confidence interval [CI], 2.01-2.07) and the validation cohort (hazard ratio; 2.82; 95% CI, 2.61-3.05). We found that loperamide induced slower gut transit in 5xFAD mice, increased Aß and microglia levels in the brain, increased transcription of genes related to norepinephrine secretion and immune responses, and decreased the transcription of defense against bacteria in the colonic tissue. CONCLUSION: Impaired gut transit may contribute to AD pathogenesis via the gut-brain axis, thus suggesting a cyclical relationship between intestinal barrier disruption and Aß accumulation in the brain. We propose that gut transit or motility may be a modifiable lifestyle factor in the prevention of AD, and further clinical investigations are warranted.