CleanSeq: A Pipeline for Contamination Detection, Cleanup, and Mutation Verifications from Microbial Genome Sequencing Data

Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained

CleanSeq: A Pipeline for Contamination Detection, Cleanup, and Mutation Verifications from Microbial Genome Sequencing Data

Contaminations frequently occur in bacterial cultures, which significantly affect the reproducibility and reliability of the results from whole-genome sequencing (WGS). Decontaminated WGS data with clean reads is the only desirable source for detecting possible variants correctly. Improvements in bioinformatics are essential to analyze the contaminated WGS dataset. Existing pipelines usually contain contamination detection, decontamination, and variant calling separately. The efficiency and results from existing pipelines fluctuate since distinctive computational models and parameters are applied. It is then promising to develop a bioinformatical tool containing functions to discriminate and remove contaminated reads and improve variant calling from clean reads. In this study, we established a Python-based pipeline named CleanSeq for automatic detection and removal of contaminating reads, analyzing possible genome variants with proper verifications via local re-alignments. The application and reproducibility are proven in either simulated, publicly available datasets or actual genome sequencing reads from our experimental evolution study in Escherichia coli. We successfully obtained decontaminated reads, called out all seven consistent mutations from the contaminated bacterial sample, and derived five colonies. Collectively, the results demonstrated that CleanSeq could effectively process the contaminated samples to achieve decontaminated reads, based on which reliable results (i.e., variant calling) could be obtained

The TFPI2–PPARγ axis induces M2 polarization and inhibits fibroblast activation to promote recovery from post-myocardial infarction in diabetic mice

Abstract Background Diabetes mellitus is one of the causes of poor ventricular remodelling and poor cardiac recovery after myocardial infarction (MI). We previously reported that tissue factor pathway inhibitor-2 (TFPI2) was downregulated in response to hyperglycaemia and that it played a pivotal role in extracellular matrix (ECM) degradation and cell migration. Nonetheless, the function and mechanism of TFPI2 in post-MI remodelling under diabetic conditions remain unclear. Therefore, in the present study, we investigated the role of TFPI2 in post-MI effects in a diabetic mouse model. Results TFPI2 expression was markedly decreased in the infarcted myocardium of diabetic MI mice compared with that in non-diabetic mice. TFPI2 knockdown in the MI mouse model promoted fibroblast activation and migration as well as matrix metalloproteinase (MMP) expression, leading to disproportionate fibrosis remodelling and poor cardiac recovery. TFPI2 silencing promoted pro-inflammatory M1 macrophage polarization, which is consistent with the results of TFPI2 downregulation and M1 polarization under diabetic conditions. In contrast, TFPI2 overexpression in diabetic MI mice protected against adverse cardiac remodelling and functional deterioration. TFPI2 overexpression also inhibited MMP2 and MMP9 expression and attenuated fibroblast activation and migration, as well as excessive collagen production, in the infarcted myocardium of diabetic mice. TFPI2 promoted an earlier phenotype transition of pro-inflammatory M1 macrophages to reparative M2 macrophages via activation of peroxisome proliferator-activated receptor gamma. Conclusions This study highlights TFPI2 as a promising therapeutic target for early resolution of post-MI inflammation and disproportionate ECM remodelling under diabetic conditions

PINK1/Parkin-Mediated Mitophagy Partially Protects against Inorganic Arsenic-Induced Hepatic Macrophage Polarization in Acute Arsenic-Exposed Mice

Inorganic arsenic is a well-known environmental toxicant and carcinogen, and there is overwhelming evidence for an association between this metalloid poisoning and hepatic diseases. However, the biological mechanism involved is not well characterized. In the present study, we probed how inorganic arsenic modulates the hepatic polarization of macrophages, as well as roles of PTEN-induced kinase 1 (PINK1)/Parkin-mediated mitophagy participates in regulating the metalloid-mediated macrophage polarization. Our results indicate that acute arsenic exposure induced macrophage polarization with up-regulated gene expression of inducible nitric oxide synthase (Inos) and arginase-1 (Arg1), monocyte chemotactic protein-1 (Mcp-1) and macrophage inflammatory protein-2 (Mip-2), tumor necrosis factor (Tnf)-α, interleukin (Il)-1β and Il-6, as well as anti-inflammatory factors Il-4 and Il-10. In parallel, we demonstrated the disrupted hepatic redox balance typically characterized by the up-regulation of hydrogen peroxide (H2O2) and glutathione (GSH), and activation of PINK1/Parkin-mediated mitophagy in the livers of acute arsenic-exposed mice. In addition, our results demonstrate that it might be the PINK1/Parkin-mediated mitophagy that renders hepatic macrophage refractory to arsenic-induced up-regulation of the genes Inos, Mcp-1, Mip-2, Tnf-α, Il-1β, Il-6 and Il-4. In this regard, this is the first time the protective effects of PINK1/Parkin-mediated mitophagy in inorganic arsenic-induced hepatic macrophage polarization in vivo have been reported. These findings add novel insights into the arsenical immunotoxicity and provide a basis for the preve.ntive and therapeutic potential of PINK1/Parkin-mediated mitophagy in arsenic poisoning