Persistence and potential risks of tetracyclines and their transformation products in two typical different animal manure composting treatments.

Abundant residues of tetracyclines in animal manures and manure-derived organic fertilizers can pose a substantial risk to environments. However, our knowledge on the residual levels and potential risk of tetracyclines and their transformation products (TPs) in manure and manure-derived organic fertilizers produced by different composting treatments is still limited. Herein, the occurrence and distribution of four veterinary tetracyclines (tetracycline, oxytetracycline, chlortetracycline, and doxycycline) and ten of their TPs were investigated in paired samples of fresh manure and manure-derived organic fertilizers. Tetracyclines and TPs were frequently detected in manure and manure-derived organic fertilizer samples in ranging from 130 to 118,137 µg·kg-1 and 54.6 to 104,891 µg·kg-1, respectively. Notably, the TPs concentrations of tetracycline and chlortetracycline were comparable to those of the parent compounds, with 4-epimers being always dominant and retained antibacterial potency. Based on paired-sampling strategy, the removal efficiency of tetracyclines and TPs in thermophilic composting was higher than that in manure storage. Toxicological data in the soil environment and the data derived from equilibrium partitioning method, indicated that tetracyclines and some TPs like 4-epitetracycline, 4-epichlortetracycline and isochlortetracycline could pose median to high ecological risk to terrestrial organisms. Total concentrations of TPs in manure-derived organic fertilizers were significantly correlated with the absolute abundance of tet(X) family genes, which provide evidence to evaluate the effects of TPs on the levels of antibiotic resistance in the environment. Among them, the 4-epitetracycline could pose ecological risk and retain antibacterial potency. Our findings emphasize the importance of monitoring and controlling the prevalence of tetracyclines and their TPs in livestock-related environments.

Occurrence, distribution, and behaviors of erythromycin A, production byproducts, transformation products, and resistance genes in a full-scale erythromycin A production wastewater treatment system.

Antibiotic production wastewater from pharmaceutical manufacturing is a significant source of antibiotic and resistance gene pollution in the environment. Given that Erythromycin A (Ery-A) is a widely used antibiotic in both human clinical and livestock breeding, it is imperative to ascertain its presence, along with related compounds, in the biological treatment processes of production wastewater. In this study, the occurrence and behavior of Ery-A, its production byproducts, transformation products, and resistance genes were first systematically investigated in a full-scale anaerobic-aerobic system for treating Ery-A production wastewater. Simultaneously, residual antibacterial activity in wastewater and sludge was evaluated throughout the wastewater treatment process. Ery-A contributes only 24.2 - 36.0% to the antibacterial activities. Ery-A-derived compounds including production byproducts (erythromycin B and erythromycin C) and transformation products (anhydro erythromycin A, N-demethyl-erythromycin A, and erythromycin A enol ether), are determined to contribute to the antibacterial activities of the wastewater treatment system. High concentrations of antibiotics with antibacterial activity (up to 1,258.9 mg/kg·TS for erythromycin A enol ether) adsorbed in the sludge result in near collapse of the first-stage anaerobic sludge system. Sludge biodegradation in second-stage anaerobic and anoxic-aerobic tanks is essential in removing Ery-A-related compounds from wastewater. The Ery-A-related compounds in the secondary effluent and excess sludge are determined to be 44.5 g/h and 1.5 g/h through the mass balance analysis, respectively. The discharge of MLS resistance genes from the secondary effluent and excess sludge is 1.0 × 1016 copies/h and 7.1 × 1015 copies/h, respectively. These findings highlight the significant concern over the release of Ery-A-related compounds and MLS resistance genes from the Ery-A production wastewater treatment system. As a result, it is crucial to implement strategies for the removal of Ery-A-related compounds from production wastewater before biological processes. This study is the first to report the occurrence and behavior of Ery-A-related compounds and resistance genes along the full-scale wastewater treatment processes. Additionally, it sheds light on the importance of byproducts and transformation products with antibacterial activity from Ery-A in the Ery-A production wastewater treatment system.

Simultaneous determination of erythromycin and its transformation products in treated erythromycin fermentation residue and amended soil.

Erythromycin fermentation residue (EFR) is a solid waste generated from the fermentation process of erythromycin A production. Some byproducts are produced during the fermentation process of erythromycin A production, and erythromycin A can also undergo hydrolysis and biodegradation reactions in the environment with the formation of transformation products. Herein, an accurate analytical method was established and validated to quantify erythromycin A, two byproducts and five hydrolysis or biodegradation products, in solid or semi-solid media of waste EFR and the amended soil. The method mainly included ultrasonic solvent extraction, solid phase extraction, and ultra-performance liquid chromatography-tandem mass spectrometry quantification. All analytes could be effectively extracted in a single process, and the recoveries ranged from 76% to 122% for different matrices. Low matrix effects and excellent precision were achieved by optimizing the mass spectrometry parameters, extraction solution, number of extractions and eluent. This method was applied to evaluate the residual analytes in EFR, treated EFR after industrial-scale hydrothermal treatment, and the subsequent soil application. Seven analytes were detected in the EFR, while six were found in the treated EFR and amended soils. The concentration of erythromycin A in EFR was 1,629 ± 100 mg/kg·TS, and the removal efficiency of hydrothermal treatment (180 °C, 60 min) was about 99.6%. Three hydrolysis products were the main residuals in treated EFR, with anhydroerythromycin A showing the highest concentration. The concentrations of the analytes in soil ranged from 2.17 ± 1.04 to 92.33 ± 20.70 µg/kg·TS, and anhydroerythromycin A contributed 65%-77% of the total concentration. Erythromycin B, a byproduct, was still detected in soil. This work provides an accurate analytical method which would be useful to evaluate the potential risk of byproducts and transformation products of erythromycin A in environment.

MultiTrans: An Algorithm for Path Extraction Through Mixed Integer Linear Programming for Transcriptome Assembly.

Recent advances in RNA-seq technology have made identification of expressed genes affordable, and thus boosting repaid development of transcriptomic studies. Transcriptome assembly, reconstructing all expressed transcripts from RNA-seq reads, is an essential step to understand genes, proteins, and cell functions. Transcriptome assembly remains a challenging problem due to complications in splicing variants, expression levels, uneven coverage and sequencing errors. Here, we formulate the transcriptome assembly problem as path extraction on splicing graphs (or assembly graphs), and propose a novel algorithm MultiTrans for path extraction using mixed integer linear programming. MultiTrans is able to take into consideration coverage constraints on vertices and edges, the number of paths and the paired-end information simultaneously. We benchmarked MultiTrans against two state-of-the-art transcriptome assemblers, TransLiG and rnaSPAdes. Experimental results show that MultiTrans generates more accurate transcripts compared to TransLiG (using the same splicing graphs) and rnaSPAdes (using the same assembly graphs). MultiTrans is freely available at https://github.com/jzbio/MultiTrans.

IsoTree: A New Framework for de novo Transcriptome Assembly from RNA-seq Reads.

High-throughput sequencing of mRNA has made the deep and efficient probing of transcriptome more affordable. However, the vast amounts of short RNA-seq reads make de novo transcriptome assembly an algorithmic challenge. In this work, we present IsoTree, a novel framework for transcripts reconstruction in the absence of reference genomes. Unlike most of de novo assembly methods that build de Bruijn graph or splicing graph by connecting k- mers which are sets of overlapping substrings generated from reads, IsoTree constructs splicing graph by connecting reads directly. For each splicing graph, IsoTree applies an iterative scheme of mixed integer linear program to build a prefix tree, called isoform tree. Each path from the root node of the isoform tree to a leaf node represents a plausible transcript candidate which will be pruned based on the information of paired-end reads. Experiments showed that in most cases IsoTree performs better than other leading transcriptome assembly programs. IsoTree is available at https://github.com/Jane110111107/IsoTree.

DTA-SiST: de novo transcriptome assembly by using simplified suffix trees.

BACKGROUND: Alternative splicing allows the pre-mRNAs of a gene to be spliced into various mRNAs, which greatly increases the diversity of proteins. High-throughput sequencing of mRNAs has revolutionized our ability for transcripts reconstruction. However, the massive size of short reads makes de novo transcripts assembly an algorithmic challenge. RESULTS: We develop a novel radical framework, called DTA-SiST, for de novo transcriptome assembly based on suffix trees. DTA-SiST first extends contigs by reads that have the longest overlaps with the contigs' terminuses. These reads can be found in linear time of the lengths of the reads through a well-designed suffix tree structure. Then, DTA-SiST constructs splicing graphs based on contigs for each gene locus. Finally, DTA-SiST proposes two strategies to extract transcript-representing paths: a depth-first enumeration strategy and a hybrid strategy based on length and coverage. We implemented the above two strategies and compared them with the state-of-the-art de novo assemblers on both simulated and real datasets. Experimental results showed that the depth-first enumeration strategy performs always better with recall and also better with precision for smaller datasets while the hybrid strategy leads with precision for big datasets. CONCLUSIONS: DTA-SiST performs more competitive than the other compared de novo assemblers especially with precision measure, due to the read-based contig extension strategy and the elegant transcripts extraction rules.