Key genes of electron transfer, the nitrogen cycle and tetracycline removal in bioelectrochemical systems.

BACKGROUND: Soil microbial fuel cells (MFCs) can remove antibiotics and antibiotic resistance genes (ARGs) simultaneously, but their removal mechanism is unclear. In this study, metagenomic analysis was employed to reveal the functional genes involved in degradation, electron transfer and the nitrogen cycle in the soil MFC. RESULTS: The results showed that the soil MFC effectively removed tetracycline in the overlapping area of the cathode and anode, which was 64% higher than that of the control. The ARGs abundance increased by 14% after tetracycline was added (54% of the amplified ARGs belonged to efflux pump genes), while the abundance decreased by 17% in the soil MFC. Five potential degraders of tetracycline were identified, especially the species Phenylobacterium zucineum, which could secrete the 4-hydroxyacetophenone monooxygenase encoded by EC 1.14.13.84 to catalyse deacylation or decarboxylation. Bacillus, Geobacter, Anaerolinea, Gemmatirosa kalamazoonesis and Steroidobacter denitrificans since ubiquinone reductase (encoded by EC 1.6.5.3), succinate dehydrogenase (EC 1.3.5.1), Coenzyme Q-cytochrome c reductase (EC 1.10.2.2), cytochrome-c oxidase (EC 1.9.3.1) and electron transfer flavoprotein-ubiquinone oxidoreductase (EC 1.5.5.1) served as complexes I, II, III, IV and ubiquinone, respectively, to accelerate electron transfer. Additionally, nitrogen metabolism-related gene abundance increased by 16% to support the microbial efficacy in the soil MFC, and especially EC 1.7.5.1, and coding the mutual conversion between nitrite and nitrate was obviously improved. CONCLUSIONS: The soil MFC promoted functional bacterial growth, increased functional gene abundance (including nitrogen cycling, electron transfer, and biodegradation), and facilitated antibiotic and ARG removal. Therefore, soil MFCs have expansive prospects in the remediation of antibiotic-contaminated soil. This study provides insight into the biodegradation mechanism at the gene level in soil bioelectrochemical remediation.

[Characteristics of Antibiotic Contamination of Soil in China in Past Fifteen Years and the Bioremediation Technology: A Review].

In recent years, the contamination of antibiotics and their resistance genes (ARGs) has attracted the extensive attention of researchers at home and abroad. Soil is an important sink for the migration and transformation of antibiotics and ARGs, which pose a threat to soil organisms and human health. According to the relevant investigations in the past 15 years, the soil has been polluted by antibiotics to varying degrees in China. Bioremediation is a green and environment-friendly remediation technology, which has great potential in the remediation of antibiotic-contaminated soil. This review summarized the spatial and temporal characteristics of antibiotic pollution of soils in China in the past 15 years and the application of plants, animals, and microorganisms in the remediation of antibiotic-contaminated soil. In particular, the recent research advances of microbial electrochemical systems in removing antibiotics and ARGs in soil were reviewed, and the unaddressed issues of relevant research and the direction of future development were proposed, in order to provide a scientific basis for soil pollution remediation.

The m6A RNA Modification Quantity and the Prognostic Effect of Reader YTHDC2 in Colorectal Cancer.

Background: N6-methyladenosine (m6A) modification plays crucial roles in cancers. However, its alteration in colorectal cancer (CRC) is still poorly described. The purpose of this study is to explore the change of m6A modification and the function of m6A binding protein YTHDC2 in CRC. Methods: The global level of m6A modification was detected by mass spectrometry and dot blotting assay. The expression of YTHDC2 was investigated using The Cancer Genome Atlas and using real-time polymerase chain reaction (RT-qPCR), western blotting, and immunohistochemistry based on CRC tissues. Kaplan-Meier analysis and Cox proportional hazards regression were performed to analyze the prognostic value of YTHDC2. RNA immunoprecipitation (RIP)-seq and m6A immunoprecipitation (MeRIP)-seq were used to explore the direct targets of YTHDC2. Gene oncology (GO) and Gene Set Enrichment Analysis (GSEA) were used to explore the pathways that could be influenced by YTHDC2. Results: No significant difference was observed in the global level of m6A modification on total RNA or mRNA between CRC and adjacent nontumor tissues. We further found a significant decreasing of YTHDC2 in CRC tissues. Kaplan-Meier analysis indicated that lower expression of YTHDC2 was related to the worse disease-free survival and overall survival. In addition, lower expression of YTHDC2 was an independent worse prognostic factor in univariate and multivariate Cox regression analysis. Using YTHDC2-RIP-seq and MeRIP-seq, we identified that YTHDC2 could participate in several important biological signal pathways. Conclusions: In summary, this study suggested that the global level of m6A did not change in CRC and identified that lower YTHDC2 as a prognostic marker for worse survival of CRC.

Regorafenib in Refractory Metastatic Colorectal Cancer: A Multi-Center Retrospective Study.

Background: Regorafenib improves progression-free survival (PFS) and overall survival (OS) in patients with refractory metastatic colorectal cancer (mCRC). Here, we report the treatment patterns of regorafenib in the third- or late-line setting for mCRC in four centers in China. Patients and Methods: Patients with refractory mCRC in four centers in China administered regorafenib from February 1, 2018 to June 31, 2021 were enrolled. Patients were grouped into 3 cohorts, namely, the monotherapy (regorafenib alone), chemo (regorafenib plus chemotherapy), and immune [regorafenib plus anti-PD1 (programmed cell death 1) antibodies] groups. Demographic, clinical, survival and safety data were retrospectively analyzed. Results: A total of 177 patients were included in this study. Of them, 116 (65.5%) were treated with regorafenib alone, while 28 (15.9%) and 33 (18.6%) were administered regorafenib plus chemotherapy and anti-PD1 antibodies, respectively. The median followed-up time was 9.2 months. The disease control rate (DCR) was 40.7%. The median PFS (mPFS) was 2.43 months and the median OS (mOS) was 12.2 months. The immune group had longer median PFS (3.5 m vs. 2.2 m, p = 0.043) compared with the monotherapy group. Patients administered regorafenib plus chemotherapy had longer median OS (15.9 m vs. 8.4 m, p = 0.032) compared with the monotherapy group. Patients who began regorafenib treatment at 120 mg had longer median PFS and OS compared with those who began at 80 mg (PFS: 3.7 m vs. 2.0 m; p <0.001; OS: 13.4 m vs. 10.2 m; p = 0.005). Patients with a final dose of 120 mg had longer median PFS and OS compared with the 80 mg or less group (PFS: 5.0 m vs. 2.3 m; p = 0.045; OS: UR (unreach) vs. 10.9 m; p = 0.003). There were 87.0% (154/177) patients who experienced AEs. Three groups had similar rates of AEs (86.2% vs. 89.3% vs. 87.9%; p = 0.89). Conclusion: Patients administered regorafenib alone or regorafenib in combination with other agents were relieved to some extent, with a disease control rate of 40.7%. Regorafenib plus anti-PD1 antibodies showed better PFS, while regorafenib plus chemotherapy had the most benefit in OS. There was no significant difference among three groups in terms of AEs.

TACN: A Topical Adversarial Capsule Network for textual network embedding.

Combining topological information and attributed information of nodes in networks effectively is a valuable task in network embedding. Nevertheless, many prior network embedding methods regarded attributed information of nodes as simple attribute sets or ignored them totally. In some scenarios, the hidden information contained in vertex attributes are essential to network embedding. For instance, networks that contain vertexes with text information play an increasingly important role in our life, including citation networks, social networks, and entry networks. In these textual networks, the latent topic relevance information of different vertexes contained in textual attributes information are valuable in the network analysis process. Shared latent topics of nodes in networks may influence the interaction between them, which is critical to network embedding. However, much prior work for textual network embedding only regarded the text information as simple word sets while ignored the embedded topic information. In this paper, we develop a model named Topical Adversarial Capsule Network (TACN) for textual network embedding, which extracts a low-dimensional latent space of the original network from node structures, vertex attributes, and topic information contained in text of nodes. The proposed TACN contains three parts. The first part is an embedding model, which extracts the embedding representation from the topological structure, vertex attributes, and document-topic distributions. To ensure a consistent training process by back-propagation, we generate document-topic distributions by the neural topic model with Gaussian Softmax constructions. The second part is a prediction model, which is used to exploit labels of vertices. In the third part, an adversarial capsule model is used to help distinguish the latent representations from node structure domain, vertex attribute domain, or document-topic distribution domain. The latent representations, which may come from the three domains, are the output of the embedding model. We incorporate the adversarial idea into the adversarial capsule model to combine the information from these three domains, rather than to distinguish the representations conventionally. Experiments on seven real-world datasets validate the effectiveness of our method.