Corn silage can usually improve the growth performance and the meat quality of ruminants, and subsequently increase the economic benefits of farming. However, little is known about the effects of corn silage on donkeys. This experiment investigated the effects of corn silage on the weight gain, gut microbiota and metabolites of Dezhou donkeys. A total of 24 Dezhou donkeys, sourced from the same farm and exhibiting similar age and average body weight, were utilized in this experiment. The donkeys were allocated into two groups: a control group receiving a basic diet and a test group receiving a basic diet supplemented with 30% corn silage. Each group comprised 12 donkeys, evenly distributed by sex (6 males and 6 females). The experiment lasted for 100 days. Results showed that dietary supplementation with corn silage significantly (P < 0.05) improved the weight gain of Dezhou donkeys at the end of the experiment. And the supplementation of corn silage in the diet significantly altered the bacterial community composition and metabolome in the feces of the donkeys. Notably, the relative abundance ratio of Bacteroidetes to Firmicutes was 0.76 in the control group compared to 0.96 in the test group. Furthermore, members of the Bacteroidetes and Firmicutes phyla were associated with differentiated metabolites enriched in the arachidonic acid metabolism and pentose and glucuronate interconversion pathways, both of which have been reported to be related to animal growth. Specifically, Bacteroidia exhibited statistically (P < 0.05) positive correlations with 15S-HpETE, while Bacilli demonstrated statistically (P < 0.05) negative correlations with D-Xylulose. The findings of this study can advance our mechanistic understanding of the remodeling of intestinal microbiota and metabolome induced by corn silage, as well as their relationships with the growth performance of Dezhou donkeys, which in turn favor the improvement in nutrition of Dezhou donkeys.

The falsely annotated protein-coding genes have been deemed one of the major causes accounting for the annotating errors in public databases. Although many filtering approaches have been designed for the over-annotated protein-coding genes, some are questionable due to the resultant increase in false negative. Furthermore, there is no webserver or software specifically devised for the problem of over-annotation. In this study, we propose an integrative algorithm for detecting the over-annotated protein-coding genes in microorganisms. Overall, an average accuracy of 99.94% is achieved over 61 microbial genomes. The extremely high accuracy indicates that the presented algorithm is efficient to differentiate the protein-coding genes from the non-coding open reading frames. Abundant analyses show that the predicting results are reliable and the integrative algorithm is robust and convenient. Our analysis also indicates that the over-annotated protein-coding genes can cause the false positive of horizontal gene transfers detection. The webserver of the proposed algorithm can be freely accessible from www.cbi.seu.edu.cn/RPGM.

Cancer is the second leading cause of death in the world, and chemotherapy is one of the main methods of cancer treatment. However, the resistance of cancer cells to chemotherapeutic drugs has always been the main reason affecting the therapeutic effect. Synthetic lethality has emerged as a promising approach to augment the sensitivity of cancer cells to chemotherapy agents. Synthetic lethality (SL) refers to the specific cell death resulting from the simultaneous mutation of two non-lethal genes, which individually allow cell survival. This comprehensive review explores the classification of SL, screening methods, and research advancements in SL inhibitors, including Poly (ADP-ribose) polymerase (PARP) inhibitors, Ataxia telangiectasia and Rad3-related (ATR) inhibitors, WEE1 G2 checkpoint kinase (WEE1) inhibitors, and protein arginine methyltransferase 5 (PRMT5) inhibitors. Emphasizing their combined use with chemotherapy drugs, we aim to unveil more effective treatment strategies for cancer patients.

Background: Small open reading frames (sORFs) with protein-coding ability present unprecedented challenge for genome annotation because of their short sequence and low expression level. In the past decade, only several prediction methods have been proposed for discovery of protein-coding sORFs and lack of objective and uniform negative datasets has become an important obstacle to sORFs prediction. The prediction efficiency of current sORFs prediction methods needs to be further evaluated to provide better research strategies for protein-coding sORFs discovery. Methods: In this work, nine mainstream existing methods for predicting protein-coding potential of ORFs are comprehensively evaluated based on a random sequence strategy. Results: The results show that the current methods perform poorly on different sORFs datasets. For comparison, a sequence based prediction algorithm trained on prokaryotic sORFs is proposed and its better prediction performance indicates that the random sequence strategy can provide feasible ideas for protein-coding sORFs predictions. Conclusions: As a kind of important functional genomic element, discovery of protein-coding sORFs has shed light on the dark proteomes. This evaluation work indicates that there is an urgent need for developing specialized prediction tools for protein-coding sORFs in both eukaryotes and prokaryotes. It is expected that the present work may provide novel ideas for future sORFs researches.