Exposure to Sri Lanka's local groundwater in a CKDu prevalent area causes kidney damage in zebrafish.

How local groundwater induces chronic kidney disease of unknown etiology (CKDu) in Sri Lanka is still elusive. This study aims to elucidate the impacts of Sri Lanka's local groundwater in a CKDu prevalent area and reveal the possible pathogenic mechanism of CKDu using zebrafish models. The drinking water from the local underground well in Vavuniya was sampled and the water quality parameters including Na+, Mg2+, K+, Ca2+, Cl-, NO3-, SO42-, and F- were analyzed. Then, local groundwater exposure to zebrafish larvae and 293T cells was performed, and water with high hardness and fluoride was prepared as parallel groups. Our result showed that exposure to Sri Lanka's local groundwater caused developmental toxicity, kidney damage, and pronephric duct obstruction as well as abnormal behavior in zebrafish. Similar results were also found after exposure to water with high hardness and fluoride in zebrafish. Further, the expression levels of marker genes related to renal development and functions (foxj1a, dync2h1, pkd2, gata3, and slc20a1) were significantly altered, which is also confirmed in the 293T cells. Taken together, those results indicated that Sri Lanka's local groundwater in a CKDu prevalent area could cause kidney damage, implying that high water hardness and fluorine might be the inducible environmental factors for the etiological cause of CKDu.

Plastic biodegradation by in vitro environmental microorganisms and in vivo gut microorganisms of insects.

Traditional plastics, such as polyethylene (PE), polystyrene (PS), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET), polyurethane (PUR), and other plastic polymers, are difficult to degrade and are gradually accumulated in the environment to cause a serious environmental problem, which is urgently needed to develop novel treatments or control technology. The biodegradation of plastics has gained great attention due to the advantages of green and safe characteristics. Microorganisms play a vital role in the biodegradation of plastics, including environmental microbes (in vitro) and gut microbes of insects (in vivo). Microbial degradation in environmental conditions in vitro is extremely slow for major plastics at degradation rates on the basis of a month or even a year time, but recent discoveries show that the fast biodegradation of specific plastics, such as PS, PE, and PUR, in some invertebrates, especially insects, could be enhanced at rates on basis of hours; the biodegradation in insects is likely to be gut microbial-dependent or synergetic bioreactions in animal digestive systems. This review comprehensively summarizes the latest 7-year (2016-2022) publications on plastic biodegradation by insects and microorganisms, elucidates the mechanism of plastic degradation in insects and environmental microbes, and highlights the cutting-edge perspectives for the potential applications of plastic biodegradation.

Role of germ-free animal models in understanding interactions of gut microbiota to host and environmental health: A special reference to zebrafish.

Numerous pieces of evidence documented the importance of gut microbiota in regulating human health and evaluating the toxicity of environmental pollutants, which are closely related to the host health in various aspects, including nutrition, energy translation, metabolism, pathogen resistance, and immune function. A variety of environmental factors can disrupt gut microbiota and their functions, and inevitably cause immune diseases, obesity and diabetes. However, deciphering the inner mechanisms involved in the functional interaction of gut microbes with host health is still needed extensive investigations. This review focused on the essential roles of intestinal microbes in host-related diseases and highlighted the development and applications of germ-free (GF) animal models, mainly zebrafish. Moreover, the generation, immunity characters, advantages and challenges of GF zebrafish models were also summarized. Importantly, the composition and isolation of zebrafish gut bacteria for further application and toxicity evaluation of aquatic environmental pollutants were also discussed. In conclusion, GF zebrafish play irreplaceable roles in understanding the potential functions and responses of customized microbiota towards human and environmental health implications.

Site-Specific Systematic Analysis of Lysine Modification Crosstalk.

Research has revealed that post-translational modifications (PTMs) that occur at lysine (PLMs) can cooperatively regulate various biological processes by crosstalk. However, the trend of the crosstalk between multiple PLMs and the properties of PLM crosstalk require additional investigation. Here, the crosstalk among acetylation, succinylation, and SUMOylation is systematically studied in a site-specific waz. First, crosstalk between SUMOylation is detected and succinylation is found to be underexpressed, whereas succinylation tends to crosstalk with acetylation and SUMOylation on the same lysine residue while PLM crosstalk is tissue-specific across different species. Further analysis reveals that different PLMs tend to occur crosstalk at diverse subcellular compartments and structural regions, and they participate in distinct biological processes and functions. Additionally, short-term evolutionary analysis shows that there is no additional evolutionary pressure on PLMs crosstalk sites, as found by comparison with singly modified sites. Finally, phylogenetic classification reveals that genes with co-occupied lysine crosstalk are more likely to have higher evolutionary similarity and possess a tendency to cluster in the specific branch. The integrated approach reported here has the potential for large-scale prioritization of in situ crosstalk of PLM candidates and provides a profound understanding of the underlying relationship between different lysine modifications.

Computing Prediction and Functional Analysis of Prokaryotic Propionylation.

Identification and systematic analysis of candidates for protein propionylation are crucial steps for understanding its molecular mechanisms and biological functions. Although several proteome-scale methods have been performed to delineate potential propionylated proteins, the majority of lysine-propionylated substrates and their role in pathological physiology still remain largely unknown. By gathering various databases and literatures, experimental prokaryotic propionylation data were collated to be trained in a support vector machine with various features via a three-step feature selection method. A novel online tool for seeking potential lysine-propionylated sites (PropSeek) ( http://bioinfo.ncu.edu.cn/PropSeek.aspx ) was built. Independent test results of leave-one-out and n-fold cross-validation were similar to each other, showing that PropSeek is a stable and robust predictor with satisfying performance. Meanwhile, analyses of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathways, and protein-protein interactions implied a potential role of prokaryotic propionylation in protein synthesis and metabolism.

Computational prediction of species-specific malonylation sites via enhanced characteristic strategy.

MOTIVATION: Protein malonylation is a novel post-translational modification (PTM) which orchestrates a variety of biological processes. Annotation of malonylation in proteomics is the first-crucial step to decipher its physiological roles which are implicated in the pathological processes. Comparing with the expensive and laborious experimental research, computational prediction can provide an accurate and effective approach to the identification of many types of PTMs sites. However, there is still no online predictor for lysine malonylation. RESULTS: By searching from literature and database, a well-prepared up-to-data benchmark datasets were collected in multiple organisms. Data analyses demonstrated that different organisms were preferentially involved in different biological processes and pathways. Meanwhile, unique sequence preferences were observed for each organism. Thus, a novel malonylation site online prediction tool, called MaloPred, which can predict malonylation for three species, was developed by integrating various informative features and via an enhanced feature strategy. On the independent test datasets, AUC (area under the receiver operating characteristic curves) scores are obtained as 0.755, 0.827 and 0.871 for Escherichia coli ( E.coli ), Mus musculus ( M.musculus ) and Homo sapiens ( H.sapiens ), respectively. The satisfying results suggest that MaloPred can provide more instructive guidance for further experimental investigation of protein malonylation. AVAILABILITY AND IMPLEMENTATION: http://bioinfo.ncu.edu.cn/MaloPred.aspx . CONTACT: jdqiu@ncu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. AVAILABILITY AND IMPLEMENTATION: The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

SuccFind: a novel succinylation sites online prediction tool via enhanced characteristic strategy.

UNLABELLED: Lysine succinylation orchestrates a variety of biological processes. Annotation of succinylation in proteomes is the first-crucial step to decipher physiological roles of succinylation implicated in the pathological processes. In this work, we developed a novel succinylation site online prediction tool, called SuccFind, which is constructed to predict the lysine succinylation sites based on two major categories of characteristics: sequence-derived features and evolutionary-derived information of sequence and via an enhanced feature strategy for further optimizations. The assessment results obtained from cross-validation suggest that SuccFind can provide more instructive guidance for further experimental investigation of protein succinylation. AVAILABILITY AND IMPLEMENTATION: A user-friendly server is freely available on the web at: http://bioinfo.ncu.edu.cn/SuccFind.aspx. CONTACT: jdqiu@ncu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Progress and challenges in predicting protein methylation sites.

Protein methylation catalyzed by methyltransferases carries many important biological functions. Methylation and their regulatory enzymes are involved in a variety of human disease states, raising the possibility that abnormally methylated proteins can be disease markers and methyltransferases are potential therapeutic targets. Identification of methylation sites is a prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles that have been implicated in the pathological processes. Due to various limitations of experimental methods, in silico approaches for identifying novel methylation sites have become increasingly popular. In this review, we summarize the progress in the prediction of protein methylation sites from the dataset, feature representation, prediction algorithm and online resources in the past ten years. We also discuss the challenges that are faced while developing novel predictors in the future. The development and application of methylation site prediction is a promising field of systematic biology, provided that protein methyltransferases, species and functional information will be taken into account.

Polymerlike statistical characterization of two-dimensional granular chains.

Statistical behaviors of packing collections of granular chains in a two-dimensional container have been investigated experimentally. On compaction from their own gravity, the longer chains pack into a structure with lower packing density due to the prevalence of backbone loops. The packing of chains can be considered as the jamming of the granular system. The structure factor of packing chains shows scaling behavior g(q)∼q(-2) in good agreement with dense polymer solutions. In addition, we compute various probability distributions of distances and estimate three crucial contact exponents, finding that the scaling behavior from granular chains is in accord with the theoretical expectation of polymers. Finally, an orientational anticorrelation of granular chains is observed by bond-bond correlation function, which agrees with the results in the two-dimensional model of compact polymers.