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The prediction of the biological function of non-coding ribonucleic acid (ncRNA) is an important step towards understanding the regulatory mechanisms underlying many diseases. Since non-coding RNAs are present in great abundance in human cells and are functionally diverse, developing functional prediction tools is necessary. With recent advances in non-coding RNA biology and the availability of complete genome sequences for a large number of species, we now have a window of opportunity for studying non-coding RNA biology. However, the computational methods used to predict the non-coding RNA functions are mostly either scarcely accurate, when based on sequence information alone, or prohibitively expensive in terms of computational burden when a secondary structure prediction is needed. We propose a novel computational method to predict the biological function of non-coding RNA genes that is based on a collection of deep network architectures utilizing solely ncRNA sequence information and which does not rely on or require expensive secondary ncRNA structure information. The approach presented in this work exhibits comparable or superior accuracy to methods that employ both sequence and structural features, at a much lower computational cost.

The submerged plant Vallisneria natans can provide an attachment matrix and habitat for diverse microorganisms and plays an important role in maintaining the structure and function of the shallow lake ecosystem. However, little is known about how V. natans-related microorganism components, especially bacteria, adapt to specific plant compartments. In this study, we investigated the assembly and network of bacterial communities living in different plant compartments (sediment, rhizosphere, rhizoplane, root endosphere, and leaf endosphere) associated with V. natans by 16S rRNA gene sequencing. The results showed that the diversity and network complexity of the bacterial community in the sediment was significantly higher than that in other plant compartments. The bacterial community composition showed that the dominant phyla were Proteobacteria, Bacteroidetes, Actinobacteria, Firmicutes, Desulfobacterota, and Chloroflexi, among which Proteobacteria were extremely abundant in all samples, and there were notable differences in bacterial community composition related to plant compartments. Different networks based on sediment and plant compartments showed distinct co-occurrence patterns and exhibited distinct topological features. Additionally, functional predictions from FAPROTAX indicate that the predominant biogeochemical cycle function of the V. natans-related bacterial community is to participate in the carbon and nitrogen cycle. These results strongly suggested how the microbial community adapted to different plant compartments and provided theoretical and technical data for isolating beneficial bacteria from macrophytes in the future.

Zhong Wang,1,* Kai Yuan,1,* Yan-Bin Ji,2,* Su-Xia Li,3 Le Shi,1 Zhe Wang,1 Xin-Yu Zhou,4 Yan-Ping Bao,3 Wen Xie,5 Ying Han,3 Jie Shi,3 Lin Lu,1,3,6 Wei Yan,1 Wen-Hao Chen1 1Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, People’s Republic of China; 2Department of Neurology, Qilu Hospital of Shandong University, Shandong University, Jinan, 250012, People’s Republic of China; 3National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191, People’s Republic of China; 4Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 5Mental Health Center of Anhui Province, Hefei, 230032, People’s Republic of China; 6Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wen-Hao Chen; Wei Yan Peking University Sixth Hospital/Peking University Institute of Mental Health, 51 Huayuanbei Road, Haidian District, Beijing, 100191, People’s Republic of ChinaTel +86-10-82801342; Tel +86-10-62723704Email chenwenhao@bjmu.edu.cn; weiyan@bjmu.edu.cnIntroduction: Accumulating evidence suggests that both sleep loss and gut dysbiosis can lead to metabolic disorders. However, less is known about the impact of total sleep deprivation (SD) and sleep recovery on the composition, function, and metabolic dynamics of the gut microbiota.Methods: Specific-pathogen free Sprague–Dawley rats were subjected to 48 h of SD with gentle handling and then allowed to recover for 1 week. Taxonomic profiles of fecal microbiota were obtained at baseline, 24 h of SD, 48 h of SD, and 1 week of recovery. We used 16S rRNA gene sequencing to analyze the gut microbial composition and function and further characterize microbiota-derived metabolites in rats.Results: The microbiota composition analysis revealed that gut microbial composition and metabolites did not change in the rats after 24 h of SD but were significantly altered after 48 h of SD. These changes were reversible after 1 week of sleep recovery. A functional analysis was performed based on Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations, indicating that 19 KEGG pathways were significantly altered in the gut microbiota in SD rats. These functional changes occurred within 24 h of SD, were more apparent after 48 h of SD, and did not fully recover after 1 week of sleep recovery.Conclusion: These results indicate that acute total SD leads to significant compositional and functional changes in the gut microbiota, and these changes are reversible.Keywords: gut microbiota, sleep deprivation, short-chain fatty acid, function prediction

Zhong Wang,1,* Kai Yuan,1,* Yan-Bin Ji,2,* Su-Xia Li,3 Le Shi,1 Zhe Wang,1 Xin-Yu Zhou,4 Yan-Ping Bao,3 Wen Xie,5 Ying Han,3 Jie Shi,3 Lin Lu,1,3,6 Wei Yan,1 Wen-Hao Chen1 1Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, 100191, People’s Republic of China; 2Department of Neurology, Qilu Hospital of Shandong University, Shandong University, Jinan, 250012, People’s Republic of China; 3National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191, People’s Republic of China; 4Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 5Mental Health Center of Anhui Province, Hefei, 230032, People’s Republic of China; 6Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wen-Hao Chen; Wei Yan Peking University Sixth Hospital/Peking University Institute of Mental Health, 51 Huayuanbei Road, Haidian District, Beijing, 100191, People’s Republic of ChinaTel +86-10-82801342; Tel +86-10-62723704Email chenwenhao@bjmu.edu.cn; weiyan@bjmu.edu.cnIntroduction: Accumulating evidence suggests that both sleep loss and gut dysbiosis can lead to metabolic disorders. However, less is known about the impact of total sleep deprivation (SD) and sleep recovery on the composition, function, and metabolic dynamics of the gut microbiota.Methods: Specific-pathogen free Sprague–Dawley rats were subjected to 48 h of SD with gentle handling and then allowed to recover for 1 week. Taxonomic profiles of fecal microbiota were obtained at baseline, 24 h of SD, 48 h of SD, and 1 week of recovery. We used 16S rRNA gene sequencing to analyze the gut microbial composition and function and further characterize microbiota-derived metabolites in rats.Results: The microbiota composition analysis revealed that gut microbial composition and metabolites did not change in the rats after 24 h of SD but were significantly altered after 48 h of SD. These changes were reversible after 1 week of sleep recovery. A functional analysis was performed based on Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations, indicating that 19 KEGG pathways were significantly altered in the gut microbiota in SD rats. These functional changes occurred within 24 h of SD, were more apparent after 48 h of SD, and did not fully recover after 1 week of sleep recovery.Conclusion: These results indicate that acute total SD leads to significant compositional and functional changes in the gut microbiota, and these changes are reversible.Keywords: gut microbiota, sleep deprivation, short-chain fatty acid, function prediction

Black-billed capercaillie (Tetrao parvirostris) was listed as a first-class state-protected animal because it was endangered in China (Category I). This study is the first to examine the diversity and composition of T. parvirostris gut microbiome in the wild. We collected fecal samples from five black-billed capercaillie flock roosting sites (each 20 km apart) in one day. Thirty fecal samples were sequenced with 16S rRNA gene amplicons on the Illumina HiSeq platform. This study is the first to analyze the fecal microbiome composition and diversity of black-billed capercaillie in the wild. At the phylum level, Camplyobacterota, Bacillota, Cyanobacteria, Actinomycetota, and Bacteroidota were the most abundant in the fecal microbiome of black-billed capercaillie. At the genus level, unidentified Chloroplast, Escherichia−Shigella, Faecalitalea, Bifidobacterium, and Halomonas were the dominant genera. Based on alpha and beta diversity analyses, we found no significant differences in the fecal microbiome between five flocks of black-billed capercaillie. Protein families: genetic information processing; protein families: signaling and cellular processes, carbohydrate metabolism; protein families: metabolism and energy metabolism are the main predicted functions of the black-billed capercaillie gut microbiome through the PICRUSt2 method. This study reveals the composition and structure of the fecal microbiome of the black-billed capercaillie under wild survival conditions, and this study provides scientific data for the comprehensive conservation of the black-billed capercaillie.

We explored the characteristics of soil bacterial communities and their ecological functions under different types of vegetation reclamation in open-pit mines on the Loess Plateau, which is the guiding significance for the selection of vegetation and the improvement of restoration effect in mining areas. The research object was to reclaim the soil of the aluminum mine waste dump in Xiaoyi County, Shanxi. The soil characteristics were measured under different types of vegetation reclamation. The soil bacterial community under different vegetation reclamation was measured using the 16S rRNA gene high-throughput sequencing technology. The ecological function was predicted using the PICRUSt method. The correlation between soil physical and chemical properties and bacterial community structure and function was analyzed. From the results, (1) the bacterial compositions of the reclaimed soil samples were 33 phyla, 90 classes, 121 orders, 207 families, 298 genera, and 140 species. The abundance and diversity of the soil microbial community showed the rule of yellow rose > lespedeza and sweet wormwood herb > alfalfa. (2) Proteobacteria were the dominant bacteria in alfalfa and sweet wormwood herb samples, accounting for 36.09–43.36%. Proteobacteria and actinobacteria were the dominant bacteria in the yellow rose and lespedeza samples accounted for 53.34–53.39%. α-Proteobacteria, actinobacteria, and β-proteobacteria were the dominant bacteria of the four vegetation types. The relative abundance of the α-proteobacteria and β-proteobacteria was positively correlated with soil organic carbon (SOC) and negatively correlated with soil total kalium (TK). Actinobacteria were positively correlated with available kalium (AK) and negatively correlated with SOC and total nitrogen (TN). (3) There was no difference in the primary functions of the soil bacterial community after the reclamation of different plants, and the main functions were metabolism, genetic information processing, and environmental information processing, with the function abundance accounting for 81.52%. (4) The abundance of functional genes in the metabolism of other amino acids, folding, sorting, and degradation and glycan biosynthesis and metabolism were relatively rich in the rhizosphere soil of yellow rose. The abundance of functional genes in signal molecules and interaction, transport, and catabolism in the rhizosphere soil of lespedeza was the highest. The abundance of functional genes in carbohydrate metabolism, translation, and energy metabolism in the rhizosphere soil of alfalfa was the highest. Therefore, there were significant differences in the structure and function of rhizosphere soil microbial communities among yellow rose, lespedeza, sweet wormwood herb, and alfalfa, and they were also affected by the soil properties. Hence, we concluded that the differences and diversity of soil microbial structure and function can help select plants for the sustainable development of soil remediation in mining areas.

The main characteristics of panic disorder (PD) include recurrent panic attacks and persistent worry, accompanied by other physical and cognitive symptoms. While recent studies have revealed that gut bacteria play an important role in anxiety and depression, little is known about the relationship between oral microbiota and PD. Therefore, the objective of this study was to explore a possible correlation between oral microbiota and PD. We conducted 16S rRNA sequencing to compare differences in the oral microbiota of patients with PD (n = 26) and healthy controls (n = 40). Patients with PD exhibited higher alpha diversity (abundance and evenness) in their oral microbiota than healthy controls, while analysis of beta diversity revealed that the two groups differed in microbial community composition. Moreover, the relative abundance of 61 genera differed between them. Overall, PD resulted in distinct oral microbial profiles that could be potential diagnostic markers and therapeutic targets.

Microorganisms play a pivotal role in nutrient cycling in aquatic ecosystems. Understanding bacterial diversity and its functional composition are crucial for aquatic ecology and protection. We investigated the bacterial community structure using 16S rRNA gene amplicons high-throughput sequencing in this study. Results showed that 105 amplicon sequence variants (ASVs) account for 43.8% of the total sequences shared by the Nandu River’s lower, middle, and upper reach and the Songtao Reservoir. The dominant bacterial phylum in the Nandu River and its connected Songtao Reservoir were Proteobacteria and Actinobacteriota, respectively. The highest Chao1 and Shannon index values were found in the lower reach of the Nandu River. Beta diversity analysis showed the divergence in bacterial communities in the Nandu River and Songtao Reservoir, but not in different reaches. Among the water properties, based on the Mantel test, dissolved oxygen, total nitrogen, and nitrite significantly affected the bacterial communities. The functional profiles predicted by Tax4Fun2 showed that metabolism was the most abundant function group. The relative abundance of genetic information processing was significantly higher in the Songtao Reservoir than in the Nandu River, while the relative abundance of human diseases was significantly lower in the Songtao Reservoir than in the Nandu River. The appearance of the xenobiotics biodegradation and metabolism function group requires us to pay more attention to possible water pollution, especially at the upper reach of the Nandu River.

Sphagnum is a fundamental ecosystem of engineers, including more than 300 species around the world. These species host diverse microbes, either endosymbiotic or ectosymbiotic, and are key to carbon sequestration in peatland ecosystems. However, the linkages between different types of Sphagnum and the diversity and ecological functions of Sphagnum-associated microbiomes are poorly known, and so are their joint responses to ecological functions. Here, we systematically investigated endophytes in Sphagnum palustre via next-generation sequencing (NGS) techniques in the Erxianyan peatland, central China. The total bacterial microbiome was classified into 38 phyla and 55 classes, 122 orders and 490 genera. The top 8 phyla of Proteobacteria (33.69%), Firmicutes (11.94%), Bacteroidetes (9.42%), Actinobacteria (6.53%), Planctomycetes (6.37%), Gemmatimonadetes (3.05%), Acidobacteria (5.59%) and Cyanobacteria (1.71%) occupied 78.31% of total OTUs. The core microbiome of S. palustre was mainly distributed mainly in 7 phyla, 9 classes, 15 orders, 22 families and 43 known genera. There were many differences in core microbiomes compared to those in the common higher plants. We further demonstrate that the abundant functional groups have a substantial potential for nitrogen fixation, carbon cycle, nitrate metabolism, sulfate respiration and chitinolysis. These results indicate that potential ecological function of Sphagnum palustre in peatlands is partially rooted in its microbiomes, and that incorporating into functional groups of Sphagnum-associated microbiomes can promote mechanistic understanding of Sphagnum ecology in subalpine peatlands.

The duodenum is an important digestive organ for poultry and houses a variety of microbes that help chickens to enhance nutrient absorption and improve production. To evaluate the characteristic of gut microbiome, duodenum content samples from 42-week-old native Taihang chickens with high (H) and low (L) egg-yielding were collected for 16S rRNA amplicon sequencing analysis. Consequently, 1,361,341 sequences were clustered into 2055 OTUs, with percentages of affiliation of 96.50 and 57.30% at phylum and genus levels. Firmicutes, Proteobacteria, Cyanobacteria and Bacteroidetes were the dominant phylum, with a lower ratio of Firmicutes/Bacteroidetes in H group than in L group (p < 0.05). At genus level, overrepresentation of Bacteroides, Faecalibacterim, and Enterococcus and underrepresentation of Romboutsia were found in H group. No significant difference in overall diversity of microbiota was observed between two groups. LEFSe analysis revealed Enterococcus was significantly enriched in H group. Importantly, Enterococcus and Lactobacillus were negatively correlated. Functional prediction analysis showed the proportion of microbiota involved in the metabolism process was the highest and enriched in H group. Differences in microbiota composition between the two groups, which may be related to intestinal function difference, also provide promising biomarkers for improving laying hen production.

Background: Transmembrane protein 95 ( TMEM95 ) plays a role in male fertility. Previous studies showed that genes with a significant impact on reproductive traits can also affect the growth traits of livestock. Thus, we speculated that the genetic variation of TMEM95 gene may have effects on growth traits of cattle. Results: Two SNPs were genotyped. The rs136174626 and rs41904693 were in the intron 4 and 3′-untranslated region, respectively. The linkage disequilibrium analysis illustrated that these two loci were not linked. The rs136174626 was associated with six growth traits of Nanyang cattle, four traits of Luxi cattle, and three traits of Ji’an cattle. For rs41904693 locus, the GG individuals had greater body height and abdominal girth in Ji’ an cattle than TT and TG individuals. In Jinnan cattle, GG and TT individuals had greater body height, height at hip cross, body length, and heart girth than TG individuals. The potential splice site prediction results suggest that the rs136174626 may influence the splicing efficiency of TMEM95, and the miRNA binding site prediction results showed that the rs41904693 may influence the expression of TMEM95 by affecting the binding efficiency of Bta-miR-1584 and TMEM95 3′-UTR. Conclusions: The findings of the study suggested that the two SNPs in TMEM95 could be a reliable basis for molecular breeding in cattle. Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Predicting enzymes function is an important and difficult problem, particularly when enzymes may have the multiplex character, i.e., some enzymes simultaneously have two or three function classes. Most of the existing enzyme function predictor can only be used to deal with the mono-functional enzymes. Actually, multi-functional enzymes should not be ignored because they usually possess diverse biological functions worthy of our special notice. By introducing the “improved Hybrid Multi-label Classifier” and “neighbor score”, a new predictor, called MF-EFP, has been developed that can be used to deal with the systems containing both mono-functional and multi-functional enzymes. As demonstration, the jackknife cross-validation was performed with MF-EFP on a benchmark dataset of enzymes classified into the following 7 functional classes: (1) EC 1 Oxidoreductase, (2) EC 2 Transferase, (3) EC 3 Hydrolase, (4) EC 4 Lyase, (5) EC 5 Isomerase, (6) EC 6 Ligase, (7) EC7 Translocases, where none of enzymes included has ≥90% pairwise sequence identity to any other in a same subset. The subset accuracy and average precision thus obtained by MF-EFP was 85.62% and 94.16% respectively. Extensive experiments also show that MF-EFP can outperform the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, MF-EFP is freely accessible to the public at the web-site http://www.jci-bioinfo.cn/MF-EFP.

Regular oxygen depletion is occurring every summer in the depression area of the Bohai Sea. The community structure and potential functions of microbes in expanding marine hypoxic area are of great importance due to their roles in biogeochemical cycling. In this study, the diversity and distribution of bacteria based on 16S rRNA gene in sediment along an inshore-offshore transect across the oxygen-depletion area in the Bohai Sea was investigated in June, July and August of 2018 by employing high-throughput sequencing. Results revealed that the bacteria community was dominated by Proteobacteria (42.67%), Actinobacteria (14.13%), Chloroflexi (13.02%), Acidobacteria (8.01%), and Bacteroidetes (6.30%). During oxygen depletion, the bacteria community from inshore site A3 subjected to dramatic variation from June to August, but the composition tended to be stable in sites from the depression area along the transect. Distinct switch of bacteria from aerobic to anaerobic group was observed when the DO concentration Anaerolineaceae in August sample. Further, KEGG prediction by PICRUSt confirmed the variations by showing significant difference in functional pathways, especially the nitrogen metabolisms, before and after DO depletion (p < 0.05). These variations could be influenced by depth, NO2– concentration and DO availability based on RDA analysis. The details in diversity and composition of bacteria under continually observation provide insights into both instant and long-term response of bacteria community to oxygen depletion, and the distinct functional switch under this process expands our knowledge on the metabolic character of bacteria in worldwide hypoxia areas.

Background Bacillus amyloliquefaciens (BA) and Bacillus subtilis (BS) are usually used as feed supplements directly or bacterial inoculants in biological feeds for animals. However, few research have reported the effects of BA and BS on fermentation characteristics and bacterial community successions of whole-plant corn silage during ensiling. If the BA and BS inoculants have positive effects on silages, then they could not only improve fermentation characteristics, but also deliver BA or BS viable cells to ruminants, which would play its probiotic effect. Therefore, the objectives of this study were to investigate the effects of BA and BS on the fermentation, chemical characteristics, bacterial community and their metabolic pathway of whole-plant corn silage. Results Freshly chopped whole-plant corn was inoculated without or with BA and BS, respectively, and ensiled for 1, 3, 7, 14 and 60 d. Results showed that BA and BS inoculations increased lactic acid concentrations of whole-plant corn silages compared with control, and BA inoculation decreased acetic acid concentrations, whereas BS inoculation decreased fiber contents and increased crude protein (CP) content. Higher water-soluble carbohydrate contents and lower starch contents were observed in BA- and BS-inoculated silages compared with that in control. The decreased CP content and increased non-protein nitrogen content were observed in BA-inoculated silage, which was consistent with the higher amino acid metabolism abundances observed in BA-inoculated silage. In addition, it was noteworthy that BA and BS inoculations increased the metabolism of cofactors and vitamins, and decreased the relative abundances of drug resistance: antimicrobial pathways. We also found that the bacterial metabolism pathways were clearly separated into three clusters based on the ensiling times of whole-plant corn silage in the present study. There were no significant differences in bacterial community compositions among the three groups during ensiling. However, BA and BS inoculations decreased the relative abundances of undesirable bacteria such as Acetobacter and Acinetobacter. Conclusion Our findings suggested that the BS strain was more suitable as silage inoculants than the BA strain in whole-plant corn silage in this study.

In this study, electrogenic microbial communities originating from a single source were multiplied using our custom-made, 96-well-plate-based microbial fuel cell (MFC) array. Developed communities operated under different pH conditions and produced currents up to 19.4 A/m3 (0.6 A/m2) within 2 days of inoculation. Microscopic observations [combined scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS)] revealed that some species present in the anodic biofilm adsorbed copper on their surface because of the bioleaching of the printed circuit board (PCB), yielding Cu2 + ions up to 600 mg/L. Beta- diversity indicates taxonomic divergence among all communities, but functional clustering is based on reactor pH. Annotated metagenomes showed the high presence of multicopper oxidases and Cu-resistance genes, as well as genes encoding aliphatic and aromatic hydrocarbon-degrading enzymes, corresponding to PCB bioleaching. Metagenome analysis revealed a high abundance of Dietzia spp., previously characterized in MFCs, which did not grow at pH 4. Binning metagenomes allowed us to identify novel species, one belonging to Actinotalea, not yet associated with electrogenicity and enriched only in the pH 7 anode. Furthermore, we identified 854 unique protein-coding genes in Actinotalea that lacked sequence homology with other metagenomes. The function of some genes was predicted with high accuracy through deep functional residue identification (DeepFRI), with several of these genes potentially related to electrogenic capacity. Our results demonstrate the feasibility of using MFC arrays for the enrichment of functional electrogenic microbial consortia and data mining for the comparative analysis of either consortia or their members.

Background The field of viromics has greatly benefited from recent developments in metagenomics, with significant efforts focusing on viral discovery. However, functional annotation of the increasing number of viral genomes is lagging behind. This is highlighted by the degree of annotation of the protein clusters in the prokaryotic Virus Orthologous Groups (pVOGs) database, with 83% of its current 9518 pVOGs having an unknown function. Results In this study we describe a machine learning approach to explore potential functional associations between pVOGs. We measure seven genomic features and use them as input to a Random Forest classifier to predict protein–protein interactions between pairs of pVOGs. After systematic evaluation of the model’s performance on 10 different datasets, we obtained a predictor with a mean accuracy of 0.77 and Area Under Receiving Operation Characteristic (AUROC) score of 0.83. Its application to a set of 2,133,027 pVOG-pVOG interactions allowed us to predict 267,265 putative interactions with a reported probability greater than 0.65. At an expected false discovery rate of 0.27, we placed 95.6% of the previously unannotated pVOGs in a functional context, by predicting their interaction with a pVOG that is functionally annotated. Conclusions We believe that this proof-of-concept methodology, wrapped in a reproducible and automated workflow, can represent a significant step towards obtaining a more complete picture of bacteriophage biology.

Plant-specialized secondary metabolites have ecological functions in mediating interactions between plants and their entophytes. In this study, high-throughput gene sequencing was used to analyze the composition and abundance of bacteria from Ginkgo leaves at five different sampling times. The results indicated that the bacterial community structure varied during leaf developmental stage. Bacterial diversity was observed to be the highest at T2 stage and the lowest at T1 stage. Proteobacteria, Firmicutes, Actinobacteria, Chloroflexi, Cyanobacteria, and Bacteroidetes were found as the dominant phyla. The major genera also showed consistency across sampling times, but there was a significant variation in their abundance, such as Bacillus, Lysinibacillus, and Staphylococcus. Significant correlations were observed between endophytic bacteria and flavonoids. Especially, Staphylococcus showed a significant positive correlation with quercetin, and changes in the abundance of Staphylococcus also showed a strong correlation with flavonoid content. In order to determine the effect of flavonoids on endophytic bacteria of Ginkgo leaves, an extracorporeal culture of related strains (a strain of Staphylococcus and a strain of Deinococcus) was performed, and it was found that the effect of flavonoids on them remained consistent. The predicted result of Tax4Fun2 revealed that flavonoids might lead to a lower abundance of endophytic microorganisms, which further proved the correlation between bacterial communities and flavonoids. This study provided the first insight into the bacterial community composition during the development of Ginkgo leaves and the correlation between the endophytic bacteria and flavonoids.

Peptide toxins generally have extreme pharmacological activities and provide a rich source for the discovery of drug leads. However, determining the optimal activity of a new peptide can be a long and expensive process. In this study, peptide toxins were retrieved from Uniprot; three positive-unlabeled (PU) learning schemes, adaptive basis classifier, two-step method, and PU bagging were adopted to develop models for predicting the biological function of new peptide toxins. All three schemes were embedded with 14 machine learning classifiers. The prediction results of the adaptive base classifier and the two-step method were highly consistent. The models with top comprehensive performances were further optimized by feature selection and hyperparameter tuning, and the models were validated by making predictions for 61 three-finger toxins or the external HemoPI dataset. Biological functions that can be identified by these models include cardiotoxicity, vasoactivity, lipid binding, hemolysis, neurotoxicity, postsynaptic neurotoxicity, hypotension, and cytolysis, with relatively weak predictions for hemostasis and presynaptic neurotoxicity. These models are discovery-prediction tools for active peptide toxins and are expected to accelerate the development of peptide toxins as drugs.

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia–Shigella, and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community.

Members of the DUF34 (domain of unknown function 34) family, also known as the NIF3 protein superfamily, are ubiquitous across superkingdoms. Proteins of this family have been widely annotated as “GTP cyclohydrolase I type 2” through electronic propagation based on one study. Here, the annotation status of this protein family was examined through comprehensive literature review and integrative bioinformatic analyses that revealed varied pleiotropic associations and phenotypes. This analysis combined with functional complementation studies strongly challenges the current annotation and suggests that DUF34 family members may serve as metal ion insertases, chaperones, or metallocofactor maturases. This general molecular function could explain how DUF34 subgroups participate in highly diversified pathways such as cell differentiation, metal ion homeostasis, pathogen virulence, redox and universal stress responses.

Rare variants in the cardiac potassium channel KV7.1 (KCNQ1) and sodium channel NaV1.5 (SCN5A) are implicated in genetic disorders of heart rhythm, including congenital long QT and Brugada syndromes (LQTS, BrS), but also occur in reference populations. We previously reported two sets of NaV1.5 (n = 356) and KV7.1 (n = 144) variants with in vitro characterized channel currents gathered from the literature. Here we investigated the ability to predict commonly reported NaV1.5 and KV7.1 variant functional perturbations by leveraging diverse features including variant classifiers PROVEAN, PolyPhen-2, and SIFT; evolutionary rate and BLAST position specific scoring matrices (PSSM); and structure-based features including “functional densities” which is a measure of the density of pathogenic variants near the residue of interest. Structure-based functional densities were the most significant features for predicting NaV1.5 peak current (adj. R2 = 0.27) and KV7.1 + KCNE1 half-maximal voltage of activation (adj. R2 = 0.29). Additionally, use of structure-based functional density values improves loss-of-function classification of SCN5A variants with an ROC-AUC of 0.78 compared with other predictive classifiers (AUC = 0.69; two-sided DeLong test p = .01). These results suggest structural data can inform predictions of the effect of uncharacterized SCN5A and KCNQ1 variants to provide a deeper understanding of their burden on carriers., Graphical abstract Unlabelled Image

Microbial communities that inhabit aging tobacco leaves play a key role in improving products quality. A better understanding of microbial communities on the aging of tobacco leaves could provide an important microbial repository for the industrial applications. Here, we examined the structural and compositional changes of microbial communities throughout the aging process of by tobacco leaves 16 S and ITS rRNA amplicon sequencing techniques and identified the potential metabolic pathways of bacteria and fungi using Functional Annotation of Prokaryotic Taxa (FAPROTAX) and Fungi Functional Guild (FUNGuild), respectively. The results showed that the diversity and structure of the microbial communities keep changing along with the aging process went on. The richness and diversity of bacterial community decreased, while the richness of fungal community was in an inverse trend. At the phylum level, the bacterial community was dominated by Proteobacteria, Actinobacteria, and Firmicutes, while Ascomycota and Basidiomycota were the dominant species in the fungal community. In the bacterial community, metabolic functions related to the carbon and nitrogen cycles which response to the degradation of harmful components, and the metabolism of aromatic hydrocarbons showed extremely dynamic at different aging periods. The change of the main nutritional mode of the fungal community also led to an increase in the abundance of saprophytic fungi. These results provide information on the succession of microbial community structure and function in the whole process of tobacco aging and suggest that the aging process of tobacco leaves can be a natural microbial collection for target microorganism and their metabolites. It also enables the further investigation of coordination mechanisms between beneficial microbial regulation and pathogenicity during aging process.

Polyaluminium chloride (PAC) has been widely used as a chemical coagulant in water treatment. However, little is known about the impact of PAC performance on the microbial community in sediments. In this study, the archaeal, bacterial, and fungal communities in urban river sediments with and without PAC treatment were investigated. Prokaryotic diversity decreased at the PAC addition site (A2) and increased along with the river flow (from A3 to A4), while eukaryotic diversity was the opposite. The abundance of core microbiota showed a similar trend. For example, the dominant Proteobacteria presented the highest relative abundance in A1 (26.8%) and the lowest in A2 (15.3%), followed by A3 (17.5%) and A4 (23.0%). In contrast, Rozellomycota was more dominant in A2 (56.6%) and A3 (58.1%) than in A1 (6.2%) and A4 (16.3%). Salinity, total dissolved solids, and metal contents were identified as the key physicochemical factors affecting the assembly of core microorganisms. The predicted functions of archaea and fungi were mainly divided into methane cycling and saprotrophic nutrition, respectively, while bacterial function was more diversified. The above findings are helpful to enhance our understanding of microorganism response to PAC and have significance for water treatment within the framework of microecology.

Antibiotics are often used to treat systemic diseases not associated with the oral cavity. This application of antibiotics may affect the healthy oral microbiota community, as it destroys the balance between specific bacterial populations throughout the ecosystem and may lead to dysbacteriosis. We hypothesized that the effects on antibiotics on oral microbiota regulation and function would affect antibody content in saliva, depending on the antibiotic type. To address this, a total of 24 Sprague Dawley rats (divided into 4 cages, 6 per pen) were administered amoxicillin (AMX), spiramycin (SP), metronidazole (MTZ), or water (control) daily for 14 days (gavage). After treatment was completed, high-throughput sequencing of 16S rRNA genes was used to determine changes in the composition, metabolic function, and diversity of oral microbiota in the rats. Enzyme-linked immunosorbent assay was used to detect antibodies in saliva, including SIgA, IgG, and IgM. Results showed that AMX, MTZ, and SP significantly affected oral microbiota composition, diversity, and metabolic function in rats. AMX induced substantial changes in the rat salivary antibody concentrations. At the genus level, the relative abundance of Rothia and Haemophilus was higher in the AMX group than in the other groups. In conclusion, antibiotics-induced changes in oral microbiota populations may be associated with changes in salivary antibody concentrations. However, the specific interaction mechanisms remain unknown, and it is still unclear whether significant changes in the oral microbiota cause changes in salivary antibody concentrations or vice versa.

Simple Summary Gut microbiota, such as Proteobacteria, Firmicutes, and Bacteroidetes, show chitin-degrading and/or antibiotic biosynthesis ability. To evaluate whether ecological factors (diet) and host conditions (body size, body mass, and body condition) affect the gut microbiota diversity, we analysed the diet composition and host condition in Fejervarya limnocharis among different seasons and/or sexes. The dietary difference was not significant among seasons and between males and females, but host condition seasonally changed. Using bioinformatics analysis, we observed that food variations and body mass were significantly correlated with gut microbial composition. Our findings suggest that gut microbiomes of F. limnocharis vary seasonally in response to diet variations when facing environmental changes. Abstract Organisms adapt to environmental fluctuations by varying their morphology and structural, physiological, and biochemical characteristics. Gut microbiome, varying rapidly in response to environmental shifts, has been proposed as a strategy for adapting to the fluctuating environment (e.g., new dietary niches). Here, we explored the adaptive mechanism of frog intestinal microbes in response to environmental changes. We collected 170 Fejervarya limnocharis during different seasons (spring, summer, autumn, and pre-hibernation) to study the compositional and functional divergence of gut microbiota and analysed the effects of seasonal feeding habits and body condition on intestinal microorganisms using 16S rRNA high-throughput sequencing, Tax4Fun function prediction analysis, and bioinformatics analysis. The results showed no significant dietary difference in various seasons and between males and females. However, a significantly positive correlation was detected between dietary diversity and food niche width. Host condition (body size, body mass, and body condition) also revealed seasonal changes. The frogs were colonised by 71 bacterial phyla and dominated by Proteobacteria, Firmicutes, and Bacteroidetes. Stenotrophomonas was the most abundant genus in the Proteobacteria. The composition, diversity, and function of intestinal microorganisms in different seasons were significantly different. Significant differences were observed in composition and function but not in the microbial diversity between sexes. Furthermore, seasonal foods and body mass were significantly correlated with gut microbial composition. Our results suggest that gut microbiomes of F. limnocharis vary seasonally in response to diet under fluctuating environments.

It was recently shown that the gut microbiota of both depression patients and depression model animals is significantly altered, suggesting that gut microbes are closely related to depression. Here, we investigated the effects of Sophora alopecuroides L.-derived alkaloids on the gut microbiota of mice with depression-like behaviors. We first established a mouse model of depression via chronic unpredictable mild stress (CUMS) and detected changes in depression-like behaviors and depression-related indicators. Simultaneously, 16S rRNA sequencing was performed to investigate gut microbiota changes. Sophora alopecuroides L.-derived alkaloids improved depression-like behaviors and depression-related indicators in mice. The alkaloids decreased the gut microbiota diversity of CUMS mice and depleted intestinal differentially abundant “harmful” microbiota genera. Spearman analysis showed that there is a certain correlation between the differential microbiota (Lactobacillus, Helicobacter, Oscillospira, Odoribacter, Mucispirillum, Ruminococcus), depression-like behaviors, and depression-related indicators. Combined with the predictive analysis of gut microbiota function, these results indicate that alkaloids improve depression in mice through modulating gut microbiota.

Cigar tobacco leaves (CTLs) contain abundant bacteria and fungi that are vital to leaf quality during fermentation. In this study, artificial fermentation was used for the fermentation of CTLs since it was more controllable and efficient than natural aging. The bacterial and fungal community structure and composition in unfermented and fermented CTLs were determined to understand the effects of microbes on the characteristics of CTLs during artificial fermentation. The relationship between the chemical contents and alterations in the microbial composition was evaluated, and the functions of bacteria and fungi in fermented CTLs were predicted to determine the possible metabolic pathways. After artificial fermentation, the bacterial and fungal community structure significantly changed in CTLs. The total nitrate and nicotine contents were most readily affected by the bacterial and fungal communities, respectively. FAPROTAX software predictions of the bacterial community revealed increases in functions related to compound transformation after fermentation. FUNGuild predictions of the fungal community revealed an increase in the content of saprotrophic fungi after fermentation. These data provide information regarding the artificial fermentation mechanism of CTLs and will inform safety and quality improvements., Tobacco‐associated bacteria and fungi affect the aroma and quality of cigars. The microbial community structure significantly changed in cigar tobacco leaves after artificial fermentation. The total nitrate and nicotine contents were altered by the bacterial and fungal communities, respectively.

Huntington’s disease is a rare neurodegenerative and autosomal dominant disorder. HD is caused by a mutation in the gene coding for huntingtin (Htt). The result is the production of a mutant Htt with an abnormally long polyglutamine repeat that leads to pathological Htt aggregates. Although the structure of human Htt has been determined, albeit at low resolution, its functions and how they are performed are largely unknown. Moreover, there is little information on the structure and function of Htt in other organisms. The comparison of Htt homologs can help to understand if there is a functional conservation of domains in the evolution of Htt in eukaryotes. In this work, through a computational approach, Htt homologs from lower eukaryotes have been analysed, identifying ordered domains and modelling their structure. Based on the structural models, a putative function for most of the domains has been predicted. A putative C. elegans Htt-like protein has also been analysed following the same approach. The results obtained support the notion that this protein is a orthologue of human Htt.

It was recently shown that the gut microbiota of both depression patients and depression model animals is significantly altered, suggesting that gut microbes are closely related to depression. Here, we investigated the effects of Sophora alopecuroides L.-derived alkaloids on the gut microbiota of mice with depression-like behaviors. We first established a mouse model of depression via chronic unpredictable mild stress (CUMS) and detected changes in depression-like behaviors and depression-related indicators. Simultaneously, 16S rRNA sequencing was performed to investigate gut microbiota changes. Sophora alopecuroides L.-derived alkaloids improved depression-like behaviors and depression-related indicators in mice. The alkaloids decreased the gut microbiota diversity of CUMS mice and depleted intestinal differentially abundant “harmful” microbiota genera. Spearman analysis showed that there is a certain correlation between the differential microbiota (Lactobacillus, Helicobacter, Oscillospira, Odoribacter, Mucispirillum, Ruminococcus), depression-like behaviors, and depression-related indicators. Combined with the predictive analysis of gut microbiota function, these results indicate that alkaloids improve depression in mice through modulating gut microbiota.

Protein catabolism by intestinal bacteria is infamous for releasing many harmful compounds, negatively affecting the health status, both locally and systemically. In a previous study, we enriched in protein degraders the fecal microbiota of five subjects, utilizing a medium containing protein and peptides as sole fermentable substrates and we monitored their evolution by 16S rRNA gene profiling. In the present study, we fused the microbiome data and the data obtained by the analysis of the volatile organic compounds (VOCs) in the headspace of the cultures. Then, we utilized ANOVA simultaneous component analysis (ASCA) to establish a relationship between metabolites and bacteria. In particular, ASCA allowed to separately assess the effect of subject, time, inoculum concentration, and their binary interactions on both microbiome and volatilome data. All the ASCA submodels pointed out a consistent association between indole and Escherichia–Shigella, and the relationship of butyric, 3-methyl butanoic, and benzenepropanoic acids with some bacterial taxa that were major determinants of cultures at 6 h, such as Lachnoclostridiaceae (Lachnoclostridium), Clostridiaceae (Clostridium sensu stricto), and Sutterellaceae (Sutterella and Parasutterella). The metagenome reconstruction with PICRUSt2 and its functional annotation indicated that enrichment in a protein-based medium affected the richness and diversity of functional profiles, in the face of a decrease of richness and evenness of the microbial community. Linear discriminant analysis (LDA) effect size indicated a positive differential abundance (p < 0.05) for the modules of amino acid catabolism that may be at the basis of the changes of VOC profile. In particular, predicted genes encoding functions belonging to the superpathways of ornithine, arginine, and putrescine transformation to GABA and eventually to succinyl-CoA, of methionine degradation, and various routes of breakdown of aromatic compounds yielding succinyl-CoA or acetyl-CoA became significantly more abundant in the metagenome of the bacterial community.

SummaryUnraveling gene functions is pivotal to understand the signaling cascades controlling plant development and stress responses. Given that experimental profiling is costly and labor intensive, the need for high-confidence computational annotations is evident. In contrast to detailed gene-specific functional information, transcriptomics data is widely available in both model and crop species. Here, we developed a novel automated function prediction (AFP) algorithm, leveraging complementary information present in multiple expression datasets through the analysis of study-specific gene co-expression networks. Benchmarking the prediction performance on recently characterized Arabidopsis thaliana genes, we showed that our method outperforms state-of-the-art expression-based approaches. Next, we predicted biological process annotations for known (n=15,790) and unknown (n=11,865) genes in A. thaliana and validated our predictions using experimental protein-DNA and protein-protein interaction data (covering >220 thousand interactions in total), obtaining a set of high-confidence functional annotations. 5,054 (42.6%) unknown genes were assigned at least one validated annotation, and 3,408 (53.0%) genes with only computational annotations gained at least one novel validated function. These omics-supported functional annotations shed light on a variety of developmental processes and molecular responses, such as flower and root development, defense responses to fungi and bacteria, and phytohormone signaling, and help alleviate the knowledge gap of biological process annotations in Arabidopsis. An in-depth analysis of two context-specific networks, modeling seed development and response to water deprivation, shows how previously uncharacterized genes function within the respective networks. Moreover, our AFP approach can be applied in future studies to facilitate gene discovery for crop improvement.Significance statementFor the majority of plant genes, it is unknown in which processes they are involved. Using a multi-omics approach, leveraging transcriptome, protein-DNA and protein-protein interaction data, we functionally annotated 42.6% of unknown Arabidopsis thaliana genes, providing insight into a variety of developmental processes and molecular responses, as well as a resource of annotations which can be explored by the community to facilitate future research.

Background Protein function prediction remains a key challenge. Domain composition affects protein function. Here we present DomFun, a Ruby gem that uses associations between protein domains and functions, calculated using multiple indices based on tripartite network analysis. These domain-function associations are combined at the protein level, to generate protein-function predictions. Results We analysed 16 tripartite networks connecting homologous superfamily and FunFam domains from CATH-Gene3D with functional annotations from the three Gene Ontology (GO) sub-ontologies, KEGG, and Reactome. We validated the results using the CAFA 3 benchmark platform for GO annotation, finding that out of the multiple association metrics and domain datasets tested, Simpson index for FunFam domain-function associations combined with Stouffer’s method leads to the best performance in almost all scenarios. We also found that using FunFams led to better performance than superfamilies, and better results were found for GO molecular function compared to GO biological process terms. DomFun performed as well as the highest-performing method in certain CAFA 3 evaluation procedures in terms of $$F_{max}$$ F max and $$S_{min}$$ S min We also implemented our own benchmark procedure, Pathway Prediction Performance (PPP), which can be used to validate function prediction for additional annotations sources, such as KEGG and Reactome. Using PPP, we found similar results to those found with CAFA 3 for GO, moreover we found good performance for the other annotation sources. As with CAFA 3, Simpson index with Stouffer’s method led to the top performance in almost all scenarios. Conclusions DomFun shows competitive performance with other methods evaluated in CAFA 3 when predicting proteins function with GO, although results vary depending on the evaluation procedure. Through our own benchmark procedure, PPP, we have shown it can also make accurate predictions for KEGG and Reactome. It performs best when using FunFams, combining Simpson index derived domain-function associations using Stouffer’s method. The tool has been implemented so that it can be easily adapted to incorporate other protein features, such as domain data from other sources, amino acid k-mers and motifs. The DomFun Ruby gem is available from https://rubygems.org/gems/DomFun. Code maintained at https://github.com/ElenaRojano/DomFun. Validation procedure scripts can be found at https://github.com/ElenaRojano/DomFun_project.

In this study, the effect of aerobic denitrification on nitrogen removal was investigated using two zero-discharge biofloc-based recirculating aquaculture systems with representative carbon-to-nitrogen (C/N) ratios of 15 (CN15) and 20 (CN20). Aquaculture wastewater, residual feed, and fish feces were treated in an aerated suspended growth reactor (SGR, dissolved oxygen > 5.0 mg L–1). Low toxic NH3 (

Background Small open reading frame (smORF) is open reading frame with a length of less than 100 codons. Microproteins, translated from smORFs, have been found to participate in a variety of biological processes such as muscle formation and contraction, cell proliferation, and immune activation. Although previous studies have collected and annotated a large abundance of smORFs, functions of the vast majority of smORFs are still unknown. It is thus increasingly important to develop computational methods to annotate the functions of these smORFs. Results In this study, we collected 617,462 unique smORFs from three studies. The expression of smORF RNAs was estimated by reannotated microarray probes. Using a speed-optimized correlation algorism, the functions of smORFs were predicted by their correlated genes with known functional annotations. After applying our method to 5 known microproteins from literatures, our method successfully predicted their functions. Further validation from the UniProt database showed that at least one function of 202 out of 270 microproteins was predicted. Conclusions We developed a method, smORFunction, to provide function predictions of smORFs/microproteins in at most 265 models generated from 173 datasets, including 48 tissues/cells, 82 diseases (and normal). The tool can be available at https://www.cuilab.cn/smorfunction.

Background Karst caves are considered as extreme environments with nutrition deficiency, darkness, and oxygen deprivation, and they are also the sources of biodiversity and metabolic pathways. Microorganisms are usually involved in the formation and maintenance of the cave system through various metabolic activities, and are indicators of changes environment influenced by human. Zhijin cave is a typical Karst cave and attracts tourists in China. However, the bacterial diversity and composition of the Karst cave are still unclear. The present study aims to reveal the bacterial diversity and composition in the cave and the potential impact of tourism activities, and better understand the roles and co-occurrence pattern of the bacterial community in the extreme cave habitats. Results The bacterial community consisted of the major Proteobacteria, Actinobacteria, and Firmicutes, with Proteobacteria being the predominant phylum in the rock, soil, and stalactite samples. Compositions and specialized bacterial phyla of the bacterial communities were different among different sample types. The highest diversity index was found in the rock samples with a Shannon index of 4.71. Overall, Zhijin cave has relatively lower diversity than that in natural caves. The prediction of function showed that various enzymes, including ribulose-bisphosphate carboxylase, 4-hydroxybutyryl-CoA dehydratase, nitrogenase NifH, and Nitrite reductase, involved in carbon and nitrogen cycles were detected in Zhijin cave. Additionally, the modularity indices of all co-occurrence network were greater than 0.40 and the species interactions were complex across different sample types. Co-occurring positive interactions in the bacteria groups in different phyla were also observed. Conclusion These results uncovered that the oligotrophic Zhijin cave maintains the bacterial communities with the diverse metabolic pathways, interdependent and cooperative co-existence patterns. Moreover, as a hotspot for tourism, the composition and diversity of bacterial community are influenced by tourism activities. These afford new insights for further exploring the adaptation of bacteria to extreme environments and the conservation of cave ecosystem.

Background There are growing evidences showing that gut microbiota should play an important role in host appetite and feeding behavior. However, what kind of microbe(s) and how they affect porcine appetite remain unknown. Results In this study, 280 commercial Duroc pigs were raised in a testing station with the circadian feeding behavior records for a continuous period of 30–100 kg. We first analyzed the influences of host gender and genetics in porcine average daily feed intake (ADFI), but no significant effect was observed. We found that the Prevotella-predominant enterotype had a higher ADFI than the Treponema enterotype-like group. Furthermore, 12 out of the 18 OTUs positively associated with the ADFI were annotated to Prevotella, and Prevotella was the hub bacteria in the co-abundance network. These results suggested that Prevotella might be a keystone bacterial taxon for increasing host feed intake. However, some bacteria producing short-chain fatty acids (SCFAs) and lactic acid (e.g. Ruminococcaceae and Lactobacillus) showed negative associations with the ADFI. Predicted function capacity analysis showed that the genes for amino acid biosynthesis had significantly different enrichment between pigs with high and low ADFI. Conclusions The present study provided important information on the profound effect of gut microbiota on porcine appetite and feeding behavior. This will profit us to regulate porcine appetite through modulating the gut microbiome in the pig industry. Electronic supplementary material The online version of this article (10.1186/s12866-018-1364-8) contains supplementary material, which is available to authorized users.

Cardiac hypertrophy (CH) is a common disease that originates from long‐term heart pressure overload and finally leads to heart failure. Recently, long non‐coding RNAs (lncRNAs) have attracted attention because they have broad and crucial functions in regulating complex biological processes. Some studies had found that lncRNAs play vital roles in complex cardiovascular diseases. However, the function and mechanism of lncRNAs in CH have not been elucidated. In our study, to investigate the potential roles of lncRNAs in CH, the Cardiac Hypertrophy‐associated LncRNAs‐Protein coding genes Network (CHLPN) was constructed by integrating gene microarray re‐annotation and subpathway enrichment analyses. After performing random walking with restart in CHLPN, we predicted 21 significant risk lncRNAs, of which 7 (Kis2, 1700110K17Rik, Gm17501, E330017L17Rik, C630043F03Rik, Gm9866 and Ube4bos1) formed a close module with their co‐expressed protein‐coding genes (PCGs). We found that the module might play crucial roles in the development of CH. In particular, 44 PCGs that were co‐expressed with six lncRNAs were enriched in CH‐related biological processes and pathways. We also found that some lncRNAs participated in the competitive endogenous RNA cross‐talk that might be involved in CH. These results indicate that the functional lncRNAs are related to post‐transcriptional regulation and could shed light on a new molecular diagnostic target of CH.

The R2R3-MYB genes make up one of the largest transcription factor families in plants, and play regulatory roles in various biological processes such as development, metabolism and defense response. Although genome-wide analyses of this gene family have been conducted in several species, R2R3-MYB genes have not been systematically analyzed in Medicago truncatula, a sequenced model legume plant. Here, we performed a comprehensive, genome-wide computational analysis of the structural characteristics, phylogeny, functions and expression patterns of M. truncatula R2R3-MYB genes. DNA binding domains are highly conserved among the 155 putative MtR2R3-MYB proteins that we identified. Chromosomal location analysis revealed that these genes were distributed across all eight chromosomes. Results showed that the expansion of the MtR2R3-MYB family was mainly attributable to segmental duplication and tandem duplication. A comprehensive classification was performed based on phylogenetic analysis of the R2R3-MYB gene families in M. truncatula, Arabidopsis thaliana and other plant species. Evolutionary relationships within clades were supported by clade-specific conserved motifs outside the MYB domain. Species-specific clades have been gained or lost during evolution, resulting in functional divergence. Also, tissue-specific expression patterns were investigated. The functions of stress response-related clades were further verified by the changes in transcript levels of representative R2R3-MYB genes upon treatment with abiotic and biotic stresses. This study is the first report on identification and characterization of R2R3-MYB gene family based on the genome of M. truncatula, and will facilitate functional analysis of this gene family in the future.

The structured vocabulary that describes gene function, the gene ontology (GO), serves as a powerful tool in biological research. One application of GO in computational biology calculates semantic similarity between two concepts to make inferences about the functional similarity of genes. A class of term similarity algorithms explicitly calculates the shared information (SI) between concepts then substitutes this calculation into traditional term similarity measures such as Resnik, Lin, and Jiang-Conrath. Alternative SI approaches, when combined with ontology choice and term similarity type, lead to many gene-to-gene similarity measures. No thorough investigation has been made into the behavior, complexity, and performance of semantic methods derived from distinct SI approaches. We apply bootstrapping to compare the generalized performance of 57 gene-to-gene semantic measures across six benchmarks. Considering the number of measures, we additionally evaluate whether these methods can be leveraged through ensemble machine learning to improve prediction performance. Results showed that the choice of ontology type most strongly influenced performance across all evaluations. Combining measures into an ensemble classifier reduces cross-validation error beyond any individual measure for protein interaction prediction. This improvement resulted from information gained through the combination of ontology types as ensemble methods within each GO type offered no improvement. These results demonstrate that multiple SI measures can be leveraged for machine learning tasks such as automated gene function prediction by incorporating methods from across the ontologies. To facilitate future research in this area, we developed the GO Graph Tool Kit (GGTK), an open source C++ library with Python interface (github.com/paulbible/ggtk). Keywords: Semantic similarity, Gene ontology, Function prediction, Machine learning, Protein–protein interaction, Gene expression

In a previous work, we in silico annotated protein sequences of Citrus genus plants as putative tryptophan decarboxylase (pTDC). Here, we investigated the structural properties of Citrus pTDCs by using the TDC sequence of Catharanthus roseus as an experimentally annotated reference to carry out comparative modeling and substrate docking analyses. The functional annotation as TDC was verified by combining 3D molecular modeling and docking simulations, evidencing the peculiarities and the structural similarities with C. roseus TDC. Docking with l-tryptophan as a ligand showed specificity of pTDC for this substrate. These combined results confirm our previous in silico annotation of the examined protein sequences of Citrus as TDC and provide support for TDC activity in this plant genus.

In a previous work, we in silico annotated protein sequences of Citrus genus plants as putative tryptophan decarboxylase (pTDC). Here, we investigated the structural properties of Citrus pTDCs by using the TDC sequence of Catharanthus roseus as an experimentally annotated reference to carry out comparative modeling and substrate docking analyses. The functional annotation as TDC was verified by combining 3D molecular modeling and docking simulations, evidencing the peculiarities and the structural similarities with C. roseus TDC. Docking with l-tryptophan as a ligand showed specificity of pTDC for this substrate. These combined results confirm our previous in silico annotation of the examined protein sequences of Citrus as TDC and provide support for TDC activity in this plant genus.

As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention.

The SQUAMOSA promoter binding protein (SBP)-box genes encode a kind of plant-specific transcription factors (TFs) and play important roles in the regulation of plant development. In this study, a genome-wide characterization of this family was conducted in maize (Zea mays). Thirty-one SBP-box genes were identified to be distributed in nine chromosomes and 16 of them were complementary to the mature ZmmiR156 sequences. All the Z. mays SBP (ZmSBP) genes were classified into two clusters with eight subgroups according to the phylogenetic analysis of proteins, which were consistent with the pattern of exon-intron structures. The phylogenetic tree of the ZmSBP, Oryza sativa SBP-like (OsSPL) and Arabidopsis thaliana SBP-like (AtSPL) genes were constructed and all the SBP-box genes were divided into eight groups, which was the same as the classification of ZmSBP genes. The comparision of the expression profiles of all SBP-box genes in these three species indicated that most orthologous genes had similar expression patterns. The results from this study provided a basic understanding of the ZmSBP genes and might facilitate future researches for elucidating the SBP-box genes function in maize.

Protein function prediction has been the most worked upon and the most challenging problem for computational biologists. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions. New un-annotated sequences are being added to the public protein databases (e.g. UniprotKB) at an enormous pace [1]. Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s). Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins [2]. For proteins with well characterised close relatives, it is trivial to infer function. Orphan proteins without discernible sequence relatives present a greater challenge [3]. Here the task of experimental characterisation is blind and becomes unwieldy. It is highly unlikely that all known proteins will ever be completely experimentally characterised [4]. Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. The Dataset was generated by calculating the sequence, sub-sequence, physiochemical, annotation-based features for each 171,212 reviewed proteins using method in [10]. These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. The Dataset contains the Entry and Entry name of each sequence corresponding to UniprotKB Database. This dataset being huge in size (171,212 samples X 9890 features, 1739 classes with multiple values) and equipped with enough number of positive and negative samples of each 1739 class, is good for testing efficiency of any upcoming deep learning models [5]. We divided the full dataset of 171,212 reviewed proteins in the ratio 3:1 to form Train/Test dataset 1; train dataset with 128,409 samples and test dataset with 42,803 samples to facilitate training of a deep learning model. The train and test datasets are stratified to contain good proportion of each 1739 classes. We then prepared a dataset 2 of pathogenic unreviewed proteins of the 9 bacterial phyla each with 9890 features same as train/train dataset of reviewed proteins but without target labels in order to predict their functions using deep learning model proposed in [5].

In the past few decades, the number and variety of genomic and proteomic data available have increased dramatically. Molecular or functional interaction networks are usually constructed according to high-throughput data and the topological structure of these interaction networks provide a wealth of information for inferring the function of genes or proteins. It is a widely used way to mine functional information of genes or proteins by analyzing the association networks. However, it remains still an urgent but unresolved challenge how to combine multiple heterogeneous networks to achieve more accurate predictions. In this paper, we present a method named ReprsentConcat to improve function inference by integrating multiple interaction networks. The low-dimensional representation of each node in each network is extracted, then these representations from multiple networks are concatenated and fed to gcForest, which augment feature vectors by cascading and automatically determines the number of cascade levels. We experimentally compare ReprsentConcat with a state-of-the-art method, showing that it achieves competitive results on the datasets of yeast and human. Moreover, it is robust to the hyperparameters including the number of dimensions.

Background Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression. Results We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes. Conclusion This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available. © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

WOS: 000445215600004, 30241466, Science Citation Index Expanded, Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers. Results: In ECPred, each EC number constituted an individual class and therefore, had an independent learning model. Enzyme vs. non-enzyme classification is incorporated into ECPred along with a hierarchical prediction approach exploiting the tree structure of the EC nomenclature. ECPred provides predictions for 858 EC numbers in total including 6 main classes, 55 subclass classes, 163 sub-subclass classes and 634 substrate classes. The proposed method is tested and compared with the state-of-the-art enzyme function prediction tools by using independent temporal hold-out and no-Pfam datasets constructed during this study. Conclusions: ECPred is presented both as a stand-alone and a web based tool to provide probabilistic enzymatic function predictions (at all five levels of EC) for uncharacterized protein sequences. Also, the datasets of this study will be a valuable resource for future benchmarking studies. ECPred is available for download, together with all of the datasets used in this study, at: https://github.com/cansyl/ECPred. ECPred webserver can be accessed through http://cansyl.metu.edu.tr/ECPred.html., YOK OYP scholarships, AD and ASR were supported by YOK OYP scholarships. The funding body did not play any role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Salinity tolerance has been extensively investigated in recent years due to its agricultural importance. Several features, such as the regulation of ionic transporters and metabolic adjustments, have been identified as salt tolerance hallmarks. Nevertheless, due to the complexity of the trait, the results achieved to date have met with limited success in improving the salt tolerance of rice plants when tested in the field, thus suggesting that a better understanding of the tolerance mechanisms is still required. In this work, differences between two varieties of rice with contrasting salt sensitivities were revealed by the imaging of photosynthetic parameters, ion content analysis and a transcriptomic approach. The transcriptomic analysis conducted on tolerant plants supported the setting up of an adaptive program consisting of sodium distribution preferentially limited to the roots and older leaves, and in the activation of regulatory mechanisms of photosynthesis in the new leaves. As a result, plants resumed grow even under prolonged saline stress. In contrast, in the sensitive variety, RNA-seq analysis revealed a misleading response, ending in senescence and cell death. The physiological response at the cellular level was investigated by measuring the intracellular profile of H2O2 in the roots, using a fluorescent probe. In the roots of tolerant plants, a quick response was observed with an increase in H2O2 production within 5 min after salt treatment. The expression analysis of some of the genes involved in perception, signal transduction and salt stress response confirmed their early induction in the roots of tolerant plants compared to sensitive ones. By inhibiting the synthesis of apoplastic H2O2, a reduction in the expression of these genes was detected. Our results indicate that quick H2O2 signaling in the roots is part of a coordinated response that leads to adaptation instead of senescence in salt-treated rice plants.

Bacteria play an important role in water purification in drinking water treatment systems. On one hand, bacteria present in the untreated water may help in its purification through biodegradation of the contaminants. On the other hand, some bacteria may be human pathogens and pose a threat to consumers. The present study investigated bacterial communities using Illumina MiSeq sequencing of 16S rRNA genes and their functions were predicted using PICRUSt in a treatment system, including the biofilms on sand filters and biological activated carbon (BAC) filters, in 4 months. In addition, quantitative analyses of specific bacterial populations were performed by real-time quantitative polymerase chain reaction (qPCR). The bacterial community composition of post-ozonation effluent, BAC effluent and disinfected water varied with sampling time. However, the bacterial community structures at other treatment steps were relatively stable, despite great variations of source water quality, resulting in stable treatment performance. Illumina MiSeq sequencing illustrated that Proteobacteria was dominant bacterial phylum. Chlorine disinfection significantly influenced the microbial community structure, while other treatment processes were synergetic. Bacterial communities in water and biofilms were distinct, and distinctions of bacterial communities also existed between different biofilms. By contrast, the functional composition of biofilms on different filters were similar. Some functional genes related to pollutant degradation were found widely distributed throughout the treatment processes. The distributions of Mycobacterium spp. and Legionella spp. in water and biofilms were revealed by real-time quantitative polymerase chain reaction (qPCR). Most bacteria, including potential pathogens, could be effectively removed by chlorine disinfection. However, some bacteria presented great resistance to chlorine. qPCRs showed that Mycobacterium spp. could not be effectively removed by chlorine. These resistant bacteria and, especially potential pathogens should receive more attention. Redundancy analysis (RDA) showed that turbidity, ammonia nitrogen and total organic carbon (TOC) exerted significant effects on community profiles. Overall, this study provides insight into variations of microbial communities in the treatment processes and aids the optimization of drinking water treatment plant design and operation for public health.

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.