Gel beads prepared by polyvinyl alcohol cross-linking with phytic acid and Fe as novel microbial carriers for simultaneous partial nitrification, anammox and denitrification.

Enhancing the stability of biomass and ensuring a stable activity of anaerobic ammonia oxidizing bacteria are crucial for successful operation of the simultaneous partial nitrification, Anammox, and denitrification (SNAD) process. In this study, gel beads of polyvinyl alcohol/phytic acid (PVA/PA) and polyvinyl alcohol/phytic acid/Fe (PVA/PA/Fe) were prepared as innovative bio-carriers. Theoretical simulations and analyses revealed that these carriers are predominantly connected via hydrogen and borate bonds, with PVA/PA/Fe also featuring metal coordination bonds. The total nitrogen removal efficiency of reactors with PVA/PA/Fe and PVA/PA increased by 13.5 % and 9.0 %, respectively, compared to reactor without carriers. The iron-enriched PVA/PA/Fe carriers significantly improve SNAD by promoting Anammox, Feammox, and nitrate-dependent Fe2+ oxidation reactions, leading to faster nitrogen conversion and higher nitrogen removal rate than reactor without carriers and with PVA/PA. Using of PVA/PA and PVA/PA/Fe gel beads as bio-carriers offers benefits to the SNAD process, including cost-effective and low carbon requirement.

Emulsion-Directed Synthesis of Poly-Porphyrin Nanoparticles as Heterogeneous Photocatalysts for PET-RAFT Polymerization.

Heterogeneous photocatalysts have attracted extensive attention in photo-induced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization due to their remarkable advantages such as easy preparation, tunable photoelectric properties, and recyclability. In this study, zinc (II) 5,10,15,20-tetrakis(4-aminophenyl)porphyrin (ZnTAPP)-based poly-porphyrin nanoparticles (PTAPP-Zn) are constructed by an emulsion-directed approach. It is investigated as a heterogeneous photocatalyst for PET-RAFT polymerization of various methacrylate monomers under visible light exposure, and the reactions show refined polymerization control with high monomer conversions. Furthermore, it is demonstrated that the PTAPP-Zn nanoparticles with the larger pore size enhance photocatalytic activity in PET-RAFT polymerization. In addition, the capabilities of oxygen tolerance and temporal control are demonstrated and PTAPP-Zn particles can be easily recycled and reused without an obvious decrease in catalytic efficiency.

Evaluation of machine learning models on protein level inference from prioritized RNA features.

The parallel measurement of transcriptome and proteome revealed unmatched profiles. Since proteomic analysis is more expensive and challenging than transcriptomic analysis, the question of how to use messenger RNA (mRNA) expression data to predict protein level is extremely important. Here, we comprehensively evaluated 13 machine learning models on inferring protein expression levels using RNA expression profile. A total of 20 proteogenomic datasets from three mainstream proteomic platforms with >2500 samples of 13 human tissues were collected for model evaluation. Our results highlighted that the appropriate feature selection methods combined with classical machine learning models could achieve excellent predictive performance. The voting ensemble model outperformed other candidate models across datasets. Adding the mRNA proxy model to the regression model further improved the prediction performance. The dataset and gene characteristics could affect the prediction performance. Finally, we applied the model to the brain transcriptome of cerebral cortex regions to infer the protein profile for better understanding the functional characteristics of the brain regions. This benchmarking work not only provides useful hints on the inherent correlation between transcriptome and proteome, but also has practical value of the transcriptome-based prediction of protein expression levels.

Impact on strain growth and butenyl-spinosyn biosynthesis by overexpression of polynucleotide phosphorylase gene in Saccharopolyspora pogona.

Polynucleotide phosphorylase is a highly conserved protein found in bacteria and fungi that can regulate the transcription of related enzymes involved in amino acid metabolism, organic acid metabolism, and cell biosynthesis. We studied the effect of polynucleotide phosphorylase on Saccharopolyspora pogona (S. pogona) growth and the synthesis of secondary metabolites. First, we generated the overexpression vector pOJ260-PermE-pnp via overlap extension PCR. The vector pOJ260-PermE-pnp was then introduced into S. pogona by conjugal transfer, thereby generating the recombination strain S. pogona-Pnp. Results showed that engineering strains possessed higher biomass than those of the wild-type strains. Moreover, the ability of these strains to produce spores on solid medium was stronger than that of the wild-type strains. HPLC results revealed that the butenyl-spinosyn yield in S. pogona-Pnp increased by 1.92-fold compared with that of S. pogona alone. These findings revealed that overexpression of polynucleotide phosphorylase effectively promoted butenyl-spinosyn biosynthesis in S. pogona. This result may be extended to other Streptomyces for strain improvement.

NFPscanner: a webtool for knowledge-based deciphering of biomedical networks.

BACKGROUND: Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis. They would benefit from the development of a standardized tool to compare functions of multiple biological networks quantitatively and promptly. RESULTS: To address this challenge, we developed NFPscanner, a web server for deciphering gene networks with pathway associations. Adapted from a recently reported knowledge-based framework called network fingerprint, NFPscanner integrates the annotated pathways of 7 databases, 4 algorithms, and 2 graphical visualization modules into a webtool. It implements 3 types of network analysis: Fingerprint: Deciphering gene networks and highlighting inherent pathway modules Alignment: Discovering functional associations by finding optimized node mapping between 2 gene networks Enrichment: Calculating and visualizing gene ontology (GO) and pathway enrichment for genes in networks Users can upload gene networks to NFPscanner through the web interface and then interactively explore the networks' functions. CONCLUSIONS: NFPscanner is open-source software for non-commercial use, freely accessible at http://biotech.bmi.ac.cn/nfs .

ICM: a web server for integrated clustering of multi-dimensional biomedical data.

Large-scale efforts for parallel acquisition of multi-omics profiling continue to generate extensive amounts of multi-dimensional biomedical data. Thus, integrated clustering of multiple types of omics data is essential for developing individual-based treatments and precision medicine. However, while rapid progress has been made, methods for integrated clustering are lacking an intuitive web interface that facilitates the biomedical researchers without sufficient programming skills. Here, we present a web tool, named Integrated Clustering of Multi-dimensional biomedical data (ICM), that provides an interface from which to fuse, cluster and visualize multi-dimensional biomedical data and knowledge. With ICM, users can explore the heterogeneity of a disease or a biological process by identifying subgroups of patients. The results obtained can then be interactively modified by using an intuitive user interface. Researchers can also exchange the results from ICM with collaborators via a web link containing a Project ID number that will directly pull up the analysis results being shared. ICM also support incremental clustering that allows users to add new sample data into the data of a previous study to obtain a clustering result. Currently, the ICM web server is available with no login requirement and at no cost at http://biotech.bmi.ac.cn/icm/.

The relationships among host transcriptional responses reveal distinct signatures underlying viral infection-disease associations.

Genome-scale DNA microarrays and computational biology facilitate new understanding of viral infections at the system level. Recent years have witnessed a major shift from microorganism-centric toward host-oriented characterization and categorization of viral infections and infection related diseases. We established host transcriptional response (HTR) relationships among 23 different types of human viral pathogens based on calculating HTR similarities using computational integration of 587 public available gene expression profiles. We further identified five virus clusters that show consensus internal HTRs and defined cluster signatures using common dysregulated genes. Individual cluster signature genes were distinguished from one another, and functional analysis revealed common and specific host cellular bioprocesses and signaling pathways involved in confronting viral infections. Through literature investigation and support from epidemiological studies, these were confirmed to be important gene factors associating viral infections with cluster-common and cluster-specific non-infectious human disease(s). Our analyses were the first to feature differential HTRs to viral infections as clusters, and they present a new perspective for understanding infection-disease associations and the underlying pathogeneses.

Inferring Infection Patterns Based on a Connectivity Map of Host Transcriptional Responses.

Host responses to infections represent an important pathogenicity determiner, and delineation of host responses can elucidate pathogenesis processes and inform the development of anti-infection therapies. Low cost, high throughput, easy quantitation, and rich descriptions have made gene expression profiling generated by DNA microarrays an optimal approach for describing host transcriptional responses (HTRs). However, efforts to characterize the landscape of HTRs to diverse pathogens are far from offering a comprehensive view. Here, we developed an HTR Connectivity Map based on systematic assessment of pairwise similarities of HTRs to 50 clinically important human pathogens using 1353 gene-expression profiles generated from >60 human cells/tissues. These 50 pathogens were further partitioned into eight robust "HTR communities" (i.e., groups with more consensus internal HTR similarities). These communities showed enrichment in specific infection attributes and differential gene expression patterns. Using query signatures of HTRs to external pathogens, we demonstrated four distinct modes of HTR associations among different pathogens types/class, and validated the reliability of the HTR community divisions for differentiating and categorizing pathogens from a host-oriented perspective. These findings provide a first-generation HTR Connectivity Map of 50 diverse pathogens, and demonstrate the potential for using annotated HTR community to detect functional associations among infectious pathogens.

Network fingerprint: a knowledge-based characterization of biomedical networks.

It can be difficult for biomedical researchers to understand complex molecular networks due to their unfamiliarity with the mathematical concepts employed. To represent molecular networks with clear meanings and familiar forms for biomedical researchers, we introduce a knowledge-based computational framework to decipher biomedical networks by making systematic comparisons to well-studied "basic networks". A biomedical network is characterized as a spectrum-like vector called "network fingerprint", which contains similarities to basic networks. This knowledge-based multidimensional characterization provides a more intuitive way to decipher molecular networks, especially for large-scale network comparisons and clustering analyses. As an example, we extracted network fingerprints of 44 disease networks in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The comparisons among the network fingerprints of disease networks revealed informative disease-disease and disease-signaling pathway associations, illustrating that the network fingerprinting framework will lead to new approaches for better understanding of biomedical networks.