Activity- and Enrichment-Based Metaproteomics Insights into Active Urease from the Rumen Microbiota of Cattle

Regulation of microbial urease activity plays a crucial role in improving the utilization efficiency of urea and reducing nitrogen emissions to the environment for ruminant animals. Dealing with the diversity of microbial urease and identifying highly active urease as the target is the key for future regulation. However, the identification of active urease in the rumen is currently limited due to large numbers of uncultured microorganisms. In the present study, we describe an activity- and enrichment-based metaproteomic analysis as an approach for the discovery of highly active urease from the rumen microbiota of cattle. We conducted an optimization method of protein extraction and purification to obtain higher urease activity protein. Cryomilling was the best choice among the six applied protein extraction methods (ultrasonication, bead beating, cryomilling, high-pressure press, freeze-thawing, and protein extraction kit) for obtaining protein with high urease activity. The extracted protein by cryomilling was further enriched through gel filtration chromatography to obtain the fraction with the highest urease activity. Then, by using SDS-PAGE, the gel band including urease was excised and analyzed using LC-MS/MS, searching against a metagenome-derived protein database. Finally, we identified six microbial active ureases from 2225 rumen proteins, and the identified ureases were homologous to those of Fibrobacter and Treponema. Moreover, by comparing the 3D protein structures of the identified ureases and known ureases, we found that the residues in the β-turn of flap regions were nonconserved, which might be crucial in influencing the flexibility of flap regions and urease activity. In conclusion, the active urease from rumen microbes was identified by the approach of activity- and enrichment-based metaproteomics, which provides the target for designing a novel efficient urease inhibitor to regulate rumen microbial urease activity.

Omics analysis coupled with gene editing revealed potential transporters and regulators related to levoglucosan metabolism efficiency of the engineered Escherichia coli

Background Bioconversion of levoglucosan, a promising sugar derived from the pyrolysis of lignocellulose, into biofuels and chemicals can reduce our dependence on fossil-based raw materials. However, this bioconversion process in microbial strains is challenging due to the lack of catalytic enzyme relevant to levoglucosan metabolism, narrow production ranges of the native strains, poor cellular transport rate of levoglucosan, and inhibition of levoglucosan metabolism by other sugars co-existing in the lignocellulose pyrolysate. The heterologous expression of eukaryotic levoglucosan kinase gene in suitable microbial hosts like Escherichia coli could overcome the first two challenges to some extent; however, no research has been dedicated to resolving the last two issues till now. Results Aiming to resolve the two unsolved problems, we revealed that seven ABC transporters (XylF, MalE, UgpB, UgpC, YtfQ, YphF, and MglA), three MFS transporters (KgtP, GntT, and ActP), and seven regulatory proteins (GalS, MhpR, YkgD, Rsd, Ybl162, MalM, and IraP) in the previously engineered levoglucosan-utilizing and ethanol-producing E. coli LGE2 were induced upon exposure to levoglucosan using comparative proteomics technique, indicating these transporters and regulators were involved in the transport and metabolic regulation of levoglucosan. The proteomics results were further verified by transcriptional analysis of 16 randomly selected genes. Subsequent gene knockout and complementation tests revealed that ABC transporter XylF was likely to be a levoglucosan transporter. Molecular docking showed that levoglucosan can bind to the active pocket of XylF by seven H-bonds with relatively strong strength. Conclusion This study focusing on the omics discrepancies between the utilization of levoglucosan and non-levoglucosan sugar, could provide better understanding of levoglucosan transport and metabolism mechanisms by identifying the transporters and regulators related to the uptake and regulation of levoglucosan metabolism. The protein database generated from this study could be used for further screening and characterization of the transporter(s) and regulator(s) for downstream enzymatic/genetic engineering work, thereby facilitating more efficient microbial utilization of levoglucosan for biofuels and chemicals production in future.

Effect of Eucommia ulmoides leaves on hyperuricemia and kidney injury induced by a high-fat/high-fructose diet in rats

Eucommia ulmoides leaves have unique advantages in the treatment of metabolic diseases. In this study, network pharmacology and molecular-docking methods were used to predict the effects and action mechanisms of the major components of E. ulmoides leaves on hyperuricemia. Combining literature collection, we used SciFinder and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform to collect E. ulmoides leaf flavonoid and iridoid components. Swiss Target Prediction, SEA, GeneCards, and the OMIM database were used to obtain core targets, and the STRING protein database was performed as core targets for gene ontology enrichment Set and KEGG analyses. Molecular docking was applied to predict the pathways regulating the metabolism of uric acid. The selected targets and targeting efficacy were validated using a rat model of hyperuricemia and renal injury induced by a high-fat and high-fructose diet. A total of 32 chemical components with effective targets, which regulated the PI3K-AKT pathway and endocrine resistance, were collected. Isoquercetin, kaempferol, and quercetin were predicted via network pharmacology to have potential bioactivities and strong docking binding forces. Molecular docking results showed that iridoids and flavonoids are bound to proteins related to inflammation and uric acid metabolism. In addition, it was verified via animal experiments that an E. ulmoides leaf extract ameliorated hyperuricemia, renal injury, and inflammation, which are closely related to the targets IL-6, TNF-α, TLR4, and GLUT9. In conclusion, E. ulmoides leaf flavonoids and iridoids ameliorate hyperuricemia and uric-acid–induced inflammation through a multi-component, multi-target, and multi-pathway mechanism, which provides a theoretical basis for the development of therapeutics from E. ulmoides leaf components.

Repurposing of Drugs for SARS-CoV-2 Using Inverse Docking Fingerprints

Severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 is a virus that belongs to the Coronaviridae family. This group of viruses commonly causes colds but possesses a tremendous pathogenic potential. In humans, an outbreak of SARS caused by the SARS-CoV virus was first reported in 2003, followed by 2012 when the Middle East respiratory syndrome coronavirus (MERS-CoV) led to an outbreak of Middle East respiratory syndrome (MERS). Moreover, COVID-19 represents a serious socioeconomic and global health problem that has already claimed more than four million lives. To date, there are only a handful of therapeutic options to combat this disease, and only a single direct-acting antiviral, the conditionally approved remdesivir. Since there is an urgent need for active drugs against SARS-CoV-2, the strategy of drug repurposing represents one of the fastest ways to achieve this goal. An in silico drug repurposing study using two methods was conducted. A structure-based virtual screening of the FDA-approved drug database on SARS-CoV-2 main protease was performed, and the 11 highest-scoring compounds with known 3CLpro activity were identified while the methodology was used to report further 11 potential and completely novel 3CLpro inhibitors. Then, inverse molecular docking was performed on the entire viral protein database as well as on the Coronaviridae family protein subset to examine the hit compounds in detail. Instead of target fishing, inverse docking fingerprints were generated for each hit compound as well as for the five most frequently reported and direct-acting repurposed drugs that served as controls. In this way, the target-hitting space was examined and compared and we can support the further biological evaluation of all 11 newly reported hits on SARS-CoV-2 3CLpro as well as recommend further in-depth studies on antihelminthic class member compounds. The authors acknowledge the general usefulness of this approach for a full-fledged inverse docking fingerprint screening in the future.

Mapping the Melanoma Plasma Proteome (MPP) Using Single-Shot Proteomics Interfaced with the WiMT Database

Plasma analysis by mass spectrometry-based proteomics remains a challenge due to its large dynamic range of 10 orders in magnitude. We created a methodology for protein identification known as Wise MS Transfer (WiMT). Melanoma plasma samples from biobank archives were directly analyzed using simple sample preparation. WiMT is based on MS1 features between several MS runs together with custom protein databases for ID generation. This entails a multi-level dynamic protein database with different immunodepletion strategies by applying single-shot proteomics. The highest number of melanoma plasma proteins from undepleted and unfractionated plasma was reported, mapping >1200 proteins from >10,000 protein sequences with confirmed significance scoring. Of these, more than 660 proteins were annotated by WiMT from the resulting ~5800 protein sequences. We could verify 4000 proteins by MS1t analysis from HeLA extracts. The WiMT platform provided an output in which 12 previously well-known candidate markers were identified. We also identified low-abundant proteins with functions related to (i) cell signaling, (ii) immune system regulators, and (iii) proteins regulating folding, sorting, and degradation, as well as (iv) vesicular transport proteins. WiMT holds the potential for use in large-scale screening studies with simple sample preparation, and can lead to the discovery of novel proteins with key melanoma disease functions.

Building a reference transcriptome for Juniperus squamata (Cupressaceae) based on single-molecule real-time sequencing

Objectives Cupressaceae is the second largest family of coniferous trees (Coniferopsida) with important economic and ecological values. However, like other conifers, the members of Cupressaceae have extremely large genome (> 8 gigabytes), which limited the researches of these taxa. A high-quality transcriptome is an important resource for gene discovery and annotation for non-model organisms. Data description Juniperus squamata, a tetraploid species which is widely distributed in Asian mountains, represents the largest genus, Juniperus, in Cupressaceae. Single-molecule real-time sequencing was used to obtain full-length transcriptome of Juniperus squamata. The full-length transcriptome was corrected with Illumina RNA-seq data from the same individual. A total of 47,860 non-redundant full-length transcripts, N50 of which was 2839, were obtained. A total of 57,393 simple sequence repeats were identified and 268,854 open reading frames were predicted for Juniperus squamata. A BLAST alignment against non-redundant protein database was conducted and 10,818 sequences were annotated in Gene Ontology database. InterPro analysis shows that 30,403 sequences have been functionally characterized against its member database. This data presents the first comprehensive transcriptome characterization of Juniperus species, and provides an important reference for researches on the genomics and evolutionary history of Cupressaceae plants and conifers in the future.

Using AnnoTree to get more assignments, faster, in DIAMOND+MEGAN microbiome analysis

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein-reference database such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr, and increased assignment rates, in particular, assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy.

In Silico Analysis of Glucose Oxidase from Aspergillus niger: Potential Cysteine Mutation Sites for Enhancing Protein Stability

Glucose oxidase (GOx) holds considerable advantages for various applications. Nevertheless, the thermal instability of the enzyme remains a grand challenge, impeding the success in applications outside the well-controlled laboratories, particularly in practical bioelectronics. Many strategies to modify GOx to achieve better thermal stability have been proposed. However, modification of this enzyme by adding extra disulfide bonds is yet to be explored. This work describes the in silico bioengineering of GOx from Aspergillus niger by judiciously analyzing characteristics of disulfide bonds found in the Top8000 protein database, then scanning for amino acid residue pairs that are suitable to be replaced with cysteines in order to establish disulfide bonds. Next, we predicted and assessed the mutant GOx models in terms of disulfide bond quality (bond length and α angles), functional impact by means of residue conservation, and structural impact as indicated by Gibbs free energy. We found eight putative residue pairs that can be engineered to form disulfide bonds. Five of these are located in less conserved regions and, therefore, are unlikely to have a deleterious impact on functionality. Finally, two mutations, Pro149Cys and His158Cys, showed potential for stabilizing the protein structure as confirmed by a structure-based stability analysis tool. The findings in this study highlight the opportunity of using disulfide bond modification as a new alternative technique to enhance the thermal stability of GOx.

In silico Identification of Potential Inhibitor Targeting Streptococcus mutans and Lactobacillus Acidophilus for the Treatment of Dental Caries

Background: Dental caries is one of the most common chronic diseases, and it is caused by the acid fermentation of bacteria that have become attached to the teeth. Streptococcus mutans (S. mutans) and Lactobacillus acidophilus (L. acidophilus) anchor surface proteins to the cell wall and form a biofilm to aid adhesion to the tooth surface. Some natural plant products, particularly several flavonoids, are effective inhibitors. However, given the scarcity of inhibitors and the emergence of drug resistance, the development of new inhibitors is critical. The high-throughput virtual screening approach was used in this study to identify new potential inhibitor of against S. mutans and L. acidophilus by using ligand (Ellagic acid). Aim: To evaluate the drug interaction ligand (Ellagic acid) and protein [A3VP1 of AgI/II] of Streptococcus mutans (PDB ID: 3IPK), glucan-1,6 - alpha-glucosidase from Lactobacillus acidophilus NCFM (PDB ID: 4AIE). Materials and Methodology: The pdb format of two selected proteins was retrieved from the RCSB protein database. Then inhibitors were docked with protein (A3VP1 of AgI/II) and glucan-1,6-alpha-glucosidase to identify the potent inhibitor. An evaluation criterion was based on the binding affinities by using AutoDock. Results: The binding energy of Ellagic acid - Streptococcus mutans docked complex-10.63 kcal/mol and with Ellagic acid – Lactobacillus acidophilus docked complex was -7.30 kcal/mol. Conclusion: In this study, Showed that lesser binding energy better is the binding of the ligand and protein. These findings can provide a new strategy for dental caries disease therapy by using Ellagic acid as a inhibitor against Streptococcus mutans and Lactobacillus acidophilus

Identification of residue inversions in large phylogenies of duplicated proteins

Connecting protein sequence to function is becoming increasingly relevant since high-throughput sequencing studies accumulate large amounts of genomic data. Protein database annotation helps to bridge this gap; however, it is fundamental to understand the mechanisms underlying functional inheritance and divergence. If the homology relationship between proteins is known, can we determine whether the function diverged? In this work, we analyze different possibilities of protein sequence evolution after gene duplication and identify "residue inversions", i.e., sites where the relationship between the ancestry and the functional signal is decoupled. Residues in these sites play a role in functional divergence and could indicate a shift in protein function. We develop a method to recognize residue inversions in a phylogeny and test it on real and simulated datasets. In a dataset built from the Epidermal Growth Factor Receptor (EGFR) sequences found in 88 fish species, we identify 19 positions that went through inversion after gene duplication, mostly located at the ligand-binding extracellular domain.