Gene and Protein Network Analysis of AmpC β Lactamase

Network analysis of atherosclerotic genes elucidates druggable targets

Background

Atherosclerosis is one of the major causes of cardiovascular disease. It is characterized by the accumulation of atherosclerotic plaque in arteries under the influence of inflammatory responses, proliferation of smooth muscle cell, accumulation of modified low density lipoprotein. The pathophysiology of atherosclerosis involves the interplay of a number of genes and metabolic pathways. In traditional translation method, only a limited number of genes and pathways can be studied at once. However, the new paradigm of network medicine can be explored to study the interaction of a large array of genes and their functional partners and their connections with the concerned disease pathogenesis. Thus, in our study we employed a branch of network medicine, gene network analysis as a tool to identify the most crucial genes and the miRNAs that regulate these genes at the post transcriptional level responsible for pathogenesis of atherosclerosis.

Result

From NCBI database 988 atherosclerotic genes were retrieved. The protein–protein interaction using STRING database resulted in 22,693 PPI interactions among 872 nodes (genes) at different confidence score. The cluster analysis of the 872 genes using MCODE, a plug-in of Cytoscape software revealed a total of 18 clusters, the topological parameter and gene ontology analysis facilitated in the selection of four influential genes viz., AGT, LPL, ITGB2, IRS1 from cluster 3. Further, the miRNAs (miR-26, miR-27, and miR-29 families) targeting these genes were obtained by employing MIENTURNET webtool.

Conclusion

Gene network analysis assisted in filtering out the 4 probable influential genes and 3 miRNA families in the pathogenesis of atherosclerosis. These genes, miRNAs can be targeted to restrict the occurrence of atherosclerosis. Given the importance of atherosclerosis, any approach in the understanding the genes involved in its pathogenesis can substantially enhance the health care system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01195-y.

Role of AmpC-Inducing Genes in Modulating Other Serine Beta-Lactamases in Escherichia coli

The consistently mutating bacterial genotypes appear to have accelerated the global challenge with antimicrobial resistance (AMR); it is therefore timely to investigate certain less-explored fields of targeting AMR mechanisms in bacterial pathogens. One of such areas is beta-lactamase (BLA) induction that can provide us with a collection of prospective therapeutic targets. The key genes (ampD, ampE and ampG) to which the AmpC induction mechanism is linked are also involved in regulating the production of fragmented muropeptides generated during cell-wall peptidoglycan recycling. Although the involvement of these genes in inducing class C BLAs is apparent, their effect on serine beta-lactamase (serine-BLA) induction is little known. Here, by using ∆ampD and ∆ampE mutants of E. coli, we attempted to elucidate the effects of ampD and ampE on the expression of serine-BLAs originating from Enterobacteriaceae, viz., CTX-M-15, TEM-1 and OXA-2. Results show that cefotaxime is the preferred inducer for CTX-M-15 and amoxicillin for TEM-1, whereas oxacillin for OXA-2. Surprisingly, exogenous BLA expressions are elevated in ∆ampD and ∆ampE mutants but do not always alter their beta-lactam susceptibility. Moreover, the beta-lactam resistance is increased upon in trans expression of ampD, whereas the same is decreased upon ampE expression, indicating a differential effect of ampD and ampE overexpression. In a nutshell, depending on the BLA, AmpD amidase moderately facilitates a varying level of serine-BLA expression whereas AmpE transporter acts likely as a negative regulator of serine-BLA.

Network topology analysis of essential genes interactome of Helicobacter pylori to explore novel therapeutic targets

The Helicobacter pylori chronic colonization produces a wide range of gastric diseases in the gastric mucosa by abetting inflammation. Amidst coevolution and reorganization of its metabolism with humans, it has become difficult still imperative to understand and prevent its growth. This study focus to explore functional insights into identification of hub proteins/genes by aggregating the behavior of genes connected in a protein-protein interaction (PPI) network. We have constructed a PPI network of 123 essential genes along with 1213 interactions in H. pylori 26695. The degree and other centrality measures analysis assist in identifying the important hub nodes, which are top-ranked proteins. A total of nine proteins (recA, guaA, dnaK, rpsB, rplQ, rpmA, rpmC, rpmF, and rpsE) were obtained with high degree (k), betweenness centrality (BC) value. Gene ontology analysis reveals 8, 5 and 3 GO terms correspond to biological processes, cellular components and molecular function respectively. Gene complexes of hypothetical proteins (HPs) were related to aminoacyl-tRNA biosynthesis, biosynthesis of secondary metabolites, bacterial secretion system and protein export. The MCODE analysis revealed that protein from module M1, M3 and M6 include the proteins which have highest degree and BC values. It is noteworthy to mention that the bifunctional GMP synthase/glutamine amidotransferase protein (guaA), molecular chaperon (dnaK), recombinase A (recA) constitute as hub proteins. As a result, these genes are considered as network hub nodes that might be used as therapeutic targets. Our analysis affords a detailed understanding of the molecular process and pathways regulated by the essential genes in H. pylori 26695.

Amalgamation of 3D structure and sequence information for protein-protein interaction prediction

Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein-protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein-protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

Gene interaction network approach to elucidate the multidrug resistance mechanisms in the pathogenic bacterial strain Proteus mirabilis

Proteus mirabilis is one among the most frequently identified pathogen in patients with the urinary tract infection. The multidrug resistance exhibited by P. mirabilis renders the treatment ineffective, and new progressive strategies are needed to overcome the antibiotic resistance (AR). We have analyzed the evolutionary relationship of 29 P. mirabilis strains available in the National Center for Biotechnology Information-Genome database. The antimicrobial resistance genes of P. mirabilis along with the enriched pathways and the Gene Ontology terms are analyzed using gene networks to understand the molecular basis of AR. The genes rpoB, tufB, rpsl, fusA, and rpoA could be exploited as potential drug targets as they are involved in regulating the vital functions within the bacterium. The drug targets reported in the present study will aid researchers in developing new strategies to combat multidrug-resistant P. mirabilis.

Gene interaction network studies to decipher the multi-drug resistance mechanism in Salmonella enterica serovar Typhi CT18 reveal potential drug targets

Salmonella enterica subsp. enterica serovar Typhi, a human enteric pathogen causing typhoid fever, developed resistance to multiple antibiotics over the years. The current study was dedicated to understand the multi-drug resistance (MDR) mechanism of S. enterica serovar Typhi CT18 and to identify potential drug targets that could be exploited for new drug discovery. We have employed gene interaction network analysis for 44 genes which had 275 interactions. Clustering analysis resulted in three highly interconnecting clusters (C1-C3). Functional enrichment analysis revealed the presence of drug target alteration and three different multi-drug efflux pumps in the bacteria that were associated with antibiotic resistance. We found seven genes (arnA,B,C,D,E,F,T) conferring resistance to Cationic Anti-Microbial Polypeptide (CAMP) molecules by membrane Lipopolysaccharide (LPS) modification, while macB was observed to be an essential controlling hub of the network and played a crucial role in MacAB-TolC efflux pump. Further, we identified five genes (mdtH, mdtM, mdtG, emrD and mdfA) which were involved in Major Facilitator Superfamily (MFS) efflux system and acrAB contributed towards AcrAB-TolC efflux pump. All three efflux pumps were seen to be highly dependent on tolC gene. The five genes, namely tolC, macB, acrA, acrB and mdfA which were involved in multiple resistance pathways, can act as potential drug targets for successful treatment strategies. Therefore, this study has provided profound insights into the MDR mechanism in S. Typhi CT18. Our results will be useful for experimental biologists to explore new leads for S. enterica.

Inhibitory ASIC2-mediated calcineurin/NFAT against colorectal cancer by triterpenoids extracted from Rhus chinensis Mill

ETHNOPHARMACOLOGICAL RELEVANCE

Studies have shown that the etiology and pathogenesis of colorectal cancer are closely related to the tumor microenvironment, and the cancer tissue is still in the state of "energy deficit" and has to promote energy generation through high glycolysis. Rhus chinensis Mill is a Chinese herbal medicine used to treat various types of solid tumors in China. Colorectal cancer (CRC) is a heterogeneous disease group caused by abnormal changes in glucose metabolism resulted in lactic acid production, which remodels acidosis.

AIM OF THE STUDY

Although previous studies have shown that the active compounds of Rhus chinensis Mill. can inhibit the proliferation of tumor cells, whether its triterpenoids could effectively regulate glycolysis involved in CRC have not been systematically investigated.

MATERIALS AND METHODS

In this study, the extraction of triterpenoids extract from Rhus chinensis Mill. was obtained, and cell viability assay, the percentage of apoptosis for CRC cells were counted, and matrigel invasion assay and production of lactic acid and glucose uptake assay was determined. we further examined the expression of the key glycolytic enzymes and acid-sending ion channel (ASIC) family members of SW620 cells, and some key proteins in the glycolytic pathway were further verified.

RESULTS

Notably, triterpenoids (TER) of Rhus chinensis Mill. showed effective anti-proliferative activity and significantly altered protein levels associated with CRC cell survival and glycolysis metabolism. TER could down-regulate the expression of ASIC2, in CRC SW620 cell line. Most importantly, the levels of ASIC2 and calcineurin/nuclear factor of activated T cells (NFAT) were also down-regulated by TER. Furthermore, inhibition of activated the ASIC2-mediated calcineurin/NFAT1 pathway and target gene transcript expression of MMP-2 and MMP-9 in parallel to reduce, and resulted in the reduced invasion ability by TER treatment.

CONCLUSION

The potential pathways and targets that involved in glycolysis to excert the anti-CRC effects of main compounds in triterpenoids of Rhus chinensis Mill. were predicted by network pharmacology methods. Our findings thus provided rational evidence that inhibition of the ASIC2-induced calcineurin/NFAT pathway by triterpenoids in Rhus chinensis Mill. profoundly suppressed cell growth and invasion in CRC, which target alternative glycolysis in colorectal tumor cells, may be a useful adjuvant therapy in the treatment of colorectal cancer.

Insights into the interaction of key biofilm proteins in Pseudomonas aeruginosa PAO1 with TiO2 nanoparticle: An in silico analysis

Pseudomonas aeruginosa is a pathogenic biofilm forming bacteria which exist in wide range of environments such as water, soil and human body. In an earlier study, we used a system biology approach based analysis of biofilm forming genes of P. aeruginosa and their possible role in TiO₂ nanoparticle binding. The major protein of P. aeruginosa targeted by TiO₂ was found to be KatA, a major catalase required for H₂O₂ resistance and acute virulence and the direct interacting protein partners of KatA were found to be DnaK, Hfq, RpoA and RpoS. To understand the protein-protein physical interaction characteristic of these key proteins involved in biofilm related processes, homology modeling, docking and molecular dynamic simulation were performed. For all these proteins, physical and chemical properties, amino acid composition, nest and cleft analysis were performed using online tools. The interactions between TiO₂NPs-KatA and four protein-protein complexes such as KatA-DnaK, KatA-Hfq, KatA-RpoA and KatA-RpoS were studied. Our results indicate that all four key proteins and TiO₂NPs can have stable complexation with KatA. The study has given enough clues to understand the interaction of TiO₂NPs with P. aeruginosa biofilm in natural environment. Further investigations could lead to development of TiO₂NPs based therapeutic and sanitary interventions to combat this pathogenic bacterium.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

Panel strain of Klebsiella pneumoniae for beta-lactam antibiotic evaluation: their phenotypic and genotypic characterization

Klebsiella pneumoniae is responsible for numerous infections caused in hospitals, leading to mortality and morbidity. It has been evolving as a multi-drug resistant pathogen, acquiring multiple resistances such as such as horizontal gene transfer, transposon-mediated insertions or change in outer membrane permeability. Therefore, constant efforts are being carried out to control the infections using various antibiotic therapies. Considering the severity of the acquired resistance, we developed a panel of strains of K. pneumoniae expressing different resistance profiles such as high-level penicillinase and AmpC production, extended spectrum beta-lactamases and carbapenemases. Bacterial strains expressing different resistance phenotypes were collected and examined for resistance genes, mutations and porin alterations contributing to the detected phenotypes. Using the Massive parallel sequencing (MPS) technology we have constructed and genotypically characterized the panel strains to elucidate the multidrug resistance. These panel strains can be used in the clinical laboratory as standard reference strains. In addition, these strains could be significant in the field of pharmaceuticals for the antibiotic drug testing to verify its efficiency on pathogens expressing various  resistances.

Gene network analysis reveals the association of important functional partners involved in antibiotic resistance: A report on an important pathogenic bacterium Staphylococcus aureus

Staphylococcus aureus (S. aureus) is an emerging concern in hospital settings as it causes serious human infections. The multidrug resistance (MDR) in S. aureus is a complicated problem that is difficult to overcome due to the presence of numerous antibiotic resistance genes and it exhibit resistance to most of the currently available antibiotics. Presently, the resistance mechanisms of these genes/proteins are not completely understood. Therefore, identifying and understanding the functional relationship between the antibiotic resistant genes and their associated proteins might provide necessary information on resistance mechanisms and thereby help in designing successful drugs to combat the antibiotic resistance. In this study, we propose a model based on protein/gene network to identify genes/proteins associated with drug resistance in S. aureus. We filtered 50 functional partners in NorA, aacA-aphD (aac6ie), aad9ib (ant), aadd (knt), baca (uppP), bl2a_pc (blaZ), ble, ermA, SAV0052 (ermb), ermc, fosB, mecA (mecI), mecR (mecr1), mepA, msrA1, qacA, vraR (str), tet38 and tetM while 40 functional partners are identified in tet and aphA-3 (aph3iiia). The average shortest path length and betweenness centrality of functional partners in the clusters are calculated and they are functionally enriched with the Gene Ontology (GO) terms with a p-value cut-off ≤0.05. Interestingly, the constructed network reveals many associated antibiotic resistant genes and proteins and their role in resistance mechanisms. Thus, our results might provide a better understanding of the molecular mechanisms of action and their mode of drug resistance that will be useful for researchers exploring in the field of antibiotic resistance mechanisms.