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

SphK2 promotes the progression of Helicobacter pylori-positive gastric cancer by regulating the Ras/MEK/ERK pathway

BACKGROUND

Helicobacter pylori (H. pylori) infection promotes gastric cancer (GC) through various mechanisms. It causes inflammation and damage to the gastric mucosa, thereby increasing the risk of developing GC. Sphingolipids can act as signaling molecules that activate or inhibit intracellular signaling pathways, and abnormal sphingolipid metabolism may promote tumorigenesis and metastasis. This study aimed to explore the relationship among sphingosine kinase 2 (SphK2) expression, GC progression, and H. pylori infection.

METHODS

Expression profile data for SphK2 were extracted from public datasets. Normal human gastric mucosal and GC cells were co-incubated with H. pylori, and SphK2 expression in these cells was detected using western blotting. GC cells with SphK2 overexpression and knockdown were established, and the effects of SphK2 and H. pylori on the proliferation, migration, and invasion abilities of GC cells were verified using CCK-8, EdU, and Transwell assays. The expression of Ras/MEK/ERK pathway-related proteins was detected using western blotting, and the secretion of pro-inflammatory cytokines TNF-α, IL-6 and IL-1β in GC cells was detected using ELISA.

RESULTS

SphK2 is highly expressed in GC cells and is associated with a poor prognosis. The expression of SphK2 in GC cells is related to H. pylori infection. SphK2 overexpression promotes the proliferation, migration, and invasion of GC cells and enhances the pro-inflammatory effects of H. pylori.

CONCLUSION

SphK2 promotes the progression of H. pylori-positive GC by activating the Ras/MEK/ERK pathway.

Subtractive genome mining in Xanthomonas citri pv. citri strain 306 for identifying novel drug target proteins coupled with in-depth protein-protein interaction and coevolution analysis - A leap towards prospective drug design

Citrus canker poses a serious threat to a highly significant citrus fruit crop, this disease caused by one of the most destructive bacterial plant pathogens Xanthomonas citri pv. citri (Xcc). Bacterial plant diseases significantly reduce crop yields worldwide, making it more difficult to supply the growing food demand. The high levels of antibiotic resistance in Xcc strains are diminishing the efficacy of current control measures, necessitating the exploration of novel therapeutic targets to address the escalating antimicrobial resistance trend. Genome subtraction approach along with protein-protein network and coevolution analysis were used to identify potential drug targets in Xcc stain 306. The study involved retrieving the Xcc proteome from the UniProt database, eliminating paralogous proteins using CD-HIT (80 % identity cutoff), and selecting nonhomologous proteins through BLASTp (e-value <0.005). Essential proteins were identified using BLAST against the DEG (e-value cutoff 0.00001). 750 essential proteins were identified that are nonhomologous to citrus plant. Subsequent analyses included metabolic pathway assessment, subcellular localization prediction, and druggability analysis. Protein network analysis, coevolution analysis, protein active site identification was also performed. In conclusion, this study identified eight potential drug targets (GlmU, CheA, RmlD, GspE, FleQ, RpoN, Shk, SecB), highlighting RpoN, FleQ, and SecB as unprecedented targets for Xcc. These findings may contribute to the development of novel antimicrobial agents in the future that can efficiently control citrus canker disease.

Protective efficacy and immune responses of largemouth bass (Micropterus salmoides) immunized with an inactivated vaccine against the viral hemorrhagic septicemia virus genotype IVa

Viral hemorrhagic septicemia virus (VHSV) poses a significant threat to the aquaculture industry, prompting the need for effective preventive measures. Here, we developed an inactivated VHSV and revealed the molecular mechanisms underlying the host's protective response against VHSV. The vaccine was created by treating VHSV with 0.05 % formalin at 16 °C for 48 h, which was determined to be the most effective inactivation method. Compared with nonvaccinated fish, vaccinated fish exhibited a remarkable increase in survival rate (99 %) and elevated levels of serum neutralizing antibodies, indicating strong immunization. To investigate the gene changes induced by vaccination, RNA sequencing was performed on spleen samples from control and vaccinated fish 14 days after vaccination. The analysis revealed 893 differentially expressed genes (DEGs), with notable up-regulation of immune-related genes such as annexin A1a, coxsackievirus and adenovirus receptor homolog, V-set domain-containing T-cell activation inhibitor 1-like, and heat shock protein 90 alpha class A member 1 tandem duplicate 2, indicating a vigorous innate immune response. Furthermore, KEGG enrichment analysis highlighted significant enrichment of DEGs in processes related to antigen processing and presentation, necroptosis, and viral carcinogenesis. GO enrichment analysis further revealed enrichment of DEGs related to the regulation of type I interferon (IFN) production, type I IFN production, and negative regulation of viral processes. Moreover, protein-protein interaction network analysis identified central hub genes, including IRF3 and HSP90AA1.2, suggesting their crucial roles in coordinating the immune response elicited by the vaccine. These findings not only confirm the effectiveness of our vaccine formulation but also offer valuable insights into the underlying immunological mechanisms, which can be valuable for future vaccine development and disease management in the aquaculture industry.

Computational prediction of crucial genes involved in gonorrhea infection and neoplastic cell transformation: A multiomics approach

Neisseria gonorrheae, the causative agent of genitourinary infections, has been associated with asymptomatic or recurrent infections and has the potential to form biofilms and induce inflammation and cell transformation. Herein, we aimed to use computational analysis to predict novel associations between chronic inflammation caused by gonorrhea infection and neoplastic transformation. Prioritization and gene enrichment strategies based on virulence and resistance genes utilizing essential genes from the DEG and PANTHER databases, respectively, were performed. Using the STRING database, protein‒protein interaction networks were constructed with 55 nodes of bacterial proteins and 72 nodes of proteins involved in the host immune response. MCODE and cytoHubba were used to identify 12 bacterial hub proteins (murA, murB, murC, murD, murE, purN, purL, thyA, uvrB, kdsB, lpxC, and ftsH) and 19 human hub proteins, of which TNF, STAT3 and AKT1 had high significance. The PPI networks are based on the connectivity degree (K), betweenness centrality (BC), and closeness centrality (CC) values. Hub genes are vital for cell survival and growth, and their significance as potential drug targets is discussed. This computational study provides a comprehensive understanding of inflammation and carcinogenesis pathways that are activated during gonorrhea infection.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches

Background

Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people.

Objectives

In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making.

Methods

An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots.

Results

Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM.

Conclusion

A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus

The bacterium Candidatus Liberibacter asiaticus (CLas) causes citrus Huanglongbing disease. Our understanding of the pathogenicity and biology of this microorganism remains limited because CLas has not yet been cultivated in artificial media. Its genome is relatively small and encodes approximately 1136 proteins, of which 415 have unknown functions. Here, we use a high-throughput yeast-two-hybrid (Y2H) screen to identify interactions between CLas proteins, thus providing insights into their potential functions. We identify 4245 interactions between 542 proteins, after screening 916 bait and 936 prey proteins. The false positive rate of the Y2H assay is estimated to be 2.9%. Pull-down assays for nine protein-protein interactions (PPIs) likely involved in flagellar function support the robustness of the Y2H results. The average number of PPIs per node in the CLas interactome is 15.6, which is higher than the numbers previously reported for interactomes of free-living bacteria, suggesting that CLas genome reduction has been accompanied by increased protein multi-functionality. We propose potential functions for 171 uncharacterized proteins, based on the PPI results, guilt-by-association analyses, and comparison with data from other bacterial species. We identify 40 hub-node proteins, including quinone oxidoreductase and LysR, which are known to protect other bacteria against oxidative stress and might be important for CLas survival in the phloem. We expect our PPI database to facilitate research on CLas biology and pathogenicity mechanisms.

Gene expression profile analysis unravelled the systems level association of renal cell carcinoma with diabetic nephropathy and Matrix-metalloproteinase-9 as a potential therapeutic target

Type 2 diabetes (T2D) and cancer share many common risk factors. However, the potential biological link that connects the two at the molecular level is still unclear. The experimental evidence suggests that several genes and their pathways may be involved in developing cancerous conditions associated with diabetes. In this study, we identified the protein-protein interaction (PPI) networks and the hub protein(s) that interlink T2D and cancer using genome-scale differential gene expression profiles. Further, the PPI network of AMP-activated protein kinase (AMPK) in cancer was analyzed to explore novel insights into the molecular association between the two conditions. The densely connected regions were analyzed by constructing the backbone and subnetworks with key nodes and shortest pathways, respectively. The PPI network studies identified Matrix-metalloproteinase-9 (MMP-9) as a hub protein playing a vital role in glomerulonephritis tubular diseases and some genetic kidney diseases. MMP-9 was also associated with different growth factors, like tumor necrosis factor (TNF-α), transforming growth factor 1 (TGF-1), and pathways like chemokine signaling, NOD-like receptor signaling, etc. Further, the molecular docking and molecular dynamic simulation studies supported the druggability of MMP-9, suggesting it as a potential therapeutic target in treating renal cell carcinoma linked with diabetic kidney disease.Communicated by Ramaswamy H. Sarma.

Genome-guided approaches and evaluation of the strategies to influence bioprocessing assisted morphological engineering of Streptomyces cell factories

Streptomyces genera serve as adaptable cell factories for secondary metabolites with various and distinctive chemical structures that are relevant to the pharmaceutical industry. Streptomyces' complex life cycle necessitated a variety of tactics to enhance metabolite production. Identification of metabolic pathways, secondary metabolite clusters, and their controls have all been accomplished using genomic methods. Besides this, bioprocess parameters were also optimized for the regulation of morphology. Kinase families were identified as key checkpoints in the metabolic manipulation (DivIVA, Scy, FilP, matAB, and AfsK) and morphology engineering of Streptomyces. This review illustrates the role of different physiological variables during fermentation in the bioeconomy coupled with genome-based molecular characterization of biomolecules responsible for secondary metabolite production at different developmental stages of the Streptomyces life cycle.

Application of TraDIS to define the core essential genome of Campylobacter jejuni and Campylobacter coli

Campylobacter species are the major cause of bacterial gastroenteritis. As there is no effective vaccine, combined with the rapid increase in antimicrobial resistant strains, there is a need to identify new targets for intervention. Essential genes are those that are necessary for growth and/or survival, making these attractive targets. In this study, comprehensive transposon mutant libraries were created in six C. jejuni strains, four C. coli strains and one C. lari and C. hyointestinalis strain, allowing for those genes that cannot tolerate a transposon insertion being called as essential. Comparison of essential gene lists using core genome analysis can highlight those genes which are common across multiple strains and/or species. Comparison of C. jejuni and C. coli, the two species that cause the most disease, identified 316 essential genes. Genes of interest highlighted members of the purine pathway being essential for C. jejuni whilst also finding that a functional potassium uptake system is essential. Protein-protein interaction networks using these essential gene lists also highlighted proteins in the purine pathway being major 'hub' proteins which have a large number of interactors across the network. When adding in two more species (C. lari and C. hyointestinalis) the essential gene list reduces to 261. Within these 261 essential genes, there are many genes that have been found to be essential in other bacteria. These include htrB and PEB4, which have previously been found as core virulence genes across Campylobacter species in other studies. There were 21 genes which have no known function with eight of these being associated with the membrane. These surface-associated essential genes may provide attractive targets. The essential gene lists presented will help to prioritise targets for the development of novel therapeutic and preventative interventions.

Exploring key molecular signatures of immune responses and pathways associated with tuberculosis in comorbid diabetes mellitus: a systems biology approach

Background

Comorbid type 2 diabetes mellitus (T2DM) increases the risk for tuberculosis (TB) and its associated complications, although the pathological connections between T2DM and TB are unknown. The current research aims to identify shared molecular gene signatures and pathways that affirm the epidemiological association of T2DM and TB and afford clues on mechanistic basis of their association through integrative systems biology and bioinformatics approaches. Earlier research has found specific molecular markers linked to T2DM and TB, but, despite their importance, only offered a limited understanding of the genesis of this comorbidity. Our investigation used a network medicine method to find possible T2DM-TB molecular mediators.

Results

Functional annotation clustering, interaction networks, network cluster analysis, and network topology were part of our systematic investigation of T2DM-TB linked with 1603 differentially expressed genes (DEGs). The functional enrichment and gene interaction network analysis emphasized the importance of cytokine/chemokine signalling, T cell receptor signalling route, NF-kappa B signalling pathway and Jak-STAT signalling system. Furthermore, network analysis revealed significant DEGs such as ITGAM and STAT1, which may be necessary for T2DM-TB immune responses. Furthermore, these two genes are modulators in clusters C4 and C5, abundant in cytokine/chemokine signalling and Jak-STAT signalling pathways.

Conclusions

Our analyses highlight the role of ITGAM and STAT1 in T2DM-TB-associated pathways and advances our knowledge of the genetic processes driving this comorbidity.

Functional insights of antibiotic resistance mechanism in Helicobacter pylori: Driven by gene interaction network and centrality-based nodes essentiality analysis

Increased antibiotic resistance in Helicobacter pylori (H. pylori), a major human pathogen, constitutes a substantial threat to human health. Understanding the pathophysiology and development of antibiotic resistance can aid our battle with the infections caused by H. pylori. The aim of this study is to discover the high-impact key regulatory mechanisms and genes involved in antimicrobial drug resistance (AMR). In this study, we constructed a functional gene interaction network by integrating multiple sources of data related to antibiotic resistant genes (number-77) from H. pylori. The gene interaction network was assortative, with a hierarchical, scale-free topology enriched in a variety of gene ontology (GO) categories and KEGG pathways. Using an iterative clustering methodology, we identified a number of communities in the AMR gene network that comprised nine genes (sodB, groEL, gyrA, recA, polA, tuf, infB, rpsJ, and gyrB) that were present at the deepest level and hence were key regulators of AMR. Further, an antibiotic-resistant gene network-based centrality analysis revealed superoxide dismutase (sodB) as a bottleneck node in the network. Our findings suggested that sodB is critically enriched in the cellular response to oxidative stress, removal of superoxide radicals, cellular oxidant detoxification processes, cellular component biogenesis, response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process and reactive oxygen species metabolic process. We demonstrated how the sodB, which are involved in the response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process, reactive oxygen species metabolic process, regulated by Fur gene/proteins, claim a major authority over regulation and signal propagation in the AMR.

Transcriptome of human neuroblastoma SH-SY5Y cells in response to 2B protein of enterovirus-A71

Infection with enterovirus-A71 (EV-A71) can cause hand-foot-mouth disease associated with fatal neurological complications. The host response to EV-A71 has not yet been fully elucidated, thus, hampering the development of a precise therapeutic approach. A nonstructural 2B protein of EV-A71 has been reported to involve with calcium dysregulation and apoptosis induction in human neuroblastoma SH-SY5Y cells. However, the molecular mechanism has not been delineated. To address this, comprehensive study of the gene expression from SH-SY5Y cells transfected with EV-A71 2B was carried out by RNA sequencing and transcriptomic analysis. It was found that the signature of the upregulated genes of SH-SY5Y cells expressing EV-A71 2B involved the Ca²⁺-related signaling pathways participating gene expression, inflammatory response, apoptosis, and long-term potentiation of the neuron. Protein-protein interaction network analysis revealed that the products encoded by CCL2, RELB, BIRC3, and TNFRSF9 were the most significant hub proteins in the network. It indicated that EV-A71 2B protein might play a role in immunopathogenesis of the central nervous system (CNS) which probably associated with the non-canonical NF-κB pathway. The data suggest that transcriptomic profiling can provide novel information source for studying the neuropathogenesis of EV-A71 infection leading to development of an effective therapeutic measure for CNS complications.

Genomic and Transcriptomic Analysis of Bovine Pasteurella multocida Serogroup A Strain Reveals Insights Into Virulence Attenuation

Pasteurella multocida is one of the primary pathogens of bovine respiratory disease (BRD), and causes huge losses in the cattle industry. The Pm3 strain was a natural isolate, which is a strong form of pathogen and is sensitive to fluoroquinolones antibiotics. A high fluoroquinolone resistant strain, Pm64 (MIC = 64 μg/mL), was formed after continuous induction with subinhibitory concentration (1/2 MIC) of enrofloxacin, with the enhanced growth characteristics and large attenuation of pathogenicity in mice. This study reports the whole genome sequence and the transcription profile by RNA-Seq of strain Pm3/Pm64. The results showed an ineffective difference between the two strains at the genome level. However, 32 genes could be recognized in the gene islands (GIs) of Pm64, in which 24 genes were added and 8 genes were lost. Those genes are involved in DNA binding, trehalose metabolism, material transportation, capsule synthesis, prophage, amino acid metabolism, and other functions. In Pm3 strain, 558 up-regulated and 568 down-regulated genes were found compared to Pm64 strain, from which 20 virulence factor-related differentially expressed genes (DEGs) were screened. Mainly differentially transcribed genes were associated with capsular polysaccharide (CPS), lipopolysaccharide (LPS), lipooligosaccharide (LOS). Iron utilization, and biofilm composition. We speculated that the main mechanism of virulence attenuation after the formation of resistance of Pm64 comes from the change of the expression profile of these genes. This report elucidated the toxicity targets of P. multocida serogroup A which provide fundamental information toward the understanding of the pathogenic mechanism and to decreasing antimicrobial drugs resistance.

Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets

The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.

Cellular and molecular level host-pathogen interactions in Francisella tularensis: A microbial gene network study

Due to the high infectivity and fatal effect on human population, Francisella tularensis (F. tularensis) is classified as a potential biological warfare agent. The interaction between host and pathogen behind the successful establishment of F. tularensis infection within the human host is largely unknown. In our present work, we have studied the molecular level interactions between the host cellular components and F. tularensis genes to understand the interplay between the host and pathogen. Interestingly, we have identified the pathways associated with the pathogen offensive strategies that help in invasion of host defensive systems. The F. tularensis genes purL, katG, proS, rpoB and fusA have displayed high number of interactions with the host genes and thus play a crucial role in vital pathogen pathways. The pathways identified were involved in adaptation to different stress conditions within the host and might be crucial for designing new therapeutic interventions against tularemia.