Computational analysis and expression profiling of two-component system (TCS) gene family members in mango (Mangifera indica) indicated their roles in stress response.

The two-component system (TCS) gene family is among the most important signal transduction families in plants and is involved in the regulation of various abiotic stresses, cell growth and division. To understand the role of TCS genes in mango (Mangifera indica ), a comprehensive analysis of TCS gene family was carried out in mango leading to identification of 65 MiTCS genes. Phylogenetic analysis divided MiTCSs into three groups (histidine kinases, histidine-containing phosphotransfer proteins, and response regulators) and 11 subgroups. One tandem duplication and 23 pairs of segmental duplicates were found within the MiTCSs . Promoter analysis revealed that MiTCSs contain a large number of cis -elements associated with environmental stresses, hormone response, light signalling, and plant development. Gene ontology analysis showed their involvement in various biological processes and molecular functions, particularly signal transduction. Protein-protein interaction analysis showed that MiTCS proteins interacted with each other. The expression pattern in various tissues and under many stresses (drought, cold, and disease) showed that expression levels varied among various genes in different conditions. MiTCSs 3D structure predictions showed structural conservation among members of the same groups. This information can be further used to develop improved cultivars and will serve as a foundation for gaining more functional insights into the TCS gene family.

Comparative genomic profiling of transport inhibitor Response1/Auxin signaling F-box (TIR1/AFB) genes in eight Pyrus genomes revealed the intraspecies diversity and stress responsiveness patterns.

In the genomics of plants and the phytoecosystem, Pyrus (pear) is among the most nutritious fruits and contains fiber that has great health benefits to humans. It is mostly cultivated in temperate regions and is one of the most cultivated pome fruits globally. Pears are highly subjected to biotic and abiotic stresses that affect their yield. TIR1/AFB proteins act as auxin co-receptors during the signaling of nuclear auxins and play a primary role in development-related regulatory processes and responses to biotic and abiotic stresses. However, this gene family and its members have not been explored in Pyrus genomes, and understanding these genes will help obtain useful insights into stress tolerance and ultimately help maintain a high yield of pears. This study reports a pangenome-wide investigation of TIR1/AFB genes from eight Pyrus genomes: Cuiguan (Pyrus pyrifolia), Shanxi Duli (P. betulifolia), Zhongai 1 [(P. ussuriensis × communis) × spp.], Nijisseiki (P. pyrifolia), Yunhong No.1 (P. pyrifolia), d'Anjou (P. communis), Bartlett v2.0 (P. communis), and Dangshansuli v.1.1 (P. bretschneideri). These genes were randomly distributed on 17 chromosomes in each genome. Based on phylogenetics, the identified TIR1/AFB genes were divided into six groups. Their gene structure and motif pattern showed the intraspecific structural conservation as well as evolutionary patterns of Pyrus TIR1/AFBs. The expansion of this gene family in Pyrus is mainly caused by segmental duplication; however, a few genes showed tandem duplication. Moreover, positive and negative selection pressure equally directed the gene's duplication process. The GO and PPI analysis showed that Pyrus TIR1/AFB genes are associated with abiotic stress- and development-related signaling pathways. The promoter regions of Pyrus TIR1/AFB genes were enriched in hormone-, light-, development-, and stress-related cis elements. Furthermore, publicly available RNA-seq data analysis showed that DaTIR1/AFBs have varied levels of expression in various tissues and developmental stages, fruit hardening disease conditions, and drought stress conditions. This indicated that DaTIR1/AFB genes might play critical roles in response to biotic and abiotic stresses. The DaTIR1/AFBs have similar protein structures, which show that they are involved in the same function. Hence, this study will broaden our knowledge of the TIR1/AFB gene family in Pyrus, elucidating their contribution to conferring resistance against various environmental stresses, and will also provide valuable insights for future researchers.

Comparative genomics analysis of pheophorbide a oxygenase (PAO) genes in eight pyrus genomes and their regulatory role in multiple stress responses in Chinese pear (Pyrus bretschneideri).

Pyrus (pear) is among the most nutritious fruits and contains fibers that have great health benefits to humans. It is mostly cultivated in temperate regions globally and is highly subjected to biotic and abiotic stresses which affect its yield. Pheophorbide a oxygenase (PAO) is an essential component of the chlorophyll degradation system and contributes to the senescence of leaves. It is responsible for opening the pheophorbide a porphyrin macrocycle and forming the main fluorescent chlorophyll catabolite However, this gene family and its members have not been explored in Pyrus genomes. Here we report a pangenome-wide investigation has been conducted on eight Pyrus genomes: Cuiguan, Shanxi Duli, Zhongai 1, Nijisseiki, Yunhong No.1, d'Anjou, Bartlett v2.0, and Dangshansuli v.1.1. The phylogenetic history, their gene structure, conservation patterns of motifs, their distribution on chromosomes, and gene duplication are studied in detail which shows the intraspecific structural conservation as well as evolutionary patterns of Pyrus PAOs. Cis-elements, protein-protein interactions (PPI), and the Gene Ontology (GO) enrichment analyses show their potential biological functions. Furthermore, their expression in various tissues, fruit hardening conditions, and drought stress conditions is also studied. Based on phylogenetics, the identified PAOs were divided into four groups. The expansion of this gene family in Pyrus is caused by both tandem and segmental duplication. Moreover, positive and negative selection pressure equally directed the gene's duplication process. The Pyrus PAO genes were enriched in hormones-related, light, development, and stress-related elements. RNA-seq data analysis showed that PAOs have varied levels of expression under diseased and abiotic stress conditions. The 3D structures of PAOs are also predicted to get more insights into functional conservation. Our research can be used further to get a deeper knowledge of the PAO gene family in Pyrus and to guide future research on improving the genetic composition of Pyrus to enhance stress tolerance.

Precision Genome Editing with CRISPR-Cas9.

The CRISPR/Cas9 system is a revolutionary technology for genome editing that allows for precise and efficient modifications of DNA sequences. The system is composed of two main components, the Cas9 enzyme and a guide RNA (gRNA). The gRNA is designed to specifically target a desired DNA sequence, while the Cas9 enzyme acts as molecular scissors to cut the DNA at that specific location. The cell then repairs the digested DNA, either through nonhomologous end joining (NHEJ) or homology-directed repair (HDR), resulting in either indels or precise modifications of DNA sequences with broad implications in biotechnology, agriculture, and medicine. This chapter provides a practical approach for utilizing CRISPR/Cas9 in precise genome editing, including identifying the target gene sequence, designing gRNA and protein (Cas9), and delivering the CRISPR components to target cells.

Comparative genomics and bioinformatics approaches revealed the role of CC-NBS-LRR genes under multiple stresses in passion fruit.

Passion fruit is widely cultivated in tropical, subtropical regions of the world. The attack of bacterial and fungal diseases, and environmental factors heavily affect the yield and productivity of the passion fruit. The CC-NBS-LRR (CNL) gene family being a subclass of R-genes protects the plant against the attack of pathogens and plays a major role in effector-triggered immunity (ETI). However, no information is available regarding this gene family in passion fruit. To address the underlying problem a total of 25 and 21 CNL genes have been identified in the genome of purple (Passiflora edulis Sims.) and yellow (Passiflora edulis f. flavicarpa) passion fruit respectively. Phylogenetic tree was divided into four groups with PeCNLs present in 3 groups only. Gene structure analysis revealed that number of exons ranged from 1 to 9 with 1 being most common. Most of the PeCNL genes were clustered at the chromosome 3 and underwent strong purifying selection, expanded through segmental (17 gene pairs) and tandem duplications (17 gene pairs). PeCNL genes contained cis-elements involved in plant growth, hormones, and stress response. Transcriptome data indicated that PeCNL3, PeCNL13, and PeCNL14 were found to be differentially expressed under Cucumber mosaic virus and cold stress. Three genes were validated to be multi-stress responsive by applying Random Forest model of machine learning. To comprehend the biological functions of PeCNL proteins, their 3D structure and gene ontology (GO) enrichment analysis were done. Our research analyzed the CNL gene family in passion fruit to understand stress regulation and improve resilience. This study lays the groundwork for future investigations aimed at enhancing the genetic composition of passion fruit to ensure robust growth and productivity in challenging environments.

Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions.

Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.

Network pharmacology and molecular dynamic simulation integrated strategy for the screening of active components and mechanisms of phytochemicals from Datura innoxia on Alzheimer and cognitive decline.

Alzheimer's disease (AD) ranks as the most prevalent neurodegenerative disorder with dementia and it accounts for more than 70% of all cases. Despite extensive reporting on the experimental investigation of Datura innoxia (DI) and its phytochemical components in the treatment of AD, the urgent need for elucidation of the principle of multi-mechanism and multi-level treatment of AD remains. In this research, molecular docking and network pharmacology were used to evaluate active compounds and molecular targets of DI for the treatment of AD. The phytochemical compounds of DI were obtained from the Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) as well as the Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening includes the 28 most abundant components of DI and the Swiss Target Prediction database was used to predict targets of these compounds. The GeneCards database was used to collect AD-related genes. Both DI and AD targets were imported into a Venn diagram, and the 28 overlapped genes were identified as potential DI anti-AD targets. The results showed that Dinoxin B, Meteloidine, Scopoline, and Tropic acid had no effect on AD-related genes. Furthermore, the GO enrichment analysis indicates that DI influences molecular functions and biological processes such as learning or memory and modulation of chemical synaptic transmission as well as the membrane raft and membrane microdomain. The KEGG pathway analysis revealed that the key pathways implicated in DI's anti-AD actions include serotonergic synapse, IL-17 signaling pathway, and AGE-RAGE signaling pathway in diabetic complications. Based on the STRING and Cytoscape network-analysis platforms, the top ten anti-AD core targets include APP, CASP3, IL6, BACE1, IL1B, ACE, PSEN1, GAPDH, GSK3B and ACHE. The molecular docking and molecular dynamic simulation of the top two molecules against the top three target proteins confirmed the strong binding affinity and stability at the docked site. Overall, our findings pave the path for further research into the development and optimization of potential anti-AD agents from DI.Communicated by Ramaswamy H. Sarma.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer.

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Unveiling MurE ligase potential inhibitors for treating multi-drug resistant Acinetobacter baumannii.

Acinetobacter baumannii is an opportunistic pathogen with ability to cause serious infection such as bacteremia, ventilator associated pneumonia, and wound infections. As strains of A. baumannii are resistant to almost all clinically used antibiotics and with the emergence of carbapenems resistant phenotypes warrants the search for novel antibiotics. Considering this, herein, a series of computer aided drug designing approach was utilized to search novel chemical scaffolds that bind stronger to MurE ligase enzyme of A. baumannii, which is involved peptidoglycan synthesis. The work identified LAS_22461675, LAS_34000090 and LAS_51177972 compounds as promising binding molecules with MurE enzyme having binding energy score of -10.5 kcal/mol, -9.3 kcal/mol and -8.6 kcal/mol, respectively. The compounds were found to achieve docked inside the MurE substrate binding pocket and established close distance chemical interactions. The interaction energies were dominated by van der Waals and less contributions were seen from hydrogen bonding energy. The dynamic simulation assay predicted the complexes stable with no major global and local changes noticed. The docked stability was also validated by MM/PBSA and MM/GBSA binding free energy methods. The net MM/GBSA binding free energy of LAS_22461675 complex, LAS_34000090 complex and LAS_51177972 complex is -26.25 kcal/mol, -27.23 kcal/mol and -29.64 kcal/mol, respectively. Similarly in case of MM-PBSA, the net energy value was in following order; LAS_22461675 complex (-27.67 kcal/mol), LAS_34000090 complex (-29.94 kcal/mol) and LAS_51177972 complex (-27.32 kcal/mol). The AMBER entropy and WaterSwap methods also confirmed stable complexes formation. Further, molecular features of the compounds were determined that predicted compounds to have good druglike properties and pharmacokinetic favorable. The study concluded the compounds to good candidates to be tested by in vivo and in vitro experimental assays.Communicated by Ramaswamy H. Sarma.

Structural insights into the mechanism of resistance to bicalutamide by the clinical mutations in androgen receptor in chemo-treatment resistant prostate cancer.

Despite advanced diagnosis and detection technologies, prostate cancer (PCa) is the most prevalent neoplasms in males. Dysregulation of the androgen receptor (AR) is centrally involved in the tumorigenesis of PCa cells. Acquisition of drug resistance due to modifications in AR leads to therapeutic failure and relapse in PCa. An overhaul of comprehensive catalogues of cancer-causing mutations and their juxta positioning on 3D protein can help in guiding the exploration of small drug molecules. Among several well-studied PCa-specific mutations, T877A, T877S and H874Y are the most common substitutions in the ligand-binding domain (LBD) of the AR. In this study, we combined structure as well as dynamics-based in silico approaches to infer the mechanistic effect of amino acid substitutions on the structural stability of LBD. Molecular dynamics simulations allowed us to unveil a possible drug resistance mechanism that acts through structural alteration and changes in the molecular motions of LBD. Our findings suggest that the resistance to bicalutamide is partially due to increased flexibility in the H12 helix, which disturbs the compactness, thereby reducing the affinity for bicalutamide. In conclusion, the current study helps in understanding the structural changes caused by mutations and could assist in the drug development process.Communicated by Ramaswamy H. Sarma.

Integrative multiomics profiling of passion fruit reveals the genetic basis for fruit color and aroma.

Passion fruit (Passiflora edulis) possesses a complex aroma and is widely grown in tropical and subtropical areas. Here, we conducted the de novo assembly, annotation, and comparison of PPF (P. edulis Sims) and YPF (P. edulis f. flavicarpa) reference genomes using PacBio, Illumina, and Hi-C technologies. Notably, we discovered evidence of recent whole-genome duplication events in P. edulis genomes. Comparative analysis revealed 7.6∼8.1 million single nucleotide polymorphisms, 1 million insertions/deletions, and over 142 Mb presence/absence variations among different P. edulis genomes. During the ripening of yellow passion fruit, metabolites related to flavor, aroma, and color were substantially accumulated or changed. Through joint analysis of genomic variations, differentially expressed genes, and accumulated metabolites, we explored candidate genes associated with flavor, aroma, and color distinctions. Flavonoid biosynthesis pathways, anthocyanin biosynthesis pathways, and related metabolites are pivotal factors affecting the coloration of passion fruit, and terpenoid metabolites accumulated more in PPF. Finally, by heterologous expression in yeast (Saccharomyces cerevisiae), we functionally characterized 12 terpene synthases. Our findings revealed that certain TPS homologs in both YPF and PPF varieties produce identical terpene products, while others yield distinct compounds or even lose their functionality. These discoveries revealed the genetic and metabolic basis of unique characteristics in aroma and flavor between the 2 passion fruit varieties. This study provides resources for better understanding the genome architecture and accelerating genetic improvement of passion fruits.

Comparative genomics profiling revealed multi-stress responsive roles of the CC-NBS-LRR genes in three mango cultivars.

The nucleotide-binding site-leucine-rich repeat (NBS-LRR) gene family is the largest group of disease resistance (R) genes in plants and is active in response to viruses, bacteria, and fungi usually involved in effector-triggered immunity (ETI). Pangenome-wide studies allow researchers to analyze the genetic diversity of multiple species or their members simultaneously, providing a comprehensive understanding of the evolutionary relationships and diversity present among them. The draft pan-genome of three Mangifera indica cultivars (Alphonso, Hong Xiang Ya, and Tommy atkins) was constructed and Presence/absence variants (PAVs) were filtered through the ppsPCP pipeline. As a result, 2823 genes and 5907 PAVs from H. Xiang Ya, and 1266 genes and 2098 PAVs from T. atkins were added to the reference genome. For the identification of CC-NBS-LRR (CNL) genes in these mango cultivars, this draft pan-genome study has successfully identified 47, 27, and 36 members in Alphonso, H. Xiang Ya, and T. atkins respectively. The phylogenetic analysis divided MiCNL proteins into four distinct subgroups. All MiCNL genes are unevenly distributed on chromosomes. Both tandem and segmental duplication events played a significant role in the expansion of the CNL gene family. These genes contain cis-elements related to light, stress, hormone, and development. The analysis of protein-protein interactions (PPI) revealed that MiCNL proteins interacted with other defense-responsive proteins. Gene Ontology (GO) analysis indicated that MiCNL genes play a role in defense mechanisms within the organism. The expression level of the identified genes in fruit peel was observed under disease and cold stress which showed that Mi_A_CNL13 and 14 were up-regulated while Mi_A_CNL15, 25, 30, 31, and 40 were down-regulated in disease stress. On the other hand, Mi_A_CNL2, 14, 41, and 45 were up-regulated and Mi_A_CNL47 is down-regulated in cold stress. Subsequently, the Random Forest (RF) classifier was used to assess the multi-stress response of MiCNLs. It was found that Mi_A_CNL14 is a gene that responds to multiple stress conditions. The CNLs have similar protein structures which show that they are involved in the same function. The above findings provide a foundation for a deeper understanding of the functional characteristics of the mango CNL gene family.

Integrated omics and machine learning-assisted profiling of cysteine-rich-receptor-like kinases from three peanut spp. revealed their role in multiple stresses.

Arachis hypogaea (peanut) is a leading oil and protein-providing crop with a major food source in many countries. It is mostly grown in tropical regions and is largely affected by abiotic and biotic stresses. Cysteine-rich receptor-like kinases (CRKs) is a family of transmembrane proteins that play important roles in regulating stress-signaling and defense mechanisms, enabling plants to tolerate stress conditions. However, almost no information is available regarding this gene family in Arachis hypogaea and its progenitors. This study conducts a pangenome-wide investigation of A. hypogaea and its two progenitors, A. duranensis and A. ipaensis CRK genes (AhCRKs, AdCRKs, and AiCRKs). The gene structure, conserved motif patterns, phylogenetic history, chromosomal distribution, and duplication were studied in detail, showing the intraspecies structural conservation and evolutionary patterns. Promoter cis-elements, protein-protein interactions, GO enrichment, and miRNA targets were also predicted, showing their potential functional conservation. Their expression in salt and drought stresses was also comprehensively studied. The CRKs identified were divided into three groups, phylogenetically. The expansion of this gene family in peanuts was caused by both types of duplication: tandem and segmental. Furthermore, positive as well as negative selection pressure directed the duplication process. The peanut CRK genes were also enriched in hormones, light, development, and stress-related elements. MicroRNA (miRNA) also targeted the AhCRK genes, which suggests the regulatory association of miRNAs in the expression of these genes. Transcriptome datasets showed that AhCRKs have varying expression levels under different abiotic stress conditions. Furthermore, the multi-stress responsiveness of the AhCRK genes was evaluated using a machine learning-based method, Random Forest (RF) classifier. The 3D structures of AhCRKs were also predicted. Our study can be utilized in developing a detailed understanding of the stress regulatory mechanisms of the CRK gene family in peanuts and its further studies to improve the genetic makeup of peanuts to thrive better under stress conditions.

Network pharmacology based virtual screening of Flavonoids from Dodonea angustifolia and the molecular mechanism against inflammation.

Inflammation is a nonspecific immune response against injury caused by a harmful agent that strives to restore tissue function and homeostasis. Dodonaea angustifolia L.f. (Sapindaceae) is a medium-sized shrub used to treat a variety of diseases in traditional medicine. In the current study, integrated network-pharmacology and molecular docking approaches were used to identify the active constituents, their possible targets, signaling pathways, and anti-inflammatory effects of flavonoids from D.angustifolia. D. angustifolia active ingredients were acquired from the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT), and Traditional Chinese Medicine System Pharmacology (TCMSP) databases. The screening included the ten most prevalent D. angustifolia components, and the SwissTargetPrediction database was utilized to anticipate the targets of these compounds. Anti-inflammatory genes were found using the GeneCards database. The 175 overlapping genes were discovered as prospective D. angustifolia anti-inflammatory targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the overlapped targets were closely related to the major pathogenic processes linked to inflammation, such as response to organonitrogen compound, protein kinase activity, phosphotransferase activity, pI3k-Akt signaling pathway, metabolic pathways, and chemical carcinogenesis. Compound-target-pathway, and protein-protein interaction networks revealed 6-Methoxykaempferol and 5-Hydroxy-7,8 dimethoxyflavone as key compounds, and AKT1, VEGFA, and EGFR as key targets. Furthermore, molecular docking followed by molecular dynamic (MD) simulation of D. angustifolia active ingredients with core proteins fully complemented the binding affinity of these compounds and indicated stable complexes at the docked site. These findings reveal D. angustifolia 's multi-target, multi-compound, and multi-pathway strategies against inflammation. Our study paved the way for further research into the mechanism for developing D. angustifolia -based natural products as alternative therapies for inflammation.

Identification and dynamics of novel scaffolds against Enterococcus faecium serine hydroxymethyltransferase enzyme: a potential target for antibiotics development.

Serine hydroxymethyltransferase enzyme is a significant player in purine, thymidylate, and L-serine biosynthesis and has been tagged as a potential target for cancer, viruses, and parasites. However, this enzyme as an anti-bacterial druggable target has not been explored much. Herein, in this work, different computational chemistry and biophysics techniques were applied to identify potential computational predicted inhibitory molecules against Enterococcus faecium serine hydroxymethyltransferase enzyme. By structure based virtual screening process of ASINEX antibacterial library against the enzyme two main compounds: Top-1_BDC_21204033 and Top-2_BDC_20700155 were reported as best binding molecules. The Top-1_BDC_21204033 and Top-2_BDC_20700155 binding energy value is -9.3 and -8.9 kcal/mol, respectively. The control molecule binding energy score is -6.55 kcal/mol. The mean RMSD of Top-1-BDC_21204033, Top-2-BDC_20700155 and control is 3.7 Å (maximum 5.03 Å), 1.7 Å (maximum 3.05 Å), and 3.84 Å (maximum of 6.7 Å), respectively. During the simulation time, the intermolecular docked conformation and interactions were seen stable despite of few small jumps by the compounds/control, responsible for high RMSD in some frames. The MM/GBSA and MM/PBSA binding free energy of lead Top-2-BDC_20700155 complex is -79.52 and -82.63 kcal/mol, respectively. This complex was seen as the most stable compared to the control. Furthermore, the lead molecules and control showed good druglikeness and pharmacokinetics profile. The lead molecules were non-toxic and non-mutagenic. In short, the compounds are promising in terms of binding to the serine hydroxymethyltransferase enzyme and need to be subjected to experimental studies.Communicated by Ramaswamy H. Sarma.

Systematic elucidation of the multi-target pharmacological mechanism of Terminalia arjuna against congestive cardiac failure via network pharmacology and molecular modelling approaches.

Congestive cardiac failure (CCF) is a pathophysiologic state when the heart is not able to maintain its cardiac output to meet the demand of metabolising tissues. CCF is responsible for approximately 2.9 million deaths worldwide. The heterogeneous nature of CCF draws the attention of researchers to find more enthralling and promising diagnostic and treatment options. Terminalia arjuna (Arjuna) is an evergreen, deciduous tree exhibited various astringent, anti-bacterial, and anti-microbial properties. T. arjuna is being used in various regions for anginal pain, hypertension, congestive heart failure, and dyslipidemia. Although previous in vitro studies have demonstrated the therapeutic potential of T. arjuna, the exact molecular mechanism underlying its protective effect on the heart remains unclear. In this study, a network pharmacology technique was used to explore the active ingredients, potential targets in T. arjuna for the treatment of CCF. In the framework of this study, we explored the active ingredient-target-pathway network and figured out that oleanolic acid, arjunolic acid, luteolin, kaempferol, cholesterol, ellagic acid 4-O-xylopyranoside 3,3'-dimethyl ether, and cyclohexyl (2,4-dimethyl phenyl) methanone contributed significantly to the development of CCF by affecting AKT1, MAPK14, TNF, IL6, ESR1, and HSP90AA1 genes. Molecular docking analysis further validated the activities of these compounds against potential targets. To sum up, integrated network pharmacology and docking analysis revealed that T. arjuna exerts its cardioprotective effect by acting on various signalling pathways, including the thyroid hormone, VEGF signalling pathway, AGE-RAGE signalling pathway in diabetic complications, HIF signalling pathway, sphingolipid signalling pathway, and oestrogen signalling pathways. Overall, this study provides valuable insights into the molecular mechanism of T. arjuna in CCF and highlights its potential as a promising preventive treatment for this condition.

LC-MS/MS-based metabolomics approach identified novel antioxidant flavonoids associated with drought tolerance in citrus species.

Citrus fruits are cultivated around the world, and they face drought stress frequently during their growth and development. Previous studies showed that citrus plants biosynthesized flavonoid compounds in response to abiotic stress. In this study, we have quantified 37 flavonoid compounds from the leaves of three distinct citrus species including sour orange (drought-tolerant), pummelo 'Majia you pummelo' (drought-sensitive), and lemon (drought-sensitive). The 37 flavonoids consisted of 12 flavones, 10 flavonols, 6 flavanones, 5 isoflavanones, and 1 each for chalcone, flavanol, flavanonol, and flavone glycoside. Drought stress differentially altered the flavonoid metabolism in drought-tolerant and drought-sensitive citrus species. The kaempferol 3-neohesperidoside was 17-fold higher in sour orange (124.41 nmol/L) after 18 days of drought stress than lemon (7.33 nmol/L). In sour orange, neohesperidin (69.49 nmol/L) was 1,407- and 37-fold higher than pummelo and lemon, respectively. In sour orange, some flavonoids were significantly increased, such as vitexin, neohesperidin, cynaroside, hyperoside, genistin, kaempferol 3-neohesperidoside, eriocitrin, and luteolin, in response to drought stress, whereas in lemon, these flavonoids were significantly decreased or not altered significantly in response to drought stress. Moreover, the total contents of flavonoids and antioxidant activity were increased in sour orange as compared with pummelo and lemon. The genes associated with flavonoid biosynthesis (PAL, CHI, FLS, GT1, F3H, F3'M, C4H, 4CL, FLS, FG2, FG3, and CYP81E1) were more highly expressed in sour orange leaves than in pummelo and lemon after drought stress. These outcomes showed that pummelo and lemon failed to biosynthesize antioxidant flavonoids to cope with the prolonged drought stress, whereas the sour orange biosynthesized fortified flavonoid compounds with increased antioxidant activity to detoxify the harmful effects of reactive oxygen species produced during drought stress.

Development of a Novel Vaccine Candidates against Cardiobacterium valvarum through Reverse Vaccinology and Computational Approaches.

Antibiotic resistance is a major public health concern that has resulted in high healthcare costs, increased mortality, and the emergence of novel bacterial diseases. Cardiobacterium valvarum, an antibiotic-resistant bacterium, is one of the leading causes of heart disease. Currently, there is no licensed vaccination against C. valvarum. In this research, an in silico-based vaccine was designed against C. valvarum using reverse vaccinology, bioinformatics, and immunoinformatics techniques. 4206 core proteins, 2027 nonredundant proteins, and 2179 redundant proteins were predicted. Among nonredundant proteins, 23 proteins were predicted in an extracellular membrane, 30 in the outer membrane, and 62 in the periplasmic membrane region. After applying several subtractive proteomics filters, two proteins, TonB-dependent siderophore receptor and hypothetical protein, were chosen for epitope prediction. In the epitope selection phase, B and T-cellepitopes were analyzed and shortlisted for vaccine design. The vaccine model was designed by linking selected epitopes with GPGPG linkers to avoid flexibility. Furthermore, the vaccine model was linked to cholera toxin B adjuvant to induce a proper immune response. The docking approach was utilized to analyze binding affinity to immune cell receptors. Molecular docking results predicted 12.75 kcal/mol for a Vaccine with MHC-I, 6.89 for a vaccine with MHC-II, and 19.51 vaccine with TLR-4. The MMGBSA estimated -94, -78, and -76 kcal/mol for TLR-4 and vaccine, MHC-I and vaccine, and MHC-II and vaccine, while the MMPBSA analysis estimated -97, -61, and -72 kcal/mol for TLR-4 with the vaccine, MHC-I with vaccine, and MHC-II with a vaccine. Molecular dynamic simulation analysis revealed that the designed vaccine construct has proper stability with immune cell receptors as it is essential for inducing an immune response. In conclusion, we observed that the model vaccine candidate has the potency to induce an immune response in the host. However, the study is designed purely on a computational basis; hence, experimental validation is strongly recommended.

Discovering common pathogenic processes between COVID-19 and HFRS by integrating RNA-seq differential expression analysis with machine learning.

Zoonotic virus spillover in human hosts including outbreaks of Hantavirus and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) imposes a serious impact on the quality of life of patients. Recent studies provide a shred of evidence that patients with Hantavirus-caused hemorrhagic fever with renal syndrome (HFRS) are at risk of contracting SARS-CoV-2. Both RNA viruses shared a higher degree of clinical features similarity including dry cough, high fever, shortness of breath, and certain reported cases with multiple organ failure. However, there is currently no validated treatment option to tackle this global concern. This study is attributed to the identification of common genes and perturbed pathways by combining differential expression analysis with bioinformatics and machine learning approaches. Initially, the transcriptomic data of hantavirus-infected peripheral blood mononuclear cells (PBMCs) and SARS-CoV-2 infected PBMCs were analyzed through differential gene expression analysis for identification of common differentially expressed genes (DEGs). The functional annotation by enrichment analysis of common genes demonstrated immune and inflammatory response biological processes enriched by DEGs. The protein-protein interaction (PPI) network of DEGs was then constructed and six genes named RAD51, ALDH1A1, UBA52, CUL3, GADD45B, and CDKN1A were identified as the commonly dysregulated hub genes among HFRS and COVID-19. Later, the classification performance of these hub genes were evaluated using Random Forest (RF), Poisson Linear Discriminant Analysis (PLDA), Voom-based Nearest Shrunken Centroids (voomNSC), and Support Vector Machine (SVM) classifiers which demonstrated accuracy >70%, suggesting the biomarker potential of the hub genes. To our knowledge, this is the first study that unveiled biological processes and pathways commonly dysregulated in HFRS and COVID-19, which could be in the next future used for the design of personalized treatment to prevent the linked attacks of COVID-19 and HFRS.

Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis.

Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan-Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.