Bioinformatics and Drug Discovery

Unraveling diabetes complexity through natural products, miRNAs modulation, and future paradigms in precision medicine and global health

BACKGROUND AND AIMS

The challenge posed by diabetes necessitates a paradigm shift from conventional diagnostic approaches focusing on glucose and lipid levels to the transformative realm of precision medicine. This approach, leveraging advancements in genomics and proteomics, acknowledges the individualistic genetic variations, dietary preferences, and environmental exposures in diabetes management. The study comprehensively analyzes the evolving diabetes landscape, emphasizing the pivotal role of genomics, proteomics, microRNAs (miRNAs), metabolomics, and bioinformatics.

RESULTS

Precision medicine revolutionizes diabetes research and treatment by diverging from traditional diagnostic methods, recognizing the heterogeneous nature of the condition. MiRNAs, crucial post-transcriptional gene regulators, emerge as promising therapeutic targets, influencing key facets such as insulin signaling and glucose homeostasis. Metabolomics, an integral component of omics sciences, contributes significantly to diabetes research, elucidating metabolic disruptions, and offering potential biomarkers for early diagnosis and personalized therapies. Bioinformatics unveils dynamic connections between natural substances, miRNAs, and cellular pathways, aiding in the exploration of the intricate molecular terrain in diabetes. The study underscores the imperative for experimental validation in natural product-based diabetes therapy, emphasizing the need for in vitro and in vivo studies leading to clinical trials for assessing effectiveness, safety, and tolerability in real-world applications. Global cooperation and ethical considerations play a pivotal role in addressing diabetes challenges worldwide, necessitating a multifaceted approach that integrates traditional knowledge, cultural competence, and environmental awareness.

CONCLUSIONS

The key components of diabetes treatment, including precision medicine, metabolomics, bioinformatics, and experimental validation, converge in future strategies, embodying a holistic paradigm for diabetes care anchored in cutting-edge research and global healthcare accessibility.

Monastrol suppresses invasion and metastasis in human colorectal cancer cells by targeting fascin independent of kinesin-Eg5 pathway

Rearrangement of the actin cytoskeleton is a prerequisite for carcinoma cells to develop cellular protrusions, which are required for migration, invasion, and metastasis. Fascin is a key protein involved in actin bundling and is expressed in aggressive and invasive carcinomas. Additionally, fascin appears to be involved in tubulin-binding and microtubule rearrangement. Pharmacophoric-based in silico screening was performed to identify compounds with better fascin inhibitory properties than migrastatin, a gold-standard fascin inhibitor. We hypothesized that monastrol displays anti-migratory and anti-invasive properties via fascin blocking in colorectal cancer cell lines. Biophysical (thermofluor and ligand titration followed by fluorescence spectroscopy), biochemical (NMR), and cellular assays (MTT, invasion of human tissue), as well as animal model studies (zebrafish invasion) were performed to characterize the inhibitory effect of monastrol on fascin activity. In silico analysis revealed that monastrol is a potential fascin-binding compound. Biophysical and biochemical assays demonstrated that monastrol binds to fascin and interferes with its actin-bundling activity. Cell culture studies, including a 3D human myoma disc model, showed that monastrol inhibited fascin-driven cytoplasmic protrusions as well as invasion. In silico, confocal microscopy, and immunoprecipitation assays demonstrated that monastrol disrupted fascin-tubulin interactions. These anti-invasive effects were confirmed in vivo. In silico confocal microscopy and immunoprecipitation assays were carried out to test whether monastrol disrupted the fascin-tubulin interaction. This study reports, for the first time, the in vitro and in vivo anti-invasive properties of monastrol in colorectal tumor cells. The number and types of interactions suggest potential binding of monastrol across actin and tubulin sites on fascin, which could be valuable for the development of antitumor therapies.

Cldn4 overexpression promotes penile cavernous smooth muscle cell fibrotic response via the JNK signaling pathway

BACKGROUND

Erectile dysfunction (ED), defined as the inability to achieve or maintain a penile erection sufficient to satisfy sexual behavior, is prevalent worldwide.

AIM

Using previous research, bioinformatics, and experimental confirmation, we aimed to discover genes that contribute to ED through regulating hypoxia in corpus cavernosum smooth muscle cells (CCSMCs).

METHODS

We used the Gene Expression Omnibus to acquire the sequencing data of the corpus cavernosum transcriptome for diabetic ED and nerve injury type ED rats. We intersected the common differentially expressed genes. Further verification was performed using single cell sequencing. Real-time quantitative polymerase chain reaction and immunofluorescence were used to investigate whether the differentially expressed genes are found in the corpus cavernosum. We used induced hypoxia to assess cell viability changes, and we developed a lentivirus overexpressing Cldn4 for in vitro and in vivo experiments to measure changes in JNK signaling, fibrosis, hypoxia, and erectile function.

OUTCOMES

Our results indicate that targeting the JNK pathway and decreasing local hypoxia may be better options for therapeutic intervention to improve erectile function.

RESULTS

We identified Cldn4 and found its expression increased in the corpora cavernosa of the 2 datasets. In addition, we found that hypoxia can increase the expression of Cldn4, activate the JNK signaling pathway, and exacerbate fibrosis in CCSMCs. Cldn4 overexpression in CCSMCs activated the JNK signaling pathway and increased fibrotic protein expression. Last, rat corpus cavernosum overexpressing Cldn4 activated the JNK signaling pathway, increased local fibrosis, and impaired erectile function.

CLINICAL IMPLICATIONS

Through bioinformatics and in vitro and in vivo experiments, we found that Cldn4 has a negative effect on ED, and targeting Cldn4 may provide new ideas for ED treatment.

STRENGTHS AND LIMITATIONS

Although we have identified Cldn4 as a potential target for ED treatment, we have only conducted preliminary validation on CCMSCs, and we still need to further validate in other cell lines.

CONCLUSION

CCSMC hypoxia leads to increased Cldn4, in both nerve injury and diabetic ED rat models, and promotes fibrosis by activating the JNK signaling pathway.

Drug repurposing for neurodegenerative diseases

Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.

Current approaches in identification of a novel drug targets for drug repurposing

Currently, millions of drugs and their licence have been expired or will be expiring in near future. Therefore, existing USFDA approved drug can be used for treating another disease. The above-mentioned approach falls under the category of drug repurposing. Drug repurposing is an alternative strategy for finding new applications of existing USFDA approved drugs. Identification of a novel drug target is one of the go to way for drug repurposing so that new therapeutic applications of USFDA approved drugs could be determined. Recent advances in computational biology and bioinformatics can help to accelerate the same. Drug repurposing can save time and resource as compared to discovery of an entirely new drug molecule. In this chapter, we explore different strategies for discovery of a novel drug target and its uses for drug repurposing to treat disease.

Novel sulfonamide-indolinone hybrids targeting mitochondrial respiration of breast cancer cells

Breast cancer (BC) still poses a threat worldwide which demands continuous efforts to present safer and efficacious treatment options via targeted therapy. Beside kinases' aberrations as Aurora B kinase which controls cell division, BC adopts distinct metabolic profiles to meet its high energy demands. Accordingly, targeting both aurora B kinase and/or metabolic vulnerability presents a promising approach to tackle BC. Based on a previously reported indolinone-based Aurora B kinase inhibitor (III), and guided by structural modification and SAR investigation, we initially synthesized 11 sulfonamide-indolinone hybrids (5a-k), which showed differential antiproliferative activities against the NCI-60 cell line panel with BC cells displaying preferential sensitivity. Nonetheless, modest activity against Aurora B kinase (18-49% inhibition) was noted at 100 nM. Screening of a representative derivative (5d) against 17 kinases, which are overexpressed in BC, failed to show significant activity at 1 μM concentration, suggesting that kinase inhibitory activity only played a partial role in targeting BC. Bioinformatic analyses of genome-wide transcriptomics (RNA-sequencing), metabolomics, and CRISPR loss-of-function screens datasets suggested that indolinone-completely responsive BC cell lines (MCF7, MDA-MB-468, and T-47D) were more dependent on mitochondrial oxidative phosphorylation (OXPHOS) compared to partially responsive BC cell lines (MDA-MB-231, BT-549, and HS 578 T). An optimized derivative, TC11, obtained by molecular hybridization of 5d with sunitinib polar tail, manifested superior antiproliferative activity and was used for further investigations. Indeed, TC11 significantly reduced/impaired the mitochondrial respiration, as well as mitochondria-dependent ROS production of MCF7 cells. Furthermore, TC11 induced G0/G1 cell cycle arrest and apoptosis of MCF7 BC cells. Notably, anticancer doses of TC11 did not elicit cytotoxic effects on normal cardiomyoblasts and hepatocytes. Altogether, these findings emphasize the therapeutic potential of targeting the metabolic vulnerability of OXPHOS-dependent BC cells using TC11 and its related sulfonamide-indolinone hybrids. Further investigation is warranted to identify their precise/exact molecular target.

Navigating the Chemical Space of ENR Inhibitors: A Comprehensive Analysis

Antimicrobial resistance is a global health threat that requires innovative strategies against drug-resistant bacteria. Our study focuses on enoyl-acyl carrier protein reductases (ENRs), in particular FabI, FabK, FabV, and InhA, as potential antimicrobial agents. Despite their promising potential, the lack of clinical approvals for inhibitors such as triclosan and isoniazid underscores the challenges in achieving preclinical success. In our study, we curated and analyzed a dataset of 1412 small molecules recognized as ENR inhibitors, investigating different structural variants. Using advanced cheminformatic tools, we mapped the physicochemical landscape and identified specific structural features as key determinants of bioactivity. Furthermore, we investigated whether the compounds conform to Lipinski rules, PAINS, and Brenk filters, which are crucial for the advancement of compounds in development pipelines. Furthermore, we investigated structural diversity using four different representations: Chemotype diversity, molecular similarity, t-SNE visualization, molecular complexity, and cluster analysis. By using advanced bioinformatics tools such as matched molecular pairs (MMP) analysis, machine learning, and SHAP analysis, we were able to improve our understanding of the activity cliques and the precise effects of the functional groups. In summary, this chemoinformatic investigation has unraveled the FAB inhibitors and provided insights into rational antimicrobial design, seamlessly integrating computation into the discovery of new antimicrobial agents.

Microarray data analysis of antileukemic action of Cinnamoylated benzaldehyde LQB-461 in Jurkat cell line

BACKGROUND

Leukemias stand out for being the main type of childhood cancer in the world. Current treatments have strong side effects for patients, and there is still a high rate of development of resistance to multidrug therapy. Previously, our research group developed a structure-activity study with novel synthetic molecules analogous to LQB-278, described as an essential molecule with in vitro antileukemic action. Among these analogs, LQB-461 stood out, presenting more significant antileukemic action compared to its derivative LQB-278, with cytostatic and cytotoxicity effect by apoptosis, inducing caspase-3, and increased sub-G1 phase on cell cycle analysis.

METHODS AND RESULTS

Deepening the study of the mechanism of action of LQB-461 in Jurkat cells in vitro, a microarray assay was carried out, which confirmed the importance of the apoptosis pathway in the LQB-461 activity. Through real-time PCR, we validated an increased expression of CDKN1A and BAX genes, essential mediators of the apoptosis intrinsic pathway. Through the extrinsic apoptosis pathway, we found an increased expression of the Fas receptor by flow cytometry, showing the presence of a more sensitive population and another more resistant to death. Considering the importance of autophagy in cellular resistance, it was demonstrated by western blotting that LQB-461 decreased LC-3 protein expression, an autophagic marker.

CONCLUSIONS

These results suggest that this synthetic molecule LQB-461 induces cell death by apoptosis in Jurkat cells through intrinsic and extrinsic pathways and inhibits autophagy, overcoming some mechanisms of cell resistance related to this process, which differentiates LQB-461 of other drugs used for the leukemia treatment.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

An AI Approach to Identifying Novel Therapeutics for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that has a significant impact on quality of life and work capacity. Treatment of RA aims to control inflammation and alleviate pain; however, achieving remission with minimal toxicity is frequently not possible with the current suite of drugs. This review aims to summarise current treatment practices and highlight the urgent need for alternative pharmacogenomic approaches for novel drug discovery. These approaches can elucidate new relationships between drugs, genes, and diseases to identify additional effective and safe therapeutic options. This review discusses how computational approaches such as connectivity mapping offer the ability to repurpose FDA-approved drugs beyond their original treatment indication. This review also explores the concept of drug sensitisation to predict co-prescribed drugs with synergistic effects that produce enhanced anti-disease efficacy by involving multiple disease pathways. Challenges of this computational approach are discussed, including the availability of suitable high-quality datasets for comprehensive analysis and other data curation issues. The potential benefits include accelerated identification of novel drug combinations and the ability to trial and implement established treatments in a new index disease. This review underlines the huge opportunity to incorporate disease-related data and drug-related data to develop methods and algorithms that have strong potential to determine novel and effective treatment regimens.

Selection of Mexican Medicinal Plants by Identification of Potential Phytochemicals with Anti-Aging, Anti-Inflammatory, and Anti-Oxidant Properties through Network Analysis and Chemoinformatic Screening

Many natural products have been acquired from plants for their helpful properties. Medicinal plants are used for treating a variety of pathologies or symptoms. The axes of many pathological processes are inflammation, oxidative stress, and senescence. This work is focused on identifying Mexican medicinal plants with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects through network analysis and chemoinformatic screening of their phytochemicals. We used computational methods to analyze drug-like phytochemicals in Mexican medicinal plants, multi-target compounds, and signaling pathways related to anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence mechanisms. A total of 1373 phytochemicals are found in 1025 Mexican medicinal plants, and 148 compounds showed no harmful functionalities. These compounds displayed comparable structures with reference molecules. Based on their capacity to interact with pharmacological targets, three clusters of Mexican medicinal plants have been established. Curatella americana, Ximenia americana, Malvastrum coromandelianum, and Manilkara zapota all have anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Plumeria rubra, Lonchocarpus yucatanensis, and Salvia polystachya contained phytochemicals with anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence reported activity. Lonchocarpus guatemalensis, Vallesia glabra, Erythrina oaxacana, and Erythrina sousae have drug-like phytochemicals with potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. Between the drug-like phytochemicals, lonchocarpin, vallesine, and erysotrine exhibit potential anti-oxidant, anti-inflammatory, anti-aging, and anti-senescence effects. For the first time, we conducted an initial virtual screening of selected Mexican medicinal plants, which was subsequently confirmed in vivo, evaluating the anti-inflammatory activity of Lonchocarpus guatemalensis Benth in mice.

TB diagnostic insights, progress made on point of care diagnostics and bioinformatics as an additional tool for improvement

Despite major efforts made to control tuberculosis disease (TB), this disease continues to present a major global health challenge and drug resistance is continuously growing. TB is caused by Mycobacterium tuberculosis and spreads exclusively via human-to-human contact transmission. Therefore, early detection and diagnosis for proper treatment with active TB have a great impact on public health. Regardless, most people in developing countries with TB or TB-associated symptoms do not have access to an adequate initial diagnosis. Available bacteriologic-based techniques are either inefficient or may require a longer turnaround time from the laboratory. Contemporarily, non-bacteriologic based methods have both questionable sensitivity and specificity and while others cannot distinguish between active and latent TB. Thus, additional efforts have been made to find accurate diagnostic tests for TB. Herein, we review the available methods used for TB diagnosis, and in addition, we explore point of care (POC) diagnostics as an alternative way to develop TB diagnostic tests and further evaluate whether bioinformatics can be used as an additional screening tool for identification of possible TB biomarkers for the development of POC TB diagnostics, which is part of our research focus.

Drug repurposing based on the similarity gene expression signatures to explore for potential indications of quercetin: a case study of multiple sclerosis

Quercetin (QR) is a natural flavonol compound widely distributed in the plant kingdom with extensive pharmacological effects. To find the potential clinical indications of QR, 156 differentially expressed genes (DEGs) regulated by QR were obtained from the Gene Expression Omnibus database, and new potential pharmacological effects and clinical indications of QR were repurposed by integrating compounds with similar gene perturbation signatures and associated-disease signatures to QR based on the Connectivity Map and Coexpedia platforms. The results suggested QR has mainly potential therapeutic effects on multiple sclerosis (MS), osteoarthritis, type 2 diabetes mellitus, and acute leukemia. Then, MS was selected for subsequent animal experiments as a representative potential indication, and it found that QR significantly delays the onset time of classical MS model animal mice and ameliorates the inflammatory infiltration and demyelination in the central nervous system. Combined with network pharmacology technology, the therapeutic mechanism of QR on MS was further demonstrated to be related to the inhibition of the expression of inflammatory cytokines (TNF-α, IL-6, IL-1β, IFN-γ, IL-17A, and IL-2) related to TNF-α/TNFR1 signaling pathway. In conclusion, this study expanded the clinical indications of QR and preliminarily confirmed the therapeutic effect and potential mechanism of QR on MS.

Investigation of the therapeutic role of native plant compounds against colorectal cancer based on system biology and virtual screening

This study investigated the anticancer effects of compounds extracted from native plants on colon cancer following drug-target-network analysis and molecular docking. Based on the ChEBI database, compounds were identified in medicinal plants and weeds in the Chaharmahal and Bakhtiari provinces of Iran. A drug-target network was constructed based on candidate colon cancer protein targets and selective compounds. Network pharmacology analysis was conducted against the identified compounds and subjected to molecular docking studies. Based on molecular dynamics simulations, the most efficient compounds were evaluated for their anticancer effects. Our study suggests that TREM1, MAPK1, MAPK8, CTSB, MIF, and DPP4 proteins may be targeted by compounds in medicinal plants for their anti-cancer effects. Multiorthoquinone, Liquiritin, Isoliquiritin, Hispaglabridin A, Gibberellin A98, Cyclomulberrin, Cyclomorusin A, and Cudraflavone B are effective anticancer compounds found in targeted medicinal plants and play an important role in the regulation of important pathways in colon cancer. Compounds that inhibit MIF, CTSB, and MAPK8-16 appear to be more effective. Additional in vitro and in vivo experiments will be helpful in validating and optimizing the findings of this study.

Study on the mechanism of action of colchicine in the treatment of coronary artery disease based on network pharmacology and molecular docking technology

Objective: This is the first study to explore the mechanism of colchicine in treating coronary artery disease using network pharmacology and molecular docking technology, aiming to predict the key targets and main approaches of colchicine in treating coronary artery disease. It is expected to provide new ideas for research on disease mechanism and drug development. Methods: Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Swiss Target Prediction and PharmMapper databases were used to obtain drug targets. GeneCards, Online Mendelian Inheritance in Man (OMIM), Therapeutic Target Database (TTD), DrugBank and DisGeNET databases were utilized to gain disease targets. The intersection of the two was taken to access the intersection targets of colchicine for the treatment of coronary artery disease. The Sting database was employed to analyze the protein-protein interaction network. Gene Ontology (GO) functional enrichment analysis was performed using Webgestalt database. Reactom database was applied for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking was simulated using AutoDock 4.2.6 and PyMOL2.4 software. Results: A total of 70 intersecting targets of colchicine for the treatment of coronary artery disease were obtained, and there were interactions among 50 targets. GO functional enrichment analysis yielded 13 biological processes, 18 cellular components and 16 molecular functions. 549 signaling pathways were obtained by KEGG enrichment analysis. The molecular docking results of key targets were generally good. Conclusion: Colchicine may treat coronary artery disease through targets such as Cytochrome c (CYCS), Myeloperoxidase (MPO) and Histone deacetylase 1 (HDAC1). The mechanism of action may be related to the cellular response to chemical stimulus and p75NTR-mediated negative regulation of cell cycle by SC1, which is valuable for further research exploration. However, this research still needs to be verified by experiments. Future research will explore new drugs for treating coronary artery disease from these targets.

Current trends in natural products for the treatment and management of dementia: Computational to clinical studies

The number of preclinical and clinical studies evaluating natural products-based management of dementia has gradually increased, with an exponential rise in 2020 and 2021. Keeping this in mind, we examined current trends from 2016 to 2021 in order to assess the growth potential of natural products in the treatment of dementia. Publicly available literature was collected from various databases like PubMed and Google Scholar. Oxidative stress-related targets, NF-κB pathway, anti-tau aggregation, anti-AChE, and A-β aggregation were found to be common targets and pathways. A retrospective analysis of 33 antidementia natural compounds identified 125 sustainable resources distributed among 65 families, 39 orders, and 7 classes. We found that families such as Berberidaceae, Zingiberaceae, and Fabaceae, as well as orders such as Lamiales, Sapindales, and Myrtales, appear to be important and should be researched further for antidementia compounds. Moreover, some natural products, such as quercetin, curcumin, icariside II, berberine, and resveratrol, have a wide range of applications. Clinical studies and patents support the importance of dietary supplements and natural products, which we will also discuss. Finally, we conclude with the broad scope, future challenges, and opportunities for field researchers.

Combined Multiomics and In Silico Approach Uncovers PRKAR1A as a Putative Therapeutic Target in Multi-Organ Dysfunction Syndrome

Despite all epidemiological, clinical, and experimental research efforts, therapeutic concepts in sepsis and sepsis-induced multi-organ dysfunction syndrome (MODS) remain limited and unsatisfactory. Currently, gene expression data sets are widely utilized to discover new biomarkers and therapeutic targets in diseases. In the present study, we analyzed MODS expression profiles (comprising 13 sepsis and 8 control samples) retrieved from NCBI-GEO and found 359 differentially expressed genes (DEGs), among which 170 were downregulated and 189 were upregulated. Next, we employed the weighted gene co-expression network analysis (WGCNA) to establish a MODS-associated gene co-expression network (weighted) and identified representative module genes having an elevated correlation with age. Based on the results, a turquoise module was picked as our hub module. Further, we constructed the PPI network comprising 35 hub module DEGs. The DEGs involved in the highest-confidence PPI network were utilized for collecting pathway and gene ontology (GO) terms using various libraries. Nucleotide di- and triphosphate biosynthesis and interconversion was the most significant pathway. Also, 3 DEGs within our PPI network were involved in the top 5 significantly enriched ontology terms, with hypercortisolism being the most significant term. PRKAR1A was the overlapping gene between top 5 significant pathways and GO terms, respectively. PRKAR1A was considered as a therapeutic target in MODS, and 2992 ligands were screened for binding with PRKAR1A. Among these ligands, 3 molecules based on CDOCKER score (molecular dynamics simulated-based score, which allows us to rank the binding poses according to their quality and to identify the best pose for each system) and crucial interaction with human PRKAR1A coding protein and protein kinase-cyclic nucleotide binding domains (PKA RI alpha CNB-B domain) via active site binding residues, viz. Val283, Val302, Gln304, Val315, Ile327, Ala336, Ala337, Val339, Tyr373, and Asn374, were considered as lead molecules.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.

Inhibitor Assessment against the LpxC Enzyme of Antibiotic-resistant Acinetobacter baumannii Using Virtual Screening, Dynamics Simulation, and in vitro Assays

BACKGROUND

Bacterial resistance is currently a significant global public health problem. Acinetobacter baumannii has been ranked in the list of the World Health Organization as the most critical and priority pathogen for which new antibiotics are urgently needed. In this context, computational methods play a central role in the modern drug discovery process. The purpose of the current study was to identify new potential therapeutic molecules to neutralize MDR A. baumannii bacteria.

METHODS

A total of 3686 proteins retrieved from the A. baumannii proteome were subjected to subtractive proteomic analysis to narrow down the spectrum of drug targets. The SWISS-MODEL server was used to perform a 3D homology model of the selected target protein. The SAVES server was used to evaluate the overall quality of the model. A dataset of 74500 analogues retrieved from the PubChem database was docked with LpxC using the AutoDock software.

RESULTS

In this study, we predicted a putative new inhibitor for the Lpxc enzyme of A. baumannii. The LpxC enzyme was selected as the most appropriate drug target for A. baumannii. According to the virtual screening results, N-[(2S)-3-amino-1-(hydroxyamino)-1-oxopropan-2-yl]-4-(4-bromophenyl) benzamide (CS250) could be a promising drug candidate targeting the LpxC enzyme. This molecule shows polar interactions with six amino acids and non-polar interactions with eight other residues. In vitro experimental validation was performed through the inhibition assay.

CONCLUSION

To the best of our knowledge, this is the first study that suggests CS250 as a promising inhibitory molecule that can be exploited to target this gram-negative pathogen.

Anti-leukemic effect of menthol, a peppermint compound, on induction of apoptosis and autophagy

Background

Menthol, a natural compound in peppermint leaves, has several biological activities, including antioxidant, anti-inflammatory, antiviral, antibacterial and anticancer properties. This study revealed the anti-leukemic effects and its underlying mechanisms of the menthol related apoptosis signaling pathway and autophagy in both NB4 and Molt-4 leukemic cell lines.

Methods

Both leukemic cells were treated with menthol in various concentration. Cell viability was assessed using MTT assay, whereas apoptosis and autophagy were analyzed by flow cytometry using Annexin V-FITC/PI and anti-LC3/FITC antibodies staining, respectively. Apoptotic and autophagic related gene and protein expression were detected using RT-qPCR and western blot analysis, respectively. Moreover, STITCH database was used to predicts the interaction between menthol and proposed proteins.

Results

Menthol significantly decreased cell viability in NB4 and Molt-4 cell lines in dose dependent manner. In combination of menthol and daunorubicin, synergistic cytotoxic effects were observed in leukemic cells. However, there was a minimal effect found on normal, peripheral blood mononuclear cells (PBMCs). Moreover, menthol significantly induced apoptosis induction via upregulation of caspase-3, BAX, p53 and downregulation of MDM2 mRNA expression. Autophagy was also induced by menthol through upregulating ATG3 and downregulating mTOR mRNA expression. For protein expression, menthol significantly increased caspase-3 whereas decreased mTOR in both leukemic cells. Conclusions. These results suggest that menthol exhibits cytotoxic activities by inhibition of cell proliferation, induction of apoptosis and autophagy through activating the caspase cascade, altering BAX and p53/MDM2, and regulating autophagy via the ATG3/mTOR signaling pathway.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science

Having played important roles in human growth and development, livestock animals are regarded as integral parts of society. However, industrialization has depleted natural resources and exacerbated climate change worldwide, spurring the emergence of various diseases that reduce livestock productivity. Meanwhile, a growing human population demands sufficient food to meet their needs, necessitating innovations in veterinary sciences that increase productivity both quantitatively and qualitatively. We have been able to address various challenges facing veterinary and farm systems with new scientific and technological advances, which might open new opportunities for research. Recent breakthroughs in multi-omics platforms have produced a wealth of genetic and genomic data for livestock that must be converted into knowledge for breeding, disease prevention and management, productivity, and sustainability. Vetinformatics is regarded as a new bioinformatics research concept or approach that is revolutionizing the field of veterinary science. It employs an interdisciplinary approach to understand the complex molecular mechanisms of animal systems in order to expedite veterinary research, ensuring food and nutritional security. This review article highlights the background, recent advances, challenges, opportunities, and application of vetinformatics for quality veterinary services.

Application of Computational Biology and Artificial Intelligence in Drug Design

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Anti-Inflammatory Effects of Compounds from Echinoderms

Chronic inflammation can extensively burden a healthcare system. Several synthetic anti-inflammatory drugs are currently available in clinical practice, but each has its own side effect profile. The planet is gifted with vast and diverse oceans, which provide a treasure of bioactive compounds, the chemical structures of which may provide valuable pharmaceutical agents. Marine organisms contain a variety of bioactive compounds, some of which have anti-inflammatory activity and have received considerable attention from the scientific community for the development of anti-inflammatory drugs. This review describes such bioactive compounds, as well as crude extracts (published during 2010-2022) from echinoderms: namely, sea cucumbers, sea urchins, and starfish. Moreover, we also include their chemical structures, evaluation models, and anti-inflammatory activities, including the molecular mechanism(s) of these compounds. This paper also highlights the potential applications of those marine-derived compounds in the pharmaceutical industry to develop leads for the clinical pipeline. In conclusion, this review can serve as a well-documented reference for the research progress on the development of potential anti-inflammatory drugs from echinoderms against various chronic inflammatory conditions.

Elucidation of Prebiotics, Probiotics, Postbiotics, and Target from Gut Microbiota to Alleviate Obesity via Network Pharmacology Study

The metabolites produced by the gut microbiota have been reported as crucial agents against obesity; however, their key targets have not been revealed completely in complex microbiome systems. Hence, the aim of this study was to decipher promising prebiotics, probiotics, postbiotics, and more importantly, key target(s) via a network pharmacology approach. First, we retrieved the metabolites related to gut microbes from the gutMGene database. Then, we performed a meta-analysis to identify metabolite-related targets via the similarity ensemble approach (SEA) and SwissTargetPrediction (STP), and obesity-related targets were identified by DisGeNET and OMIM databases. After selecting the overlapping targets, we adopted topological analysis to identify core targets against obesity. Furthermore, we employed the integrated networks to microbiota-substrate-metabolite-target (MSMT) via R Package. Finally, we performed a molecular docking test (MDT) to verify the binding affinity between metabolite(s) and target(s) with the Autodock 1.5.6 tool. Based on holistic viewpoints, we performed a filtering step to discover the core targets through topological analysis. Then, we implemented protein-protein interaction (PPI) networks with 342 overlapping target, another subnetwork was constructed with the top 30% degree centrality (DC), and the final core networks were obtained after screening the top 30% betweenness centrality (BC). The final core targets were IL6, AKT1, and ALB. We showed that the three core targets interacted with three other components via the MSMT network in alleviating obesity, i.e., four microbiota, two substrates, and six metabolites. The MDT confirmed that equol (postbiotics) converted from isoflavone (prebiotics) via Lactobacillus paracasei JS1 (probiotics) can bind the most stably on IL6 (target) compared with the other four metabolites (3-indolepropionic acid, trimethylamine oxide, butyrate, and acetate). In this study, we demonstrated that the promising substate (prebiotics), microbe (probiotics), metabolite (postbiotics), and target are suitable for obsesity treatment, providing a microbiome basis for further research.

A Potential Anticancer Mechanism of Finger Root (Boesenbergia rotunda) Extracts against a Breast Cancer Cell Line

Breast cancer is the most common type of cancer women suffer from worldwide in 2020 and the 4th leading cause of cancer death. Boesenbergia rotunda is an herb with high potential as an anticancer agent. This study explores the potential bioactive compounds in B. rotunda as anti-breast cancer agents using in silico and in vitro approaches. The in silico study was used for active compound analysis, selection of anticancer compound candidates, prediction of target protein, functional annotation, molecular docking, and molecular dynamics simulation, respectively. The in vitro study was conducted by measurement toxicity, rhodamine 123, and apoptosis assays on T47D cells. Based on the KNApSAcK database, B. rotunda contained 20 metabolites, which are dominated by chalcone and flavonoid groups. Seven of them were predicted to have anticancer activity, namely, sakuranetin, cardamonin, alpinetin, 2S-pinocembrin, 7.4'-dihydroxy-5-methoxyflavanone, 5.6-dehydrokawain, and pinostrobin chalcone. These compounds targeted proteins related to cancer progression pathways such as the PI3K/Akt, FOXO, JAK/STAT, and estrogen signaling pathways. Therefore, these compounds are predicted to inhibit growth and induce apoptosis of cancer cells through their interactions with MMP12, MMP13, CDK4, JAK3, VEGFR1, VEGFR2, and KCNA3. Anticancer activity of B. rotunda through in vitro study confirmed that B. rotunda extract is strong cytotoxic and induces apoptosis of breast cancer cell lines. This study concludes that Boesenbergia rotunda has potency as an anticancer candidate.

Virtual screening of substances used in the treatment of SARS-CoV-2 infection and analysis of compounds with known action on structurally similar proteins from other viruses

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is considered the etiological agent of the disease that caused the COVID-19 pandemic, and for which there is currently no effective treatment. This pandemic has shown that the rapid identification of therapeutic compounds is critical (when a new virus with high transmissibility occurs) to prevent or reduce as much as possible the loss of human lives. To meet the urgent need for drugs, many strategies were applied for the discovery, respectively the identification of potential therapies / drugs for SARS-CoV-2. Molecular docking and virtual screening are two of the in silico tools/techniques that provided the identification of few SARS-CoV-2 inhibitors, removing ineffective or less effective drugs and thus preventing the loss of resources such as time and additional costs. The main target of this review is to provide a comprehensive overview of how in-silico tools have been used in the crisis management of anti-SARS-CoV-2 drugs, especially in virtual screening of substances used in the treatment of SARS-CoV-2 infection and analysis of compounds with known action on structurally similar proteins from other viruses; also, completions were added to the way in which these methods came to meet the requirements of biomedical research in the field. Moreover, the importance and impact of the topic approached for researchers was highlighted by conducting an extensive bibliometric analysis.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

Network pharmacology and molecular docking-based investigations of Kochiae Fructus’s active phytomolecules, molecular targets, and pathways in treating COVID-19

COVID-19 disease is caused by SARS-CoV-2. Hyper-inflammation mediated by proinflammatory cytokines is humans’ primary etiology of SARS-CoV-2 infection. Kochiae Fructus is widely used in China as traditional Chinese medicine (TCM) to treat inflammatory diseases. Due to its anti-inflammatory properties, we hypothesized that Kochiae Fructus would be a promising therapeutic agent for COVID-19. The active phytomolecules, targets, and molecular pathways of Kochiae Fructus in treating COVID-19 have not been explored yet. Network pharmacology analysis was performed to determine the active phytomolecules, molecular targets, and pathways of Kochiae Fructus. The phytomolecules in Kochiae Fructus were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and their potential targets were predicted with the SwissTargetPrediction webserver. COVID-19-related targets were recovered from the GeneCards database. Intersecting targets were determined with the VENNY tool. The Protein-protein interaction (PPI) and Molecular Complex Detection (MCODE) network analyses were constructed using the Cytoscape software. Using the DAVID tool, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the intersecting targets. AutoDock Vina (version 1.2.0.) was used for molecular docking analysis. Six active phytomolecules and 165 their potential targets, 1,745 COVID-19-related targets, and 34 intersecting targets were identified. Network analysis determined 13 anti-COVID-19 core targets and three key active phytomolecules (Oleanolic acid, 9E,12Z-octadecadienoic acid, and 11,14-eicosadienoic acid). Three key pathways (pathways in cancer, the TNF signaling pathway, and lipid and atherosclerosis) and the top six anti-COVID-19 core targets (IL-6, PPARG, MAPK3, PTGS2, ICAM1, and MAPK1) were determined to be involved in the treatment of COVID-19 with active phytomolecules of Kochiae Fructus. Molecular docking analysis revealed that three key active phytomolecules of Kochiae Fructus had a regulatory effect on the identified anti-COVID-19 core targets. Hence, these findings offer a foundation for developing anti-COVID-19 drugs based on phytomolecules of Kochiae Fructus.

Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches

Chlamydia pneumonia, a species of the family Chlamydiacea, is a leading cause of pneumonia. Failure to eradicate C. pneumoniae can lead to chronic infection, which is why it is also considered responsible for chronic inflammatory disorders such as asthma, arthritis, etc. There is an urgent need to tackle the major concerns arising due to persistent infections caused by C. pneumoniae as no FDA-approved drug is available against this chronic infection. In the present study, an approach named subtractive proteomics was employed to the core proteomes of five strains of C. pneumonia using various bioinformatic tools, servers, and software. However, 958 non-redundant proteins were predicted from the 4754 core proteins of the core proteome. BLASTp was used to analyze the non-redundant genes against the proteome of humans, and the number of potential genes was reduced to 681. Furthermore, based on subcellular localization prediction, 313 proteins with cytoplasmic localization were selected for metabolic pathway analysis. Upon subsequent analysis, only three cytoplasmic proteins, namely 30S ribosomal protein S4, 4-hydroxybenzoate decarboxylase subunit C, and oligopeptide binding protein, were identified, which have the potential to be novel drug target candidates. The Swiss Model server was used to predict the target proteins’ three-dimensional (3D) structure. The molecular docking technique was employed using MOE software for the virtual screening of a library of 15,000 phytochemicals against the interacting residues of the target proteins. Molecular docking experiments were also evaluated using molecular dynamics simulations and the widely used MM-GBSA and MM-PBSA binding free energy techniques. The findings revealed a promising candidate as a novel target against C. pneumonia infections.

PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling

Background

Computational methods based on initial screening and prediction of peptides for desired functions have proven to be effective alternatives to lengthy and expensive biochemical experimental methods traditionally utilized in peptide research, thus saving time and effort. However, for many researchers, the lack of expertise in utilizing programming libraries, access to computational resources, and flexible pipelines are big hurdles to adopting these advanced methods.

Results

To address the above mentioned barriers, we have implemented the peptide design and analysis under Galaxy (PDAUG) package, a Galaxy-based Python powered collection of tools, workflows, and datasets for rapid in-silico peptide library analysis. In contrast to existing methods like standard programming libraries or rigid single-function web-based tools, PDAUG offers an integrated GUI-based toolset, providing flexibility to build and distribute reproducible pipelines and workflows without programming expertise. Finally, we demonstrate the usability of PDAUG in predicting anticancer properties of peptides using four different feature sets and assess the suitability of various ML algorithms.

Conclusion

PDAUG offers tools for peptide library generation, data visualization, built-in and public database peptide sequence retrieval, peptide feature calculation, and machine learning (ML) modeling. Additionally, this toolset facilitates researchers to combine PDAUG with hundreds of compatible existing Galaxy tools for limitless analytic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04727-6.

Scope of computational biology and bioinformatics towards the discovery of potential therapeutic agents against viral diseases

Tweetable abstract

Computational biology and bioinformatics resources provide a cutting-edge platform for the screening and development of novel therapeutic agents against probable targets of emerging viral diseases.

Molecular and genetic markers in hepatocellular carcinoma: In silico analysis to clinical validation (current limitations and future promises)

Hepatocellular carcinoma (HCC) is the second cause of cancer-related mortality. The diagnosis of HCC depends mainly on α-fetoprotein, which is limited in its diagnostic and screening capabilities. There is an urgent need for a biomarker that detects early HCC to give the patients a chance for curative treatment. New targets of therapy could enhance survival and create future alternative curative methods. In silico analysis provides both; discovery of biomarkers, and understanding of the molecular pathways, to pave the way for treatment development. This review discusses the role of in silico analysis in the discovery of biomarkers, molecular pathways, and the role the author has contributed to this area of research. It also discusses future aspirations and current limitations. A literature review was conducted on the topic using various databases (PubMed, Science Direct, and Wiley Online Library), searching in various reviews, and editorials on the topic, with overviewing the author’s own published and unpublished work. This review discussed the steps of the validation process from in silico analysis to in vivo validation, to incorporation into clinical practice guidelines. In addition, reviewing the recent lines of research of bioinformatic studies related to HCC. In conclusion, the genetic, molecular and epigenetic markers discoveries are hot areas for HCC research. Bioinformatics will enhance our ability to accomplish this understanding in the near future. We face certain limitations that we need to overcome.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Recent advances in the design of antimicrobial peptide conjugates

Antimicrobial peptides (AMPs) are ubiquitous host defense peptides characterized by antibiotic activity and lower propensity for developing resistance compared to classic antibiotics. While several AMPs have shown activity against antibiotic-sensitive...

The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers

In the present article we propose the application of variants of the mutual information function as characteristic fingerprints of biomolecular sequences for classification analysis. In particular, we consider the resolved mutual information functions based on Shannon-, Rényi-, and Tsallis-entropy. In combination with interpretable machine learning classifier models based on generalized learning vector quantization, a powerful methodology for sequence classification is achieved which allows substantial knowledge extraction in addition to the high classification ability due to the model-inherent robustness. Any potential (slightly) inferior performance of the used classifier is compensated by the additional knowledge provided by interpretable models. This knowledge may assist the user in the analysis and understanding of the used data and considered task. After theoretical justification of the concepts, we demonstrate the approach for various example data sets covering different areas in biomolecular sequence analysis.

Cardioprotective effect of icariin against myocardial fibrosis and its molecular mechanism in diabetic cardiomyopathy based on network pharmacology: Role of ICA in DCM

BACKGROUND

Diabetic cardiomyopathy (DCM) is one of the most severe symptoms of diabetes. It continues to be a major clinical problem, but our knowledge of its molecular mechanisms and effective treatments are limited. Traditional Chinese medicine has been shown to be a pool of novel drugs for diabetes.

PURPOSE

Herein, we aim to define the molecular mechanism of icariin (ICA), an extract from a traditional Chinese medicine herb, in protecting cardiac structures and restoring cardiac functions of in a rat model of type 2 diabetes mellitus (T2DM).

STUDY DESIGN AND METHODS

Candidate genes related to T2DM were identified through bioinformatics screening and their interactions were constructed by molecule docking technique, followed by pathway enrichment analyses of their cellular functions. A T2DM rat model was then established to evaluate the effects of ICA on cardiac structures, myocardial fibrosis, and cellular Ca²⁺ inflow, as reflected by HE and Masson staining, qRT-PCR and Western blot determination of related genes, and measurement of the L-type Ca²⁺ current.

RESULTS

Four potential target genes (Jun, p65, NOS3, and PDE5A) were identified. ICA ameliorated the structural damage and myocardial fibrosis in T2DM rats. Intracellular Ca2+ hyperactivities and dysfunction in myocardium of T2DM rats were also repressed by ICA treatment. Furthermore, ICA-induced inhibition of Jun and p65 ameliorated the irregular collagen metabolism and myocardial fibrosis. NOS3, PDE5A and the related sGC-cGMP-PKG signaling pathway mediated the ICA-induced improvement of intracellular Ca²⁺ inflow.

CONCLUSION

In conclusion, these results demonstrate the regulatory roles of potential target genes in DCM and suggest ICA as an effective treatment of DCM by targeting these genes specifically.

Identification of Potential Prognostic Biomarker for Predicting Survival in Multiple Myeloma Using Bioinformatics Analysis and Experiments

Multiple myeloma (MM) is a malignant disease of plasma cells, which remains incurable because of its unclear mechanism and drug resistance. Herein, we aimed to explore new biomarkers and therapeutic targets in MM. After screening differentially expressed genes (DEGs) in GSE6477 and GSE13591 dataset, we performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs using DAVID online database. The results indicated that the downregulated DEGs were mainly enriched in the immune-associated biological process. The protein–protein interaction network was constructed by STRING database, on which we performed module analysis and identified key genes. Gene set enrichment analysis (GSEA) and Kaplan–Meier analysis showed that RRM2 could be a novel biomarker in MM diagnosis. We further confirmed that novel RRM2 inhibitor osalmid inhibited MM cell proliferation and triggered cell cycle S phase arrest. Targeting RRM2 was expected to develop new therapeutic strategies for malignant MM.

Microbial Metabolites: The Emerging Hotspot of Antiviral Compounds as Potential Candidates to Avert Viral Pandemic Alike COVID-19

The present global COVID-19 pandemic caused by the noble pleomorphic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created a vulnerable situation in the global healthcare and economy. In this pandemic situation, researchers all around the world are trying their level best to find suitable therapeutics from various sources to combat against the SARS-CoV-2. To date, numerous bioactive compounds from different sources have been tested to control many viral diseases. However, microbial metabolites are advantageous for drug development over metabolites from other sources. We herein retrieved and reviewed literatures from PubMed, Scopus and Google relevant to antiviral microbial metabolites by searching with the keywords "antiviral microbial metabolites," "microbial metabolite against virus," "microorganism with antiviral activity," "antiviral medicine from microbial metabolite," "antiviral bacterial metabolites," "antiviral fungal metabolites," "antiviral metabolites from microscopic algae' and so on. For the same purpose, the keywords "microbial metabolites against COVID-19 and SARS-CoV-2" and "plant metabolites against COVID-19 and SARS-CoV-2" were used. Only the full text literatures available in English and pertinent to the topic have been included and those which are not available as full text in English and pertinent to antiviral or anti-SARS-CoV-2 activity were excluded. In this review, we have accumulated microbial metabolites that can be used as antiviral agents against a broad range of viruses including SARS-CoV-2. Based on this concept, we have included 330 antiviral microbial metabolites so far available to date in the data bases and were previously isolated from fungi, bacteria and microalgae. The microbial source, chemical nature, targeted viruses, mechanism of actions and IC50/EC50 values of these metabolites are discussed although mechanisms of actions of many of them are not yet elucidated. Among these antiviral microbial metabolites, some compounds might be very potential against many other viruses including coronaviruses. However, these potential microbial metabolites need further research to be developed as effective antiviral drugs. This paper may provide the scientific community with the possible secret of microbial metabolites that could be an effective source of novel antiviral drugs to fight against many viruses including SARS-CoV-2 as well as the future viral pandemics.

mRNA-miRNA-lncRNA Regulatory Network in Nonalcoholic Fatty Liver Disease

AIM

we aimed to construct a bioinformatics-based co-regulatory network of mRNAs and non coding RNAs (ncRNAs), which is implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), followed by its validation in a NAFLD animal model.

MATERIALS AND METHODS

The mRNAs-miRNAs-lncRNAs regulatory network involved in NAFLD was retrieved and constructed utilizing bioinformatics tools. Then, we validated this network using an NAFLD animal model, high sucrose and high fat diet (HSHF)-fed rats. Finally, the expression level of the network players was assessed in the liver tissues using reverse transcriptase real-time polymerase chain reaction.

RESULTS

in-silico constructed network revealed six mRNAs (YAP1, FOXA2, AMOTL2, TEAD2, SMAD4 and NF2), two miRNAs (miR-650 and miR-1205), and two lncRNAs (RPARP-AS1 and SRD5A3-AS1) that play important roles as a co-regulatory network in NAFLD pathogenesis. Moreover, the expression level of these constructed network-players was significantly different between NAFLD and normal control. Conclusion and future perspectives: this study provides new insight into the molecular mechanism of NAFLD pathogenesis and valuable clues for the potential use of the constructed RNA network in effective diagnostic or management strategies of NAFLD.

New tyrosinases with putative action against contaminants of emerging concern

Tyrosinases (EC 1.14.18.1) are type-3 copper metalloenzymes with strong oxidative capacities and low allosteric selectivity to phenolic and non-phenolic aromatic compounds, which have been used as biosensors and biocatalysts to mitigate the impacts of environmental contaminants over aquatic ecosystems. However, the widespread use of these polyphenol oxidases is limited by elevated production costs and restricted knowledge on their spectrum of action. Here, six tyrosinase homologs were identified and characterized from the genomes of four widespread freshwater ciliates using bioinformatics. Next, we performed a virtual screening to calculate binding energies between 3D models of these homologs and ~ 1000 contaminants of emerging concern (CECs), as an indirect approach to identify likely and unlikely targets for tyrosinases. Many fine chemicals, pharmaceuticals, personal care products, illicit drugs, natural toxins, and pesticides exhibited strong binding energies to these new tyrosinases, suggesting the spectrum of targets of these enzymes might be considerably broader than previously thought. Many ciliates, including those carrying tyrosinase genes, are fast-growing unicellular microeukaryotes that can be efficiently cultured, at large scales, under in vitro conditions, suggesting these organisms should be regarded as potential low-cost sources of new environmental biotechnological molecules.

Alteraciones Hepaticas en pacientes con infección por VIH en un centro de investigacion en Bogotá Colombia 2009 – 2019

Introducción: Las alteraciones de la bioquímica hepática son frecuentes en los pacientes con infección por VIH, la etiología es variada, la esteatosis hepática es frecuente con una prevalencia estimada del 60% Objetivos: Caracterizar las alteraciones hepáticas en una serie de pacientes con infección por VIH en un centro de investigación de Bogotá Colombia durante el periodo 2009 – 2019. Materiales y Métodos: Estudio descriptivo, retrospectivo, observacional de pacientes con infección por VIH que asistieron a un centro de investigación durante los años 2009-2019. Resultados: 94% fueron hombres y 6% mujeres con edad promedio de 44 años, 92,5% de los pacientes presentaba uso de terapia antiretroviral. Las principales hepatopatías fueron la coinfección VIH-Hepatitis C y el hígado graso en iguales porcentajes, 31,3%. El promedio del indice HOMA fue de 2,58. Discusión: Las enfermedades hepáticas son una causa importante de morbimortalidad en pacientes con infección por VIH, las coinfecciones virales y el hígado graso pueden ser muy frecuentes en nuestro medio a diferencia de otros estudios Conclusiones: Este es el primer estudio a nivel local en describir las alteraciones hepáticas en pacientes con VIH, las comorbilidades no SIDA, juegan un papel importante dentro de la enfermedad. La hepatitis C continúa siendo una coinfección frecuente en la población VIH.

In Silico Approach for Phytocompound-Based Drug Designing to Fight Efflux Pump-Mediated Multidrug-Resistant Mycobacterium tuberculosis

Tuberculosis (TB), caused by the bacteria Mycobacterium tuberculosis, is one of the principal causes of death in the world despite the existence of a significant number of antibiotics aimed against it. This is mainly due to the drug resistance mechanisms present in the bacterium, which leads to multidrug-resistant tuberculosis (MDR-TB). Additionally, the development of new antibiotics has become limited over the years. Although there are various drug resistance mechanisms present, efflux pumps are of utmost importance because they extrude out several dissimilar antitubercular drugs out of the cell. There are many efflux pump proteins present in Mycobacterium tuberculosis. Therefore, blocking these efflux pumps by inhibitors can raise the efficacy of the existing antibiotics and may also pave the path for the discovery and synthesis of new drugs. Plant compounds can act as a resource for the development of efflux pump inhibitors (EPIs), which may eventually replace or augment the current therapeutic options. This is mainly because plants have been traditionally used for ages for food or treatment and are considered safe with little or no side effects. Various computational tools are available which are used for the virtual screening of a large number of phytocompounds within a short span of time. This review aims to highlight the mechanism and appearance of drug resistance in Mycobacterium tuberculosis with emphasis on efflux pumps along with the significance of phytochemicals as inhibitors of these pumps and their screening strategy by computational approaches.

A Review on Rheumatoid Arthritis Interventions and Current Developments

Rheumatoid arthritis is a chronic autoimmune disorder characterized by inflammation, swelling, and joint destruction primarily affecting the peripheral joints. In recent years, RA has become an alarming concern affecting more than 1.5% of the population worldwide. The majority of the drugs in clinical trials for rheumatoid arthritis are immunomodulatory. The development of novel drugs for RA is impending and scientists are exploring new strategies through various innovative approaches for RA drug development. Treat-to-target and window of opportunity hypothesis are the new approaches that are used to treat, improve outcomes, and prevent long-term use of ineffective therapy, respectively. Novel therapeutic agents (e.g. GM-CSF inhibitors, Matrix metalloproteinase inhibitors) and delivery systems (e.g., Liposomes, Superparamagnetic iron oxide nano particles (SPIONs)) are under investigation for more target based therapy with reduced side effects and toxicity. The new drug discovery and repositioning of previously FDA-approved drugs are also being considered for chronic inflammatory disorder. The review encompasses a vast array of information, including genetics, etiology, clinical symptoms, current treatment, and newer therapeutics approaches, focused on the development of RA interventions. The introduction of the bioinformatics-based approach in RA has also been significantly discussed in the review. This review provides a general understanding of the challenges and uncertainties in the treatment of RA and summarizes the evolving scenario as well as innovative approaches taken into consideration for drug development in rheumatoid arthritis.