The COVID-19 pandemic has been ongoing since its onset in late November 2019 in Wuhan, China. Understanding and monitoring the genetic evolution of the virus, its geographical characteristics, and its stability are particularly important for controlling the spread of the disease and especially for the development of a universal vaccine covering all circulating strains. From this perspective, we analyzed 30,983 complete SARS-CoV-2 genomes from 79 countries located in the six continents and collected from 24 December 2019, to 13 May 2020, according to the GISAID database. Our analysis revealed the presence of 3206 variant sites, with a uniform distribution of mutation types in different geographic areas. Remarkably, a low frequency of recurrent mutations has been observed; only 169 mutations (5.27%) had a prevalence greater than 1% of genomes. Nevertheless, fourteen non-synonymous hotspot mutations (>10%) have been identified at different locations along the viral genome; eight in ORF1ab polyprotein (in nsp2, nsp3, transmembrane domain, RdRp, helicase, exonuclease, and endoribonuclease), three in nucleocapsid protein, and one in each of three proteins: Spike, ORF3a, and ORF8. Moreover, 36 non-synonymous mutations were identified in the receptor-binding domain (RBD) of the spike protein with a low prevalence (<1%) across all genomes, of which only four could potentially enhance the binding of the SARS-CoV-2 spike protein to the human ACE2 receptor. These results along with intra-genomic divergence of SARS-CoV-2 could indicate that unlike the influenza virus or HIV viruses, SARS-CoV-2 has a low mutation rate which makes the development of an effective global vaccine very likely.

Breast cancer is one of the main global priorities in terms of public health. It remains the most frequent cancer in women and is the leading cause of their death. The human microbiome plays various roles in maintaining health by ensuring a dynamic balance with the host or in the appearance of various pathologies including breast cancer. In this study, we performed an analysis of bacterial signature differences between tumor and adjacent tissues of breast cancer patients in Morocco. Using 16S rRNA gene sequencing, we observed that adjacent tissue contained a much higher percentage of the Gammaproteobacteria class (35.7%) while tumor tissue was characterized by a higher percentage of Bacilli and Actinobacteria classes, with about 18.8% and 17.2% average abundance, respectively. Analysis of tumor subtype revealed enrichment of genus Sphingomonodas in TNBC while Sphingomonodas was predominant in HER2. The LEfSe and the genus level heatmap analysis revealed a higher abundance of the Rothia genus in tumor tissues. The identified microbial communities can therefore serve as potential biomarkers for prognosis and diagnosis, while also helping to develop new strategies for the treatment of breast cancer patients.

The COVID-19 pandemic has been ongoing since its onset in late November 2019 in Wuhan, China. Understanding and monitoring the genetic evolution of the virus, its geographical characteristics, and its stability are particularly important for controlling the spread of the disease and especially for the development of a universal vaccine covering all circulating strains. From this perspective, we analyzed 30,983 complete SARS-CoV-2 genomes from 79 countries located in the six continents and collected from December 24, 2019, to May 13, 2020, according to the GISAID database. Our analysis revealed the presence of 3,206 variant sites, with a uniform distribution of mutation types in different geographic areas. Remarkably, a low frequency of recurrent mutations has been observed; only 169 mutations (5.27%) had a prevalence greater than 1% of genomes. Nevertheless, fourteen non-synonymous hotspot mutations (> 10%) have been identified at different locations along the viral genome; eight in ORF1ab polyprotein (in nsp2, nsp3, transmembrane domain, RdRp, helicase, exonuclease, and endoribonuclease), three in nucleocapsid protein and one in each of three proteins: spike, ORF3a, and ORF8. Moreover, 36 non-synonymous mutations were identified in the RBD of the spike protein with a low prevalence (<1%) across all genomes, of which only four could potentially enhance the binding of the SARS-CoV-2 spike protein to the human ACE2 receptor. These results along with mutational frequency dissimilarity and intra-genomic divergence of SARS-CoV-2 could indicate that the SARS-CoV-2 is not yet adapted to its host. Unlike the influenza virus or HIV viruses, the low mutation rate of SARS-CoV-2 makes the development of an effective global vaccine very likely.

Estrogen receptor alpha (ERα) plays a critical role in breast cancer (BC) progression, with endocrine therapy being a key treatment for ERα + BC. However, resistance often arises due to somatic mutations in the ERα ligand-binding domain (LBD). Lasofoxifene, a third-generation selective estrogen receptor modulator, has shown promise against Y537S and D538G mutations. However, the emergence of a novel F404 mutation in patients with pre-existing LBD mutations raises concerns about its impact on lasofoxifene efficacy. This study investigates the impact of the dual Y537S and F404V mutations on lasofoxifene’s efficacy. Using molecular dynamics simulations and molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energy calculations, we found that the dual mutation reduces lasofoxifene binding affinity and binding free energy, disrupts crucial protein-ligand interactions, and induces significant conformational changes in the ligand-binding pocket. These alterations are likely due to the loss of the pi-pi stacking interaction in the F404V mutation. These findings suggest a potential reduction in lasofoxifene efficacy due to the dual mutation. Further experimental validation is required to confirm these results and fully understand the impact of dual mutations on lasofoxifene’s effectiveness in ERα + metastatic BC.

Pseudomonas aeruginosa is a major cause of nosocomial infections and is often associated with biofilm-mediated antibiotic resistance. The LasR protein is a key component of the quorum system in P. aeruginosa, allowing it to regulate its biofilm-induced pathogenicity. When the bacterial population reaches a sufficient density, the accumulation of N-(3-oxododecanoyl) acyl homoserine lactone (3O-C12-HSL) leads to the activation of the LasR receptor, which then acts as a transcriptional activator of target genes involved in biofilm formation and virulence, thereby increasing the bacteria’s antibiotic resistance and enhancing its virulence. In this study, we performed a structure-based virtual screening of a natural food database of 10 997 compounds against the crystal structure of the ligand-binding domain of the LasR receptor (PDB ID: 3IX4). This allowed us to identify four molecules, namely ZINC000001580795, ZINC000014819517, ZINC000014708292, and ZINC000004098719, that exhibited a favorable binding mode and docking scores greater than −13 kcal/mol. Furthermore, the molecular dynamics simulation showed that these four molecules formed stable complexes with LasR during the 150-ns molecular dynamics (MD) simulation, indicating their potential for use as inhibitors of the LasR receptor in P. aeruginosa. However, further experimental validation is needed to confirm their activity.

Purpose: The enoyl-acyl carrier protein reductase (InhA) is one of the important key enzymes employed in mycolic acids biosynthesis pathway and an important component of mycobacterial cell walls. This enzyme has also been identified as major target of isoniazid drug, except that isoniazid needs to be activated first by the catalase peroxidase (KatG) protein to form the isonicotinoyl-NAD (INH-NAD) adduct that inhibits the action of InhA enzyme. However, this activation becomes more difficult and unreachable with the problem of mutation-related resistance caused mainly by acquired mutations in KatG and InhA protein. Our main interest in this study is to identify direct InhA inhibitors using computer-aided drug design. Methods: Computer-aided drug design was used to solve this problem by applying three different approaches including mutation impact modelling, virtual screening and 3D-pharmacophore search. Results: A total of 15 mutations were collected from the literature, then a 3D model was generated for each of them and their impact was predicted. Of the 15 mutations, 10 were found to be deleterious and have a direct effect on flexibility, stability and SASA of the protein. In virtual screening, from 1,000 similar INH-NAD analogues obtained by the similarity search method, 823 compounds passed toxicity filter and drug likeness rules, which were then docked to the wild-type of InhA protein. Subsequently, 34 compounds with binding energy score better than that of INH-NAD were selected and docked against the 10 generated mutated models of InhA. Only three leads showed a lower binding affinity better than the reference. The 3D-pharmacophore model approach was used to identify the common features between those three compounds by generating a pharmacophoric map. Conclusion: The result of this study may pave the way to develop more potent mutant-specific inhibitors to overcome this resistance.