Multiple Alignment Research Articles

Genome mining of Bacillus thuringiensis strain SY49.1 reveals novel candidate pesticidal and bioactive compounds.

Bacillus thuringiensis SY49.1 (Bt SY49.1) strain has promising insecticidal and fungicidal activity against phytopathogens and pests. Therefore, we selected this strain for whole-genome sequencing (WGS), annotation and analysis, with the aim of identifying genes responsible for producing putative pesticidal toxins, antimicrobial metabolites and plant growth-promoting features. Our results showed that the SY49.1 genome is 6. 32 Mbp long with a GC content of 34.68%. Genome mining revealed the presence of multiple gene inventories for the biosynthesis of bioactive compounds such as insecticidal delta endotoxins, secondary metabolites, and several plant growth-promoting proteins. Multiple sequence alignment revealed residue variations in the toxic core of Cry1Ab when compared with known Cry1Ab sequences from Bt nomenclature databases. This suggests that the cry1Ab of SY49.1 is a new kind of its group. Among the predicted secondary metabolites, we found a kurstakin with a predicted peptide that differs from the known kurstakin peptide available in the NORINE database. In addition, lipopeptides extracted from SY49.1 suppressed the growth of Verticillium dahliae and Fusarium oxysporum. We anticipate that the complete genome of Bt SY49.1 may provide a model for properly understanding and studying antimicrobial compound mining, genetic diversity among the B. cereus group, and pathogenicity against insects. This is the first report on the WGS and mining of the Bt strain isolated from Turkey. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Diversity, Distribution and Structural Prediction of the Pathogenic Bacterial Effectors EspN and EspS.

Many Gram-negative enterobacteria translocate virulence proteins (effectors) into intestinal epithelial cells using a type III secretion system (T3SS) to subvert the activity of various cell functions possess. Many T3SS effectors have been extensively characterized, but there are still some effector proteins whose functional information is completely unknown. In this study, two predicted effectors of unknown function, EspN and EspS (Escherichia coli secreted protein N and S), were selected for analysis of translocation, distribution and structure prediction. The TEM1 (β-lactamase) translocation assay was performed, which showed that EspN and EspS are translocated into host cells in a T3SS-dependent manner during bacterial infection. A phylogenetic tree analysis revealed that homologs of EspN and EspS are widely distributed in pathogenic bacteria. Multiple sequence alignment revealed that EspN and its homologs share a conserved C-terminal region (673-1133 a.a.). Furthermore, the structure of EspN (673-1133 a.a.) was also predicted and well-defined, which showed that it has three subdomains connected by a loop region. EspS and its homologs share a sequence-conserved C-terminal (146-291 a.a.). The predicted structure of EspS (146-291 a.a.) is composed of a β-sheet consisting of four β-strands and several short helices, which has a TM score of 0.5014 with the structure of the Vibrio cholerae RTX cysteine protease domain (PDBID: 3eeb). These results suggest that EspN and EspS may represent two important classes of T3SS effectors associated with pathogen virulence, and our findings provide important clues to understanding the potential functions of EspN and EspS.

Variability of a number of Pm gene sequences in representatives of Triticeae

Aim. Comparison of the sequences of selected resistance genes deposited in GenBank to find types of differences in their nucleotide composition and to determine the significance of these differences for primer design. Methods. Sequence searches were performed in the GenBank biological database. Comparison of different sequences of the same gene was carried out in DnaSP6 by calculating the Tajima test for Pm2, Pm3 and the Kolmogorov-Smirnov test in GeneDoc for Pm21. Multiple alignment was performed in CLUSTALW before using the specified programs. Primers were designed in Primer3. Results. Variability of the compared sequences is represented by nucleotide substitutions, insertions/deletions, the presence of a transposon in one of the Pm3 sequences. The regions coding for leucine-rich repeats are the most variable. The sequences isolated from Triticum dicoccoides have two such regions, within each of which some of the changes in the nucleotide sequence will be reflected in the amino acid composition of the proteins. Conclusions. The differences between the compared sequences of Pm2, Pm3, Pm21 (within each of the genes) are random (p < 0.05). According to the results of the sequences analysis, combinations of primers were developed for the identification of sequences characteristic to resistance genes on the available plant material.

Impact of phylogeny on the inference of functional sectors from protein sequence data.

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection.

Sequences derived from organisms sharing common evolutionary origins exhibit similarity, while unique sequences, absent in related organisms, act as good diagnostic marker candidates. However, the approach focused on identifying dissimilar regions among closely-related organisms poses challenges as it requires complex multiple sequence alignments, making computation and parsing difficult. To address this, we have developed a biologically inspired universal NAUniSeq algorithm to find the unique sequences for microorganism diagnosis by traveling through the phylogeny of life. Mapping through a phylogenetic tree ensures a low number of cross-contamination and false positives. We have downloaded complete taxonomy data from Taxadb database and sequence data from National Center for Biotechnology Information Reference Sequence Database (NCBI-Refseq) and, with the help of NetworkX, created a phylogenetic tree. Sequences were assigned over the graph nodes, k-mers were created for target and non-target nodes and search was performed over the graph using the depth first search algorithm. In a memory efficient alternative NoSQL approach, we created a collection of Refseq sequences in MongoDB database using tax-id and path of FASTA files. We queried the MongoDB collection for the target and non-target sequences. In both the approaches, we used an alignment free sliding window k-mer-based procedure that quickly compares k-mers of target and non-target sequences and returns unique sequences that are not present in the non-target. We have validated our algorithm with target nodes Mycobacterium tuberculosis, Neisseria gonorrhoeae, and Monkeypox and generated unique sequences. This universal algorithm is a powerful tool for generating diagnostic sequences, enabling the accurate identification of microbial strains with high phylogenetic precision.

A study on the development of O2O operation service platform using augmented reality

An attempt of sequence selection is made for multiple alignments of transmembrane proteins in order to detect the structural similarity in the protein families. Treatment for the sequence selection, which is based on the pairwise sequence identities, is applied to ten sequence data sets of functional group of transmembrane proteins extracted from SWISS-PROT. The treatment excludes the sequences with low sequence identity from the divergent data sets effectively. Obtained multiple alignments are evaluated using two newly developed indices. The selected sequences, which are well aligned with few inserted gaps, seem to contain enough information for extracting the structural features of transmembrane functional groups.

Automated evaluation of multiple sequence alignment methods to handle third generation sequencing errors

Automated evaluation of multiple sequence alignment methods to handle third generation sequencing errors

Integrated subtractive genomics and structure-based approach to unravel the therapeutic drug target of Leishmania species.

Leishmaniasis is a complex vector-borne disease caused by intracellular protozoan parasites of the Leishmania genus. It presents a significant public health challenge in tropical and subtropical regions globally. As resistance to treatment increases, managing and controlling Leishmaniasis becomes more challenging, necessitating innovative approaches. To address this challenge, our study utilized subtractive genomics and structure-based approaches to identify common drug targets and combat antimicrobial resistance (AMR) across five Leishmania species strains. The subtractive genomics approach unraveled Glutamate Dehydrogenase (GDH) as a promising drug target for treating Leishmania infections. The investigation considered established methodologies observed in analogous studies, orthologous group, and druggability tests. Multiple sequence alignment revealed conserved sequences in GDH, while phylogenetic tree analysis provided insights into the evolutionary origin and close relationships of GDH across Leishmania species. Conserved sequences in GDH along with its function in pathogenicity provided insights into the close relationships of GDH across Leishmania species. Using a structure-based approach, our study showed the molecular interactions between GDH and three ligands-Bithionol, GW5074, and Hexachlorophene-through molecular docking and 100ns molecular dynamics (MD) simulations. GW5074 exhibited a significant affinity for GDH, as indicated by stable RMSD values, a more compact conformation, and a higher number of hydrogen bonds than Bithionol. MMPBSA analysis confirmed the superior binding energy of the GW5074-GDH complex, emphasizing its potential as a potent ligand for drug development. This comprehensive analysis identified GW5074 as a promising candidate for inhibiting GDH activities in Leishmania species, contributing to the development of effective therapeutics against Leishmania infections.

Predicting Mutation-Induced Allosteric Changes in Structures and Conformational Ensembles of the ABL Kinase Using AlphaFold2 Adaptations with Alanine Sequence Scanning.

Despite the success of AlphaFold2 approaches in predicting single protein structures, these methods showed intrinsic limitations in predicting multiple functional conformations of allosteric proteins and have been challenged to accurately capture the effects of single point mutations that induced significant structural changes. We examined several implementations of AlphaFold2 methods to predict conformational ensembles for state-switching mutants of the ABL kinase. The results revealed that a combination of randomized alanine sequence masking with shallow multiple sequence alignment subsampling can significantly expand the conformational diversity of the predicted structural ensembles and capture shifts in populations of the active and inactive ABL states. Consistent with the NMR experiments, the predicted conformational ensembles for M309L/L320I and M309L/H415P ABL mutants that perturb the regulatory spine networks featured the increased population of the fully closed inactive state. The proposed adaptation of AlphaFold can reproduce the experimentally observed mutation-induced redistributions in the relative populations of the active and inactive ABL states and capture the effects of regulatory mutations on allosteric structural rearrangements of the kinase domain. The ensemble-based network analysis complemented AlphaFold predictions by revealing allosteric hotspots that correspond to state-switching mutational sites which may explain the global effect of regulatory mutations on structural changes between the ABL states. This study suggested that attention-based learning of long-range dependencies between sequence positions in homologous folds and deciphering patterns of allosteric interactions may further augment the predictive abilities of AlphaFold methods for modeling of alternative protein sates, conformational ensembles and mutation-induced structural transformations.

MOLECULAR CHARACTERIZATION AND PATHOGENICITY TEST ON CITRUS FRUITS BY GREEN MOLD AND BLUE MOLD

Citrus fruits are essential for preventing various health conditions, including diabetes, neurological diseases, and cancer. Among the postharvest diseases affecting citrus fruits, Penicillium digitatum and Penicillium italicum are particularly significant. This study aimed to characterize Penicillium spp. isolated from three citrus varieties: orange (Citrus sinensis), small orange, and Malta. Pathogenicity tests confirmed that these Penicillium isolates were capable of infecting the tested citrus fruits. For molecular characterization, PCR amplification of the D1/D2 domain of 26S rDNA was performed using universal primers, which target the conserved regions of the nucleotide sequence. The PCR products were inserted into the pGEM-T Easy vector and transformed into E. coli Dh5α. The presence of the D1/D2 domain was verified by endonuclease digestion with EcoR1. Sequencing was conducted using the T7 promoter primer, and the resulting DNA sequences were analyzed with the DNAMAN analysis system. Sequence analysis revealed that the D1/D2 region of 26S rDNA from the orange isolate showed 99% similarity with Penicillium sp., while the D1/D2 region from the small orange isolate had 99.84% similarity with P. digitatum strain CBS 112082. The D1/D2 region from the Malta isolate showed 100% similarity with P. digitatum. Multiple sequence alignments among the three Penicillium isolates revealed a 98.81% identity. This study highlights the use of molecular techniques for understanding the pathogenicity of Penicillium spp. in citrus fruits.

Molecular Cloning of the scd1 Gene and Its Expression in Response to Feeding Artificial Diets to Mandarin Fish (Siniperca chuatsi).

Background/Objectives: Stearoyl-coenzyme A desaturase 1 (SCD1) plays a crucial role in fatty acid metabolism. However, its roles in the feeding habit transformation of mandarin fish (Siniperca chuatsi) remain largely unknown. Methods: Juvenile mandarin fish (10.37 ± 0.54)g were trained to feed on an artificial diet and then divided into artificial diet feeders and nonfeeders according to their feed preference. Afterwards, the scd1 gene of mandarin fish (Sc-scd1) was identified and characterized, and its transcription difference was determined between S. chuatsi fed live artificial diets and those fed prey fish. Results: Our results show that Sc-scd1 coding sequence is 1002 bp long, encoding 333 amino acids. The assumed Sc-SCD1 protein lacks a signal peptide, and it contains 1 N-linked glycosylation site, 24 phosphorylation sites, 4 transmembrane structures, and 3 conserved histidine elements. We found that Sc-SCD1 exhibits a high similarity with its counterparts in other fish by multiple alignments and phylogenetic analysis. The expression level of Sc-scd1 was detected with different expression levels in all tested tissues between male and female individuals fed either live prey fish or artificial diets. Conclusions: In particular, the Sc-scd1 expression level was the highest in the liver of both male and female mandarin fish fed artificial diets, indicating that scd1 genes may be associated with feed adaption of mandarin fish. Taken together, our findings offer novel perspectives on the potential roles of scd1 in specific domestication, and they provide valuable genetic information on feeding habits for the domestication of mandarin fish.

Guidelines in Designing a Universal Primer Mixture to Probe and Quantify Antibiotic-Resistant Genes Using the Polymerase Chain Reaction (PCR).

Multidrug resistance efflux pumps (MDREPs) in biofilm communities have become an increasingly expensive problem in clinical settings. Polymerase chain reaction (PCR)-based detection can be used to diagnose and characterize these genes, but this requires effective primer design to minimize false positives and negatives in test conclusions. A universal primer approach has previously been used to detect conserved core genes but not for accessory genes such as MDREPs. This study describes a guideline for the design of primers used in the detection of MDREP genes and an optimization approach for creating primers by using multiple sequence alignments to target conserved regions in silico, progressing from in silico to in vitro to generate working primers. Using thisapproach, this paper was able to generate primers to target sugE, a small multidrug resistance (SMR) protein found in microbial species. Primers were tested positively against synthetic DNA sequences but were inconsistent with DNA extracted from the organism of interest. Primer design informs the shortfalls of this detection technique and the difficulty in characterizing such genomic elements.

Unraveling barley's PAL gene family: a genome-wide study on defense mechanisms against Puccinia graminis f. sp. tritici

Phenylalanine ammonia lyase (PAL) is a pivotal enzyme bridging primary and secondary phenylpropanoid metabolism, influencing plant growth, development, and stress responses. Despite extensive studies on PAL genes across various plant species, their investigation in barley, a critical staple food globally, has been relatively scarce. In this study, we have successfully identified 10 HvPAL genes, designated as HvPAL genes, in Hordeum vulgare (barley). These HvPAL genes were categorized based on their conserved sequences, which revealed patterns through MEME analysis and multiple sequence alignment. Interestingly, we found cis elements related to stress in the promoter regions of HvPAL genes, indicating their involvement in the response to pathogens. Furthermore, these gene promoters contained components associated with light, development, and hormone responsiveness. This suggests that they may play a role in hormonal developmental processes. MicroRNAs were also identified as regulators of the HvPAL genes we identified highlighting their significance in barley. To further investigate these gene expression patterns, we analyzed the RNA-seq data revealed the upregulating of HvPAL 2, HvPAL3, and HvPAL8, and downregulating HvPAL 5, HvPAL 6, and HvPAL9 genes in this study. This study focused on the regulation of PAL genes in response to 23 different races of Puccinia graminis f. sp. tritici in barley. These results suggest ways to improve traits and develop barley varieties that are resistant to pathogens by selectively increasing the expression of certain HvPAL genes that were not previously regulated. This thorough investigation aims to expand our knowledge of the versatility of the PAL gene family, providing insights for advancements in host -pathogen genetics.

Evaluating the Genome-Based Average Nucleotide Identity Calculation for Identification of Twelve Yeast Species.

Classifying a yeast strain into a recognized species is not always straightforward. Currently, the taxonomic delineation of yeast strains involves multiple approaches covering phenotypic characteristics and molecular methodologies, including genome-based analysis. The aim of this study was to evaluate the suitability of the Average Nucleotide Identity (ANI) calculation through FastANI, a tool created for bacterial species identification, for the assignment of strains to some yeast species. FastANI, the alignment of in silico-extracted D1/D2 sequences of LSU rRNA, and multiple alignments of orthologous genes (MAOG) were employed to analyze 644 assemblies from 12 yeast genera, encompassing various species, and on a dataset of hybrid Saccharomyces species. Overall, the analysis showed high consistency between results obtained with FastANI and MAOG, although, FastANI proved to be more discriminating than the other two methods applied to genomic sequences. In particular, FastANI was effective in distinguishing between strains belonging to different species, defining clear boundaries between them (cutoff: 94-96%). Our results show that FastANI is a reliable method for attributing a known yeast species to a particular strain. Moreover, although hybridization events make species discrimination more complex, it was revealed to be useful in the identification of these cases. We suggest its inclusion as a key component in a comprehensive approach to species delineation. Using this approach with a larger number of yeasts would validate it as a rapid technique to identify yeasts based on whole genome sequences.

AARS Online: A collaborative database on the structure, function, and evolution of the aminoacyl-tRNA synthetases.

The aminoacyl-tRNA synthetases (aaRS) are a large group of enzymes that implement the genetic code in all known biological systems. They attach amino acids to their cognate tRNAs, moonlight in various translational and non-translational activities beyond aminoacylation, and are linked to many genetic disorders. The aaRS have a subtle ontology characterized by structural and functional idiosyncrasies that vary from organism to organism, and protein to protein. Across the tree of life, the 22 coded amino acids are handled by 16 evolutionary families of Class I aaRS and 21 families of Class II aaRS. We introduce AARS Online, an interactive Wikipedia-like tool curated by an international consortium of field experts. This platform systematizes existing knowledge about the aaRS by showcasing a taxonomically diverse selection of aaRS sequences and structures. Through its graphical user interface, AARS Online facilitates a seamless exploration between protein sequence and structure, providing a friendly introduction to the material for non-experts and a useful resource for experts. Curated multiple sequence alignments can be extracted for downstream analyses. Accessible at www.aars.online, AARS Online is a free resource to delve into the world of the aaRS.

Engineering of Substrate-Binding Domain to Improve Catalytic Activity of Chondroitin B Lyase with Semi-Rational Design.

Dermatan sulfate and chondroitin sulfate are dietary supplements that can be utilized as prophylactics against thrombus formation. Low-molecular-weight dermatan sulfate (LMWDS) is particularly advantageous due to its high absorbability. The enzymatic synthesis of low-molecular-weight dermatan sulfates (LMWDSs) using chondroitin B lyase is a sustainable and environmentally friendly approach to manufacturing. However, the industrial application of chondroitin B lyases is severely hampered by their low catalytic activity. To improve the activity, a semi-rational design strategy of engineering the substrate-binding domain of chondroitin B lyase was performed based on the structure. The binding domain was subjected to screening of critical residues for modification using multiple sequence alignments and molecular docking. A total of thirteen single-point mutants were constructed and analyzed to assess their catalytic characteristics. Out of these, S90T, N103C, H134Y, and R159K exhibited noteworthy enhancements in activity. This study also examined combinatorial mutagenesis and found that the mutant H134Y/R159K exhibited a substantially enhanced catalytic activity of 1266.74 U/mg, which was 3.21-fold that of the wild-type one. Molecular docking revealed that the enhanced activity of the mutant could be attributed to the formation of new hydrogen bonds and hydrophobic interactions with the substrate as well as neighbor residues. The highly active mutant would benefit the utilization of chondroitin B lyase in pharmaceuticals and functional foods.

Assessing the role of evolutionary information for enhancing protein language model embeddings

Embeddings from protein Language Models (pLMs) are replacing evolutionary information from multiple sequence alignments (MSAs) as the most successful input for protein prediction. Is this because embeddings capture evolutionary information? We tested various approaches to explicitly incorporate evolutionary information into embeddings on various protein prediction tasks. While older pLMs (SeqVec, ProtBert) significantly improved through MSAs, the more recent pLM ProtT5 did not benefit. For most tasks, pLM-based outperformed MSA-based methods, and the combination of both even decreased performance for some (intrinsic disorder). We highlight the effectiveness of pLM-based methods and find limited benefits from integrating MSAs.

A putative scenario of how de novo protein-coding genes originate in the Saccharomyces cerevisiae lineage

BackgroundNovel protein-coding genes were considered to be born by re-organization of pre-existing genes, such as gene duplication and gene fusion. However, recent progress of genome research revealed that more protein-coding genes than expected were born de novo, that is, gene origination by accumulating mutations in non-genic DNA sequences. Nonetheless, the in-depth process (scenario) for de novo origination is not well understood.ResultsWe have conceived bioinformatic analysis for sketching a scenario for de novo origination of protein-coding genes. For each de novo protein-coding gene, we firstly identified an edge of a given phylogenetic tree where the gene was born based on parsimony. Then, from a multiple sequence alignment of the de novo gene and its orthologous regions, we constructed ancestral DNA sequences of the gene corresponding to both end nodes of the edge. We finally revealed statistical features observed in evolution between the two ancestral sequences. In the analysis of the Saccharomyces cerevisiae lineage, we have successfully sketched a putative scenario for de novo origination of protein-coding genes. (1) In the beginning was GC-rich genome regions. (2) Neutral mutations were accumulated in the regions. (3) ORFs were extended/combined, and then (4) translation signature (Kozak consensus sequence) was recruited. Interestingly, as the scenario progresses from (2) to (4), the specificity of mutations increases.ConclusionTo the best of our knowledge, this is the first report outlining a scenario of de novo origination of protein-coding genes. Our bioinformatic analysis can capture events that occur during a short evolutionary time by directly observing the evolution of the ancestral sequences from non-genic to genic. This property is suitable for the analysis of fast evolving de novo genes.

KegAlign: Optimizing pairwise alignments with diagonal partitioning.

Our ability to generate sequencing data and assemble it into high quality complete genomes has rapidly advanced in recent years. These data promise to advance our understanding of organismal biology and answer longstanding evolutionary questions. Multiple genome alignment is a key tool in this quest. It is also the area which is lagging: today we can generate genomes faster than we can construct and update multiple alignments containing them. The bottleneck is in considerable computational time required to generate accurate pairwise alignments between divergent genomes, an unavoidable precursor to multiple alignments. This step is typically performed with lastZ, a very sensitive and yet equally slow tool. Here we describe an optimized GPU-enabled pairwise aligner KegAlign. It incorporates a new parallelization strategy, diagonal partitioning, with the latest features of modern GPUs. With KegAlign a typical human/mouse alignment can be computed in under 6 hours on a machine containing a single NVidia A100 GPU and 80 CPU cores without the need for any pre-partitioning of input sequences: a ∼150× improvement over lastZ. While other pairwise aligners can complete this task in a fraction of that time, none achieves the sensitivity of KegAlign's main alignment engine, lastZ, and thus may not be suitable for comparing divergent genomes. In addition to providing the source code and a Conda package for KegAlign we also provide a Galaxy workflow that can be readily used by anyone.

Genomic profiling of pan-drug resistant proteus mirabilis Isolates reveals antimicrobial resistance and virulence gene landscape

Proteus mirabilis is a gram-negative pathogen that caused significant opportunistic infections. In this study we aimed to identify antimicrobial resistance (AMR) genes and virulence determinants in two pan-drug resistant isolate “Bacteria_11” and “Bacteria_27” using whole genome sequencing. Proteus mirabilis “Bacteria_11” and “Bacteria_27” were isolated from two different hospitalized patients in Egypt. Antimicrobial susceptibility determined using Vitek 2 system, then whole genome sequencing (WGS) using MinION nanopore sequencing was done. Antimicrobial resistant genes and virulence determinants were identified using ResFinder, CADR AMR database, Abricate tool and VF analyzer were used respectively. Multiple sequence alignment was performed using MAFFT and FastTree, respectively. All genes were present within bacterial chromosome and no plasmid was detected. “Bacteria_11” and “Bacteria_27” had sizes of approximately 4,128,657 bp and 4,120,646 bp respectively, with GC content of 39.15% and 39.09%. “Bacteria_11” and “Bacteria_27” harbored 43 and 42 antimicrobial resistance genes respectively with different resistance mechanisms, and up to 55 and 59 virulence genes respectively. Different resistance mechanisms were identified: antibiotic inactivation, antibiotic efflux, antibiotic target replacement, and antibiotic target change. We identified several genes associated with aminoglycoside resistance, sulfonamide resistance. trimethoprim resistance tetracycline resistance proteins. Also, those responsible for chloramphenicol resistance. For beta-lactam resistance, only blaVEB and blaCMY-2 genes were detected. Genome analysis revealed several virulence factors contribution in isolates pathogenicity and bacterial adaptation. As well as numerous typical secretion systems (TSSs) were present in the two isolates, including T6SS and T3SS. Whole genome sequencing of both isolates identify their genetic context of antimicrobial resistant genes and virulence determinants. This genomic analysis offers detailed representation of resistant mechanisms. Also, it clarifies P. mirabilis ability to acquire resistance and highlights the emergence of extensive drug resistant (XDR) and pan-drug resistant (PDR) strains. This may help in choosing the most appropriate antibiotic treatment and limiting broad spectrum antibiotic use.