Unveiling the Therapeutic Potential of Paclitaxel Combinations Against Breast Carcinoma and Identification of In Vivo Biomarkers.

Breast cancer (BC) is one of the leading causes of high mortality rates in women worldwide. Although advancements have been made in the design of therapeutic strategies and drug discovery, drug resistance remains one of the key challenges. One of the ways to overcome drug resistance is finding potential drug combinations since the efficacy of combined drugs is higher than their individual efficacies if the combination is a synergistic pair. Therefore, the current study uses a BC patient-derived xenograft (PDX) dataset to evaluate the effects of various cancer drugs on breast cancer in vivo models. The drug effects are further validated by four machine learning models, namely Elastic Net, Least Absolute Shrinkage and Selection (LASSO), Support Vector Machine (SVM), Random Forests (RF), as well as exploring the shortlisted drugs in combination with paclitaxel, a baseline drug for enhanced efficacy on tumor volume reduction. Additionally, the study also shortlists the top 50 in vivo biomarkers correlated with the effects of the drugs. The outcomes could be significantly important for the design of an effective anti-breast cancer therapy.

Molecular dynamic simulation reveals spider antimicrobial peptide Latarcin-1 and human eosinophil cationic protein as peptide inhibitors of SARS-CoV-2 variants.

COVID-19 has rapidly proliferated around 180 countries, and new cases are reported frequently. No peptide medication has been developed that can reliably block SARS-CoV-2 infection. The investigation focuses on the crucial host receptors angiotensin-converting enzyme 2 (ACE2) , which can bind receptor-binding domain (RBD) on the SARS-CoV-2 spike protein (S). To investigate the inhibitory effects of human Eosinophil Cationic Protein (hECP) and Latarcin-1 (L1)on SARS-CoV-2 infection, we have selected them as research subjects. Further, we ran extensive molecular dynamics simulations to bring the docked peptide-ACE2 complex into its equilibrium state. The outcomes were then evaluated with g_MMPBSA and interaction analysis. We have also considered the Delta and Omicron variants to examine these peptides' inhibitory effects. The experimental findings revealed an enhanced capability of L1 and hECP as SARS-CoV-2 inhibitors, occupying hot spots and numerous key residues in ACE2. These include ASP30, ASP38, GLU35 and GLU75, which significantly inhibit the binding of RBD and ACE2 and are effective against two common variants in a similar manner. In addition, this study can serve as a springboard for future research on SARS-CoV-2 inhibitors.Communicated by Ramaswamy H. Sarma.

MiR-210-3p enhances intermittent hypoxia-induced tumor progression via inhibition of E2F3.

PURPOSE: Intermittent hypoxia (IH) is a hallmark of obstructive sleep apnea (OSA), which is related to tumorigenesis and progression. Although micro-ribonucleic acid-210-3p (miR-210-3p) is correlated with hypoxia-induced tumor development, its role in the relationship between IH and tumor function remains poorly understood. The present work focused on elucidating the molecular mechanism through which miR-210-3p drives tumor progression under IH. METHODS: MiR-210-3p levels were quantified within tumor samples from patients with lung adenocarcinoma who had or did not have OSA. Correlations between miR-210-3p and polysomnographic variables were analyzed. For in vitro experiments, miR-210-3p was inhibited or overexpressed via transfection under IH conditions. Cell viability, growth, invasion and migration assays were carried out. For in vivo modeling of IH using mouse xenografts, a miR-210-3p antagomir was intratumorally injected, tumor biological behaviors were evaluated, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR), immunohistochemistry and western blot were carried out for detecting miR-210-3p and E2F transcription factor 3 (E2F3) expression. RESULTS: For patients with lung adenocarcinoma and OSA, high miR-210-3p levels showed positive relation to polysomnographic variables, such as oxygen desaturation index, apnea-hypopnea index, and proportion of total sleep time with oxygen saturation in arterial blood < 90%. IH enhanced tumor viability, proliferation, migration, and invasion, downregulated E2F3 expression, and increased miR-210-3-p levels. miR-210-3p overexpression induced similar changes. These changes were reversed by miR-210-3p inhibition in vitro or miR-210-3p antagomir through intratumoral injection in vivo. CONCLUSIONS: IH-induced tumor development is driven through miR-210-3p by E2F3 suppression. MiR-210-3p represents a potential therapeutic target among patients with concomitant cancer and OSA.

16S rRNA gene sequencing and machine learning reveal correlation between drug abuse and human host gut microbiota.

Over the past few years, there has been increasing evidence highlighting the strong connection between gut microbiota and overall well-being of the host. This has led to a renewed emphasis on studying and addressing substance use disorder from the perspective of brain-gut axis. Previous studies have suggested that alcohol, food, and cigarette addictions are strongly linked to gut microbiota and faecal microbiota transplantation or the use of probiotics achieved significant efficacy. Unfortunately, little is known about the relationship between drug abuse and gut microbiota. This paper aims to reveal the potential correlation between gut microbiota and drug abuse and to develop an accurate identification model for drug-related faeces samples by machine learning. Faecal samples were collected from 476 participants from three regions in China (Shanghai, Yunnan, and Shandong). Their gut microbiota information was obtained using 16S rRNA gene sequencing, and a substance use disorder identification model was developed by machine learning. Analysis revealed a lower diversity and a more homogeneous gut microbiota community structure among participants with substance use disorder. Bacteroides, Prevotella_9, Faecalibacterium, and Blautia were identified as important biomarkers associated with substance use disorder. The function prediction analysis revealed that the citrate and reductive citrate cycles were significantly upregulated in the substance use disorder group, while the shikimate pathway was downregulated. In addition, the machine learning model could distinguish faecal samples between substance users and nonsubstance users with an AUC = 0.9, indicating its potential use in predicting and screening individuals with substance use disorder within the community in the future.

Comparative binding analysis of WGX50 and Alpha-M with APP family proteins APLP1 and APLP2 using structural-dynamics and free energy calculation approaches.

A.D. is a common disease among other neurodegenerative disorders primarily developing due to amyloid-ß (Aß) neurotoxicity derived from the amyloid-ß protein precursor (AßPP). The amyloid precursor-like proteins 1 and 2 (APP1 and APLP2) biochemically behave similarly in many aspects to AßPP. We, therefore, proposed to test WGX-50 and Alpha-M for their interaction mechanism with APLP1 and APLP2 because both these drug candidate compounds previously showed inhibition of Aß aggregation. We employed a comparative atomic investigation on Alpha-M and WGX-50 in complex with novel targets, i.e., APLP1 and APLP2, using biophysical and molecular simulation methods. The docking score was -6.83 kcal mol-1 for Alpha-M-APLP1, -8.41 kcal mol-1 for WGX-50-APLP1, -7.02 kcal mol-1 for Alpha-M-APLP2 and -8.25 kcal mol-1 for the WGX-50-APLP2 complex. Our results also elaborate that in the case of their interaction with both APLP1 and APLP2, the WGX-50 complex exhibits better stability than the APLP1/2-Alpha-M complexes during simulation. Furthermore, WGX50 in both APLP1 and APLP2 stabilized the internal flexibility upon binding in contrast to the Alpha-M complexes. The data showed that the BFE for Alpha-M-APLP1 was calculated to be -27.38 ± 0.93 kcal mol-1, for WGX-50-APLP1 -39.65 ± 0.95 kcal mol-1, for Alpha-M-APLP2 -24.80 ± 0.63 kcal mol-1 while for WGX-50-APLP2 the BFE was -57.16 ± 1.03 kcal mol-1 respectively. These results highlight that APLP2-WGX50 has greater binding energies in all four systems. PCA and FEL analysis further revealed variations in the dynamic behavior of these complexes. Overall, our findings demonstrate that WGX50 potentially acts as a more potent inhibitor for APLP1 and APLP2 than Alpha-M and thus shows the diverse pharmacological potential of WGX50. Due to its stable binding interaction, WGX50 might be a suitable candidate drug compound for targeting these precursors under pathological conditions.

[Preparation of luciferase-expressing mRNA and expression characteristics of mRNA delivered by electroporation in vivo].

In this study, we aimed to construct a non-replication mRNA platform and explore the side effects of electroporation-mediated delivery of mRNA on the mice as well as the expression features of the mRNA. With luciferase gene as a marker, in vitro transcription with T7 RNA polymerase was carried out for the synthesis of luciferase-expressed mRNA, followed by enzymatic capping and tailing. The mRNA was delivered in vivo by electroporation via an in vivo gene delivery system, and the expression intensity and duration of luciferase in mice were observed via an in vivo imaging system. The results demonstrated that the mRNA transcripts were successfully expressed both in vitro and in vivo. The electroporation-mediated delivery of mRNA had no obvious side effects on the mice. Luciferase was expressed successfully in all the mRNA-transduced mice, while the expression intensity and duration varied among individuals. Overall, the expression level peaked on the first day after electroporation and rapidly declined on the fourth day. This study is of great importance for the construction of non-replication mRNAs and their application in vaccine or antitumor drug development.

Investigating the stabilisation of IFN-α2a by replica exchange molecular dynamics simulation.

Current biopharmaceutical drugs are mainly a class of peptides or proteins that play an essential role in the treatment of many diseases. Such peptides/proteins are usually thermally unstable and may lose their bioactivity when exposed to ambient conditions. Therefore, they are not suitable for long-term storage. Lyophilisation is the most common method to prolong shelf life of solid peptide/protein drugs; however, the freeze-drying process can lead to irreversible damage. In the present study, human interferon-alpha 2a (IFN-α2a) was selected as a model protein drug; four disaccharides (ß-lactose, ß-maltose, sucrose, and trehalose) were selected as bioactive protectants. We investigated the effects of different protectants on IFN-α2a under various ambient conditions (vacuum, dry state, and aqueous solution) using replica exchange molecular dynamics simulation. The protective effect of ß-maltose on IFN-α2a was the highest in aqueous solution and dry state, ß-lactose showed a poor protective effect in all three conditions, the performance of sucrose was good in all conditions, and trehalose showed a better protective effect under vacuum conditions and in aqueous solution. Disaccharides form H-bonds with water, thereby preventing water from the tertiary structure of proteins. Trehalose forms strong H-bonds with water which explains its extraordinary stability.

Insight into substrate-assisted catalytic mechanism and stereoselectivity of bifunctional nocardicin thioesterase.

The inversion from L- to D-stereochemistry endows peptides improved bioactivity and enhanced resistance to many proteases and peptidases. To strengthen the biostability and bioavailability of peptide drugs, enzymatic epimerization becomes an important way to incorporate D-amino acid into peptide backbones. Recently, a bifunctional thioesterase NocTE, which is responsible for the epimerization and hydrolysis of the C-terminal (p-hydroxyphenyl)glycine residue of ß-lactam antibiotic nocardicin A, exclusively directs to the generation of D-diastereomers. Different from other epimerases, NocTE exhibits unique stereochemical selectivity. Herein, we investigated the catalytic mechanism of NocTE via molecular dynamic (MD) simulations and quantum mechanical/molecular mechanics (QM/MM) calculations. Through structural analyses, two key water molecules around the reaction site were found to serve as proton mediators in epimerization. The structural characteristics inspired us to propose a substrate-assisted mechanism for the epimerization, where multi-step proton transfers were mediated by water molecules and ß-lactam ring, and the free energy barrier was calculated to be 20.3 kcal/mol. After that, the hydrolysis of D-configured substrate was energetically feasible with the energy barrier of 14.3 kcal/mol. As a comparison, the energy barrier for the direct hydrolysis of L-configured substrate was obtained to be 24.0 kcal/mol. Our study provides mechanistic insights into catalytic activities of bifunctional thioesterase NocTE, uncovers more clues to the molecular basis for stereochemical selectivity and paves the way for the directed biosynthesis of novel peptide drugs with various stereostructural characteristics by enzyme rational design.

Discovery of a Natural Product with Potent Efficacy Against SARS-CoV-2 by Drug Screening.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide for almost 2 years. It starts from viral adherence to host cells through an interaction between spike glycoprotein 1 (S1) containing a receptor-binding domain (RBD) and human angiotensin-converting enzyme-2 (ACE2). One of the useful strategies to prevent SARS-CoV-2 infection is to inhibit the attachment of RBD to ACE2. Therefore, the current work proposed potent peptides against SARS-CoV-2 infection by carrying out MM-PBSA calculation based on the binding of 52 antiviral peptides (AVPs) to RBD. Considering the binding free energies of AVPs to RBD, cyanovirin-N (CV-N) showed the strongest RBD binding affinity among 52 AVPs. Upon structural analysis of RBD complex with CV-N, it was observed that 12 of the 13 key residues of RBD binding to ACE2 were hijacked by CV-N. CV-N bound to RBD at a smaller affinity of 14.9 nM than that of ACE2 and inhibited the recruitment of S1 to human alveolar epithelial cells. Further analysis revealed that CV-N suppressed SARS-CoV-2 S pseudovirion infection with a half-maximal inhibitory concentration (IC50) of 18.52 µg/mL. This study demonstrated a drug screening for AVPs against SARS-CoV-2 and discovered a peptide with inspiring antiviral properties, which provided a promising strategy for the COVID-19 therapeutic approach.

Improved thermostability of proteinase K and recognizing the synergistic effect of Rosetta and FoldX approaches.

Proteinase K (PRK) is a proteolytic enzyme that has been widely used in industrial applications. However, poor stability has severely limited the uses of PRK. In this work, we used two structure-guided rational design methods, Rosetta and FoldX, to modify PRK thermostability. Fifty-two single amino acid conversion mutants were constructed based on software predictions of residues that could affect protein stability. Experimental characterization revealed that 46% (21 mutants) exhibited enhanced thermostability. The top four variants, D260V, T4Y, S216Q, and S219Q, showed improved half-lives at 69°C by 12.4-, 2.6-, 2.3-, and 2.2-fold that of the parent enzyme, respectively. We also found that selecting mutations predicted by both methods could increase the predictive accuracy over that of either method alone, with 73% of the shared predicted mutations resulting in higher thermostability. In addition to providing promising new variants of PRK in industrial applications, our findings also show that combining these programs may synergistically improve their predictive accuracy.

Deep learning analysis and age prediction from shoeprints.

Human gaits are the patterns of limb movements which involve both the upper and lower body parts. These patterns in terms of step rate, gait speed, stance widening, stride, and bipedal forces are influenced by different factors including environmental (such as social, cultural, and behavioral traits) and physical changes (such as age and health status). These factors are reflected on the imprinted shoeprints generated with body forces, which in turn can be used to predict age, a problem not systematically addressed using any computational approach. We collected 100,000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age. The model integrates various convolutional neural network models together using a skip mechanism to extract age-related features, especially in pressure and abrasion regions from pair-wise shoeprints. The results show that 40.23% of the subjects had prediction errors within 5-years of age and the prediction accuracy for gender/sex classification reached 86.07%. Interestingly, the age-related features mostly reside in the asymmetric differences between left and right shoeprints. The analysis also reveals interesting age-related and gender-related patterns in the pressure distributions on shoeprints; in particular, the pressure forces spread from the middle of the toe toward outside regions over age with gender-specific variations of forces on heel regions. Such statistics provide insight into new methods for forensic investigations, medical studies of gait pattern disorders, biometrics, and sport studies.

HD5 and LL-37 Inhibit SARS-CoV and SARS-CoV-2 Binding to Human ACE2 by Molecular Simulation.

The coronavirus (COVID-19) pandemic is still spreading all over the world. As reported, angiotensin-converting enzyme-2 (ACE2) is a receptor of SARS-CoV-2 spike protein that initializes viral entry into host cells. Previously, the human defensin 5 (HD5) has been experimentally confirmed to be functional against the SARS-CoV-2. The present study proposes a human cathelicidin known as LL37 that strongly binds to the carboxypeptidase domain of human ACE2 compared to HD5. Therefore, LL37 bears a great potential to be tested as an anti-SARS-CoVD-2 peptide. We investigated the molecular interactions formed between the LL37 and ACE2 as well as HD5 and ACE2 tailed by their thermodynamic stability. The MM-PBSA and free energy landscape analysis outcomes confirmed its possible inhibitory effect against the SARS-CoV-2. The results obtained here could help propose a promising therapeutic strategy against the havoc caused by SARS-CoV-2 infections.

Human Cathelicidin Inhibits SARS-CoV-2 Infection: Killing Two Birds with One Stone.

SARS-CoV-2 infection begins with the association of its spike 1 (S1) protein with host angiotensin-converting enzyme-2 (ACE2). Targeting the interaction between S1 and ACE2 is a practical strategy against SARS-CoV-2 infection. Herein, we show encouraging results indicating that human cathelicidin LL37 can simultaneously block viral S1 and cloak ACE2. LL37 binds to the receptor-binding domain (RBD) of S1 with high affinity (11.2 nM) and decreases subsequent recruitment of ACE2. Owing to the RBD blockade, LL37 inhibits SARS-CoV-2 S pseudovirion infection, with a half-maximal inhibitory concentration of 4.74 µg/mL. Interestingly, LL37 also binds to ACE2 with an affinity of 25.5 nM and cloaks the ligand-binding domain (LBD), thereby decreasing S1 adherence and protecting cells against pseudovirion infection in vitro. Intranasal administration of LL37 to C57 mice infected with adenovirus expressing human ACE2 either before or after pseudovirion invasion decreased lung infection. The study identified a versatile antimicrobial peptide in humans as an inhibitor of SARS-CoV-2 attachment using dual mechanisms, thus providing a potential candidate for coronavirus disease 2019 (COVID-19) prevention and treatment.

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level.

Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and more attention in the field of diagnosing food-borne pathogens in recent years. In the research, 15,890 single-cell Raman spectra of 23 common strains from 7 genera were obtained at the single cell level. Then, the nonlinear features of raw data were extracted by kernel principal component analysis, and the individual bacterial cell was evaluated and discriminated at the serotype level through the decision tree algorithm. The results demonstrated that the average correct rate of prediction on independent test set was 86.23 ± 0.92% when all strains were recognized by only one model, but there were high misjudgment rates for certain strains. Therefore, the four-level classification models were introduced, and the different hierarchies of the identification models achieved accuracies in the range of 87.1%-95.8%, which realized the efficient prediction of strains at the serotype level. In summary, Raman spectroscopy combined with machine learning based on fingerprint difference was a prospective strategy for the rapid diagnosis of pathogenic bacteria.

An ultrahigh-throughput screening platform based on flow cytometric droplet sorting for mining novel enzymes from metagenomic libraries.

Uncultivable microbial communities provide enormous reservoirs of enzymes, but their experimental identification by functional metagenomics is challenging, mainly due to the difficulty of screening enormous metagenomic libraries. Here, we propose a reliable and convenient ultrahigh-throughput screening platform based on flow cytometric droplet sorting (FCDS). The FCDS platform employs water-in-oil-in-water double emulsion droplets serving as single-cell enzymatic micro-reactors and a commercially available flow cytometer, and it can efficiently isolate novel biocatalysts from metagenomic libraries by processing single cells as many as 108 per day. We demonstrated the power of this platform by screening a metagenomic library constructed from domestic running water samples. The FCDS assay screened 30 million micro-reactors in only 1 h, yielding a collection of esterase genes. Among these positive hits, Est WY was identified as a novel esterase with high catalytic efficiency and distinct evolutionary origin from other lipolytic enzymes. Our study manifests that the FCDS platform is a robust tool for functional metagenomics, with the potential to significantly improve the efficiency of exploring novel enzymes from nature.

Complete sequences of two new KPC-harbouring plasmids in Klebsiella pneumoniae ST11 strains in China.

OBJECTIVES: Klebsiella pneumoniae carbapenemase (KPC) has spread across the world. The present study focused on exploring the sequences of two new KPC-harbouring plasmids in K. pneumoniae. METHODS: Eighteen KPC-harbouring K. pneumoniae isolates were collected from a tertiary teaching hospital in 2014 in Fujian, China, among which two new KPC-harbouring plasmids (pF77 and pF5) we identified. The characteristics of the plasmids and the isolates carrying them were investigated in detail. RESULTS: The two KPC-harbouring plasmids (pF5 and pF77) carried the antimicrobial resistance genes blaKPC-2, blaCTX-M-65, blaSHV-12, catA2 and fosA3. Detailed sequence comparison revealed that the two plasmids might have evolved from recombination of the previously reported plasmids pKP1034 and pCT-KPC, which were considered to evolve from ancestor plasmids pHN7A8, pKPC-LK30 and pKPHS2. Plasmids pF5 and pF77 were non-conjugative and were mainly identified in sequence type 11 (ST11) K. pneumoniae isolates. Additionally, 4-55 core single nucleotide polymorphisms (SNPs) were identified in each pair of sequenced isolates that carried the identified plasmids. CONCLUSION: Plasmids pF5 and pF77 as well as the previously reported plasmids pKP1034 and pCT-KPC were all detected in 2013-2014 in South China and were carried by ST11 K. pneumoniae isolates. SNP analysis indicated high similarity of the sequenced isolates. Therefore, spread of the group of plasmids may be due to an outbreak of clonal dissemination of ST11 KPC-producing K. pneumoniae. This study also highlights the importance of plasmid analysis in the surveillance and control of antibiotic resistance spread in clinical isolates.

Improved thermostability of creatinase from Alcaligenes Faecalis through non-biased phylogenetic consensus-guided mutagenesis.

BACKGROUND: Enzymatic quantification of creatinine has become an essential method for clinical evaluation of renal function. Although creatinase (CR) is frequently used for this purpose, its poor thermostability severely limits industrial applications. Herein, we report a novel creatinase from Alcaligenes faecalis (afCR) with higher catalytic activity and lower KM value, than currently used creatinases. Furthermore, we developed a non-biased phylogenetic consensus method to improve the thermostability of afCR. RESULTS: We applied a non-biased phylogenetic consensus method to identify 59 candidate consensus residues from 24 creatinase family homologs for screening afCR mutants with improved thermostability. Twenty-one amino acids of afCR were selected to mutagenesis and 11 of them exhibited improved thermostability compared to the parent enzyme (afCR-M0). Combination of single-site mutations in sequential screens resulted in a quadruple mutant D17V/T199S/L6P/T251C (M4-2) which showed ~ 1700-fold enhanced half-life at 57 °C and a 4.2 °C higher T5015 than that of afCR-M0. The mutant retained catalytic activity equivalent to afCR-M0, and thus showed strong promise for application in creatinine detection. Structural homology modeling revealed a wide range of potential molecular interactions associated with individual mutations that contributed to improving afCR thermostability. CONCLUSIONS: Results of this study clearly demonstrated that the non-biased-phylogenetic consensus design for improvement of thermostability in afCR is effective and promising in improving the thermostability of more enzymes.

SERS-based lateral flow assay combined with machine learning for highly sensitive quantitative analysis of Escherichia coli O157:H7.

In the present study, surface-enhanced Raman scattering-based lateral flow assay (SERS-LFA) strips were applied to promptly and sensitively detect Escherichia coli O157:H7 (E. coli O157:H7) to ensure food safety. The SERS nanotags were prepared by connecting peculiar monoclonal antibody (McAb) against E. coli O157:H7 directly onto the surfaces of gold-silver core-shell nanostructures loaded with two-layer Raman reporter molecules of 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB). The Raman signal intensity at 1335 cm-1 on the test line (T line) of SERS-LFA strips was detected in the wide range of 101-109 colony-forming units/mL (CFU/mL), and regression models based on machine learning were combined to accurately and quantitatively analyze E. coli O157:H7. The limit of detection (LOD) of the extreme gradient boosting regression (XGBR) based on the Raman signal intensity of DTNB was 6.94 × 101 CFU/mL for E. coli O157:H7, which was approximately four orders of magnitude lower than that of visual limits. In addition, although E. coli O157:H7 was spiked into the food matrices including milk and beef at an ultra-low dose of 10 CFU/mL, the SERS-LFA combined with XGBR was able to successfully explore E. coli O157:H7 from the mixture that was incubated for only 2 h, in which the recoveries were mainly distributed between 86.41 and 128.25%. In summary, these results demonstrated that the SERS-LFA had a significant potential as a powerful tool for the point-of-care testing (POCT) of E. coli O157:H7 in the early food contamination stage.

Genetic factors related to the widespread dissemination of ST11 extensively drug-resistant carbapenemase-producing Klebsiella pneumoniae strains within hospital.

BACKGROUND: Carbapenemase-producing Klebsiella pneumoniae (CP-Kp) poses distinct clinical challenges due to extensively drug resistant (XDR) phenotype, and sequence type (ST) 11 is the most dominant blaKPC-2-bearing CP-Kp clone in China. The purpose of this current retrospective study was to explore the genetic factors associated with the success of XDR CP-Kp ST11 strains circulated in the intensive care unit (ICU) of a Chinese tertiary hospital. METHODS: Six ST11 XDR CP-Kp strains were identified between May and December 2014 and validated by minimum inhibitory concentration examination, polymerase chain reaction, and pyrosequencing. The six ST11 XDR CP-Kp, as well as three multi-drug resistant (MDR) and four susceptible strains, were sequenced using single-molecule real-time method. Comprehensively structural and functional analysis based on comparative genomics was performed to identify genomic characteristics of the XDR ST11 CP-Kp strains. RESULTS: We found that ST11 XDR blaKPC-2-bearing CP-Kp strains isolated from inpatients spread in the ICU of the hospital. Functionally, genes associated with information storage and processing of the ST11 XDR CP-Kp strains were more abundant than those of MDR and susceptible strains, especially genes correlative with mobile genetic elements (MGEs) such as transposons and prophages. Structurally, eleven large-scale genetic regions taken for the unique genome in these ST11 XDR CP-Kp strains were identified as MGEs including transposons, integrons, prophages, genomic islands, and integrative and conjugative elements. Three of them were located on plasmids and eight on chromosomes; five of them were with antimicrobial resistance genes and eight with adaptation associated genes. Notably, a new blaKPC-2-bearing ΔΔTn1721-blaKPC-2 transposon, probably transposed and truncated from ΔTn1721-blaKPC-2 by IS903D and ISKpn8, was identified in all six ST11 XDR CP-Kp strains. CONCLUSION: Our findings suggested that together with clonal spread, MGEs identified uniquely in the ST11 XDR CP-Kp strains might contribute to their formidable adaptability, which facilitated their widespread dissemination in hospital.