Molecular Characteristics and Virulence Profile of Clinical Listeria monocytogenes Isolates in Northern Taiwan, 2009-2019.

Listeria monocytogenes is a critical foodborne pathogen that causes severe invasive and noninvasive diseases and is associated with high mortality. Information on the prevalence of L. monocytogenes infections in Taiwan is very limited. This study aimed to analyze the molecular epidemiological surveillance and virulence gene distribution of 176 human clinical L. monocytogenes isolates collected between 2009 and 2019 in northern Taiwan. Our results showed that the isolates belonged to 4 serogroups (IIa, IIb, IVb, and IIc), with most isolates in serogroups IIa (81/176, 46%) and IIb (71/176, 40.3%). Multilocus sequence typing analysis revealed 18 sequence types (STs) and 13 clonal complexes (CCs). Eighty-four percent of all isolates belonged to six STs: CC87-ST87 (40/176, 22.7%), CC19-ST378 (36/176, 19.9%), CC155-ST155 (28/176, 15.5%), CC1-ST710 (16/176, 8.8%), CC5-ST5 (16/176, 8.8%), and CC101-ST101 (11/176, 6.1%). Furthermore, our analysis showed the distributions of four Listeria pathogenicity islands (LIPI) among all isolates. LIPI-1 and LIPI-2 existed in all isolates, whereas LIPI-3 and LIPI-4 only existed in specific STs and CCs. LIPI-3 existed in the STs, CC1-ST710, CC3-ST3, CC288-ST295, and CC191-ST1458, whereas LIPI-4 could be found in the STs, CC87-ST87 and CC87-ST1459. Strains containing LIPI-3 and LIPI-4 are potentially hypervirulent; thus, 68/176 isolates (39.1%) collected in this study were potentially hypervirulent. Since L. monocytogenes infections are considered highly correlated with diet, molecular epidemiological surveillance of Listeria in food is important; continued surveillance will provide critical information to prevent foodborne diseases.

Molecular characterization of lugdunin inactivation mechanisms and their association with Staphylococcus lugdunensis genetic types.

BACKGROUND AND PURPOSE: Our previous studies showed that lugdunin activities are associated with Staphylococcus lugdunensis genotypes, and most isolates do not exhibit lugdunin activity. As a continuation of our previous analysis, we focused on the reasons for defects in lugdunin production in S. lugdunensis clinical isolates. METHODS: A comparative analysis of 36 S. lugdunensis whole genome sequencing data revealed three major mutation types, unknown deletion mechanism that caused most of lug operon genes lost, mobile genetic element (MGE) insertion, and nonsense mutations, which potentially damaged lugdunin production. A total of 152 S. lugdunensis clinical isolates belonging to lugdunin nonproducers were further examined for the above three mutation types. PCR products were sequenced to examine these variations. RESULTS: Forty-six of the 152 isolates were CRISPR-Cas IIC isolates, including 26 ST27, 14 ST4, and 6 ST29 isolates; further investigation confirmed that all of their lug operons had lost almost all lug operon genes except lugM. An IS256 insertion in lugA was identified in 16 isolates, and most isolates (15 over 16) belonged to ST3. In addition, three nonsense mutations caused by single nucleotide substitutions (an adenine deletion in lugB at the 361th and 1219th nucleotides and an adenine deletion in lugC at the 1612nd nucleotide) that were frequently observed among 36 S. lugdunensis whole genome sequencing data were further observed in our clinical isolates. These three nonsense mutations were frequently found in most of CRISPR-Cas IIIA strains, especially in ST6 isolates. CONCLUSION: Our findings suggest that the mechanisms affecting lugdunin production are associated with S. lugdunensis molecular types.

High Incidence of Multiple-Drug-Resistant Pheromone-Responsive Plasmids and Transmissions of VanA-Type Vancomycin-Resistant Enterococcus faecalis between Livestock and Humans in Taiwan.

A total of seventy VanA-type vancomycin-resistant enterococci (VRE) isolates obtained in Taiwan in the early 2000s were retrospectively characterized. Forty isolates were obtained from human patients and thirty from livestock. Of these VRE isolates, twenty-three (57.5%) of the human VRE and thirty (100%) of the livestock VRE were Enterococcus faecalis, and the remaining seventeen (42.5%) of the human VRE were E. faecium. Of the 53 E. faecalis isolates, twenty-two (96%) of the human VRE and thirty (100%) of the livestock VRE exhibited a high level of resistance to vancomycin and sensitivity to teicoplanin. They also had three amino acid substitutions in the N-terminal region of the deduced VanS sequence. The vancomycin resistance of all of the 22 human isolates, and 20 of the 30 livestock isolates, transferred to E. faecalis FA2-2 at a frequency of 10-5 to 10-3 per donor cell in broth. Each of the transconjugants responded to E. faecalis pheromone (i.e., E. faecalis FA2-2 culture filtrate), indicating that the conjugative plasmids were pheromone-responsive plasmids. Three of the conjugative plasmids originated from human isolates, and five plasmids from livestock isolates were corresponded and classified as type A plasmid. Two plasmids originated from human isolates and six plasmids from livestock isolates were corresponded and classified as type B plasmid. E. faecalis FA2-2 containing either the type A or type B plasmid responded to the synthetic pheromone cAD1. The type A and type B plasmids transferred between E. faecalis FA2-2 and JH2SS at a frequency of about 10-2 per donor cell and conferred vancomycin, bacitracin, and erythromycin resistances. The complete DNA sequence of the representative type A plasmid pTW9 (85,068 bp) showed that the plasmid carried a Tn1546-like element encoding vanA-type resistance, erythromycin resistance (ermB), and bacitracin resistance (bcrABDR). The plasmid contained the regulatory region found in the pheromone-responsive plasmid and encoded the genes traA, traD and iad1, which are the key negative regulatory elements, and traE1, a key positive regulator of plasmid pAD1, indicating that plasmid pTW9 was pAD1-type pheromone-responsive plasmid. PFGE analysis of SmaI-digested chromosomal DNAs showed that several E. faecalis strains harboring an identical type A pheromone-responsive plasmid were indistinguishable, and that these were identified both in human and livestock isolates, indicating the transmissions of the VRE strains between livestock and humans. These data showed that the multiple-drug-resistant pheromone-responsive conjugative plasmids have been widely spread in both human and livestock VRE, and there was high potential for transfers of VRE from food animals to humans in Taiwan in the early 2000s.

The Clinical and Genetic Characteristics of Streptococcus agalactiae Meningitis in Neonates.

Streptococcus agalactiae (Group B Streptococcus, GBS) is an important pathogen of bacterial meningitis in neonates. We aimed to investigate the clinical and genetic characteristics of neonatal GBS meningitis. All neonates with GBS meningitis at a tertiary level medical center in Taiwan between 2003 and 2020 were analyzed. Capsule serotyping, multilocus sequence typing, antimicrobial resistance, and whole-genome sequencing (WGS) were performed on the GBS isolates. We identified 48 neonates with GBS meningitis and 140 neonates with GBS sepsis. Neonates with GBS meningitis had significantly more severe clinical symptoms; thirty-seven neonates (77.8%) had neurological complications; seven (14.6%) neonates died; and 17 (41.5%) survivors had neurological sequelae at discharge. The most common serotypes that caused meningitis in neonates were type III (68.8%), Ia (20.8%), and Ib (8.3%). Sequence type (ST) is highly correlated with serotypes, and ST17/III GBS accounted for more than half of GBS meningitis cases (56.3%, n = 27), followed by ST19/Ia, ST23/Ia, and ST12/Ib. All GBS isolates were sensitive to ampicillin, but a high resistance rates of 72.3% and 70.7% to erythromycin and clindamycin, respectively, were noted in the cohort. The virulence and pilus genes varied greatly between different GBS serotypes. WGS analyses showed that the presence of PezT; BspC; and ICESag37 was likely associated with the occurrence of meningitis and was documented in 60.4%, 77.1%, and 52.1% of the GBS isolates that caused neonatal meningitis. We concluded that GBS meningitis can cause serious morbidity in neonates. Further experimental models are warranted to investigate the clinical and genetic relevance of GBS meningitis. Specific GBS strains that likely cause meningitis requires further investigation and clinical attention.

A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology.

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision-recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus.

Lugdunin production and activity in Staphylococcus lugdunensis isolates are associated with its genotypes.

Lugdunin produced by Staphylococcus lugdunensis has been shown to have broad inhibitory activity against Gram-positive bacteria; however, lugdunin activity among S. lugdunensis isolates and its association with different agr, SCCmec, and sequence types remain unclear. We used matrix-assisted laser desorption ionization-time-of-flight mass spectrometry to identify S. lugdunensis and collected 202 S. lugdunensis samples for further assays. Agar spot tests were performed to characterize S. lugdunensis lugdunin production and activity. Multilocus sequence typing, SCCmec, and agr genotyping were performed on S. lugdunensis. In all, 91 Staphylococcus aureus strains with varying vancomycin susceptibilities were used to examine lugdunin activity in S. lugdunensis. In total, 48 S. lugdunensis strains (23.8%) were found to be oxacillin-resistant S. lugdunensis (ORSL), whereas 154 (76.2%) were classified as oxacillin-sensitive S. lugdunensis (OSSL). Moreover, 16 (33.3%) ORSL and 35 (22.7%) OSSL strains showed antibacterial activity against S. aureus. Our data showed that most lugdunin-producing ORSL strains (14/48, 29.2%) were of ST3-SCCmec V-agr II genotypes, whereas most lugdunin-producing OSSL strains (15/154, 9.7%) were of ST3-agr II, followed by ST1-agr I (10/154, 6.5%). Our data also revealed that lugdunin exhibited weak inhibitory activity against the VISA ST239 isolate. In addition, we observed that ST239 VSSA was more resistant to lugdunin than ST5, ST59, and ST45 VSSA. Taken together, our data pioneered the epidemiology of lugdunin production in S. lugdunensis isolates and revealed its association with genotypes. However, further molecular and bioinformatics investigations are needed to elucidate the regulatory mechanisms of lugdunin production and activity. IMPORTANCE Lugdunin is active against both methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci by dissipating their membrane potential. However, the association of lugdunin activity with the genotypes of Staphylococcus lugdunensis has not been addressed. Here, we show the high prevalence of lugdunin-producing strains among ST1 (83.3%), ST2 (66.7%), and ST3 (53.3%) S. lugdunensis. Moreover, we identified the antibacterial activity of lugdunin-producing strains against VISA and hVISA. These results shed light on the potential application of lugdunin for the treatment of drug-resistant pathogens.

Clonal Spreading of ST42 Staphylococcus haemolyticus Strains Occurs Possibly Due to fusB and tetK Resistant Genes and Capsule-Related Genes.

Multi-drug resistant Staphylococcus haemolyticus is a frequent nosocomial invasive bacteremia pathogen in hospitals. Our previous analysis showed one of the predominant strains, ST42 originated from ST3, had only one multilocus sequence typing (MLST) variation among seven loci in SH1431; yet no significant differences in biofilm formation observed between ST42 and ST3, suggesting that other factors influence clonal lineage change. Whole genome sequencing was conducted on two isolates from ST42 and ST3 to find phenotypic and genotypic variations, and these variations were further validated in 140 clinical isolates. The fusidic acid- and tetracycline-resistant genes (fusB and tetK) were found only in CGMH-SH51 (ST42). Further investigation revealed consistent resistant genotypes in all isolates, with 46% and 70% of ST42 containing fusB and tetK, respectively. In contrast, only 23% and 4.2% ST3 contained these two genes, respectively. The phenotypic analysis also showed that ST42 isolates were highly resistant to fusidic acid (47%) and tetracycline (70%), compared with ST3 (23% and 4%, respectively). Along with drug-resistant genes, three capsule-related genes were found in higher percentage distributions in ST42 than in ST3 isolates. Our findings indicate that ST42 could become endemic in Taiwan, further constitutive surveillance is required to prevent the spread of this bacterium.

Data-Driven Two-Stage Framework for Identification and Characterization of Different Antibiotic-Resistant Escherichia coli Isolates Based on Mass Spectrometry Data.

In clinical microbiology, matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is frequently employed for rapid microbial identification. However, rapid identification of antimicrobial resistance (AMR) in Escherichia coli based on a large amount of MALDI-TOF MS data has not yet been reported. This may be because building a prediction model to cover all E. coli isolates would be challenging given the high diversity of the E. coli population. This study aimed to develop a MALDI-TOF MS-based, data-driven, two-stage framework for characterizing different AMRs in E. coli. Specifically, amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM) were used. In the first stage, we split the data into two groups based on informative peaks according to the importance of the random forest. In the second stage, prediction models were constructed using four different machine learning algorithms-logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). The findings demonstrate that XGBoost outperformed the other four machine learning models. The values of the area under the receiver operating characteristic curve were 0.62, 0.72, 0.87, 0.72, and 0.72 for AMC, CAZ, CIP, CRO, and CXM, respectively. This implies that a data-driven, two-stage framework could improve accuracy by approximately 2.8%. As a result, we developed AMR prediction models for E. coli using a data-driven two-stage framework, which is promising for assisting physicians in making decisions. Further, the analysis of informative peaks in future studies could potentially reveal new insights. IMPORTANCE Based on a large amount of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, a data-driven two-stage framework was established to evaluate the antimicrobial resistance of E. coli. Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for the two-stage model training, and the values of the area under the receiver operating characteristic curve (AUC) were 0.62 for AMC, 0.72 for CAZ, 0.87 for CIP, 0.72 for CRO, and 0.72 for CXM. Further investigations revealed that the informative peak m/z 9714 appeared with some important peaks at m/z 6809, m/z 7650, m/z 10534, and m/z 11783 for CIP and at m/z 6809, m/z 10475, and m/z 8447 for CAZ, CRO, and CXM. This framework has the potential to improve the accuracy by approximately 2.8%, indicating a promising potential for further research.

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra.

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods--FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods--to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism.

Clonal Complex 12 Serotype Ib Streptococcus agalactiae Strain Causing Complicated Sepsis in Neonates: Clinical Features and Genetic Characteristics.

Streptococcus agalactiae (group B Streptococcus [GBS]) is well known to cause serious diseases in infants. A serotype Ib GBS strain has recently emerged and become prevalent in Southeast Asia. We aimed to investigate the clinical and genetic characteristics of this strain. All neonates with invasive GBS diseases from a tertiary-level medical center in Taiwan between 2003 and 2020 were analyzed. The capsule serotyping, multilocus sequence typing, and antimicrobial resistance analyses were performed on all the invasive GBS isolates, and whole-genome sequencing (WGS) was performed specifically on the type Ib GBS strain. A total of 188 neonates with invasive GBS disease during the study period were identified. The type Ib GBS strain accounted for 7.4% (n = 14) of neonatal GBS invasive diseases. Almost all type Ib GBS isolates belonged to sequence type 12 (13/14, 92.9%) and clonal complex 12. Neonates with type Ib GBS disease had a significantly higher rate of complicated sepsis (10/14, 71.4%; P < 0.05) and sepsis-attributable mortality (6/14, 42.9%; P < 0.05). Additionally, type Ib GBS isolates had significantly higher rates of resistance to erythromycin and clindamycin (both 100%; P < 0.05) than other GBS serotypes. WGS revealed the presence of an ~75-kb integrative and conjugative element, ICESag37, comprising multiple antibiotic resistance and virulence genes, and PI-1 plus PI-2a were noted in all type Ib serotype 12 (ST12) GBS isolates; these isolates may be responsible for its high invasiveness and antimicrobial resistance rates. The genomic characteristics of the type Ib clonal complex 12 (CC12) GBS strain may account for the high illness severity associated with this strain and its antibiotic resistance. Continuous monitoring and advanced strategies to control the spread of type Ib CC12 GBS should be considered. IMPORTANCE A type Ib ST12 GBS strain is not a common isolate in neonatal invasive diseases and has been ignored for a long time. However, the recent literature and our data showed that such a GBS strain is associated with a significantly higher risk of severe sepsis, higher illness severity, and a significantly higher rate of sepsis-attributable mortality. This study found a novel gene cluster, including the presence of ICESag37 and specific pilus genes, carrying multiple antimicrobial resistance and virulence genes, which may be responsible for the clinical characteristics. Because of the higher mortality and severity of illness, we concluded that continuous monitoring of the type Ib ST12 GBS strain is warranted in the future.

Characterization of oxacillin-resistant Staphylococcus lugdunensis isolated from sterile body fluids in a medical center in Taiwan: A 12-year longitudinal epidemiological study.

BACKGROUND: In this study, our objective was to characterize Staphylococcus lugdunensis isolated from sterile body fluids (SBFs) in a medical center in Taiwan between 2009 and 2020. METHODS: We used MALDI-TOF MS, disk diffusion testing, agar dilution assay, SCCmec typing, and antibiotic resistance gene screening to identify and investigate the characteristics of oxacillin-resistant S. lugdunensis (ORSL). RESULTS: A total of 438 S. lugdunensis isolates were collected and 146 (33.3%) isolates were identified as ORSL. SCCmec type V was dominant (65.7%) in our ORSL isolates, followed by SCCmec type II (18.5%), and type IV (8.9%). After 2013, a slight increase in SCCmec types IV and V was revealed. Moreover, all ORSL isolates with type II and untypable SCCmec were highly resistant to oxacillin (MIC >32 µg/mL), compared to ORSL that had SCCmec types IV, V, and VT. All 146 ORSL isolates were resistant to penicillin and susceptible to teicoplanin and vancomycin. High resistance rates of ORSL to clindamycin (43.2%), erythromycin (43.2%), gentamicin (78.1%) and tetracycline (46.6%) was observed. Moreover, only two (1.4%) and six (4.1%) ORSL isolates were resistant to trimethoprim/sulfamethoxazole and ciprofloxacin, respectively. The erythromycin-resistant ORSL isolates mostly exhibited constitutive MLSB resistant phenotype (61/63, 96.8%) and contained either ermC alone (27/63, 42.9%) or a combination of ermC with ermA (28/63, 44.4%). CONCLUSION: Our present study showed a stable rate of ORSL from SBFs during 2009-2020. Moreover, teicoplanin, vancomycin, trimethoprim/sulfamethoxazole, and ciprofloxacin were shown to be highly efficient for the treatment of ORSL in vitro.

Amelioration of Maternal Immune Activation-Induced Autism Relevant Behaviors by Gut Commensal Parabacteroides goldsteinii.

Autism spectrum disorder (ASD) is characterized by cognitive inflexibility and social deficits. Probiotics have been demonstrated to play a promising role in managing the severity of ASD. However, there are no effective probiotics for clinical use. Identifying new probiotic strains for ameliorating ASD is therefore essential. Using the maternal immune activation (MIA)-based offspring ASD-like mouse model, a probiotic-based intervention strategy was examined in female mice. The gut commensal microbe Parabacteroides goldsteinii MTS01, which was previously demonstrated to exert multiple beneficial effects on chronic inflammation-related-diseases, was evaluated. Prenatal lipopolysaccharide (LPS) exposure induced leaky gut-related inflammatory phenotypes in the colon, increased LPS activity in sera, and induced autistic-like behaviors in offspring mice. By contrast, P. goldsteinii MTS01 treatment significantly reduced intestinal and systemic inflammation and ameliorated disease development. Transcriptomic analyses of MIA offspring indicated that in the intestine, P. goldsteinii MTS01 enhanced neuropeptide-related signaling and suppressed aberrant cell proliferation and inflammatory responses. In the hippocampus, P. goldsteinii MTS01 increased ribosomal/mitochondrial and antioxidant activities and decreased glutamate receptor signaling. Together, significant ameliorative effects of P. goldsteinii MTS01 on ASD relevant behaviors in MIA offspring were identified. Therefore, P. goldsteinii MTS01 could be developed as a next-generation probiotic for ameliorating ASD.

Clinical manifestations and antimicrobial susceptibility of Nocardia species at a tertiary hospital in Taiwan, 2011-2020.

BACKGROUND: The study aimed to assess the clinical characteristics of patients with nocardiosis, to evaluate the in vitro susceptibility of antimicrobial agents against Nocardia species, and to explore changes in antimicrobial susceptibilities in this era of multidrug resistance. METHODS: Nocardia isolates were identified to the species level using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) and 16S rRNA, hsp65, and secA1 gene sequencing, and minimum inhibitory concentrations (MICs) of 15 antimicrobial agents were assessed with the broth microdilution method. RESULTS: Eighty-nine isolates from 68 patients were identified to species level. The most common species were Nocardia brasiliensis (n = 28, 31.5%), followed by N. farcinica (n = 24, 27%) and N. cyriacigeorgica (n = 16, 18%). Skin and soft tissue were the most common sites of nocardiosis. In multivariate analysis, cutaneous infection (OR, 0.052; p = 0.009), immunosuppressant use (OR, 16.006; p = 0.013) and Charlson combidity index (OR, 1.522; p = 0.029) were significant predictors for death. In total, 98.9% isolates were susceptible to trimethoprim-sulfamethoxazole and linezolid. Further, the MIC range and resistance rate of all Nocardia species to ceftriaxone, imipenem, and amoxicillin-clavulanic acid were found to generally increase over time. CONCLUSION: Considering that trimethoprim-sulfamethoxazole is effective against most Nocardia species, it is the antibiotic of choice in Taiwan. Besides, amikacin, tigecycline, and linezolid showed high activity against Nocardia species and are thus good alternatives or additional therapies to treat nocardiosis, depending on patient's underlying conditions and site of infection.

An lnu(A)-Carrying Multi-Resistance Plasmid Derived from Sequence Type 3 Methicillin-Resistant Staphylococcus lugdunensis May Contribute to Antimicrobial Resistance in Staphylococci.

Methicillin-resistant Staphylococcus lugdunensis (MRSL) strains showing resistance to several common antibiotics have been reported recently. Sequence type (ST) 3 MRSL carrying SCCmec types IV, V, or Vt is the major lineage associated with health care-associated infections. We aimed to investigate the distribution and dissemination of antimicrobial resistance determinants in this lineage. Two representative ST3-MRSL strains, CGMH-SL131 (SCCmec V) and CGMH-SL138 (SCCmec IV), were subjected to whole-genome sequencing. Detection of antibiotic resistance genes and screening of susceptibility patterns were performed for 30 ST3-MRSL and 16 ST6-MRSL strains via PCR and standard methods. Except for mecA and blaZ, antimicrobial resistance genes were located within two plasmids: a 28.6 kb lnu(A)-carrying plasmid (pCGMH_SL138) in CGMH-SL138 and a 26 kb plasmid carrying non-lnu(A) resistance genes (pCGMH_SL131) in CGMH-SL131. Both plasmids shared common genetic features with multiple copies of IS257 flanked by genes conferring resistance to aminoglycoside (aacA-aphD and aadD), TET (tetk), and cadmium (cadDX) and tolerance to chlorhexidine (qacA/R); however, only pCGMH_SL138 harbored lnu(A) that conferred resistance to lincomycin and rep13 that encodes a replication initiation protein. Unlike ST6-MRSL, none of the ST3-MRSL isolates contained the ermA gene. Instead, most isolates harbored lnu(A) (20/30, 66.7%), and several other resistance genes found on pCGMH_SL138. These isolates and transformants containing pCGMH_SL138 exhibited susceptibility to ERY and higher MICs for lincomycin and aforementioned antibiotics. A novel lnu(A)-carrying plasmid, pCGMH_SL138, that harbored a multiresistance gene cluster, was identified in ST3-MRSL strains and may contribute to the dissemination of antibiotic resistance in staphylococci.

Efficiently Predicting Vancomycin Resistance of Enterococcus Faecium From MALDI-TOF MS Spectra Using a Deep Learning-Based Approach.

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species and predict antibiotic-resistant strains. However, the conventional approach using few single peaks would result in insufficient prediction power without using complete information of whole MS spectra. In the past few years, machine learning algorithms have been successfully applied to analyze the MALDI-TOF MS peaks pattern for rapid strain typing. In this study, we developed a convolutional neural network (CNN) method to deal with the complete information of MALDI-TOF MS spectra for detecting Enterococcus faecium, which is one of the leading pathogens in the world. We developed a CNN model to rapidly and accurately predict vancomycin-resistant Enterococcus faecium (VREfm) samples from the whole mass spectra profiles of clinical samples. The CNN models demonstrated good classification performances with the average area under the receiver operating characteristic curve (AUROC) of 0.887 when using external validation data independently. Additionally, we employed the score-class activation mapping (CAM) method to identify the important features of our CNN models and found some discriminative signals that can substantially contribute to detecting the ion of resistance. This study not only utilized the complete information of MALTI-TOF MS data directly but also provided a practical means for rapid detection of VREfm using a deep learning algorithm.

Rapid Antibiotic Resistance Serial Prediction in Staphylococcus aureus Based on Large-Scale MALDI-TOF Data by Applying XGBoost in Multi-Label Learning.

Multidrug resistance has become a phenotype that commonly exists among Staphylococcus aureus and is a serious concern for infection treatment. Nowadays, to detect the antibiotic susceptibility, antibiotic testing is generated based on the level of genomic for cure decision consuming huge of time and labor, while matrix-assisted laser desorption-ionization (MALDI) time-of-flight mass spectrometry (TOF/MS) shows its possibility in high-speed and effective detection on the level of proteomic. In this study, on the basis of MALDI-TOF spectra data of discovery cohort with 26,852 samples and replication cohort with 4,963 samples from Taiwan area and their corresponding susceptibilities to oxacillin and clindamycin, a multi-label prediction model against double resistance using Lowest Power set ensemble with XGBoost is constructed for rapid susceptibility prediction. With the output of serial susceptibility prediction, the model performance can realize 77% of accuracy for the serial prediction, the area under the receiver characteristic curve of 0.93 for oxacillin susceptibility prediction, and the area under the receiver characteristic curve of 0.89 for clindamycin susceptibility prediction. The generated multi-label prediction model provides serial antibiotic resistance, such as the susceptibilities of oxacillin and clindamycin in this study, for S. aureus-infected patients based on MALDI-TOF, which will provide guidance in antibiotic usage during the treatment taking the advantage of speed and efficiency.

Large-Scale Samples Based Rapid Detection of Ciprofloxacin Resistance in Klebsiella pneumoniae Using Machine Learning Methods.

Klebsiella pneumoniae is one of the most common causes of hospital- and community-acquired pneumoniae. Resistance to the extensively used quinolone antibiotic, such as ciprofloxacin, has increased in Klebsiella pneumoniae, which leads to the increase in the risk of initial antibiotic selection for Klebsiella pneumoniae treatment. Rapid and precise identification of ciprofloxacin-resistant Klebsiella pneumoniae (CIRKP) is essential for clinical therapy. Nowadays, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is another approach to discover antibiotic-resistant bacteria due to its shorter inspection time and lower cost than other current methods. Machine learning methods are introduced to assist in discovering significant biomarkers from MALDI-TOF MS data and construct prediction models for rapid antibiotic resistance identification. This study examined 16,997 samples taken from June 2013 to February 2018 as part of a longitudinal investigation done by Change Gung Memorial Hospitals (CGMH) at the Linkou branch. We applied traditional statistical approaches to identify significant biomarkers, and then a comparison was made between high-importance features in machine learning models and statistically selected features. Large-scale data guaranteed the statistical power of selected biomarkers. Besides, clustering analysis analyzed suspicious sub-strains to provide potential information about their influences on antibiotic resistance identification performance. For modeling, to simulate the real antibiotic resistance predicting challenges, we included basic information about patients and the types of specimen carriers into the model construction process and separated the training and testing sets by time. Final performance reached an area under the receiver operating characteristic curve (AUC) of 0.89 for support vector machine (SVM) and extreme gradient boosting (XGB) models. Also, logistic regression and random forest models both achieved AUC around 0.85. In conclusion, models provide sensitive forecasts of CIRKP, which may aid in early antibiotic selection against Klebsiella pneumoniae. The suspicious sub-strains could affect the model performance. Further works could keep on searching for methods to improve both the model accuracy and stability.

Long short-term memory model - A deep learning approach for medical data with irregularity in cancer predication with tumor markers.

BACKGROUND: Machine learning (ML) has emerged as a superior method for the analysis of large datasets. Application of ML is often hindered by incompleteness of the data which is particularly evident when approaching disease screening data due to varied testing regimens across medical institutions. Here we explored the utility of multiple ML algorithms to predict cancer risk when trained using a large but incomplete real-world dataset of tumor marker (TM) values. METHODS: TM screening data were collected from a large asymptomatic cohort (n = 163,174) at two independent medical centers. The cohort included 785 individuals who were subsequently diagnosed with cancer. Data included levels of up to eight TMs, but for most subjects, only a subset of the biomarkers were tested. In some instances, TM values were available at multiple time points, but intervals between tests varied widely. The data were used to train and test various machine learning models to evaluate their robustness for predicting cancer risk. Multiple methods for data imputation were explored and models were developed for both single time-point as well as time-series data. RESULTS: The ML algorithm, long short-term memory (LSTM), demonstrated superiority over other models for dealing with irregular medical data. A cancer risk prediction tool was trained and validated for a single time-point test of a TM panel including up to four biomarkers (AUROC = 0.831, 95% CI: 0.827-0.835) which outperformed a single threshold method using the same biomarkers. A second model relying on time series data of up to four time-points for 5 TMs had an AUROC of 0.931. CONCLUSIONS: A cancer risk prediction tool was developed by training a LSTM model using a large but incomplete real-world dataset of TM values. The LSTM model was best able to handle irregular data compared to other ML models. The use of time-series TM data can further improve the predictive performance of LSTM models even when the intervals between tests vary widely. These risk prediction tools are useful to direct subjects to further screening sooner, resulting in earlier detection of occult tumors.

Investigating Unfavorable Factors That Impede MALDI-TOF-Based AI in Predicting Antibiotic Resistance.

The combination of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) spectra data and artificial intelligence (AI) has been introduced for rapid prediction on antibiotic susceptibility testing (AST) of Staphylococcus aureus. Based on the AI predictive probability, cases with probabilities between the low and high cut-offs are defined as being in the "grey zone". We aimed to investigate the underlying reasons of unconfident (grey zone) or wrong predictive AST. In total, 479 S. aureus isolates were collected and analyzed by MALDI-TOF, and AST prediction and standard AST were obtained in a tertiary medical center. The predictions were categorized as correct-prediction group, wrong-prediction group, and grey-zone group. We analyzed the association between the predictive results and the demographic data, spectral data, and strain types. For methicillin-resistant S. aureus (MRSA), a larger cefoxitin zone size was found in the wrong-prediction group. Multilocus sequence typing of the MRSA isolates in the grey-zone group revealed that uncommon strain types comprised 80%. Of the methicillin-susceptible S. aureus (MSSA) isolates in the grey-zone group, the majority (60%) comprised over 10 different strain types. In predicting AST based on MALDI-TOF AI, uncommon strains and high diversity contribute to suboptimal predictive performance.