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  • br Methodology The following subsections describe the steps

    2019-09-11


    Methodology The following subsections describe the steps of the Systematic Literature Review (SLR) process we followed [9].
    Results In our review of the literature, we used the definitions provided by the IEEE Standard Computer Dictionary [15] to categorize the claims about the effectiveness of software engineering practices in scientific software into 11 practices. We then divided these practices into two groups: (1) those that are primarily part of the software development workflow, and (2) those that are part of the infrastructure that supports software development. Table 4 lists the 11 practices and the two larger groupings. The remainder of this section describes the claims about each of these 11 practices in more detail. Throughout the discussion, we emphasize the claims with bold-faced text and provide additional discussion to substantiate the claim. While the standard practice in Systematic Literature Reviews is to provide separate answers for each research question, in this review we believed it made more sense to answer them together so that each claim would be presented along with the evidence that supports it. Additionally, while any particular claim may be positive or negative, the claims are worded so that all evidence supports them.
    Conclusion
    Acknowledgments The authors acknowledge support from NSF grant 1445344.
    Introduction The worldwide emergence of Klebsiella pneumoniae carbapenemase (KPC)-producing Enterobacteriaceae poses a great challenge to healthcare [1]. Expression of KPC-type β-lactamases confers decreased susceptibility to virtually all β-lactam WAY-600 including cephalosporins and carbapenems [2]. High-level production of β-lactamases (carbapenemases) and porin deficiency are recognised as major causes of resistance development in K. pneumoniae[3], [4]. However, less attention has been paid towards other cellular proteins that might be equally important in the development of resistance. The bacterial proteome is a dynamic entity that responds rapidly to external stimuli such as antibiotic stress WAY-600 [5]. The presence of antimicrobials in bacterial cells disturbs the internal harmony of the system and quickly cellular functions are diverted to revert the effect. Studying the proteomics of resistant bacteria under drug stress could identify novel strategies employed by bacteria to overcome the effects of antimicrobials. Recent advances in proteomics and bioinformatics offer great potential in unravelling biological problems. Proteomic approaches have been used to elucidate the cellular responses of model micro-organisms such as Bacillus subtilis, Escherichia coli and Mycobacterium tuberculosis and many other micro-organisms to different antimicrobial agents [6], [7], [8], [9]. Since proteins are the functional entities of the cell, we investigated the effect of meropenem exposure on the proteome of a blaKPC-2-carrying multidrug-resistant clinical K. pneumoniae strain (NP6). The primary objective of this study was to identify proteins that were differentially expressed under antibiotic stress and that might play an important role in bacterial defence and survival mechanisms, besides KPC-2 carbapenemase. The soluble whole-cell proteomes of K. pneumoniae NP6 were studied in the presence and absence of meropenem by employing two-dimensional gel electrophoresis (2DE) coupled with mass spectrometry. To the best of our knowledge, this is the first study looking into differential protein expression of KPC-2 β-lactamase-producing K. pneumoniae in response to meropenem treatment. This study revealed that several cellular proteins are differentially expressed upon exposure to meropenem, thus providing a glimpse of the changes occurring in the cells following antibiotic treatment.
    Materials and methods
    Results The main focus of this study was to identify upregulated proteins in carbapenem-resistant K. pneumoniae NP6 grown in the presence and absence of meropenem. The MIC of meropenem for the resistant strain was 256mg/L and that of the susceptible control strain MTCC 432 was 0.125mg/L. Fig. 1 shows the 2DE profile of NP6 grown in the absence and presence of a subinhibitory concentration (0.5×MIC) of meropenem. On comparing the two proteomic profiles, 16 spots were upregulated by ≥1.5-fold in the drug-treated condition. These protein spots were further analysed by MALDI-TOF/MS.