Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration

b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 w w w. elsevier.com/locate/bjid The Brazilian Journal of INFECTIOUS DISEASES Original Article Influence of microbiome species in hard-to-heal wounds on disease severity and treatment duration Dagmar Chudobovaa,b, Kristyna Cihalovaa,b, Roman Gurana,b, Simona Dostalovaa,b, Kristyna Smerkovaa,b, Radek Veselyc, Jaromir Gumuleca,d, Michal Masarika,d, Zbynek Hegera,b, Vojtech Adama,b, Rene Kizeka,b,∗ aDepartment of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska, Czech Republic bCentral European Institute of Technology, Brno University of Technology, Technicka, Czech Republic cDepartment of Traumatology at the Medical Faculty, Masaryk University and Trauma Hospital of Brno, Ponavka, Czech Republic dDepartment of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice, Czech Republic a r t i c l e i n f o Article history: Received 9 January 2015 Accepted 8 August 2015 Available online 27 October 2015 Keywords: Bacterial strains MALDI-TOF Sequencing Superficial wounds a b s t r a c t Background: Infections, mostly those associated with colonization of wound by different pathogenic microorganisms, are one of the most serious health complications during a medical treatment. Therefore, this study is focused on the isolation, characterization, and identification of microorganisms prevalent in superficial wounds of patients (n = 50) presenting with bacterial infection. Methods: After successful cultivation, bacteria were processed and analyzed. Initially the identification of the strains was performed through matrix-assisted laser desorption/ionization time-of-flight mass spectrometry based on comparison of protein profiles (2–30 kDa) with database. Subsequently, bacterial strains from infected wounds were identified by both matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and sequencing of 16S rRNA gene 108. Results: The most prevalent species was Staphylococcus aureus (70%), and out of those 11% turned out to be methicillin-resistant (mecA positive). Identified strains were compared with patients’ diagnoses using the method of artificial neuronal network to assess the association between severity of infection and wound microbiome species composition. Artificial neuronal network was subsequently used to predict patients’ prognosis (n = 9) with 85% success. ∗Corresponding author. E-mail address: [email protected] (R. Kizek). http://dx.doi.org/10.1016/j.bjid.2015.08.013 1413-8670/© 2015 Elsevier Editora Ltda. All rights reserved. b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 605 Conclusions: In all of 50 patients tested bacterial infections were identified. Based on the proposed artificial neuronal network we were able to predict the severity of the infection and length of the treatment. © 2015 Elsevier Editora Ltda. All rights reserved. Introduction Skin has an important role in preventing the entry of undesirable substances, organisms, and bacteria into the bloodstream.1,2 Loss of skin integrity leads to different types of wounds which have the humidity, warmth, and a nurturing environment ideal for colonization and proliferation of undesirable bacterial strains, changing the naturally occurring microbiome. Colonized sites are usually polymicrobial, i.e. contain more than one bacterium with pathogenic potential.3 Wound infections are marked by disturbed host–bacteria equilibrium in a traumatized tissue environment favoring the pathogenic bacteria. A wound infection not only has the possibility to elicit a systemic response (sepsis), but is also able to inhibit the multiple processes involved in the orchestrated progression of normal wound healing.4 The concept of microbiome was first suggested in 2001 by Lederberg and McCray and was described as an ecological cohort of commensal, symbiotic, and pathogenic microorganisms sharing a body space.5Previously, it was estimated that as much as 20 to 60% of human-associated microbiome is hard-to-identify, which has likely resulted in an underestimation of microbiome diversity.6One of the most frequent microorganisms in infected wound is Staphylococcus aureus.7–10 According to numerous studies,11–13 another common organism in infected wounds is Pseudomonas aeruginosa with up to 10% occurrence, causing nosocomial infections together with S. aureus and other bacteria. In addition, the Enterobacteriaceae family is most often identified in connection with immunocompromised patients or those who have undergone abdominal surgery.1 Bacterial infections, increasingly occurring in medical facilities, can seriously complicate the outcome of treated patients.14,15 This is particularly connected with rising resistance of bacterial strains toward antibiotics or metals,16,17 thus significantly hindering treatment success. Although being highly debated the mechanism of resistance development has not been satisfactorily elucidated.18–21 The elevated occurrence of resistant bacterial strains is strictly linked with increased utilization of invasive surgical techniques, which are often performed in elderly, immunocompromised patients. Simultaneously, with the use of antibiotics, bacterial resistance can evolve in surgical sites, leading to bacteremia and sepsis, and thus significantly prolonging the healing phase of a patient. Although bacterial resistance presents a problem in healthcare facilities, there still exist few possibilities to eliminate the most frequent resistant strains that cause hospital-acquired infection – methicillin-resistant S. aureus (MRSA),22,23 e.g. highly potent glycopeptide vancomycin.24 However, for a correct choice of antibiotics one needs to accurately identify the microbiome composition of infected wounds. Knowledge of the bacterial ecology of wounds may thus lead to increase treatment success, coupled with curbing bacterial resistance as a result of inadequate utilization of antibiotics.25–28 Accordingly, this work is focused on identification of the microbiome associated with serious infections in hard-to-heal wounds with the aim to propose a prediction model, comprising both the microbiome composition and patients ´ health conditions. Materials and methods Chemicals, preparation of deionized water and pH measurement Chemicals used in this study were acquired from SigmaAldrich (St. Louis, MO, USA) in ACS purity unless noted otherwise. Deionized water was prepared using reverse osmosis equipment Aqual 25 (Aqual s.r.o., Brno, Czech Republic) and further purified using Milli-Q Direct QUV equipped with the UV lamp, with 18 M resistance. pH was measured using the pH meter WTW inoLab (Weilheim, Germany). Preparation of hospital samples and their cultivation Cohort of patients with bacterial infections For evaluation, patients with superficial or deep wounds were selected according to infection severity. Detailed information concerning the patients is documented in S1. A total of 50 patients aged 19 through 93 years were enrolled into the clinical study, and 13 patients were 70–79 years old; 23 patients superficial wounds and 27 deep wounds. For all patients, the treatment duration was determined by the traumatologist based severity and extent of infection, associated diseases potentially interfering with treatment outcome and healing of wounds, and other factors such as patient age, concomitant medications, and previous medical history. For confirmation of the functionality of the neural network 9 blank samples from 9 patients identified by MALDI-TOF MS were used. Enrollment of patients into the clinical study was approved by the Ethics Committee of Trauma hospital in Brno. Collection of wound swabs from patients with bacterial infections The smears, collected from infected wounds with the agreement of patients, were sampled by rolling motion at the wound using a sterile swab sampler. All patients were divided into two subgroups, on the grounds of infection severity: deep and superficial wound. A detailed description of comorbidities and duration of treatment was obtained. Patients were classified 606 b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 according to the Classification of surgical wounds – SSI (surgical site infections).29–31 Infected wounds were sampled by using disposable tampon swabs maximizing collection of representative microflora. Tampons were subsequently stored in transport medium (inorganic salts, sodium thioglycolate, 1% agar, activated charcoal). The important part of our workflow process comprised sampling in duplicates with further transport in both aerobic and anaerobic conditions to preserve bacterial viability. Cultivation of clinical specimens Four types of selective nutrient media (blood agar enriched by 10% NaCl, Endo agar, blood agar without any other component, and blood agar with amikacin) we employed for further microbiological selection. Petri dishes, containing the above mentioned media were subsequently incubated according to conventional protocols, as described elsewhere,32–35 to maintain suitable conditions for growth of all types of bacteria. These Petri dishes were incubated for 24–48 h at 37 ◦C supplemented by TGY medium (1 g L−1glucose, 5 g L−1tryptone, 2.5 g L−1yeast extract). Subsequently, individual colonies were collected from each Petri dish and stored in 1 ␮L of enriched media. These samples were processed as described in the DNA sequencing section and utilized for both – MALDI-TOF MS identification and PCR with subsequent sequencing. MALDI-TOF MS identification of bacteria The following extraction protocol was based on MALDI BiotyperTM 3.0 User Manual Revision 2, also used in a previous report.36 500 ␮L of bacterial culture, cultivated overnight, was centrifuged at 14,000 × g for 2 min. The supernatant was discarded and the pellet was re-suspended in 300 ␮L of deionized water besides adding 900 ␮L of ethanol. After centrifugation at 14,000 × g for 2 min, the supernatant was discarded and the obtained pellet was air-dried. The pellet was then dissolved in 25 ␮L of 70% formic acid (v/v) and 25 ␮L of acetonitrile and mixed. The samples were centrifuged at 14,000 × g for 2 min and 1 ␮L of the clear supernatant was spotted in duplicate onto the MALDI target and air-dried at room temperature. Then, each spot was overlaid with 1 ␮L of ␣-cyano-4-hydroxycinnamic acid (HCCA) matrix solution (20 mg mL−1) in organic solvent (50% acetonitrile and 2.5% trifluoroacetic acid, both v/v) and air-dried completely prior to MALDI-TOF MS measurement on UltrafleXtreme MS (Bruker Daltonik GmbH, Bremen, Germany). As matrix solution 2,5dihydroxybenzoic acid (DHB) was also used in the same concentration and solvent as HCCA. Spectral data were taken in the m/z range of 2000–30,000 Da, resulted from the accumulation of 240 laser shots targeted to different regions of the same sample spot. These data were analyzed with the Flex Analysis software (Version 3.4). Final preparation of dendrograms was carried out in the MALDI BioTyperTM 3.1 (Bruker Daltonik GmbH, Bremen, Germany). DNA sequencing Bacterial cells were centrifuged at 4450 × g and 20 ◦C for 10 min. The pellet was resuspended in 400 ␮L of lysis buffer (6 M guanidium hydrochloride, 0.1 M sodium acetate) and cells were lysed for 1 hour at 20 ◦C and 600 rpm on Multi RS60 (Biosan, Riga, Latvia). Genomic DNA was isolated from lysed bacterial cultures via MagNA Pure Compact (Roche, Mannheim, Germany), using Nucleic Acid Isolation Kit I, protocol DNA Bacteria. 16S rRNA gene was amplified using Taq PCR Mix (New England Biolabs, Ipswich, MA, USA) and MasterCycler realplex4epgradient S (Eppendorf, Hamburg, Germany). 100 ␮L of reaction mixture consisted of: 1× Standard Taq Reaction Buffer, 1.6 U of Taq DNA polymerase, 200 ␮M Deoxynucleotide Solution Mix, 0.5 ␮M primers and 5 ␮L of isolated genomic DNA. The forward primer E9F 5-GAGTTTGATCCTGGCTCAG3and reverse primer U1510R 5-GGTTACCTTGTTACGACTT-3 were synthesized by Sigma–Aldrich (St. Louis, MO, USA). The reaction profile was as follows: initial denaturation at 94 ◦C for 4 min; 30 cycles of denaturation at 94 ◦C for 30 s, annealing at 52 ◦C for 30 s and elongation at 72 ◦C for 105 s; with terminal elongation at 72 ◦C for 10 min. Amplified fragments were purified using MinElute PCR Purification Kit (Qiagen, Hilden, Germany). For sequencing reaction the DTCS Quick Start Kit (Beckman Coulter, Pasadena, CA, USA) was used. To 20 ␮L sequencing reaction mixture, 98 ng of purified fragment, 0.75 ␮L of 10 ␮M forward primer, 4 ␮L of DTCS Quick Start Master Mix and 1 ␮L of Sequencing Buffer were added. The conditions of 30 cyclereactions were as follows: 96 ◦C for 20 s; 50 ◦C for 20 s and 60 ◦C for 4 min. The purification of sequencing product was carried out using CleanSEQ kit (Beckman Coulter). Purified samples in Sample Loading Solution were transferred to the plate and DNA sequencing was performed using Genetic Analysis System CEQ 8000 (Beckman Coulter). After denaturation at 90 ◦C for 2 min, a fluorescence-marked DNA fragments were separated in 33 cm long capillary with 75 ␮m i.d. (Beckman Coulter) filled with linear polyacrylamide denaturing gel. The separation was run at capillary temperature of 50 ◦C and voltage of 4.0 kV for 85 min. Sequences were identified by comparison with NCBI database. Amplification of S. aureus genes mecA and fnbA Isolation of genomic DNA was performed using the same method as described in section DNA sequencing. The mecA and fnbA genes were amplified using polymerase chain reaction (PCR) as previously reported.37 The primers were synthesized by Sigma-Aldrich and the sequences of forward and reverse primers for mecA gene were 5-CCCAATTTGTCTGCCAGTTT-3, and 5-TGGCAATATTAACGCACCTC-3and for fnbA gene were 5-GATACAAACCCAGGTGGTGG-3, and 5-TGTGCTTGACCATGCTCTTC-3. The volume of PCR reaction mixture was 100 ␮L containing 1× Taq reaction buffer, 0.2 mM dNTP, 1.6 U of Taq DNA polymerase (New England Biolabs) and 0.5 mM of each primer. The reaction profile was as follows: initial denaturation at 94 ◦C for 4 min, 30 cycles of denaturation at 94 ◦C for 30 s, annealing at 53 ◦C for 30 s and extension at 72 ◦C for 1 min with a final extension of 7 min. The amplification generated a 223 bp for mecA and 191 bp for fnbA. Agarose gel (2% (v/v), high melt, Mercury, San Diego, CA, USA) was prepared with 1× TAE buffer (40 mM Tris, 20 mM acetic acid and 1 mM ethylenediaminetetraacetic acid) and ethidium bromide (5 ␮L per 100 mL of the gel) as described b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 607 elsewhere.38 100 bp DNA ladder (New England Biolabs) within the size range from 100 to 1517 bp was used to monitor the size of the analyzed fragment. The electrophoresis (Bio-Rad, Hercules, CA, USA) was run at 60 V and 6◦C for 160 min. The bands were visualized by UV transilluminator at 312 nm (Vilber-Lourmant, Marne-la-Vallée, France). Statistical processing of obtained results Automated neuronal network was used as a predictive model. Classification analysis automated neuronal network was used for the estimation of categorical data. The dataset was randomly divided as follows: 80% for learning, 10% for testing, and 10% for validation. Following network types were tested using automated network search: multilayer perceptron network (MLP), and radial basis function (RBF). Number of hidden units to search was determined as follows: 8–24 and 8–11 for MLP and RBF, respectively. Total 1000 networks were trained, and activation functions were searched for identity, logistic, tanh, exponential. Weight decay of 0.0001–0.001 was used for hidden layer and output layer. Weight of input variables for learning was used based on MALDI-TOF classification score. Unless noted otherwise, p-value less than 0.05 was considered significant. Software Statistica 12 (StatSoft, CA, USA) was used for analysis. Results and discussion We decided to employ a variety of cultivation approaches (in presence of O2, CO2or in microaerophilic conditions) to reveal the presence of real microbiota associated with superficial infections. MALDI-TOF MS was explored as an accurate and rapid identification tool, using the protein mass patterns, which are compared with patterns from a commercial Bruker Daltonics database (BDAL) of MALDI BiotyperTM software.39 Due to a powerful software support, the method can be used for identification within few minutes, which is one of the advantages.36 Moreover, sequencing of amplified 16S rRNA gene40 was employed for identification independent of protein patterns. Finally, an artificial neural network (ANN) was developed as a predictive model for evaluation of infection severity and using developed ANN we attempted to find the relationship between disease severity and the microorganisms identified in clinical specimens. Identification of bacterial strains by MALDI-TOF MS and Sanger sequencing For the identification of bacterial entities we employed complementary methods for independent evaluation of different biomolecules – proteins and DNA.36,41,42 Sanger sequencing was utilized as a confirmation method, based on sequencing of 16S rRNA gene. This gene contains hypervariable regions, providing species-specific sequences, hence it can provide enough information for a confident discrimination, and thus became popular in medical microbiology to classify bacteria.43,44 When compared to sequencing, MALDI-TOF MS offers much shorter analysis time. By using this technique, wound microbiome could be discriminated within one hour of incubation, and thus this will likely become the method of choice for future microbiome identification. Nevertheless, the classification is based on a still developing database34; hence MALDI-TOF MS identification of non-databased bacteria has still to be connected with other confirmation methods. From this reason we firstly employed MALDI-TOF MS with a condition: If score <2.00 = 16S rRNA sequencing. As shown in S2, 108 bacterial strains were identified37 of them had to be confirmed by sequencing and confirmed strains were immediately databased to increase future classification success. Strains of S. aureus were the most often identified (n = 35). Thus, methicillin-resistant S. aureus (MRSA) is highly associated with severe infections in post-surgical wounds45; we further analyzed the mecA gene, encoding a modified penicillin binding protein (PBP) known as PBP2a, with decreased affinity toward ␤-lactams.46 The mecA positivity was determined in four isolates. Since 67% of patients had deep wound infections and were treated for more than 8 weeks after admission to infectious Department of Trauma Hospital of Brno, presence of mecA was shown to be a crucial microbiological factor, affecting patients prognosis. Further, we determined the presence of fnbA gene, responsible for adhesins production. Adhesion to human extracellular matrix components and serum proteins is an important facet in the interaction between bacteria and its host cells.47 Lim and coworkers identified the presence of fnbA in 96% of all isolated MRSA strains.35 In our case, fnbA presence was confirmed in all MRSA isolates and in 89% of methicillinsensitive S. aureus isolates. Similarly to mecA, fnbA was found to be associated with infection severity. In patients with negative fnbA and mecA the treatment duration was less than four weeks in 75% of cases, despite the fact that patients had deep wound infections. This finding suggests that the severity of staphylococcal infections does not depend solely on antimicrobial resistance, but also on adhesins expression, which enhance the interaction with the target host cells. Distribution of identified strains within various cohorts of patients According to duration of treatment, the patients were divided into specific subgroups, where each sector represents one bacterial strain. The subdivision of patients was based on surgical wounds classification SSI. As shown, patients were divided into two groups - deep and superficial wounds and the associated bacterial strains are depicted in Fig. 1A and B. As it is obvious from Fig. 1A, in the more serious infections (deep) S. aureus was the main bacterium of microbiome composition (28% of identified strains), followed by Enterococcus faecalis (15%), and Escherichia coli (11%). On the other hand, E. coli was not so often identified in surficial wounds (5% – Fig. 1B). Taken together, the microbiome composition in both groups exhibits substantial differences, and thus it can be hypothesized that presence of minority representatives as Hafnia alvei, Proteus vulgaris, Staphylococcus lugdunensis, or 608 b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 Staphy lococc us aureus 40% Staphy lococc us epidermis 9% Staphy lococc us lugdune nsis 3% Staphy lococc us haemolytic us 2% Enterococcus faec alis 15% Enterococcus faecium 3% Escherichia coli 11% Pseudomonas aerug inos a 8% Klebsiella pneumoniae 3% Acinetobacter baumannii 3% Enterobac ter cloaca e 3% Klebsiella oxytoca 2% Proteus vulgaris 2% Corynebacterium striatum 2% Shigella flexneri 2% Steno troph omonas maltopilia 2% Hafnia alvei 5% Deep Staphy lococc us epidermis 2% Staphy lococc us cohnii 2% Staphy lococc us haem olytic us 2% Enterococcus faec alis 2% Enterococcus faecium 2% Strep tococcus agalacti ae 5% Escherichia coli 5% Pseudomonas aerug inos a 16% Pseudomonas mendocina 2% Klebsiella pneumoniae 2% Acinetobacter genomospec . 5% Acinetobacter lwoffii 2% Acinetobacter baumannii 5% Corynebac ter ium striatum 5% Neisseria mucosa 2% Surface A B Staphy lococc us aureus 28% Fig. 1 – Representation of microorganism species present in patients’ wounds. Patients were grouped based on infection severity. The graphs show bacterial cultures grown on different selective nutrient media. (A) Infection severity – deep wounds and (B) infection severity – superficial wounds. Enterobacter cloacae in the wound can significantly influence the infection severity. It can be also stated that increasing duration of treatment leads to increased number of identified Enterobacteriaceae and opportunistic pathogens (Pseudomonas, Enterococcus). Phylogenetic analysis of protein alterations As was shown by Rettinger and colleagues,48 MALDI-TOF mass spectra-based phylogenetic analysis is considered equivalent to 16S rRNA gene sequencing. Therefore, we employed MALDI BiotyperTM for preparation of dendrograms for our groups, divided by treatment duration (Fig. 2). Dendrograms showed similarity of same bacterial strains (low distance level), but in some cases larger differences were found – usually among bacterial strains from different patients. These differences were caused probably by modifications of bacterial proteins. Karger et al. found methylation as a cause of higher distance level in dendrogram between Burkholderia pseudomallei and other types of B. pseudomallei.49 Thus it can be concluded that not only changes in microbiome representatives affect treatment duration and success, but also small changes in protein posttranslation modifications can be highly important for patients’ recovery. b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 609 750800 850900 950 1.000 16b_Entero coccus faec ali s 16d_Entero coccus faec ali s 16c_Klebsiella oxytoca 16a_Staphyl ococcus epiderm idis Distance level 0100 200300400 500 600 700800 900 1.000 11a_Staphylococcus aureu s 17d_Staphylococcus aureu s 5a_Staphylococcus aureu s 18a_Staphylococcus aureu s 12a_Staphylococcus aureu s 6b_Staphylococcus aureu s 18b_Staphylococcus aureu s 12b_Staphylococcus aureu s 14b_Staphylococcus aureu s 14a_Staphylococcus aureu s 4a_Staphylococcus hae molyticu s 23c_Staphylococcus hae molyticu s 5b_Corynebacterium striatu m 5d_Ente rococ cus faeciu m 11d_Entero coccus faec ali s 18e_Enteroco ccus faecali s 14d_Entero coccus faec ali s 11b_Proteus vulgari s 14c_Ha fnia alve i 11c_Escherichia coli 9e_Shigella flexner i 23d_Streptococcus agalactia e 17b_Pseudomon as aeruginos a 22c_Pseudomo nas aeruginos a 17c_Pseudomo nas aeruginos a 19b_Pseudomon as aeruginos a Distance level 0 100 200300400 500600700 800 9001.000 30d_Entero coccus faec ali s 36d_Entero coccus faec ali s 15b_Pseudomon as mendocin a 27d_Enterococ cus faeciu m 15c_Escherichia col i 30b_Esche richia coli 30c_Escherichia col i 36c_Escherichia coli 36b_Pseudomonas aeruginosa 27b_Pseudomon as aeruginos a 27e_Pseudomonas aeruginosa 33b_Klebsiella pneumonia e 33c_Klebsiella pneumoniae 46b_Enterobacter cloaca e 46c_Enterobact er cloaca e 37b_Acinetobacter baumannii 37c_Acinetoba cter baumanni i 13b_Staphylococcus aureu s 43b_Staphylococcus aureus 49b_Staphylococcus aureu s 48b_Staphylococcus aureu s 48a_Staphylococcus aureus 43a_Staphylococcus aureu s 43e_Staphylococcus aureu s 13a_Staphylococcus aureu s 21a_Staphyl ococcus epiderm idis 27a_Staphyl ococcus epiderm idis Distance level 0100 200300 400 500 600 700 800 900 1.000 50b_Staphylococcus aureu s 50a_Staphylococcus aureu s 45e_Staphylococcus aureu s 39a_Staphyl ococcus epiderm idis 50c_Staphyl ococcus epidermidi s 8b_Staphylococcus aureu s 26a_Staphylococcus aureu s 8a_Staphylococcus aureu s 24b_Staphylococcus aureu s 31b_Staphylococcus aureu s 31a_Staphylococcus aureu s 38e_Staphylococcus aureu s 44e_Staphylococcus aureus 40e_Staphylococcus aureu s 42e_Staphylococcus aureu s 40b_Staphylococcus aureus 40a_Staphylococcus aureu s 25c_Staphylococcus aureus 25b_Staphyl ococcus epiderm idis 3a_Corynebacterium striatu m 24a_Staphyl ococcus epiderm idis 25a_Staphylococcus lugdunensi s 20b_Staphylococcus lugdunensi s 2a_Corynebacterium striatu m 47a_Staphylococcus cohni i 1b_Hafni a alve i 1c_Hafnia alvei 7b_Neisseria mucos a 8d_Streptococcus agalactiae 26b_Escherichia coli 26c_Escherichia coli 29c_Escherichia coli 26e_Escherichia coli 29b_Klebsiella pneumonia e 2c_Pseudomon as aeruginos a 10b_Pseudomon as aeruginos a 10c_Pseudomo nas aeruginos a 35b_Pseudomon as aeruginos a 35c_Pseudomo nas aeruginos a 3d_Enterococcus faecali s 39e_Enterococcus faecali s 26d_Entero coccus faec ali s 32d_Entero coccus faec ali s 39d_Enterococ cus faeciu m 28b_Acinetobact er geno mospe cies 3 28c_Acinetobacter genomospecies 3 32b_Acinetobact er baumanni i 32c_Acinetoba cter baumanni i 34b_Acinetobacter lwoffi i 39c_Stenotrophomon as maltophili a Distance level AB C D Fig. 2 – Dendrograms from protein mass profiles of microorganisms in different groups based on treatment duration. Created in MALDI BiotyperTM. (A) Treatment duration less than four weeks. (B) Treatment duration 4–7 weeks. (C) Treatment duration eight and more weeks. (D) Exitus. Artificial neural network Two neuronal networks were created: (1) for the prediction of time-to-heal, and (2) for the prediction of infection severity. The following input parameters were used for the construction of networks: from 2000 networks five were retained and one was used for further final custom neuronal network. The settings of the network created using automated algorithm and used for the custom final learning were Multilayer perceptron 89-13-3 (input-hidden-output neurons), Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm, sum of squares error function, identity function for hidden layer, and then for output layer. The design of the network is displayed in Fig. 3A. With stopping conditions enabled (Fig. 3B), a final network was created in the 17th training cycle with performances of 91.4%, 85.7%, and 71.4% for training, testing, and validation (accuracy in prediction up to 85% – Fig. 3C), respectively. Consequently, a second neuronal network for the prediction of infection severity was created using an automated algorithm. The best-performing network was trained under following settings: multilayer perceptron 89-19-2 (inputhidden-output neurons) (Fig. 3D) BFGS training algorithm, cross entropy error function, and exponential and softmax activation function for hidden and output layer. The training process is depicted in Fig. 3E (accuracy in prediction up to 85% – Fig. 3F). The performances of the network were 100.0%, 85.7%, and 85.7% for training, testing, and validation (Table 1), respectively. The accuracy for individual cases is displayed in Table 2. Sensitivity analysis of input variables for both networks was carried out. For the prediction of infection severity, Table 1 – Characterization of neuronal network performance for the prediction of patient outcome. Performance displayed in % for training, testing, and validation samples. The number of training cycle for custom network training is displayed in training algorithm column. BFGS, Broyden–Fletcher–Goldfarb–Shanno training algorithm; SOS, sum of squares. Prediction target Net. name Performance Training algorithm Error function Activation Training Testing Validation Train Infection severity MLP 89-19-2 100.00 85.71 85.71 BFGS 24 Infection severity MLP 89-19-2 100.00 Time-to-heal MLP 89-13-3 91.43 85.71 71.43 BFGS 17 Time-to-heal MLP 89-13-3 91.43 610 b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 hidden layer* n=13 identit y functi on output layer n=3 tanh. functi on input layer* n=89 identity function bacteria n=23 related disease n=11 main disease n=1 gender n=1 age n=1 hidden layer* n=19 expon ential function outpu t layer n=2 softmax function inpu t layer* n=89 identity fun cti on bacteria n= 23 related disease n=11 main disease n= 1 gender n= 1 age n= 1 activation direction D AInfection severity Time to heal 403020100 0 10 20 30 40 50 60 70 80 90 100 C BEF Missclasification Train cycl e Train Test 0 10 20 30 40 50 Counts Accuracy 0 10 20 30 40 50 60 -10 0 10 20 30 40 50 60 Trai n cycl e Train Test 0 10 20 30 40 50 Accuracy Counts Missclasification Correct Incorrect Correct Incorrect Fig. 3 – Design and performance of the neuronal networks. (A) Design of classification network for the prediction of time-to-heal. The number of neurons/inputs is indicated by n. *Note the number of input and hidden neurons is not displayed exactly. (B) Training process of the classification network with stopping conditions activated. (C) Accuracy of the final network for classification of time-to-heal. (D) Design of classification network for the prediction of infection severity. (E) Training process for creation of this network with stopping criteria activated. (F) Accuracy of the network for the prediction of infection severity. Table 2 – Performance of the network: verification of the test and validation cohort. Analysis for both networks for prediction of infection severity and time-to-heal. Test cohort was employed for stopping conditions. Validation sample was used to test final network. “target” indicates input data, network output reflects calculated result from the neuronal network. id, identification of patient; w, week. Sample ID Case weights Infection severity Time-to-heal Target Network output Accuracy Conf. level Target Network output Accuracy Conf. level Test 2 1.71 Superficial Superficial Correct 1.00 <4 w <4 w Correct 0.40 6 1.81 Deep Deep Correct 1.00 >8 w <4 w Incorrect 0.47 8 1.65 Superficial Superficial Correct 1.00 <4 w <4 w Correct 0.36 22 2.00 Superficial Superficial Correct 1.00 >8 w >8 w Correct 0.41 38 2.06 Deep Superficial Incorrect 1.00 <4 w <4 w Correct 0.49 39 2.01 Deep Deep Correct 1.00 <4 w <4 w Correct 0.56 40 2.12 Superficial Superficial Correct 1.00 <4 w <4 w Correct 0.43 Validation 7 1.68 Superficial Deep Incorrect 1.00 <4 w <4 w Correct 0.40 23 1.62 Superficial Superficial Correct 1.00 >8 w >8 w Correct 0.49 27 2.23 Deep Deep Correct 0.87 4–7 w <4 w Incorrect 0.58 28 2.11 Superficial Superficial Correct 1.00 <4 w <4 w Correct 0.47 36 2.27 Deep Deep Correct 1.00 4–7 w 4–7 w Correct 0.63 45 1.88 Deep Deep Correct 1.00 <4 w <4 w Correct 0.43 49 2.25 Deep Deep Correct 1.00 4–7 w <4 w Incorrect 0.56 b r a z j i n f e c t d i s . 2 0 1 5;1 9(6):604–613 611 age 0% Alzheimer 0% paresis/plegia 0% Staph ylo cocc us haemolyticus 0% S. aur eus 2 mec A gene 1% Enterobacter cloacae 1% Neisseria mucos a 1% Proteus vulgaris 1% Streptococcus agalactiae 1% Steno tro phom onas maltopili a 1% Acinetobacte r genomospec. 1% ICHS 1% Shigella flexneri 1% peptic ulcer 1% Hafni a alve i 1% Klebsiella pneumonia e 1% Acinetobacte r bauman nii 1% S. aureus 2 fnbA gene 2% Staphylococcus aureus 2% Staphylococcus epidermidis 2% Staphylococcus lugdunensis 2% DM 3% osteomyelitis 3% main diagno sis 3% Corynebacterium striatum 3% Pseudo monas aeruginosa 3% Parkinson 4% revmatoid 4% Pseudo monas mendocin a 4% Staph ylo cocc us cohnii 5% Acinet obacter lw offi i 5% gender 5% Enterococcus faecalis 5% Escherichia col i 5% obesity 6% ulcerous colitis 9% hyperten sion 11% Steno tro phom onas maltopili a 2% Corynebacterium striatum 2% Streptococcus agalactiae 2% Neisseria mucos a 2% Acinetobacter genomospec. 2% S. aureus 1 fnbA gene 2% Enterobacter cloacae 2% Acinetobacter bauman nii 2% Staphylococcus aureus 2% age 2% Acinetobacter lwoffii 2% S. aureus 1 mecA gene 2% Staphylococcus lugdunensis 2% Klebsiella pneumonia e 2% Ente roco ccus faecal is 2% Hafnia alve i 2% Staphylococcus cohnii 2% S. aureu s 2 mec A gene 2% ICHS 2% Proteus vulgar is 2% S. aureus 2 fnbA gene 2% DM 2% revmatoid 2% obesity 3% peptic ulcer 3% Shigella flexneri 3% Escherichia coli 3% main diagnosis 3% paresis/plegia 3% Staph ylo cocc us epidermidis 3% hypertension 3% Pseudo monas aeruginosa 3% gender 3% Alzheimer 3% Staphylococcus haemolyticus 4% Pseudomonas mendocin a 4% osteomyelitis 4% Parkinson 4% ulcerous coliti s 4% AB Fig. 4 – Sensitivity analysis of all factors for prediction of time-to-heal and infection severity. Sensitivity of individual factors depicted as a percentage of total sensitivity. (A) Sensitivity of individual factors for the prediction of time-to-heal. (B) Sensitivity of individual factors for the prediction of infection severity. the mean sensitivity level was 4.66, ranging from 0.63 to 20.1, and a total sensitivity = 179.9 (Fig. 4) The highest level of sensitivity (thus highest impact on prediction of a network) was observed for hypertension (20.15), ulcerative colitis (17.06), obesity (10.13), E. coli (8.87), E. faecalis (8.50), and other factors. The factors with sensitivity <1 were P. vulgaris, Neisseria mucosa, E. cloacae, S. aureus 2 mecA genes, Staphylococcus haemolyticus, paresis/plegia, Alzheimer’s disease, age. For the prediction of time-to-heal, the sensitivity was distinctly more homogeneous for the input factors with mean sensitivity = 1.35, (0.99–2.35), total sensitivity = 52.49. 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