- Email: [email protected]
Rapid identification of Paracoccidioides lutzii and P. Brasiliensis using Fourier Transform Infrared spectroscopy
Accepted Manuscript Rapid identification of Paracoccidioides lutzii and P. brasiliensis using Fourier Transform Infrared spectroscopy Olavo O. Comparato Filho, Flavia V. Morais, Tanmoy T. Bhattacharjee, Maiara L. Castilho, Leandro Raniero PII:
S0022-2860(18)31098-6
DOI:
10.1016/j.molstruc.2018.09.016
Reference:
MOLSTR 25650
To appear in:
Journal of Molecular Structure
Received Date: 22 May 2018 Revised Date:
20 August 2018
Accepted Date: 11 September 2018
Please cite this article as: O.O. Comparato Filho, F.V. Morais, T.T. Bhattacharjee, M.L. Castilho, L. Raniero, Rapid identification of Paracoccidioides lutzii and P. brasiliensis using Fourier Transform Infrared spectroscopy, Journal of Molecular Structure (2018), doi: https://doi.org/10.1016/ j.molstruc.2018.09.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Rapid identification of Paracoccidioides lutzii and P. brasiliensis using Fourier Transform Infrared spectroscopy Olavo O. Comparato Filho1, Flavia V. Morais2, Tanmoy T. Bhattacharjee1, Maiara L. Castilho3, Leandro Raniero1* 1
SC
RI PT
Laboratório de Nanossensores, Instituto de Pesquisa & Desenvolvimento, Universidade do Vale do Paraíba, São José dos Campos, São Paulo, Brazil. 2 Laboratório de Biologia Celular e Molecular de Fungos, Instituto de Pesquisa & Desenvolvimento, Universidade do Vale do Paraíba, São José dos Campos, São Paulo, Brazil. 3 Laboratório de Bionanotecnologia, Instituto de Pesquisa & Desenvolvimento, Universidade do Vale do Paraíba, São José dos Campos, São Paulo, Brazil. * Corresponding author E-mail address: [email protected]
M AN U
Present Address: Laboratório de Nanossensores (24), IP&D (11), UNIVAP, Av. Shishima Hifumi, 2911, Urbanova, 12244-000 - São José dos Campos, SP – Brazil _____________________________________________________________________________________
ABSTRACT
______________________________________________________________________
AC C
EP
TE D
The inhalation of the fungus Paracoccidioides brasiliensis (P. brasiliensis, Pb03 and Pb18) causes the Parcoccidioidomycosis. Initially, only P. brasiliensis was thought to be responsible for it. Later the researchers found out that another pathogen: Paracoccidioides lutzii (P. lutzii, Pb01) may also cause the disease. Clinical manifestation and treatment differ based on the causative agent, making it important to identify them correctly. Therefore, this study focusses on distinguishing P. brasiliensis from P. lutzii using Fourier Transform Infrared (FTIR) spectroscopy - a rapid, highly specific and sensitive technique that can identify biochemical composition with minimal sample preparation. We acquired thirty spectra each from pure cultures of P. lutzii, and P. brasiliensis. The FTIR spectra showed the difference in biochemical composition, the major differences being in the polysaccharide and proteins vibrations. Principal Component Linear Discriminant Analysis (PC-LDA) could distinguish the species. We acquired nearly 10 spectra per sample in triplicates, resulting around 30 spectra per group. The analysis of spectra indicates biochemical difference. Multivariate analysis is important to identify spectral differences in specific chosen spectra regions where we can take biological or vibration information. PC-LDA could distinguish Pb01 (P. lutzii) with 96% efficiency and Pb03/Pb18 (P.brasiliensis) with 100% efficiency. Thus, FTIR may contribute to clinical diagnosis of this highly prevalent Latin American disease. Keywords: Paracoccidioides brasiliensis; Paracoccidioides lutzii; FTIR.
1
ACCEPTED MANUSCRIPT ______________________________________________________________________ 1. Introduction
AC C
EP
TE D
M AN U
SC
RI PT
Paracoccidioidomycosis (PCM) is systemic deep mycoses most prevalent in South America, with 80% of cases reported in Brazil [1]. The estimated annual incidence rate in rural endemic areas ranges from 1-3 new cases per 1,000,000 inhabitants to 1-3 new cases per 100,000 inhabitants. PCM is considered the third largest cause of death by chronic infectious disease with mortality rate of 1.65 cases per 1,000,000 inhabitants in these regions [2]. Clinical presentation of PCM may be asymptomatic with positive skin test; acute – involving spleen, liver, lymph nodes and bone marrow; chronic, Sequelae – characterized by inadequate adrenal function and pulmonary obstruction; or be associated with immunosuppressing diseases [3]. PCM is caused by exposure, generally in the form of inhalation, of the dimorphic temperature dependent fungi of the genus Paracoccidioides. Previously thought to be caused by only one species - P. brasiliensis, the general consensus currently is that two distinct species - P. brasiliensis and P. lutzii can cause the disease [2, 4, 5]. Barbosa and Andrade had reported clinical manifestations that are different from typical PCM, and that may be attributed to the different causative species [5-8]. It is also hypothesized that response to therapy for the two species may be different [5, 9]. These observations may have great implications in management of the disease if proven correct. A major barrier to answering the above questions is diagnosis. The main diagnostic test used detects the presence of Gp43 antigen expressed by P. brasiliensis in serum. However, numerous PCM patients in different regions of Brazil have less or no expression of Gp43, presumably due to infection by P. lutzii [10-12]. In Rondônia State of Brazil, the false-negative rate was found to be as high as 98% [13]. As Marques observed, this regional variation in antigen expression would necessitate administration of different tests for different regions for high sensitivity [3]. Alternative techniques that can identify these different species would greatly improve diagnosis and help answer some basic questions regarding PCM. In this study, we investigate the ability of FTIR vibrational spectroscopy to distinguish P. brasiliensis from P. lutzii. Since biochemical makeup of two distinct species is different, the technique may be used to distinguish them. The advantages of using FTIR are their high sensitivity and specificity, minimal sample preparation, rapid results, possibility of automation, identification and quantification of functional groups. Several studies have demonstrated the use of FTIR in microbiological studies [14, 15], identifying microorganisms [16-23], distinguishing species [24] and mycology [16, 2527]. We cultured pure P. lutzii (Pb01) and two variants of P. brasiliensis (Pb03 and Pb18), and acquired FTIR spectra from the same. The spectra were then used to understand the difference in biochemical composition as well as to determine whether multivariate statistical analysis Principal Component Linear Discriminant Analysis (PCLDA) could distinguish the species. Two variants of P. brasiliensis were used to provide a more complex challenge to PC-LDA in distinguishing the species.
2
ACCEPTED MANUSCRIPT 2. Materials and Methods 2.1. Culture of Paracoccidioides spp.
RI PT
Z. P. Camargo, UNIFESP, São Paulo, Brazil provided the isolates of Paracoccidioides brasiliensis, Pb18 and Pb03, and of P. lutzii, Pb01. Fungi stored at 4ºC, were inoculated in liquid SD medium (0.17% yeast nitrogen base, 2% glucose, 0.5% casamino acids, 0.5% ammonium sulfate, pH 4.5), and grown for 5 days at 36ºC, under rotation at 100 rpm. 2.2. Sample preparation
2.3. FTIR spectroscopy and data analysis:
SC
Yeasts of all isolates were harvested by centrifugation at 4,000 rpm, 1 minute at 4ºC, killed by sodium hypochlorite, and washed three times in cold ultrapure water (1 mL). The pellets were fixed in 60% isopropanol (250 µL) for 24 h and left to dry on a vertical laminar flow station for 24 h. After two additional ultrapure water washings, the fungi were resuspended in 200 µL of ultrapure water.
M AN U
Point spectra were recorded in the mid infrared (4000–700 cm−1) spectral range with a Spectrum Spotlight 400 FTIR imaging system (Perkin Elmer Life Sciences, France), with spatial resolution of 6.25 µm and spectral resolution of 4 cm−1. A background of 60 scans was acquired per sample, and 30 scans were acquired per spectrum. The background is automatically subtracted from sample spectrum by the software. Approximately 10 spectra were acquired per sample, and the experiment was done in triplicates; resulting in approximately 30 spectra per group.
TE D
2.4. Data analysis:
AC C
EP
For mean spectra and standard deviations, all spectra were corrected for baseline by subtracting polynomial 5 curve followed by vector normalization in LabSpec 5.0. Student’s t-test was performed between each group for intensities at each wavenumber using GraphPad Prism 7. The difference spectra along with p-values of significance were included in graphs showing mean spectra comparison between groups. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Leave One Out Cross Validation (LOOCV) was carried out in MATLAB 2011, after first derivatization, interpolation in 1,200-1,438 cm-1 spectral range and vector normalization. Deconvulation by fitting if Gaussian curves and calculation of area under the curve was performed using OriginPro 8.5, based on peaks discovered by second derivatization of mean spectra. 3. Results and Discussion Figures 1, 2 and 3 show the mean spectra comparison, standard deviation in form of gray color overlap, difference spectra and p-values of t-test on comparison of intensity values at each wavenumber for Pb01/Pb03, Pb01/Pb18 and Pb03/Pb18, respectively. The Figures help compare the spectra visually, and highlight the difference between the spectra. The associated p-values help point out spectral regions wherein the differences are statistically significant. Majority of peaks in the mean spectra can be assigned to protein vibrations (1032, 1150, 1204, 1234, 1256, 1310, 1450, 1536, 1628, 1656 cm-1), lipids (1744, 2852, 2922, 2956 cm-1), nuclear material (1076, 1116 cm-1)
3
ACCEPTED MANUSCRIPT and water (3290, 3432 cm-1) [28]. No major shift in peak position is observed, although there are differences in peak intensities.
Table 1:Spectral assignments.
RI PT
Fig. 1. Comparison of Pb01 and Pb03 mean spectra, along with difference spectrum and p-values of t-test performed between intensities at each wavenumber.
M AN U
SC
The difference spectra Pb01 – Pb03, Pb01 – Pb18, and Pb03 – Pb18 help identify differences that may not be apparent in visual comparison of spectra. In such spectrum (e.g. Pb01 – Pb03), peaks in minuend (Pb01 in Pb01 – Pb03) that have more intensity than subtrahend (Pb03 in Pb01 – Pb03) appear as positive peaks; while those peaks in minuend with intensities less than subtrahend appear as negative peaks. As seen in Figure 1, Pb01 – Pb03 show negative peaks 1032, 1076, 1150, 2852 and 2932 cm-1 (mostly lipids and DNA), and positive peaks 1526, 1628 and 1656 cm-1 (protein). This indicates more lipids/ DNA and less protein in Pb03 than Pb01. Pb01 – Pb18 negative peaks 1236, 1556 cm-1 (proteins) and 2958 (lipids) have very low intensity compared to Pb01 – Pb03 and Pb03 – Pb18, suggesting that the difference between two groups is less. Pb03 – Pb18 difference spectra suggests higher lipid and DNA, and lower protein content in Pb03 compared to Pb18.
TE D
Fig. 2. Comparison of Pb01 and Pb18 mean spectra, along with difference spectrum and p-values of t-test performed between intensities at each wavenumber.
Fig. 3. Comparison of Pb03 and Pb18 mean spectra, along with difference spectrum and p-values of t-test performed between intensities at each wavenumber.
AC C
EP
The analysis of spectra indicates biochemical difference. However, the analysis is restricted to mean spectra. Even taking standard deviation into account would not fully represent each individual spectra, since these measures can be peturbed by various factors. To circumvent this problem, multivariate analysis can be performed. In this case, we use PCA and then LDA. PCA identifies differences in spectrum regardless of group. It calculates average of all the spectra, then taking into account the variation of each spectrum from the average, it generates principal components (PCs) that are assigned ranks; with the PC1 describing most variation, PC2 less and so on. When PC scores of each spectrum are plotted (say PC1 as x-axis and PC2 as y-axis), spread in data can be visualized. If there are variation between groups, they will be separated in the PC1 – PC2 co-ordinate system; or will show no separation if the spectra of groups have similar features. Since PCA does not need input of group information, its called unsupervised analysis; and helps discern patterns in the data. Moreover, since the aim of multivariate analysis is to highlight spectral differences, choosing spectral regions for analysis can become important. The spectral region can broadly be divided into fingerprint region (in this case 900-1800 cm-1) and functional group region (2800-3500 cm-1). The former contains a large number of biological information, while the latter is
4
ACCEPTED MANUSCRIPT
SC
RI PT
restricted to few vibrations. This is observed in Table 1, where the fingerprint region has various types of vibrations; while functional group region have fewer. Several PCAs were carried out using different spectral ranges. The best separation between groups was achieved in the 1200-1438 cm-1 range. The PCA scatter plot in this range of PC1 and PC2 is depicted in Figure 4 (left) and clearly shows separation between the three groups. Interestingly, Pb03 appears to be more distinct from Pb01 compared to Pb18. However, since the picture may change depending on the PC plotted, further analysis is warranted. Its also important to note that PC can provide information on the specific peaks responsible for the separation. In this PCA plot, it is clear that PC1 and PC2 (Figure 4, right side) are responsible for separation. PC1 shows peaks – 1213, 1266 (phosphate), 1230 (protein) and 1382 (nucleic acids); while PC2 shows 1218 (phosphate), 1300, 1364 (protein or nucleic acids) and 1436 cm-1 (lipids). This suggests that phosphate, proteins, lipids and nucleic acids are responsible for group separation. Fig. 4. PCA scatter plot of PC1 and PC2 (left) and loading plots of PC1 and PC2 (right).
AC C
EP
TE D
M AN U
Further analysis involves performing LDA using the PCs. LDA is a supervised technique; wherein as input we use group information. LDA then presents a view from n-dimensions that best increases inter group variability at the same time reducing intragroup variability. The results are presented in the form of a confusion matrix that shows which spectrum belongs to which group with respect to the new orientation. The PCLDA shows all spectra from each group were correctly placed in their own groups – 28 out of 28 Pb01 spectra were placed in Pb01, 31 of 31 Pb03 in Pb03 and 30 Pb18 of 30 in Pb18. It is important to cross-validate the results, since LDA will give the best possible scenario. We have used LOOCV, where in the entire analysis is repeated after removing one spectrum, and the removed spectrum is put in a group based on its spectral features. This is then repeated for each spectrum. If the results are not robust, removal of spectrum will change the results; and the spectrum will not be placed in its proper group. The results of LOOCV are shown in Table 2. Table 2 is a confusion matrix, wherein diagonal elements show correct classification, whereas non-diagonal elements show erroneous placements. As can be seen, 27 out of 28 Pb01 are correctly placed in Pb01, while 1 is wrongly placed in Pb18. All Pb03 and Pb18 spectra are correctly placed. This shows the robustness of the analysis, as results after validation are very similar to pre-validation results. Overall, PC-LDA could Pb01 (P. lutzii) with 96% and Pb03 and Pb18 (P. brasiliensis) with 100% efficiency. Table 2:LOOCV confusion matrix.
Since the best separation was obtained in a limited spectral range (1200 1438 cm- 1), it is interesting to further explore this region for biochemical information. To this end, the peaks in this region were deconvoluted, fitted with Gaussian curves, and area calculated. Any FTIR spectrum is an overlapping mix of signals from several molecular vibrations. Deconvolution is a method of un-mixing these individual signals by fitting Gaussian curves to each signal. The information regarding number of signals present (and how many Gaussian curves to fit) can be obtained from second derivatives of the spectra. After the curves are fitted, the area under the same can be calculated. The area is directly proportional to strength of the particular signal. Since these areas of 5
ACCEPTED MANUSCRIPT peaks representing sample biochemistry can be compared, it provides further insights into biochemical differences. Fig. 5. Representative depiction of spectral fitting done using Origin.
SC
RI PT
The major differences between P. lutzii and P. brasiliensis variants appear to be in the proteins and polysaccharide vibrations (Table 3). The protein vibration 1397 – 1398 cm-1 is higher in P. lutzii compared to P. brasiliensis. As opposed to this, the C-O stretching/C-H deformation/N-H deformation band can be assigned to proteins or nucleic acids and 1422-1426 cm-1 polysaccharide band is lower in P. lutzii compared to P. brasiliensis. Within the P. brasiliensis group, differences exist in protein, phosphate and cytosine vibrations. Pb18 and Pb01 resemble each other in terms of 1367-1368 cm-1 polysaccharide, and 1281-1282 cm-1 collagen vibrations; whereas Pb03 is more similar to Pb01 than Pb18 is to Pb01 when area of 1326-1327 cm-1 thymine and adenine band is considered.
M AN U
Table 3: Percent area under the curve for specific peaks along with assignments derived after deconvulation and Gaussian curve fitting.
AC C
EP
TE D
Since P. lutzii has only recently been identified as a species separate from P. brasiliensis, most of earlier proteomic and genomic studies on these fungi have not incorporated these types in their studies. However, chromosomal polymorphism, and a difference in ploidy and genome size has been well established within the unclassified P. brasiliensis species [29, 30]. Our results concur with this fact, and additionally show a difference between P. lutzii and P. brasiliensis and within P. brasiliensis. Similarly, comparative proteomics have shown extensive differences within the unclassified P. brasiliensis group [31]. Mass spectroscopic studies have shown the possibility of distinguishing P. lutzii from P. brasiliensis based on protein differences [32]. We have also demonstrated that differences in protein play an important role in the classification of the three studied groups. Future studies will involve identification of these pathogens sensitively and uniquely from blood samples. This will provide basis for development of quick diagnostic tests. Potential of ATR-FTIR applications to blood/blood components for diagnostics have received enthusiastic support of spectroscopists, owing to ease of use, fairly minimum sample preparation, and quick results. Unlike tissues, such samples do not suffer from sample thickness variations, and can provide information regarding the entire body, instead of having to first isolate and obtain the affected tissue. This broadens the scope and horizons of blood-based spectroscopic diagnosis. With respect to Mass spectrometry that also provides molecular information, sample preparation based distortions are avoided. Mass spectroscopy, on the other hand, would not be so advantageous because of the sample preparation, long time resulting, the method also transforms the sample’s molecules into gas phase ions. Care should be taken not to heat the sample to a temperature where it eventually decomposes. The molecular mass of the compound can not be determined in such cases. This and other disadvantages hamper the analysis by this technique of high molecular weight compounds and most biomolecules [33]. However, several concerns need to be addressed before body-fluid based FTIR can become clinically acceptable tool. The biggest problem is masking of relevant peaks due to presence of water. Sample drying can take up to 30 minutes or more, increasing the time required to get results [34]. This may significantly hamper the techniques clinical translation. Further, sample preparation may need to be tailored, 6
ACCEPTED MANUSCRIPT based on the diagnostic objective. Reports of high biological variation within samples despite stringent preparation [35], along with reproducibility issues, also require due consideration when assessing its medical applicability. 4. Conclusion
M AN U
SC
RI PT
PCM is an important fungal disease prevalent in South America. Two species, P. lutzii and P. brasiliensis, have been identified as causative agents. It is hypothesized that the PCM clinical manifestation as well as therapeutic response vary depending on the infecting species. A rapid, sensitive and specific diagnostic method may help convincingly prove or disprove the same. Moreover, owing to shortcomings of the current diagnostic tests available, a new and more efficient test may also be invaluable in clinics. Therefore, in this study, we use FTIR to distinguish the two species. We find that they can be classified with 96% and 100% efficiency. The advantage of FTIR lies in the fact that both nucleic acid and protein differences could be used to classify the groups. Furthermore, the technique is rapid and requires little sample preparation for highly sensitive identification of said groups. Further studies using patient blood to test ability of FTIR in identifying the species will aid translation of this technique into clinics. Acknowledgements
TE D
This work was supported by the grant of the FAPESP (Project 2009/09559-5 and 2013/17404-7), CNPq (470534/2012-5 and 302132/2015-5), FINEP (Conv. 01.13.0275.00) and CAPES for the scholarship. . References
AC C
EP
[[1] A.L. Colombo, A. Tobón, A. Restrepo, F. Queiroz-Telles, M. Nucci, Epidemiology of endemic systemic fungal infections in Latin America, Medical mycology 49(8) (2011) 785-798. [2] M.A. Shikanai-Yasuda, F.d.Q. Telles Filho, R.P. Mendes, A.L. Colombo, M.L. Moretti, Guideliness in paracoccidioidomycosis, Revista da Sociedade Brasileira de Medicina Tropical 39(3) (2006) 297-310. [3] S.A. Marques, Paracoccidioidomycosis: epidemiological, clinical, diagnostic and treatment up-dating, Anais brasileiros de dermatologia 88(5) (2013) 700-711. [4] J. Batista Jr, Z.P. De Camargo, G.F. Fernandes, A.P. Vicentini, C.J.F. Fontes, R.C. Hahn, Is the geographical origin of a Paracoccidioides brasiliensis isolate important for antigen production for regional diagnosis of paracoccidioidomycosis?, Mycoses 53(2) (2010) 176-180. [5] M.d.M. Teixeira, R.C. Theodoro, F.F.M.d. Oliveira, G.C. Machado, R.C. Hahn, E. Bagagli, G. San-Blas, M.S. Soares Felipe, Paracoccidioides lutzii sp. nov.: biological and clinical implications, Medical mycology 52(1) (2013) 19-28. [6] W. Barbosa, A.L.S.S.d. Andrade, Hepatic involvement in paracoccidioidomycosis, (1983). [7] W. Barbosa, R. Daher, A. de Oliveira, Lymphatic abdominal form of South American blastomycosis, Revista do Instituto de Medicina Tropical de Sao Paulo 10(1) (1968) 16-27. [8] E. Molinari-Madlum, M. Felipe, C. Soares, Virulence of Paracoccidioides brasiliensis isolates can be correlated to groups defined by random amplified polymorphic DNA analysis, Medical mycology 37(4) (1999) 269-276. [9] R.C. Hahn, A.M. Macedo, C.J.F. Fontes, R.D. Batista, N.L. Santos, J.S. Hamdan, Randomly amplified polymorphic DNA as a valuable tool for epidemiological studies of Paracoccidioides brasiliensis, Journal of clinical microbiology 41(7) (2003) 2849-2854.
7
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
[10] E. Bagagli, A. Sano, K.I. Coelho, S. Alquati, M. Miyaji, Z.P. de Camargo, G.M. Gomes, M. Franco, M.R. Montenegro, Isolation of Paracoccidioides brasiliensis from armadillos (Dasypus noveminctus) captured in an endemic area of paracoccidioidomycosis, The American journal of tropical medicine and hygiene 58(4) (1998) 505-512. [11] A. Gelderen de Komaid, E. Duŕan, A. Madero, V. Carizo, Histoplasmosis in northwestern Argentina. Epidemiological survey of Chuscha and La Higuera in the province of Tucuman, European journal of epidemiology 8(2) (1992) 206-210. [12] A. Restrepo, M. Salazar, L. Cano, E. Stover, D. Feldman, D. Stevens, Estrogens inhibit mycelium-to-yeast transformation in the fungus Paracoccidioides brasiliensis: implications for resistance of females to paracoccidioidomycosis, Infection and immunity 46(2) (1984) 346-353. [13] M.T. Rodrigues, M.A. de Resende, Epidemiologic skin test survey of sensitivity to paracoccidioidin, histoplasmin and sporotrichin among gold mine workers of Morro Velho Mining, Brazil, Mycopathologia 135(2) (1996) 89-98. [14] J. Legal, M. Manfait, T. Theophanides, Applications of FTIR spectroscopy in structural studies of cells and bacteria, Journal of Molecular structure 242 (1991) 397-407. [15] H. Wu, W. Chen, X. Rong, P. Cai, K. Dai, Q. Huang, In situ ATR-FTIR study on the adhesion of Pseudomonas putida to Red soil colloids, Journal of soils and sediments 14(3) (2014) 504514. [16] A. Naumann, L. Kurtze, A. Krähmer, H. Hagels, H. Schulz, Discrimination of Solanaceae taxa and quantification of scopolamine and hyoscyamine by ATR-FTIR spectroscopy, Planta medica 80(15) (2014) 1315-1320. [17] R.G. Saraiva, J.A. Lopes, J. Machado, P. Gameiro, M.J. Feio, Discrimination of single-porin Escherichia (E.) coli mutants by ATR and transmission mode FTIR spectroscopy, Journal of biophotonics 7(6) (2014) 392-400. [18] P. Zarnowiec, L. Lechowicz, G. Czerwonka, W. Kaca, Fourier transform infrared spectroscopy (FTIR) as a tool for the identification and differentiation of pathogenic bacteria, Current medicinal chemistry 22(14) (2015) 1710-1718. [19] F.S. Costa, P.P. Silva, C.L. Morais, T.D. Arantes, E.P. Milan, R.C. Theodoro, K.M. Lima, Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy as a new technology for discrimination between Cryptococcus neoformans and Cryptococcus gattii, Analytical Methods 8(39) (2016) 7107-7115. [20] F. Li, W. Song, J. Wei, S. Wang, C. Liu, Rapid Isolation of Phenol Degrading Bacteria by Fourier Transform Infrared (FTIR) Spectroscopy, Guang pu xue yu guang pu fen xi= Guang pu 35(5) (2015) 1222-1227. [21] M. Martin, D. Perez-Guaita, D.W. Andrew, J.S. Richards, B.R. Wood, P. Heraud, The effect of common anticoagulants in detection and quantification of malaria parasitemia in human red blood cells by ATR-FTIR spectroscopy, Analyst 142(8) (2017) 1192-1199. [22] G. Sockalingum, W. Bouhedja, P. Pina, P. Allouch, C. Mandray, R. Labia, J. Millot, M. Manfait, ATR–FTIR Spectroscopic Investigation of Imipenem-Susceptible andResistantPseudomonas aeruginosaIsogenic Strains, Biochemical and biophysical research communications 232(1) (1997) 240-246. [23] B.R. Wood, P. Heraud, D. Perez-Guita, A. Khoshmanesh, P. Jearanaikoon, M.W. Dixon, L. Tilley, D. McNaughton, Malaria Diagnosis Using ATR-FTIR Spectroscopy, FTIR Spectroscopy in Microbiological and Medical Diagnostics (2015). [24] Ç. Ergin, Y. Gök, Y. Bayğu, R. Gümral, B. Özhak-Baysan, A. Döğen, D. Öğünç, M. Ilkit, S. Seyedmousavi, ATR-FTIR Spectroscopy Highlights the Problem of Distinguishing Between Exophiala dermatitidis and E. phaeomuriformis Using MALDI-TOF MS, Microbial ecology 71(2) (2016) 339-346. [25] A. Naumann, Fourier Transform Infrared (FTIR) Microscopy and Imaging of Fungi, Advanced Microscopy in Mycology, Springer2015, pp. 61-88. [26] J. Oberle, J. Dighton, G. Arbuckle-Keil, Comparison of methodologies for separation of fungal isolates using Fourier transform infrared (FTIR) spectroscopy and Fourier transform 8
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
infrared-attenuated total reflectance (FTIR-ATR) microspectroscopy, Fungal biology 119(11) (2015) 1100-1114. [27] A. Rajendran, B. Hu, Mycoalgae biofilm: development of a novel platform technology using algae and fungal cultures, Biotechnology for biofuels 9(1) (2016) 112. [28] Z. Movasaghi, S. Rehman, D.I. ur Rehman, Fourier transform infrared (FTIR) spectroscopy of biological tissues, Applied Spectroscopy Reviews 43(2) (2008) 134-179. [29] L.d.S. Feitosa, P.S. Cisalpino, M.R.M. dos Santos, R.A. Mortara, T.F. Barros, F.V. Morais, R. Puccia, J.F. da Silveira, Z.P. de Camargo, Chromosomal polymorphism, syntenic relationships, and ploidy in the pathogenic fungus Paracoccidioides brasiliensis, Fungal Genetics and Biology 39(1) (2003) 60-69. [30] A. Almeida, D. Matute, J. Carmona, M. Martins, I. Torres, J. McEwen, A. Restrepo, C. Leão, P. Ludovico, F. Rodrigues, Genome size and ploidy of Paracoccidioides brasiliensis reveals a haploid DNA content: flow cytometry and GP43 sequence analysis, Fungal genetics and biology 44(1) (2007) 25-31. [31] L.L. Pigosso, A.F.A. Parente, A.S.G. Coelho, L.P. Silva, C.L. Borges, A.M. Bailão, C.M. de Almeida Soares, Comparative proteomics in the genus Paracoccidioides, Fungal genetics and biology 60 (2013) 87-100. [32] J.N. de Almeida, G.M. Del Negro, R.C. Grenfell, M.S. Vidal, D.Y. Thomaz, D.S. De Figueiredo, E. Bagagli, L. Juliano, G. Benard, Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Differentiation of the Dimorphic Fungal Species Paracoccidioides brasiliensis and Paracoccidioides lutzii, Journal of clinical microbiology 53(4) (2015) 1383-1386. [33] J.-H. Rabe, D.A. Sammour, S. Schulz, B. Munteanu, M. Ott, K. Ochs, P. Hohenberger, A. Marx, M. Platten, C.A. Opitz, Fourier transform infrared microscopy enables guidance of automated mass spectrometry imaging to predefined tissue morphologies, Scientific reports 8(1) (2018) 313. [34] C. Hughes, M. Brown, G. Clemens, A. Henderson, G. Monjardez, N.W. Clarke, P. Gardner, Assessing the challenges of Fourier transform infrared spectroscopic analysis of blood serum, Journal of biophotonics 7(3-4) (2014) 180-188. [35] V. Erukhimovitch, V. Pavlov, M. Talyshinsky, Y. Souprun, M. Huleihel, FTIR microscopy as a method for identification of bacterial and fungal infections, Journal of pharmaceutical and biomedical analysis 37(5) (2005) 1105-1108.
9
Wavenumber
Assignment
1032
υ(CC) skeletal cis formation, υ(CH2OH), υ(CO) stretching, collagen
1076
Skeletal cis conformation (CC) of DNA
1116
C-O stretching vibration of C-OH group of ribose (RNA)
1150
CHδ deformation
1204
Vibrational modes of collagen protein – amide III
1234
amide III
1256
PO2 asymmetric
1310
Amide III
1450
Asymmetric CH3 bending of protein methyl group
1536
Stretching C=N, C=C
1628
Amide I
1744
RI PT
SC
M AN U
Amide I
C=O stretching mode of lipids Symmetric stretching vibration of CH2 of lipid acyl chains
2922
2956
AC C
EP
2852
TE D
1656
ACCEPTED MANUSCRIPT
Asymmetric stretching vibration of CH2 of lipid acyl chains
Asymmetric stretching vibration of CH2 of lipid acyl chains
3290
Symmetric O-H stretching
3432
Symmetric O-H stretching
ACCEPTED MANUSCRIPT LOOCV of PCPb01
Pb03
Pb18
Pb01
27 (96%)
0
1
Pb03
0
31 (100%)
0
Pb18
0
0
RI PT
LDA
AC C
EP
TE D
M AN U
SC
30 (100%)
ACCEPTED MANUSCRIPT Wavenumber (cm-1) Assignment
%Area
Fungal species/variant
Pb01
Pb03
Pb18
collagen
2.4
2.1
2.2
1230 - 1233
protein/ nucleic acid
14.3
6.8
15.3
1260 - 1261
phospahte
10.7
6.0
13.7
1281 - 1282
Collagen
0.6
0.3
0.5
1296-1300
cytosine
5.1
8.9
1309 - 1312
Protein Amide III
1.8
8.5
2.1
1326 - 1327
tymine, adenine
5.4
4.1
6.3
SC M AN U
polysaccharides nucleic acids
AC C
1378 - 1379
3.9
EP
1367 - 1368
10.0
TE D
1345 - 1347 1352 - 1355
1397 - 1398
RI PT
1204
proteins
3.2
3.4 19.1
9.3
9.6
8.3
14.1
10.8
8.7
3.1
2.7
Stretching C-O, deformation C-
1412 - 1415
H,deformation N-H
4.2
10.5
9.6
1422 - 1426
polysaccharides
0.1
3.2
1.7
11.1
6.1
1.4
1431 - 1436
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT The efficacy of FTIR in identification of P. brasiliensis and P. Lutzii The different spectra in biochemical composition of the fungi that cause PCM
AC C
EP
TE D
M AN U
SC
RI PT
The major differences appear to be in the proteins and polysaccharide vibrations
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
S0022-2860(18)31098-6
DOI:
10.1016/j.molstruc.2018.09.016
Reference:
MOLSTR 25650
To appear in:
Journal of Molecular Structure
Received Date: 22 May 2018 Revised Date:
20 August 2018
Accepted Date: 11 September 2018
Please cite this article as: O.O. Comparato Filho, F.V. Morais, T.T. Bhattacharjee, M.L. Castilho, L. Raniero, Rapid identification of Paracoccidioides lutzii and P. brasiliensis using Fourier Transform Infrared spectroscopy, Journal of Molecular Structure (2018), doi: https://doi.org/10.1016/ j.molstruc.2018.09.016. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT
Rapid identification of Paracoccidioides lutzii and P. brasiliensis using Fourier Transform Infrared spectroscopy Olavo O. Comparato Filho1, Flavia V. Morais2, Tanmoy T. Bhattacharjee1, Maiara L. Castilho3, Leandro Raniero1* 1
SC
RI PT
Laboratório de Nanossensores, Instituto de Pesquisa & Desenvolvimento, Universidade do Vale do Paraíba, São José dos Campos, São Paulo, Brazil. 2 Laboratório de Biologia Celular e Molecular de Fungos, Instituto de Pesquisa & Desenvolvimento, Universidade do Vale do Paraíba, São José dos Campos, São Paulo, Brazil. 3 Laboratório de Bionanotecnologia, Instituto de Pesquisa & Desenvolvimento, Universidade do Vale do Paraíba, São José dos Campos, São Paulo, Brazil. * Corresponding author E-mail address: [email protected]
M AN U
Present Address: Laboratório de Nanossensores (24), IP&D (11), UNIVAP, Av. Shishima Hifumi, 2911, Urbanova, 12244-000 - São José dos Campos, SP – Brazil _____________________________________________________________________________________
ABSTRACT
______________________________________________________________________
AC C
EP
TE D
The inhalation of the fungus Paracoccidioides brasiliensis (P. brasiliensis, Pb03 and Pb18) causes the Parcoccidioidomycosis. Initially, only P. brasiliensis was thought to be responsible for it. Later the researchers found out that another pathogen: Paracoccidioides lutzii (P. lutzii, Pb01) may also cause the disease. Clinical manifestation and treatment differ based on the causative agent, making it important to identify them correctly. Therefore, this study focusses on distinguishing P. brasiliensis from P. lutzii using Fourier Transform Infrared (FTIR) spectroscopy - a rapid, highly specific and sensitive technique that can identify biochemical composition with minimal sample preparation. We acquired thirty spectra each from pure cultures of P. lutzii, and P. brasiliensis. The FTIR spectra showed the difference in biochemical composition, the major differences being in the polysaccharide and proteins vibrations. Principal Component Linear Discriminant Analysis (PC-LDA) could distinguish the species. We acquired nearly 10 spectra per sample in triplicates, resulting around 30 spectra per group. The analysis of spectra indicates biochemical difference. Multivariate analysis is important to identify spectral differences in specific chosen spectra regions where we can take biological or vibration information. PC-LDA could distinguish Pb01 (P. lutzii) with 96% efficiency and Pb03/Pb18 (P.brasiliensis) with 100% efficiency. Thus, FTIR may contribute to clinical diagnosis of this highly prevalent Latin American disease. Keywords: Paracoccidioides brasiliensis; Paracoccidioides lutzii; FTIR.
1
ACCEPTED MANUSCRIPT ______________________________________________________________________ 1. Introduction
AC C
EP
TE D
M AN U
SC
RI PT
Paracoccidioidomycosis (PCM) is systemic deep mycoses most prevalent in South America, with 80% of cases reported in Brazil [1]. The estimated annual incidence rate in rural endemic areas ranges from 1-3 new cases per 1,000,000 inhabitants to 1-3 new cases per 100,000 inhabitants. PCM is considered the third largest cause of death by chronic infectious disease with mortality rate of 1.65 cases per 1,000,000 inhabitants in these regions [2]. Clinical presentation of PCM may be asymptomatic with positive skin test; acute – involving spleen, liver, lymph nodes and bone marrow; chronic, Sequelae – characterized by inadequate adrenal function and pulmonary obstruction; or be associated with immunosuppressing diseases [3]. PCM is caused by exposure, generally in the form of inhalation, of the dimorphic temperature dependent fungi of the genus Paracoccidioides. Previously thought to be caused by only one species - P. brasiliensis, the general consensus currently is that two distinct species - P. brasiliensis and P. lutzii can cause the disease [2, 4, 5]. Barbosa and Andrade had reported clinical manifestations that are different from typical PCM, and that may be attributed to the different causative species [5-8]. It is also hypothesized that response to therapy for the two species may be different [5, 9]. These observations may have great implications in management of the disease if proven correct. A major barrier to answering the above questions is diagnosis. The main diagnostic test used detects the presence of Gp43 antigen expressed by P. brasiliensis in serum. However, numerous PCM patients in different regions of Brazil have less or no expression of Gp43, presumably due to infection by P. lutzii [10-12]. In Rondônia State of Brazil, the false-negative rate was found to be as high as 98% [13]. As Marques observed, this regional variation in antigen expression would necessitate administration of different tests for different regions for high sensitivity [3]. Alternative techniques that can identify these different species would greatly improve diagnosis and help answer some basic questions regarding PCM. In this study, we investigate the ability of FTIR vibrational spectroscopy to distinguish P. brasiliensis from P. lutzii. Since biochemical makeup of two distinct species is different, the technique may be used to distinguish them. The advantages of using FTIR are their high sensitivity and specificity, minimal sample preparation, rapid results, possibility of automation, identification and quantification of functional groups. Several studies have demonstrated the use of FTIR in microbiological studies [14, 15], identifying microorganisms [16-23], distinguishing species [24] and mycology [16, 2527]. We cultured pure P. lutzii (Pb01) and two variants of P. brasiliensis (Pb03 and Pb18), and acquired FTIR spectra from the same. The spectra were then used to understand the difference in biochemical composition as well as to determine whether multivariate statistical analysis Principal Component Linear Discriminant Analysis (PCLDA) could distinguish the species. Two variants of P. brasiliensis were used to provide a more complex challenge to PC-LDA in distinguishing the species.
2
ACCEPTED MANUSCRIPT 2. Materials and Methods 2.1. Culture of Paracoccidioides spp.
RI PT
Z. P. Camargo, UNIFESP, São Paulo, Brazil provided the isolates of Paracoccidioides brasiliensis, Pb18 and Pb03, and of P. lutzii, Pb01. Fungi stored at 4ºC, were inoculated in liquid SD medium (0.17% yeast nitrogen base, 2% glucose, 0.5% casamino acids, 0.5% ammonium sulfate, pH 4.5), and grown for 5 days at 36ºC, under rotation at 100 rpm. 2.2. Sample preparation
2.3. FTIR spectroscopy and data analysis:
SC
Yeasts of all isolates were harvested by centrifugation at 4,000 rpm, 1 minute at 4ºC, killed by sodium hypochlorite, and washed three times in cold ultrapure water (1 mL). The pellets were fixed in 60% isopropanol (250 µL) for 24 h and left to dry on a vertical laminar flow station for 24 h. After two additional ultrapure water washings, the fungi were resuspended in 200 µL of ultrapure water.
M AN U
Point spectra were recorded in the mid infrared (4000–700 cm−1) spectral range with a Spectrum Spotlight 400 FTIR imaging system (Perkin Elmer Life Sciences, France), with spatial resolution of 6.25 µm and spectral resolution of 4 cm−1. A background of 60 scans was acquired per sample, and 30 scans were acquired per spectrum. The background is automatically subtracted from sample spectrum by the software. Approximately 10 spectra were acquired per sample, and the experiment was done in triplicates; resulting in approximately 30 spectra per group.
TE D
2.4. Data analysis:
AC C
EP
For mean spectra and standard deviations, all spectra were corrected for baseline by subtracting polynomial 5 curve followed by vector normalization in LabSpec 5.0. Student’s t-test was performed between each group for intensities at each wavenumber using GraphPad Prism 7. The difference spectra along with p-values of significance were included in graphs showing mean spectra comparison between groups. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Leave One Out Cross Validation (LOOCV) was carried out in MATLAB 2011, after first derivatization, interpolation in 1,200-1,438 cm-1 spectral range and vector normalization. Deconvulation by fitting if Gaussian curves and calculation of area under the curve was performed using OriginPro 8.5, based on peaks discovered by second derivatization of mean spectra. 3. Results and Discussion Figures 1, 2 and 3 show the mean spectra comparison, standard deviation in form of gray color overlap, difference spectra and p-values of t-test on comparison of intensity values at each wavenumber for Pb01/Pb03, Pb01/Pb18 and Pb03/Pb18, respectively. The Figures help compare the spectra visually, and highlight the difference between the spectra. The associated p-values help point out spectral regions wherein the differences are statistically significant. Majority of peaks in the mean spectra can be assigned to protein vibrations (1032, 1150, 1204, 1234, 1256, 1310, 1450, 1536, 1628, 1656 cm-1), lipids (1744, 2852, 2922, 2956 cm-1), nuclear material (1076, 1116 cm-1)
3
ACCEPTED MANUSCRIPT and water (3290, 3432 cm-1) [28]. No major shift in peak position is observed, although there are differences in peak intensities.
Table 1:Spectral assignments.
RI PT
Fig. 1. Comparison of Pb01 and Pb03 mean spectra, along with difference spectrum and p-values of t-test performed between intensities at each wavenumber.
M AN U
SC
The difference spectra Pb01 – Pb03, Pb01 – Pb18, and Pb03 – Pb18 help identify differences that may not be apparent in visual comparison of spectra. In such spectrum (e.g. Pb01 – Pb03), peaks in minuend (Pb01 in Pb01 – Pb03) that have more intensity than subtrahend (Pb03 in Pb01 – Pb03) appear as positive peaks; while those peaks in minuend with intensities less than subtrahend appear as negative peaks. As seen in Figure 1, Pb01 – Pb03 show negative peaks 1032, 1076, 1150, 2852 and 2932 cm-1 (mostly lipids and DNA), and positive peaks 1526, 1628 and 1656 cm-1 (protein). This indicates more lipids/ DNA and less protein in Pb03 than Pb01. Pb01 – Pb18 negative peaks 1236, 1556 cm-1 (proteins) and 2958 (lipids) have very low intensity compared to Pb01 – Pb03 and Pb03 – Pb18, suggesting that the difference between two groups is less. Pb03 – Pb18 difference spectra suggests higher lipid and DNA, and lower protein content in Pb03 compared to Pb18.
TE D
Fig. 2. Comparison of Pb01 and Pb18 mean spectra, along with difference spectrum and p-values of t-test performed between intensities at each wavenumber.
Fig. 3. Comparison of Pb03 and Pb18 mean spectra, along with difference spectrum and p-values of t-test performed between intensities at each wavenumber.
AC C
EP
The analysis of spectra indicates biochemical difference. However, the analysis is restricted to mean spectra. Even taking standard deviation into account would not fully represent each individual spectra, since these measures can be peturbed by various factors. To circumvent this problem, multivariate analysis can be performed. In this case, we use PCA and then LDA. PCA identifies differences in spectrum regardless of group. It calculates average of all the spectra, then taking into account the variation of each spectrum from the average, it generates principal components (PCs) that are assigned ranks; with the PC1 describing most variation, PC2 less and so on. When PC scores of each spectrum are plotted (say PC1 as x-axis and PC2 as y-axis), spread in data can be visualized. If there are variation between groups, they will be separated in the PC1 – PC2 co-ordinate system; or will show no separation if the spectra of groups have similar features. Since PCA does not need input of group information, its called unsupervised analysis; and helps discern patterns in the data. Moreover, since the aim of multivariate analysis is to highlight spectral differences, choosing spectral regions for analysis can become important. The spectral region can broadly be divided into fingerprint region (in this case 900-1800 cm-1) and functional group region (2800-3500 cm-1). The former contains a large number of biological information, while the latter is
4
ACCEPTED MANUSCRIPT
SC
RI PT
restricted to few vibrations. This is observed in Table 1, where the fingerprint region has various types of vibrations; while functional group region have fewer. Several PCAs were carried out using different spectral ranges. The best separation between groups was achieved in the 1200-1438 cm-1 range. The PCA scatter plot in this range of PC1 and PC2 is depicted in Figure 4 (left) and clearly shows separation between the three groups. Interestingly, Pb03 appears to be more distinct from Pb01 compared to Pb18. However, since the picture may change depending on the PC plotted, further analysis is warranted. Its also important to note that PC can provide information on the specific peaks responsible for the separation. In this PCA plot, it is clear that PC1 and PC2 (Figure 4, right side) are responsible for separation. PC1 shows peaks – 1213, 1266 (phosphate), 1230 (protein) and 1382 (nucleic acids); while PC2 shows 1218 (phosphate), 1300, 1364 (protein or nucleic acids) and 1436 cm-1 (lipids). This suggests that phosphate, proteins, lipids and nucleic acids are responsible for group separation. Fig. 4. PCA scatter plot of PC1 and PC2 (left) and loading plots of PC1 and PC2 (right).
AC C
EP
TE D
M AN U
Further analysis involves performing LDA using the PCs. LDA is a supervised technique; wherein as input we use group information. LDA then presents a view from n-dimensions that best increases inter group variability at the same time reducing intragroup variability. The results are presented in the form of a confusion matrix that shows which spectrum belongs to which group with respect to the new orientation. The PCLDA shows all spectra from each group were correctly placed in their own groups – 28 out of 28 Pb01 spectra were placed in Pb01, 31 of 31 Pb03 in Pb03 and 30 Pb18 of 30 in Pb18. It is important to cross-validate the results, since LDA will give the best possible scenario. We have used LOOCV, where in the entire analysis is repeated after removing one spectrum, and the removed spectrum is put in a group based on its spectral features. This is then repeated for each spectrum. If the results are not robust, removal of spectrum will change the results; and the spectrum will not be placed in its proper group. The results of LOOCV are shown in Table 2. Table 2 is a confusion matrix, wherein diagonal elements show correct classification, whereas non-diagonal elements show erroneous placements. As can be seen, 27 out of 28 Pb01 are correctly placed in Pb01, while 1 is wrongly placed in Pb18. All Pb03 and Pb18 spectra are correctly placed. This shows the robustness of the analysis, as results after validation are very similar to pre-validation results. Overall, PC-LDA could Pb01 (P. lutzii) with 96% and Pb03 and Pb18 (P. brasiliensis) with 100% efficiency. Table 2:LOOCV confusion matrix.
Since the best separation was obtained in a limited spectral range (1200 1438 cm- 1), it is interesting to further explore this region for biochemical information. To this end, the peaks in this region were deconvoluted, fitted with Gaussian curves, and area calculated. Any FTIR spectrum is an overlapping mix of signals from several molecular vibrations. Deconvolution is a method of un-mixing these individual signals by fitting Gaussian curves to each signal. The information regarding number of signals present (and how many Gaussian curves to fit) can be obtained from second derivatives of the spectra. After the curves are fitted, the area under the same can be calculated. The area is directly proportional to strength of the particular signal. Since these areas of 5
ACCEPTED MANUSCRIPT peaks representing sample biochemistry can be compared, it provides further insights into biochemical differences. Fig. 5. Representative depiction of spectral fitting done using Origin.
SC
RI PT
The major differences between P. lutzii and P. brasiliensis variants appear to be in the proteins and polysaccharide vibrations (Table 3). The protein vibration 1397 – 1398 cm-1 is higher in P. lutzii compared to P. brasiliensis. As opposed to this, the C-O stretching/C-H deformation/N-H deformation band can be assigned to proteins or nucleic acids and 1422-1426 cm-1 polysaccharide band is lower in P. lutzii compared to P. brasiliensis. Within the P. brasiliensis group, differences exist in protein, phosphate and cytosine vibrations. Pb18 and Pb01 resemble each other in terms of 1367-1368 cm-1 polysaccharide, and 1281-1282 cm-1 collagen vibrations; whereas Pb03 is more similar to Pb01 than Pb18 is to Pb01 when area of 1326-1327 cm-1 thymine and adenine band is considered.
M AN U
Table 3: Percent area under the curve for specific peaks along with assignments derived after deconvulation and Gaussian curve fitting.
AC C
EP
TE D
Since P. lutzii has only recently been identified as a species separate from P. brasiliensis, most of earlier proteomic and genomic studies on these fungi have not incorporated these types in their studies. However, chromosomal polymorphism, and a difference in ploidy and genome size has been well established within the unclassified P. brasiliensis species [29, 30]. Our results concur with this fact, and additionally show a difference between P. lutzii and P. brasiliensis and within P. brasiliensis. Similarly, comparative proteomics have shown extensive differences within the unclassified P. brasiliensis group [31]. Mass spectroscopic studies have shown the possibility of distinguishing P. lutzii from P. brasiliensis based on protein differences [32]. We have also demonstrated that differences in protein play an important role in the classification of the three studied groups. Future studies will involve identification of these pathogens sensitively and uniquely from blood samples. This will provide basis for development of quick diagnostic tests. Potential of ATR-FTIR applications to blood/blood components for diagnostics have received enthusiastic support of spectroscopists, owing to ease of use, fairly minimum sample preparation, and quick results. Unlike tissues, such samples do not suffer from sample thickness variations, and can provide information regarding the entire body, instead of having to first isolate and obtain the affected tissue. This broadens the scope and horizons of blood-based spectroscopic diagnosis. With respect to Mass spectrometry that also provides molecular information, sample preparation based distortions are avoided. Mass spectroscopy, on the other hand, would not be so advantageous because of the sample preparation, long time resulting, the method also transforms the sample’s molecules into gas phase ions. Care should be taken not to heat the sample to a temperature where it eventually decomposes. The molecular mass of the compound can not be determined in such cases. This and other disadvantages hamper the analysis by this technique of high molecular weight compounds and most biomolecules [33]. However, several concerns need to be addressed before body-fluid based FTIR can become clinically acceptable tool. The biggest problem is masking of relevant peaks due to presence of water. Sample drying can take up to 30 minutes or more, increasing the time required to get results [34]. This may significantly hamper the techniques clinical translation. Further, sample preparation may need to be tailored, 6
ACCEPTED MANUSCRIPT based on the diagnostic objective. Reports of high biological variation within samples despite stringent preparation [35], along with reproducibility issues, also require due consideration when assessing its medical applicability. 4. Conclusion
M AN U
SC
RI PT
PCM is an important fungal disease prevalent in South America. Two species, P. lutzii and P. brasiliensis, have been identified as causative agents. It is hypothesized that the PCM clinical manifestation as well as therapeutic response vary depending on the infecting species. A rapid, sensitive and specific diagnostic method may help convincingly prove or disprove the same. Moreover, owing to shortcomings of the current diagnostic tests available, a new and more efficient test may also be invaluable in clinics. Therefore, in this study, we use FTIR to distinguish the two species. We find that they can be classified with 96% and 100% efficiency. The advantage of FTIR lies in the fact that both nucleic acid and protein differences could be used to classify the groups. Furthermore, the technique is rapid and requires little sample preparation for highly sensitive identification of said groups. Further studies using patient blood to test ability of FTIR in identifying the species will aid translation of this technique into clinics. Acknowledgements
TE D
This work was supported by the grant of the FAPESP (Project 2009/09559-5 and 2013/17404-7), CNPq (470534/2012-5 and 302132/2015-5), FINEP (Conv. 01.13.0275.00) and CAPES for the scholarship. . References
AC C
EP
[[1] A.L. Colombo, A. Tobón, A. Restrepo, F. Queiroz-Telles, M. Nucci, Epidemiology of endemic systemic fungal infections in Latin America, Medical mycology 49(8) (2011) 785-798. [2] M.A. Shikanai-Yasuda, F.d.Q. Telles Filho, R.P. Mendes, A.L. Colombo, M.L. Moretti, Guideliness in paracoccidioidomycosis, Revista da Sociedade Brasileira de Medicina Tropical 39(3) (2006) 297-310. [3] S.A. Marques, Paracoccidioidomycosis: epidemiological, clinical, diagnostic and treatment up-dating, Anais brasileiros de dermatologia 88(5) (2013) 700-711. [4] J. Batista Jr, Z.P. De Camargo, G.F. Fernandes, A.P. Vicentini, C.J.F. Fontes, R.C. Hahn, Is the geographical origin of a Paracoccidioides brasiliensis isolate important for antigen production for regional diagnosis of paracoccidioidomycosis?, Mycoses 53(2) (2010) 176-180. [5] M.d.M. Teixeira, R.C. Theodoro, F.F.M.d. Oliveira, G.C. Machado, R.C. Hahn, E. Bagagli, G. San-Blas, M.S. Soares Felipe, Paracoccidioides lutzii sp. nov.: biological and clinical implications, Medical mycology 52(1) (2013) 19-28. [6] W. Barbosa, A.L.S.S.d. Andrade, Hepatic involvement in paracoccidioidomycosis, (1983). [7] W. Barbosa, R. Daher, A. de Oliveira, Lymphatic abdominal form of South American blastomycosis, Revista do Instituto de Medicina Tropical de Sao Paulo 10(1) (1968) 16-27. [8] E. Molinari-Madlum, M. Felipe, C. Soares, Virulence of Paracoccidioides brasiliensis isolates can be correlated to groups defined by random amplified polymorphic DNA analysis, Medical mycology 37(4) (1999) 269-276. [9] R.C. Hahn, A.M. Macedo, C.J.F. Fontes, R.D. Batista, N.L. Santos, J.S. Hamdan, Randomly amplified polymorphic DNA as a valuable tool for epidemiological studies of Paracoccidioides brasiliensis, Journal of clinical microbiology 41(7) (2003) 2849-2854.
7
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
[10] E. Bagagli, A. Sano, K.I. Coelho, S. Alquati, M. Miyaji, Z.P. de Camargo, G.M. Gomes, M. Franco, M.R. Montenegro, Isolation of Paracoccidioides brasiliensis from armadillos (Dasypus noveminctus) captured in an endemic area of paracoccidioidomycosis, The American journal of tropical medicine and hygiene 58(4) (1998) 505-512. [11] A. Gelderen de Komaid, E. Duŕan, A. Madero, V. Carizo, Histoplasmosis in northwestern Argentina. Epidemiological survey of Chuscha and La Higuera in the province of Tucuman, European journal of epidemiology 8(2) (1992) 206-210. [12] A. Restrepo, M. Salazar, L. Cano, E. Stover, D. Feldman, D. Stevens, Estrogens inhibit mycelium-to-yeast transformation in the fungus Paracoccidioides brasiliensis: implications for resistance of females to paracoccidioidomycosis, Infection and immunity 46(2) (1984) 346-353. [13] M.T. Rodrigues, M.A. de Resende, Epidemiologic skin test survey of sensitivity to paracoccidioidin, histoplasmin and sporotrichin among gold mine workers of Morro Velho Mining, Brazil, Mycopathologia 135(2) (1996) 89-98. [14] J. Legal, M. Manfait, T. Theophanides, Applications of FTIR spectroscopy in structural studies of cells and bacteria, Journal of Molecular structure 242 (1991) 397-407. [15] H. Wu, W. Chen, X. Rong, P. Cai, K. Dai, Q. Huang, In situ ATR-FTIR study on the adhesion of Pseudomonas putida to Red soil colloids, Journal of soils and sediments 14(3) (2014) 504514. [16] A. Naumann, L. Kurtze, A. Krähmer, H. Hagels, H. Schulz, Discrimination of Solanaceae taxa and quantification of scopolamine and hyoscyamine by ATR-FTIR spectroscopy, Planta medica 80(15) (2014) 1315-1320. [17] R.G. Saraiva, J.A. Lopes, J. Machado, P. Gameiro, M.J. Feio, Discrimination of single-porin Escherichia (E.) coli mutants by ATR and transmission mode FTIR spectroscopy, Journal of biophotonics 7(6) (2014) 392-400. [18] P. Zarnowiec, L. Lechowicz, G. Czerwonka, W. Kaca, Fourier transform infrared spectroscopy (FTIR) as a tool for the identification and differentiation of pathogenic bacteria, Current medicinal chemistry 22(14) (2015) 1710-1718. [19] F.S. Costa, P.P. Silva, C.L. Morais, T.D. Arantes, E.P. Milan, R.C. Theodoro, K.M. Lima, Attenuated total reflection Fourier transform-infrared (ATR-FTIR) spectroscopy as a new technology for discrimination between Cryptococcus neoformans and Cryptococcus gattii, Analytical Methods 8(39) (2016) 7107-7115. [20] F. Li, W. Song, J. Wei, S. Wang, C. Liu, Rapid Isolation of Phenol Degrading Bacteria by Fourier Transform Infrared (FTIR) Spectroscopy, Guang pu xue yu guang pu fen xi= Guang pu 35(5) (2015) 1222-1227. [21] M. Martin, D. Perez-Guaita, D.W. Andrew, J.S. Richards, B.R. Wood, P. Heraud, The effect of common anticoagulants in detection and quantification of malaria parasitemia in human red blood cells by ATR-FTIR spectroscopy, Analyst 142(8) (2017) 1192-1199. [22] G. Sockalingum, W. Bouhedja, P. Pina, P. Allouch, C. Mandray, R. Labia, J. Millot, M. Manfait, ATR–FTIR Spectroscopic Investigation of Imipenem-Susceptible andResistantPseudomonas aeruginosaIsogenic Strains, Biochemical and biophysical research communications 232(1) (1997) 240-246. [23] B.R. Wood, P. Heraud, D. Perez-Guita, A. Khoshmanesh, P. Jearanaikoon, M.W. Dixon, L. Tilley, D. McNaughton, Malaria Diagnosis Using ATR-FTIR Spectroscopy, FTIR Spectroscopy in Microbiological and Medical Diagnostics (2015). [24] Ç. Ergin, Y. Gök, Y. Bayğu, R. Gümral, B. Özhak-Baysan, A. Döğen, D. Öğünç, M. Ilkit, S. Seyedmousavi, ATR-FTIR Spectroscopy Highlights the Problem of Distinguishing Between Exophiala dermatitidis and E. phaeomuriformis Using MALDI-TOF MS, Microbial ecology 71(2) (2016) 339-346. [25] A. Naumann, Fourier Transform Infrared (FTIR) Microscopy and Imaging of Fungi, Advanced Microscopy in Mycology, Springer2015, pp. 61-88. [26] J. Oberle, J. Dighton, G. Arbuckle-Keil, Comparison of methodologies for separation of fungal isolates using Fourier transform infrared (FTIR) spectroscopy and Fourier transform 8
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
infrared-attenuated total reflectance (FTIR-ATR) microspectroscopy, Fungal biology 119(11) (2015) 1100-1114. [27] A. Rajendran, B. Hu, Mycoalgae biofilm: development of a novel platform technology using algae and fungal cultures, Biotechnology for biofuels 9(1) (2016) 112. [28] Z. Movasaghi, S. Rehman, D.I. ur Rehman, Fourier transform infrared (FTIR) spectroscopy of biological tissues, Applied Spectroscopy Reviews 43(2) (2008) 134-179. [29] L.d.S. Feitosa, P.S. Cisalpino, M.R.M. dos Santos, R.A. Mortara, T.F. Barros, F.V. Morais, R. Puccia, J.F. da Silveira, Z.P. de Camargo, Chromosomal polymorphism, syntenic relationships, and ploidy in the pathogenic fungus Paracoccidioides brasiliensis, Fungal Genetics and Biology 39(1) (2003) 60-69. [30] A. Almeida, D. Matute, J. Carmona, M. Martins, I. Torres, J. McEwen, A. Restrepo, C. Leão, P. Ludovico, F. Rodrigues, Genome size and ploidy of Paracoccidioides brasiliensis reveals a haploid DNA content: flow cytometry and GP43 sequence analysis, Fungal genetics and biology 44(1) (2007) 25-31. [31] L.L. Pigosso, A.F.A. Parente, A.S.G. Coelho, L.P. Silva, C.L. Borges, A.M. Bailão, C.M. de Almeida Soares, Comparative proteomics in the genus Paracoccidioides, Fungal genetics and biology 60 (2013) 87-100. [32] J.N. de Almeida, G.M. Del Negro, R.C. Grenfell, M.S. Vidal, D.Y. Thomaz, D.S. De Figueiredo, E. Bagagli, L. Juliano, G. Benard, Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass Spectrometry for Differentiation of the Dimorphic Fungal Species Paracoccidioides brasiliensis and Paracoccidioides lutzii, Journal of clinical microbiology 53(4) (2015) 1383-1386. [33] J.-H. Rabe, D.A. Sammour, S. Schulz, B. Munteanu, M. Ott, K. Ochs, P. Hohenberger, A. Marx, M. Platten, C.A. Opitz, Fourier transform infrared microscopy enables guidance of automated mass spectrometry imaging to predefined tissue morphologies, Scientific reports 8(1) (2018) 313. [34] C. Hughes, M. Brown, G. Clemens, A. Henderson, G. Monjardez, N.W. Clarke, P. Gardner, Assessing the challenges of Fourier transform infrared spectroscopic analysis of blood serum, Journal of biophotonics 7(3-4) (2014) 180-188. [35] V. Erukhimovitch, V. Pavlov, M. Talyshinsky, Y. Souprun, M. Huleihel, FTIR microscopy as a method for identification of bacterial and fungal infections, Journal of pharmaceutical and biomedical analysis 37(5) (2005) 1105-1108.
9
Wavenumber
Assignment
1032
υ(CC) skeletal cis formation, υ(CH2OH), υ(CO) stretching, collagen
1076
Skeletal cis conformation (CC) of DNA
1116
C-O stretching vibration of C-OH group of ribose (RNA)
1150
CHδ deformation
1204
Vibrational modes of collagen protein – amide III
1234
amide III
1256
PO2 asymmetric
1310
Amide III
1450
Asymmetric CH3 bending of protein methyl group
1536
Stretching C=N, C=C
1628
Amide I
1744
RI PT
SC
M AN U
Amide I
C=O stretching mode of lipids Symmetric stretching vibration of CH2 of lipid acyl chains
2922
2956
AC C
EP
2852
TE D
1656
ACCEPTED MANUSCRIPT
Asymmetric stretching vibration of CH2 of lipid acyl chains
Asymmetric stretching vibration of CH2 of lipid acyl chains
3290
Symmetric O-H stretching
3432
Symmetric O-H stretching
ACCEPTED MANUSCRIPT LOOCV of PCPb01
Pb03
Pb18
Pb01
27 (96%)
0
1
Pb03
0
31 (100%)
0
Pb18
0
0
RI PT
LDA
AC C
EP
TE D
M AN U
SC
30 (100%)
ACCEPTED MANUSCRIPT Wavenumber (cm-1) Assignment
%Area
Fungal species/variant
Pb01
Pb03
Pb18
collagen
2.4
2.1
2.2
1230 - 1233
protein/ nucleic acid
14.3
6.8
15.3
1260 - 1261
phospahte
10.7
6.0
13.7
1281 - 1282
Collagen
0.6
0.3
0.5
1296-1300
cytosine
5.1
8.9
1309 - 1312
Protein Amide III
1.8
8.5
2.1
1326 - 1327
tymine, adenine
5.4
4.1
6.3
SC M AN U
polysaccharides nucleic acids
AC C
1378 - 1379
3.9
EP
1367 - 1368
10.0
TE D
1345 - 1347 1352 - 1355
1397 - 1398
RI PT
1204
proteins
3.2
3.4 19.1
9.3
9.6
8.3
14.1
10.8
8.7
3.1
2.7
Stretching C-O, deformation C-
1412 - 1415
H,deformation N-H
4.2
10.5
9.6
1422 - 1426
polysaccharides
0.1
3.2
1.7
11.1
6.1
1.4
1431 - 1436
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
ACCEPTED MANUSCRIPT The efficacy of FTIR in identification of P. brasiliensis and P. Lutzii The different spectra in biochemical composition of the fungi that cause PCM
AC C
EP
TE D
M AN U
SC
RI PT
The major differences appear to be in the proteins and polysaccharide vibrations
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT
AC C
EP
TE D
M AN U
SC
RI PT
ACCEPTED MANUSCRIPT