MitoTarget Modeling Using ANN-QSTR Approach Based on Fractal

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MitoTarget Modeling Using ANN-QSTR Approach Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors Michael Gonzalez Durruthy, Silvana Manske Nunes, Juliane Ventura Lima, Marcos A Gelesky, Humberto González-Díaz, José M. Monserrat, Riccardo Concu, and M. Natalia D.S. Cordeiro J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00631 • Publication Date (Web): 09 Nov 2018 Downloaded from http://pubs.acs.org on November 9, 2018

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Journal of Chemical Information and Modeling

MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM NanoDescriptors: Carbon Nanotubes as Mitochondrial F0F1-ATPase Inhibitors Michael González-Durruthy

a,*,

Silvana Manske Nunes

Geleskyf, Humberto González-Díaz

g,h,

b,c,

José M. Monserrat

Juliane Ventura-Lima

b,c,d,e,

b,c,d,e,

Marcos A.

Riccardo Concua,* , M. Natália D.S.

Cordeiro a,* aLAQV-REQUIMTE,

Department of Chemistry and Biochemistry, Faculty of Sciences, 4169-007, University of Porto, Porto, Portugal bInstitute of Biological Sciences (ICB), Universidade Federal do Rio Grande -FURG, 96270-900, Rio Grande, RS, Brazil. cICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900, Rio Grande, RS, Brazil dNational Institute of Carbon Nanomaterial Science and Technology, 30123970, Belo Horizonte, MG, Brazil. eNanotoxicology Network (MCTI/CNPq), 96270-900, Rio Grande, RS, Brazil. fPost-Graduate Program in Technological and Environmental Chemistry, 96270-900, Rio Grande, RS, Brazil. gDepartment of Organic Chemistry II, College of Science and Technology, University of the Basque Country UPV/EHU, 48940, Leioa, Bizkaia. hIKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Bizkaia.

* To whom correspondence should be addressed: M. González-Durruthy, Email: [email protected], Fax: +351220402659 R. Concu, Email: [email protected], Fax: +351220402659 M. Natália D.S. Cordeiro, Email: [email protected], Fax: +351220402659

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Abstract: Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and non-linear classification algorithms on structure-toxicity relationship with artificial neural network (ANN) models were setup using the fractal dimensions calculated from CNTs as source of supra-molecular chemical-information. To predict the potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays. The attained experimental data suggest that CNTs have high ability to inhibit the F0-ATPase active-binding site following the order: oxidizedCNT (CNTCOOH > CNTOH) > pristineCNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile the performance of the ANN models was found to be improved by including different non-linear combinations of the calculated fractal Scanning Electron Microscopy (SEM) nano-descriptors. Leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and non-mitotoxic with specificity (Sp > 98.9 %) and sensitivity (Sn > 99.0 %) from ANN models compared with linear approaches (LNN) with Sp ≈ Sn > 95.5 %. Finally, the present study can contribute towards the rational-design of carbon nanomaterials and opens new opportunities towards mitochondrial nanotoxicology-based in silico models.


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Introduction The H+ protons flux through F0-ATPsynthase subunit membrane-embedded of mitochondrial F0F1ATP synthase favors the mechanical rotation coupled to the γ and the ε subunits co-rotating within the F1-ATP synthase subunit, which induces synchronized conformational changes resulting in the synthesis and release of ATP.1-3 However, under pathological conditions like cancer, heart ischemia/reperfusion, cerebrovascular events, and mitochondrial encephalomyopathy the F0ATPsynthase subunit membrane-embedded paradoxically hydrolyses ATP associated with the reverse biochemical reaction consuming the ATP reserves and quickly compromising the cellular homeostasis and viability.4 The toxicological modulation of F0-ATP hydrolase-based inhibition may provide a novel mechanism to prevent different pathological conditions where mitochondrial ATP-hydrolysis mechanisms are exacerbated. Recent experimental evidences suggest that the mitochondrial Oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel within the mitochondrial permeability transition pore, which is responsible for inducing mitochondrial dysfunction linked to apoptosis.4-8 This in turn can thus be an interesting mitotoxic-targeting therapy based on the F0ATP hydrolase inhibition under pathological conditions; as it has been suggested in previous investigations using experimental in vitro assays combined with traditional approaches-based quantitative structure-activity relationship models (SAR/QSAR).4,5,7,8 Over the last decade, nanotechnology has brought great advances in the understanding of nanotoxicity mechanisms and applications.9-11 In this regard, multiple applications of novel nanomaterials have been found based on their physicochemical and selective biological properties. Particularly, carbon nanotubes (CNT) have rapidly become one of the most widely studied nanomaterials, essentially due to their high selectivity towards subcellular components mainly mitochondria, allowing novel applications based on toxicological modulation.9,10 For example, multi-walled carbon nanotubes (MWCNT) have recently been used to enhance the fast and selective H+ protons transport through their

inner

core,

when

vertically-aligned

with

phosphatidylcholine

(DOPC)

and

dipalmitoylphosphatidylcholine (DPPC) membrane phospholipids that are components of the inner mitochondrial membranes.10 However, the discovery of new carbon nanomaterials with low nanotoxicity/high biocompatibility is a complex, time-consuming and costly process. In this regard, current works on Nano-Quantitative Structure-Activity/Toxicity Relationships (Nano-QSAR/QSTR models) applied to carbon nanomaterials (CNT) have become major tools for nanotoxicological



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predictions, and at the same time have allowed to classify with greater clarity their toxic impact on human and environmental health.12-19 An important task in any nano-QSAR/QSTR modeling is to express the structural properties of nanosystems (nano-descriptors) in a quantitative way, which is not always straightforward.20,21 In fact, the relevant problem in setting up theoretical nano-descriptors is the complexity and non-uniformity of manufactured-nanoparticles.22 Owing to this, we used here for the first time the fractal theory brought by Mandelbrot,23-29 based on the calculation of the fractal dimensions (FD) of irregular objects (like CNT) as a new source of raw and supra-molecular geometrical information associated with CNTnanotoxicity. Following this idea, Scanning Electron Microscopy (SEM) images obtained from CNT irregular surface,30 could be used to extract raw structural information-based FD as CNT-nanodescriptors towards predictive classification approaches which remains largely ignored so far. In this context, the present study aims thus at developing a new ANN-classification models based on fractal SEM nano-descriptors for predicting the mitochondrial nanotoxicity on F0-ATPase subunit inhibition (ATP-hydrolysis inhibition) induced by carbon nanotubes.

Materials and methods Surface morphological characterization of carbon nanotubes. Nine different carbon nanotube samples were analyzed by Scanning Electron Microscopy (SEM), using a microscope (JEOL JSM 6610) in the secondary ion mode (SEI) with a voltage of 30 kV (See Figure 1). More detailed information in Supporting Information (SI) like Tables S1 and S2.


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Figure 1. SEM images of carbon nanotubes used in this study: (A) Pristine-MWCMT (CNT1), (B) Mixed- SW/DWCNT-OH (CNT2), (C) MWCNT-OH (CNT3), (D) MWCNT-OH (CNT4), (E) MWCNT-OH (CNT5), (F) MWCNT-COOH (CNT6), (G) MWCNT-COOH (CNT7), (H) SWCNT-COOH (CNT8), (I) MWCNT-COOH (CNT9). Calculation of fractal surface SEM nano-descriptors. To this end, the box counting algorithm-based on the original SEM-image was employed, and the fractal dimension of the whole shell obtained by a linear fit of data from non-Euclidean objects,23-30 see Figure. S2 of SI. The fractal geometry is considered as self-similar, and we checked that its complex structure is the same regardless of the scale used to measure it. In so doing, a fractal dimension near to 2 revealed high complexity (high variety of geometric information) and low self-similarity, while a fractal dimension closer to 1 little complexity and high self-similarity. Indeed, a fractal is rigorously self-similar if it can be expressed as a union of sets, each of which is an exactly reduced copy (geometrically similar) of the full set  Sierpinski triangle, Koch flake.28,29 Following these criteria, the fractal properties of the SEM-image 5 ACS Paragon Plus Environment


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were analyzed by computing the number of binary black and white boxes required to entirely cover an object (individual CNT/SEM-image). A logarithmical function  f(N(r))  was then fitted, where r is the box size and N(r) the number of boxes needed to completely cover the fractal of SEM-image from a particular CNT-sample. The fractal surface SEM-nanodescriptors from CNT-samples are the slopes (DBW, DBBW and DWBW) of the

log–log plot of measured box counts (f(N(r))) according to the

following linear functions:

f  N BW (r )  = lnN BW (r ) = ln  K BW  + DBW ln(r )

(1)

f  N BBW (r )  = lnN BBW (r ) = ln  K BBW  + DBBW ln(r )

(2)

f  NWBW (r )  = lnNWBW (r ) = ln  KWBW  + DWBW ln(r ) (3)

Herein, DBW characterizes the CNT-properties from the border of SEM-fractal pattern. DBBW characterizes the SEM-fractal pattern on the white background, and DWBW the SEM-fractal pattern on the black background of the SEM-images calculated for each CNT-sample.30 For this instance, the mask from original SEM-image of CNT-family members was fixed as default in the intensity option in the range from 0 (black) to 255 (white). The mesh box counting parameters ranged from 2 to 252, the number of steps being 30, and the number of random offsets 1,000. Then, the analysis was performed by superimposing regular grids over individual SEM-images and by counting the number (N) of occupied boxes (pixels) of the raster, i.e.: NBW, NBBW = NB + NBW or NWBW = NW + NBW, needed to cover the fractal completely, where NB is the number of black boxes, NW the number of white boxes, NBW the number of black plus white boxes, NBBW the number of black boxes and black plus white boxes, and NWBW the number of white boxes and black plus white boxes. To verify that a calculated fractal dimension was in fact an invariant geometric-property, three representative SEM-images from the same CNT-samples (CNT1-CNT9) were analyzed see Figures S2 and S3 of Supporting Information (SI). Rat-liver mitochondria preparation and isolation. The mitochondria were isolated through standard differential centrifugation. Male Wistar rats weighing (approximately 150 g) were euthanized by decapitation following the approved procedures of the Directive 2010/63/EU of the European Parliament of the Council on the protection of animals. Next, the livers (10–15 g) were immediately removed and sliced in medium (50 ml), consisting of 250 mM of sucrose, 1 mM of ethyleneglycol-bis (β-aminoethyl)-N,N,N′,N′-tetraacetic acid (EGTA) and 10 mM of HEPES-KOH, pH 7.2, and 6 ACS Paragon Plus Environment

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homogenized three times for 15 s at 1 min intervals using a Potter-Elvehjem homogenizer. For this purpose, homogenates were centrifuged (580 × g, 5 min at 4°C) and the resulting supernatant further centrifuged (10300 × g, 10 min at 4°C). Pellets were then suspended in medium (10 ml) containing 250 mM of sucrose, 0.3 mM of EGTA and 10 mM of HEPES-KOH, pH 7.2, and centrifuged again (3400 × g, 15 min 4°C) [31]. The final mitochondrial pellet was suspended in medium (1 ml) consisting of 250 mM of sucrose and 10 mM of HEPES-KOH, pH 7.2, and used within 3 h. Mitochondrial proteins content (> 90 %) was determined by the Biuret method. Determination of mitochondrial toxicity-based F0-ATPase inhibition. Toxicological inhibition of the F0-ATPase subunit by CNT-family members was evaluated in isolated rat liver mitochondria. The assay was performed measuring the mitochondrial retention of the fluorescent cationic probe safranine O,32 using a spectrofluorimeter (model F-4010, Hitachi) at excitation and emission wavelengths of 495 and 586 nm, respectively, with a slit width of 5 nm. For this purpose, the mitochondria (1 mg of protein/ml) were incubated in 2 ml of standard medium consisting of 125 of mM sucrose, 65 mM of KCl, 2 mM of inorganic phosphate and 10 mM of HEPES-KOH pH 7.4 at 30 °C and supplemented with 2 µM of safranin O.32 Relative changes in the mitochondrial membrane potential (Ψm) allowed the continuous monitoring of changes over time of the F0F1-ATPase hydrolytic activity previous RLM-incubation with individual CNT-samples (CNT1-9) at concentration range of 0.5-5µg/ml and RLM-incubation with Oligomycin A (1 µM), a specific inhibitor of F0-ATPase subunit (as control group

or

100%

of

F0-ATPase

mitotoxicity).

Different

mitochondrial

F0F1-ATPase

inhibitors/modulatory substrates as Mg2+/ATP 1mM, KCN 1 µM (as mitochondrial cytochrome C oxidase inhibitor-induced ischemic conditions) and CCCP 1 µM (a recognized protonophoric agent or mitochondrial uncoupling electron transport chain) were used and the mitochondrial nanotoxicitybased inhibition of the F0-ATPase induced by the different CNT was obtained by comparison with the Oligomycin A-treated RLM (control group). The F0F1-ATPase response was expressed as arbitrary fluorescence units (AFU).32 Determination of mitochondrial membrane potential (Ψm). RLM-suspensions were pre-incubated with a specific mitochondrial membrane potential probe as 5, 5’, 6, 6’-tetrachloro-1, 1’, 3, 3’tetraethyl-benzimidazolcarbocyanine iodide (JC-1) in 0.2 mg/ml for 15 min.33 JC-1 is widely used in apoptosis studies to monitor mitochondrial health and this dye exhibits potential-dependent accumulation in mitochondria, indicated by a fluorescence emission that shifts from green (~529 nm) to red (~590 nm). To this end, mitochondrial depolarization was indicated by decreasing the red to



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green fluorescence intensity color. The potential-sensitive color shift is due to concentrationdependent formation of red fluorescent JC-1 aggregates. To evaluate the mitochondrial membrane potential (Ψm), RLM-suspensions were exposed to CNT-family members at 5µg/ml and concomitantly treated with KCN 1µM (as classical mitochondrial cytochrome-c-oxidase inhibitor-induced ischemic conditions) in four different groups, namely: 1) untreated-RLM, 2) KCN 1µM +pristine-CNT-treatedRLM, 3) KCN 1µM + CNT-OH-treated-RLM, 4) KCN 1µM + CNT-COOH-treated-RLM. Fluorescent images were analyzed using a fluorescence microscope (Olympus IX81, Markham, Ontario, Canada) equipped with a DP72 digital camera to study the effects on mitochondrial membrane potential linked to mitochondrial F0F1-ATPase inhibition. Before the spectrofluorometric F0F1ATPase inhibition-measurements, blanks with each CNT were setup to compare with RLM exposed to CNT. For these blanks, the presence of Stern-Volmer quenching fluorescent processes associated to carbon nanotubes UV-visible optical interferences were not detected at 400-590 nm, according to their semi-metallic properties.34 Here, it is important to notice that at the time of the exposure to isolated mitochondria suspensions for the measurement of c-ring F0-ATPase subunit hydrolase nanotoxicity-based inhibition, each CNTsample was added under continuous stirring by using magnetic stirrer cuvettes which favors optimal exposure conditions according to monodisperse state and prevents spontaneous agglomeration in CNT-sample dispersions.35,36 Theoretical details of the predictive fractal classification models. Herewith, we proposed a theoretical approach based on a dose-response time series model to predict the F0-ATPase nanotoxicity-based inhibition from mitochondria assays ( AFU ij ), using as inputs the values of fractal dimensions (Dm,p) to improve the classical dose-response predictions.21,37,38 These are raw nano-descriptors of the complex geometry non-Euclidean structure of individual CNT-family members obtained from SEM images (see previous section). The general mathematical model employed is the following: kmax

m=1,p= 2

k=1

m , p=0

E ( AFU ij ) = S ( AFU ij ) pred = a0 0 f +  ak  k f (ΔVkj ) +



E ( AFU ij ) = S ( AFU ij ) pred = a0 f + a1  f (tij ) + a2  f (cij ) + 0

1

2

ak (m, p )  k f ( Dm , p ) + e0

m=1, p= 2



a3 (m, p )  3 f ( Dm , p ) + e0

(4) (5)

m , p=0


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In the equations above, the functions kf (like: 1f, 2f, 3f ) represent transformations k f (ΔVkj ) of the Box-Jenkins moving averages operators ( ΔVkj ) of the original CNT-input variables ( kVi ). The latter are c0  the CNT structure, c1  the exposure time (ti), and c2  the CNT concentration (ci) of the i-th type of CNTi in the j-th F0-ATP hydrolase nanotoxicity-based inhibition assay for one specific experimental boundary condition ( c j ). The CNT-parameters ΔVkj , i.e. the moving averages of the original CNT-input variables, are useful to quantitatively predict the output variable ATP-hydrolysis inhibition (F0-ATPase nanotoxicity) as S ( AFU ij ) pred , or to classify the discrete value of F0-ATPase nanotoxicity as E ( AFU ij ) in different experimental conditions, that is, like k f (ΔVkj )  (Vi   Vij ) using kVi -functions such as 1f(Δtij) = (ti  ); 2f(Δcij) = (ci  )2. The  Vkj  value can be interpreted as the average (or mean) for all the k-th physicochemical properties of CNTs-tested, according to: 1  Vkj   nj

 nj i    Vkj   ic j 

(6)

where nj is the number of experimental entries for a given condition cj according to the total number of experimental entries measured in this work (nj-total). The proposed fractal ANN dose-response time series model is intended to generate a predictive classification of the output variable, that is, of the F0-ATPase-mitochondrial nanotoxicity effect (E) of different CNT-family members at a set of experimental conditions. In so doing, the model considers the variation of the effect (E) for different concentrations c (µg/mL1) of the CNTs over a given time t (s) after the initial dose. To discriminate the strength of such effect, the observed variable E ( AFU ij )  i.e. the F0-ATPase nanotoxicity-based inhibition in arbitrary fluorescence units, was discretized as going from strong (= 1) to weak (= 0) inhibition. Different linear and non-linear alternatives based on Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) were then used to setup the QSTR models, respectively.12-19 Regarding the latter, different ANN schemes and activation functions were investigated such as multilayer perceptrons (MLP), radial basis functions (RBF), and linear neural networks (LNN)39. Then, the optimal values for the equation coefficients ak (a0, a1, a2, a3) and e0 (i.e. the error or independent term) of the dose-response LDA and ANN time series models were determined using the STATISTICA software [38], including statistical parameters such as Chisquare (2) and the corresponding probability of error p-level (p < 0.005). Regarding the ANN classification models, has been extensively reported by many authors that may have the tendency to be 9 ACS Paragon Plus Environment


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over-fitted and we have extensively discussed this point in a recent work18. In any case, in order to avoid an unlikely overfitted model we checked all the relevant statistics that may clearly indicate whether a model is robust or not. In this sense, the statistical quality (training set) and predictive power (validation set) of the ANN classification models were assessed by considering diverse statistical parameters. These included, for example, the percentages of correct classifications for nonmitotoxic (sensitivity, Sn) and mitotoxic (specificity, Sp) cases, the overall percentage of correct classifications (accuracy), and the areas under the receiver operating characteristic (ROC) curves40. Table 1 summarizes and defines the input variables employed in both predictive models. Table 1. CNT-input variables (iVk), Box-Jenkins operators (ΔVkj) and functions (kf) used for setting up the ANN models and their definitions. Coefficients 

(iVk) 

ΔVkj

kf

Function examples

Definition





S(AFUij)pred, 1/S[(AFUij)pred]2,

Predicted fluorescence

log S(AFUij)pred a0





0f

Average of value of fluorescence (or AFU- expected values) for all CNTs

samples

for

multiple

T

experimental

o

F0-ATPase nanotoxicity-based

conditions

(i.e.:

inhibition assay, CNT-type, and t

chemical function)

h a1

ti

Δtij

1f

Δtij, exp(Δtij)

Exposure time (in s)

e a2

ci

Δcij

2f

Δcij, 1/(1+Δcij)

CNT concentration (in µg/ml)

e0









Error term

f These fractal SEM nano-descriptors, are firstly calculated with the HarFA software,24 and then included into the dose-response time series modeling in two different ways (m): 1) a discrete way (m = 0) and 2) a continuous one (m = 1), in order to compute the fractal dimensions from the SEM-image box counting CNT-processing method. To do so, the SEM-image nano-descriptors consider three different sets from the mesh box counting of (N)-binary pixels (p) denoted as NBW (p = 0), NBBW (p = 1), or NWBW (p = 2). Following this methodology, we have three fractal SEM nano-descriptors Dm,p from the box counting discrete way (i.e., m = 0: D0,0; D0,1; D0,2), and another three ones from the box


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counting continuous way (i.e., m = 1: D1,0; D1,1; D1,2); see details in Table 2, and Table S3 and Table S4 of SI. Table 2. Values of the fractal SEM nano-descriptors (Dm,p) and their averages () for the different CNTs tested. Function

CNT

D0,0

D0,1

D0,2

D1,0

D1,1

D1,2

H

1

1.486

1.997

1.505

1.296

1.993

1.385

OH

2

1.71

1.99

1.71

1.53

1.99

1.57

OH

3

1.84

1.99

1.85

1.66

1.97

1.71

OH

4

1.77

1.99

1.78

1.57

1.98

1.63

OH

5

1.88

1.98

1.90

1.74

1.96

1.80

COOH

6

1.73

2.00

1.73

1.51

1.99

1.54

COOH

7

1.14

2.00

1.19

1.73

2.00

1.73

COOH

8

1.86

1.99

1.86

1.71

1.98

1.73

COOH

9

1.69

2.00

1.67

1.46

1.99

1.47

Function CNTs

H

1

1.486

1.997

1.505

1.296

1.993

1.385

OH

2-5

1.802

1.989

1.812

1.625

1.975

1.677

COOH

6-9

1.607

1.997

1.614

1.604

1.992

1.616

These moving averages (MA) have been largely used in structure-toxicity relationship studies [16-19]. The output of the LDA-dose response time series model based on SEM-fractal nano-descriptors is E ( AFU ij ) = S ( AFU ij ) pred , which is a real-valued score of the observed F0-ATPase nanotoxicity-based

inhibition effect ( E ) . In order to improve the predictions on mitochondrial F0-ATPase mitotoxicity the centered moments (DDm,pn) were also calculated from the fractal dimensions Dm,p. These centered moments are the natural powers of the moving averages of the fractal descriptors, that is: DDm,pn = (DDm,p – )n, the moving averages being the deviations DDm,p (= (Dm,p – ) for all CNTs with the same function (H, OH, or COOH). Further validation of the quality of the derived ANN models was checked up by determining the percentages of correct classification for nontoxic cases (Sensitivity: Sn) and toxic cases (Specificity: Sp), as well as the area under the receiving operating characteristic (ROC) curve.40 The latter is obtained by plotting Sn versus Sp, and the characteristics of this curve provide easier recognition of



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the precision of the targeted response. The general workflow applied in this experimental-theoretical study is depicted step by step in Figure 2.

Figure 2. Experimental-theoretical workflow on mitochondrial F0-ATPase mitotoxic-inhibition using isolated rat-liver mitochondria and modeling the ANN-classification models-based fractal dimension for carbon nanotubes family members.

Results and Discussion Effects of CNT-family members on the F0-ATPase nanotoxicity-based inhibition. The CNT-family (CNT1-9) was tested regarding its potential ability to induce nanotoxicity-based inhibition of rat-liver mitochondrial F0-ATPase in a range of concentration of 0.5-5 µg/ml, according to previous studies that also used in vitro assays with isolated rat-liver mitochondria.31 The results show that the oxidizedCNT family members (CNT2-9) are able to significantly inhibit the enzyme (p < 0.05) at concentration of 5 µg/ml, save for the pristine multi-walled carbon nanotube (CNT-1) that interestingly is nearly devoid of F0-ATPase inhibition maybe because of the absence of oxidized moieties like (OH and COOH) allow the H+-protons flux through the F0-ATPase subunit meanwhile 12 ACS Paragon Plus Environment

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for oxidized-CNT family members (CNT2-9) the deprotonated oxidized moieties (like CNT-O- and CNT-COO-) could interact electrostatically with H+-protons and in this way avoid the mechanical F0ATPase subunit rotation coupled to the γ and the ε subunits co-rotating within the F1-ATP synthase subunit. It was found that the CNT-family members induce F0-ATPase nanotoxicity-based inhibition in the following order: Oligomycin A (positive control as classical F0-ATPase inhibitor) > CNTCOOH (MWCNT-9, MWCNT-7, MWCNT-6, SWCNT-8) > CNT-OH (SWCNT-2, MWCNT-5, MWCNT-3, MWCNT-4) > pristine-CNT (MWCNT-1) ~ DMSO (as CNT solvent) ~ untreated-CNT group (or untreated-RLM control). In addition, the oxidized-CNT family members (CNT-OH and CNT-COOH) did not dissipate the mitochondrial membrane electrochemical-potential (Ψm), showing healthy integrity mitochondria or J-aggregates (red to pseudo-colored red fluorescence) when compared with the results obtained for pristine-CNT where the presence of miptotic J-monomers (green fluorescence) was detected by using the JC-1 dye probe.33 see Figure 3.



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Figure 3. H+-F0-ATPase save for the pristine multi-walled carbon nanotube (CNT-1) that show low ability to induce F0-ATPase nanotoxicity-based inhibition in isolated rat-liver mitochondria. a) ATPhydrolysis inhibition by CNT-family members at maximum concentration of 5µg/ml in isolated-rat liver mitochondria (RLM). Experimental conditions are described under Materials and Methods section. Different treatments are depicted: mitochondria control (untreated RLM, red line), DMSOtreated RLM (yellow line), Oligomycin A-treated RLM (c-ring/F0-ATPase inhibitor used as positive control, blue line), pristine-CNT1-treated RLM (orange line), CNT-OH (CNT2-CNT5) treated RLM (black lines), CNT-COOH (CNT6-CNT9) treated RLM (green lines). RLM, KCN (cytochrome c oxidase inhibitor induced mitochondrial ischemic conditions), MgATP (1mM) and CCCP 1µM (uncoupling mitochondrial electron transport chain) were added where indicated by the arrows. Results are representative of three experiments (n = 3). To denote the absence of statistical differences (p > 0.05) these were compared to mitochondrial control (untreated RLM, red line) and DMSO-treated RLM (CNT-solvent, yellow line), and to denote statistical differences (p < 0.05 *, **, ***) between positive control (Oligomycin A-treated RLM, blue line) and CNT-treated RLM experiments (CNT19). Fluorescent images show the effects on mitochondrial membrane potential (Ψm) according to the previous mitochondrial F0-ATPase nanotoxicity-based inhibition assay (ATP-hydrolysis inhibition) at maximum concentration of 5µg/ml in isolated-rat liver mitochondria. RLM were incubated with a specific mitochondrial dye (JC-1) in 0.1 mg/ml for 15 min. b) Untreated-RLM (J-aggregates: red fluorescence); c) KCN + p-CNT-treated-RLM (miptotic J-monomers: green fluorescence); d) KCN + CNT-OH (CNT2-CNT5) treated-RLM (J-aggregates mixed miptotic J-monomers: red to pseudocolored red fluorescence); e) KCN + CNT-COOH (CNT6-CNT9) treated-RLM (J-aggregates: red fluorescence). Predictive ANN models based on fractal SEM nano-descriptors. The predictive classification models (LDA and ANN models) obtained in the present work are a canonical generalization of the toxicodynamics problems of dose-response. As mentioned earlier, in order to train the model and discriminate the strength of F0-ATPase nanotoxicity-based inhibition

the observed variable

considered is E ( AFU ij ) , discretized regarding such strength as strong or ( E ( AFU ij ) = 1 ), when the AFU ij -values > 2 (< AFU ij >), or as weak otherwise ( E ( AFU ij ) = 0 ); < AFU ij > being the average-

value of AFU ij for all the CNT-samples studied. Firstly, we used LDA for setting up an alternative linear model, using as input the fractal descriptors Dmi , p of the i-th CNT and its j-th experimental conditions (concentration and time). As such, the output E ( AFU ij ) = S ( AFU ij ) pred of the LDA-model based on SEM fractal nano-descriptors is a real-valued score of the observed F0-ATPase inhibition effect, increasing the S ( AFU ij ) pred -score for higher values of the probability with which a CNT presents a F0-ATPase nanotoxicity-based inhibition effect ( E = 1 ). The resulting best-fit model found (a four-variable equation) is given below together with the statistical parameters of the LDA:


Page 15 of 27 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Chemical Information and Modeling i i E (AFU ij ) = S ( AFU ij ) pred = 3.642  0.014  tij ( s ) + 0.040  cij (μg/ml) + 8.162  D1,0  6.100  D1,2

N = 72617 ,

χ 2 = 15786.30 ,

p < 0.05

(7)

The large sample size, large χ 2 value, and small p value are indicative of the model’s statistical discriminatory significance, showing that it displays an adequate power for differentiating both strong and weak F0-ATPase mitotoxicity. The latter is also confirmed by the classification results; the model correctly classified the non-mitotoxic (Sn = 100%) and mitotoxic (Sp = 80.2%) for a priori probabilities of p ( E = 0 ) = 0.55 and p ( E = 1 ) = 0.45, respectively. To this end, we tested different combinations of linear and non-linear functions of the three main factors, namely of the time (t) as 1f, concentration (c) as 2f, and fractal SEM nano-descriptors (Dm,p) as 3f. The non-linear functions were considered according to the general dose-response model, i.e.: as sigmoidal profile for concentration, and exponential for time. Furthermore, for improving the predictivity performance, the centered moments from fractal SEM nano-descriptors (DDm,pn) as 3f were calculated and included as input parameters in the model. The obtained results are summarized in Table 3.



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Table 3. LDA model results using linear and/or non-linear forms of the input functions kf. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

Time (1f)

Concentration (2f )

Fractal (3f )

Data set a

Obs. class b

Linear

Linear

Linear

Train.

1

Sp

80.19

54084

13360

t

c

Dm,p

0

Sn

100

0

5173

1

Sp

80.21

18031

4450

0

Sn

100

0

1724

1

Sp

99.77

67290

154

0

Sn

12.00

4552

621

1

Sp

99.77

22429

52

0

Sn

11.89

1519

205

1

Sp

80.18

54079

13365

0

Sn

100

0

5173

1

Sp

80.17

18023

4458

0

Sn

100

0

1724

1

Sp

99.77

67290

154

0

Sn

12.00

4552

621

1

Sp

99.77

22429

52

0

Sn

11.89

1519

205

1

Sp

95.59

64468

2976

0

Sn

100

0

5173

1

Sp

95.59

21489

992

0

Sn

100

0

1724

Val. Exponential

Linear

Linear

exp(kt)

c

Dm,p

Train.

Val. Linear

Sigmoid

Linear

t

c/(1 + c)

Dm,p

Train. Val.

Exponential

Sigmoid

Linear

exp(kt)

c/(1 + c)

Dm,p

Train.

Val. Linear

Linear

Centered moments

t

c

DDm,pn

Train. Val.

a

Data set: Training (Train.) and Validation (Val.) sets.

b

Classf. (%) c

E =1

d

E =0

d

Observed class: Non-toxic (0) and toxic (1). c Percentages of correct classifications for toxic cases (Sp:

Specificity) and for non-toxic cases (Sn: Sensitivity). d Number of toxic cases (E = 1) and non-toxic cases (E = 0) from F0-ATPase nanotoxicity-based inhibition induced by CNT considering experimental conditions like (concentration (cij) and time (tij)) and the fractal SEM-nanodescriptors like Dm,p and DDm,pn = (DDm,p – )n centered moments of moving average. 16 ACS Paragon Plus Environment

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Notably, the model with centered moments forms of the fractal SEM nano-descriptors (DDm,pn) presents excellent results with Sp = 95.59 % and Sn = 100%. Apart from those descriptors, this model uses as input linear forms of experimental conditions (time and concentration). However, it is much more complicated as it can be perceived below (see Eq. 5 and Tables 2 and 3 for the symbols and notation used in the model): i i S ( AUFij ) pred = 7.162  0.012  tij (s) + 0.472  cij (μg/ml) + 3071.078  DD1,0  2755.391  DD1,2 i 2 i 3 i 2 i 3  4268.716  DD1,2 + 70587.698  DD1,2 + 3355.170  DD1,0  97213.870  DD1,0

N = 72617

χ 2 = 63706.60

(8)

p < 0.05

Then, an ANN analysis39 of this dataset was also carried out to test the existence of more complex non-linear relationships between the centered moments inputs DDm,pn (as fractal SEM nanodescriptors) and the S ( AUFij ) output (as F0-ATPase inhibition). Interestingly, the LNN model using the same variables of the simplest LDA model shows a better and more balanced result with respect to Sp ≈ Sn > 95% for the training and external validation series (see Table 4).



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Table 4. ANN-classification models’ results. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

ANN

Data set a

RBF 4:4-1-1:1

Obs. class b

Classf. (%) c

E =1

d

E =0

1

Sp

61.5

41477

1878

0

Sn

63.7

25967

3295

1

Sp

61.5

13823

626

0

Sn

63.7

8658

1098

1

Sp

96.1

64803

231

0

Sn

95.5

2641

4942

1

Sp

96.1

21605

77

0

Sn

95.5

876

1647

1

Sp

98.9

66720

53

0

Sn

99.0

724

5120

1

Sp

98.9

22239

18

0

Sn

99.0

242

1706

1

Sp

99.6

67146

35

0

Sn

99.3

298

5138

1

Sp

99.5

22379

13

0

Sn

99.2

102

1711

d

Method e kM, kNN, PI

Train.

Val.

LNN 4:4-1:1

Train.

PI

Val.

MLP 4:4-5-1:1

Train.

BP, CG

Val.

MLP 4:4-8-1:1

BP, CG

Train.

Val.

a Data

set: Training (Train.) and Validation (Val.) sets. b Observed class: Non-toxic (0) and toxic (1). c Percentages of correct classifications for toxic cases (Sp: Specificity)

and for non-toxic cases (Sn: Sensitivity). d Number of toxic cases (E = 1) and non-toxic cases (E = 0). e Training methods: kM = k-Means (Center Assignment), kNN = k-Nearest Neighbor (Deviation Assignment), PI = Pseudo-Invert (Linear Least Squares Optimization), BP = Back Propagation, and CG = Conjugate Gradient Descent.


Page 19 of 27

In addition, the predictive performance of both the LDA-dose response time series and LNN-dose response time series models were more successful than the RBF-ANN model for predictive classification of F0-ATPase inhibition using the same linear combination of input parameters. The two MLP topologies tested showed an excellent classification behavior with Sp ≈ Sn > 98% in training and external validation sets. The classification performance of the ANN-models is usually perform better than linear models (LDA), this tendency is well reported in the literature39, and is due to the fact that this kind of models can deal with nonlinearities as in this case. After analyzing all the statistics of the ANN models, we may suggest that our model it is not affected by the over-fit problem. In fact, the number of neurons is quite low considering our dataset, the area under the ROC (AUROC) did not show any kind of variation that may suggest an over-fit problem. Lastly, the values of specificity and sensitivity of the train and validation series are almost the same. However, an increment on Sp and Sn lower than 5% does not clearly justify the necessity of a higher computing complexity for these non-linear models with respect to the LDA/LNN linear models. Figure 4 depicts the overall behavior of the area under the ROC (AUROC) curves for these different classifiers. Only the LNN and MLP topologies showed area values notably higher than that typical (AUROC = 0.5) of a Random (RND) classifier.40 1.1 1.0

MLPs

0.9 LNN

0.8

RBF

0.7 1-Specificity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.6 RND

0.5 0.4 0.3 0.2 0.1 0.0 -0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Sensitivity

Figure 4. ROC curve analysis for the different classification methods applied.



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This predictive classification model can be regarded as a generalized model that includes new raw information based on fractal dimensions obtained by SEM analysis for making predictions of F0ATPase nanotoxicity-based inhibition induced by CNTs. The acceptability and applicability of the model for such prediction purpose have been statistically judged in quantitative terms using different validation parameters as Sp and Sn according to the best classification models (ANN) obtained. Additional information on the ANN models’ dataset can be found in file S5 of supporting information (SI). Two relevant structure-mitotoxicity relationships were identified, which involve the original input variables and experimental boundary conditions and are able to explain the F0-ATPase nanotoxicityinduced by the carbon nanotubes tested, namely: (i) between the CNT-concentration and time of exposure (ci ↔ ti), and (ii) between the CNT-type and SEM-fractal dimensions (c0 ↔ Dm,p). Regarding the first structure-mitotoxicity relationship (i), the observed F0-ATPase nanotoxicity-based inhibition effects are dependent on the concentration of CNTs and duration of exposure to a fixed level of response for a given endpoint, vis-à-vis to the dose/time-response models following the Haber’s rule.41 Therefore, the CNT-induced F0-ATPase mitotoxicity-based inhibition-effect is a function of (ci ↔ ti) relationships in the mitochondrial assays using the isolated-RLM, with a mitotoxicity pattern-like oligomycin A that is a well-recognized F0-ATPase inhibitor. Besides, it is important to notice that the observed nanotoxicity-based inhibition effects of oxidized-CNT family members on the F0-ATPase-hydrolitic activity do not affect the (Ψm)-mitochondrial membrane potential despite the presence of KCN (mitochondrial cytochrome-c-oxidase inhibitor-induced ischemic conditions), according to the results obtained in the JC-1 dye probe based on the presence of healthy-RLM (J-aggregates: red and pseudo-colored red fluorescence). However, for KCN + pristineCNT-treated-RLM, a dissipation of (Ψm)-mitochondrial membrane potential was observed, and the presence of miptotic J-monomers (or green fluorescence) detected. Due to this, we hypothesize that the oxidized CNT-family members tested (CNT2-CNT9) should be more selective mitotoxic for the F0-ATPase (ATP-hydrolysis) than for the F1-ATP synthase subunit (ATP-synthesis) coupled to H+protons flux. Then, the deprotonated moieties (CNT-COO > CNT-O) of the oxidized-CNTs could module a withdrawal ability of H+-uncoupling protons from the mitochondrial matrix (pH = 7.4) preventing the (Ψm)-mitochondrial membrane potential dissipation, and consequently inhibiting the ATP-hydrolysis induced by KCN-mimicking ischemic conditions in isolated rat-liver mitochondria. However, the pristine-CNT (CNT-H) could have low withdrawal ability for H+-uncoupling protons associated to (Ψm)-mitochondrial membrane potential dissipation as previously shown (Figure 3c). In addition, current experimental evidences on pristine multi-walled carbon nanotubes vertically-aligned 20 ACS Paragon Plus Environment

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with membrane phospholipids (DOPC and DPPC) have shown a fast and selective ion transport (H+) through pristine-MWCNT2. That is, pristine-MWCNTs could contribute to the mitochondrial membrane potential dissipation since these allow the H+-uncoupling protons passage and increase the ATP-hydrolysis induced by KCN-mimicking ischemic conditions in isolated rat-liver mitochondria, affecting therefore in this way Ψm. The CNT-properties are expected to significantly affect the mitochondrial mechanisms, but these are largely ignored in in vitro toxicological studies of CNTs. Herein the (ci ↔ ti) relationships suggest that the experimental conditions cij and tij do have a relevant influence on the dose-response time-series models, just as found in previous studies.19 On the second structure-mitotoxicity relationship found (i.e., the CNT-type and the SEM-fractal dimensions (c0 ↔ Dm,p) or ii), it may provide supra-molecular chemical information (non-Euclidean geometry properties) on the surface structure-toxicity relationships unveiling the ability of CNTfamily members as new mitotoxic-targeting nanoparticles (ATP-hydrolysis inhibitors). From the structural point of view, it is important to note that the centered moments fractal SEM-nanodescriptors (DD1,pn: , ) of the ANN-classification model have a different influence on this structuremitotoxicity relationship. Herein, the F0-ATPase mitotoxicity-based fractal SEM-nanodescriptors like (first-(DDi1,0), second-(DDi1,0)2, and third-(DDi1,2)3 spectral moments statistic order) is higher for oxidized-CNT members (CNT-OH ≈ CNT-COOH) compared with the pristine-CNT (CNT-1) due to the contribution of their corresponding positive statistical coefficients (see Eq. 8); and the previously calculated FDs (Tables 2). Please, refer to Eq. 5, Tables 2 and Table 3 for the symbols and notation used in the ANN model. Interestingly, the average values of the different DD1,pn-fractal dimensions for the cited oxidized-CNT members are closer to 2, due to both the high complexity and low selfsimilarity across different scales in terms of non-Euclidean topological properties.23-30 However, the calculated DD1,pn (, ) value for pristine-CNT (CNT-1) are closer to 1, indicating little complexity, variety, or information (see Table 2). We strongly suggest that the potential ability of CNT-family members to induce ATP-hydrolysis inhibition (F0-ATPase mitotoxicity) has an important non-linear dependence relationship on the centered moments of fractal SEM nano-descriptors based on binary pixels (p) like p = 0 (BW) and p = 2 (WBW) and the box-counting continuous way (m = 1). Following this idea, we suggest that chemical functionalization with oxidized-moieties (OH, COOH) could affect the fractal surface properties (i.e., the experimentally obtained fractal dimensions) of oxidized-CNT members by forming topological defects (through carboxylation and hydroxylation linked to several oxygen functionalized-defects generated in the tips and walls CNT-graphiticstructure) and consequently increasing the CNT-surface reactivity-associated to F0-ATPase 21 ACS Paragon Plus Environment


Page 22 of 27

mitotoxicity obtained experimentally. Furthermore, box-counting continuous method (m = 1) can efficiently model all defects, irregularities and cracks from CNT-SEM images studied toward experimental and theoretical mitotoxicity studies.

Conclusions In this work, we present a combined experimental and theoretical study on the CNT-family members as mitotoxic-targeting nanoparticles in the H+-F0-ATPase subunit (ATP-hydrolysis inhibitors). Nanotoxicity-based H+-F0-ATPase inhibition was probed experimentally in in vitro assays through fluorescence and followed by setting up predictive LDA-linear and non-linear ANN classification models based on fractal-SEM nano-descriptors as means of raw supra-molecular information. Our results show the high ability of oxidized-CNT members to affect the mitochondrial H+-F0-ATPase hydrolytic activity in isolated-RLM with an mitotoxic pattern like oligomycin A (specific H+-F0ATPase inhibitor). In addition, carboxylated-CNTs have higher H+-F0-ATPase mitotoxic response than their hydroxylated and pristine-CNT counterparts. Both the obtained classification models exhibit an excellent internal accuracy and predictivity performance to discriminate correctly CNT-mitotoxic and non-mitotoxic with specificity (Sp > 80 %) and sensitivity (Sn > 99.0 %) in both cases. This being accomplished by including new nanodescriptors taken from the fractal centered moments (DD1, pn) from CNTs’ SEM-images through the box counting-continuous analysis (m = 1). However, the non-linear ANN model is suggested to be the best model, judging from its statistical parameters. At the same time, it was shown that the CNTs’ mitotoxic response clearly depends on their concentration and time of exposure. To sum up, considering the potential applications of oxidized-CNT family members, the toxicological modulation of the F0-ATPase could be used to attenuate the bioenergetics unbalance associated to the pathological increase of ATP-hydrolysis by the F0-ATPase subunit during mitochondrial ischemic conditions KCN-induced. Besides, the experimental and theoretical results suggest that the fractal dimensions of the CNT-family members can be efficiently applied as new CNT nano-descriptors and be used in turn to predict different carbon nanomaterials (not tested here), as well as other useful biochemical responses. Finally, the present study can contribute for the rational design of carbon nanomaterials based on rigorous structural-classification criteria, opening new opportunities to emergent areas of research as Mitochondrial Nanotoxicology.


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Disclosure statement The authors declare no competing interests. Acknowledgements This work received financial support from Fundação para a Ciência e a Tecnologia (FCT/MEC) through national funds and co-financed by the European Union (FEDER funds) under the Partnership Agreement PT2020, through projects UID/ QUI/50006/2013, POCI/01/0145/FEDER/007265, NORTE-01-0145-FEDER-000011 (LAQV@REQUIMTE), and the Interreg SUDOE NanoDesk (SOE1/P1/E0215; UP). RC acknowledges FCT and the European Social Fund for financial support (Grant SFRH/BPD/80605/2011). José M. Monserrat acknowledge the Nanotoxicology Network from CNPq (Project number 552131/2011-3). J.M. Monserrat acknowledges funds INCT of Carbon Nanomaterials (Project 421701/2017-0). Finally, the authors would like also to acknowledge the support from CEMESUL-FURG for logistical support in the carbon nanotubes characterization. To all financing sources the authors are greatly indebted. Supporting Information Available The Supporting Information associated with this paper is available like: Table S1. Physico-chemical parameters of CNT family and detailed information on carbon nanotubes characterization, reagents and solutions. Figure S2. SEM binary images from HarFA software. Figure S3. Fractal analysis. Table S3. Calculated fractal dimensions Table S4. ANN-Classification Models dataset.

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MitoTarget Modeling Using ANN-QSTR Approach Based on Fractal

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