*3.4. Cerium Concentration in Plant Fractions*

A general view of the Ce uptake and accumulation in plant tissues as affected by experimental factors is given in Table S6, Supplementary Materials. The factor "dose" result was statistically significant only for Ce concentration in plant stems (*p* = 0.0000 \*\*\*), while the Ce accumulation in each plant tissue, as expected, increased responding to the

factor "concentration". A statistically significant interaction "dose x concentration" was observed in the roots (*p* = 0.0313 \*) and stems (*p* = 0.0021 \*\*).

Table 2 reports data regarding the Ce concentrations in plant fractions. At first glance, the data indicate that plant Ce uptake was not very high compared to the treatments. Concerning the plant fractions, as expected, Ce in the roots was higher than the others.

**Table 2.** Ce concentration in plant fraction and Ce translocation factor in *S. flos cuculi* grown in the presence of different inputs of *n*CeO2 (20 and 200 mg kg−1.) Data are mean ± standard deviation. Statistically significant differences (*p* ≤ 0.05) are indicated by the letters using one-way ANOVA follow by Tukey's test. Dashed boxes indicate ANOVA *p*-values (*p* ≤ 0.05) within *n*CeO2 concentration. ns: not significant at *p* ≤ 0.05; \*\* significant at *p* ≤ 0.01.

On average, the treatment concentration of Ce in the root tissues (3074 μg kg−1) was four times higher than that of the stems (779 μg kg−1) and two times higher than that found in the leaves (1594 μg kg−1), respectively. However, we do not observe a clear and statistically significant response to the *n*CeO2 doses regardless of the treatment concentration. However, the statistically significant interaction "dose X concentration" is explained by the different response in terms of Ce accumulation in roots after dose D3 *n*CeO2 200 mg kg−1 that was about 49% higher than the average D1–D2. (Table 2).

After being taken up by the roots, a fraction of Ce moved towards the aerial plant fractions to be allocated in the stems. A statistically significant effect of the dose factor and of the interaction is visible by observing the concentration of Ce in the stems (Table 2). Here, although the highest Ce concentration was detected at D3 *n*CeO2 20 mg kg−1, the most evident effect of the "dose" factor can be appreciated for plants exposed to *n*CeO2 200 mg kg−1 (*p* = 0.0015 \*\*; Table 2). Finally, the leaves represent the final allocation of Ce in plant aerial biomass. Here, Ce accumulation was higher in than in stems; however, due to a certain variability, it was not possible to statistically verify a significant effect of the experimental treatments (Table 2)

#### **4. Discussion**

Only in 2012 were the effects of ENMs over the whole plant cycle studied [33]. In soybeans (*Glycine max* L.), it was demonstrated that Ce concentrations in the roots and the concentration of *n*CeO2 in soil were correlated. Nanoceria negatively influenced the yield of soybean and N2-fixation by affecting the efficiency of the symbiotic system established with *Bradyrhizobium*: a dramatic example of the influence on cultivated plants and wild species' ecological services, as well.

A large body of literature reports negative responses observed at different plant growth stages. When germinating seeds are exposed to *n*CeO2, other effects could be verified, basically depending on particle size and concentration. Additionally, statistically significant species-specific responses were reported, regarding root elongation being more sensitive to *n*CeO2 than germination [34–36]. Other studies explored the physiological implications of the *n*CeO2 plant uptake, concluding that plants responded to the treatments increasing the antioxidant enzyme activities. However, the oxidative stress induced by high concentrations of *n*CeO2 cannot be attenuated by the antioxidant system [37–40].

The growth of *S. flos-cuculi* was negatively affected by *n*CeO2. Suppose the root apparatus development in plants treated at the lowest *n*CeO2 concentration has not undergone apparent alterations at the highest concentration; in this case, the effect is evident and progressively increases as the *n*CeO2 dose increases. The impact of *n*CeO2 on plant growth was much more apparent in the biomass of plant stems. We observed a slowdown in plant growth. The number of plant stems did not change, but they were shorter than the controls'. No statistically significant evidence was found regarding the effects of treatments on leaf biomass (evaluated by counting the number of leaves per plant, the leaf area, and the leaf dry weight). However, likely the relevant data variability detected in the treated plants compared to that of the control plants was an early signal of plant stress.

SLA is a very informative parameter in plant ecology. The total leaf area ratio to total leaf dry mass correlates with whole-plant growth linking C gain and water loss [41]. Even though we calculated the SLA using data from a single biomass sampling at the end of the growth cycle of *S. flos-cuculi*, the response of SLA to the *n*CeO2 treatments allowed us to interpret the experimental data more effectively. In particular, the increase in SLA responded to the dose of *n*CeO2 received by the plant. Moreover, this could be a consequence of the slowing of the vegetative growth rate and could lead us to conclude that the *n*CeO2 negatively affects the C accumulation by leaf tissues. Our data do not allow us to identify the specific cause precisely. However, this observation corroborates the literature evidence regarding the slowing of the plant growth cycle [42] and photosynthesis, both in terrestrials and aquatic plants [43,44].

The growing number of nanotechnology applications in various fields inevitably results in the release of nanomaterials into the environment. Models demonstrated that wastewater and sewage sludge are the primary vectors by which ENMs end up in the environment [45]. Apart from the quantitative aspect, nanomaterials' flows can occur differently concerning the position of the target to the source (e.g., a single massive event or events repeated over time). Literature papers concerning the effects of ENMs on plants always report experiments where the nanomaterials were applied in a single concentration, whereas a more realistic exposure scenario involves repeated pulses.

In our study, plants of *S. flos-cuculi* were grown in soil amended with *n*CeO2. The experimental design was conceived assuming that the soil could receive different *n*CeO2 pulses over time, thereby obtaining three different doses of *n*CeO2 supplied at different growth stages of *S. flos-cuculi*. At the moment, we cannot compare our data with other works having the same experimental approach. We have already cited a paper reporting Ag and Cu nanoparticles' effects on seedlings of *Pinus sylvestris* and *Quercus robur*. A single dose of nanomaterials was administered to plants by three subsequent foliar applications in that study, whereas in our experiment were provided three amounts of *n*CeO2. However, in both experiments, the experimental factor "dose" or merely the phenological stage at which plants received the treatments showed some influence on the consequences of the treatment. Therefore, this early indication suggests that this type of study should be further developed. Other studies of soil ecology have used the same approach. In particular, it was demonstrated that soil enzyme activity is differently affected by repeated ENM doses, indicating that additive effects occur [46,47]. It will be necessary to compile these different works to achieve a complete evaluation of the effects of ENMs on the soil–plant system.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2079-4 991/11//229/s1: Figure S1. Number of stems per plant in specimens of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S2. Number of leaves per plant in specimens of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way

ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S3. Total leaf area in plants of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S4. Leaf dry matter in plants of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S5. Stem mass fraction of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S6. Leaf mass fraction of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S7. Shoot to root ratio in *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Figure S8. Leaf area ratio of *S. flos-cuculi*. Comparison of effects based on single (D1) and repeated applications (D2, D3) of 20 and 200 mg kg−1 *n*CeO2, respectively. Letters indicate statistically significant difference between treatments (*p* ≤ 0.05) using one-way ANOVA followed by Tukey's test. † One-way ANOVA *p*-value within each concentration. Table S1. Average, PDI and ζ-potentials of *n*CeO2 25 nm. Table S2. Most frequent particle size, mean particle size, number of pulses and concentration of dissolved Ce determined by sp-ICP-MS analysis after enzymatic extraction from roots and leaves of Silene flos-cuculi. Table S3. Two-way ANOVA *p*-values testing the statistically significant effects of dose and concentration and their interaction of *n*CeO2 on biometric variables of *S. flos-cuculi*. Table S4. Biomass allocation variables calculated from plant measurements (Poorter et al., 2011). Table S5. Two-way ANOVA *p*-values testing the statistically significant effects of dose and concentration and their interaction of *n*CeO2 on growth indices of *S. flos-cuculi*. Table S6. Two-way ANOVA *p*-values testing the statistically significant effects of dose and concentration and their interaction on Ce concentrations in fractions of *S. flos-cuculi* and Ce translocation factor.

**Author Contributions:** Conceptualization, D.L. and L.M.; formal analysis, D.L. and L.M.; funding acquisition, L.M.; investigation, D.L.; methodology, A.M., B.P. and E.G.; supervision, L.M.; Writing— Original draft, L.M.; Writing—Review and editing, G.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Regione Friuli Venezia Giulia, General Directorate of Environment, project "Nanomateriali 2017–2020".

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Acknowledgments:** The authors are grateful to PhD course in "Environmental Life Sciences" jointly offered by the Universities of Trieste (Italy) and University of Udine (Italy). L.M. is grateful to Regione Friuli Venezia Giulia, General Directorate of Environment, for the financial support.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


**4. Molecular typing based on proteome**

Proteins are the direct executors of life activities and proteomics is one of the effective methods to search for molecular markers. The molecular characteristics of 110 cases of early hepatocellular carcinoma were analyzed and compared by proteomics [27]. The heterogeneity of early hepatocellular carcinoma was divided into the subtypes S-I, S-II, and S-III, each of which has a different clinical outcome. TGF-β and other tumor proliferation-related proteins were highly expressed in the S-III subtype, which was consistent with the Hoshida S1 subtype, and the prognosis was poor. S-II and S-I subtypes were characterized by high expression of Wnt and CTNNB1, consistent with Hoshida S2 and S3 subtypes. Proteomics is also an effective way to find drug targets. At present, the direct targets of liver cancer-targeted drugs with multi-kinase inhibitors and immunotherapy with immune checkpoint inhibitors are all proteins. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)-high expression of which is a signature specific to the S-III subtype-alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumor xenograft mouse model of hepatocellular carcinoma, that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumors that had high levels of SOAT1 expression, which indicates that SOATI may become a new target of S-III subtype, namely Hoshida S1 subtype liver cancer [27]. Gene mutation induced by aristolochic acid is a characteristic pathogenic factor in China and even in Asia except for viral hepatitis B [28]. The mutation "fingerprint" of aristolochic acid is significantly positively correlated with tumor mutation burden, tumor neoantigen burden, CD8+ T cell infiltration, and immune microenvironment tolerance, suggesting these patients may benefit from immunotherapy [18]. On the other hand, the microenvironment of CTNNB1 mutation patients is immuno-privileged and may not benefit from immunotherapy. Further multi-omics analysis of liver cancer found that CTNNB1 mutation is related to the phosphorylation of serine 36 in ALDOA (fructose-1,6-bisphosphate aldolase) [29]. ALDOA phosphorylation promotes tumor cell proliferation by promoting anaerobic glycolysis and knocking *Research Frontier of Accurate Diagnosis and Treatment Guided by Molecular Typing… DOI: http://dx.doi.org/10.5772/intechopen.99836*

down ALDOA significantly inhibits tumor proliferation. Therefore, ALDOA may be an important potential therapeutic target for CTNNB1 mutant liver cancer.


**4. Discussion**

In this study, we evaluated the histopathological and clinical implications of obesity in patients with IgAN. Intriguingly, multivariable logistic regression demonstrated that a BMI greater than 25 kg/m2 was independently associated with high MME in patients with IgAN. Patients with elevated BMI were more hypertensive and had increased proteinuria, but the association of BMI with renal function and proteinuria was modest in IgAN.

Only a few studies have investigated the influence of obesity on histological parameters in IgAN. First, Bonnet et al. suggested that vascular, tubular, and interstitial indices were higher in obese patients than non-obese patients using a semi-quantitative classification scheme, but the glomerular index did not di ffer significantly. A recent cohort analysis of 481 IgAN patients also showed that high BMI was associated with a high risk of IF [22]. These previous studies did not focus and evaluate the association between increased BMI and MME in IgAN. Tanaka et al. [29] also reported that obese patients with IgAN showed significantly increased proteinuria, accompanied by GBM thickening and glomerulomegaly, mimicking obesity-related glomerulopathy. This study showed no significant di fferences in the severity of mesangial proliferation and matrix expansion between obese and non-obese group, but the number of enrolled patients were very small. Our study is the first study to show an independent association between obesity and MME in IgAN. Moreover, in subjects with tubulointerstitial disease, obesity did not show a significant association with MME, which means that the association between obesity and MME in IgAN is not an incidental finding seen in obese patients. Although the precise mechanism by which obesity causes MME remains unclear, obesity can lead to a rise in the concentration of leptin, which stimulates the expression of transforming growth factor-β1 (TGF-β1). TGF-β1 is known to act as the main driver of extracellular matrix accumulation, mesangial cell proliferation, and progressive glomerulosclerosis [30,31]. These in turn may be responsible for the proliferation of mesangial cells and expanded mesangial matrix. MME has been regarded as a step towards glomerular sclerosis [32]. Although FSGS has long been recognized as the hallmark lesion of obesity-related glomerulopathy [13], Serra et al. [33] also found that increased mesangial matrix, podocyte hypertrophy, and mesangial cell proliferation were more frequent in extremely obese patients compared to normal weight controls. Of note, our study supports these findings in that obesity could independently increase MME after adjustment for confounding factors and strengthens our knowledge of the pathogenic role of obesity in mesangial lesions in IgAN.

Obesity-induced subclinical inflammation is responsible for activation of the complement system [34]. C3 and C4 are expressed and secreted by adipose tissue [35], and serum C3 is strongly associated with components of metabolic syndrome, such as insulin resistance, lipid profile, and BMI [36–38]. Furthermore, weight gain increases C3, which decreases upon weight loss [39]. Therefore, C3 has been linked to the etiology of obesity, and to a wide range of obesity sequelae. In this study, serum C3 and C4 levels were increased in patients with higher BMI. Serum C3 level was positively associated with 24 h proteinuria using multivariable analysis. Consistent with our study, some researchers previously reported that serum C3 and C4 levels were positively correlated with increased BMI and severe proteinuria in IgAN patients [19,21]. High serum C4 level was also associated with severe proteinuria and renal pathological damage in IgAN [40]. Altogether, these findings suggest that obesity may activate the complement system and is related to the severity of renal presentation in patients with IgAN.

In this study, mesangial C3 deposition decreased as BMI increased, but multivariable linear regression and logistic regression analyses showed that mesangial C3 deposition was not independently associated with BMI. Additionally, mesangial C3 deposition was not significantly associated with eGFR or 24 h proteinuria. Therefore, we speculate that the e ffect of obesity on the activation of the local complement system is modest in IgAN. Previous studies revealed conflicting results, suggesting an important role of local complement activation in the pathogenesis of IgAN. Mesangial C3c deposition was associated with active inflammation in IgAN [41], and mesangial C3 deposition was an independent risk factor for progression of IgAN [42]. A recent study also reported that mesangial C3 deposition was associated with poor outcome in IgAN; however, eGFR or proteinuria at the time of kidney biopsy did not differ according to the degree of mesangial C3 deposition [43]. The implications of mesangial complement deposition may have differential effects on renal presentation and prognosis, and further analysis is needed to clarify the pathologic role of mesangial complement deposition in IgAN.

Another interesting finding is that obesity was not independently associated with clinical parameters such as renal function and proteinuria in IgAN. In this study, obese patients showed decreased eGFR and increased 24 h proteinuria compared to other groups, but there were no significant relationships between BMI and eGFR or 24-h proteinuria in multivariable analysis. In addition, the histologic classification of IgAN, such as the Oxford classification or WHO classification, was not different between the groups. Previous studies demonstrated similar findings to our results. A prospective cohort study of 331 French IgAN patients showed that patients with elevated BMI had worse renal presentations and outcomes, but that obesity, per se, did not directly affect these outcomes [18]. In a study of 481 Chinese IgAN patients, there was no significant difference in clinical parameters or Oxford classification scores among BMI groups [22]. In a study including a small number of IgAN patients, obesity had no significant impact on serum creatinine levels or pathological severity in multiple linear regression models [44]. These findings suggest that obesity may not be independently related to decreased renal function, increased proteinuria, or severe forms of histologic classification in IgAN, although it likely affects histologic damage. Obese patients may simultaneously have multiple unhealthy lifestyle behaviors, such as smoking, increased energy intake, low physical activity, and various medical conditions including hypertension, diabetes mellitus, and dyslipidemia, which are known risk factors for renal disease progression [45]. In this study, we also showed that obese patients with IgAN had more the presence of hypertension and dyslipidemia, and were more actively treated with supportive managements, such as anti-hypertensive medications, thiazide, and statins, as well as corticosteroids, than underweight or normal/overweight patients. Therefore, increased weight alone may not be enough to induce severe renal parameters and histologic classifications. To elucidate a direct association between obesity and long-term kidney damage, we need to perform further studies involving a larger number of patients with IgAN.

This study has several limitations. First, our findings are limited by the cross-sectional and retrospective design, uncertain causality, and possible influence of confounding factors that could not be captured; in addition, there were no longitudinal follow-up data. Therefore, a prospective, multi-center, large scale study with a longer follow-up period is required to define the histopathological impact of BMI more clearly in IgAN. Second, although this study included eight university hospitals, the evaluation of renal biopsy specimens was conducted by expert renal pathologists in each center. Therefore, there may be interobserver variability. However, each hospital shares the same renal biopsy report form, and the pathologists at each hospital were trained by one pathologist. Thus, observer bias in histologic assessment may be relatively small. To compensate for this limitation, the histologic grading and staging of IgAN were described according to both the Oxford and WHO classifications. Third, the inclusion of only Korean patients may limit the generalizability of our findings to other ethnic populations. Current practice guidelines suggested that obesity is typically defined quite simply as excess body weight for height, but this simple definition of obesity cannot reflect the consideration of age or race/ethnicity. Therefore, further studies may be needed to elucidate the association with obesity and clinicopathologic parameters in various ethnic groups of IgAN. Fourth, we did not measure the anthropometric parameters associated with metabolic syndrome, such as waist circumference, skin fold thickness and waist-to-hip ratio, and the index of insulin resistance in this study. Further studies will be required to determine whether these markers may be more reliable measures than BMI in IgAN.