*2.2. Analysis Methods*

A range of systems are available to analyse environmental data, such as air quality measurements. These systems can be used to detect uncharacteristic data points by taking into account trends, variations between neighbouring network stations and expected values with respect to the sampling location. An example of these expert systems of data and environmental parameter validation would be the trends analysis throughout R-programming (openair) [33].

With classical analysis, the data are only analysed statically. The proposed methodology includes using a large amount of existing data to extract conclusions. Today, the amount of data that has been stored in environmental databases requires automated analysis techniques. Actually, the analysis methodology presented here is oriented to knowledge discovery in databses (KDD) [34], which provides a complete process for extracting information and also provides a clear methodology for the preparation of data and interpreting the results obtained. KDD involves an iterative and interactive process of searching for models, patterns and parameters that are useful for detection, classification and/or prediction in order to generate knowledge and help in decision making.


**5. Conclusions**

This study evaluated humeral biomechanical asymmetry across complete humeral diaphysis based on high-resolution micro-CT, and by quantifiable visualization and statistical methods. Using specimens from three Chinese archaeological populations that varied in geographic location, chronological age, and subsistence pattern, the pattern of humeral asymmetry in East Asian modern humans was investigated. Distinct humeral asymmetry patterns are observed on the mean morphometric map, but are not statistically significant. Analogous distributions of highly asymmetrical regions and CV are observed across nearly all the sexes and populations, indicating possible universality of the humeral asymmetry pattern in East Asian modern humans. These highly asymmetrical regions correspond with major muscle attachments. The diaphyseal regions that are highly varied in bilateral asymmetry tend to present a low asymmetrical level. Although J35 and J50 asymmetry are related to the overall humeral asymmetry, it can only explain about half of the total variation. These findings suggest that the overall biomechanical asymmetry of humeral diaphysis is more complicated than previously assumed. This study complements previous findings on humeral asymmetry, and accumulate data and knowledge for future works in this area.

**Author Contributions:** Conceptualization, Y.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z., S.X.; formal analysis, Y.Z.; investigation, Y.Z.; resources, M.Z., H.L., J.H.; data curation, Y.Z., S.X.; writing—original draft preparation, Y.Z.; writing—review and editing, P.W., S.X.; visualization, Y.Z.; supervision, S.X.; project administration, S.X.; funding acquisition, H.L., S.X. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB26000000), the National Natural Science Foundation of China (41872030), and the National Social Science Fund of China (19BKG039, 19VJX066).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

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

**Acknowledgments:** The authors thank Yemao Hou, Pengfei Yin, and Jiawei Ma for their help in scanning and image processing. The authors also thank Xiujie Wu and Mackie O'Hara Ali for optimizing the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

**Appendix A**

**Figure A1.** Morphometric maps exhibiting SMA asymmetry values of all specimens.


*Editorial* **Post-Transcriptional Regulation through Long Non-Coding RNAs (lncRNAs)**

**Giuseppina Pisignano 1,\* and Michael Ladomery 2,\***

The discovery of thousands of non-coding RNAs (ncRNAs) pervasively transcribed from the eukaryotic genome has revolutionized the "central dogma" of biology and shifted the attention on the role of RNAs as regulatory molecules, more than simply traditional mediators of genomic information. Non-coding RNAs are transcripts that do not encode proteins and are generally classified as short or long depending on their average size (< or >200 nt). Non-coding RNAs are found in nearly all organisms. Among them, the long non-coding RNAs (lncRNAs) play key roles in many biological processes in development and disease. Since their discovery, the lncRNA field has exploded, and new roles for lncRNAs are constantly emerging, making their investigation a priority in studying gene expression regulation at any level.

This Special Issue encases seven review papers and one original research article from experts in the ncRNA field and illustrates the main mechanisms through which lncRNAs modulate gene expression at the post-transcriptional level. This collection of articles provides a complete overview of their multifunctional roles and presents an additional layer of complexity in the regulation of gene expression and associated cellular processes.

LncRNA length, low expression, and lack of sequence conservation have frequently represented a major technical limitation in their identification and characterization. In their review, Carter et al. provide an exhaustive guide of both in silico and low-to-high throughput experimental approaches to assist researchers to face this challenge. They also offer critical insights to advance our understanding of how lncRNAs are involved in tumorigenesis [1].

A wide range of RNA-binding proteins (RBPs) have been shown to cooperate with lncRNAs to regulate gene expression. In their review, Briata and Gherzi draw attention to the complexity of lncRNA–RBP associations [2]. They illustrate the variety of mechanisms through which lncRNA–RBP complexes can control essentially all post-transcriptional processes in the cell. Sadeq et al. discuss how endogenous lncRNA-associated dsRNA structures are tolerated, whereas viral-derived dsRNA triggers a complex defence network; and further examine the potential implications in the context of autoimmune disease and cancer treatments [3].

In their review, Pisignano and Ladomery describe multiple mechanisms through which lncRNAs contribute to the regulation of alternative splicing and how their action further enhances the expression of mRNA-splicing variants, thereby increasing proteomic diversity in complex organisms [4].

In a more cytoplasmic context, Karakas and Ozpolat discuss how lncRNAs can affect mRNA translation by controlling translation factors and signalling pathways in normal and tumour conditions [5], while Sebastian-delaCruz et al. highlight the importance of lncRNAs in the regulation of mRNA stability and turnover as the basis for the correct functionality of cellular processes and homeostasis [6]. In this regard, in another work presented in this Special Issue, Munz et al. found in a diffuse, large B cell lymphoma cell

**Citation:** Pisignano, G.; Ladomery, M. Post-Transcriptional Regulation through Long Non-Coding RNAs (lncRNAs). *Non-coding RNA* **2021**, *7*, 29. https://doi.org/10.3390/ ncrna7020029

Received: 26 April 2021 Accepted: 27 April 2021 Published: 29 April 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

line a lncRNA (*lncTNK2-2:1*) associated with the increased stability of transcripts that are affected by mTOR inhibition and responsible for the DNA damage response [7].

Fonouni-Farde et al. conclude this Special Issue by describing how plant lncRNAs use sophisticated mechanisms to regulate RNA degradation, alternative splicing, translation, post-translational modifications and even protein localisation [8].

Taken together, this Special Issue highlights the relevance of lncRNAs as crucial regulatory molecules in most post-transcriptional regulation mechanisms, both in animals and plants, and aims to encourage research groups and young researchers to further develop new studies in the field. A more comprehensive understanding of the molecular mechanisms of post-transcriptional regulation by lncRNAs will certainly advance our understanding of the many intricate cellular processes that are still far from being fully elucidated.

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

#### **References**


**Author details**

Amjad M. Husaini1 \* and Sofi NR2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Division of Plant Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir, India

2 Mountain Research Centre for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, Jammu and Kashmir, India


*Article* **Security Ontology Structure for Formalization of Security Document Knowledge**

**Simona Ramanauskaite˙ 1,\*, Anatoly Shein 2, Antanas Cenys ˇ 3 and Justinas Rastenis 3**

**\***Correspondence<EMAIL_ADDRESS>
**Abstract:** Cybersecurity solutions are highly based on data analysis. Currently, it is not enough to make an automated decision; it also has to be explainable. The decision-making logic traceability should be provided in addition to justification by referencing different data sources and evidence. However, the existing security ontologies, used for the implementation of expert systems and serving as a knowledge base, lack interconnectivity between different data sources and computer-readable linking to the data source. Therefore, this paper aims to increase the possibilities of ontologybased cyber intelligence solutions, by presenting a security ontology structure for data storage to the ontology from different text-based data sources, supporting the knowledge traceability and relationship estimation between different security documents. The proposed ontology structure is tested by storing data of three text-based data sources, and its application possibilities are provided. The study shows that the structure is adaptable for different text data sources and provides an additional value related to security area extension.

**Keywords:** security; ontology; structure; formalization

**Citation:** Ramanauskaite, S.; Shein, ˙ A.; Cenys, A.; Rastenis, J. Security ˇ Ontology Structure for Formalization of Security Document Knowledge. *Electronics* **2022**, *11*, 1103. https:// doi.org/10.3390/electronics11071103

Academic Editor: Vijayakumar Varadarajan

Received: 13 March 2022 Accepted: 29 March 2022 Published: 31 March 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).


PTM sites identified within CLDN2, CX32, and ZO-1 in this study have highlighted some crucial and important sites which can be targeted by the pathogen to dysregulate the normal functioning of barrier proteins, thus defecting the cell polarity. These sites may be targeted for therapeutic interventions in diseased condition. All predicted sites are highly conserved, emphasizing on their evolutionary importance. Whereas, the location of these sites also play significant role in their targeted function. H. pylori infection has also been reported to be linked with regulation and dysregulation of genes, proteins and their promoters via various modifications, especially methylation, initiating gastric carcinoma (Parsonnet et al., 1991; Akhtar et al., 2001; Blaser and Berg, 2001; Perri et al., 2007). Their ability to induce methylation has also been confirmed within animal model (Niwa et al., 2010). Thus, we found it quite crucial to target important methylation sites within TJ proteins. Some methylation sites of TJ proteins lie within functional domains, thus, predicting their effective roles in maintaining the cell integrity. R164 predicted within ECL2 of CX32 can be of great importance as these loops are maintaining the cell integrity by docking with neighboring connexins. Whereas, other four predicted sites lying on C-terminal of protein may also trigger major functions, as tail of connexins have been reportedly involved in modulating gene expression and cell-cycle control via binding proteins (Giepmans, 2004). Similarly, R-28 within PDZ1 domain and R-251 in PDZ2 domain of ZO-1 might be of great interest as these domains are responsible for anchoring ZO-1 with claudins and connexins (Thévenin et al., 2013). R-579 lies within Src Homology-3 (SH3) domain much important for protein interactions. Modification of residues in this domain may lead to disrupted protein-protein interactions, thus, modulating signaling pathways (Thévenin et al., 2013). R-635, 677 and 752 are within Guanylate kinase (GK) domain of ZO-1, where occludins and adherens junction proteins can bind.

Phosphorylation, one of the most common PTM is also triggered by pathogens to influence some significant pathways. H. pylori also targets TJ proteins for unwanted phosphorylation/dephosphorylation modifications to loosen the cell to cell junction. Effect of modification truly depends in number and location of PTM sites. Phosphorylation sites identified within CLDN2, CX32, and ZO-1 also revealed some crucial points that can be targeted by H. pylori during infection. Tyr-224 of CLDN2 has already been studied as potential phosphorylation site which modulates the binding of CLDN2 with ZO-1, as it has already been identified as key factor for regulating affinity between claudins and PDZ domain (Nomme et al., 2015). Presence of such a crucial site makes adjacent amino acids (S-223 and S-225) more significant to be tested experimentally as they have also been identified as potential curated phosphorylation sites by PhosphoSite Plus (Hornbeck et al., 2015) and also in our data set. As CLDN2 is involved in barrier leakiness and PTMs use to influence its pore forming ability, therefore, it should be systematically analyzed for its phosphorylation and other modification sites to unravel the mechanism of its regulation or dysregulation. Similarly, in CX32 Ser at position 229 and 233 are experimentally validated, verified by Phospho.ELM database (Diella et al., 2004) and S240 has already been predicted in a study to be a potential phosphorylation site (Locke et al., 2006). Whereas, other five sites may be novel and would needs to be evaluated experimentally. The prediction of experimentally validated residues among our data set validates the potential of phosphorylation for selected residues. Most of these phosphorylation sites harbor C-terminal of CX32 except Ser at 50th position that lies within ECL1. C-terminal of connexins is a major influential part for protein trafficking and studies have reported that phosphorylation of some amino acids at the tail can also alter the protein half-life

and gap junction assembly (Johnstone et al., 2012). Sites having potential to be phosphorylated within ZO-1 are also found within crucial domains directly involved in the binding with occludins, important TJ proteins. Modification in such domains may lead to disassociation of ZO-1 from TJ proteins, hence, loosening the epithelial barriers and facilitate the entry of pathogen. H. pylori alters the expression of different kinases, proteins which catalyze phosphorylation events, to infer normal modifications through their virulence factors (Ribet and Cossart, 2010). All predicted phosphorylation sites when checked for their kinases, reveal some specific kinases triggered during infection. Kinases affected especially by H. pylori and its virulence factors were dug out through extensive literature search. The regulation in expression of these kinases by CagA are then modeled using HPNs, which presents the possible route adopted by CagA to induce PTMs in TJ proteins. The model designed in this study helps us to investigate the downstream effect of injected CagA within host cells under pathophysiological conditions. For now,

red curve represents the activity level of proteins after infection.

we ignored the effect of other factors or proteins modulating the expression of H. pylori specific kinases. Our model was refined using experimental data and current knowledge of protein interactions and their influence on each other's expression both in normal and diseased states. One of the major key factor in the regulation of kinases is activation of cytokines IL1β and IL8. Production of these proinflammatory cytokines is one of the hallmarks of the gastric mucosa infection by H. pylori which plays significant role in disease progression. Usually CagA+ strains promote higher production of IL1β and IL8 resulting in an increased risk of peptic ulcer and gastric cancer (Noach et al., 1994; Harris et al., 1996; Nagashima et al., 2015; Ferreira et al., 2016). The increased level of IL1β and IL8 during infection is stimulated by both phosphorylated and unphosphorylated CagA, as it triggers the production of NF-κB to almost double. Elevated expression of NF-κB I turn activates various kinases like MLCK, MAPK, PKC, P38 etc., which induce phosphorylations at different sites of TJ proteins. Phosphorylation of MLC by myosin light chain kinase (MLCK) causes distension in TJs thus affecting the cell permeability. Increased IL1β also stimulates the activation of MLCK during H. pylori infection (Ashida et al., 2012). Phosphorylation and activation of these kinases thus induce specific PTMs within TJ proteins which help the pathogen in cellular vacuolation, loss of membrane integrity and apoptosis leading to gastric atrophy or intestinal metaplasia. Pattern of all these changes have been predicted through our model under the influence of CagA. Simulation results of this model (of disease condition) are validated by comparing it by modeling expression of these proteins under normal conditions. Simulation results of our proposed HPN models are in complete agreement with the experimental results (**Table 1**), thus verifying the predictions of our model.

In conclusion, our HPN model of intracellular signaling after H. pylori infection provides insight on the mechanism adopted by pathogen to induce PTMs within TJ proteins. Model clearly shows that, as a result of action of specific kinases, expression of CX32 and ZO-1 decreases up to significant levels whereas CLDN2 gets overexpressed to promote paracellular cation leak. Despite the limitation of being adapted from previous literature, our model reflects the sequence of events and captures the logical interactions among entities through various mechanisms. Such models can further be expanded to unveil the altered or adapted mechanisms by pathogen during or before the induction of infection and pathogenesis.

#### CONCLUSION

We believe that H. pylori significantly modulate the host TJ proteins and their interactions to direct its entry within host

#### REFERENCES

epithelial cells via PTMs. To induce specific PTMs, pathogen disrupts the signaling pathways to express or suppress specific proteins and kinases leading to alteration within the barrier proteins. Specific PTM sites have been predicted within selected TJ proteins, these can be further targeted to infer their potential impact in vitro and in vivo models. The HPN model proposed in this study was interesting and revealed a possible mechanism adapted by the H. pylori to promote the leaky barrier. The results presented here are truly qualitative, hence need experimental validation, which can help to improve the model by inclusion of quantitative data. Similar approaches can also help to infer specific targets for specific interventions such as drugs and vaccines or their combinations to combat the H. pylori infection. The approach can be extended to other host-pathogen interactions model for understating the progression of diseases and prediction of potential therapeutic targets.

#### AUTHOR CONTRIBUTIONS

AA and AN designed the study. AN contributed to analysis, interpretation of data and draft composition. AO, FA, and AI helped in designing mathematical models and proteins' analysis. AA, AO, FA, JA, and AI critically analyzed the draft and helped AN in organizing the final version.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2017.01682/full#supplementary-material

permeability requires NF-κB activation. Am. J. Physiol. Gastrointest. Liver Physiol. 286, G367–G376. doi: 10.1152/ajpgi.00173.2003

validation of an in-situ hybridisation assay for therapeutic studies. PLoS ONE 11:e0162076. doi: 10.1371/journal.pone.0162076

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Naz, Obaid, Awan, Ikram, Ahmad and Ali. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Modeling Kick-Kill Strategies toward Hiv Cure

#### *Esteban A. Hernandez-Vargas\**

*Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany*

Although combinatorial antiretroviral therapy (cART) potently suppresses the virus, a sterile or functional cure still remains one of the greatest therapeutic challenges worldwide. Reservoirs are infected cells that can maintain HIV persistence for several years in patients with optimal cART, which is a leading obstacle to eradicate the virus. Despite the significant progress that has been made in our understanding of the diversity of cells that promote HIV persistence, many aspects that are critical to the development of effective therapeutic approaches able to purge the latent CD4+ T cell reservoir are poorly understood. Simultaneous purging strategies known as "kick-kill" have been pointed out as promising therapeutic approaches to eliminate the viral reservoir. However, longterm outcomes of purging strategies as well as the effect on the HIV reservoir are still largely fragmented. In this context, mathematical modeling can provide a rationale not only to evaluate the impact on the HIV reservoir but also to facilitate the formulation of hypotheses about potential therapeutic strategies. This review aims to discuss briefly the most recent mathematical modeling contributions, harnessing our knowledge toward the uncharted territory of HIV eradication. In addition, problems associated with current models are discussed, in particular, mathematical models consider only T cell responses but HIV control may also depend on other cell responses as well as chemokines and cytokines dynamics.

#### *Edited by:*

*Juarez Antonio Simões Quaresma, Federal University of Pará, Brazil*

#### *Reviewed by:*

*David Andrew Moskowitz, Northwestern University, United States Paolo Casali, The University of Texas Health Science Center San Antonio, United States Namal P. M. Liyanage, Ohio State University, United States*

#### *\*Correspondence:*

*Esteban A. Hernandez-Vargas<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology*

*Received: 12 June 2017 Accepted: 04 August 2017 Published: 28 August 2017*

#### *Citation:*

*Hernandez-Vargas EA (2017) Modeling Kick-Kill Strategies toward HIV Cure. Front. Immunol. 8:995. doi: 10.3389/fimmu.2017.00995*

Keywords: HIV infection, HIV cure, reservoirs, mathematical modeling, LRA, ART, vaccination

#### 1. INTRODUCTION

According to UNAIDS estimates for the year 2015, 36 million persons are infected with the HIV worldwide, and there are approximately 2.3 million new infections and 1.6 million AIDS-related deaths that occurred that year (1). Combined antiretroviral therapies (cART) are not able to eradicate the virus and HIV rebounds if therapy is discontinued. Upon HIV infection, a subset of latently infected cells carrying transcriptionally inactive integrated proviral DNA (the HIV reservoir) is rapidly established (2, 3). These cells are the main force behind HIV persistence under cART and, therefore, the main obstacle for an HIV cure (4–6). Thus far, there is only one reported case of a potential cure, known in the popular press as the Berlin patient (7). Unfortunately, the unique circumstances of the Berlin patient case would make it highly implausible to achieve a cure on large scales (8).

Two different approaches are envisaged for curing HIV infection: a sterilizing cure if there is a complete eradication of the virus and infected cells; and a functional cure if there is permanent control of viral replication without therapy (2). There was a growing recognition that a cure for HIV infection could be feasible (4, 8, 9). Recent clinical observations have hypothesized that an early initiation of cART is crucial to a progressive contraction of the latent HIV reservoir ("shrink"). This

**270**

could possibly be accomplished with simultaneous strategies that activate ("kick" or "shock") the latent reservoir and increase the clearance of virus-infected cells ("kill"), known as a "kick-kill" or "shock-kill" strategy (10, 11). The time window to intervene at an early stage of infection, while reservoirs are limited, is envisaged to be narrow but critical to the performance of an effective "shrink-kick-kill" strategy (**Figure 1**).

Disentangling the leading mechanisms of HIV reservoirs is essential for the design of optimal therapeutic strategies. Although there are animal models such as the non-human primate and the BLT humanized mouse available to recapitulate HIV infection or even eradication, they are not perfect (12). Mathematical models can serve as a framework to interpret data of ongoing clinical trials, to evaluate the long-term of new therapeutic interventions, and to tailor future clinical trials. HIV modeling research twisted in a new dimension when the two works from Perelson et al. (13) and Nowak and Bangham (14) obtained a mathematical interpretation of viral decay data presented in HIV patients treated with anti-HIV drugs. Since then, modeling HIV infection has been a very active research topic over the past decades. Most of these modeling works initially aimed to represent the basic relation between the host cells and virus (15–21). In addition, significant efforts were invested to understand HIV disease progression (22–29), viral persistence (30–34), drug resistance (35–39), and optimal cART scheduling (40–46) among many others. Mathematical modeling was also pointed out as a tool to assess the potential of "kick-kill" strategies on long-term outcomes from short-term studies (47).

This short review focuses on discussing briefly the uncharted territory of HIV eradication as well as the most recent mathematical modeling contributions aiming to shed light on major clinical implications toward an HIV cure, see **Table 1**.

### 2. "SHRINKING" THE RESERVOIRS

A very debatable question in HIV research has been if the existence of an HIV latent cellular reservoir is maintained by long-lived resting memory CD4+ T cells or through residual virus replication that replenishes the HIV reservoir (2, 56). Till now, latently infected resting memory CD4+ T cells are the only cell type in which it has been clearly demonstrated that replication-competent virus can persist for several years in patients (4, 57–59).

Memory CD4+ T cells represent the largest lymphocyte population in the adult human body and play critical roles in maintaining a life-long immune defense against specific pathogens (60). Reservoir maintenance is disputable, it has been mainly attributed to the replenishment of the pool, presumably by homeostatic or antigen-driven clonal proliferation and *de novo* infection of memory CD4+ T cells, ensuring the continuous replenishment of the HIV reservoir (57). Recent works from different labs (4, 57–59) revealed a progressive reduction of the size of the blood latent reservoir around a core of lessdifferentiated memory subsets (central memory and stem celllike memory CD4+ T cells). These works indicated an extreme stability of different sub-reservoirs, the size of which is directly related to cumulative plasma virus exposure before the onset

of cART (58, 59), stressing the importance of early initiation of effective cART. Nevertheless, very recent studies demonstrated that the viral reservoir is seeded rapidly after SIV infection of rhesus monkeys, even before detectable viremia (61). Therefore, the multifactorial mechanisms of HIV reservoirs and their establishment according to the time of optimal cART are still a matter of debate.

The VISCONTI study (9) dissected for the first time that the initiation of cART at very early stages of infection could decrease the size of the HIV reservoirs. In this study, cART was provided for 3 years after primary infection (PHI) to 14 patients and then interrupted. This study revealed that the 14 patients presented a sustained control for a median of 7 years named as posttreatment controllers (PTCs), implying that perhaps the nature of the viral reservoir (levels of TCM cells) could play an important role in controlling the infection in the absence of cART. Note that PTCs are not the only ones that can control HIV infection, a small group of individuals identified in 2005 showed the ability to control HIV infection in the absence of cART named as "Elite controllers (ECs)" (62). Although both ECs and PTCs can control the HIV infection, ECs can naturally maintain undetectable viral loads mainly attributed to a measurably stronger CTL response than non-controllers. Note that ECs undetectable viral loads are not only due to strong CTL but also other cell types, HLA type, and cytokines and chemokines (63).

Although the VISCONTI study revealed crucial information for a successful therapeutic strategy inducing viral remission, it left open several questions, in particular, how can we increase the probability of HIV-infected patients becoming PTC? The answer is not intuitive, in this direction, Hill et al. (48) proposed a mathematical model based on a two-type branching process assuming only four types of events: a latently infected cell can either activate or die, an actively infected cell can either die or produce a collection of virions that results in the infection of other cells. The model provided a relevant prediction that it might not be necessary to deplete completely the reservoir pool to prevent viral rebound, representing a PTC. The reasoning behind these predictions is that the high variability in viral progeny generated from actively infected cells increases the probability that the progeny of an activated provirus will go extinct because of stochastic diffusion. Hill et al. (48) results suggested that in order to achieve the goal of eradication for 95% of patients, a 5.8 logs reduction in the reservoir size may be necessary. Alternatively, to reach 1 year average without rebound, approximately 2,000 fold reduction would be required (48).

Controverting Hill et al. (48) predictions, Pinkevych et al. (49) combined data from four independent clinical cohorts of patients with cART interruption together using a simple model with exponential phase with a "shoulder" that represents the time for drug "washout" and viral growth. Pinkevych et al. (49) estimated that viral replication is initiated on average once every 6 days approximately, which is about 24 times lower than previous estimations. Furthermore, the model indicated that a modest 50–70-fold reduction of the reservoir would be required for 1 year without viral rebound after cART interruption. Six months later, Hill et al. (64) questioned the estimation approach by Pinkevych et al. (49). Mathematical models including modest interpersonal variations in Ref. (64) were also able to explain the observed variation in rebound times, rejecting the simplifying assumption of homogeneity in Ref. (49). In response to the critic of Hill et al. (64), Pinkevych et al. (65) derived an analytical approximation incorporating multiple reactivation events and consequently fitted to four datasets. Overall, fitting results in Ref. (65) indicated similar results to their original work (49). Furthermore, Pinkevych et al. (65) argued back to the work in Hill et al. (48) for using reactivation rates from previous publications that were not based on data on reactivation from latency after treatment interruption.

In a separate modeling lab, Conway et al. (50) tested the immune system response led by cytotoxic T cell (CTL) would be sufficient to control the infection due to the rate of new productively infected cells is small. To this end, using ordinary differential equations (ODEs), numerical simulations in Ref. (50) pointed out that for very strong CTL responses, HIV infection would be controlled in a similar fashion as with the ECs. Moreover, within the first 6 months of cART interruption, the model represented qualitatively similar the viral rebound to those reported in some individuals in the VISCONTI study. Interestingly, the model analysis reveals that not only a very low latent reservoir size is necessary to guarantee PTCs but also the CTL strength. A shortcoming of the Conway et al. model is the missing quantitative information to precise CD8+ T response leading to a subjective selection of the model and parameters.

### 3. "KICKING" THE LATENT RESERVOIRS

The central dogma for an HIV cure is to reverse latency of resting CD4+ T cells that harbor replication competent proviruses (30), but at the same time, a major challenge in "purging" treatment strategies is the reduction of the virus without causing global T cell activation (66). The ability to induce HIV viremia or at least cell surface expression of viral proteins and presentation of viral antigens is a fundamental requirement for enabling the immunemediated killing of latently infected cells and, thus, defines the key goal of latency-reversing agents (LRAs) in eradication strategies (11). These agents include histone methyltransferase inhibitors and histone deacetylases (HDAC) inhibitors (67). The potential to activate HIV production from latently infected cell lines and resting CD4+ T cells from HIV-infected patients on suppressive cART is under large debate last 5 years (11, 68). Thus far, multiple HDAC inhibitors can potently activate viral production *in vitro*, however, results of initial clinical trials in HIV-infected patients are just very recently published (10, 69, 70).

Essential steps in the life cycle of HIV within host cells, cell-associated HIV RNA markers, have been identified and currently used in several clinical trials as a surrogate to measure the degree of HIV persistence (71). Among HIV RNA markers, unspliced RNA (UsRNA) also referred as cell-associated unspliced (CA-US) is more easy to detect and thus several studies have linked to virus persistence (71).

For vorinostat, a promising LRA, 20 HIV-infected individuals on suppressive cART were treated with 400 mg of vorinostat for 14 days and then followed by 70 days (69). Although highly variable outcomes among the participants, vorinostat induced a significant and sustained increase UsRNA. Ke et al. (51) proposed three different models based on ODEs to further understand how latently infected cells respond dynamically to vorinostat: a direct activation model, a delay activation model, and a multistage delayed model. The model analysis in Ref. (51) revealed that a multistage delayed activation model could recapitulate the short-term and the long-term changes induced by vorinostat in UsRNA in most of the participants. This can be interpreted as latently infected cells may need to go through several stages before becoming sustainably activated. Clinically relevant for HIV persistence, parameter estimates by Ke et al. (51) evoked the idea that vorinostat treatment may not induce killing of transcriptionally activated cells leading to a minimal or absent reduction in reservoir size.

Another important LRA is romidepsin, Søgaard et al. (70) reported in a small clinical trial of six HIV-infected individuals who received romidepsin once weekly for 3 weeks while keeping cART. In contrast to vorinostat that did not induce plasma HIV RNA, romidepsin treatment promoted in five patients an increase of plasma HIV RNA to detectable levels ranging from 46 to 103 copies/ml. However, romidepsin and vorinostat did not alter the size of the HIV reservoir (72). To demonstrate that romidepsin may successfully activate the latent reservoir, Policicchio et al. (52) developed a non-human primate (NH) model to capture the characteristics of PTCs. Unexpectedly, stopping cART 7 days after romidepsin administration showed that viral rebound occurred as early as 3 days after cART interruption. Note that the average time of viral rebounds in humans is approximately 8 weeks (73). Employing a simple mathematical model of viral production, Policicchio et al. (52) indicated that the slopes of plasma viral load increase after romidepsin treatment are related to the intensification of viral replication attributed to romidepsin. Fitting results showed that the estimated slope was 0.418 log10/day.

Thus far, in clinical trials, HDACs have demonstrated an increase of UsRNA in total but a minimal reduction in reservoir size. Based on simple mathematical models assuming "direct activation," Petravic et al. (53) suggested that several mechanisms such as maintenance and clearance of the reservoirs as well as other mechanisms may significantly impact the relationship between HIV reactivation and the reduction of latently infected cells. In particular, Petravic et al. (53) considered the impact of panobinostat and romidepsin, both drugs revealed 3–4 increased of CA-RNA in clinical trials. On one hand, cells reactivated with panobinostat have a long life span (half-life >1 month) suggesting a modest increase in reactivation rate (approximately 8%). On the other hand, cells activated with romidepsin have a short life span (2 days), implying that HIV reactivation rate may have doubled with romidepsin (53).

Overall, it is envisaged that additional interventions will be needed to eliminate efficiently latently infected cells (69). It is, therefore, very likely that HDACs will form part of a multipronged strategy (74). Consequently, mathematical models merging dynamics from different HDAC inhibitors may help to propose "kick" strategies to eliminate latently infected cells to achieve the ultimate goal of HIV eradication.

### 4. "KILLING" THE ACTIVATED RESERVOIR

Purging strategies reached a new level of complexity due to recently published works addressing the frequency of CTL escape mutations in archived proviruses, indicating an unexpected and exceptionally dynamic nature of the latent reservoir (75). Although cART is started early, the vast majority of latent viruses carry CTL mutations that render infected cells unrecognizable by CTLs directed at common epitopes. While the non-protective responses may not be harmful *per se*, they may dominate and suppress the true protective ones. Thus, it is critical to (re) focus T-cell responses on the protective, biologically conserved epitopes of the HIV-1-proteome by effective vaccination (76). However, the biggest obstacle for vaccine development is the HIV-1 variability and escape from mounted responses. T-cell strategies focus on vaccine-elicited responses on the most conserved regions of the HIV-1 proteome are very promising, due to these are common to most variants and cause replicative fitness loss if mutated (76, 77). In this context, the best "kill" strategy could be based on innovative vaccines aiming to induce CD8+ T cell responses in conserved regions of the HIV-1 proteome.

To the best knowledge of the author, there is not any mathematical work to evaluate vaccines aiming to induce CD8+ T cell responses. Till now, mathematical models have focused on incorporating affinity antibody maturation (54, 55). Using agent-based simulations of the Germinal Center (GC) reaction, simulation results from Wang et al. (55) suggested that the induction of cross-reactive antibodies occurs with low probability because of conflicting forces by different antigens, ultimately frustrating affinity maturation. Wang et al. (55) provided a critical prediction that sequential immunization with different antigens would be preferred over a cocktail for induction of cross-reactive antibodies. In a similar vein, Luo et al. (54) proposed a Markov process model to simulate coevolving multi-type virus and antibodies populations. Simulations results provided also the hint that competitive exclusion by autologous antibodies could avoid the appearance of broadly neutralizing antibodies.

### 5. A ROADMAP FOR HIV MODELING

HIV modeling is on uncharted territory. Modeling "kill" strategies aiming to induce CD8+ T cell responses in cooperation with

#### REFERENCES

a combination of HDAC inhibitors has the potential to advance understanding toward HIV eradication. There are at the moment several clinical trials based on "kick-kill" therapies such as the RIVER study (78), for which a long-term follow-up out to 5 years is envisaged. Furthermore, several mechanisms may be underestimated in mathematical modeling research. Recent experimental evidence revealed that clonal proliferation of infected cell may play a central role maintaining the reservoirs (79). On the other hand, viral control may not be only associated with restoration of CD8+ T cells (80). Mathematical models presented till now assume only T cell responses as a main component. However, further modeling efforts including host factors and immune responses responsible for the HIV elite status may uncover clues for the design of therapeutic vaccines and functional cures (63). Ultimately, mathematical models of HIV compartments (e.g., different places where the virus is present) and sanctuaries (e.g., limited penetration of drugs that maintains persistent replication) are needed to weight HIV persistence.

Above all, the difference in opinion of modeling approaches between Pinkevych et al. and Hill et al. points out that there is a great need to unify the efforts in modeling practices such as develop good practice guidelines for reporting parameter fitting results. Although assuming there exists a model that represents properly the problem at hand, model fitting to experimental data is subject to a large number of factors that can distort parameter estimates (81). Efforts in dealing with errors in parameter estimation shall be well documented in next mathematical models to strengthen and support further development toward HIV eradication.

# AUTHOR CONTRIBUTIONS

EAHV conceived and wrote the manuscript.

### FUNDING

This work was supported by the Alfons und Gertrud Kassel-Stiftung.

homeostatic proliferation. *Nat Med* (2009) 15(8):893–900. doi:10.1038/ nm.1972

in vivo. *PLoS Pathog* (2015) 11(9):e1005142. doi:10.1371/journal.ppat. 1005142

**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Hernandez-Vargas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Modulation of Host miRNAs Transcriptome in Lung and Spleen of Peste des Petits Ruminants Virus Infected Sheep and Goats

Aruna Pandey<sup>1</sup>† , Amit R. Sahu<sup>1</sup>† , Sajad A. Wani<sup>1</sup>† , Shikha Saxena<sup>1</sup> , Sonam Kanchan<sup>1</sup> , Vaishali Sah<sup>1</sup> , Kaushal K. Rajak<sup>2</sup> , Alok Khanduri<sup>1</sup> , Aditya P. Sahoo<sup>1</sup> , Ashok K. Tiwari<sup>3</sup> , Bina Mishra<sup>2</sup> , D. Muthuchelvan<sup>4</sup> , Bishnu P. Mishra<sup>1</sup> , Raj K. Singh<sup>1</sup> and Ravi K. Gandham<sup>1</sup> \*

#### Edited by:

Lars Kaderali, Technische Universität Dresden, Germany

#### Reviewed by:

Muhammad Zubair Shabbir, University of Veterinary and Animal Sciences, Pakistan Balamurugan Vinayagamurthy, National Institute of Veterinary Epidemiology and Disease Informatics (ICAR), India

#### \*Correspondence:

Ravi K. Gandham<EMAIL_ADDRESS>†These authors have contributed equally to this work.

#### Specialty section:

This article was submitted to Microbial Immunology, a section of the journal Frontiers in Microbiology

Received: 23 March 2017 Accepted: 06 June 2017 Published: 26 June 2017

#### Citation:

Pandey A, Sahu AR, Wani SA, Saxena S, Kanchan S, Sah V, Rajak KK, Khanduri A, Sahoo AP, Tiwari AK, Mishra B, Muthuchelvan D, Mishra BP, Singh RK and Gandham RK (2017) Modulation of Host miRNAs Transcriptome in Lung and Spleen of Peste des Petits Ruminants Virus Infected Sheep and Goats. Front. Microbiol. 8:1146. doi: 10.3389/fmicb.2017.01146 <sup>1</sup> Computational Biology and Genomics Facility Lab, Division of Veterinary Biotechnology, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India, <sup>2</sup> Division of Biological Products, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India, <sup>3</sup> Division of Biological Standardization, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Bareilly, India, <sup>4</sup> Division of Virology, Indian Council of Agricultural Research-Indian Veterinary Research Institute, Nainital, India

Peste des petits ruminants (PPR) is one of the highly contagious viral disease, characterized by fever, sore mouth, conjunctivitis, gastroenteritis, and pneumonia, primarily affecting sheep and goats. Reports suggested variable host response in goats and sheep and this host response vis-a-vis the expression of microRNAs (miRNAs) has not been investigated. Here, miRNAs were sequenced and proteomics data were generated to identify the role of differentially expressed miRNA (DEmiRNA) in PPR virus (PPRV) infected lung and spleen tissues of sheep and goats. In lungs, 67 and 37 DEmiRNAs have been identified in goats and sheep, respectively. Similarly, in spleen, 50 and 56 DEmiRNAs were identified in goats and sheep, respectively. A total of 20 and 11 miRNAs were found to be common differentially expressed in both the species in PPRV infected spleen and lung, respectively. Six DEmiRNAs—miR-21-3p, miR-1246, miR-27a-5p, miR-760-3p, miR-320a, and miR-363 were selected based on their role in viral infections, apoptosis, and fold change. The target prediction analysis of these six selected DEmiRNAs from the proteome data generated, revealed involvement of more number of genes in lung and spleen of goats than in sheep. On gene ontology analysis of host target genes these DEmiRNAs were found to regulate several immune response signaling pathways. It was observed that the pathways viz. T cell receptor signaling, Rap1 signaling, Toll-like receptor signaling, and B cell receptor signaling governed by DEmiRNAs were more perturbed in goats than in sheep. The data suggests that PPRV-induced miR-21-3p, miR-320a, and miR-363 might act cooperatively to enhance viral pathogenesis in the lung and spleen of sheep by downregulating several immune response genes. The study gives an important insight into the molecular pathogenesis of PPR by identifying that the PPRV—Izatnagar/94 isolate elicits a strong host response in goats than in sheep.

Keywords: microRNAs, PPR, sheep, goats, host–pathogen interaction, molecular pathogenesis

# INTRODUCTION

fmicb-08-01146 June 23, 2017 Time: 16:52 # 2

MicroRNAs (miRNAs) are an important part of the host's regulatory system, involved in post-transcriptional regulation of gene expression in animals, plants, and some DNA viruses (Sevignani et al., 2006). They regulate gene expression by recognizing partial complementary sites, typically within the 3<sup>0</sup> untranslated region (30UTR) of specific mRNAs. Evidence also supports that miRNAs may regulate gene expression by binding to 50UTR or coding region (Tay et al., 2008; Roberts et al., 2011a). miRNAs are shown to be involved in different biological processes, including reproduction, development, pathogenesis, apoptosis, and signal transduction (Ambros, 2004; Bartel, 2004; Sahu et al., 2015). It has also been suggested that miRNAs may be the effectors in controlling immune regulation, including cellular differentiation and immune response (Rodriguez et al., 2007; Thai et al., 2007; Johnnidis et al., 2008). They are considered as the centered factors in the interaction network between viruses and host. Studies demonstrated that numerous cellular miRNAs (host miRNAs) play a regulatory role in the host–virus interaction network (Scaria et al., 2006; Grassmann and Jeang, 2008).

Cellular miRNAs can greatly influence viral replication and pathogenesis by promoting or inhibiting virus replication (Guo et al., 2013; Li et al., 2014; Mizuguchi et al., 2015). Viral infection also exerts a profound impact on cellular miRNA expression profile, by altering the expression of cellular miRNAs, thereby regulating host or viral RNA targets (Skalsky and Cullen, 2010). It has been observed that miR-142 suppresses replication of Eastern Equine Encephalitis virus (Trobaugh et al., 2014) and miR-122 enhances replication of Hepatitis C virus (Chang et al., 2008). HIV-1, boosts the expression of several host miRNAs, including miR-122, miR-370, miR-373, and miR-297 and suppresses the expression of the miR-17-92 cluster via an unknown mechanism (Roberts et al., 2011b). The emergence of deep sequencing technology has overcome the limitations of miRNA research. Several studies have explored this technology to evaluate global changes in miRNAs expression in response to virus infection (Wang et al., 2009; Cui et al., 2010).

Peste des petits ruminants (PPR) is an acute, highly contagious viral disease of sheep and goats characterized by fever, sore mouth, conjunctivitis, gastroenteritis, and pneumonia. Goats have been found to be more susceptible with severe form of clinical disease than sheep (Lefevre and Diallo, 1990; Nanda et al., 1996; Dhar et al., 2002; Singh et al., 2004a; Delil et al., 2012; Truong et al., 2014). It has also been observed that the rate of recovery is lower in goats than in sheep (Singh et al., 2004a). However, severe outbreaks of PPR in regions having large sheep populations have also been reported (Singh et al., 2004a; Raghavendra et al., 2008; Maganga et al., 2013). Recently, host–virus interaction studies in PPR have uncovered transcription factors modulating immune response to Sungri/96 live attenuated vaccine strain and predicted an immune signaling pathway that induces immune response (Manjunath et al., 2015, 2017). However, the host miRNAome in PPR has not been explored till date. In the present study, miRNAs were sequenced and proteomics data were generated to examine the effect of PPR virus (PPRV) on host miRNAs expression vis-a-vis protein expression in lung and spleen tissues of sheep and goats infected with PPR.

# MATERIALS AND METHODS

### Ethics Statement and Animal Experiment

The vaccine potency testing experiment was carried out at ICAR-Indian Veterinary Research Institute Mukteshwar Campus as per the guidelines of Indian Pharmacopoeia-2014. The study was done after obtaining permission from Indian Veterinary Research Institute Animal Ethics Committee (IVRI-IAEC) under the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), India. The protocols were approved vide letter no 387/CPCSEA. Animals (ca. 1 year of age) for the experiment were initially tested to be negative for the presence of PPRV antibody by competitive ELISA (Singh et al., 2004b) and serum neutralization test (SNT; Dhinakar Raj et al., 2000). The animals were also found negative for PPRV antigen in nasal, ocular, buccal, and rectal swabs by sandwich ELISA (Singh et al., 2004c). A highly virulent PPRV (Izatnagar/94 - lineage IV) isolate maintained at PPR Laboratory, Division of Virology, Indian Veterinary Research Institute, Mukteshwar was used as a challenge virus (Sreenivasa et al., 2002). The accession number of this isolate is (KR140086.1; Sahu et al., 2017). Splenic suspension (10%) of virulent virus was inoculated subcutaneously (4 ml suspension, 2 ml each at two different sites). The unvaccinated infected group animals were monitored diurnally for, rectal temperature, any secretion from natural orifices, and feeding habit throughout the experimental period. The unvaccinated animals infected with the PPRV, developed symptoms characteristics of PPRV. The infected animals in which the temperature dropped subnormal were euthanized at 10 days post-infection. As PPRV is epitheliotropic and lymphotropic virus, the tissue samples—lung (epithelial) and spleen (lymphoid) were collected from PPRV infected sheep and goats (n = 2 for each of the species). The counterpart healthy tissues (control) were collected from nearby slaughter house from apparently healthy animals that were screened for the absence of PPRV antigen by sandwich ELISA and antibodies by competitive ELISA and SNT.

### Confirmation of PPRV Infection

PPRV infection was confirmed in lung and spleen tissues by, RT-PCR, qRT-PCR, and sandwich ELISA.

### Small RNA Library Construction

Total RNA from each of the collected samples (lung and spleen) was isolated using the RNeasy Mini kit (Qiagen GmbH, Germany) according to the manufacturer's protocol. The integrity and quantity of isolated RNA were assessed on a Bioanalyzer (Agilent Technologies, Inc). The RNA integrity number (RIN) value of all the samples was found greater than 8, which is considered suitable for further processing (Kukurba and Montgomery, 2015). The library was prepared using NEBNext Multiplex Small RNA Library Prep Kit (New England Biolabs Inc.) following the manufacturer's protocol. Hundred nanograms

of total RNA from each sample was used for small RNA library preparation. The quality of the libraries was assessed on Bioanalyzer. Libraries were quantified using a Qubit 2.0 Fluorometer (Life Technologies) and by quantitative real-time PCR (qPCR; Robin et al., 2016). The high-throughput sequencing was performed on Illumina – NextSeq500 (75 bp single-end) (manufacturer's protocol).

#### miRNAs Prediction and Analysis

The cattle genome sequence was obtained from ftp://ftp. ensembl.org/pub/release-89/fasta/bos\_taurus/dna/ The genome was indexed using Bowtie short read aligner program (Langmead et al., 2009). miRNAs are conserved across species (Altuvia et al., 2005). Since there is no complete sheep and goats miRNAs dataset available in miRBase, known mature miRNAs and precursor sequences for cattle were obtained using a Perl script from miRBase database. Data were further processed using miRDeep2 software (Friedlander et al., 2008, 2012). After filtering (read length ≥18 nt) the set of collapsed, non-redundant, clean reads were mapped to the indexed cattle genome using a mapper module. To identify known miRNAs, clean reads were aligned against miRNA precursor sequences reported in the miRBase database using quantifier.pl module. Read counts for each miRNA identified using miRDeep2 were further used for the downstream analysis. The relative expression levels of miRNAs were normalized as TMM (trimmed mean of M-values) using edgeR R/Bioconductor package (Robinson et al., 2010) to identify differentially expressed miRNAs (DEmiRNAs) in lung and spleen.

# Proteomics Data Generation and Data Analysis

#### Protein Extraction and Analysis

Approximately, 1 g of tissue—spleen and lung, from PPRV infected/apparently healthy goats and sheep was taken in 10 ml lysis buffer (50 mM Tris buffer + PIC + PMSF), homogenized on ice, centrifuged at 14,000 rpm and the supernatant was collected into a separate tube (Tris buffer extract). The cell pellet was further added with urea lysis buffer, centrifuged at 14,000 rpm and the resultant supernatant was collected. Supernatants from Tris and urea extractions were run on an SDS-PAGE for quality check (QC) and for further downstream processing. Protein concentration was determined by using Bradford assay and 100 µg of the samples was taken for digestion. Protein samples were treated with 100 mM dithiothreitol at 95◦C for 1 h, added with 250 mM iminodiacetic acid and kept at room temperature for 45 min in dark. Samples were then digested with trypsin and incubated overnight at 37◦C. Further, 1% of formic acid was added and incubated at 37◦C for 45 min. The resulting samples were vacuum dried and dissolved in 10 µl of 0.1% formic acid and centrifuged at 10,000 g. The supernatant was injected on C18 Nano-LC column for separation of peptides followed by analysis on the Waters Synapt G2 Q-TOF instrument for MS and MSMS. The raw data was processed by MassLynx 4.1 WATERS. The individual peptides MSMS spectra were matched to the database sequence for protein identification on PLGS software, WATERS. Based on the m/z values and their probability to match with a specific peptide present in proteins cleaved at arginine (R) or lysine (K) the protein identification was carried with thresholds, minimum number of peptides to be found for a protein—2; minimum number of fragments (MSMS) ions in a peptide—3; minimum number of fragments (MSMS) ions in a protein—7; peptide mass tolerance—30 ppm; and fragment ion mass tolerance—70 ppm. The identified proteins in the three runs of each sample were compared with each other as control (healthy) and infected samples. Expression Analysis package of the PLGS software was then used for quantification. The ion counts matching with the peptides of a specific protein corresponding between the two samples in the three runs, were averaged and the ratio was calculated for the whole protein.

# Target Prediction of miRNAs

To better understand the biological function of DEmiRNA, TargetScan tool (Aggarwal et al., 2015) with default parameters was used to predict target genes of the selected DEmiRNAs (six miRNAs selected based on their role). From these predicted genes, the dysregulated genes from the proteomics data were identified for the miRNA selected (downregulated proteins for upregulated miRNA and upregulated proteins for downregulated miRNA). These common target genes from TargetScan and proteomics data were considered for further analysis. The miRNA–protein network was created based on the expression profile of target genes and miRNAs using Cytoscape (ver. 3.1.1; Shannon et al., 2003).

# Gene Ontology Enrichment and Pathway Analysis

Functional annotation of the selected DEmiRNAs in each tissue was performed using target genes governed by them in ClueGO (ver. 2.1.4; Bindea et al., 2009) in Cytoscape (ver. 3.1.1; Shannon et al., 2003). Immune system processes and KEGG pathways were selected to generate a functionally organized GO/pathway term networks.

# Validation Using qPCR

Total RNA, including small RNA from the lung and spleen of control and infected sheep and goats were isolated using mirVanaTM miRNA isolation kit (Invitrogen). Reverse transcriptase reactions were performed using RT specific primers of miR-363, miR-760-3p, miR-21-3p, and U6snRNA by TaqMan <sup>R</sup> MicroRNA Reverse Transcription Kit. Real-time PCR was performed using a standard TaqMan PCR kit protocol on an Applied Biosystems 7500 fast Sequence Detection System. The 10 µl PCR included 5 µl of 2× TaqMan Gene Expression Master Mix (Thermo Fisher Scientific Inc., Wilmington, DE, United States, Cat. No. 4369016), 0.5 µl of 20× TaqMan probe, 2 µl (0.134 ng) of RT product and 2.5 µl of NFW. The reactions were incubated in a 96-well plate at 95◦C for 10 min, followed by 40 cycles of 95◦C for 15 s and 60◦C for 1 min. All reactions were run in triplicate. The threshold cycle (Aad et al., 2015) is defined as the fractional cycle number at which the fluorescence passes

the fixed threshold. The expression of the selected miRNAs described above was assayed taking the expression of U6snRNA as an internal control. The relative expression of each miRNA was calculated using the 2−11CT method with a control group as calibrator (Schmittgen and Livak, 2008).

## RESULTS

#### Confirmation of PPRV Infection

Viral infection in the lung and spleen of sheep and goats infected with PPRV was confirmed by RT-PCR of 351 bp N gene amplicon in lung and spleen (**Supplementary Figure S1**). The viral infection was further confirmed by sandwich ELISA and qRT-PCR in both lung and spleen of goats and sheep (data not shown).

## miRNAs Prediction and Identification of DEmiRNAs

In goats, miRDeep2 identified 298 and 283 miRNAs in control and PPRV infected lung, and 277 and 274 miRNAs in control and PPRV infected spleen. In sheep, 290 and 298 miRNAs in control and PPRV infected lung, and 274 and 256 miRNAs in control and PPRV infected spleen, respectively, were predicted. DEmiRNAs [FDR ≤ 0.05 and fold change (log2FC) ≥ 1] in lung and spleen of PPRV infected sheep and goats are presented in **Table 1**. A total of 67 miRNAs (34 downregulated and 33 upregulated) were dysregulated in the lungs of PPRV infected goats. However, a relatively small number of DEmiRNAs—37 miRNAs (16 miRNAs downregulated and 21 miRNAs upregulated) were identified in the lungs of sheep (**Table 1**). In infected goat's spleen, 50 miRNAs were dysregulated with 26 of them downregulated and 24 upregulated. In spleen of sheep, 56 miRNAs were differentially expressed after PPRV infection and of these, 26 miRNAs were downregulated and 30 miRNAs were upregulated (**Table 1**).

On comparing tissues across species, 20 and 11 miRNAs were found to be commonly differentially expressed in PPRV infected spleen and lung, respectively, in both sheep and goats. Among these 20 common DEmiRNAs in spleen, 11 DEmiRNAs (miR-199b, miR-1271, miR-217, miR-2887-1, miR-2887-2, miR-6119-3p, miR-221, miR-744, miR-30c, let-7a-5p-2, and miR-211) were downregulated and nine DEmiRNAs (miR-17-3p, miR-486, miR-146b, miR-363, miR-451, miR-193a-3p, miR-760-3p, miR-144, and miR-21-5p) were upregulated in goats, and in

TABLE 1 | Differentially expressed (log2FC ≥ 1 and FDR ≤ 0.05 up/down regulated miRNAs in goats and sheep spleen and lung.

sheep, nine DEmiRNAs (miR-199b, miR-1271, miR-217, miR-6119-3p, miR-221, miR-744, miR-30c, let-7a-5p-2, and miR-211) were downregulated and 11 DEmiRNAs (miR-2887-1, miR-2887- 2, miR-17-3p, miR-486, miR-146b, miR-363, miR-451, miR-193a-3p, miR-760-3p, miR-144, and miR-21-5p) were upregulated (**Figure 1A** and **Table 2**). Of these 20 common DEmiRNAs, miR-21-5p was the most upregulated (log2FC = 2.35) and miR-199b was the most downregulated DEmiRNA (log2FC = −3.03) in the spleen of goats. While in infected spleen of sheep the most upregulated DEmiRNAs were miR-451 (log2FC = 2.75) and miR-144 (log2FC = 2.63) and the most downregulated DEmiRNAs were miR-217 (log2FC = −4.08) and miR-221 (log2FC = −2.60).

The expression profile of the 11 common DEmiRNAs in the lung tissue varied in sheep and goats. miR-328 was found downregulated in goats but upregulated in sheep; two miRNAs miR-2285f-2 and miR-27a-5p were found upregulated in goats but downregulated in sheep; six miRNAs—miR-320a-1, miR-320a-2, miR-1246, miR-363, miR-760-3p, and miR-21-3p were upregulated and two miRNAs—miR-34b and miR-150 were downregulated in both species (**Figure 1B** and **Table 3**). Among these 11 common DEmiRNAs, the expression of miR-21-3p, miR-760-3p, and miR-27a-5p was more abundant in lungs of PPRV infected goats with log<sup>2</sup> fold change of 5.82, 3.79, and 3.07, respectively, while miR-34b (log2FC = −2.53) and miR-2285f-2 (log2FC = −2.53) were found least abundant in lungs of goats and sheep, respectively.

Among these 31 commonly DEmiRNAs, six DEmiRNAs were selected based on their role in viral infections, apoptosis, and fold change (**Table 4**). These miRNAs include miR-21-3p, miR-320a, miR-27a-5p, and miR-1246—expressed in lung of both species; miR-760-3p and miR-363—expressed in lung and spleen of both species (**Figure 2**). The miRNAs—miR-363 and miR-760-3p commonly present in PPRV infected lung and spleen of both species were identified to be upregulated. In lung, the expression of miR-363 and miR-760-3p was higher in goats with log<sup>2</sup> fold change of 2.42 and 3.79, respectively, than in sheep (miR-363, log2FC = 1.04 and miR-760-3p, log2FC = 1.24) after PPRV infection. In spleen, the expression profile of miR-363 was higher in goats (log2FC = 1.55) than in sheep (log2FC = 1.28), however, no difference in expression of miR-760-3p was observed in goats (log2FC = 1.63) and sheep (log2FC = 1.67) infected with PPRV.

#### Target Prediction and miRNA–Protein Regulatory Network Analysis

A total of 1149 (714 downregulated, 435 upregulated) and 1565 (1041 downregulated, 524 upregulated) differentially expressed proteins were identified in lung of sheep and goats, respectively, and 944 (281 downregulated, 663 upregulated) and 909 (590 downregulated, 319 upregulated) differentially expressed proteins were identified in spleen of sheep and goats, respectively. The number of dysregulated proteins identified through mass spectrophotometry by each of these six miRNAs is shown in **Figure 2**. The miRNA–protein interactions for each species and tissue are represented in a network (**Figure 3**). In the miRNA–protein network of lung tissue of goats, three

miRNAs—miR-21-3p, miR-363, and miR-320a mutually regulate EGFR (epidermal growth factor receptor), which is involved in immune response. Similarly, IGF1R (insulin like growth factor 1) protein, involved in regulation of immune response was the target of miR-27a-5p, miR-363, miR-320a, and miR-760-3p. The TRIM (tripartite motif family) family members TRIM24, TRIM36, and TRIM45 were identified to be modulated by miR-1246, miR-320a, and miR-21-3p, respectively. The expression level of NF-κB signaling-related molecules IRAK2 and TRAF4 was regulated by miR-1246 and miR-320; and miR-1246 and miR-760-3p, respectively (**Figure 3A**). In PPRV infected sheep lung, the upregulated miRNAs—miR-21-3p and miR-320a govern immune genes—TRAF6, EGFR, and ERBB4 and the downregulated miR-27a-5p potentially modulates the expression of genes—MAP3K7 and MAPK8IP3, involved in JNK signaling pathways (**Figure 3B**).

In PPRV infected spleen of goats, miR-363 and miR-760- 3p regulate apoptotic molecules (NFATC2, NOX5, and MYO6) and target genes involved in immunological processes (NFATC2, IGF1R, KLHL21, and NOX5) (**Figure 3C**). The immune effector molecules IFIT5 and TRIM33 were targeted by miR-363, and CD244 and NFKBIE were regulated by miR-760-3p in infected spleen of sheep (**Figure 3D**).

# Gene Ontology and KEGG Pathway-Based Network Analysis

Functional annotation of a total of 770 and 1226 target proteins governed by selected six miRNAs in infected sheep and goats infected lung, respectively, resulted in higher number of significantly enriched pathways and GO terms in goats than in sheep (**Figure 4**). The highly enriched common GO terms and

TABLE 2 | DEmiRNAs commonly identified in PPRV infected spleen tissue of sheep and goats.

TABLE 3 | DEmiRNAs commonly identified in PPRV infected lung tissue of sheep and goats.

pathways targeted by the miRNAs in the lung tissue of sheep and goats includes T cell receptor signaling pathway, Rap1 signaling pathway, Toll-like receptor TLR6:TLR2 signaling pathway, etc. (**Figures 4A,B**).

Similarly, functional annotation of 84 and 173 target proteins governed by two out of selected six miRNAs in infected sheep and goats spleen, respectively, identified enrichment of B cell receptor signaling and FC epsilon RI signaling pathways in both the species (**Figures 4C,D**). Furthermore, targets of DEmiRNAs in sheep spleen were also found enriched in FC-gamma R mediated phagocytosis and myeloid leukocyte mediated immunity. The targets of DEmiRNAs of goat's spleen were found involved in NF-κB signaling pathway, alpha–beta T cell differentiation, ErbB TABLE 4 | Six selected DEmiRNAs.

signaling pathway and regulation of B cell activation. In addition, the number of significantly enriched pathways and GO terms were higher in lung and spleen tissue of goats than in sheep.

# Validation of DEmiRNAs by qPCR

To further validate the expression of DEmiRNAs from highthroughput sequencing, qPCR was performed on three DEmiRNAs—miR-21-3p, miR-363, and miR-760-3p. The expression of miR-363 and miR-760-3p in sheep and goats in both the tissues was in concordance with small RNA sequencing results. The expression of miR-21-3p was found to be in concordance with the sequencing results in PPRV infected spleen of both the species. However, in infected lung miR-21-3p was found upregulated on qPCR though not found in small RNA sequencing data in both the species (**Figure 5** and **Table 5**).

## DISCUSSION

PPR is a major threat to livestock keepers in developing countries, causing a severe disease in goats and sheep. Host encoded miRNAs have been demonstrated to be key regulators of host– virus interactions, and their expression is often affected by viral infection (Hussain and Asgari, 2010; Skalsky and Cullen, 2010). Currently there is no report available suggesting PPRV infectioninduced changes in expression of cellular or host miRNAs. In the present study, we investigated the expression pattern of host miRNAs in spleen and lung of sheep and goats infected with PPRV vis-a-vis protein expression.

Detailed analysis revealed many differences in the global expression profile of miRNAs among lung and spleen, suggesting common and unique miRNA transcriptome landscape against PPRV. PPRV infection altered the expression of host miRNAs in lung and spleen. Under PPRV infection, a total of 37 and 67 DEmiRNAs were identified in lung of sheep and goats; and, 56 and 50 DEmiRNAs in the spleen of sheep and goats, respectively. PPRV infection in spleen and lung triggered the expression of many immune-related miRNAs, including, miR-21, miR-150, miR-146b, and let-7 family as reported in Japanese encephalitis virus infection (Cai et al., 2015). Moreover, 20 and 11 common DEmiRNAs expressed in spleen and lung of both species, respectively, suggested variable tissue response to PPRV infection. Among these 31 DEmiRNAs, six DEmiRNAs—miR-21-3p, miR-320a, miR-27a-5p, miR-1246 (expressed in lung of both species), miR-760-3p and miR-363 (expressed in lung and

spleen of both species) were selected based on their role in viral infection, apoptosis and fold change. In infected goat's lung all these six DEmiRNAs were found to upregulated. However, in infected sheep's lung, miR-27a-5p was found to be downregulated and the rest of the five DEmiRNAs were upregulated.

miR-21-3p induce apoptosis (Lo et al., 2013) and PPRV is also reported to cause apoptosis of host cells (Mondal et al., 2001). The upregulated miR-363 is also known to induce apoptosis (Zhang et al., 2014; Zhou et al., 2014; Hu et al., 2015; Li et al., 2015). The upregulation of miR-21-3p and miR-363 in PPRV infections suggests synergistic effect of these miRs along with the virus in inducing apoptosis. The upregulation of miR-363 has also been reported in Human papillomavirus (HPV)+ and HPV− pharyngeal squamous cell carcinoma and in HPV16+ HNSCC cell lines (Lajer et al., 2011; Wald et al., 2011). Recently, miR-27a-5p was found to be highly expressed in vaccinia virus infection (Buck et al., 2010). Under PPRV infection miR-27a-5p was found to be upregulated in infected lung of goats but downregulated in sheep suggesting a species-specific response. Further, miR-320a is known to inhibit mink enteritis virus infection by downregulating its receptor, transferrin receptor (TfR; Sun et al., 2014). Significant upregulation of miR-320a expression in PPRV infected lung tissue of sheep and goats suggests that miR-320a might serve in triggering antiviral response against PPRV infection. Sheng et al. (2014) reported that the upregulated miR-1246 decreased the expression of cell adhesion target genes and hence promotes the cytotoxicity induced by Ebola virus glycoprotein. Similarly, upregulated miR-1246 was found to promote cell death pathway by reducing the expression levels of DLG3 protein during HEV71 infection in human neuroblastoma cells (Xu et al., 2014). The increased expression of miR-1246 in the PPRV infected lung of goats and sheep in our study, could be a factor contributing to the pathogenesis of PPRV.

For miRNA studies, it is critical to identify targets for understanding its biological function and molecular mechanism (Tang et al., 2015). The miRNA–protein network analysis suggests that one miRNA could participate in several biological processes by targeting different mRNAs, and one biological process could be influenced by multiple miRNAs. The upregulated miR-27a-5p, miR-363, miR-320a, and miR-760-3p were observed to bring

about the downregulation of IGF1R in the PPRV infected lung of goats. IGF1R, is a multifunctional receptor that plays an important role in the regulation of immune response, including cell differentiation and proliferation (Smith, 2010). Similarly, TRIM24, tripartite motif containing 24, which is involved in cytokine signaling and secretion (Tisserand et al., 2011) was downregulated by miR-1246 and miR-320a in lung tissue of goats. miRNAs play an important role in regulation of NF-κB signaling pathway during viral infections (Gao et al., 2014) and activation of NF-κB is important for immune defense (Hoesel and Schmid, 2013). NF-κB signaling-related molecules IRAK2 were found modulated by miR-1246 and miR-320a, and TRAF4 was found modulated by miR-1246 and miR-760-3p in PPRV infected lung of goats in the present study. Interferon (IFN)-mediated pathway is a crucial part of the cellular response against viral infection (Wu et al., 2015). TRAF6, a major element in IFN production (Yoshida et al., 2008) was suppressed by PPRV-induced miR-21-3p and miR-320a in the lung of sheep. Similarly, the expression of IFIT5, which is involved in stimulating anti-viral response (Zhang et al., 2013) was suppressed by miR-363 in the spleen of sheep. This suggests that PPRV-induced miR-21-3p, miR-320a, and miR-363 might act cooperatively to enhance viral pathogenesis in the lung and spleen of sheep by downregulating several immune response genes. Further, this could be corroborated by the GO and pathway analysis of the potential targets of all the six DEmiRNAs. It was observed that the pathways governed by DEmiRNAs were more perturbed in goats than in sheep, thereby reflecting on the severity of disease in goats than in sheep.

### CONCLUSION

This study demonstrated for the first time DEmiRNAs in sheep and goats under PPRV infection. The DEmiRNAs identified in this study govern genes involved in immune response processes.

2.1.4) plugin of Cytoscape (ver. 3.1.1). (B) Gene ontology of immune-related KEGG pathways in goats lung visualized in ClueGO (ver. 2.1.4) plugin of Cytoscape (ver. 3.1.1). (C) Gene ontology of immune-related KEGG pathways in sheep spleen visualized in ClueGO (ver. 2.1.4) plugin of Cytoscape (ver. 3.1.1). (D) Gene ontology of immune-related KEGG pathways in goats spleen visualized in ClueGO (ver. 2.1.4) plugin of Cytoscape (ver. 3.1.1). Color from red to green of the nodes in the network depicts increase in significance. The diameter indicates the percent associated genes to a particular node.

#### TABLE 5 | qPCR validation of small RNA sequencing data.

reference gene for normalization. Fold change is represented as log2FC.

It was observed that PPRV elicits a strong host response in goats than in sheep as evident from the number of significantly enriched immune system pathways and genes perturbed. This study revealed that PPRV-induced miR-21-3p, miR-320a, and miR-363 might act cooperatively to enhance viral pathogenesis, which warrants further research.

# AUTHOR CONTRIBUTIONS

RS, BPM, AT, and RG conceived and designed the research. ARS, SW, SS, SK, and AK conducted the wet lab work. AP, SW, ARS, and RG analyzed the data. AP, ARS, SW, and RG wrote the manuscript. AP, ARS, SW, RG, VS, APS, KR, BM, and DM helped in manuscript drafting and editing. RS, BPM, AT, and RG proofread the manuscript.

# REFERENCES

# FUNDING

This study was supported in part by Centre for Agricultural Bioinformatics (ICAR-IASRI) and SubDIC (BTISnet), ICAR-IVRI.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2017.01146/full#supplementary-material

FIGURE S1 | Confirmation of PPRV infection in spleen and lung tissues of sheep and goats. Amplification of 351 bp N gene by RT-PCR. Lane M, 100 bp ladder; lane 1, NTC; lane 2, sheep spleen; lane 3, goats spleen; lane 4, goats lung; lane 5, sheep lung; lane 6, PPRV positive control.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Pandey, Sahu, Wani, Saxena, Kanchan, Sah, Rajak, Khanduri, Sahoo, Tiwari, Mishra, Muthuchelvan, Mishra, Singh and Gandham. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


**Mirosław Jabło ´nski**

Mirosław Jabłonski, Dr., is the Associate Professor in the Department of Quantum Chemistry ´ and Atomic Spectroscopy in the Faculty of Chemistry at the Nicolaus Copernicus University in Torun. He deals with computational chemistry in the field of molecular modeling and physical ´ organic chemistry. The subject of his scientific research concerns various intra- and intermolecular interactions, especially hydrogen, hydride, halogen, triel and agostic bonds. He is also interested in methods of studying electron density distribution, endohedral chemistry, carbenes and methods of determining the energy of intramolecular interactions.


**Mathematics and Physics of Computed Tomography (CT): Demonstrations and Practical Examples**

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**5.1 Menorrhagia (Case 6)**

A 12-year-old female was referred to pediatric hematology clinic for a possible bleeding disorder. She had had heavy menstrual periods since menarche 6 months previously. She had been admitted to a local community hospital 4 months earlier at which time she was found to be profoundly anemic with a hemoglobin of 5.9 g/dL, MCV 68.2 fL with symptoms of anemia requiring several blood transfusions. She was started on oral birth control pills with resolution of her menorrhagia. She was also started on daily iron sulfate tablets.

Apart from dysfunctional uterine bleeding, the patient did not have a past medical history of bleeding problems, nosebleeds, easy bruising, petechiae or mucosal bleeding. She denied any loose, bloody, tarry stools or abdominal pain. She had no food allergies. She was healthy before the onset of menarche except for daily headaches which had been evaluated by CT. Her father said he experienced similar headaches as an adolescent. Her father denied a family history of bleeding disorders or menorrhagia. She was doing well in 7th grade and enjoyed cheerleading.

In clinic the patient was alert and in no apparent distress. She was afebrile, with normal vital signs. Skin was unremarkable with no petechiae or bruising. Her conjunctivae were pink without pallor. Abdomen was soft, nontender, nondistended without hepatosplenomegaly. A comprehensive coagulation workup showed no evidence of a systemic bleeding disorder. At the time of her visit to pediatric hematology clinic her hemoglobin was 12.5 g/dL, MCV 82.4, fL, both normal. A ferritin level was normal.


2.2.2. Experimental Apparatus

The experimental apparatus is shown in Figure 1.

**Figure 1.** Flowsheet of the experimental apparatus: PI—pressure indicator; V-i—valves; MFC—mass flow controller; MFM—mass flow meter; PC—pressure controller; AC—adsorption column; analyzer—URAS ABB.

Details of the piping and control instrumentation are reported in a previous paper [22].


**Youqiang Xing**

Youqiang Xing, Ph.D, is an Associate Professor at the School of Mechanical Engineering, Southeast University, China. Dr. Xing's research interests include the interrelated areas of machining processes, micro-/nano-manufacturing, surface engineering, friction and wear, etc. He has published over 80 articles and authorized 30 invention patents in China. His article citation number is more than 2000, his h-index is 26, and his h10-index is 43. He obtained the Second Prize of Natural Science of Ministry of Education, China, among others.


**1. Introduction**

The production of items derived from sustainable and renewable resources, not toxic for humans and the environment, is a pressing challenge facing our society [1]. In this context, the production of sustainable coatings with improved and multifunctional performances is necessary [2]. As such, the search for coatings that have to be bio-based, with good barrier, water resistance and antimicrobial features is underway [3]. Nowadays, extensively used materials, with excellent moisture barrier properties for the production of coatings, are fundamentally petro-based. This must be the barrier to break down in research in the coming years [4].

Before going into the detail of the review subject, it is necessary to underline and to clarify the definition of biodegradable and bio-based polymers used for coatings formulation. The concept of biodegradation refers to biodegradable polymers that can be disintegrated and catabolized to CO2 and H2O by bacteria and/or enzymes [5,6]. Instead, bio-based polymers can be categorized based on their source, process technique, and formulation following the classification shown in Figure 1 [7]. In detail, bio-based polymers

**Citation:** Gigante, V.; Panariello, L.; Coltelli, M.-B.; Danti, S.; Obisesan, K.A.; Hadrich, A.; Staebler, A.; Chierici, S.; Canesi, I.; Lazzeri, A.; et al. Liquid and Solid Functional Bio-Based Coatings. *Polymers* **2021**, *13*, 3640. https://doi.org/10.3390/ polym13213640

Academic Editor: Begoña Ferrari

Received: 20 September 2021 Accepted: 20 October 2021 Published: 22 October 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

can be derived from biomass (like polysaccharides, lipids and proteins), from bio-derived monomers achieved by fermentation, such as lactic acid oligomers (OLAs); finally it is possible to find polymers developed from microorganisms (e.g., polyhydroxyalkanoates, PHAs) [8].

**Figure 1.** Schematic overview of bio-based polymers' differences.

In the field of coatings, these bio-based polymers represent the "new pathway to follow" because they can specifically provide to substrates multiple functionalities, also in relation to their processing conditions, without being petro-based [9].

Generally, functional bio-based coatings can be applied with the aim to improve the surface characteristics of a substrate (adhesion, wettability, water repellence, anti-corrosion properties and gas barrier. In other cases the coating can guarantee new properties in the final product, being an essential part of it [10].

This review will, therefore, be focused on the state of the art of bio-based and sustainable coatings production, with a detailed analysis of their application on cellulosic and plastic substrates. Moreover, the involvement of biomolecules in the coating formulations, but also the main technological innovations and the difference among liquid and solid preparation of bio-based sustainable coatings will be described in the following sections.

Indeed, to develop sustainable coatings for cellulose or bioplastic substrates is a technological goal of huge importance and it has become mandatory in the bioeconomy and circular economy context, aimed at imparting proper functional characteristics, based on biopolymer and functional materials coming from agro-food waste streams [11].

Coatings based on polymers, polymeric composites, and nanocomposites are used in several applications and sectors: (aerospace, automotive, marine structures, biomedical devices, decorative stuff, energy items, packaging). High-quality material is usually attained by thoroughly modulating layer/substrate.

Starting from paper substrates, it is well known that they are made of the most available bio-based material: the cellulose [12]. The use of cellulose-coated materials in personal care and disposable items for food (i.e., tableware) could be helpful for the environment and it is a route that has been followed in recent years [13]. The limits yet to be overcome are the hydrophilicity and low barrier properties typical of a non-woven fibrous system. For this reason, materials based on cellulose combined with poly(ethylene) are still widely present on the market, and these petro-based products are currently preferred, despite their negative environmental impact.

Regarding the application of coatings on plastic substrates, this is becoming increasingly necessary with the development of items with novel bio-based and biodegradable plastics based for example on poly (lactic acid) PLA, poly (butylene succinate) PBS, poly(butylene succinate-adipate) PBSA, as they do not show adequate barrier properties and are not able to withstand the rigours of the market [14–17]. In fact, since they do not present barrier properties comparable to traditional plastics [18], they need a protective layer. Obviously, in order not to affect the renewability and biodegradability of the product, a coating must also be developed with the characteristics of being bio-based and environmentally friendly [19].

Therefore, a considerable research effort "is on the agenda" investigating and formulating new bioplastics and new sustainable coating systems [20]. While their actual impact on the market is growing, it needs to be sharpened in many other applications.

The critical issue to achieving real progress towards sustainable materials is to intercept society's willingness to achieve sustainability; consumers must understand that obtaining sustainable products also means reducing global costs [21].


*Article* **A Morphological Study of Dynamically Vulcanized Styrene-Ethylene-Butylene-Styrene/Styrene-Butylene-Styrene/MethylVinylSilicon Rubber Thermoplastic Elastomer**

**Chunxu Zhao, Xiaohan Chen † and Xian Chen \*,†**

Room 602, Yifu Science and Technology Building, Wangjiang Campus, Sichuan University, Chengdu 610065, China<EMAIL_ADDRESS>(C.Z<EMAIL_ADDRESS>(X.C.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-028-85468166 † These authors contributed equally to this work.

**Abstract:** In this work, we prepared thermoplastic silicone rubber (TPSiV) by dynamically vulcanizing different relative proportions of methyl vinyl silicone rubber (MVSR), styrene ethylene butene styrene block copolymer (SEBS), and styrene butadiene styrene block copolymer (SBS). The compatibility and distribution of the MVSR phase and SEBS/SBS phase were qualitatively characterized by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) tests on TPSiV. Subsequently, the backscattered electron signal image was analyzed using a colorimeter, and it was found that the size of the interface layer between the MVSR phase and the SEBS-SBS phase could be quantitatively characterized. This method overcomes the defect of the etching method, which cannot quantitatively analyze the size of the compatible layer between the two polymers. The final experiment proved that the two phases in TPSiV exhibited a "sea-island" structure, in which the MVSR phase acted as a dispersed phase in the SEBS-SBS phase. In addition, the addition of the silane coupling agent KH-907 (γ-isocyanatopropyltriethoxysilane) improved the mechanical properties of TPSiV, increasing the tensile strength by about 40% and the elongation at break by 30%. The permanent tensile deformation increase rate was about 15%. Through the quantitative measurement of the compatible layer, it was found that KH-907 could increase the thickness of the interface layer between the MVSR phase and the SEBS-SBS phase by more than 30%, which explained why the silane coupling agent KH-907 improved the mechanical properties of TPSiV at the micro level.

**Keywords:** thermoplastic silicone rubber; backscattered electrons; compatibility layer; scanning electron microscope; dynamic vulcanization

## **1. Introduction**

Rubber is a class of polymers that is commonly used in daily life, and vulcanization is a necessary process to impart various properties to rubber [1,2]. Dynamic vulcanization refers to the melt blending of rubber and thermoplastic polymer at a high temperature and then vulcanizing the rubber under the action of a cross-linking agent, thereby obtaining a vulcanized rubber phase with a size in the micron level and uniformly dispersed in the thermoplastic polymer. Compared with other rubber vulcanization methods, dynamic vulcanization has the advantages of greatly improving the performance of blended thermoplastic elastomers, reducing equipment investment, and improving efficiency [3]. Since Gessler first proposed the concept of dynamic vulcanization in 1962, researchers have successively discovered polypropylene (PP)/ethylene propylene diene rubber (EPDM) [4], nitrile rubber (NBR)/polylactic acid (PLA) [5], and other dynamic vulcanization systems.

Silicone rubber is a polymer material with a silicon–oxygen bond as the main chain and phenyl, vinyl, and other groups as side chains and has excellent comprehensive properties [6]. At present, researchers often improve some properties of silicone rubber by

**Citation:** Zhao, C.; Chen, X.; Chen, X. A Morphological Study of Dynamically Vulcanized Styrene-Ethylene-Butylene-Styrene/Styrene-Butylene-Styrene/MethylVinylSilicon Rubber Thermoplastic Elastomer. *Polymers* **2022**, *14*, 1654. https://doi.org/ 10.3390/polym14091654

Academic Editors: Wei Wu, Hao-Yang Mi, Chongxing Huang, Hui Zhao and Tao Liu

Received: 16 March 2022 Accepted: 15 April 2022 Published: 20 April 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

blending it with different inorganic substances or polymers. In recent years, the development of special functions, such as high-voltage electrical properties, thermal conductivity, flame retardant properties, and antistatic properties of silicone rubber, has been a trend [7,8]. In addition to silicone rubber, thermoplastic elastomer is widely used because it has the physical and mechanical properties of vulcanized rubber and the processing properties of thermoplastic plastics [9,10]. As one of the most important types of thermoplastic elastomers, SBS is often used for asphalt modification [11]. Since the aging resistance of SBS is not ideal, SEBS obtained by hydrogenating SBS came into being, and it is often used in situations where high aging resistance is required [12]. In this paper, TPSiV prepared by melt blending MVSR and SEBS/SBS is also a material that can combine the biocompatibility of MVSR with the excellent mechanical properties of SEBS/SBS. This TPSiV can be used for medical elastomers and other applications. Before this, the study of this blend system was rarely reported.

Although a variety of materials with excellent properties can be obtained by melting and blending various polymers, because most polymers have poor compatibility with each other, solving the compatibility between polymers and enhancing the interaction between different phase interfaces has become the focus of researchers. Among many solutions, adding a silane coupling agent to the polymer blend system is one of the commonly used methods [13,14]. Therefore, in this paper, we chose the silane coupling agent KH-907 as the compatibilizer of TPSiV.

Scanning electron microscopy (SEM) is one of the most commonly used methods to characterize polymer compatibility and phase interface conditions [15]. The characterization of copolymer compatibility and phase interface by scanning electron microscopy can be roughly divided into two categories. One is to directly observe the surface with obvious phase separation by observing the difference in the phase separation before and after adding the compatibilizer as evidence of the enhanced interaction and improved compatibility of the two-phase interface [16–18]. The other is to dissolve the material section in a good solvent of a phase, dissolve and remove the phase, observe the section of the material, and measure the strength of the interaction between the phases by the roughness of the surface of the undissolved phase, to prove that a substance has an obvious effect on the improvement of the compatibility between the two phases [19–21]. However, this method is only applicable to the situation where one phase is completely dissolved by the solvent, and the other phase is not dissolved by the solvent at all, which is difficult in some dynamic vulcanization blend systems. In addition, SEM is often used in conjunction with Transmission Electron Microscopy (TEM) and Energy Dispersive X-Ray Spectroscopy (EDX) to characterize more information on the surface of the material [22,23]. However, TEM testing has high requirements for samples, and it is necessary to find suitable dyes.

The SEM test described above usually uses the secondary electron signal [24]. In addition to the secondary electron signal, the backscattered electron signal reflected from the sample surface can also be analyzed to obtain information about the sample surface [25,26]. This characterization method is usually used to observe the changes in alloy phases [27–29] and the phase distribution of cement cross-sections [30–32]. However, there are few reports on the characterization of dynamic vulcanization blend systems. Therefore, we applied the backscattered electron signal characterization method to the dynamic vulcanization system and quantitatively characterized the phase interface between the dispersed phase and the continuous phase by using the method combined with the colorimeter, which can avoid many shortcomings of the etching method. For example, it is impossible to effectively prove that the etched phase is completely etched, and the unetched phase collapses and easily adheres to the cross-section, which affects the observation and cannot quantitatively characterize the phase interface between the dispersed phase and the continuous phase. At the same time, this method can also observe and analyze the dynamic vulcanization blend system without obvious phase separation.