**1. Introduction**

[19] Bacon TH, Levin MJ, Leary JJ, Sarisky RT, & Sutton D (2003) Herpes simplex virus resistance to acyclovir and penciclovir after two decades of antiviral therapy. Clinical

microbiology reviews 16[1]:114-128.

250 Current Issues in Molecular Virology - Viral Genetics and Biotechnological Applications

The history of plant virology dates to the late 19th century, when Iwanowski and Beijerinck, who were investigating the cause of a mysterious disease of tobacco, independently described an unusual agent that caused tobacco mosaic disease. This agent was later named *Tobacco mosaic virus* (TMV) [1]. During this period, viruses including *Potato virus X* (PVX), *Potato virus Y* (PVY) and *Lettuce mosaic virus* (LMV) were described. These viruses could be distinguished based on their transmission and method of disease induction. In addition, numerous techni‐ ques for the study of viruses were developed.

Viruses are among the most agriculturally important groups of plant pathogens, causing serious economic losses in many major crops by reducing yield and quality. A virus can be defined as a set of one or more nucleic acid template molecules, often encased in a protective coat of protein or lipoprotein, which is able to organise its own replication only within suitable host cells [1]. Because the genetic information encoded by viral genomes is limited, viruses depend entirely on host cells to replicate their genome and produce infectious progeny. Both plant and animal viruses can be classified according to the type of nucleic acid that makes up their genome. In plants, the vast majority of viruses have positive-sense (+) RNA genomes (i.e., the RNA genome has the same polarity as cellular mRNA), although negative-sense (−) RNA and double-stranded RNA viral genomes also exist. Other plant viruses have DNA genomes; the DNA can be double-stranded (caulimoviruses) or single-stranded (geminiviruses) [1, 2]. Cell-to-cell movement of plant viruses occurs through cytoplasmic "bridges" between cells

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called plasmodesmata, and viruses are able to move systemically throughout plants via the phloem [1].

alternative pathway of host defence that occurs in parallel to RNA silencing and reinforces

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Over time, the strategies used by the virus to overcome these elaborate host defences can lead to a number of fundamental changes in the plant's physiology. Such changes, including structural modifications in the host cell, may give rise to intranuclear inclusions of various types and may affect the nucleolus or the size and shape of the nucleus. Within the mitochon‐ dria, abnormal membrane systems may develop [16, 17]. In plants infected with *Turnip yellow mosaic virus* (TYMV - Tymovirus), abnormalities such as clumping of chloroplasts and abnormal size and number of starch grains in leaf cells may occur, and small vesicles near the periphery and chloroplasts may become greatly enlarged and filled with starch grains [18]. These abnormalities were also observed in squash infected by the *Zucchini yellow mosaic potyvirus* (ZYMV - Potyvirus) [17]. In plant cell walls, three types of abnormality have been observed: abnormal thickening due to the deposition of callose near the edges of virus-induced lesions; cell wall protrusions involving the plasmodesmata (these protrusions may have one or more canals and may be quite short or of considerable length); paramural bodies, which are depositions of electron-dense material between the cell wall and the plasma membrane, may appear and extend over substantial areas of the cell wall, or be limited in extent occurring in association with plasmodesmata. Moreover, in the cytoplasm of an infected cell, virus particles may accumulate in sufficient numbers to form three-dimensional crystalline arrays. The ability to form crystals within the host cell cytoplasm depends on properties of the virus itself, and is not related to the overall concentration reached in the tissue or to the ability of the purified

Intriguingly, in carrot plants (*Daucus carota* L.) infected by *Cucumber mosaic virus* (CMV) some cytological and physiological changes were observed due to alterations in various host metabolites. With respect to cytological changes, scattered metaphase was observed in the diseased plant cells. The mitotic index of the diseased cells was decreased, while the nucleus / cytoplasm ratio was increased. Chromatin bridges were also observed at anaphase I and II. Physiological changes resulting in decreased carbon, nitrogen and protein content and

increased phosphorous content of the virally infected plants have been observed [19].

Other viral counterattack mechanisms involve changes in the plant cell cycle. In plants, as in all eukaryotes, the four phases of the mitotic cell cycle (G1, S, G2 and M) are conserved. During development, plant cells leave the cell division cycle, and in mature plants, DNA replication and the corresponding enzymes are confined to meristematic tissues [20]. Geminiviruses are good models for the study of the relationship between the cell cycle and viral DNA replication because they replicate in differentiated cells, such as mature cells of the leaves, stems and roots, in which most of the cellular factors required for viral DNA replication are normally absent. These cells have left the cell division cycle and no longer contain detectable levels of plant DNA replication enzymes necessary for geminivirus replication [21]. Due to the requirement for cellular factors, geminiviral DNA replication must be coupled to a special state of the infected cell, suggesting that the virus may have evolved mechanisms that affect the expression of cellular genes involved in S-phase progression and G1/S transition [22]. One such a mechanism involved in regulating changes of the host cell cycle appears to be the inactivation

cellular antiviral defence in plants [14].

virus to form crystals [1].

Viruses depend on other organisms (vectors) to transmit them from diseased to healthy plants. These vectors are often sap-sucking insects such as aphids, thrips, whiteflies, leaf-feeding beetles, plant-feeding mites, soil-inhabiting nematodes or fungal pathogens. Some viruses can be mechanically transmitted, on pruning knives or gardeners hands; or by grafting material, and a relatively small number of species can pass through infected seed [3]. Viruses use a variety of strategies that frequently induce disease in the plants they infect. Different viruses induce distinct diseases, and this can be true even for different strains of the same virus. Virus infection can profoundly alter the physiology of the host due to the interaction with cellular components. In plants, the severity of viral diseases varies considerably depending on the host genotype, the stage of infection and the environmental conditions. Diseases caused by viruses can vary broadly in intensity, from very mild symptoms observed in tolerant plants up to very severe symptoms and plant death [1, 4, 5].

Each plant virus encodes an average of 4-10 proteins necessary to coordinate the complex biochemical and intermolecular interactions required for viral infection cycles. The cycle of infection includes viral genome replication, cell-to-cell and systemic movement and transmis‐ sion [6]. For efficient viral infection to occur, viral proteins must be able to interact with factors in the host cell, thereby manipulating metabolic pathways and coordinating biochemical interactions that promote infection. Thus, during co-evolution between viruses and their hosts, a variety of complex interactions have developed that involve several distinct mechanisms of plant defence and virus attack.

During evolution, plants have developed diverse defence mechanisms that are activated during viral infection. One of these is the hypersensitive response (HR), which activates initial defence responses that prevent the infection from spreading further and then kills the cells within the infected zone. The onset of a second mechanism, systemic acquired resistance (SAR), then protects the plant together with HR against new attacks by the same pathogen. SAR is induced by a variety of agents after initial infection and can provide resistance to a wide range of pathogens for days [7-10]. The HR and SAR responses are accompanied by changes in gene expression that include the production of pathogenesis-related (PR) proteins and of several proteins involved in cell signalling [11].

Plants also possess other antiviral defence mechanisms such as RNA silencing, a remarkable type of gene regulation based on sequence-specific targeting and degradation of RNA [12], and the more recently described ubiquitin/26S proteasome system (UPS), which plays a central role in the degradation of proteins. The latter system is involved in almost all phases of the defence mechanisms of plants, regardless of the type of pathogen [13]. In addition to its proteolytic activity against ubiquitinated pathogen proteins, which directs their degradation by the 20S proteasome, the degradation of viral RNA can also occur via the ribonuclease activity of the 20S proteasome [14, 15]. While the proteasome as a structure, and RNA silencing as a mechanism, are two conserved features among eukaryotes, several lines of evidence suggest that the proteasome-linked RNase activity is most likely not directly related to RNA silencing. The selective degradation of viral RNAs by the 20S complex can represent an alternative pathway of host defence that occurs in parallel to RNA silencing and reinforces cellular antiviral defence in plants [14].

called plasmodesmata, and viruses are able to move systemically throughout plants via the

252 Current Issues in Molecular Virology - Viral Genetics and Biotechnological Applications

Viruses depend on other organisms (vectors) to transmit them from diseased to healthy plants. These vectors are often sap-sucking insects such as aphids, thrips, whiteflies, leaf-feeding beetles, plant-feeding mites, soil-inhabiting nematodes or fungal pathogens. Some viruses can be mechanically transmitted, on pruning knives or gardeners hands; or by grafting material, and a relatively small number of species can pass through infected seed [3]. Viruses use a variety of strategies that frequently induce disease in the plants they infect. Different viruses induce distinct diseases, and this can be true even for different strains of the same virus. Virus infection can profoundly alter the physiology of the host due to the interaction with cellular components. In plants, the severity of viral diseases varies considerably depending on the host genotype, the stage of infection and the environmental conditions. Diseases caused by viruses can vary broadly in intensity, from very mild symptoms observed in tolerant plants up to very

Each plant virus encodes an average of 4-10 proteins necessary to coordinate the complex biochemical and intermolecular interactions required for viral infection cycles. The cycle of infection includes viral genome replication, cell-to-cell and systemic movement and transmis‐ sion [6]. For efficient viral infection to occur, viral proteins must be able to interact with factors in the host cell, thereby manipulating metabolic pathways and coordinating biochemical interactions that promote infection. Thus, during co-evolution between viruses and their hosts, a variety of complex interactions have developed that involve several distinct mechanisms of

During evolution, plants have developed diverse defence mechanisms that are activated during viral infection. One of these is the hypersensitive response (HR), which activates initial defence responses that prevent the infection from spreading further and then kills the cells within the infected zone. The onset of a second mechanism, systemic acquired resistance (SAR), then protects the plant together with HR against new attacks by the same pathogen. SAR is induced by a variety of agents after initial infection and can provide resistance to a wide range of pathogens for days [7-10]. The HR and SAR responses are accompanied by changes in gene expression that include the production of pathogenesis-related (PR) proteins and of several

Plants also possess other antiviral defence mechanisms such as RNA silencing, a remarkable type of gene regulation based on sequence-specific targeting and degradation of RNA [12], and the more recently described ubiquitin/26S proteasome system (UPS), which plays a central role in the degradation of proteins. The latter system is involved in almost all phases of the defence mechanisms of plants, regardless of the type of pathogen [13]. In addition to its proteolytic activity against ubiquitinated pathogen proteins, which directs their degradation by the 20S proteasome, the degradation of viral RNA can also occur via the ribonuclease activity of the 20S proteasome [14, 15]. While the proteasome as a structure, and RNA silencing as a mechanism, are two conserved features among eukaryotes, several lines of evidence suggest that the proteasome-linked RNase activity is most likely not directly related to RNA silencing. The selective degradation of viral RNAs by the 20S complex can represent an

phloem [1].

severe symptoms and plant death [1, 4, 5].

plant defence and virus attack.

proteins involved in cell signalling [11].

Over time, the strategies used by the virus to overcome these elaborate host defences can lead to a number of fundamental changes in the plant's physiology. Such changes, including structural modifications in the host cell, may give rise to intranuclear inclusions of various types and may affect the nucleolus or the size and shape of the nucleus. Within the mitochon‐ dria, abnormal membrane systems may develop [16, 17]. In plants infected with *Turnip yellow mosaic virus* (TYMV - Tymovirus), abnormalities such as clumping of chloroplasts and abnormal size and number of starch grains in leaf cells may occur, and small vesicles near the periphery and chloroplasts may become greatly enlarged and filled with starch grains [18]. These abnormalities were also observed in squash infected by the *Zucchini yellow mosaic potyvirus* (ZYMV - Potyvirus) [17]. In plant cell walls, three types of abnormality have been observed: abnormal thickening due to the deposition of callose near the edges of virus-induced lesions; cell wall protrusions involving the plasmodesmata (these protrusions may have one or more canals and may be quite short or of considerable length); paramural bodies, which are depositions of electron-dense material between the cell wall and the plasma membrane, may appear and extend over substantial areas of the cell wall, or be limited in extent occurring in association with plasmodesmata. Moreover, in the cytoplasm of an infected cell, virus particles may accumulate in sufficient numbers to form three-dimensional crystalline arrays. The ability to form crystals within the host cell cytoplasm depends on properties of the virus itself, and is not related to the overall concentration reached in the tissue or to the ability of the purified virus to form crystals [1].

Intriguingly, in carrot plants (*Daucus carota* L.) infected by *Cucumber mosaic virus* (CMV) some cytological and physiological changes were observed due to alterations in various host metabolites. With respect to cytological changes, scattered metaphase was observed in the diseased plant cells. The mitotic index of the diseased cells was decreased, while the nucleus / cytoplasm ratio was increased. Chromatin bridges were also observed at anaphase I and II. Physiological changes resulting in decreased carbon, nitrogen and protein content and increased phosphorous content of the virally infected plants have been observed [19].

Other viral counterattack mechanisms involve changes in the plant cell cycle. In plants, as in all eukaryotes, the four phases of the mitotic cell cycle (G1, S, G2 and M) are conserved. During development, plant cells leave the cell division cycle, and in mature plants, DNA replication and the corresponding enzymes are confined to meristematic tissues [20]. Geminiviruses are good models for the study of the relationship between the cell cycle and viral DNA replication because they replicate in differentiated cells, such as mature cells of the leaves, stems and roots, in which most of the cellular factors required for viral DNA replication are normally absent. These cells have left the cell division cycle and no longer contain detectable levels of plant DNA replication enzymes necessary for geminivirus replication [21]. Due to the requirement for cellular factors, geminiviral DNA replication must be coupled to a special state of the infected cell, suggesting that the virus may have evolved mechanisms that affect the expression of cellular genes involved in S-phase progression and G1/S transition [22]. One such a mechanism involved in regulating changes of the host cell cycle appears to be the inactivation of the retinoblastoma protein (pRb), which negatively regulates the G1/S transition in cells. The Rep protein, which is encoded by all geminiviruses and is the only viral protein necessary for viral DNA replication, has been found to induce expression and also interacts with the host "proliferating cell nuclear antigen" (PCNA), an auxiliary protein of DNA polymerases required during replication and repair in non-dividing plant cells. This observation suggests that Rep protein can provide the necessary stimulus to induce the dedifferentiation process. Mechanisms other than sequestering plant pRb most likely contribute to the multiple effects of geminivirus proteins on cellular gene expression, cell growth control and cellular DNA replication [21, 22].

three purposes: 1) creating and maintaining heterochromatin at repetitive DNA and transpo‐ sons; 2) regulating development, stress responses and other endogenous regulatory functions; 3) defending against viral and bacterial infections [29]. Silencing is utilised in developmental pathways and in cellular differentiation to repress genes whose products are not required at specific stages of development or in specific cell types; in plants silencing is also used to respond to internal and external stresses by changing the expression of specific genes involved

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Initially discovered in transgenic plants, especially in those created to acquire virus resistance, RNA silencing is now also believed to be responsible for various epigenetic effects and their maintenance; the silencing of transgenic loci in plants most likely results from the activation of defence mechanisms. A number of silencing studies with different plant systems have explored transgenes as indicators of silencing pathways; these works have received important attention in part because silencing reduces the reliability of transgenic approaches in biotech‐

RNA silencing can be classified into two major categories: transcriptional gene silencing (TGS) and post-transcriptional gene silencing (PTGS). TGS is defined as inhibition of transcription, whereas PTGS involves the post-transcriptional degradation of RNA species but does not affect the transcription rate [30]. TGS occurs when double-stranded RNA molecules (dsRNA) containing promoter sequences are present, and PTGS occurs when dsRNA comprise open reading frames (ORF). Together, TGS and PTGS depend on small interfering RNAs (siRNA) or microRNAs (miRNA) that are produced from dsRNA precursors [31]. Because RNA silencing, mainly PTGS, also contributes to antiviral immunity in plants, fungi and inverte‐ brates, it is an important part of innate immunity [32]. The silencing may persist over many

The basic steps in common to all RNA silencing pathways (Figure 1) include: (i) formation of a dsRNA; (ii) processing of the dsRNA by an RNase III–like enzyme named Dicer (DCL) to shorter (20-30 nucleotide) dsRNA duplexes, the so called siRNA (iii) binding of the small RNA duplexes to a protein from the Argonaute (AGO) family; and (iv) targeting of the RNA-induced complex to mRNA (or DNA) guided by strand complementary to the small dsRNA, which is called the guide [34]. At present, there is good evidence for the existence of at least four different types of RNA silencing pathways in plants. These pathways involve different types of small RNA molecules, specially siRNA and miRNA. Heterochromatin-associated siRNA (hcsiRNA), trans-acting siRNA (tasiRNA) and viral siRNA (primary and secondary) are also important in silencing [35]. A better understanding of silencing pathways is very important because of the potential usefulness of silencing as a powerful tool for gene function studies

As already mentioned, PTGS is essential to antiviral immunity in plants, thus our focus will be concentrated in this phenomenon. PTGS was first observed in 1990, and initially referred to as 'co- suppression', it was first discovered in transgenic petunia plants in which the

in the response. In some situations, tissue- or cell-specific silencing is desirable.

nology of several agriculturally important cultures.

cell divisions or plant generations [33].

and crop improvement.

**2.1. Post-transcriptional gene silencing**

Ultimately, RNA silencing suppressor (RSS) proteins are able to block or attenuate plant host defence mechanisms, particularly post-transcriptional gene silencing and efficiently inhibit host antiviral responses by interacting with the key components of cellular silencing machi‐ nery, often mimicking their normal cellular functions [23]. Viral suppressors of RNA silencing have been identified from almost all plant virus genera; these VSRs are surprisingly diverse, exhibiting no obvious sequence similarities. Most identified VSRs are multifunctional; in addition to being RNA silencing suppressors, they often perform essential roles, functioning as coat proteins, replicases, movement proteins, helper components for viral transmission, proteases or transcriptional regulators. The first viral RNA silencing suppressor identified was the helper component-proteinase (HC-Pro) of potyviruses [24]; currently, many different suppressors are known.

The aim of this chapter review is to discuss the current status of knowledge regarding various components of host silencing machinery and viral suppression. We also pretend to describe how the defence response in plants is directed against the virus and, in particular, how the virus can sidetrack the host's defence response. Relevant topics on the molecular bases of the induction and suppression of the RNA silencing mechanism, as well as the applications and perspectives of the use of silencing suppression in plant biotechnology, will be emphasised.


**5. Strategies to eliminate latent reservoirs**

**4.9. RNA based therapeutics**

to HIV infection, which is discussed later [106].

222 Trends in Basic and Therapeutic Options in HIV Infection - Towards a Functional Cure

strategies to enhance target cell resilience to HIV infection).

RNA based therapeutic strategies exert their action between the phases of transcription and translation. Numerous RNA based techniques have been developed which are classified by their mechanism of action. They include; inhibitors of messenger RNA (mRNA) translation (antisense oligonucleotides), the agents of RNA interference (RNAi), catalytically active RNA molecules (ribozymes) and RNAs that bind proteins and other molecular ligands (aptamers) [104, 105] (Table-5). These techniques can be utilized for the treatment of any viral disease by engineering specific, complementary inhibitory RNA particles to the viral transcription components. Among these techniques, RNAi and to a lesser extent antisense oligonucleotides, have been tried out to inhibit retroviral replication. As these techniques can also target the host cellular processes, they are being exploited in strategies to increase the resilience of target cells

RNAi is an endogenous mechanism which involves the down regulation of mRNA activity during transcription and post transcription phases using short double stranded RNA (dsRNA) called micro RNA (miRNA), which are about 20-30bp long. The identification of this regulatory process has provoked the interest of controlling unwanted viral replication using exogenously administered specific sequences of short dsRNA. The exogenously administered agents of RNAi therapy include small interfering RNA (siRNA) and short hairpin RNA (shRNA). These agents act on the post transcript mRNA and either cause direct sequence specific cleavage when there is a perfect sequence complementarity match, or lead to translational repression and degradation of mRNA when the interfering RNA sequence is of limited complimentarity to the targeted mRNA. As the siRNA get cleared off after their action, their effects are only transient and need repeated administration similar to agents of chemotherapy. On the other hand the action of shRNA is similar to gene therapy, as they get expressed on promoters and cause long term effects. Various viral and non-viral delivery mechanisms and active targeting strategies have been developed to deliver these active agents into the target cells [107, 108].

Systems employing siRNA and shRNA to target the gene products of tat, rev, nef, env, vif and pol have been designed and evaluated for efficacy [47, 106]. However, the use of this technol‐ ogy against viral replicatory processess is threatened by the genetic variation exhibited by HIV. Very simple mutations allow HIV to escape from the action of both siRNA and shRNA [109, 110]. Four possible solutions are being tried to tackle the problem of these escape mutants. The first attempt is to expand the RNAi technique to simulataneously inhibit multiple HIV mRNA targets similar to the concept of multidrug use. Recent studies have demonstrated that concurrent inhibition of HIV mRNA with three different shRNAs can prevent viral escape *in vitro* [111, 112]. In the second possible solution, inhibitory RNAs with a complete match to the most commonly encountered viral escape sequences are being designed. When used along with the inhibitory RNA of the wild type virus, these could prevent a majority of the mutants from escape [113]. The third solution involves identifying novel, genetically conserved sequences of HIV which do not usually undergo mutation. Targeting these stable sites would favour the success of RNAi [114]. Finally, RNAi techniques are also being designed to restrict the 'genetically more stable' host factors that help in HIV replication (discussed later under

The ability to integrate its nucleic acids with host cell genome and co-exist in a genetically quiescent proviral state is one of the distinct features of HIV, which makes eradication of infection next to impossible. The latent reservoirs of HIV are categorized into two broad groups namely the cellular and the anatomical reservoir. The cellular reservoir comprises the long lived resting CD4 T-cells. With an extended lifespan averaging 400 days, these cells bear the provirus and release the progeny upon activation at a later time [116]. Infection of the cells of the monocyte-macrophage lineage and subsequent compartmentalization of these infected cells into various organs / tissues such as the reticulo-endothelial system, lymph nodes, gastrointestinal tract, brain and lungs, lead to the formation of anatomical reservoirs. Within the macrophages of these anatomical reservoirs, the HIV either remains quiescent inside the chronically infected cells or maintains a continuous low level replication [117]. Thus, the reservoirs act as Trojan horses spilling the progeny virions into circulation at periodic intervals. Although the mechanisms involved in proviral repression are gradually being unraveled, specific factors / agents reactivating the viral replication are yet to be clearly identified. Although early aggressive treatment helps in achieving a functional cure by limiting the reservoir formation, it does not offer a solution to the already established reservoirs. The new strategies and techniques that are being developed for eradicating the established viral reservoirs are subsequently discussed.

#### **5.1. Targeted drug delivery to the reservoirs**

Apart from attempts to limit viral replication in actively infected CD4 T-cells, nanotechnology methods are widely studied for the elimination of the latent viral reservoirs. Strategies include passive and active targeting and the use of surface moieties which enhance the penetration of biological barriers by the nanoparticles.

Upon systemic administration, most of the nanoparticles are instantaneously opsonized with plasma proteins and rapidly cleared off from circulation by the phagocytosis. The macro‐ phages of the reticulo-endothelial system are the principal cells which are involved in the degradation and clearance of the nanoparticles [118]. This forms the basis of passive targeting which aims at achieving high concentrations of anti-retroviral agents in the reticulo-endothe‐ lial reservoir as a consequence of phagocytosis of the drug loaded nanoparticles. Studies have demonstrated the achievement of higher drug concentrations in the reticulo-endothelial cells following the administration of liposomes, nanocapsules or polymeric nanoparticles loaded with anti-retroviral drugs [119]. Likewise, passive targeting of the lymphatic reservoir can be achieved by incorporation of the nanoparticles with lipids such as phosphatidylcholine and cholesterol or by surface coating of the nanocarriers with polyethylene glycol [120, 121].

Apart from passive targeting, specific drug delivery to the anatomical reservoirs can be accomplished by active targeting strategies. Nanocarriers tagged on their surface with galactose or mannose residues or anti-HLA-DR monoclonal antibodies, effectively localize in the reticulo-endothelial system which have abundant receptors for these ligands such as the galactose and lectin receptors and the HLA-DR determinant of MHC-II respectively. Active targeting of the cellular reservoirs is based on the aforementioned principle of employing nanocarriers with homing ligands to viral components on infected cell surface. The infected resting CD4 T-cells containing HIV envelope glycoproteins on their surface attract the drug loaded nanoparticles coated with recombinant CD4 molecules or Fab fragments of monoclonal anti-gp120 antibodies [13].

Nanotechnology also offers a solution for the problem of poor drug penetration in certain anatomical sites. One such anatomical reservoir is the brain, where the infected microglial cells rest safely with the protection conferred by the blood brain barrier against the system‐ ically administered anti-retroviral agents.  With the ability to increase the crossing and reduce the efflux of drugs, nanotechnol‐ ogy methods help in reservoir elimination by overcoming the privilege offered by the blood brain barrier [122].

Nanotechnology methods that increase the drug transport across the blood brain barrier function by mimicry of natural substrates, utilization of cell penetrating peptides or by active targeting of molecules of abundance in neuronal vasculature with suitable surface ligands. The non-ionic surfactant 'polysorbate-80' has found to be an effective enhancer of drug delivery to the brain using the principle of substrate mimicry. Upon systemic administration, the drug loaded nanoparticles coated with polysorbate-80 adsorb various apolipoproteins in circulation to form a complex that mimics lipoproteins.  Nanoparticles coated with cell penetrating peptides such as the HIV-1 tat peptide has shown enhanced efficacy in crossing the blood brain barrier. As the microvasculature of the brain is rich in receptors such as transferring receptor, low-density lipoprotein receptor and β2 receptors, their respective ligands such as transferrin, apolipoprotein-E and β2 agonists are considered possible candidates for active targeting.

The use of nanotechnology for reservoir elimination poses some peculiar problems in addition to other drawbacks mentioned earlier. The effectiveness of nanotechniques that rely on antiretroviral drugs to eliminate the reservoir is questionable as these agents are lethal to the virus only during active replication and do not affect the inactive provirus. The final clearance of the nanoparticles from the anatomical sites, especially from the brain is not clearly known and accumulation overtime with repeated doses can possibly lead to neurotoxicity. Acquisition of more knowledge through carefully designed clinical trials is needed so that the potential of this technology could be put into adequate use [122].


*6.1. A Brief Description of the Training Testing Set*

Before the study, we needed correlation planes references. The good and bad planes of references were made like it is described in step 2 of Section 4. There are also training and testing sets. In order to clarify these notions, we refer to Figure 5. We call the training set the reference images of step 1 in Figure 5. The testing set represents the set of all the images of the target images.


CD45 can be expressed in various immunocompetent cells, including dendritic cells, T lymphocytes, B lymphocytes, macrophages, etc. [25,26]. CD45 was used as a general marker of immunocompetent cells in the synovial layer in this study. The CD3 antigen binds to the membranes of all T lymphocytes and virtually no other cell types, which makes it a useful immunohistochemical marker of T lymphocytes in tissue sections [27]. The CD68 antigen is mainly expressed by monocytes and macrophages. When monocytes migrate into local tissues, they differentiate into macrophages [28,29]. Therefore, we used CD68 as a marker of macrophages in this study. To analyze the systemic biological reactions caused by metal particles and metal ions in our murine model, the immunohistochemical markers mentioned above were analyzed in the right knees of all groups.

Images of the stained samples were collected under a light microscope at 200× magnification (Carl Zeiss Micro Imaging GmbH, Oberkochen, Germany). Every image included the most synovial tissue (region of interest) of each sample. If cells or tissues were stained from light yellow to brown, positive immunostaining was recorded. Two observers independently performed manual counts of the obtained images with the assistance of Image J software (National Institutes of Health, Bethesda, MD, USA) (Ver.1.43, available at rsbweb.nih.gov/ij). Preceding manual counting, images were cropped, scaled to μm and separated by a color channel, and artifacts were removed. An area tool was used to select the synovial region and calculate the area. The Image J cell counter tool recorded mouse clicks on cells that were labeled with colored dots. The results were saved to a spreadsheet and screen shots were used to record the session. Positive cells from the synovial membrane were counted by each observer. If the results were inconsistent, specific samples were collected and counted by both observers for a third time.

#### *2.5. Statistics*

The data obtained from the immunohistochemical evaluation were evaluated using IBM SPSS®Statistics 22 (IBM Deutschland GmbH, Ehningen, Germany). Statistical analyses were carried out with the non-parametric Kruskal–Wallis test for independent samples. *P*-values below 0.05 were considered statistically significant and were adjusted by Bonferroni correction. A graphical presentation of the results was produced using box plots.

#### **3. Results**

#### *3.1. Characterization of Metal Particles and Ions*

For the generated metal particles, the mean size was in the nanometer range (ECD: 61.25 ± 18.47 nm). Meanwhile, their aspect ratio was 1.69 ± 0.66 and roundness was 0.64 ± 0.16 (Table 1). The particle shape was predominantly round and oval; in addition, a small proportion of acicular particles were formed (shape: round, 44%; oval, 49%; needle, 7%). This size and morphology of the particles were consistent with clinically found metal particles after a MoM total hip replacement [30,31].

**Table 1.** Morphological parameters of particles. The CoCr29Mo6 particle shape is predominantly oval and round with a small proportion of acicular particles (shape: round, 44%; oval, 49%; needle, 7%). Equivalent circular diameter (ECD).

The CoCr29Mo6 particle shape was predominantly oval and round with a small proportion of acicular particles (shape: round, 44%; oval, 49%; needle, 7%).

Using HR-ICP-MS, a total metal ion content (cobalt, chromium, nickel and molybdenum) of 20.5 mg/L was determined, which was adjusted to the desired target concentration of 200 μg/L using PBS (Table 2) [32]. The target concentration of ions was based on the concentration measured in the synovial fluid of patients with endoprosthesis during revision surgery [22].

Total ion concentrations of the CoCrMo stock solution as well as in the experimental solution (200 μg/L in total) according to Jonitz-Heincke et al. [32].

#### *3.2. Results of the Left Knee Joints*

#### 3.2.1. Expression of TNF-α, IL-1β and IL-6

With regard to the staining results of TNF-α, IL-1β and IL-6, the MP group showed a significantly increased number of positive cells compared with the PBS group (*p* < 0.05). However, the MI group did not express significantly more TNF-α (*p* = 0.056), IL-1β (*p* = 0.420) and IL-6 (*p* = 0.124) than the PBS group. Therefore, according to these results, the MP group had stronger inflammatory reactions in the synovial layers of the left knees (Figure 1).

**Figure 1.** Expression of inflammatory markers (IL-1β, IL-6 and TNF-α) in the synovial layer of left murine knees. The staining results of TNF-α in the negative control (NC), phosphate-buffered saline (PBS), metal ion (MI) and metal particles (MP) group. Both the strongest expression and a thickened synovial layer are found in the MP group. (**A**) The expression of TNF-α in the synovial layer of left murine knees. (**B**) The expression of IL-1β in the synovial layer of left murine knees. (**C**) The expression of IL-6 in the synovial layer of left murine knees. The staining results of IL-1β, TNF-α and IL-6 are consistent. The MP group had the strongest inflammatory reactions. (Magnification: 200x; \* *p* < 0.05).

#### 3.2.2. Expression of CD68, CD3 and CD45

The MP group showed a significantly higher number of CD45-positive cells compared with the PBS group (*p* < 0.05). Likewise, the MI group had a significantly different number of CD45-positive cells compared with the PBS group (*p* < 0.05). Interestingly, in terms of CD45, there was no significant difference between the MP group and the MI group (Figure 2A).

**Figure 2.** Expression of CD45, CD3 and CD68 in the synovial layer of left murine knees. The staining results of CD45 in the NC, PBS, MI and MP group. Numerous CD45 positive cells were found in the MP and MI group. MP and MI groups had significantly increased positive cells compared with the PBS group (*p* < 0.05). (**A**) The expression of CD45 in the synovial layer of left murine knees. (**B**) The expression of CD3 in the synovial layer of left murine knees. No significant difference was found in all groups. (**C**) The expression of CD68 in the synovial layer of left murine knees. (Magnification: 200x; \* *p* < 0.05).

With respect to the expression of CD3 positive cells in our study, there was no statistically significant difference among the three groups (*p* = 0.45) (Figure 2B).

The MP group showed a significantly increased number of CD68 positive cells compared with the PBS group (*p* < 0.05), and the MI group (*p* < 0.05) in the synovial layer of the left knees. There was no significant difference between the MI group and the PBS group (*p* = 1.0). Considering that macrophages play an important role in inflammatory reactions, these results indicate that the MP group had the strongest inflammatory reactions, which is consistent with the TNF-α, IL-1β and IL-6 results (Figure 2C).

#### *3.3. Results of the Right Knee Joints*

#### 3.3.1. Expression of TNF-α, IL-1β and IL-6

For TNF-α and IL-1β, the number of positive cells in the MP group was only significantly increased compared with the PBS group (*p* < 0.05). There was no significant difference among all groups in the expression of IL-6 in the synovial layer of the right murine knee joints, (*p* = 0.13) (Figure 3A–C).

**Figure 3.** Expression of all biological markers in the synovial layer of right murine knees. The aim of examining the right knees was to evaluate systemic reactions caused by the dissemination of metal particles and metal ions. (**A**) The expression of TNF-α in the synovial layer of right murine knees. (**B**) The expression of IL-6 in the synovial layer of right murine knees. (**C**) The expression of IL-1β in the synovial layer of right murine knees. (**D**) The expression of CD45 in the synovial layer of right murine knees. The MI group exhibited a significant difference compared with the PBS groups (*p* < 0.05). (**E**) The expression of CD3 in the synovial layer of right murine knees. (**F**) The expression of CD68 in the synovial layer of right murine knees. (\* *p* < 0.05).

#### 3.3.2. Expression of CD68, CD3 and CD45

The expression of CD45-positive cells in the MI group was significantly increased compared with all other groups (PBS: *p* < 0.05, MP: *p* < 0.05). No statistically significant difference was found in the direct comparison of the PBS and MP groups (*p* = 1.0). Meanwhile, there was no significant difference among all groups in the expression of CD68 and CD3 in the synovial layer of the right knees (CD68: *p* = 0.55, CD3: *p* = 0.91) (Figure 3E–G).

#### **4. Discussion**

Our initial hypothesis for this study could not be proved. No increased T-lymphocyte markers were found in either the MP or MI group. However, in terms of proinflammatory reactions (IL-1β, IL-6 and TNF-α), the MP group had stronger reactions than the MI group. In addition, in terms of systemic reactions, the MP group and the MI group both had significantly increased biological reactions in the synovial membrane of right-sided knee joints compared to the PBS (control) group.

Histopathological studies can decisively contribute to the determination of the main cell populations underlying the biological mechanisms of aseptic inflammation and osteolysis [33]. In addition, the key protein molecules (such as IL-1β, IL-6 and TNF-α) involved in aseptic loosening can also be precisely detected by immunohistochemistry [34,35]. Therefore, periprosthetic tissues harvested during revision surgery, especially the bone–implant interface membrane and pseudo-synovial tissues, are frequently examined via histological techniques in clinical work [14,36]. However, periprosthetic tissues from revision surgeries mainly reflect end-stage failure and may not adequately reveal the primary biological reactions caused by metal particles and ions released immediately after the initial surgery. Nevertheless, the primary biological reactions caused by metal products in vivo may be crucial to elucidate the evolution of the pathophysiological events that lead to prosthetic osteolysis. In light of the limitations mentioned above, an established murine model was used in this study, which mimics the initial biological reactions in the synovial-like tissues around prostheses caused by metal particles and ions [35,37–39]. In contrast to various in vitro cell culture studies that focus on one cell type [32,40], the murine model can not only reflect complex cellular and tissue interactions, but can also mimic the dynamic process of joints. Additionally, compared with other models, such as hamsters' skinfold-chamber models [41] and "air pouch" models [42], results from this model, as the suspensions were injected intraarticularly, are easier to translate into a clinical scenario because the generated wear debris primarily accumulates in the joint that has been replaced. In terms of the characteristics of metal materials, the CoCrMo particles and ions used in our study were consistent with those found in some clinical studies.

CD45 can be expressed on many types of cells in the immune system, such as lymphocytes, natural killer cells, granulocytes, dendritic cells and monocytes/macrophages [26,43]. Therefore, CD45 was chosen as a general marker of immunocompetent cells in the synovial layer. For the left (treated) mice knees, the MP group and MI group both showed significantly increased CD45-positive cells compared with the PBS group, assuming that some immunocompetent cells were recruited to the synovial layer as a result of the metal particles and metal ions. However, it is difficult to distinguish specific immunocompetent cells and biological reactions solely from marking CD45. CD3 and CD68 were used as specific markers for T lymphocytes and macrophages [27], while IL-1β, IL-6 and TNF-α were used as common inflammatory markers [16].

For the groups of left knees that received an intra-articular injection, the MP group's inflammatory reactions (IL-1β, TNF-α, IL-6 and CD68) increased considerably more than those of the other groups. Considering the physical properties of CoCrMo particles, released nanometer-sized particles probably cause physical harm, especially when the knee joint is in motion [21]. This may be one reason why CoCrMo particles had very intensive inflammatory reactions in this in vivo study. Additionally, via various released cellular mediators, damaged cells can lead to the activation and recruitment of immunocompetent cells, especially macrophages.  Inside the cells, the particles are exposed to oxidative attacks, and consequently, high levels of ions might be released during the chemical corrosion processes [45]. Therefore, CoCrMo particles not only have a destructive effect because of their physical properties—the corrosion process and the presence of metal ions can also cause some biological reactions in the surrounding tissues [46]. Furthermore, because of the presence of phagocytized debris, cellular necrosis can occur and many mediators of inflammation are released by macrophages, which can recruit more macrophages, amplifying the inflammatory cascade [20]. Numerous macrophages (CD68 positive cells) were found in the MP group in this study. Although metal ions can also initiate oxidation-reduction reactions, the metal ions were rapidly quenched, while metal particles offered a reservoir of redox-reactive metals for continuous Reactive oxygen species (ROS) generation, which could further explain why metal particles resulted in the strongest local inflammatory reactions in this study.

However, in contrast with the inflammatory reaction results in our murine model, some in vitro studies have shown different results. Chamaon et al. indicated that the treatment of a human monocytic cell line with ionic cobalt led to a decrease in metabolic activity [Water Soluble Tetrazolium-1(WST-1) assay], while CoCrMo particles had no effect [47]. They explained that the rather large abrasive particles (from 200 nm to several micrometers) used in their study might have resulted in the relatively low impact of CoCrMo particles [47]. Because particle size is considered a critical parameter that influences the biological reactions to wear particles, the majority of MoM-produced particles are usually from 40 to 60 nm [2,48]. One easily neglected factor should also be considered: the in vitro cell cultivation process was static and could not reflect physical injury from metal particles in vivo. Moreover, Caicedo et al. showed that similar increases of IL-1β, TNF-α and IL-6 were evoked by cobalt, molybdenum ions, and Co-Cr-Mo alloy particles in human monocytes/macrophages [49]. Meanwhile, they found that ions and cobalt alloy particles induced inflammasome activation in vitro in a dose-dependent manner [50]. In our murine model, metal ions might be unavoidably systemically disseminated via lymphatics and blood vessels and then circulated throughout the host body, probably leading to a more general distribution than the metal particles. The gradually decreased metal ion level could attenuate local inflammatory stimulation effects, which might be one important reason why the level of inflammatory mediators (IL-1β, IL-6 and TNF-α) and the number of macrophages (CD68+ cells) did not significantly increase in the MI group in our study.

According to the literature, a cell-mediated (type-IV delayed hypersensitivity) response, which is mainly characterized by the activation of T lymphocytes, can be triggered by metal particles and ions during the process of aseptic loosening [19,51]. At least theoretically, metal ions are able to activate type-IV hypersensitivity by forming haptens with host proteins [16,52]. Compared with toxic reactions, type-IV hypersensitivity reactions do not have a simple dose–response relationship, with higher doses being more potent than lower doses, because even small amounts of antigens can cause strong reactions [17]. In our study, we attempted to count T lymphocytes (CD3-positive cells) to verify the existence of a type-IV hypersensitivity response. However, unexpectedly, regarding T lymphocytes, there were no significant differences among the groups. A delayed hypersensitivity response typically occurred between 24 and 48 h after exposure to an antigen [53]. One possibility was that a delayed-type hypersensitivity response did not play an important role in aseptic implant loosening, and that T lymphocytes were scarce in the primary process. Many researchers support this opinion, because there is a relatively low revision rate related to "allergic" loosening [20]. Approximately 10% of the population is hypersensitive to the materials found in jewelry and joint replacements [54]. If metallic debris and ions can induce a serious hypersensitivity response in the periprosthetic tissues, then more revision surgeries might have been performed as a result of "allergic osteolysis and aseptic loosening"; however, this is not actually the case [20,54].

Some studies have indicated that small nanoparticles and high levels of metal ions from implants can be detected in remote organs, including the liver, lung, spleen and kidney [55]. Whether the systemic dissemination of wear particles and metal ions can cause side effects is a matter of debate [20,23]. As such, in our study, the synovial membrane of each right-sided knee joint after the intra-articular particle or ion injection (left knee) was also analyzed for the presence of biological markers (IL-1β, IL-6 TNF-α, CD45, CD68 and CD3). For IL-1β and TNF-α, only the right knees of the MP group had a stronger response than the PBS group, which means that a systemic response caused by metal particles occurred. By contrast, IL-6 was not significantly increased in the right knees of the MP group. One possible explanation for this result is the presence of a temporal factor, because IL-6 secretion in the periprosthetic membrane is preceded by the expression of TNF-α and IL-1β [56]. With regard to CD45, the results of the right knees in the MI group were consistent with those of the left knees, indicating that some immunocompetent cells (CD45-positive cells) were activated by metal ions, and that metal ions also led to systemic reactions. In terms of CD3 and CD68, there were no significant differences in the right knees of all groups, which means that no macrophages or T lymphocytes were recruited in the right knees of all groups.

There are some limitations to this study. Although the murine model can reflect inflammatory reactions caused by implant materials, it cannot imitate the osteolysis process around implants. Additionally, the single-injection murine model used in this study might not completely reflect the chronic production of wear debris in patients with joint replacements. Some researchers have used osmotic pumps in a murine model to achieve a continuous infusion of degeneration products [57]. This model is more like the clinical scenario, but irritation from osmotic pumps might be an interference factor. In a further study, we will aim to establish one continuous infusion model in combination with existing techniques. To investigate the systemic reactions caused by particles and ions, we used untreated knees. In the future, the impact of metal particles and ions on other organs, such as the liver, spleen, heart and kidney, will also be evaluated, which would further reflect the overall reactions caused by metal debris. Additionally, only one concentration of metal particles and ions was used, referring to the national animal laws. In a future study, different metal particle and ion concentrations, and the impact of different concentrations on aseptic inflammation, will be investigated.

#### **5. Conclusions**

The results of this study clearly demonstrate that the primary process which occurs after the release of metal particle and ions, especially CoCrMo particles, can lead to an intensive proinflammatory response in vivo. Metal ions can also cause the recruitment of immunocompetent cells but, in view of local inflammatory reactions, macrophages and inflammatory mediators were scarce in vivo. During the process by which metal particles and ions were released in present study, T lymphocytes were not recruited in our murine model. Systemic reactions by metal particles and ions were found according to the observation of untreated right knees.

**Author Contributions:** Data curation, X.C. and S.C.D.; formal analysis, X.C. and S.C.D.; funding acquisition, J.P.K., R.B., S.U. and A.C.P.; investigation, S.H.; methodology, S.H. and A.C.P.; project administration, S.U. and A.C.P.; resources, J.P.K., S.U. and A.C.P.; supervision, A.B.-M. and S.U.; validation, S.C.D.; writing—original draft, X.C.; writing—review & editing, S.U. and A.C.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by German Research Foundation, grant number UT 119/3-1, BA 3347/12-1, KR 3755/5-1. And Xiangyun Cheng's doctoral work is funded by the China Scholarship Council (CSC) under the State Scholarship Fund (Grant No.201708140085).

**Acknowledgments:** The authors would like to thank the German Research Foundation (DFG) and China Scholarship Council (CSC) for funding of this project. We also gratefully thank Dr. Paul Johan Høl for his assistance in ICP-MS techniques.

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

#### **References**

proinflammatory cytokines critical to metal-induced lymphocyte reactivity. *J. Biomed. Mater. Res. A* **2010**, *93*, 1312–1321. [CrossRef]

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).

*Article*


*3.5. Evaluation for Antiproliferative Activity of* **1**–**5**

New bromopyrrole alkaloids **1**–**5** were evaluated for their antiproliferative activity against human cancer cell lines (HeLa, A549, and MCF7) according to the following procedure. The human cancer cell lines were cultured in Dulbecco's modified eagle medium (DMEM) supplemented with 5% fetal bovine serum (FBS). All cells were incubated at 37 ◦C in a humidified atmosphere with 5% CO2–95% air. Cells were seeded at 1 × 104 cells/well in a 96-well plate and preincubated for 24 h. Test samples were dissolved in small amounts of DMSO and diluted in the appropriate culture medium (final concentration of DMSO < 1%). After removal of the preincubated culture medium, 100 μL of medium containing various concentrations of test compound was added and further incubated for 48 h. A cell proliferation assay was performed with the Cell Counting Kit-8 (WST-8; Dojindo, Japan) according to the manufacturer's instruction. Briefly, the WST-8 reagent solution (10 μL) was added to each well of a 96-well microplate containing 100 μL of cells in the culture medium at various densities, and the plate was incubated for 2 h at 37 ◦C. Absorbance was measured at 450 nm using a microplate reader. Cisplatin was used as a positive control, whose IC50 values against HeLa, A549, and MCF7 cells were 11.7, 7.2, and 52.4 mM, respectively.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1660-3397/18/9/455/s1. Figure S1: 1H NMR spectrum of agesasine A (**1**) in DMSO-*d*6 (500 MHz), Figure S2: 13C NMR spectrum of agesasine A (**1**) in DMSO-*d*6 (125 MHz), Figure S3: 1H-1H COSY spectrum of agesasine A (**1**) in DMSO-*d*6 (500 MHz), Figure S4: HSQC spectrum of agesasine A (**1**) in DMSO-*d*6 (500 MHz), Figure S5: HMBC spectrum of agesasine A (**1**) in DMSO-*d*6 (500 MHz), Figure S6: ROESY spectrum of agesasine A (**1**) in DMSO-*d*6 (500 MHz), Figure S7: HRESIMS spectrum (pos.) of agesasine A (**1**), Figure S8: Chiral HPLC chart of agesasine A (**1**), Figure S9: 1H NMR spectrum of agesasine B (**2**) in DMSO-*d*6 (500 MHz), Figure S10: 13C NMR spectrum of agesasine B (**2**) in DMSO-*d*6 (125 MHz), Figure S11: 1H-1H COSY spectrum of agesasine B (**2**) in DMSO-*d*6 (500 MHz), Figure S12: HSQC spectrum of agesasine B (**2**) in DMSO-*d*6 (500 MHz), Figure S13: HMBC spectrum of agesasine B (**2**) in DMSO-*d*6 (500 MHz), Figure S14: HRESIMS spectrum (neg.) of agesasine B (**2**), Figure S15: 1H NMR spectrum of 9-hydroxydihydrodispacamide (**3**) in DMSO-*d*6 (500 MHz), Figure S16: 13C NMR spectrum of 9-hydroxydihydrodispacamide (**3**) in DMSO-*d*6 (125 MHz), Figure S17: 1H-1H COSY spectrum of 9-hydroxydihydrodispacamide (**3**) in DMSO-*d*6 (500 MHz), Figure S18: HSQC spectrum of 9-hydroxydihydrodispacamide (**3**) in DMSO-*d*6 (500 MHz), Figure S19: HMBC spectrum of 9-hydroxydihydrodispacamide (**3**) in DMSO-*d*6 (500 MHz), Figure S20: HRESIMS spectrum (pos.) of 9-hydroxydihydrodispacamide (**3**), Figure S21: Chiral HPLC chart of 9-hydroxydihydrodispacamide (**3**), Figure S22: 1H NMR spectrum of 9-hydroxydihydrooroidin (**4**) in DMSO-*d*6 (500 MHz), Figure S23: 13C NMR spectrum of 9-hydroxydihydrooroidin (**4**) in DMSO-*d*6 (125 MHz), Figure S24: 1H-1H COSY spectrum of 9-hydroxydihydrooroidin (**4**) in DMSO-*d*6 (500 MHz), Figure S25: HSQC spectrum of 9-hydroxydihydrooroidin (**4**) in DMSO-*d*6 (500 MHz), Figure S26: HMBC spectrum of 9-hydroxydihydrooroidin (**4**) in DMSO-*d*6 (500 MHz), Figure S27: HRESIMS spectrum (pos.) of 9-hydroxydihydrooroidin (**4**), Figure S28: ECD spectrum of 9-hydroxydihydrooroidin (**4**) in MeOH, Figure S29: 1H NMR spectrum of 9*E*-keramadine (**5**) in DMSO-*d*6 (500 MHz), Figure S30: 13C NMR spectrum of 9*E*-keramadine (**5**) in DMSO-*d*6 (125 MHz), Figure S31: 1H-1H COSY spectrum of 9*E*-keramadine (**5**) in DMSO-*d*6 (500 MHz), Figure S32: HSQC spectrum of 9*E*-keramadine (**5**) in DMSO-*d*6 (500 MHz), Figure S33: HMBC spectrum of 9*E*-keramadine (**5**) in DMSO-*d*6 (500 MHz), Figure S34: ROESY spectrum of 9*E*-keramadine (**5**) in DMSO-*d*6 (500 MHz), Figure S35: HRESIMS spectrum (pos.) of 9*E*-keramadine (**5**), Figure S36: 1H NMR spectrum of tauroacidin A in DMSO-*d*6 (500 MHz), Figure S37: 1H NMR spectrum of taurodispacamide A in DMSO-*d*6 (500 MHz), Figure S38: 1H NMR spectrum of oroidin in DMSO-*d*6 (500 MHz), Figure S39: 1H NMR spectrum of keramadine in DMSO-*d*6 (500 MHz), Figure S40: 1H NMR spectrum of 2-bromo-9,10-dihydrokeramadine in DMSO-*d*6 (500 MHz), Figure S41: 1H NMR spectrum of nagelamide L in DMSO-*d*6 (500 MHz), Figure S42: Structures of known bromopyrrole alkaloids, tauroacidin A, taurodispacamide A, oroidin, keramadine, 2-bromokeramadine, and nagelamide L, Figure S43: Antiproliferative activity of **1**–**5** against HeLa cells, Figure S44: Antiproliferative activity of **1**–**5** against A549 cells, Figure S45: Antiproliferative activity of **1**–**5** against MCF7 cells, Table S1: 1D and 2D NMR data for agesasine A (**1**) in DMSO-*d*6, Table S2: 1D and 2D NMR data for agesasine B (**2**) in DMSO-*d*6, Table S3: 1D and 2D NMR data for 9-hydroxydihydrodispacamide (**3**) in DMSO-*d*6, Table S4: 1D and 2D NMR data for 9-hydroxydihydrooroidin (**4**) in DMSO-*d*6, Table S5: 1D and 2D NMR data for 9*E*-keramadine (**5**) in DMSO-*d*6, Table S6: 1H NMR data for tauroacidin A and taurodispacamide A in DMSO-*d*6, Table S7: 1H NMR data for oroidin, keramadine, and 2-bromo-9,10-dihydrokeramadine in DMSO-*d*6, Table S8: 1H NMR data for nagelamide L in DMSO-*d*6.

**Author Contributions:** Conceptualization, S.L., N.T., D.T., S.-Y.K., M.K., K.I., J.K., and Y.K.; methodology, S.L. and N.T.; validation, S.L. and N.T.; formal analysis, S.L., N.T., and Y.K.; investigation, S.L., S.T., D.T., and S.-Y.K.; resources, J.K.; writing—original draft preparation, S.L.; writing—review and editing, N.T., M.K., K.I., and Y.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was partly supported by JSPS KAKENHI, grant number JP17K08337.

**Acknowledgments:** We thank Z. Nagahama for his help with the sponge collection.

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

#### **References**

© 2020 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 (http://creativecommons.org/licenses/by/4.0/).


**3.2. Identification by 16–23S intergenic region**

202 The Complex World of Polysaccharides

species.

**Figure 1.** Phylogenetic neighbor-joining tree obtained by 16S rRNA sequence analysis of the tested isolates and other *Bacillus* spp. present in the gene bank database (accession numbers in parentheses).

The DNA of the isolates was extracted as described in Section 2 and the 1.5 kb 16S rRNA gene was amplified for each DNA by PCR using primers bac-F and bac-R. The PCR amplification, purification and sequencing were performed as described previously. The 1.5 kb obtained sequences were aligned and clustered with sequences from the NCBI database. 16S rRNA gene sequence analysis indicated that the six isolates (K, M, A, C, E, and G) were *Bacillus* spp. with 99% identity any of these three species *B. subtilis, or B. licheniformis, or B. amyloliquefaciens* and they clustered into a monophyletic line in a phylogenetic tree. To distinguish and clear identification of these strains on the species level the analogical electrophoresis, using NEB cutter was applied to identify the 16S rRNA results, which have been sequenced, as the strains of the same species expected to have almost the same sites when digested with AluI. Fig. 2 showed that the isolates M and G have the same size fragments as *B. subtilis* gi 269313996 while the other isolates A, C, E; K showed different AluIfragments which differ to the AluI fragments generated from 16S rRNA sequence of *B. subtilis, or B. licheniformis or B. amyloliquefaciens*. It was clear that 16S rRNA gene alone could not distinguish these three closely related Bacillus To distinguish between these three closely related strains the 16–23S intergenic region was amplified by primers L516SF × L523SR, and then the sequence was determined for the six isolates. The homology results for the 16–23S intergenic region showed that: K and M strains showed 100% similarity to *B. subtilis* but strains A, C, E, G could be either *B. subtilis (99%) or B. amyloliquefaciens* (85%) **(Table. 1.).**


**Exploring Tumor Heterogeneity Using PET Imaging: The Big Picture**

**Clément Bailly 1,2, Caroline Bodet-Milin 1,2, Mickaël Bourgeois 1,2,3, Sébastien Gouard 1, Catherine Ansquer 2, Matthieu Barbaud 2, Jean-Charles Sébille 2, Michel Chérel 1,3,4, Françoise Kraeber-Bodéré 1,2,4 and Thomas Carlier 1,2,\***

Received: 25 June 2019; Accepted: 28 August 2019; Published: 31 August 2019

**Abstract:** Personalized medicine represents a major goal in oncology. It has its underpinning in the identification of biomarkers with diagnostic, prognostic, or predictive values. Nowadays, the concept of biomarker no longer necessarily corresponds to biological characteristics measured ex vivo but includes complex physiological characteristics acquired by different technologies. Positron-emission-tomography (PET) imaging is an integral part of this approach by enabling the fine characterization of tumor heterogeneity in vivo in a non-invasive way. It can effectively be assessed by exploring the heterogeneous distribution and uptake of a tracer such as 18F-fluoro-deoxyglucose (FDG) or by using multiple radiopharmaceuticals, each providing different information. These two approaches represent two avenues of development for the research of new biomarkers in oncology. In this article, we review the existing evidence that the measurement of tumor heterogeneity with PET imaging provide essential information in clinical practice for treatment decision-making strategy, to better select patients with poor prognosis for more intensive therapy or those eligible for targeted therapy.

**Keywords:** PET; heterogeneity; radiomics; radiopharmaceuticals; SUV; nuclear medicine

#### **1. Introduction**

Heterogeneity is a concept familiar to pathologists. Phenotypical and functional differences arise among cancer cells during the course of the disease because of genetic changes [1]. Similarly, the interactions of cancer cells with their microenvironment or the local variation in angiogenesis and hypoxia are not uniform in the tumor. Not to mention the perpetual clonal remodeling under the pressure of microenvironment and treatments. This large biological, cellular, and tissue heterogeneity exist at the intratumoral level (molecular differences within one tumor), intrapatient level (variation of tumor features between lesions within one patient), and interpatient level (variation of tumor features between patients). This heterogeneity conditions tumor aggressiveness and therapeutic resistance and represents a significant challenge in the design of effective treatment strategies [2]. The prerequisite for personalized medicine relies on the report of such heterogeneities. Yet, the realization of multi-region sampling from each tumor of a single patient raises ethical or technical questions. Positron-emission-tomography (PET) imaging appears as a perfect tool to overcome this obstacle, providing a whole-body non-invasive method of assessing tumor heterogeneity, through the use of multiple radiopharmaceuticals, each providing different information. In parallel, the information derived from the uptakes' analysis of a tracer such as 18F-fluoro-deoxyglucose (FDG) has enabled the emergence of a wide variety of PET quantitative metrics including simple semi-quantitative

approaches such as standardized uptake value (SUV) and "high-order metrics" that involve a segmentation step and supplementary image processing. These parameters, besides their utility for therapeutic response, should play a key role in the prognostic characterization of tumors, along with the development of personalized medicine. Radiomics—the high throughput extraction of large amounts of imaging elements from radiographic images—tackles this challenge and is one of the most promising strategies [3,4]. The purpose of this short review is to present the latest developments in the exploration of tumor heterogeneity in PET imaging. Different examples of neoplasias are presented during the three developed axes. Nevertheless, to emphasize to the reader that these tools can be used in all diseases, lymphoma is used as a common thread throughout this review.

#### **2. Inter- and Intra-patient Tumor Heterogeneity Exploration through Multiple Tracers PET Imaging**

Nuclear medicine is one of the most dynamic medical fields, in constant evolution over the past decades. The main strength of this discipline lies in its incredible catalog of radiopharmaceuticals allowing exploration of virtually every major organ system in the body (Table 1). Personalized medicine has never been so relevant today and nuclear medicine is on its leading edge, probing deep inside each patient or tumor to reveal its inner workings. Predictive biomarkers are an essential tool of precision medicine and individualized treatment. Yet, as mentioned above, tumor heterogeneity contributes to sampling error, especially for metastatic diseases; target's expression at one site does not guarantee expression at all sites. Moreover, target accessibility of drugs is not assessed by biopsy, and target expression does not provide evidence of targeted-therapy impact on the target. In this context, PET imaging overcomes many of these limitations exploring target heterogeneity, assessing target expression and potential accessibility across the whole disease burden, to aid clinical decision making. A perfect example was recently published by Bensch et al. with the initial results from the first-in-human imaging with 89Zirconium-labeled atezolizumab [5]. The programmed cell death protein 1 (PD1)/programmed death-ligand 1 (PD-L1) axis is an important immune checkpoint for T-cell activation. PD-L1 overexpression is associated with a poor prognosis in a variety of cancers yet these patients typically have a stronger response to anti-PD-L1 therapy such as atezolizumab [6–9]. The PD-L1 expression is usually evaluated using immunohistochemistry or RNA sequencing. In Bensch et al. study, clinical responses were better correlated with PET uptake before treatment than these two evaluations.

**Table 1.** Main validated positron-emission-tomography (PET) tracers and their principal indications (based on [10,11]).

**Table 1.** *Cont.*

Breast cancer also represents a great model for this type of approach [12,13]. Indeed, in this pathology, 18F-fluoroestradiol (FES) PET has been validated as an accurate method for providing information on estrogen receptor (ER) status of tumor lesions to determine need for endocrine therapy [14–17]. Indeed, the uptake of FES has been proven to correlate with ER expression in biopsy sample [16]. Similarly, several works showed that radiolabeled monoclonal antibodies could non-invasively identify lesions with positive or over-expression of the human epidermal growth factor receptor 2 (HER2) and predict response to anti-HER2 antibody-based therapy [18–21]. In particular, Gebhart et al. recently reported the promising results of the ZEPHIR trial. This work successfully evaluated two PET imaging as tools to investigate heterogeneity of advanced HER2-positive breast cancer (PET imaging using trastuzumab radiolabeled with 89Zirconium) and to predict patient outcome under trastuzumab emtansine (PET imaging with FDG) [22,23]. This innovative study showed the clear benefit of combining both imaging methods in predicting whether adequate tumor targeting is followed by sufficient efficacy and cytotoxicity.

Beyond the "simple" search for the expression of a target before initiating a treatment directed against it, the use of several radiotracers in the same patient can allow to comprehensively assess disease activity, extent, and heterogeneity. Tumors derived from cells of the neural crest represent the perfect historical model for this approach [24]. This large group of neoplasms includes a large variety of tumors, such as gastroenteropancreatic neuroendocrine tumors (GEPs), neuroblastoma, paraganglioma, pheochromocytoma, medullary thyroid carcinoma, and small cell lung cancer. These tumors are characterized by similar appearances and expression of different peptides and amines. Prognostic criteria of this group are generally related to the metastatic extension of the disease but also to the functional activity, degree of differentiation of the tumor and proliferative indices. These parameters are essential in the management of these patients. Today, multiple molecular imaging methods are available to explore these various biologic and histologic characteristics with very high specificity and can be performed at the whole-body scale [25,26]. Tracers used can be grouped in three different categories. 123-metaiodobenzylguanidine (123MIBG), an analog of norepinephrine and 18F-fluorodihydroxyphenylalanine (FDOPA), an amine precursor, exploit catecholamine synthesis, storage, and secretion pathways. 111In-pentetreotide and 68Gallium-labeled somatostatin analog peptides (68Ga-DOTA-TOC, 68Ga-DOTA-NOC, 68Ga-DOTA-TATE) assess the somatostatin receptors expression. Finally, FDG uptake has been found to correlate with de-differentiation, increasing aggressiveness and proliferation rate, and poor prognosis. This phenomenon was first described in differentiated thyroid carcinomas. Indeed, de-differentiated thyroid carcinomas lose their capacity to capture radioiodine and can be detected by FDG-PET since glycolysis increase at the same time. Thus, imaging assessment of two or more tracers may yield more clinical information than each alone [27–32] (Figures 1 and 2).

**Figure 1.** An example of a 46-year-old patient with pluri-metastatic intestinal neuroendocrine tumor (grade 2, ki67 at 4%). 68Ga DOTA-TOC (**A**), FDG-PET (18F-fluoro-deoxyglucosepositron-emission-tomography) (**B**) and FDOPA (18F-fluorodihydroxyphenylalanine) (**C**) imaging were realized. MIP (maximum intensity projections) images from the respective PET data sets are shown. The subject has positive on both FDOPA and somatostatine-receptor imaging, dominant disease which exhibits no FDG uptake (green arrows). One hepatic lesion was FDOPA-negative and 68Ga-DOTA-TOC-positive (red arrow) and one gastric lesion was FDOPA-positive and 68Ga-DOTA-TOC negative (blue arrow). Images courtesy of Pr C. Bodet-Milin.

**Figure 2.** An example of 37-year-old patient with pluri-metastatic paraganglioma. MIP (maximum intensity projections) images of the realized 123MIBG-scintigraphy (**A**), FDG-PET (**B**) and FDOPA-PET (**C**) are shown. The subject has a mediastinal lesion, barely seen on 123MIBG-scintigraphy and clearly positive with the others tracers (red arrows). Pulmonary and skull lesions (green arrows) were only visible on FDOPA-PET. Images courtesy of Dr C. Ansquer © Catherine Ansquer

Lymphomas represent another cancer group where multiple tracers' exploration might allow a better potential characterization of tumor heterogeneity. Indeed, today, FDG-PET occupies a central position in accurate staging and therapeutic evaluation of lymphomas [33]. Nevertheless, a certain number of crucial questions remain regarding its optimal application. While risk-based strategies may appear to enhance patients' outcomes for those with Hodgkin's lymphoma, findings are not so impressive in non-Hodgkin's lymphoma [33]. Similarly, novel therapies that may generate an immune response may lead to false-positive FDG-PET results, requiring the incorporation of these flare-ups reactions in existing interpretation criteria [34]. Therefore, the development of other radiotracers with different uptake mechanisms from FDG could be of interest [35]. 18F-fluorothymidine (FLT) and 11C-methionine (MET), for instance, were both reported to correlate with cellular proliferation activity and lymphoma histological grade of malignancy, respectively, through the exploration of DNA and protein synthesis [36,37]. Moreover, FLT showed excellent results in treatment monitoring and particularly in the setting of early interim evaluation, with more specific and accurate analyses than FDG [38,39]. In the same way, 18F-fludarabine, an adenine nucleoside analog, owing to its specificity for lymphoid cells and its absence of uptake in inflammatory tissues, holds great promise for therapeutic evaluation [40]. Finally, the first in-human study of 68Ga-CXCR4, targeting chemokine receptor CXCR4, which is frequently overexpressed in various tumor types, showed high lesions' uptake [41,42]. Moreover, voxel-by-voxel analysis in one patient identified striking inter- and intralesional heterogeneity in the uptake of 68Ga CXCR4 and FDG, implying that the biological information given by the two probes may be complementary even in lesions that show avidity for both [42].

Systematic multiple tracers imaging could be used to reveal different profiles with highly different prognoses. This multiple-tracers imaging associated with an appropriate scoring system might also influence patients' management and help selecting between different therapy options [28,43,44]. Indeed, these tumors may be treated with molecular radiotherapy using the same pathways: 131-metaiodobenzylguanidine (131-MIBG) and 177Lutetium-DOTA-TATE. Impressive reports were reported with these targeted therapies in GEPs and neuroblastoma [45–49]. A theranostic approach, integrating imaging and therapy in the same system, providing individualized tailored treatment, despite intratumor and interlesional heterogeneities, is expected to play an increasingly pivotal role in this large tumor group [50,51].

The theranostic approach indeed represents a formidable field of expansion for nuclear medicine in an era where targeted therapies have become essential tools in oncology pharmacopoeia [52,53]. As described above in breast cancer and neuroendocrine tumors, it offers a non-invasive method for quantitatively evaluating target expression in vivo, selecting patients for costly and potentially toxic treatments, and monitoring responses [50,51,54–56]. In addition, this approach constitutes a valuable asset in the development of new drugs by pharmaceutical companies. Drug development being a fairly time-consuming and costly process, it represents an effective solution to rapidly monitor drug candidates' pharmacokinetics and biodistribution. This strategy can improve the strength and effectiveness of early trials by enhancing patient selection, optimizing dose, and rationalizing treatment reactions [54].

#### **3. Intrapatient Tumor Heterogeneity Exploration through Quantitative Analysis of PET Imaging**

FDG-PET has become an essential tool for cancer diagnosis, staging, and therapeutic evaluation. It has undoubtedly changed the landscape of lymphoma, lung, head and neck or breast cancers management [57–59]. This spread of the PET imaging technique was particularly enabled by its quantification ability which allows the use of a reproducible metric for cancer monitoring. The SUV and particularly SUVmax (defined as the SUV value of the maximum intensity voxel within a region of interest) is widely used in everyday clinical practice. It is popularly adopted as a surrogate of tissue accumulation of tracers and particularly as the overall net rate of FDG uptake. It is defined as the ratio between the radiopharmaceutical concentration (expressed in Bq/mL) and the decay-corrected injected activity normalized by a given factor (mass of the patient, body surface area or lean body

mass) [60]. The precise description of underlying technical limitations of this metric is beyond the scope of this short review and is already largely discussed in the literature [60–62]. Only its use under clinical situation is highlighted here. Indeed, the computation of intrapatient tumor heterogeneity in FDG-PET imaging using SUV has been already applied in a wide variety of indications even if not intentionally or explicitly. FDG uptake heterogeneity may reflect different tumor profiles with different aggressiveness and consequently prognosis. This contribution offered by the calculation of SUV has been particularly investigated in lymphoma.

Some histological subtypes of lymphoma such as follicular and mantle cell lymphomas are heterogeneous diseases with a variety of clinical, genetic, biological features and related different outcomes. In these pathologies, the intensity of FDG uptake on PET imaging at baseline varies greatly between patients [33]. Some present barely detectable uptakes while others exhibit very intense fixations. Yet, this spectrum of SUV, from very low to very high uptakes, provides clinically relevant information. Indeed, several reports demonstrated that the level of FDG uptake on PET imaging is largely correlated to lymphoma histology [63,64]. More particularly, indolent disease with low proliferation rate is generally associated with low uptake, while a more aggressive disease presented higher FDG uptake. In the same way, very intense FDG uptake associated to a clear uptake gradient may pertain transformation from indolent to aggressive lymphoma. These findings were confirmed in a prospective study conducted to evaluate the value of FDG-PET as an accurate guide for biopsies in suspected transformed tissues [65]. In patients with newly diagnosed indolent lymphoma, low SUV numbers may reduce the suspicion of transformation in disease sites that were not biopsied. Conversely, in patients with histologically proven indolent lymphoma, an uncharacteristically higher than expected SUV may herald an aggressive subtype, warranting a targeted biopsy. Our team reported similar findings in mantle cell lymphomas. A broad inter-individual tumor cell heterogeneity regarding FDG avidity was observed in several works with a strong prognostic value on survival of quantitative parameters such as SUVmax of the lesions with the highest uptake determined at diagnosis [66–69]. This close relationship between high SUVmax values and a more aggressive mantle cell lymphoma behavior was also supported by the concordance between SUVmax, aggressive variants, and high percentage (> 30%) of Ki67 positive cells.

This hypothesis that the prognosis of the disease is linked to the most aggressive contingent corresponding to the lesion with the highest FDG uptake is also reinforced by some studies exploring the predictive prognostic value of FDG-PET during treatment. Since the development of normalized criteria for assessment of tumor burden changes, the required number of lesions to consider for response determination remained a fundamental question [70–72]. The RECIST criteria used for radiological evaluation recommended the measurement of five target-lesions, selected "randomly," only on their suitability for accurate repeated measures [70]. On the contrary, several works using PET imaging showed that only the most metabolically active lesions, representing the most aggressive portions of tumors, are critical to consider [73–77]. This approach implies taking the single hottest area as the reference point on the pre-treatment and post-treatment studies, even if not necessarily the same area, considering only the worst biologic behaviors of the malignancy. This controversial concept was explored in many cancers and was applied both in solid tumors with the PERCIST criteria and in lymphomas. For example, Lin et al. were the first to measure the reduction of SUVmax in the "hottest" lesion before and during the treatment, in diffuse large B-cell lymphoma (Figure 3). In their study, they also investigated the FDG uptake changes on interim FDG-PET within the initial hottest tumor site on baseline FDG-PET (18% of 92 patients) which resulted in more false-negative exams in predicting PFS [76].

**Figure 3.** Selection of regions of interest in 57-y-old patient before chemotherapy. (**A**) Graded color-scaled parametric analysis applied in reconstructed coronal PET image shows most active tumor in upper abdomen. (**B**) Transverse PET image with a higher scale reveals celiac tumor (T) with activity profile crossing the hottest point (red spot). (**C**) Corresponding activity profile in counts-per-pixel. Isocontours are drawn with lower autocontour threshold of 4500 counts-per-pixel (red isocontour at inset in B). (**D**) Normal background tissue (N): two large ROIs are manually selected on gluteal muscles, avoiding iliac bone marrow activity. This research was originally published in JNM [76]. © SNMMI.

This approach was also recently reported in multiple myeloma patients [78]. In this pathology, multi-clonal heterogeneity remains one of the main challenges in developing effective strategies. Multiple myeloma is indeed characterized by spatial differences in the clonal architecture, with potential non-homogeneous distribution of high-risk disease for which multi-region investigations appear critical [79–81]. Yet, in a study by our team, the percentage difference of SUVmax between baseline and interim FDG-PET was a powerful tool to predict long-term outcomes in patients with FDG-avid multiple myeloma [78]. There again, similar to previous work in lymphomas or in solid tumors, the hottest lesion in any region was used for comparison even if its location differed from the initial hottest lesion on PET at diagnosis, to assess the most aggressive portion of the disease on each PET examination, to free oneself from intrapatient heterogeneity.

#### **4. Intratumor Heterogeneity Exploration through Quantitative Analysis of PET Imaging**

In addition to conventional measurements of SUV, a new class of metrics has recently emerged in PET imaging and is currently being clinically investigated [61]. A simple visual analysis of the FDG uptake in PET images indeed suggests that the spatial distribution of voxels of different intensities in a selected region and thus the spatial distribution of the radiotracer can be extremely heterogeneous. And one can assume that this localized heterogeneity in medical images "partly" reflects heterogeneity on a lower scale and underlying variations in metabolism, cellular proliferation or necrosis [82]. Advanced image analysis of a tumor could then capture additional information and some researchers suggested that genomic, proteomics, and other -omics patterns could be expressed in terms of macroscopic image-based features [83]. This concept requiring the extraction of a large number of quantitative data from medical multimodal images has become popular under the term "radiomics" [3,84,85]. In recent years, considerable efforts have been made by the medical imaging community to obtain correlations between these image characteristics and tumor heterogeneity. Those metrics are often referred to as "textural features" and belong to "high order parameters" (Table 2) along with shape-descriptors or other descriptors based on fractal analysis or wavelet decomposition [61,86]. They measure the relationships between groups of two or more voxels in the image (Figure 4). Numerous textural features can be extracted from medical images, yet only a handful are sufficiently reliable, robust, and reproducible. Texture analysis remains limited and biased by many methodological and technical factors inherent to PET images' acquisition, reconstruction algorithms, or segmentation technique that can affect the quantification of image heterogeneity [86]. A number of recommendations are available to help and guide researchers in making the right choices in the calculation and selection of parameters [86–89].

**Figure 4.** Whole-body 18F-FDG PET scan (**A**) tumor segmentation (**B**) and voxel-intensity resampling (**C**) allowing extraction of different features (**D**) by analysis of consecutive voxels in a direction (for cooccurrence matrices) (a), alignment of voxels with same intensity (b), difference between voxels and their neighbors (c), and zones of voxels with same intensity (d). This research was originally published in JNM [90]. © SNMMI.

**Table 2.** *Cont.*

To date, numerous studies explored the potential value of textural features in PET imaging with encouraging results in a number of cancers [86,92]. However, only a limited number adopted rigorous methodological choices with particularly large cohorts of patients and robust statistical analysis [93,94]. Keeping in mind these limitations, the evidence supporting the additional value of advanced image features from FDG-PET continues to expand year after year. Several of the most recent studies have used techniques such as external cohort validation [95–97], or even machine-learning technique [98,99] and concluded in the usefulness of textural analysis regarding patient management. Several method were also proposed to minimize the effect of inter-center variability related to textural features computation [95,100,101] with encouraging results both on a methodological and prognostic level. One should keep in mind that several hundred, if not thousands of handcrafted features can be extracted, when the number of patients used to construct the predictive model is often several order of magnitude lower than the number of features analyzed. The use of machine learning approaches in this context is thus very useful but opens other issues related to the choice of suitable algorithm for selecting features and subsequent classifier which were shown to be not unique [102] The use of more complex approaches relying on deep learning (especially convolutional neural networks) may alleviate most of the difficulties raised by handcrafted features even if other challenges arise like the number of data used for training and tuning hyper-parameters of the model. A very good overview of the available technique that may be potentially clinically efficient in a near future can be found in [103].

Finally, a few studies have investigated the potential combination of image-derived features from multimodal imaging or associated to clinical data [95,104–106]. One example of a nomogram construction, published by Desseroit et al. [104], combining tumor and heterogeneity features extracted from both PET and CT components of routinely acquired FDG-PET scans in non-small cell lung cancers is shown in Figure 5. Reports in patients with mantle cell lymphoma also successfully applied this approach [107–109]. In this pathology, as demonstrated in a prospective study [107,108] and confirmed in a recent work by Mayerhoefer et al. [109], the combination of radiomic features with bio-clinical scores may possibly improve risk stratification. All these approaches form the basis for future works investigating the value of textural features in PET imaging and combining these methods will only reinforce the validity of the studies. In this regard, a recent study focused on this topic and successfully applied some of these approaches. In this work [95], Lucia et al. validated in two independent external cohorts of patients previously developed textural features-based models [110] relying on FDG PET and MRI for prediction of disease-free survival and locoregional control in locally advanced cervical cancer. Moreover, to adjust for the multicenter effects, they used the ComBat method, derived from genomic data analysis. They were able to identify two radiomics features indeed associated with worse outcome, confirming that more heterogeneous tumors have a poorer prognosis [95].

**Figure 5.** Workflow of a nomogram construction combining tumor and heterogeneity features extracted from both PET and CT components of routinely acquired FDG-PET scans in non-small cell lung cancers, allowing for better stratification among patients with stage II–III, compared to stage I. This research was originally published in EJNMMI [104]. © Springer.

Radiomic is a promising field. Unlike histological biomarkers derived from invasive biopsy which sample only a small limited tumor region, as described above, radiomics non-invasively interrogate the whole tumor. Visualization of tumor heterogeneity is essential in the assessment of tumor aggressiveness and prognosis. Radiomic has an exceptional potential and may prove critical toward personalized medicine [83].

#### **5. Conclusions**

Nuclear medicine is one of the most dynamic medical fields. Advances in cancer biology knowledge together with the rise of new imaging techniques (new detection system and progress in imaging analysis) make this discipline a domain of tremendous and growing evolution. This dynamism is a real asset as personalized medicine has never been so relevant today. Indeed, PET imaging appears as an essential tool for non-invasive exploration of intratumoral and interlesional heterogeneity through the exploration of the distribution and uptake of a tracer or by using multiple radiopharmaceuticals, each providing different information. There is convincing evidence that the integration of PET imaging "profiling," combining these approaches, associated to clinical, biological, or genomic data could improve tumor characterization and prognosis prediction to allow adequate patients stratification to therapeutic regimens. By combining at least one metabolic tracer with a phenotypic one, and by quoting Mankoff et Dehdashti [31], "it may then be possible to show that when it comes to molecular imaging, 1 plus 1 is greater than 2."

**Author Contributions:** All authors have directly participated in the review process and in the writing of this paper, and have read and approved the final version submitted.

**Funding:** This research was funded in part by grants from the French National Agency for Research, called "Investissements d'Avenir" IRON LabEx n◦ ANR-11-LABX-0018-01, IGO LabEx n◦ ANR-11-LABX-0016-01, Siric ILIAD, ArronaxPlus Equipex n◦ ANR-11-EQPX-0004 and NExT n◦ ANR-16-IDEX-0007.

**Acknowledgments:** Figure 2 was reproduced with permission from Catherine Ansquer © Catherine Ansquer. Figure 3 was originally published in JNM by Chieh Lin et al. Figure 4 was originally published in JNM by Florent Tixier et al. Figure 5 was originally published in EJNMMI by Desseroit et al.

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

#### **References**

© 2019 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 (http://creativecommons.org/licenses/by/4.0/).


*2.1. Materials*

Divinyl sulfone (DVS, 118.15 Da), Diethylenetriaminepentaacetic acid gadolinium (III) dihydrogen salt hydrate (Gd-DTPA, 547.57 Da) and Sodium hydroxide pellets (NaOH) are purchased from Sigma Aldrich (Milan, Italy). Sodium Hyaluronate, with an average molecular weight of 42 kDa is supplied Bohus Biotech (Strömstad, Sweden) as dry powder and used without purification. Milli-Q water is systematically used for sample preparation, purification, and analysis.