**3. Results and discussion**

#### **3.1. Evolutionary relationships of palm wine yeasts and their relatives**

Yeasts facilitate several industrial food fermentation processes, which often consist of a desired specific strain [19]. This may be why domestication is believed to be the main driver of specific yeast prevalence in a geographical location. The understanding of the ecological basis of yeast diversity in nature remains fragmented and cross-kingdom competition has been proposed as a method to generate industrially useful yeast strains with new metabolic traits [20]. Palm wine yeasts are yet to enjoy significant diversity study hence a look at their relatives will enable more information to be generated.

In the last decade, there has been increase in submissions of palm wine yeast sequences based on 26S rRNA genes mainly due to quality checks by academicjournals. The identification of new strains is accompanied by performing a search with the basic local alignment search tool [21] followed by submission of DNA sequences to the GenBank. According to Benson et al. [22], GenBank is a comprehensive database that contains publicly available nucleotide sequences for up to 370,000 formally described species. It is common knowledge that these submissions which contain a lot of information are generated mainly through submissions from investigators around the world. Each sequence data received is curated by the GenBank annotation staff to ensure that it is free from errors after which accession numbers are assigned.-

All the sequences used in this study were the first versions submitted by investigators. The maximum likelihood method was preferred for the trees constructed because it is computationally intense and all possible trees are considered. Also the method can be useful for widely divergent groups or other difficult situations [23].

#### **3.2.** *Candida ethanolica*

The yeast *C. ethanolica* is not widely reported in palm wine. It has been reported as a nonconventional yeast which may present massive resource of yeast biodiversity for industrial applications because it has been found to be adapted to some of the stress factors present in harsh environmental [24]. In that report, it was found that *C. ethanolica* tolerated up to 7% v/v ethanol. This could be useful information for new palm wine drink development especially now that there is increasing interest in non-*Saccharomyces* yeasts with peculiar features able to replace or accompany *S. cerevisiae* during specific industrial fermentations [25].

The *C. ethanolica* strain from *Raphia* sp. (**Figure 1**) and *Elaeis* sp. (**Figure 2**) palm wine showed close relationships with other *Candida* species*.* The relatives of *Raphia* sp. palm wine that emanated from the same node (**Figure 1**) came from diverse sources. The flanking close relatives- (KY283163 and DQ466540) of *C. ethanolica* (HG425332) were isolated from composite microbial powders for aquaculture in China [26] and composite cocoa fermentation in Ghana [27]. Other close relatives included species from the genus *Pichia.* The *P. deserticola* strain (KM005182) from the same node as the reference strain was from aerobic deterioration of total mixed ration silage in China [28]. For *Elaeis* sp. (**Figure 2**) palm wine, close relatives to *C. ethanolica* (HG425336) strain were from a laboratory culture collection with unidentified source [29] and a tannin tolerant yeasts associated with naturally fermented *Miang* leaves in Thailand [30]. A close *P. deserticola* strain of unstated source in GenBank was from a large characterization study [31].

In both *Elaeis* sp. and *Raphia* sp. palm wine, several monophyletic groups were formed with other *Pichia* species namely *P. deserticola*, *P. Manshurica* and *P. galeiformis* which indicate polyphyletic relationships. The polyphyletic nature of *Pichia* has been demonstrated by Kurtzman and Robnett [29] in the analysis of gene sequences that included all known ascomycetous yeasts. Apart from possible similar conserved regions, previous nomenclature at the time of submission of the sequences may also be the reason why *Pichia* species of different genus were observed as close relatives of *C. ethanolica* from *Elaeis* sp. and *Raphia* sp. palm trees.

It has been reported that ascomycetic fungi submitted to the database previously have been- assigned names based on their life stages [32, 33]. For example, it was shown that the name for the fungi *Candida krusei* is based on the anamorphic stage whereas its telemorph stage

**Figure 1.** Phylogenetic analysis of *Candida ethanolica* (HG425332-underlined) from *Raphia* sp. palm wine. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.

name is *Pichia kudriavzevii*. It also has an older name *Issatchenkia orientalis*. The whole *Candida*  species consists of up to 850 organisms, which can be distantly related [34]. Hence in order to avoid the confusion, the International Botanical Congress in Melbourne in July 2011, made a change in the international code of nomenclature for fungi and adopted the principle of one fungus can only have one name and ended the system of permitting separate names to- be used for anamorphs [35]. The report emphasized that this validated all legitimate names proposed for a species, regardless of what stage they were typed and can serve as the correct name for that species.

#### **3.3.** *Sachharomyces cerevisiae*

The yeast *S. cerevisiae* is generally known to be the most used microorganism in the food and drink manufacturing sector. The organism is the dominant yeast species isolated from many studies on palm wine. However, it is unclear whether *S. cerevisiae* as a species occurs naturally or exists solely as a domesticated species [36]. *S. cerevisiae* strains are genetically diverse, largely as a result of human efforts to develop strains specifically adapted to various fermentation processes. These adaptive pressures from various ecological niches may generate behavioral differences among these strains [37]. In a review [8], it was suggested that domestication in *Saccharomyces*, is most pronounced in beer strains, because they live in their industrial niche always and allow only limited genetic admixture with wild stocks and minimal contact with natural environments. Due to this restriction, it was pointed out that

**Figure 2.** Phylogenetic analysis of *Candida ethanolica* (HG425336-underlined) from *Elaeis* sp. palm wine. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.

beer yeast genomes show complex patterns of domestication and divergence, making both ale (*S. cerevisiae*) and lager (*S. pastorianus*) strains ideal models to study domestication.

The relatives of palm wine *S. cerevisiae* was not distributed among many species or different genus observed for *Candida species*. Two nodes were observed for the *S. cerevisiae* trees constructed for *Elaeis* sp. (**Figure 3**) and *Raphia* sp. (**Figure 4**). The yeast strain isolated from *Elaeis* sp. (**Figure 3**) was in a different branch from most of its relative whereas it was vice versa for the palm wine yeast from *Raphia* sp. (**Figure 4**) palm wine. As observed for *Candida species*, isolation of *S. cerevisiae* species was from different sources. The close relatives flanking the palm wine strain from *Elaeis* sp. palm wine (HG425328, **Figure 3**) with accession numbers KU862639 and MF966566 were isolated from grape surface [38] and pear sough dough [39] whereas the close relatives of *Raphia* sp. palm wine (HG425338, **Figure 4**) with accession numbers GU080046 and HM191669 were isolated from must of spontaneous fermentation [40] and grape juice used to brew *Musalais,* a beverage made from compressed grapes [41]*.*

It is believed that 99% of yeasts is still unknown [42], and *S. cerevisiae* fermentation could be specific to a particular substrate, hence more studies of *S. cerevisiae* from different palm trees will be beneficial. The genus *Saccharomyces* was previously divided into two groups namely

**Figure 3.** Phylogenetic analysis of *S. cerevisiae* (HG425328-underlined) from *Elaeis* sp. palm wine. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.

*Saccharomyces sensu stricto* and *Saccharomyces sensu lato* and the sensu stricto strains are mostly associated with the fermentation industry [43]. The *S. cerevisiae* in this study are *sensu stricto*. Comparative genomics analysis of *S. cerevisiae* and closely related species has contributed to our understanding of how new species emerge and has shed light on various mechanisms that contribute to reproductive isolation [44]. This knowledge can be applied to palm wine yeasts to ascertain how they differ from well characterized yeasts.-

#### **3.4.** *Pichia kudriavzevii*

From recent molecular studies of yeasts present in palm wine, the yeast species *Pichia kudriavzevii*  has emerged as a prevalent non-*Saccharomyces* yeast species in the drink. The genus has shown probiotic potentials [45] and multistress-tolerance [46]. It is worth looking closely at this genus because it has been shown that some *P. kudriavzevii* strains can produce higher quantities of ethanol from lignocellulosic biomass than conventional cells of *S. cerevisiae* at 45°C [47].

The tree constructed for *P. kudriavzevii* showed the least divergence when compared to *S. cerevisiae* or *Candida* palm wine yeast relatives. All the relatives and the *Elaeis* sp. palm wine strain (HG425333) originated from one node and formed separate taxonomic units

**Figure 4.** Phylogenetic analysis of *S. cerevisiae* (HG425338-underlined) from *Raphia* sp. palm wine. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.

(**Figure 5**). In contrast, the *P. kudriavzevii* (HG425335) from *Raphia* sp. palm wine formed a separate clade and did not lie on the same branch with the relatives (**Figure 6**). This indicates intraspecies diversity and confirms findings reported previously [11]. In that study, intraspecies diversity was suggested because *P. kudriavzevii* (HG425335) from *Raphia* sp. palm wine formed a separate clade with palm wine isolates from Mexico instead of isolates from the same geographical location.

The information contained in the sequence submission of close relatives of *P. kudriavzevii*  strains also shows different sources of isolation. The strains close to the yeast from *Elaeis* sp. palm wine (HG425333, **Figure 5**) with accession numbers KY283159 and KM234455 show- that isolation was from composite microbial powders for aquaculture [21] and naturally fermented cashew apple juice [48] whereas a close relative of *Raphia* sp. palm wine (HG425335, **Figure 6**) with accession number KU167717 was isolated from activated sludge from textile dyeing [49].

#### **3.5. Geographical origin and sources of palm wine yeast relatives**

After ascertaining the sources of very close relatives from the phylogenetic trees constructed, the shortlisted 600 sequences from the aforementioned yeast genera were further examined and the information found was used to group the isolates according to country of isolation, food, beverage, and non-edible source.

**Figure 5.** Phylogenetic analysis of *P. kudriavzevii* (HG425333-underlined) from *Elaeis* sp. palm wine. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.

#### *3.5.1. Isolates submitted by country of origin and source-*

Overall, sequences examined for the aforementioned yeasts genera were submitted from 38- countries [18] and the top 6 countries is presented in this report. Sequence data for both *Elaeis*  sp. (**Figure 7**) and *Raphia* sp. (**Figure 8**) palm trees show that highest number of submissions to the Genbank database was from China. The top three countries from which palm wine yeast relatives originated were the same for both palm tree species. This suggests that a large number of palm wine yeasts may have common ancestors with yeasts found in China. The origins or sources of palm wine yeasts relatives were spread across beverages, food, and non-food sources. The prevalence of *S. cerevisiae*, *P. kudriavzevii*, and *C. ethanolica* from these sources is shown for *Elaeis* sp. palm tree (**Figure 9**) and *Raphia* sp. palm tree (**Figure 10**). In both palm wine from *Elaeis*  and *Raphia* palm trees, yeasts relatives of *S. cerevisiae* and *P. kudriavzevii* species were isolated mainly from beverage sources whereas relatives representing *C. ethanolica* species were isolated from non-food sources. The sources of isolation revealed that the closest relatives of palm wine yeasts were from various sources and not specific to any particular food or fermentation mix.-

**Figure 6.** Phylogenetic analysis of *P. kudriavzevii* (HG425335-underlined) from *Raphia* sp. palm wine. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site.

**Figure 7.** Top six countries from which sequences of palm wine yeast relatives of *Elaeis* sp. palm tree were submitted to the GenBank.

**Figure 8.** Top six countries from which sequences of palm wine yeast relatives of *Raphia* sp. palm tree were submitted to the GenBank.

**Figure 9.** Distribution of palm wine yeast relatives with reference to yeasts from *Elaeis* sp. palm wine according to beverage (), food (∎), and non-food (⊞) sources.

A report [50] found that laboratory estimates of optimum growth temperature could be used to predict global distributions of free-living microbes. Also, it was pointed out that population genetic analyses show that the genetic diversity of *S. cerevisiae* is high in the tropics and subtropics of China [51, 52]. It was suggested that without further sampling in tropical and subtropical regions, it is not possible to differentiate whether the higher diversity of *S. cerevisiae*  in Asia reflects a greater habitat area or an Asian origin for *S. cerevisiae*. It would be beneficial- to carry out further studies in order to establish if palm wine yeasts were taken from Africa to Asia or vice versa. The diversity could also be high in temperate regions because a study examined *S. cerevisiae* and *S. paradoxus* in northeast America and uncovered a large diversity of yeasts [53]. Up to 24 yeast isolates could not be assigned to any known species and it was suggested that the yeasts identified may be of taxonomic, medical, or biotechnological importance.-

**Figure 10.** Distribution of palm wine yeast relatives with reference to yeasts from *Raphia* sp. palm wine according to beverage (), food (∎), and non-food (⊞) sources.

#### **3.6. G+C composition of palm wine yeast relatives**

The G+C composition is a well known evolutionary property of eukaryotes, archaea, and bacteria. There are suggestions by Chen et al. [54], that concordance between proteomic architecture and the genetic code is related closely to genomic G+C content and phylogeny. It has been suggested that yeasts with higher G+C content have a higher recombination rate [55] and recombination is believed to be suppressed around centromeres [56]. The data in **Table 1** present the average nucleotide composition and G+C content of partial sequences of 26S rRNA genes analyzed. It shows concentration of arginine, guanine and thiamine, and cytosine concentration in *S. cerevisiae, P. kudriavzevii*, *or C. ethanolica* obtained from the aforementioned palm trees. Data were obtained after measuring nucleotide frequencies (%)

Nucleotide concentration was obtained after analysis with MEGA 7.0 software. T/U, thiamine/uracil; C, cytosine; A, arginine; G, guanine.

**Table 1.** Average nucleotide composition and G+C content obtained from yeasts from *Raphia* sp. (R) or *Elaeis* sp. (E) palm wine and their relatives after measuring nucleotide frequencies (%) in 100 sequences relative to each yeast species shown.

in 100 sequences of strains relative to each palm wine yeast species listed. It was observed that the G+C content in *P. kudriavzevii* and *C. ethanolica* was higher than that of *S. cerevisiae*. This suggests that the *P. kudriavzevii* and *C. ethanolica* have a higher recombination rate than *S. cerevisiae* strains analyzed in this report. The G+C range observed is within the reported average genomic G+C-content range (13–75%) among species [57]. It was also found to be within range of G+C content (38.3–52.9%) of the *MAT* locus reported [58] in different- *Saccharomycetaceae* species.

Further studies are required because G+C-content is associated with multiple biases of different nature during down stream operations and these biases may include sequencing technologies, biological, and methodological reasons [57]. Another factor that could affect- the G+C content is that some yeasts like *Lachancea kluyveri* show an intriguing compositional heterogeneity in that a region of the chromosome has an average G+C content of 52.9% which is significantly higher than the 40.4% global G+C content of the rest of the- genome [58].


# **Hybrid Systems for Marine Energy Harvesting**

Edited by

Paulo Jorge Rosa-Santos, Francisco Taveira Pinto, Mario López Gallego and Claudio Alexis Rodríguez Castillo

> Printed Edition of the Special Issue Published in *JMSE*

www.mdpi.com/journal/jmse


**2. Autoantibodies in viral disease**

It is well established that there is often an association of transient, low-titer, polyspecific autoantibodies with common viral infections [6]. Autoantibodies may be detected in a variety of viral illnesses including hepatitis A, B, and C, parvovirus B19, enteroviruses, cytomegalovirus (CMV), and Epstein-Barr viruses (EBV) [1, 7–11]. Infectious agents have been implicated as an initial environmental trigger of AD in general, and in the induction of autoantibodies specifically [12–17]. It has been suggested that transient autoimmune responses are induced by acute viral infections in children and adults. Such responses may include generation of transient autoantibodies of typically low titer. The progression of such an immune state to an established autoimmune disease is rare [6, 18] as usually virally induced autoantibodies typically resolve with time. Hence, it may be difficult to differentiate autoimmune disease and self-limited illness.

Some of the commonly tested autoantibodies in viral infections include antinuclear antibodies (ANAs), antibodies directed against DNA, antibodies against proteins that bind to nucleic acids i.e. extractable nuclear antibodies (ENA), those directed against phospholipids, and antineutrophil cytoplasmic antibodies (ANCAs). In addition to these, the immune system may produce antibodies specific to certain tissues or organs (e.g., against hepatic, renal, gastric, intestinal, thyroid, pancreatic, muscular, testicular, dermatological, or neurological tissues). Also, the tendency of some viral infections to induce inflammatory responses in a variety of organ systems may also result in the development of autoimmune conditions. Hepatitis C and B virus, human immunodeficiency virus, parvovirus B19, cytomegalovirus, and Epstein-Barr virus appear to be associated with autoantibodies more commonly than other viruses.

### **2.1. Mechanism**

The mechanisms responsible for the generation of autoantibodies as a result of viral infections remain unclear. A few proposed mechanisms include cross-reactivity between viral proteins and autoantigens [19], molecular mimicry [16, 17, 20], and the induction of apoptosis of virusinfected cells [21], all leading to the production of autoantibodies. Another theory suggests that autoantibodies are anti-idiotypic antibodies to antiviral antibodies [22]. However, molecular or antigenic mimicry between microbial proteins and self-components (i.e., proteins, carbohydrates, or DNA epitopes) remains the most likely mechanism of autoimmunity post viral infection [20, 23].


**5. Conclusion**

Information produced by the CCVA can be used to identify threatened and endangered plants most vulnerable to climate change in the Mojave Desert. Species vulnerability assessments break down the complexity of climate change impacts on overall biodiversity, and facilitate the integration of societal, economic, and other environmental concerns into conservation planning efforts. Our assessments also identified knowledge gaps for each species, which promotes the development and testing of new hypotheses about climatic tolerances. Furthermore, the identification of particular traits that make a species vulnerable allow for targeted management actions. Recovery and management plans for threatened or endangered species can be updated to include climate change vulnerability and its implications, which may necessitate the inclusion of different stakeholders or increased frequency of monitoring to detect distribution shifts. The Mojave Desert is a unique ecoregion beloved by many and there is still much uncertainty related to the magnitude and extent of global atmospheric and climatic change. Our hope is that results presented here contribute to the larger body of knowledge for the region, and aid in better stewardship of these irreplaceable ecological systems and inhabitants.


**3. Results and discussion**

Screening for inhibitors of the genetically validated drug target *Tb*CK is problematic due to the difficulty in following the reaction either continuously or directly. A direct choline kin‐ ase activity assay assessing the production of phosphocholine, utilising a modified method of Kim *et. al.* [39], using *Tb*CK and radiolabelled choline has been performed previously [23]. However this is not suitable for screening purposes, so choline kinase activity was measured by a spectrophotometric coupled assay (Figure 1). This coupled enzyme assay utilises regen‐ eration of ATP from the ADP by-product of the choline kinase by pyruvate kinase, and sub‐ sequent oxidation of NADH as the resulting pyruvate is converted to lactate, by lactate dehydrogenase. This assay using coupled enzymes is also problematic, as a compound could potentially inhibit the coupled enzymes giving rise to a false positive.

An alternative approach for screening is differential scanning fluorimetry (Figure 2), allow‐ ing identification of compounds that interact with the *Tb*CK protein, either to stabilise or de‐ stabilise it, therefore influencing the protein's Tm (melting point) [38-40].

Initially *Tb*CK was subjected to differential scanning fluorimetry to ascertain if this approach was possible. Known components required for enzyme activity were tested to see if thermal shifts were observed.In the presence of 6 mM MgCl2, a Tm of 41.2°C was obtained (Figure 1C, solid dark line). The addition of 0.5 mM ATP resulted in a > 3°C Tm shift for *Tb*CK(Figure 1C, dashed-line). These encouraging results showed *Tb*CK was amenable to differential scanning fluorimetry and allowed validation of this screening method.It is worth noting the surprising low Tm of *Tb*CK, considering that these parasites live within the bloodstream of a mammalian host, i.e. 37°C, or higher with a fever. However, the presence of physiological

Differential fluorimetric scans were performed and analysed as described in Experimental. *Tb*CK + DMSO (control) solid dark line, *Tb*CK + 0.5 µM ATP (positive control) dashed line, *Tb*CK + 1 mM compound 242, solid light line, *Tb*CK + 1 mM compound 269, dotted line.Tm of *Tb*CK in the presence of 0.5%DMSO is 41.21 ± 0.03°C (control); Tm of *Tb*CK and 0.5 mM ATP is 44.46 ± 0.05°C (positive control). Insert: schematic representation of the thermal shift assay.A protein will unfold exposing hydrophobic domains as it is denatured due to the in‐ creasing temperature. Dyes such as sypro orange (star) are able to bind to these exposed hy‐ drophobic areas giving rise to fluorescence. A plot of this increased fluorescence versus temperature allows determination of Tm (melting point) of the protein. If a compound (squares) is able to interact with the protein it may alter the protein's Tm and thus a library of compounds can be screened to see if they stabilise (increase in Tm) or destabilise (decrease in

Coupled Enzyme Activity and Thermal Shift Screening of the Maybridge Rule of 3 Fragment Library...

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An alternative approach for screening is differential scanning fluorimetry (Figure 2), allow‐ ing identification of compounds that interact with the *Tb*CK protein, either to stabilise or de‐

Initially *Tb*CK was subjected to differential scanning fluorimetry to ascertain if this ap‐ proach was possible. Known components required for enzyme activity were tested to see if thermal shifts were observed. In the presence of 6 mM MgCl2, a Tm of 41.2°C was obtained (Figure 1C, solid dark line). The addition of 0.5 mM ATP resulted in a > 3°C Tm shift for *Tb*CK(Figure 1C, dashed-line). These encouraging results showed *Tb*CK was amenable to differential scanning fluorimetry and allowed validation of this screening method.It is worth noting the surprising low Tm of *Tb*CK, considering that these parasites live within the blood‐ stream of a mammalian host, i.e. 37°C, or higher with a fever. However, the presence of physiological relevant levels of ATP does stabilize *Tb*CK by > 3 °C, which may prolong the

The respective controls in both assay types allowed Z-factors to be determined for all of the plates screened (Figure 3). Both the coupled enzyme activity assay and the thermal shift analysis showed Z-factors to be above 0.5 for all plates, except for plate 5 for the thermal shift assay (but still above 0.45), this is indicative of good reliable assays, with meaningful

The MayBridge Rule of 3 Fragment Library was distributed over 9 plates (80 compounds per plate) providing space for adequate positive and negative controls, allowing Z-factors to be determined. This was done for each plate for both the choline kinase assay (+) and thermal shift analysis (x). A Z-factor above 0.4 is acceptable and validates the data on that plate as

The ~630 compounds from the MayBridge Rule of 3 Fragment Library were assessed for their ability to inhibit the *Tb*CK coupled enzyme activity assay at a single concentration of 0.5 mM (Figure 4A). At this relative high concentration only 9 of the compounds (1.4%) showed > 70% inhibition. These primary hits were retested in triplicate at 0.5 mM (Table 1), 2 of the 9(compounds 320 and 635) were confirmed as being false positives, while the re‐ maining 7 were confirmed to show good inhibition (80-100%) against the *Tb*CK coupled en‐

stabilise it, therefore influencing the protein's Tm (melting point) [38-40].

Tm) the target protein.

half-life of the protein in the parasite.

results [41].

being reliable.

**Figure 1.** Schematic of the *Tb*CK reaction and coupled assay.

*T. brucei* choline kinase (*Tb*CK) catalyses the ATP dependent phosphorylation of choline, the ADP is converted back to ATP by pyruvate kinase (PK), which converts phosphoenolpyru‐ vate (PEP) to pyruvate in the process. The resulting pyruvate is reduced to lactate by the NADH dependent lactate dehydrogenase (LDH). The resulting conversion of NADH to NAD+ is monitored, by measuring the reduction in absorbance at 341 nM.

**Figure 2.** Thermal shift assay; typical differential fluorimetry scans of TbCK.

Differential fluorimetric scans were performed and analysed as described in Experimental. *Tb*CK + DMSO (control) solid dark line, *Tb*CK + 0.5 µM ATP (positive control) dashed line, *Tb*CK + 1 mM compound 242, solid light line, *Tb*CK + 1 mM compound 269, dotted line.Tm of *Tb*CK in the presence of 0.5%DMSO is 41.21 ± 0.03°C (control); Tm of *Tb*CK and 0.5 mM ATP is 44.46 ± 0.05°C (positive control). Insert: schematic representation of the thermal shift assay.A protein will unfold exposing hydrophobic domains as it is denatured due to the in‐ creasing temperature. Dyes such as sypro orange (star) are able to bind to these exposed hy‐ drophobic areas giving rise to fluorescence. A plot of this increased fluorescence versus temperature allows determination of Tm (melting point) of the protein. If a compound (squares) is able to interact with the protein it may alter the protein's Tm and thus a library of compounds can be screened to see if they stabilise (increase in Tm) or destabilise (decrease in Tm) the target protein.

dashed-line). These encouraging results showed *Tb*CK was amenable to differential scanning fluorimetry and allowed validation of this screening method.It is worth noting the surprising low Tm of *Tb*CK, considering that these parasites live within the bloodstream of a mammalian

*T. brucei* choline kinase (*Tb*CK) catalyses the ATP dependent phosphorylation of choline, the ADP is converted back to ATP by pyruvate kinase (PK), which converts phosphoenolpyru‐ vate (PEP) to pyruvate in the process. The resulting pyruvate is reduced to lactate by the NADH dependent lactate dehydrogenase (LDH). The resulting conversion of NADH to

is monitored, by measuring the reduction in absorbance at 341 nM.

host, i.e. 37°C, or higher with a fever. However, the presence of physiological

**Figure 1.** Schematic of the *Tb*CK reaction and coupled assay.

**Figure 2.** Thermal shift assay; typical differential fluorimetry scans of TbCK.

NAD+

418 Drug Discovery

An alternative approach for screening is differential scanning fluorimetry (Figure 2), allow‐ ing identification of compounds that interact with the *Tb*CK protein, either to stabilise or de‐ stabilise it, therefore influencing the protein's Tm (melting point) [38-40].

Initially *Tb*CK was subjected to differential scanning fluorimetry to ascertain if this ap‐ proach was possible. Known components required for enzyme activity were tested to see if thermal shifts were observed. In the presence of 6 mM MgCl2, a Tm of 41.2°C was obtained (Figure 1C, solid dark line). The addition of 0.5 mM ATP resulted in a > 3°C Tm shift for *Tb*CK(Figure 1C, dashed-line). These encouraging results showed *Tb*CK was amenable to differential scanning fluorimetry and allowed validation of this screening method.It is worth noting the surprising low Tm of *Tb*CK, considering that these parasites live within the blood‐ stream of a mammalian host, i.e. 37°C, or higher with a fever. However, the presence of physiological relevant levels of ATP does stabilize *Tb*CK by > 3 °C, which may prolong the half-life of the protein in the parasite.

The respective controls in both assay types allowed Z-factors to be determined for all of the plates screened (Figure 3). Both the coupled enzyme activity assay and the thermal shift analysis showed Z-factors to be above 0.5 for all plates, except for plate 5 for the thermal shift assay (but still above 0.45), this is indicative of good reliable assays, with meaningful results [41].

The MayBridge Rule of 3 Fragment Library was distributed over 9 plates (80 compounds per plate) providing space for adequate positive and negative controls, allowing Z-factors to be determined. This was done for each plate for both the choline kinase assay (+) and thermal shift analysis (x). A Z-factor above 0.4 is acceptable and validates the data on that plate as being reliable.

The ~630 compounds from the MayBridge Rule of 3 Fragment Library were assessed for their ability to inhibit the *Tb*CK coupled enzyme activity assay at a single concentration of 0.5 mM (Figure 4A). At this relative high concentration only 9 of the compounds (1.4%) showed > 70% inhibition. These primary hits were retested in triplicate at 0.5 mM (Table 1), 2 of the 9(compounds 320 and 635) were confirmed as being false positives, while the re‐ maining 7 were confirmed to show good inhibition (80-100%) against the *Tb*CK coupled en‐ zyme activity assay. These 7 compounds were then tested against just the coupled enzymes, some inhibition was observed for some of the compounds, but this was insufficient to ac‐ count for the strong inhibition against the *Tb*CK, thus these 7 compounds were believed to show true *Tb*CK inhibition.

those occasions where this interaction destabilizes a protein, i.e. lowering Tm, a thermo‐ dynamic model has been proposed which explains the how the same ligand can stabilise and destabilise different proteins [42]. While the same protein may be stabilized and de‐ stabilized by very similar ligands, this was exquisitely demonstrated by the changes in thermal stability of Acyl-CoA thioesterase, upon incubation with either CoA (destabilise)

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**Figure 4.** A) Percentage inhibition of *Tb*CK enzyme activity assay for each of the compounds tested. B) Observed ther‐

mal shifts of *Tb*CK for each of the compounds tested

or Acyl-CoA (stabilise) [45].

**Figure 3.** Quality control (Z factors) for the *Tb*CK thermal shift analysis and coupled enzyme activity assay.

It is worth noting the lack of false positives arising from significant inhibition of either of the coupling enzymes, pyruvate kinase and lactate dehydrogenase this is encouraging when screening other ATP utilizing enzymes.

Thermal shift analysis of *Tb*CK with the ~630 compounds from the MayBridge Rule of 3 Fragment Library showed that the vast majority of compounds had little or no affect on the Tm of *Tb*CK (Figure 4B). Relatively few compounds showed an increase in Tm (stabilisation), and only a handful of these showing an increase in Tm> 1°C, i.e. compound 269 (Figure 2, dotted line), this was rather surprising given that ATP stabilises *Tb*CK by > 3°C. Significant‐ ly more compounds showed a destabilisation affect, with 3 compounds having > 10°C de‐ crease in the Tm, i.e. compound 242 (Figure 2, solid light line). Most of the compounds observed in this screen that show significant destabilisation of *Tb*CK, do not cause similar destabilisation affects with other enzymes that we have screened in a similar manner, the only exceptions are compounds 68 (2-aminothiophene-3-carbonitrile) and 565 (4-(2-ami‐ no-1,3-thiazol-4-yl)phenol) (Table 1).

Several drug discovery style studies have shown that an increase in the thermal stability of a protein is proportional to the concentration and affinity of the ligand to the protein in keeping with the equilibrium associated with ligand-protein binding [38, 41-44]. On those occasions where this interaction destabilizes a protein, i.e. lowering Tm, a thermo‐ dynamic model has been proposed which explains the how the same ligand can stabilise and destabilise different proteins [42]. While the same protein may be stabilized and de‐ stabilized by very similar ligands, this was exquisitely demonstrated by the changes in thermal stability of Acyl-CoA thioesterase, upon incubation with either CoA (destabilise) or Acyl-CoA (stabilise) [45].

zyme activity assay. These 7 compounds were then tested against just the coupled enzymes, some inhibition was observed for some of the compounds, but this was insufficient to ac‐ count for the strong inhibition against the *Tb*CK, thus these 7 compounds were believed to

**Figure 3.** Quality control (Z factors) for the *Tb*CK thermal shift analysis and coupled enzyme activity assay.

It is worth noting the lack of false positives arising from significant inhibition of either of the coupling enzymes, pyruvate kinase and lactate dehydrogenase this is encouraging when

Thermal shift analysis of *Tb*CK with the ~630 compounds from the MayBridge Rule of 3 Fragment Library showed that the vast majority of compounds had little or no affect on the Tm of *Tb*CK (Figure 4B). Relatively few compounds showed an increase in Tm (stabilisation), and only a handful of these showing an increase in Tm> 1°C, i.e. compound 269 (Figure 2, dotted line), this was rather surprising given that ATP stabilises *Tb*CK by > 3°C. Significant‐ ly more compounds showed a destabilisation affect, with 3 compounds having > 10°C de‐ crease in the Tm, i.e. compound 242 (Figure 2, solid light line). Most of the compounds observed in this screen that show significant destabilisation of *Tb*CK, do not cause similar destabilisation affects with other enzymes that we have screened in a similar manner, the only exceptions are compounds 68 (2-aminothiophene-3-carbonitrile) and 565 (4-(2-ami‐

Several drug discovery style studies have shown that an increase in the thermal stability of a protein is proportional to the concentration and affinity of the ligand to the protein in keeping with the equilibrium associated with ligand-protein binding [38, 41-44]. On

show true *Tb*CK inhibition.

420 Drug Discovery

screening other ATP utilizing enzymes.

no-1,3-thiazol-4-yl)phenol) (Table 1).

**Figure 4.** A) Percentage inhibition of *Tb*CK enzyme activity assay for each of the compounds tested. B) Observed ther‐ mal shifts of *Tb*CK for each of the compounds tested

All of the compounds in the two data sets (the coupled enzyme activity assay and the ther‐ mal shift analysis), were compared to assess any correlation between the two very different methods. In other words looking for compounds that showed a significant change in Tm and a significant inhibition in *Tb*CK enzyme activity (Figure 5A). The vast majority of com‐ pounds showed little or no inhibition and little or no shift in Tm. Compounds showing < 40% inhibition of the enzyme activity were removed for clarification (Figure 5B), this highlighted that the majority of compounds that show *Tb*CK enzyme inhibition do not significantly alter the Tm of *Tb*CK. The exceptions are compound 565 with a decrease in Tm~9°C, and com‐ pound 68 and 95 with a decrease in Tm~2°C respectively, all show complete inhibition at 0.5 mM (Table 1). Twenty-one compounds showed > 40% enzyme inhibition, 9 of these (43%) displayed > 1°C change in *Tb*CK Tm. This is a substantial enrichment compared to the 7% of

Coupled Enzyme Activity and Thermal Shift Screening of the Maybridge Rule of 3 Fragment Library...

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423

**Figure 5.** Scatter graph representation showing the correlation between the observed thermal shifts and percentage inhibition of *Tb*CK enzyme activity. A) All of the compounds from the MayBridge Rule of 3 Fragment Library tested. B)

For those compounds with > 40% (dotted line) inhibition of *Tb*CK enzyme activity.

compounds with Tm shifts > 1°C observed for the entire library.

a Arbitrary library number

b CAS numbers are unique identifiers assigned by the "Chemical Abstracts Service" to describe every chemical descri‐ bed in open access scientific literature.

c Tm shift in °C, observed for TbCK in the presence of compound (1mM), value is mean ± SD from the Boltzman curve fitting, see Experimental for details.

d Cytotoxicity studies, see Major and Smith 2011 for details, values are percentage of controls in the absence of com‐ pound, either mean ± SD (n=3) or mean ± SE (n=2), the latter being in bold.

**Table 1.** The compounds from the Maybridge Rule of 3 library that show >70% inhibition of TbCK.

All of the compounds in the two data sets (the coupled enzyme activity assay and the ther‐ mal shift analysis), were compared to assess any correlation between the two very different methods. In other words looking for compounds that showed a significant change in Tm and a significant inhibition in *Tb*CK enzyme activity (Figure 5A). The vast majority of com‐ pounds showed little or no inhibition and little or no shift in Tm. Compounds showing < 40% inhibition of the enzyme activity were removed for clarification (Figure 5B), this highlighted that the majority of compounds that show *Tb*CK enzyme inhibition do not significantly alter the Tm of *Tb*CK. The exceptions are compound 565 with a decrease in Tm~9°C, and com‐ pound 68 and 95 with a decrease in Tm~2°C respectively, all show complete inhibition at 0.5 mM (Table 1). Twenty-one compounds showed > 40% enzyme inhibition, 9 of these (43%) displayed > 1°C change in *Tb*CK Tm. This is a substantial enrichment compared to the 7% of compounds with Tm shifts > 1°C observed for the entire library.

**Library numbera**

422 Drug Discovery

68 4651-82-5

95 933-67-5

278 199590-00-6

320 39270-39-8

346 57976-57-5

372 143426-51-1

565 57634-55-6

635 64354-50-3

Arbitrary library number

bed in open access scientific literature.

fitting, see Experimental for details.

pound, either mean ± SD (n=3) or mean ± SE (n=2), the latter being in bold.

a

c

**CAS numberb Molecular Structure**

**Compound Name**

2 aminothiophene-3-carbonitrile

> 7-methyl-1Hindole

(1-methyl-1Hindol-6 yl)methanol

2,3-dihydro-1,4 benzodioxin-6 ylmethanol

> 3-pyridin-3 ylaniline

[4-(1H-pyrrol-1-yl) phenyl] methanol

4-(2-amino-1,3 thiazol-4 yl)phenol

5-methyl-2 phenyl-3-furoic acid

**Table 1.** The compounds from the Maybridge Rule of 3 library that show >70% inhibition of TbCK.

b CAS numbers are unique identifiers assigned by the "Chemical Abstracts Service" to describe every chemical descri‐

Tm shift in °C, observed for TbCK in the presence of compound (1mM), value is mean ± SD from the Boltzman curve

d Cytotoxicity studies, see Major and Smith 2011 for details, values are percentage of controls in the absence of com‐

257 59147-02-3 4-(2-furyl)aniline 80 ± 3 ~234 63 0.46 ± 0.08 9.3 ± 8.5

101 ± 2

100 ± 0

80 ± 1

*Tb***CK activity % inhibition at 500μM Mean ± SD (***n***=3)**

*TbCK IC50* **(υM)**

> 25.45 ±1.16

12.35 ± 0.64

109.7 ± 10.6

**PK/LDH % inhibition at 500 υM mean (n=2)**

101 ± 3 ~758 40 -2.15 ± 0.08 10.3 ± 11

84 ± 4 ~380 49 -2.16 ± 0.14 4.1 ± 5.2

6 ± 4 ND ND -0.17 ± 0.07 63.1 ± 9.5

100 ± 0 ~120 85 -8.91 ± 0.49 28.7±19.3

23 ± 4 ND ND -2.94 ± 0.16 20.5 ± 7.4

*TbCK TmShiftc*

28 0.26 ± 0.09 8.8 ± 10.8

68 -1.18 ± 0.06 15.6 ± 9.3

26 1.24 ± 0.04 27 ± 14

*T. brucei % survivald*

**Figure 5.** Scatter graph representation showing the correlation between the observed thermal shifts and percentage inhibition of *Tb*CK enzyme activity. A) All of the compounds from the MayBridge Rule of 3 Fragment Library tested. B) For those compounds with > 40% (dotted line) inhibition of *Tb*CK enzyme activity.

Of the compounds identified that alter the Tm of *Tb*CK by > 1°C, ~20% of them inhibit *Tb*CK enzyme activity by > 40%. This suggests that for *Tb*CK thermal shift analysis has al‐ lowed significant enrichment, but not total capture of the potential inhibitors of *Tb*CK. However, if a direct assay for a potential drug target was very problematic, prior thermal shift analysis could significantly streamline the number of compounds to be screened, thereby increasing the potential to identify lead compounds. Thermal shift analysis has the disadvantage that good inhibitors could be missed if they do not significantly alter the Tm of the protein.

This raises an interesting question, is it a viable option to target compounds that specifi‐ cally destabilise an enzyme, causing a decrease in enzyme activity? One could argue this is exactly what pharmaceutical companies are focusing their research efforts upon, but with a slightly different approach. Some of their therapeutic targets rely on finding com‐ pounds that disrupt various interactions; hetero- or homo-oligomeric protein-protein, DNA-binding protein and RNA polymerases, many of these are associated with signaling events. Success stories include the identification of HDM2 antagonists associated with P53 activation [46], the identification of anti-cancer agent for the BCL-XL protein-protein com‐ plex and several others, reviewed by Wells and McClendon [47] and more recently by Coyne and colleagues [48].

**Figure 6.** Correlation of the percentage inhibition of *Tb*CK enzyme activity assay and *T. brucei* survival.

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425

**Figure 7.** IC50 curves of the promising compounds (278 and 346) that show significant TbCK inhibition.

selectivity index.

IC50 values were determined for these compounds (Table 1, Figure 7) ranging from 100s of µM to low µM. For example, compound 278 (1-methyl-(1H-indol-6-yl)methanol) has an IC50 value of 25.45 ± 1.16 µM however the selectivity index is not very good, while compound 346 (3-pyridin-3-ylaniline) has an IC50 value of 12.35 ± 0.64 µM with a high

The techniques utilized to study the formation / disruption of protein-protein complexes are driven by high throughput drug discovery, including fragment based approaches, these in‐ clude X-ray crystallography, NMR, dynamic light scattering, differential static light scatter‐ ing, differential scanning fluorimetry [42-50].

In summary, destabilisation by a ligand could affect the oligomeric state of a protein, or in the case of a monomer disrupt intra-molecular interactions, i.e. between stacking α-helixes or β-sheets, causing partial unfolding and thus destabilisation.In the case of *Tb*CK, which we know exists as a dimer, one of several potential mechanisms of destabilisation could be dis‐ ruption of the dimer interface, whereby a ligand is able to bind to freshly exposed hydro‐ phobic surfaces on the protein, and this interaction allows further destabilisation of the monomer structure.

As it was clear that compounds that inhibit *Tb*CK enzyme activity do not necessarily show a significant increase or decrease in Tm, it was decided to compare inhibition of *Tb*CK enzyme activity with previously determined trypanocidal activities for the com‐ pounds [51]. From this comparison (Figure 6) a group of compounds above a threshold of > 70% inhibition of *Tb*CK showed significant trypanocidal activity (circled), suggesting a direct correlation.

Compounds from the May Bridge Rule of 3 Fragment Library with greater than 70% inhibi‐ tion (dotted line) of the TbCK enzyme activity are circled and numbered. Numbers corre‐ spond to arbitrary compound library numbers; see Table 1 for chemical structures and extra data. *T. brucei* survival data was previously determined [51].

Coupled Enzyme Activity and Thermal Shift Screening of the Maybridge Rule of 3 Fragment Library... http://dx.doi.org/10.5772/52668 425

**Figure 6.** Correlation of the percentage inhibition of *Tb*CK enzyme activity assay and *T. brucei* survival.

Of the compounds identified that alter the Tm of *Tb*CK by > 1°C, ~20% of them inhibit *Tb*CK enzyme activity by > 40%. This suggests that for *Tb*CK thermal shift analysis has al‐ lowed significant enrichment, but not total capture of the potential inhibitors of *Tb*CK. However, if a direct assay for a potential drug target was very problematic, prior thermal shift analysis could significantly streamline the number of compounds to be screened, thereby increasing the potential to identify lead compounds. Thermal shift analysis has the disadvantage that good inhibitors could be missed if they do not significantly alter the

This raises an interesting question, is it a viable option to target compounds that specifi‐ cally destabilise an enzyme, causing a decrease in enzyme activity? One could argue this is exactly what pharmaceutical companies are focusing their research efforts upon, but with a slightly different approach. Some of their therapeutic targets rely on finding com‐ pounds that disrupt various interactions; hetero- or homo-oligomeric protein-protein, DNA-binding protein and RNA polymerases, many of these are associated with signaling events. Success stories include the identification of HDM2 antagonists associated with P53 activation [46], the identification of anti-cancer agent for the BCL-XL protein-protein com‐ plex and several others, reviewed by Wells and McClendon [47] and more recently by

The techniques utilized to study the formation / disruption of protein-protein complexes are driven by high throughput drug discovery, including fragment based approaches, these in‐ clude X-ray crystallography, NMR, dynamic light scattering, differential static light scatter‐

In summary, destabilisation by a ligand could affect the oligomeric state of a protein, or in the case of a monomer disrupt intra-molecular interactions, i.e. between stacking α-helixes or β-sheets, causing partial unfolding and thus destabilisation.In the case of *Tb*CK, which we know exists as a dimer, one of several potential mechanisms of destabilisation could be dis‐ ruption of the dimer interface, whereby a ligand is able to bind to freshly exposed hydro‐ phobic surfaces on the protein, and this interaction allows further destabilisation of the

As it was clear that compounds that inhibit *Tb*CK enzyme activity do not necessarily show a significant increase or decrease in Tm, it was decided to compare inhibition of *Tb*CK enzyme activity with previously determined trypanocidal activities for the com‐ pounds [51]. From this comparison (Figure 6) a group of compounds above a threshold of > 70% inhibition of *Tb*CK showed significant trypanocidal activity (circled), suggesting a

Compounds from the May Bridge Rule of 3 Fragment Library with greater than 70% inhibi‐ tion (dotted line) of the TbCK enzyme activity are circled and numbered. Numbers corre‐ spond to arbitrary compound library numbers; see Table 1 for chemical structures and extra

data. *T. brucei* survival data was previously determined [51].

Tm of the protein.

424 Drug Discovery

Coyne and colleagues [48].

monomer structure.

direct correlation.

ing, differential scanning fluorimetry [42-50].

**Figure 7.** IC50 curves of the promising compounds (278 and 346) that show significant TbCK inhibition.

IC50 values were determined for these compounds (Table 1, Figure 7) ranging from 100s of µM to low µM. For example, compound 278 (1-methyl-(1H-indol-6-yl)methanol) has an IC50 value of 25.45 ± 1.16 µM however the selectivity index is not very good, while compound 346 (3-pyridin-3-ylaniline) has an IC50 value of 12.35 ± 0.64 µM with a high selectivity index.

One of the strengths of the Maybridge Rule of 3 Fragment Library is the chemical diversity, additionally a range of analogous structures can normally be found within it allowing initial structure activity relationships to be formulated. There are several close analogues of com‐ pound 278 (1-methyl-(1H-indol-6-yl)methanol), which highlight that the N-methyl indole moiety seems necessary to have any *Tb*CK inhibition and the methanol portion of the mole‐ cule can not be replaced by a carboxylic acid. Investigation of the CHEMBL database for similar compounds identified 1-Methyl-1H-pyrrolo[2,3-c]pyridine (CHEMBL594467) which was screened as one of a library of tricyclic and bicyclic analogues of indoloquinoline alka‐ loids against a variety of protozoan parasites. The analogue mentioned here showed weak trypanocidal activity (624 µM) against *Trypanosoma brucei rhodesiense*, but significantly better (37 µM) against *Plasmodium falciparum* [52].

be investigated by undertaking various *in vivo* biochemical phenotyping experiments to as‐ certain if they are inhibiting *Tb*CK, thus causing a lack of *de novo* PC synthesis, known to be

Coupled Enzyme Activity and Thermal Shift Screening of the Maybridge Rule of 3 Fragment Library...

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427

The ultimate goal is to identify new easy to make, affordable, easy to administer, drugs in the fight against African sleeping sickness and other closely related protozoan transmitted

This work was supported in part by a Wellcome Trust Senior Research Fellowship (067441) and Wellcome Trust project grants 086658 and 093228. We thank the late and sorely missed Dr Rupert Russell (St Andrews), supported by SUSLA, for access to the May Bridge Rule of

Biomedical Sciences Research Centre, The North Haugh, The University, St. Andrews, Fife,

[3] Priotto, G.; Kasparian, S.; Mutombo, W.; Ngouama, D.; Ghorashian, S.; Arnold, U.; Ghabri, S.; Baudin, E.; Buard, V.; Kazadi-Kyanza, S.; Ilunga, M.; Mutangala, W.; Poh‐ lig, G.; Schmid, C.; Karunakara, U.; Torreele, E.; Kande, V. Lancet 2009, 374, 56.

[4] Delespaux, V.; de Koning, H. P. Drug resistance updates : reviews and commentaries

[6] Bouteille, B., O. Oukem, S. Bisser, and M. Dumas. 2003. Treatment perspectives for

[7] Priotto, G., C. Fogg, M. Balasegaram, O. Erphas, A. Louga, F. Checchi, S. Ghabri, and P. Piola. 2006. Three drug combinations for late-stage *Trypanosoma brucei gambiense* sleeping sickness: a randomized clinical trial in Uganda. PLoSClin. Trials 1:e39

[1] WHO web site: http://www.who.int/topics/trypanosomiasis\_african/en/

in antimicrobial and anticancer chemotherapy 2007, 10, 30. [5] Baker, N.; Alsford, S.; Horn, D. MolBiochemParasit 2011, 176, 55.

human African trypanosomiasis. Fundam. Clin. Pharmacol. 17:171

essential for the parasite.

Third World diseases.

**Acknowledgements**

3 Fragment Library.

**Author details**

Scotland, U.K.

**References**

Louise L. Major, Helen Denton and Terry K. Smith

[2] Steverding, D. Parasites & vectors 2010, 3, 15.

Another analogue, 1-Methyl-1H-indole (CHEMBL19912) has been shown to interact with human intracellular adhesion molecules and highlights the importance of selectivity [53]. 1H-indol-5-yl-methanol (CHEMBL1650258) has previously been screened against *Leishmania* as a potential PTR1 inhibitor but was shown to be inactive at 500 µM [54]

For the relatively simple compound 346 (3-pyridin-3-ylaniline), there are several analo‐ gous structures in the library, including compound 262 (2-(1H-imidazo-1-yl)aniline) which shows ~55% *Tb*CK enzyme inhibition and is trypanocidal. Compound 347 (4-pyri‐ din-3-ylaniline) is a structural isomer of 346 but shows no *Tb*CK enzyme inhibition and is not trypanocidal. The only related structure in the CHEMBL database was 3-(pyri‐ din-3-yl)benzenaminium (CHEMBL1778131) which was shown to be a weakinhibitor of metallo-β−lactamase IMP-1 [55].


**1. Introduction**

Distributed-order fractional operators were introduced and studied by Caputo at the end of the previous century [1,2]. They can be seen as a kind of generalization of fractional order derivatives/integrals in the sense that these operators are defined by a weighted integral of different orders of differentiation over a certain range. This subject gained more interest at the beginning of the current century by researchers from different mathematical disciplines, through attempts to solve differential equations with distributed-order derivatives [3–6]. Moreover, at the same time, in the domain of applied mathematics, those distributed-order fractional operators have started to be used, in a satisfactory way, to describe some complex phenomena modeling real world problems—see, for instance, works in viscoelasticity [7,8] and in diffusion [9]. Today, the study of distributed-order systems with fractional derivatives is a hot subject—see, e.g., [10–12] and references therein.

Fractional optimal control deals with optimization problems involving fractional differential equations, as well as a performance index functional. One of the most important results is the Pontryagin Maximum Principle, which gives a first-order necessary optimality condition that every solution to the dynamic optimization problem must verify. By applying such a result, it is possible to find and identify candidate solutions to the optimal control problem. For the state of the art on fractional optimal control, we refer the readers to [13–15] and references therein. Recently, distributed-order fractional problems of the calculus of variations were introduced and

investigated in [16]. Here, our main aim is to extend the distributed-order fractional Euler–Lagrange equation of [16] to the Pontryagin setting (see Remark 2).

Regarding optimal control for problems with distributed-order fractional operators, the results are rare and reduce to the following two papers: [17,18]. Both works develop numerical methods while, in contrast, here we are interested in analytical results (not in numerical approaches). Moreover, our results are new and bring new insights. Indeed, in [17], the problem is considered with Riemann–Liouville distributed derivatives, while in our case we consider optimal control problems with Caputo distributed derivatives. We must also note an inconsistency in [17]: when one defines the control system with a Riemann–Liouville derivative, then in the adjoint system it should appear as a Caputo derivative—when one considers optimal control problems with a control system with Caputo derivatives, the adjoint equation should involve a Riemann–Liouville operator—as a consequence of integration by parts (cf. Lemma 1). This inconsistency has been corrected in [18], where optimal control problems with Caputo distributed derivatives (as in this paper) are considered. Unfortunately, there is still an inconsistency in the necessary optimality conditions of both [17,18]: the transversality conditions are written there exactly as in the classical case, with the multiplier vanishing at the end of the interval, while the correct condition, as we prove in our Theorem 1, should involve a distributed integral operator—see condition (3).

The text is organized as follows. We begin by recalling definitions and necessary results of the literature in Section 2 of preliminaries. Our original results are then given in Section 3. More precisely, we consider fractional optimal control problems where the dynamical system constraints depend on distributed-order fractional derivatives. We prove a weak version of Pontryagin's maximum principle for the considered distributed-order fractional problems (see Theorem 1) and investigate a Mangasarian-type sufficient optimality condition (see Theorem 2). An example, illustrating the usefulness of the obtained results, is given (see Examples 1 and 2). We end with Section 4 of conclusions, mentioning also some possibilities of future research.


[23] Rangel-Lucio JA,

Rodríguez-Mendoza M, Ferrera-Cerrato R, Castellanos-Ramos J, Ramírez-Gama RM, Alvarado-Bárcenas E. Afinidad y efecto de *Azospirillum* sp. en maíz. Agronomía Mesoamericana. 2011;*22*(2):269-279

[24] Iannucci, A., Fragasso, M., Beleggia, R., Nigro, F., & Papa, R. (2017).

Evolution of the crop rhizosphere: Impact of domestication on root exudates in tetraploid wheat (*Triticum turgidum* L.). *Frontiers in plant science*, 8, 2124.

[25] Hassani, M. A., Özkurt, E., Franzenburg, S., & Stukenbrock, E. H. (2020). Ecological assembly processes of the bacterial and fungal microbiota of wild and domesticated wheat species. *Phytobiomes Journa*l, PBIOMES-01.

[26] Carrillo J, Ingwell LL, Li X, Kaplan I. Domesticated tomatoes are more vulnerable to negative plant-soil feedbacks than their wild relatives. Journal of Ecology. 2019;*107*(4):1753-1766

[27] Hardoim PR, van Overbeek LS, Berg G, Pirttilä AM, Compant S, Campisano A, et al. The hidden world within plants: Ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiology and Molecular Biology Reviews. 2015;*79*(3):294-320. DOI: 10.1128/ MMBR.00050-14

[28] Corbin KR, Bolt B, López CMR. Breeding for beneficial microbial communities using Epigenomics. Frontiers in Microbiology. 2020;**11**

[29] Tosi M, Mitter EK, Gaiero J, Dunfield K. It takes three to tango: The importance of microbes, host plant, and soil management to elucidate manipulation strategies for the plant microbiome. Canadian Journal of Microbiology. 2020;*66*(7):413-433

[30] Sessitsch A, Mitter B. 21st-century agriculture: Integration of plant microbiomes for improved crop production and food security. Microbial Biotechnology. 2015;*8*(1):32-33. DOI: 10.1111/1751-7915.12180

[31] Arif I, Batool M, Schenk PM. Plant microbiome engineering: Expected

benefits for improved crop growth and resilience. Trends in Biotechnology. 2020;**S0167-7799**(20):30121- 30129 https://doi.org/10.1016/j. tibtech.2020.04.015

[32] Bakker MG, Manter DK, Sheflin AM, Weir TL, Vivanco JM. Harnessing the rhizosphere microbiome through plant breeding and agricultural management. Plant and Soil. 2012;*360*(1-2):1-13

[33] Prashar P, Kapoor N, Sachdeva S. Rhizosphere: Its structure, bacterial diversity and significance. Reviews in Environmental Science and Bio/ Technology. 2014;*13*(1):63-77

[34] Kennedy, A. C., & Smith, K. L. 1995. *Soil microbial diversity and the sustainability of agricultural soils*. In: The significance and regulation of soil biodiversity (pp. 75-86). Springer Netherlands.

[35] Brussaard L, De Ruiter PC, Brown GG. Soil biodiversity for agricultural sustainability. Agriculture, Ecosystems & Environment. 2007;*121*(3):233-244

[36] Maheshwari, D.K. (2011). *Bacteria in Agrobiology: Crop Ecosystems*. DOI 10.1007/978-3-642-18357-7\_2, Springer-Verlag Berlin, Heidelberg

[37] Berendsen RL, Pieterse CM, Bakker PA. The rhizosphere microbiome and plant health. Trends in Plant Science. 2012;*17*(8):478-486

[38] Finkel OM, Castrillo G, Paredes SH, González IS, Dangl JL. Understanding and exploiting plant beneficial microbes. Current Opinion in Plant Biology. 2017;*38*:155-163

[39] Busby PE, Soman C, Wagner MR, Friesen ML, Kremer J, Bennett A, et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biology. 2017;*15*(3):e2001793

[40] Dent D, Cocking E. Establishing symbiotic nitrogen fixation in cereals and other non-legume crops: The greener nitrogen revolution. Agriculture & Food Security. 2017;*6*(1):7-9

[41] Van Deynze A, Zamora P, Delaux P-M, Heitmann C, Jayaraman D, Rajasekar S, et al. Nitrogen fixation in a landrace of maize is supported by a mucilage-associated diazotrophic microbiota. PLoS Biology. 2018;*16*(8):e2006352

[42] Pineda A, Kaplan I, Bezemer TM. Steering soil microbiomes to suppress aboveground insect pests. Trends in Plant Science. 2017;*22*(9):770-778

[43] Berg G, Köberl M, Rybakova D, Müller H, Grosch R, Smalla K. Plant microbial diversity is suggested as the key to future biocontrol and health trends. FEMS Microbiology Ecology. 2017;*93*(5)

[44] Lugtenberg BJ, Caradus JR, Johnson LJ. Fungal endophytes for sustainable crop production. FEMS Microbiology Ecology. 2016;*92*(12)

[45] Horner A, Browett SS, Antwis RE. Mixed-cropping between field pea varieties alters root bacterial and fungal communities. Scientific Reports. 2019;**9**(1):1-10

[46] Naqqash T, Hameed S, Imran A, Hanif MK, Majeed A, van Elsas JD. Differential response of potato toward inoculation with taxonomically diverse plant growth promoting Rhizobacteria. Frontiers in Plant Science. 2016;*7*:144. DOI: 10.3389/fpls.2016.00144