IDENTIFICATION OF BIOACTIVE COMPOUNDS AND ANTIBACTERIAL ACTIVITY OF MARINE GASTROPOD CONUS ACHATINUS GMELIN, 1791

aim of the present study is to screen a molluscan gastropod, Conus achatinus to find out the possible bioactive compounds present in the whole body tissues of experimental animal. The antibacterial activity was assayed against ten bacterial pathogens by Disc Diffusion Technique at three different concentrations. The crude methanolic extract showed significant antibacterial activity with minimum inhibitory zone of 2 mm against Pseudomonas aeruginosa and maximum 18 mm against Klebsiella pneumoniae at 100mg/ml concentration. It was further fractionated into four fractions using the different solvent system. Of all the fractions tested, (F2) and) (F3) showed the potent activities. The fraction F2 showed minimum inhibition zone of 9 mm against Pseudomonas aeruginosa and Proteus vulgaris and the maximum of 14 mm against Streptococcus pyogenes and F3 showed the minimum inhibition zone of 12 mm against Klebsiella pneumoniae and 20 mm against Staphylococcus aureus at 100 mg/ml concentration . The GC-MS analysis of the whole body tissue extract of Conus achatinus revealed the presence of alkaloid, amino, nitrogen, aldehyde, alcoholic, sulphur, nitrogen and steroid compounds which might be responsible for the antibacterial activity. of wide spectrum activity by developing maximum inhibition zone of maximum 20mm against S. aerues and minimum 12 mm against K. The present finding is a supportive

ThThe aim of the present study is to screen a molluscan gastropod, Conus achatinus to find out the possible bioactive compounds present in the whole body tissues of experimental animal. The antibacterial activity was assayed against ten bacterial pathogens by Disc Diffusion Technique at three different concentrations. The crude methanolic extract showed significant antibacterial activity with minimum inhibitory zone of 2 mm against Pseudomonas aeruginosa and maximum 18 mm against Klebsiella pneumoniae at 100mg/ml concentration. It was further fractionated into four fractions using the different solvent system. Of all the fractions tested, (F2) and) (F3) showed the potent activities. The fraction F2 showed minimum inhibition zone of 9 mm against Pseudomonas aeruginosa and Proteus vulgaris and the maximum of 14 mm against Streptococcus pyogenes and F3 showed the minimum inhibition zone of 12 mm against Klebsiella pneumoniae and 20 mm against Staphylococcus aureus at 100 mg/ml concentration. The GC-MS analysis of the whole body tissue extract of Conus achatinus revealed the presence of alkaloid, amino, nitrogen, aldehyde, alcoholic, sulphur, nitrogen and steroid compounds which might be responsible for the antibacterial activity.

Introduction:-
Oceans offers a large biodiversity of fauna and flora which is estimated to be over 5,00,000 species and more than double that of land. The marine environment is a huge source for of natural products and most of them yet to be discovered. Apart from human medicines, the research on marine natural products in the last three decades has also brought to the discoveries of many chemically and biologically interesting molecules, that have become indispensable tools in biochemical research and played significant roles in the recent advancement of life sciences. Tincu and Tailor (2004) reported that sessile and slow moving benthic marine organisms are the rich source of bioactive metabolites. These metabolites can be considered as a kind of autogenic protection and are proved to be the timely alternative natural medicine to human beings. Generally, the ocean meets the food requirements of human, a far better profitability is obtained by producing human consumables and the highest profitability is currently expected from bioactive compounds (Je et al., 2005;Jeon and Kim, 2002;Kim et al., 2001). There are more than thousands of bioactive compounds discovered in molluscs. So far, many bioactive compounds like peptides, sterols, terpenes, polypropionates, nitrogeneous compounds, macrolides, prostaglandins, fatty acids derivatives, alkaloids etc., showing specific type of activities have been identified and isolated from molluscs Members of many classes of molluscs possessing bioactive compounds having antitumour, antileukemic, and antibacterial properties have been reported worldwide (Anand et al., 1997;Ilangedone et al., 1999;Rajaganapathy et al., 2000 andChellaram et al., 2004). Conversely, most medicines are derived from shelled gastropods and bivalves (Kirsten Benkendroff, 2010). The bioactive compounds isolated from these shelled gastropods are considered a role as chemical defense against their predators. Today, the great majority of bacterial infections are easily controlled with natural or synthetic drugs. Antibacterial activity has been previously proved in a wide range of molluscan species (Mitta et al., 2000;Rajaganapathy et al., 2000;Prem Anand and Patterson Edward, 2002;Prema., 2006;Annamalai et al., 2007;Maripandi et al., 2010 andSubavathy, 2012). We are still in need of safer, cheaper and effective drugs. Marine molluscs, are the source of biologically active products is largely unexplored in India. Hence, a broad based screening of marine molluscs is necessary to explore the bioactive compounds.
Due to the above facts, the present study has been undertaken to test the whole body tissue of marine molluscan extract against bacteria and to isolate and characterize the possible antibacterial compound from the test mollusc Conus achatinus.

Material And Methods:-
Conus achatinus were collected from the Gulf of Mannar in Thoothukudi coastal region during low tides from the sea in its natural habitat. The freshly collected samples were cleaned and washed with fresh seawater to remove all impurities. The shells were broken and the tissues were made into small pieces with aseptic scissors and then dried in hot air oven at56˚Cfor 48 hours and used for further study.

Preparation Of Extracts:-
Methanol extract of the body tissue was prepared by the technique given by Thilaga (2005) which was slightly modified. Air dried materials were taken and immersed in 100% A.R. grade methanol for 10 days in room temperature. The extract from the solvent was filtered using Whatman No.1 filter paper. The filtrate was poured into petriplates and evaporated to dryness. The dried substance was used for further studies. Solids were dissolved in deionized water and concentration series of 1mg/ml, 10mg/ml and 100mg/ml were prepared and used.
Antibacterial Assay:-Antibacterial assay of both crude and column fractionated extracts of Conus achatinus were determined against ten bacterial pathogens through disc diffusion technique. Pseudomonas aeruginosa, Bacillus cereus, Shigella flexneri, Vibrio cholerae, Salmonella typhi, Escherichia coli, Proteus vulgaris, Klebsiella pneumoniae, Streptococcus pyogenes and Staphylococcus aureus which were obtained from the Microbiology Department of Sri Paramakalyani College, Alwarkurichi. These bacterial strains were inoculated in sterile nutrient broths resectively and incubated at 28 ± 2˚C for 24 hours. The impregnated discs along with the Control were kept at the centre of the agar plates, seeded with test bacterial cultures. The plates were incubated at 37˚C for 24 hours and the inhibition zone was measured from the outer side of the discs to the inner side of the inhibition zone. The extract showing broad spectrum antibacterial activity was examined by testing different concentrations viz., 1mg/ml, 10mg/ml and 100mg/ml. Each activity was tested thrice for confirmation of activity. The most potent fraction was characterized to know the functional groups through GC-MS study. GC-MS analysis was carried out on a GC Clarus 500 Perkin Elmer system at Indian Institute of Crop Processing Technology, Tanjore.

Identification Of Compounds:-
Interpretation on mass spectrum GC-MS was conducted using the data base of National Institute of Standard Technology (NIST) having more than 62,000 patterns. The unknown components with electrons impact fragmentation patterns of the mass spectra of column fractioned test mollusc was matched with the spectrum of the known components stored in NIST ver.21 National Institute of Standard Technology, the mass spectra library and predicted from Dr. Duke's Phytochemical and Ethnobotanical Database.

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Results:-In the present study, antibacterial activity of whole body tissue extract of Conus achatinus was tested and presented in figures (1-3) and plates (1)(2)(3). The extract showed more pronounced antibacterial activities against some human pathogenic bacterial strains. The crude methanolic extract of Conus achatinus showed the range of activity from 2mm to 18mm. At 100mg/ml concentration, the minimum inhibitory zone of 2mm against Pseudomonas aeruginosa and maximum of 18 mm against Klebsiella pneumonia were recorded. Of all the fractions tested, (F2) and (F3) showed the most potent activities. F2 developed minimum inhibitory zone of 9 mm against Pseudomonas aeruginosa and Proteus vulgaris and maximum 14 mm against Streptococcus pyogenes. F3 developed inhibition zones which varied from 12 mm and 20 mm. It showed the minimum inhibitory activity of 12 mm against Klebsiella pneumoniae and 20 mm against Staphylococcus aureus at 100 mg/ml concentration. These promising results revealed, the presence of alkaloid, amino, nitrogen, aldehyde, alcoholic, sulphur, nitrogen and steroid compounds by GC-MS analysis of whole body tissue of Conus achatinus, which might be responsible for the antibacterial activity.

Discussion:-
In the present study, the whole body tissue extract of the marine gastropod Conus achatinus showed significant antibacterial activity against ten bacterial pathogens. The crude extract showed more pronounced antibacterial activities against some human pathogenic bacterial strains. It could be explained that the bioactive compounds present in the experimental organism might have been developed as chemical defense of animals against their predators (Chellaram and Edward, 2009) living in their specific environment. Anand and Edward (2001) noted that the crude methanol extract of Cypraea errones exhibited promising antibacterial activity. Chellaram et al, (2004) reported that chloroform and acetone extracts of Ptera chinensis showed broad spectral antibacterial activity against eight fish pathogens tested. Similarly Annamalai et al., (2007) reported that Perna viridis showed maximum inhibition against E. coli and Staphylococcus aureus. Anbuselvi (2009) found that acetone column purified fractions of Trochus tentorium showed higher antibacterial activity. Santhi (2011) reported antibacterial activity of the whole body tissue extract of Purpura persica and the results showed wide spectral activity against some pathogens. Antibacterial activity has been previously proved in a wide range of molluscan species (Mitta et  Antibacterial activity of marine animals and their bioactive products at molecular level have been studied by various authors and the molecules responsible for antibacterial activity have been identified and characterized. The GC-MS analysis of the extract of Conus achatinus revealed eighteen compounds (Table 1). Of the 18 compounds, the following 15 compounds viz., Aziridine, I-methyl, Cyclohexanol, 2-aminotrans, Azocine, octahydro-, Butanal, Omethyloxime, L-Homocitrulline, Pentanal oxime, E-2-Tetradecen-1-ol, Z-10-pentadecen-1-ol, Trimethylamine compound with borane, 1,1-cyclopropane decarbonitrile, 2,2-dimethyl, Cyclopentanol, 2, (aminomethyl)-cis-, Tricyclo [4.2.1.1 (2,5)] decan-3-ol, Thiomorpholine, Semioxamazide and cholan-24-oic acid, 3-oxo-methyl ester (5a)-could be responsible for the antibacterial activity of the present study.
To conclude, the present study clearly showed that among all the fractions, F2 and F3 fractions of the study animal Conus achatinus showed more activity. This may be due to the presence of above mentioned compounds in the body extracts. So, further studies are needed to isolate and characterize these compounds to study their mode of action in pharmacological field.


Effects of cell packing on chemoattractant distribution within a tissue

Diffusible signals provide critical information to cells in biological systems, often in a concentration-dependent manner. In animal development, such signals can determine different cell fates or guide motile cells to their proper locations. It is well-known that migrating cells respond to graded chemoattractant cues by moving toward areas of higher concentrations. However, it is not clear how cell-dense animal tissues impact the distribution of chemoattractants in three dimensions. We leverage the simple architecture of the Drosophila egg chamber to explore this idea. In this context, sixteen large germline cells are packed together, enveloped by a somatic epithelium. A small set of epithelial cells, the border cells, form a motile cell cluster and respond to guidance signals by moving across the egg chamber during oogenesis. We created a geometrically-realistic model of the egg chamber and determined the distribution of the chemoattractants through that domain using a reaction-diffusion system. We used this information to determine reasonable biophysical parameters of chemoattractant that would facilitate gradient formation in the appropriate developmental time, and to explore the effects of different secretion locations in the egg chamber. Our model revealed several interesting features: The chemoattractant is more concentrated and the gradient sets up more quickly in a cell-packed space, and cell packing creates dips in the concentration and changes in gradient along the migratory path. We simulated migration with our calculated chemoattractant gradient and compared it to that with a constant gradient. We found that with our calculated gradient,

Introduction
Extracellular, diffusible signals provide critical pieces of information to cells in biological systems. Often, the concentration of these signals conveys additional, spatial information. For example, during animal development, morphogens prompt concentration-dependent cell fate decisions to pattern tissues [1][2][3], and chemoattractants provide directional information to migratory cells [4][5][6][7][8]. Work in vitro clearly demonstrates these principles; however, the situation in vivo is more complex. While there is debate about how diffusible protein-based signals traverse through tissues to create gradients (see examples in [1]), one simple possibility is that such molecules move between and around cells. In this case, the signals are likely to be hindered by cell packing and to pool in interstitial spaces, which could effectively change their perceptible concentration and gradients. Even in cell-dense animal tissues, small spaces or gaps can exist between non-epithelial cells, through which signals can move. Such domains have been characterized in amphibian embryo [9,10], and in mammalian brain, where extracellular spaces may make up 20% of the overall tissue [11]. Although extracellular matrix components may occupy some of this interstitial space and detain diffusible molecules, often not all space is occupied, leaving significant areas unoccupied/fluid-filled for diffusion.
To examine how the interstitial space in tissue affects signal distribution, we modeled the elegant case of the Drosophila melanogaster egg chamber. Fly oogenesis has been extensively characterized and has provided a tractable model to elucidate the genetic regulation of coordinated cell development, tissue patterning, stem cell maintenance, and cell migration [12][13][14][15]. In Drosophila, eggs develop within an egg chamber, which is comprised of an oocyte, fifteen large, germline sister cells called -nurse cells‖ that foster oocyte growth, and an outer, somatic, epithelial cell layer that is essential for patterning and eggshell formation [16]. Although nurse cells are space-filling, their large size and constraints created by cell junctions result in notable spaces between these cells and between them and the enveloping follicle cells. Among the epithelial cells, a set of migratory cellsthe border cells-arise during mid-oogenesis, and migrate as a cluster between nurse cells in response to extracellular chemoattractant cues at oogenesis stages 9 and 10 [17,18] (Figure 1). Motile cells often move directionally in response to gradients of chemoattractant or chemorepellant molecules. Border cell migration to the oocyte requires several chemoattractants, which bind and activate two conserved receptor tyrosine kinases (RTK) on the border cells: Epithelial Growth Factor Receptor (EGFR) and a receptor equally related to mammalian Platelet Derived Growth Factor Receptor and Vascular Endothelial Growth Factor Receptor, which is called PVR (reviewed in [17,18]). Both receptors appear to be redundantly required for directed migration to the oocyte at stage 9 [19][20][21][22], and EGFR is additionally required for migration to the dorsal side at a later stage [23]. Multiple ligands exist for each receptor and may act redundantly as guidance cues [19,20,24]. These factors are produced at the oocyte, but may also be generated from some somatic epithelial cells [20,24,25]. Since the RTK ligands function as chemoattractants, the simplest model is that these cues are released from the oocyte surface and evolve into a gradient across the egg chamber, driving clustered cell migration. It has been shown experimentally that uniform, local overexpression of chemoattractant or reduced RTK activity disrupts migration [19][20][21][22][23][24], and ectopic chemoattractant expression can mis-direct the cells [20,24], demonstrating that the concentration and gradient are both important. However, chemoattractants are challenging to detect, observe, or measure in vivo, so their precise distribution in time and space is unknown. Moreover, we postulated that the gradient may be altered by hindrance due to the large nurse cells that are packed into this domain. Several questions surround the characteristics of chemoattractant in the extracellular regions within the egg chamber: (1) How does the heterogeneous egg chamber environment impact the local quantitative values of chemoattractant? (2) How does secretion location impact chemoattractant localization? (3) How does the extracellular space between the nurse cells impact the gradient of chemoattractant in a realistic geometry? (4) How does the gradient in this environment compare without the nurse cell heterogeneity? (5) How does migration timing differ due to the chemoattractant distribution in this geometry?
To address how cell packing in a tissue influences signaling, we generated a geometricallyrealistic model of the egg chamber then mapped the distribution of chemoattractants in this domain using a reaction-diffusion system. Using a biophysically-relevant range of parameters, our model indicates that guidance cues diffusing only in interstitial space create a more dramatic concentration gradient than if they moved throughout the whole egg chamber unimpeded by nurse cells. In addition, the gradient is established much more quickly when large cells are packed in the domain, which may be important to allow development to proceed on time. Interestingly, the directionality and the magnitudes of gradient vectors along the migratory route varied by position in the egg chamber, which may explain some aspects of the cell migration observed in vivo. Thus, our model reveals several unanticipated features of diffusible signaling within complex animal tissue and provides a realistic input that can inform models of cell migration in response to chemoattractants [26][27][28].

Creating the extracellular domain within the egg chamber
In previous work, we modeled border and polar cell cluster migration in the egg chamber of Drosophila melanogaster [26]. The Stonko model represents each cell (epithelial follicle, border, polar, nurse cells) in the anterior egg chamber at stage 9 with one or more Identical Math Cells (IMCs) depending on each cell's relative size. Several forces impact the motion of the cluster, and one of these forces is chemotactic, the response to chemoattractant. In our previous work, the gradient of chemoattractant was assumed to be constant and uniform from low at the anterior end to high at the oocyte, and the border cells moved steadily up the gradient.
Here, we model the diffusion and reaction of chemoattractant in the extracellular domain within an egg chamber using a linear parabolic partial differential equation. Chemoattractant is secreted from the oocyte boundary, and in some simulations from other areas of the epithelia, as flux boundary conditions. Degradation of the chemoattractant is assumed within the extracellular space, while some uptake is assumed on the surface of the nurse cells and taken as a mixed boundary condition.
To obtain the geometry of the extracellular domain, we imported the IMCs from the force-based Stonko model, and generated the convex hull of the center of the IMCs representing the initial boundary of the anterior half of an egg chamber (the border, polar and epithelial cells, as well as the oocyte). Then for each nurse cell, we generated an alpha shape containing the centers of the IMCs, and used the volume formed by the outer edges of these boundaries to exclude nurse cells from the extracellular domain ( Figure 2). In this model, the egg chamber is approximately a paraboloid of length 409.5 μm ending in the oocyte, a circle of radius 146.25 μm, and centered around y = z = 146.25 μm. This is an upper bound for a Drosophila stage 9 egg chamber size. We also consider a domain with length and radius half that size for comparison.

Model of secretion and diffusion in the egg chamber extracellular space
Chemoattractants originate from a source at the oocyte, and potentially from the epithelium, and diffuse within the egg chamber, increasing the concentration at the anterior across the egg chamber. The polar and border cells exit from the anterior epithelium and respond to chemoattractants by migration towards the oocyte [19,20,23,24]. Included below are the equations used in our model of chemoattractant diffusion and reaction. The base of our model, Eq 1, is the diffusion-reaction equation for the chemoattractant concentration, u, where D is the diffusion coefficient, and k (1/s) represents the rate at which the chemoattractant breaks down in the extracellular space of the egg chamber. The boundary conditions, Eqs 2-4, model the behavior of diffusion at different types of surfaces within the egg chamber. These are Neumann conditions, modeling the derivative of the concentration in the direction normal to the boundary, in this case, the flux. Eq 2 represents the secretion of chemoattractants with parameter  (pM m/s) being the value of the source, taken to be constant, while Eq 3 models the uptake of the chemoattractant, which depends on the concentration at the boundary, with parameter  (m/s) being the rate constant. The no-flux boundary condition in Eq 4 indicates that the boundaries at all other points do not let any chemoattractant in or out. The boundary conditions can easily be changed to model secretion or uptake at any boundaries to test different hypotheses.
Typically we simulated for 18000 seconds (5 hours), as this timespan corresponds with the developmental time line of the oogenesis stages during which border cells form and migrate in D. melanogaster. We varied several of the critical model parameters from their default values: D μm 2 s −1 , k = 1 × 10 −4 s −1 ,  = 0 μm s −1 , and = 100 pM μm s −1 [29]. In our tests, unless otherwise stated, the chemoattractant is secreted from the region of the epithelium where the x-value is greater than μm. The chemoattractant is also secreted from the face of the oocyte, located at x-value 9 μm. Given that these default choices can only be estimates, we varied the parameter values to check the sensitivity of our measures after nondimensionalizing the system.
We non-dimensionalize to reduce the number of independent variables. Let x = LX, y = LY, z = LZ for some system length and non-dimensional spatial variables, X, Y and Z. Let t = a for some system time scale a and non-dimensional time variable. Let u = u 0 U for some system concentration scale, u 0 , and non-dimensional concentration U. Applying these changes of variables, Eqs 1-4 become: We pick L = 400 m, the length of the egg chamber, to fix the domain. We scale time by the domain decay constant a = 1/k. We let u 0 = 1 pM so the threshold to register chemoattractant, u = 1 pM is fixed. With these changes the non-dimensional equations become: ). The typical simulation time will then be about = (18000 s) (1 × 10 −4 s −1 ) = 1.8.
To get a preliminary idea of how this chemoattractant distribution affects clustered cell migration, we created simulations based on our previous work [26], but now allow for a non-constant gradient. So the gradient [U x ,U y ,U z ] T experienced by each border cell may either be a constant [1,0,0] T or interpolated from the solution to Eqs 1-4.

Numerical implementation of model
Due to the complex shape and boundary conditions, we used the Matlab partial differential equation toolbox and function pdesolve to solve the system 1-4 using finite elements. This solver implements the finite element method to approximate a solution. The maximal element size input parameter was hmax = 100/L. Lower values of hmax did not appreciably alter the output values. Simulations were done in Matlab Version 2016a. Some simulations were performed on the HPCF at UMBC (www.hpcf.umbc.edu).

Chemoattractant can spread across the egg chamber in minutes
It is understood that a chemoattractant is necessary for the directed migration of the polar and border cell cluster (reviewed in [17,18,30]). Therefore it is important, when considering diffusion, to know when the chemoattractant reaches a critical level at the anterior end of the egg chamber, where these cells initially reside. For this reason, we chose the farthest point from the source of secretion to test for a substantial concentration of chemoattractant. We chose the point (20 μm/L, 146.25 μm/L, 146.25 μm/L), located on the anterior end of our egg chamber central in the y and z directions. Before simulating how long it would take for the chemoattractant to reach the anterior side of the egg chamber, we defined a minimal concentration of chemoattractant that anterior cells might detect and that could trigger directed migration. For this we chose 1 pM as a baseline for considering what has sufficient chemoattractant concentration. We show the time to reach this threshold in Figure 3 with nondimensional parameters but also include values for dimensional parameters for comparison (these results are listed in Table 1, see Supplementary movie 1). Further results will appear in dimensional values.  To determine reasonable value ranges for dimensional parameters, we varied them one at a time while holding the others fixed. We first tested the time needed for the chemoattractant to be perceived by the motile cells -time across‖ using various D values ranging from 0. μm 2 s −1 to μm 2 s −1 on a logarithmic scale, as listed in Table 1. The -time across‖ is iologically important since if the migratory cells are not triggered at the right developmental stage, they will not be able to reach their required destination. With the values of 0. μm 2 s −1 and 0. μm 2 s −1 , the diffusion of the chemoattractant did not reach the end of the egg chamber by five hours, roughly the time to transition between stages 8 and 9 (when the cells are specified then must migrate), leading us to believe that D needs to be greater than 0. μm 2 s −1 to elicit the response observed in vivo. This supports a D value at least on the order of 1 μm 2 s −1 as we have estimated for a diffusible signal, Unpaired, in the same biological system [29], and near the ranges determined experimentally for diffusible morphogens in Drosophila [31][32][33][34]. Different values of k were also tested to find their impact on the time to the 1 pM mark at the anterior. The parameter k is the degradation of chemoattractant in the extracellular space. The values tested were 1 × 10 −2 s −1 to 1 × 10 −6 s −1 on a logarithmic scale. With k values of 1 × 10 −2 s −1 and 1 × 10 −3 s −1 , the time for the chemoattractant to diffuse across the egg chamber surpassed the five hour mark. This suggests that k values of 1 × 10 −2 s −1 and 1 × 10 −3 s −1 may be too high. For k sufficiently small we see little change in time to reach the 1 pM threshold. A value of k near 1 × 10 −4 s −1 seems to be a reasonable order of magnitude for the degradation rate of an extracellular signaling molecule [29,[32][33][34]. The next parameter we tested is .
Values from 1 × 10 −1 μm s −1 to 1 × 10 −5 μm s −1 were tested for the uptake of chemoattractant into the nurse cells,  on a logarithmic scale. We found that for a value of  = 0.1 μm s −1 , the chemoattractant did not reach 1 pM at the anterior of the egg chamber in under five hours, suggesting that  should be less than 0.1 μm s −1 . Similar to our results for k, small values of  caused minimal change in the time to reach threshold. The secretion constant  is the amount of secreted chemoattractant per unit of space in a given time. The values of  that we tested ranged from 1 pM μm s −1 to 10,000 pM μm s −1 on a logarithmic scale. We found that the higher values of  resulted in less time for the point in the anterior of the egg chamber to reach a 1 pM concentration, as would be expected. Changing this variable resulted in a large range of times for the chemoattractant to reach the end, as can be seen in Table 1. However, it is important to note that  did not make the chemoattractant diffuse faster but rather amplified the concentration of the chemoattractant participating so that it rose above the 1 pM anterior level in fewer seconds;  does not change the diffusion but rather it changes the magnitude of the chemoattractant influx.

Equilibrated chemoattractant distribution and gradient occurs within two to three hours
When the concentration of chemoattractant at each point reaches a level where it will consistently remain, it has achieved a steady state. Similarly, when the gradient of the concentration no longer changes, it has reached a steady state. To estimate our rate of change, we define an error estimate, find a vector of the concentration or gradient at every element at a particular time step, subtract the same vector for the previous time step, and take the Euclidean norm. When this value goes below a certain cutoff, we decide that the chemoattractant has reached a steady state. We used a cutoff of 0.0008 (Table 2).  To calculate the direction of the gradient, we first normalized it. Then when the average difference of the normalized gradient between timesteps fell below a cutoff (e.g., 10 −2 ), we recorded that time. As with the concentration, the measure of changing gradient direction decreases over time, meaning the direction of the gradient becomes more stable (Table 3). With our definition of the steady state of the concentration and of the directional gradient, the directional gradient comes to a steady state typically more quickly than the concentration does; the direction often can be established more quickly.

Varying parameters impacts the average amount of chemoattractant in the egg chamber extracellular space
Multiple cell types in the egg chamber, including the oocyte and follicle cells, express the genes encoding EGF-like chemoattractants, but it is not clear exactly which cells process and secrete the proteins [24,25,40]. We considered that the epithelial cells along with the surface of the oocyte may be secreting the chemoattractant, so we tested how changing the region of epithelial cells secreting and the total amount of chemoattractant secreted would affect the level of concentration in the egg chamber after 5 hours. We were searching for a combination that would produce an average chemoattractant concentration of approximately 4500 pM, an in vitro experimental activating value for other secreted signaling molecules [35], and in the range of chemotactic-activating concentrations for EGF ligands [36][37][38][39]. With this goal, we changed the approximate number of epithelial cells that were secreting the chemoattractant by altering which locations of the epithelium were actively secreting during certain trials. Since the epithelial cells condense toward the oocyte while the gradient is being generated, secretion from more dense, posterior regions of the epithelium may effectively be higher. Considering that the coordinates of the egg chamber in the x direction go from 0 µm to 409.5 µm, we used epithelium locations including the oocyte and starting at 200 µm, 250 µm, 300 µm, 350 µm, and just the oocyte (at 409.5 µm). Then, for each of these chosen active areas, we changed the value of σ, the flux of chemoattractant entering the egg chamber, to determine the average chemoattractant concentration in the egg chamber after 5 hours under each condition (Table 4). Many conclusions could be drawn from such a test. As expected, keeping the area of secreting epithelium cells the same, as we increased σ, the concentration in the egg chamber increased proportionally. Keeping σ the same and increasing the amount of the epithelium secreting also caused the final average chemoattractant concentration in the extracellular space of the egg chamber to increase. We then tested the same σ values and locations of secretion with two different values of k and φ, the extracellular degradation and nurse cell uptake of chemoattractant respectively. When we increased k and φ by a factor of 10, changing them from 1 × 10 −4 s −1 to 1 × 10 −3 s −1 and from 1 × 10 −4 µm s −1 to 1 × 10 −3 µm s −1 respectively, we observed the concentration average decrease for corresponding combinations of secretion location and σ value. This is logical because more of the chemoattractant is taken up by the nurse cells. However, it is interesting to note that it cuts the average concentration almost exactly in half.

Figure 5. Average chemoattractant concentration for different secretion profiles.
Looking at Figure 5, each pair of adjacent bars, starting with any tall one and looking at the shorter one directly to its right, shows that increasing k and φ by a factor of 10 and doubling σ, but maintaining all other parameters will cause the average concentration to be cut in half. Therefore, when thinking about the value of σ needed to produce an average concentration level of approximately 4500 pM after 5 hours of diffusion, maintaining the same secretion location and increasing k by a factor of 10 will necessitate doubling the value of σ in order to reach this realistic chemoattractant value. So, for a measured average concentration of chemoattractant, these ta les can e used to find appropriate secretion values. More experimental tests will be needed to determine where the chemoattractants originate, which can be used in combination with this result to estimate values.

Large chemoattractant difference across egg chamber precludes a linear migratory response to gradient
A gradient of chemoattractant is understood to be necessary for cell migration. The mechanism by which the cells interpret directional cues from the gradient is unclear, although several models have been proposed (for example [4,17,30,41]). We considered the possibility that the cluster responds linearly to the magnitude of the gradient. To determine if this is plausible, we tested the magnitude of the x-component of the gradient along with the chemoattractant concentration at points near the anterior and posterior ends of the egg chamber. We chose a point near the posterior end (Back: x = 400, y = 150, z = 150) and a point near the anterior end (Front: x = 20, y = 150, z = 150) (as in Figure 2) and found the ratio of the gradient and concentration (Tables 5 and 6, respectively).  there was a greater difference in concentration between the anterior and posterior because more chemoattractant would deteriorate before reaching the end. When k = 1 × 10 −1 s −1 , there was too little concentration at the anterior end to give a reliable value. In some nearby locations the y or z component of the gradient pointed in opposing directions (Figure 6), which could cause disruption during directional migration due to competing migratory forces experienced on different sides of the migrating cluster of cells. The concentration decreased everywhere, but less so near the oocyte. A very high D, such as μm 2 s −1 , gave a concentration that was lower near the oocyte, but higher in the anterior than with lower D because the chemoattractant was able to diffuse more freely.
Changing  causes no difference in the ratio of either concentration or gradient because it is only a scaling factor. Using a larger area of secretion makes the ratio decrease. In general, changing concentration ratios corresponded with changing gradient ratios. No matter how we changed the parameters within the biologically plausible ranges, every simulation resulted in gradients at opposite ends of the egg chamber whose value differed by at least one order of magnitude. We concluded that the migratory force cannot scale linearly to the magnitude of the gradient because in vivo experiments show a less than a two-fold difference in migration speed [21,22,42]. This distribution agrees with steady state, one-dimensional estimates of a more exponential chemoattractant distribution described in [27]. It may be that there is a low saturation point where the force stops increasing linearly, the force is very nonlinearly related, or that the migratory force only depends on the direction of the gradient and not the magnitude.
Next, we analyzed the chemoattractant concentration and the x, y and z-components of the gradient over time, along the center of the egg chamber, which is the migratory path for border cells (Figure 7). We observed that a gradient of chemoattractant is established quickly, within the first 30 minutes of the 5-hour timespan. Interestingly, separation of the spatial components of the gradient revealed major declines in the x-component near nurse cell junctions and near the oocyte, with reversals in the y and z-components nearby, particularly near x = 300 µm and x = 390 µm. These results suggest a significant impact of interstitial spaces between nurse cells on the shape of the chemoattractant gradient. Some studies in vivo have shown the migratory cells alter their behaviors at these points, for example switching positions with each other (or tumbling) [21,22,26,27]. The weak reversals of gradient could help to explain this unexpected behavior.

Egg chamber without nurse cell packing exhibits a less steep gradient
An important question about cell migration is how the nurse cells interact with the diffusion of the chemoattractant. In our model, the nurse cells either act as an impenetrable barrier and completely impede the chemoattractant, when  = 0, or uptake some level of chemoattractant when  is nonzero. Our model leaves space (approximately 19.5 μm, the diameter of 2.8 IMCs and the approximate diameter of the migratory border cell cluster) between nurse cells for the chemoattractant to travel, but in reality, it can be inferred that the interstitial pathways are likely to be narrower. We also considered what would happen to the established gradient if the chemoattractant could travel freely through the nurse cells or be absorbed by them as a part of a sink in the egg chamber. To get an idea of the effect of nurse cells blocking chemoattractant, we tested the concentration and x-component of the chemoattractant at different points (at x 2 μm; μm; 2 μm; μm, μm along the central corridor after a simulation was completed on one geometry with nurse cells and one without them. We found that the chemoattractant is more evenly dispersed without border and nurse cells (which potentially mimics the effect of chemoattractant moving freely through entirely permeable nurse cells) ( Table 7). There is a greater disparity between the concentration values at the posterior and anterior ends of the egg chamber after the diffusion with the nurse cells impeding chemoattractant; the same was found for the x component of the gradient. We can also observe that the nurse cells in the geometry cause the gradient to be stronger than what it would be had the nurse cells not been present (Table 7). A similar effect on the gradient was observed for models in which the egg chamber domain was half of the size (2 μm, Supplementary Table 1 and 2). We believe this result arises not because of dilution effects but because the nurse cells provide a barrier that prevents the chemoattractant from freely spreading farther away from the oocyte face. The results of this experiment imply that the narrower the domain in which the chemoattractant can disperse, the larger the range of the gradient and concentration in the egg chamber.

Cell migration kinetics are altered by chemoattractant distribution model
To obtain a sense of how of migratory cells respond to the chemoattractant distribution using our cell-packed geometry, we used a previously-described collective cell migration model [26] and input resultant chemoattractant concentration gradients derived from the chemoattractant model.  Figure 9 shows the early phase of migration with the chemoattractant in a cell-packed model or a unit gradient. When signal distribution is modeled in a realistic tissue geometry, the cells migrate more slowly. In the early phase, the gradient is shallow and cells move slowly; later, the cells' speed increases e ponentiall , reflecting the e ponential nature of the gradient.

Conclusions
Many biological systems rely on diffusible, extracellular signals to convey diverse kinds of information to cells. For example, migratory cell types respond to gradients of chemoattractants and chemorepellants. To understand this response, it is critical to determine the spatial distribution of signals within a complex tissue or biological system. However, these signals can be present at levels that are not easily detected, and methods to measure them spatio-temporally in vivo are inadequate. Mathematical modeling provides a way to gain insight into the possible distribution of these molecules within the complex geometry of animal tissues.
We developed a mathematical model of the well-described Drosophila egg chamber to examine possible chemoattractant distribution in this context. A set of motile cells called border cells chemotax towards several cues produced from and around the oocyte, but the exact distribution of the cues is unclear. Large nurse cells line the path of the border cells and likely impede movement of the chemoattractant signals ( Figure 1). Using estimates for chemoattractant diffusion rate, decay, secretion rate, and uptake by other cells, we determined reasonable parameters that would allow the chemoattractant to reach the border cells in the appropriate timeframe to guide their movement.
Previous work postulated that chemoattractants move uniformly through all cells of the egg chamber to generate a linear or exponential gradient [26,27]. We contrasted that model with one in which signals did not move through the nurse cells, but instead were blocked by them.
Notably, the physical constraint of the nurse cells dramatically increases the local concentration of chemoattractants along the migratory route and decreases the time at which the gradient sets up across the tissue, compared to a case in which nurse cell packing was not considered a hindrance. In the model, due to computational limitations with meshing inflated nurse cells, we overestimated the space between nurse cells and nurse cells and epithelium, however, a small deflation of the nurse cells only affected the time to reach the 1 pM threshold at the anterior end of the cell for sensitive  eff = 0.36 taking more time, about 4.95 hours compared to 3.6 hours. The time to threshold while changing both D eff and  eff were unaffected by this slight deflation. In general, the smaller gaps between the cells seen in vivo would result in even more dramatic differences in chemoattractant distribution (compared to non-hindrance, see Supplementary Tables 1 and 2). This idea has important biological implications since cellular obstacles that alter extracellular signal distribution would effectively concentrate the signals, allowing development to proceed more quickly, and would require less signal to be produced, conserving cellular energy.
The idea that tissue geometry impacts signal concentration is made more important by recent studies that suggest ligand concentrations have key impacts on signal transduction. In both mammals and Drosophila, multiple ligands are capable of binding to the guidance receptor tyrosine kinases. Recent studies suggest that different ligands at the same concentration produce more similar downstream effects than the same ligand at different concentrations [36,43] similar to how morphogens are thought to act in a concentration dependent way [2,3,32]. Thus, it is essential to consider how tissue shape may alter the perceived concentrations of signaling molecules.
Interestingly, live imaging studies show that migratory border cells alter their movements at the points where multiple nurse cell meet, exhibiting -tum ling‖ ehaviors [21,22]. This could in part be explained by physical constraints that change available traction points for the migratory cell, as indicated by Stonko et al. [26], and other possible molecular explanations have been proposed [21,22]. The model presented here suggests that changes in the shape/steepness of the gradient could also influence this behavior, as we detect drops in the gradient at the junctions between nurse cell boundaries. Most cell migration models presume the cells are responding to a linear or exponential, steadily-increasing chemoattractant gradients [4,6,7,41,44], but in vivo it is likely that non-uniform landscapes of cellular architecture or ECM could produce differences in signal distribution that need to be considered more carefully. This could be important to determine for animal development, and also immune response and tumor metastasis [5,45].
In pilot tests, collective cell migration simulations show that chemoattractant distribution impacted by cell boundaries also impacts the kinetics of cell movement. Figure 9 shows the early phase of migration with the chemoattractant in a cell-packed model or a unit gradient. When signal distribution is modeled in the realistic tissue geometry, the cells migrate more slowly overall. The exponential nature of the gradient is realized in the migration with speed increasing as the cluster nears the oocyte. For time-dependent developmental or immunological migrations, this difference in response could have a dramatic phenotypic output. Additional in depth simulations and analysis will be needed to determine the details of individual cellular responses to the distribution, and to ascertain if the slower movement is due to the more shallow gradient or variations in gradient magnitude and direction.
Currently, it is technically challenging to detect diffusible molecules accurately in vivo. Prior work has shown that diffusible signaling molecules are clearly restricted in tissues, moving much more slowly than in aqueous solution. Experimentally, the measured values vary widely, from 0.1 to 100 µm 2 /s, in part depending on the methods used [1,33,34,46,47]. Chemoattractants have not been examined in vivo in living egg chambers, and for EGF ligands, the predominant sources of the secretion is unclear, which is why we modeled different possibilities for domains of release. With either secretion from the oocyte only or from the oocyte and follicle cells, while keeping the other parameters the same, we found reasonable timeframes to activate cell migration. The impact of the different origins of signal on migration will be interesting to explore further.
The egg chamber provides a unique context in which to study the impact of cell packing on signal transduction. This work is facilitated by the diversity of cells in this tissue, and the ease with which Drosophila can be genetically manipulated and imaged live. While this tissue may be somewhat unique in structure, we believe interstitial gaps may be prevalent in animal tissues but difficult to discern with common imaging techniques. Thus, the impact of the cytoarchitecture on signaling should be more widely considered and modeled.


extfieldsofstudy: Chemistry Medicine

text:
The Unexpected Essentiality of glnA2 in Mycobacterium smegmatis Is Salvaged by Overexpression of the Global Nitrogen Regulator glnR, but Not by L-, D- or Iso-Glutamine

Nitrogen metabolism plays a central role in the physiology of microorganisms, and Glutamine Synthetase (GS) genes are present in virtually all bacteria. In M. tuberculosis, four GS genes are present, but only glnA1 is essential, whereas glnA2 was shown to be non-essential for in-vitro as well as in-vivo growth and pathogenesis, and is postulated to be involved in D-glutamine and iso-glutamine synthesis. Whilst investigating the activity of an antimicrobial compound in M. smegmatis, we found a spontaneous temperature-sensitive mutant in glnA2 (I133F), and used it to investigate the role of glnA2 in M. smegmatis. We deleted the native glnA2 and replaced it with a mutated allele. This re-created the temperature sensitivity—as after 3–4 seemingly normal division cycles, glnA2 became essential for growth. This essentiality could not be salvaged by neither L, D- nor iso-glutamine, suggesting an additional role of glnA2 in M. smegmatis over its role in M. tuberculosis. We also found that overexpression of the global nitrogen regulator glnR enabled bypassing the essentiality of glnA2, allowing the creation of a complete deletion mutant. The discrepancy between the importance of glnA2 in Mtb and M. smegmatis stresses the caution in which results in one are extrapolated to the other.

INTRODUCTION
Nitrogen metabolism is important for all bacteria, from harmless soil organisms to pathogens, including various mycobacteria. The synthesis of glutamine is a key metabolic pathway, and in many bacteria, a sole glutamine synthetase (GS, usually called glnA) is present, is essential, and its loss leads to glutamine auxotrophy (Merrick and Edwards, 1995;Weisschuh et al., 2000). In M. tuberculosis, four GS genes exist, called glnA1, A2, A3, and A4. Deletion mutants of each of these genes were previously constructed, but only glnA1 (Rv2220) was found to be essential; the deletion mutant was a glutamine auxotroph, and as such, was unable to proliferate in macrophages or mice, and was comparable to BCG as a vaccine candidate in mice (Tullius et al., 2003;Harth et al., 2005;Lee et al., 2006). However, deletion mutants of the other GSs [glnA2(Rv2222c), glnA3 (Rv1878), and glnA4 (Rv2860c)] were fully viable even without glutamine supplementation. Unlike the other glnA genes, glnA2 is sometimes thought to be responsible for synthesis of Dglutamine and possibly iso-glutamine, but still, a deletion mutant could grow in un-supplemented media and replicate in mice (and cause death) with no apparent defect as compared to wt (Tullius et al., 2003;Harth et al., 2005).
Homologues of all four glnA genes exist in Mycobacterium smegmatis, but their role there has been investigated to a lesser extent. In this study, we started by screening for spontaneous mutants that would be resistant to the chemical substance SG-1, previously described. We isolated several resistant mutants, and sequenced their genomes to try to pinpoint the mutation responsible for the resistance phenotype. We discovered that a point mutation in the glnA2 gene of M. smegmatis (Msmeg_4294, homolog of Rv2222c) rendered the bacterium temperaturesensitive, and decided to use this finding to explore the role of glnA2 in M. smegmatis.

An I133F Mutation in glnA2 Renders the Bacterium Temperature Sensitive
During the study of an antimicrobial compound derived from Allium stipitatum that is active against mycobacteria, we isolated a spontaneous M. smegmatis mutant that was both resistant to the compound and temperature sensitive in the sense that it grew at 32 • C, but not at 42 • C. We performed whole genome sequencing on this mutant, and found five point mutations as compared to the parent wt M. smegmatis mc 2 -155. One of these point mutations was at the 397th base of the glnA2 gene, replacing the A with a T, causing an I133F amino acid change. To test if this mutation was involved in the drug resistance phenotype, the temperature sensitivity, or both, we decided to replace the glnA2 gene in wt mc 2 -155 with the mutated gene. To do this, we cloned a mutated glnA2 (glnA2 mut−I133F ) into a kanamycinselected, attp-integrating vector (pDB221) and electroporated it into wt M. smegmatis, creating a merodiploid mutant for glnA2 (glnA2 wt and glnA2 mut ). From this merodiploid mutant we deleted the native glnA2 wt using the two-step allelic exchange technique (and plasmid pDB240) (Figure 1A), thus creating a mutant with only a glnA2 mut copy (mDB67, M.smeg I133F ). This mutant was not resistant to the antimicrobial compound we tested, suggesting that the resistance mechanism was either not related to the mutation, or involved a combination with any of the other identified point mutations. However, the M.smeg I133F mutant was temperature sensitive, as it grew normally at 32 • C, but not at 42 • C, suggesting the function of GlnA2 was essential to normal growth, contrary to the situation in Mtb ( Figure 1B).
Determination of the Death Rate of M.smeg I133F at 42 • C To further clarify the kinetics of death of mDB67 at 42 • C, we decided to conduct a "competition" experiment of mDB67 vs. the M. smegmatis wt-like mutant (mDB149, a kanamycin resistant, luminescent mutant). Both mutants were separately grown to log-phase (OD 600 = 0.2), mixed together in a 1:1 ratio, the mixture diluted X10 −5 , and left to grow in 7H9 media with kanamycin at either 32 or 42 • C. Every 3 h at first, and then in longer intervals, a sample was plated, and the ratio of luminescent bacteria (wt, mDB149) to non-luminescent (mDB67) was determined by colony observation. Luminescence was visualized with an IVIS machine. As the generation time of M. smegmatis is approximately 3-4 h, it was evident that during the first 2-3 generations mDB67 and mDB149 grew in a similar fashion both at 32 and 42 • C ( Figure 1C). However, after 10 h, the percentage of mDB67, that remained fairly constant at circa 50% at 32 • C, was sharply reduced at 42 • C to barely detectable levels. At 24 h, the culture at 42 • C was completely dominated by mDB149 (with no detectable mDB67), whereas at 32 • C, the ratio of 50% remained unchanged for at least 48 h. This growth pattern (normal for 2-3 generations, then a sharp deterioration) suggested either one of two situations: a lack of some vital metabolite or co-factor -at first, the amount of the metabolite was sufficient, but after 2-3 divisions, the consumption and/or dilution of the metabolite was reduced to below critical levels, and there was either growth arrest, or lysis of the cells. Alternatively, a metabolic bottleneck created by an inactive/low activity glnA2 could prompt the accumulation of an as-yet unidentified metabolite with a toxic effect, reaching lethal levels after approximately 3 generation times.
To more exactly pinpoint the temperature requirements of mDB67, we diluted a 30 ml culture to 1 cfu/µl (confirmed by plating 100 µl on a plate for colony count), split it into three identical tubes of 10 ml each, allowed it to grow for 24 h, and then plated it in dilutions for a cfu count. At 37 • C the culture grew to 65 cfu/µl (a generation time of approximately 4 h), at 32 • C cfu/µl grew to 20 cfu/µl (generation time ∼ 6 h), whereas in the 42 • C tube there were no viable bacteria (detection limit was 0.05 cfu/µl). After additional 24 h, the tubes were photographed ( Figure 1D).
glnA2 Cannot be Deleted From M. smegmatis To further show that glnA2 is indeed essential in M. smegmatis, we attempted to completely remove it, by exchanging the complementing cassette in mDB67 (M.smeg I133F ) for either one of three zeocin-selected plasmids: pDB234 (empty vector), pDB247 (vector+wt Msmeg_glnA2) and pDB259 (vector+Rv2222c, the Mtb homolog of glnA2). As anticipated, we obtained multiple colonies with the pDB247 plasmid (as this simply reverted the bacteria to the wt genotype). We also obtained multiple colonies with pDB259 (albeit the colonies appeared a day later, and where somewhat smaller). In contrast, no zeocin-resistant colonies could be obtained with the empty vector pDB234 (despite lengthy incubations of up to 14 days), strongly suggesting that the function of GlnA2 is essential (Figure 2). The attempts to electroporate pDB234 (thus obtaining a complete deletion mutant) were repeated several times, but the few colonies that did appear on plates failed to grow in subculture (except for one colony-see analysis below). We also did not obtain colonies with two additional Confirmation of glnA2 deletion. PCR was done with primers producing a 2.6 kb fragment in wt (right), and 1.3 kb fragment for a complete deletion. The 1.3 kb fragment was also examined by Sanger sequencing. (B) 5 × 10 6 cfu/mL of wt or mDB67 were plated at 32 • C or 42 • C. mDB67 failed to grow at 42 • C. (C) A growth-competition experiment of mDB67 vs. wt (mDB149) at 32 • C (circles, broken line) and 42 • C (squares, solid line). The graph shows the percentage of mDB67 in the mDB67:wt mixed culture. After 3-4 generation times, at 42 • C mDB67 rapidly disappears from the mixture. At 32 • C, both strains grow equally. (D) mDB67 was diluted to 1 cfu/µl in a volume of 30 ml. the culture was split to 3 tubes, and left to grow at 32, 37, or 42 • C. the picture was taken after 48 h.
plasmids namely pDB294 and pDB295 [with Rv1878 (glnA3) and Rv2220 (glnA1), respectively], suggesting that these genes could not compensate for the loss of glnA2 either (data not shown).
These results, together with the lack of growth of mDB67 at 42 • C, suggested that in M. smegmatis glnA2 is an essential gene, whose function can be partially compensated for by the Mtb homolog (Rv2222c), but not by other Mtb_glnA genes.
The Essentiality of glnA2 Cannot be Salvaged by L-, D-, Iso-Glutamine, Nor by Other Supplements In Mtb, the glnA2 deletion mutant grew normally and was not a glutamine auxotroph, in contrast to the glnA1 deletion mutant, which is a glutamine auxotroph. To test if in M. smegmatis glnA2 deletion causes glutamine auxotrophy, we plated M.smeg I133F and Mc 2 -155 (wt) at 32 or 42 • C, on regular 7H10 plates, or plates supplemented by glutamine, asparagine or both. Neither supplementation salvaged M.smeg I133F growth at the non-permissive 42 • C temperature (Figure 3, Top), suggesting that the growth defect was related to some other, unidentified, function of this gene/enzyme. Since some data have suggested that Mtb_glnA2 is responsible for the synthesis of D-glutamine or iso-glutamine, and not L-glutamine, we attempted to grow M.smeg I133F with D-glutamine, but this did not salvage the temperature-sensitivity phenotype either (Figure 3, Bottom).

Upregulation of the Global Nitrogen Regulator glnR Enables Complete glnA2 Deletion
In one of our repeated attempts to completely delete glnA2 from M.smeg I133F by cassette exchange, we obtained a mutant where the cassette exchange was confirmed, and was thus a full glnA2 deletion mutant. We postulated that the deletion was made possible due to a compensating mutation, and sent the fulldeletion mutant (called mDB76) for whole genome sequencing. The only mutation discovered (except for the full absence of glnA2) was a point mutation A=>G at position minus 5 (5 bp before the start codon) in M_smeg 5784, which is also called glnR. This gene was previously shown to be a global regulatoractivator of nitrogen metabolism, and involved in the regulation of hundreds of genes (Amon et al., 2008;Malm et al., 2009;Jenkins et al., 2013;Liao et al., 2015). We suspected that the finding of the A=>G point mutation right before the ATG of this particular gene in a glnA2 full deletion mutant was not a coincidence, and decided to first examine the effect of the FIGURE 2 | glnA2 is essential, as it cannot be completely removed from the M. smegmatis genome. mDB67 ( glnA2; attp:kana:glnA2 I133F ) was electroporated by an empty control vector (left), wt glnA2 or Mtb_glnA2 (Rv2222c, right), and plated on 7H10 plates supplemented by oADC, glycerol and zeocin 33 µg/mL.  mutation on glnR promoter activity. We cloned the sacB gene under either the native glnR promoter (sacB glnR−prom ) or under the mutated one (sacB mut−glnR−prom ), constructed the respective M. smegmatis mutants (mDB153 and mDB154), and examined the transcription levels by RT-PCR. We found that transcription with the mutated promoter was approximately 3-fold higher than that with the wt (Figure 4).
To test if this indeed was the factor enabling glnA2 deletion, we cloned glnR with the mutated promoter from mDB76 into a multi-copy episomal plasmid (hygromycin-selected), creating plasmid pDB342. We electroporated pDB342 into mDB67 (M.smeg I133F ), creating mDB167. This mutant overexpresses glnR due to the mutated promoter and multiple copies of the gene. We then repeated the cassette exchange experiments attempted previously with mDB67, with the newly constructed mDB167. To make true cassette replacement more easily identifiable, we re-cloned the genes into another zeocin-selected empty vector: pDB299, which has lacZ, therefore producing blue colonies (when plated on IPTG/Xgal). The three plasmids used were pDB328 (with wt M_smeg glnA2), pDB329 (wt Mtb glnA2, Rv2222c), or pDB299 (vector control, no complementation). Whereas with mDB67 only pDB238 and pDB329 yielded blue colonies, with mDB167 all three plasmids yielded multiple blue colonies, including pDB299 (empty vector) ( Figure 5). The cassette exchange was confirmed by PCR, showing a full deletion mutant of glnA2 could now be readily created.
Since glnR is a nitrogen-metabolism regulator, we postulated that its' overexpression does not per-se allow the deletion of glnA2, but rather that it lowers the threshold for a metabolic adaptation, allowing this deletion. To test this, we plated 10 6 or 10 5 cfu/mL of either mDB67 or mDB167, and kept them at 32 • C (permissive temperature) or 42 • C (non-permissive). If overexpression of glnR (in mDB167) would be enough to counter the loss of glnA2, we would expect multiple colonies of mDB167 to arise at 42 • C. Instead, most of the growth at 42 • C was still inhibited, but we obtained "breakthrough" colonies for both mDB67 and mDB167. However, the number of breakthrough colonies for mDB167 was at least 10-times higher than that for mDB67 ( Figure 6). We therefore concluded that a metabolic adaptation compensating for a complete glnA2 loss (perhaps compendations for lack of D-or iso-glutamine?) was facilitated by high-level glnR expression. This implied that another compensatory mutation in nitrogen metabolism pathways may also enable glnA2 deletion, making a "low threshold" for such mutations.

DISCUSSION
This study shows that for M. smegmatis, unlike in Mtb, glnA2 is essential, and suggests that it may have an additional role aside iso-, D-or L-glutamine synthesis. The function of glnA2 is salvaged by high-level expression of glnR, a global nitrogen metabolism regulator.
Nitrogen metabolism plays a central role in all bacteria, and its different components are potential drug targets in pathogenic bacteria, including mycobacteria. Glutamine is one of the core substances in nitrogen metabolism, and glutamine synthetases (GSs), are therefore important genes in all bacteria. M. tuberculosis has four glutamine synthetases (glnA1-4), and several other genes involved in glutamine synthesis and glutamine synthesis regulation (such as glnE), but surprisingly, of these four, only glnA1 (Rv2220c) was shown to be essential, and its deletion rendered the bacteria a glutamine auxotroph (Harth et al., 2005;Lee et al., 2006). Mutants deleted in glnA2, 3, or 4 were unaffected, were not auxotrophs, and produced in-vivo disease in mice indistinguishable from that of the wt (Lee et al., 2006). Although one would expect this to be the situation in other mycobacteria as well, surprisingly, in the very closely related organism M. bovis, a glnA1 deletion was viable even without the addition of glutamine (Chandra et al., 2010), although it did fail to proliferate in vivo. However, in that publication other additives were used, with no clear mention of what they were (Chandra et al., 2010). Still, it appears that some of these genes have slightly different roles even in closely related mycobacteria.
In this work, we discovered a spontaneous mutant in M. smegmatis, with a phenotype of resistance to an antibacterial chemical with a poorly defined mechanism (O'Donnell et al., 2009), and the mutant had temperature sensitivity. We anticiptaed that identifying the mutation in this strain would shed light on the mechanism of the antimicrobial chemical, and identified the mutation as an I133F mutation in the glnA2 gene. However, introducing the mutation into the wt did not re-create the resistance phenotype, but did maintain the temperature sensitivity. We therefore took the FIGURE 5 | Over-expression of glnR allowed for the complete deletion of glnA2 using cassette-exchange. Top row: mDB67 was electroporated by an empty vector, wt glnA2 or Rv2222c. Correct cassette exchanges yielded blue colonies, whereas background colonies are white. Bottom row: the same cassette exchanges were attempted in mDB167. Multiple blue colonies arised on the empty vector plate, yielding complete deletion mutants.
FIGURE 6 | The effect of glnR overexpression on "escape" from glnA2 essentiality. mDB67 and mDB167 were plated in even numbers (10 6 and 10 5 cfu/plate) at the non-permissive temperature of 42 • C. mDB167 had approximately 10 times more spontaneous "escape" mutants than mDB67. opportunity to examine the role of glnA2 in M. smegmatis. In M. tuberculosis this non-essential gene is thought to be involved in D-glutamine, rather than L-glutamine biosynthesis. However, our experiments convincingly showed that in M. smegmatis the glnA2 gene is essential as its complete removal was not possible, and when grown at a nonpermissive temperature, the mutant failed to "keep up" with wt M. smegmatis for more than 3-4 generations. More surprisingly, supplementation with neither iso-, L-, nor D-glutamine could salvage the phenotype at the non-permissive temperature, suggesting another role, and not glutamine synthesis, for glnA2 in these bacteria. However, the Mtb_glnA2 (Rv2222c) could compensate for the loss of glnA2 in M. smegmatis, albeit with a growth defect. The question of which role of glnA2 in M. smegmatis is the essential one, remains to be resolved.
Of note, another possibility is that glnA2 is indeed responsible for D-or Iso-glutamine synthesis, and that this is the basis for essentiality-but an inability of M. smegmatis to use exogenous D-or iso-glutamine is causing the failure of salvage with these AA. L-glutamine auxotrophs of Mtb could be salvaged by exogenous L-glutamine, but it is possible that D-and iso-amino acids behave differently.
M.smeg_5784, also known as glnR, is an ompR-like regulatoractivator of nitrogen metabolism (Jenkins et al., 2013), that was previously shown to control dozens of genes in M. smegmatis (and in other bacteria) (Williams et al., 2015;Xu et al., 2017). We have shown here that up-regulation of glnR expression can facilitate the emergence of "glnA2-deletion" resistance, as the rate of spontaneous mutant-appearance to glnA2 inactivation (by non-permissive temperature) was 10 times higher in bacteria overexpressing glnR compared to that of wt levels. Furthermore, overexpression of glnR allowed for the relatively easy creation of a complete glnA2 deletion-mutant, suggesting that the transcriptomic (resulting in proteomic, and finallymetabolomic) alteration induced by this overexpression favored the metabolic adaptation needed to sustain such a deletion. This is somewhat similar to the situation described in recently published research, where the murL gene was shown to be essential in M. smegmatis, and was a D-glutamate auxotroph. However, a point mutation in the promoter of another gene, Msmeg_5795, which up-regulated the latter's expression, salvaged the auxotroph phenotype (Mortuza et al., 2018). How exactly the upregulation of glnR salvages the essentiality of glnA2 is not completely clear-this could be by upregulation of metabolic pathways bypassing the bottleneck created by glnA2 deletion, by inducing a gene with a redundant function or other possibilities. It is also possible that glnA2 is needed for the proper function of glnR itself (by stabilizing it as a dimer), and higher expression of glnR compensates for lesser stability of the dimer. A somewhat similar mechanism is present in Bacillus subtilis Wray, 2008, 2009;Wray and Fisher, 2008). Also, work in Paenibacillus riograndensis showed that the binding of glnR to its targets is facilitated when glnR is complexed with a glutamine synthetase-again, upregulation of glnR may bypass the lack of glnA2 [Hauf et al., 2016;Fernandes et al., 2017].
It remains unresolved as to what is the reason for glnA2 essentiality. The possibilities are numerous but could include the depletion of some metabolite, the accumulation of a toxic metabolite due to a metabolic bottleneck, or an essential role in a process such as cell wall remodeling, needed for cell proliferation. Of special interest is the finding that despite great similarity between the GS genes of Mtb and M. smegmatis, and specifically the glnA2 gene (which has 83% homology of DNA sequence, and 88% homology of amino acid sequence), found to be non-essential in one is highly important for the physiology of the other. If glnA2 in M. smegmatis does indeed have additional roles over those of glnA2 in Mtb, it remains to be explored if these roles are unique to M. smegmatis, and if not, what are the genes responsible for these functions in Mtb.

Bacteria and Growth Conditions
The M. smegmatis used was the standard laboratory-strain mc 2 -155, grown as previously widely described in 7H9 liquid broth or on solid 7H10 plates, both supplemented with glycerol and OADC (and Tween 80 for 7H9 broth). When appropriate, antibiotics were added in the following concentrations: kanamycin 20 µg/mL, zeocin 33 µg/mL, and hygromycin 50 µg/mL. When appropriate, L-glutamine, Dglutamine, or asparagine were added to the growth media at 20 mM (Harth et al., 2005).

Whole Genome Sequencing (WGS)
Genomic DNA was extracted from mycobacteria as previously described, and sent for WGS at the DNA Services Facility in the University of Chicago, Illinois.

Construction of an Unmarked glnA2 Deletion Mutant
To completely delete glnA2 from mc 2 -155, we first created a merodiploid strain, as described in the results section. We then cloned the 600 bp upstream and downstream flanking regions immediately next to glnA2 (including the first and last few nucleotides) into the plasmid pAJF013, that also carried the zeocin resistance gene, and the two negative selection markers galK and sacB. This plasmid was then used to create a complete, unmarked deletion mutant in a two-step allelic exchange technique, as described before (Barkan et al., 2011), and the mutant was named mDB67. The complete deletion of glnA2 was demonstrated by amplifying the glnA2 region with primers external to the 600 bp flanking regions. Whereas in wt the PCR reaction yielded a 2,600 bp product, in a full deletion mutant the product was only 1,300 bp long. The 1,300 bp product was gel-purified, sequenced, and shown to contain the upstream and downstream regions of glnA2, but not the gene itself. The deletion was later also confirmed by whole genome sequencing.
Testing the wt or Mutated glnR-Promoter Activity by RT-PCR We used sacB as a reporter gene, and cloned it either under the wt or the mutated glnR promoter, using a PCR primer extension, into an attp-integrating vector (pDB213). This vector was electroporated into wt M. smegmatis, creating the mutants mDB153 (with sacB under wt glnR promoter) and mDB154 (sacB under the mutated promoter). Bacteria were grown to log phase, and RNA was isolated as follows: 1 mL of OD 600 = 0.4 bacteria were harvested by centrifugation at 3000 g for 1 min and resuspended in 1 mL of Trizol Reagent (Thermo Fisher Scientific, #15596026). After incubation of 1 min on ice, bust bead-beating was carried out (Precellys Lysing Kit) with 30 s cycles of beating/resting for 10 cycles. The lysate was centrifuged and the RNA extraction was then performed (Pure Link RNA mini Kit # 12183018A). For real-time reverse transcription PCR (qRT-PCR) analysis, one microgram of each RNA sample was reverse transcribed with the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher # 4368814). Real-time PCR was performed in a StepOne real-time PCR instrument (Applied Biosystems) with SYBR Green PCR Supermix (Invitrogen). The primers for sacB were: reverse 5 ′ -GCTGGCCATTACAAAACGCT-3 ′ , forward 5 ′ -GACGATGTGGTAGCCGTGAT-3 ′ ; for glnR: F: 5 ′ -GAAGAAG TCGTAACCCC -3 ′ , R: 5 ′ -AGTGATCAACGAAGGCG−3 ′ . Normalizing primers: s16-f: 5 ′ -GTGCATGTCAAACCCAGGT AAGG-3 ′ , s16-r: 5 ′ -GGGATCCGTGCCGTAGCTAAC-3 ′ . The results shown are the average of five independent experiments.
A detailed list of plasmids and strains used in this study can be found at Supplementary Material.

AUTHOR CONTRIBUTIONS
SG, OZ, and DB conceived the design of the study. NR, KG, MB, and DB performed the acquisition, analysis, and the interpretation of the data. NR, SG, OZ, and DB wrote the manuscript.


s2fieldsofstudy: Biology Psychology

text:
Heat Shock Cognate 70 Inhibitor, VER-155008, Reduces Memory Deficits and Axonal Degeneration in a Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder resulting in structural brain changes and memory impairment. We hypothesized that reconstructing neural networks is essential for memory recovery in AD. Heat shock cognate 70 (HSC70), a member of the heat shock protein family of molecular chaperones, is upregulated in AD patient brains, and recent studies have demonstrated that HSC70 facilitates axonal degeneration and pathological progression in AD. However, the direct effects of HSC70 inhibition on axonal development and memory function have never been investigated. In this study, we examined the effects of a small-molecule HSC70 inhibitor, VER-155008, on axonal morphology and memory function in a mouse model of AD (5XFAD mice). We found that VER-155008 significantly promoted axonal regrowth in amyloid β-treated neurons in vitro and improved object recognition, location, and episodic-like memory in 5XFAD mice. Furthermore, VER-155008 penetrated into the brain after intraperitoneal administration, suggesting that VER-155008 acts in the brain in situ. Immunohistochemistry revealed that VER-155008 reduced bulb-like axonal swelling in the amyloid plaques in the perirhinal cortex and CA1 in 5XFAD mice, indicating that VER-155008 also reverses axonal degeneration in vivo. Moreover, the two main pathological features of AD, amyloid plaques and paired helical filament tau accumulation, were reduced by VER-155008 administration in 5XFAD mice. This is the first report to show that the inhibition of HSC70 function may be critical for axonal regeneration and AD-like symptom reversal. Our study provides evidence that HSC70 can be used as a new therapeutic target for AD treatment.

INTRODUCTION
Deposits of amyloid β (Aβ) and hyperphosphorylated tau in the brain are the two main pathological features of Alzheimer's disease (AD). These deposits trigger the disruption of neural networks in the brain, which is the direct cause of memory dysfunction in AD. Reconstruction of damaged neural networks, including neurite regeneration and synapse re-formation, may be essential for memory function recovery. New drugs and new therapeutic targets that prevent or reverse axonal degeneration are needed to preserve memory function in AD.
Heat shock cognate protein 70 (HSC70) is a molecular chaperone from the heat shock protein (HSP) family (Stricher et al., 2013). Multiple intracellular functions of HSC70 have been reported, including binding and promoting the folding of nascent polypeptides, degrading client proteins via the ubiquitin proteasome system (UPS) (Meimaridou et al., 2009), and chaperone-mediated autophagy (CMA) (Cuervo, 2011). HSC70 expression is increased in AD model mice and patient brains (Piedrahita et al., 2016), and HSC70 prevents the accumulation of phosphorylated tau (Jinwal et al., 2010). Importantly, neurofilament medium chain protein (NF-M), a component of neurites, interacts with HSC70 and is degraded by the UPS (Wang et al., 2011), possibly facilitating axonal degeneration in AD. Therefore, HSC70 upregulation in AD brains might contribute to memory deficits. However, no reports have demonstrated a direct effect of HSC70 inhibition on axonal development or memory function.
The 5XFAD (Tg6799) AD mouse model co-expresses mutant human amyloid precursor protein (APP) containing the Swedish (K670N and M671L), Florida (I716V), and London (V717I) mutations, and presenilin-1 (PS1; M146L and L286V) specifically in neurons. These five familial AD mutations increase the level of Aβ 1−42 peptide in the brain (Oakley et al., 2006). Aβ plaques deposition begins at 2 months, and memory deficit is first observed at 4 months of age in 5XFAD mice.
VER-155008 is an HSC70 functional inhibitor that competitively binds to the ATP-binding pocket of HSC70 (Schlecht et al., 2013). Approximately 10 small-molecule inhibitors of HSC70 are known (Goloudina et al., 2012); however, it is difficult to isolate the specific inhibitory effect on HSC70 because of its sequence similarity to other HSPs (86% for HSP70). We selected VER-155008 because of its sustained inhibitory effect on HSC70 but not HSP70 (Schlecht et al., 2013).
In the present study, we investigated the effects of HSC70 inhibition by VER-155008 on axonal development in cultured neurons and memory function in 5XFAD mice. We also examined the pathological changes in the brain tissue of VER-155008-treated 5XFAD mice. The goal of this study was to evaluate HSC70 function inhibition as a potential strategy for treatment of AD.

MATERIALS AND METHODS
All experiments were performed in accordance with the Guidelines for the Care and Use of Laboratory Animals of the University of Toyama. The Committee for Animal Care and Use at the Sugitani Campus of the University of Toyama approved the study protocols (approval number for the animal experiments is A2017INM-1). All efforts were made to minimize the number of animals used.

Animal Studies
Transgenic mice (5XFAD) were obtained from the Jackson Laboratory (Bar Harbor, ME, United States) and maintained by crossing hemizygous animals with B6/SJL F1 breeders. To investigate the effect of VER-155008 on 5XFAD mice, we used hemizygous female mice (age: 32-38 weeks in the experiments shown in Figures 2A-D, and 24-28 weeks old in Figures 2E-H) or wild-type female littermates. All mice were housed with free access to food and water in a controlled environment (22 ± 2 • C, 12 h light/dark cycle starting at 7:00 am).

Behavioral Tests
To investigate the relationship between HSC70 inhibition and memory recovery, VER-155008 (IC 50 = 0.5 µM; Sigma-Aldrich, St. Louis, MO, United States) was dissolved in 10% dimethyl sulfoxide (DMSO)-containing saline and administered intraperitoneally (10 µmol/kg/day) to 5XFAD mice for 18 days (Figures 2A-D) or 15 days (Figures 2E-H). Vehicle solution (10% DMSO in saline) was used as a control. On day 14 of administration, the novel object recognition or location test and on day 15, the episodic-like memory test were performed as described previously (Kuboyama et al., 2015). Testing was performed in a dimly illuminated (81 lux) room. On the last day of administration, mice were individually habituated to an open-field box composed of polyvinyl chloride (33 cm × 29 cm; height, 26.5 cm) for 10 min. Their paths were tracked using a digital camera system. The total distance moved (cm) and total immobility time (s) for 10 min were analyzed with EthoVision 3.0 (Noldus, Wageningen, Netherlands).

Brain Penetration of VER-155008
To detect the blood brain barrier (BBB) penetration, VER-155008 or vehicle solution (10% DMSO in saline) was intraperitoneally administered (89.9 µmol/kg) to female 5XFAD mice (36 weeks old). Five minutes after drug administration, the mice were euthanized by isoflurane and blood was collected. The plasma was obtained after centrifugation at 10,000 g for 10 min at 4 • C. Plasma (120 µl) was extracted with methanol, dried, and resolubilized in 100 µl of methanol. The cerebral cortex perfused with saline before dissection was homogenized, extracted with methanol, dried, and resolubilized in 100 µl of methanol before LC-MS analysis. To calculate the VER-155008 concentration in the cortex, a standard curve was plotted. Briefly, standard solutions of VER-155008 were mixed with a control cortex extract, prepared as the above, and subjected to LC-MS analyses. A Thermo Scientific TM Accela HPLC system interfaced with an LTQ Orbitrap XL hybrid Fourier Transform Mass Spectrometer (Thermo Fisher Co., San Jose, CA, United States) was used to chemically profile VER-155008. Liquid chromatographic analyses were performed on a Capcell Pak C18 MGIII S-5 (1.5 mm i.d. × 150 mm, Shiseido, Tokyo, Japan) column held at 40 • C with a flow rate of 200 µl/min. Ultrapure water and ethanol (E) were used as the mobile phase. The following linear elution gradient was used: 0-5 min, 40-70% E; 5-10 min, 70-85% E; 10-13 min, 85-100% E; 13-14 min, 100-40% E; 14-18 min, 40% E. The following electrospray ionization (ESI) parameters were used: spray voltage, 4.5 kV; capillary voltage, 40.0 kV; tube lens, 150 V; capillary temperature, 330 • C; sheath gas flow rate, 50 units; and aux gas flow rate, 10 units. We operated the mass spectrometer in the positive ESI mode; scanning from 50 to 2,000 m/z; and calibrated the instrument using a polytyrosine solution before each experiment.

Statistical Analysis
Statistical comparisons were performed using one-way analysis of variance (ANOVA) with post hoc Dunnett's tests, repeated measures two-way ANOVA with the post hoc Bonferroni test, and unpaired t-tests in GraphPad Prism 5 (GraphPad Software, La Jolla, CA, United States). p < 0.05 was considered significant. Data are presented as the mean ± standard deviation.

VER-155008 Reverses Aβ-induced Axonal Atrophy in Cultured Neurons
To investigate the effect of HSC70 inhibition on axonal morphology, Aβ-treated neurons were further treated with VER-155008. After 3 days of Aβ (10 µM) treatment, the length of the pNF-H-positive axons was significantly reduced. However, a subsequent addition of 0.05-5 µM VER-155008, but not vehicle, restored the axonal length to the control levels (Figure 1). This result suggests that the inhibition of HSC70 function can induce axonal regrowth after Aβ treatment of neurons.

VER-155008 Improves Memory Function in 5XFAD Mice
Next, to investigate the relationship between the inhibition of HSC70 and memory recovery, VER-155008 or vehicle solution was intraperitoneally administered to 5XFAD mice for 18 days. Axons regrowth in the adult mouse brain takes at least 7 days after injury (Jin et al., 2016). Our previous study indicated that synaptic maturation after axonal growth required at least another 14 days (Tohda et al., 2006). We hypothesized that VER-155008 could stimulate axonal regeneration in vivo and thus improve memory. For expecting the axonal regeneration and subsequent synapse formation in the brain, we firstly tried to evaluate the effect VER-155008 on memory improvement at 14-18 days after administration. On day 18, a locomotion test was performed on each mouse. The total distance moved and total immobility time were not significantly different between transgenic mice treated with VER-155008 or vehicle or between either of these groups and wild-type mice treated with vehicle (Figures 2A,B). In addition, no significant changes in body weight were observed in any of the groups during drug administration ( Figure 2C). These results indicate that VER-155008 treatment did not significantly affect general health and behavior.
On administration day 14, an object recognition memory test was performed. In this test, all mice showed similar exploratory behaviors toward two identical objects in the training session (preferential indexes of approximately 50%). In the test session, the VER-155008-treated 5XFAD mice showed a significantly higher preferential index to the new object did than the vehicletreated 5XFAD mice (Figure 2D), indicating that VER-155008 treatment improved object recognition memory in 5XFAD mice.
To test whether spatial memory was also improved by VER-155008 administration, we performed an object location test in 5XFAD mice on administration day 14. In the test session, one of the identical objects was located in a different place compared with its location in the training session ( Figure 2E). While wildtype mice showed higher preferential indexes for the moved object, vehicle-treated 5XFAD mice could not distinguish the two objects. However, VER-155008 administration significantly improved object location memory in the 5XFAD mice. On administration day 15, an episodic-like memory test was performed. This test requires the ability to integrate object (what), location (where), and context (when) recognition (Dere et al., 2005). Among the four objects used in test session, E 1 and E 4 were located at the same places in training session 2 10 min before test session ( Figure 2F). During the test session, the objects D 2 and D 4 should be less familiar to the mice than objects E 1 and E 4 . Wild-type mice showed a significantly higher preferential index for the unfamiliar (D 2 and D 4 ) than the familiar objects (E 1 and E 4 ) ( Figure 2G). However, vehicle-treated 5XFAD mice showed equal preferential indexes to both sets of objects. VER-155008 administration increased the preferential index for the unfamiliar objects in the 5XFAD mice. Object D 2 was placed in the test session at the same location as in training session 1, whereas D 4 was located at a novel position compared with its location in training session 1. Therefore, mice with normal location memory should show higher preferential indexes for object D 4 than D 2 . However, vehicle-treated 5XFAD mice failed to distinguish between D 2 and D 4 . In contrast, VER-155008treated mice showed significantly increased preferential indexes for D 4 , suggesting that VER-155008 administration improved episodic-like memory in the 5XFAD mice ( Figure 2H).
Taken together, these results indicate that HSC70 inhibition ameliorated the deficits in recognition, location, and episodic-like memory in 5XFAD mice.

VER-155008 Penetrates into the Brain after Intraperitoneal Administration in Mice
To evaluate the possibility that VER-155008 could stimulate the brain directly, we investigated whether VER-155008 crossed the BBB. By detecting the high-accuracy quasi-molecular ion ([M+H] + ) with a mass error of ±10 mmu, we could identify the standard mass data and fragmentation pattern of VER-155008 ( Figure 3A). Usually, doses much higher than the effective dose are used to detect compounds in biological samples by mass spectrometry (Durairajan et al., 2012;Yang et al., 2017); therefore, we administered a dose approximately 9 times higher than the dose of VER-155008 (89.9 µmol/kg) used in the experiments shown in Figure 2 (10 µmol/kg) to 5XFAD mice. We compared extracted ion current (EIC) chromatograms of VER-155008 in plasma and cortex samples obtained from vehicle-or VER-155008-treated 5XFAD mice. In both samples from both groups of mice, total ion current peaks were detected at retention times similar to that of VER-155008 standard ( Figure 3B). MS-MS fragmentation patterns of the peaks were also similar to that of the standard. A corresponding ion peak was not detected in the plasma and cerebral cortex of vehicle-treated mice. These results suggest that VER-155008 penetrated the BBB and reached the brain. Using a VER-155008 standard curve, the predicted concentrations of VER-15008 in the plasma FIGURE 2 | VER-155008 administration rescued memory deficits in 5XFAD mice. (A-D) Wild-type and 5XFAD mice (female, 32-38 weeks old) were treated with VER-155008 (VER; 10 µmol/kg/day, i.p.) or vehicle solution (Veh; 10% DMSO in saline) for 18 days. (A,B) On administration day 18, a locomotion test was performed. Total distance moved (cm) and total immobility time (s) were measured. The differences were not significant vs. 5XFAD/Veh, one-way ANOVA post hoc Dunnett's test, n = 4. (C) Change in body weight during the drug administrations (±g compared with day 0). A significant drug × day interaction was found by repeated-measures two-way ANOVA [F(34,153) = 1.420, p = 0.0788, n = 4]. (D) On administration day 14, an object recognition memory test was performed. The preferential indexes in the training and test sessions are shown. A significant drug × test interaction was found by repeated measures two-way ANOVA [F(2,9) = 4.230, p = 0.0507; * p < 0.05, * * * p < 0.001, post hoc Bonferroni test, n = 4]. (E-H) Wild-type and 5XFAD mice (female, 24-28 weeks old) were treated with VER-155008 (10 µmol/kg/day, i.p.) or vehicle solution (10% DMSO in saline) for 15 days. (E) On administration day 14, an object location test was performed. The preferential indexes in the training and test sessions are shown. A significant drug × test interaction was found by repeated-measures two-way ANOVA [F(2,12) = 35.12, p < 0.0001. * * * * p < 0.0001, post hoc Bonferroni test, n = 5]. (F) On administration day 15, an episodic-like memory test was performed. (G) The preferential indexes of E 1 + E 4 (gray columns) and D 2 + D 4 (blue columns), (H) The preferential indexes of D 2 (light blue columns) and D 4 (blue columns) in the test session are shown. * * * * p < 0.0001, unpaired t-test, n = 5.

VER-155008 Reduced AD Pathologies in the 5XFAD Mouse Brain
After behavioral testing of drug-treated mice, we also examined the effects of VER-155008 administration on the AD-like pathological changes in 5XFAD mouse brains.
As shown in Figure 1, VER-155008 reversed Aβ-induced axonal atrophy in cultured neurons. Therefore we examined the effect of VER-155008 on abnormal axonal structures in the 5XFAD mouse brain. In AD patient brains, ring-or bulblike deformed neurofilaments were located within the amyloid plaques (Dickson and Vickers, 2001). Similarly, we observed pNF-H-positive abnormal bulb-like swollen axons in the amyloid plaques in 5XFAD mice (Tohda et al., 2012). In the present study, the swollen axon area within the plaques in the perirhinal cortex or hippocampal CA1 was quantified (Figure 4). In wildtype mice, no deposition of amyloid plaques was observed, and the axons had fiber-like shapes. However, in the vehicle-treated 5XFAD group, the axons in the amyloid plaques were swollen. In contrast, in VER-155008-treated 5XFAD mice, the percentage of swollen axons within each amyloid plaque was reduced both in the perirhinal cortex and CA1.
To investigate the effect of VER-155008 administration on the accumulation of amyloid plaques in vivo, the amyloid plaque areas in the perirhinal cortex and CA1 were measured (Figure 5). While no amyloid deposition was found in wild-type brains, the brains of age-matched vehicle-treated 5XFAD mice showed severe plaque deposition. However, the VER-155008-treated 5XFAD mice had significantly reduced total plaque areas in the perirhinal cortices and CA1 regions.
FIGURE 4 | VER-155008 administration reduced axonal swelling associated with amyloid plaques in 5XFAD mice. Wild-type and 5XFAD mice (female, 32-38 weeks old) were treated with VER-155008 (VER; 10 µmol/kg/day, i.p.) or vehicle solution (Veh; 10% DMSO in saline) for 18 days. After behavioral tests, the mice were sacrificed, and the perirhinal cortex and hippocampal CA1 were immunostained for Aβ 1−40/42 and phosphorylated neurofilament heavy subunit (pNF-H).  We also examined the effect of VER-155008 on the expression levels of PHF-tau (Figure 6). We have previously detected increased AT8-positive PHF-tau staining closely associated with amyloid plaques in 5XFAD mouse brains (Tohda et al., 2012;Yang et al., 2017). In addition, AT8-positive PHF-tau was present in 5XFAD brain lysates (Stygelbout et al., 2014). Therefore, we evaluated the AT8-positive PHF-tau levels in 5XFAD brains. While no PHF-tau was observed in wild-type mice, high PHFtau levels were present in 5XFAD brains. The PHF-tau protein was closely associated with the amyloid plaques. Treatment with VER-155008 resulted in significant reduction of PHF-tau within the plaques in the perirhinal cortices and CA1 regions of the 5XFAD mice.
These results suggest that HSC70 activity is required for sustained axonal degeneration, amyloid deposition, and tau hyperphosphorylation in the brains of 5XFAD mice.

DISCUSSION
VER-155008, an inhibitor of HSC70, promoted axonal regrowth in cultured neurons even after axonal degeneration induced by Aβ treatment (Figure 1). In addition, object recognition, location, and episodic-like memory tests showed remarkable improvements in VER-155008-treated 5XFAD mice (Figure 2). VER-155008 penetrated the brain after intraperitoneal administration (Figure 3), indicating that it might affect impaired neurons directly. Axonal swelling, amyloid deposition, and PHF-tau formation were significantly reduced in the perirhinal cortices and hippocampal CA1 regions of VER-155008-treated 5XFAD mice (Figures 4-6). Altogether, these results suggest that VER-155008 can reverse axonal degeneration in the brain and reduce the pathological features of AD. This is the first report to demonstrate that HSC70 inhibition is sufficient to ameliorate axonal atrophy and memory dysfunction in an animal model of AD.
As a molecular chaperone, HSC70 has various client proteins, and its downstream signaling depends on which protein it associates with. The activity of HSC70 is ADP/ATP-dependent. When HSC70 has ATP in its N-terminal nucleotide-binding domain (NBD), it can associate with clients with low affinities. Through interaction with co-chaperones, the HSC70-bound ATP is hydrolyzed to give rise to an ADP-bound HSC70 with a high affinity for client proteins (Stricher et al., 2013). VER-155008 directly binds to the ATP-binding pocket of HSC70, blocking the HSC70 ATPase activity and likely preventing HSC70 binding to client proteins involved in AD-associated axonal degeneration and amyloid deposition. As mentioned above, HSC70 associates with NF-M and targets it for degradation by the UPS (Wang et al., 2011). HSC70 also associates with tau and maintains it in a hyperphosphorylated state in the AD environment (Jinwal et al., 2010). Therefore, NF-M and tau may mediate the axonal regrowth and PHF-tau reduction induced by HSC70 inhibition in 5XFAD brains. Our results are consistent with the findings FIGURE 6 | VER-155008 administration reduced PHF-tau associated with amyloid plaques in 5XFAD mice. Double-immunostaining for Aβ 1−40/42 and PHF-tau was performed in the perirhinal cortex and hippocampal CA1. (A) Representative images of Aβ 1−40/42 -positive amyloid plaques, PHF-tau, and DAPI staining in the perirhinal cortex are shown. The amyloid plaques area is encircled with white dotted lines. (B) The total area of PHF-tau staining per 100 mm 2 of amyloid plaque was quantified. * * * p < 0.001 vs. 5XFAD/Veh, one-way ANOVA post hoc Dunnett's test, n = 4. that allosteric HSP70 inhibitors reduced tau levels and stimulated long-term potentiation in ex vivo brain slices from a transgenic mouse tauopathy model (Abisambra et al., 2013).
No direct evidence exists that HSC70 is related to amyloid plaque accumulation. Sp1 is transcription activator of the genes encoding beta-site amyloid precursor protein-cleaving enzyme (BACE) 1 and amyloid precursor protein (APP), abnormal processing of which causes Aβ accumulation in AD brains (Christensen et al., 2004). Sp1 expression is increased in AD patients and model mouse brains (Citron et al., 2008). Importantly, Sp1 is also a client protein of HSC70, which protects Sp1 from degradation (Yang et al., 2014). This finding indicates that HSC70 activity correlates with Sp1 upregulation, possibly leading to the accumulation of amyloid plaques. Therefore, VER-155008 may downregulate Sp1 expression and prevent amyloid plaque formation by inhibiting HSC70 activity. However, in the present study, we used 7-8-month-old 5XFAD mice, and the drug administration period was only 18 days. At this age, the accumulation of amyloid plaques in the mouse brain had almost plateaued (Oakley et al., 2006). VER-155008 treatment decreased the plaque areas in the 5XFAD brains by 50% in the perirhinal cortex and 43% in the CA1 relative to the vehicle-treated group (Figure 5). Therefore, we speculate that the VER-155008induced reduction of amyloid plaques is caused not only through prevention of plaque production, but also through other effects, such as increased plaque degradation. Further investigation is required to clarify the mechanism underlying the reduction of amyloid plaques by of HSC70 inhibition.
Pharmacokinetics studies of VER-155008 showed that VER-155008 penetrated into tumor tissues after intravenous administration (Massey et al., 2010). However, no reports have focused on BBB penetration by VER-155008. The hydrophobicity of VER-155008 is comparatively high (Clog P = 2.71). Figure 3 indicates that VER-155008 reached the brain. Moreover, we found that several pathological features (swollen axons, amyloid plaques, and PHF-tau levels) in the brain were reduced by VER-155008 administration (Figures 4-6), suggesting that VER-155008 acts in the brain. During VER-155008 administration, no significant changes in body weight or abnormal behavior were observed (Figures 2A-C), suggesting that administration of VER-155008 is safe and effective, at least at the dose used in this study.
This is the first report to show that HSC70 inhibition effectively reverses axonal degeneration and recovers memory function in AD-like pathologies. Fortunately, several HSC70 inhibitors have recently been developed. However, these inhibitors, including VER-155008, were created to treat cancer because HSC70 activates autophagy pathways and inhibits apoptosis for the survival of cancer cells . In the present study, we utilized these properties of VER-155008 to stimulate axonal regrowth and memory recovery. HSC70 may be a new, promising therapeutic target for the treatment of AD and other neurodegenerative diseases.