**2. HTS in drug discovery for infectious diseases**

Infectious diseases arise in any person due to certain microbes which enter the body and multiply to give clinical symptoms of the disease. While some infections are contagious and spread from one person to another person, others may spread in the community through infectious vectors (insect/animal bites) or contaminated air, water, and food [6]. These microbes undergo mechanisms of resistance to antibiotics either under the direct influence of antibiotics or through adaptive processes unrelated to the chemical structures of antibiotics [7, 8]. The increase in the number of antibiotic-resistant microbial strains makes it evident to discover and develop newer efficacious drugs. However, developing a new drug is a tedious and complex process with uncertain outcomes; therefore, the process needs to be rational in approach. HTS offers a highly rationalized automation approach to explore large chemical space in a time-efficient manner. However, it requires complex and costly technological platforms which are generally available in pharmaceutical companies [4]. Nevertheless, it is not expensive because HTS screens a huge number of chemical compounds as compared to manual methods for target-to-lead discovery. An overall success rate of HTS to find leads is considered ⁓50%. However, vHTS is considered

*High-Throughput Screening for Drug Discovery toward Infectious Diseases: Options and Challenges DOI: http://dx.doi.org/10.5772/intechopen.102936*

to have a higher success rate, but every method has its strengths and weaknesses, and therefore both the methods, HTS and vHTS, should be coupled for lead discovery. Few examples, among successful HTS drug discoveries in anti-infective agents, are (i) G-protein-coupled receptor (GPCR) inhibitor, Maraviroc (anti-HIV), (ii) reverse transcriptase (RT) inhibitor, etravirine (anti-HIV), [9] and (iii) hepatitis C virus (HCV) genotype 1a/b or 3 RNA replication inhibitor, Daclatasvir [10].

#### **2.1 Need of HTS in drug discovery for infectious diseases**

Bacterial enzymes play a significant role in developing antibiotic resistance through several key mechanisms and genetically derived mutations happening in: (i) drug-modifying enzymes (such as *transferases* and *hydrolases*), (ii) drug-metabolizing enzymes (such as *pyrazinamidase*, *catalase-peroxidase*, and *monooxygenase*), (iii) antibiotic's target enzymes (such as *RNA-dependent RNA polymerase* (RdRp) and *Topoisomerase II*), and (iv) antibiotic's cellular target-modifying enzymes (*rRNAmethyltransferases* and *phosphoethanolaminetransferase*). The structural changes in these enzymes not only lead to resistance among microbes but also open the 'omics gates to identify newer targets that originated after modifications in enzymes [8–10]. The rapid spread of resistance among microbes makes it imperative to rapidly identify new classes of antibiotics. Traditionally, growth inhibition assays are used for antimicrobial drug discovery which is a slow process [11, 12]. However, to match the pace of microbial resistance to antibiotics, a robotic automation screening process with efficient, accurate, and robust scientific methodology is required. HTS offers an economic advantage of screening huge chemical spaces accurately within defined timelines. Therefore, time, cost, and quality are termed as the "magic triangle of HTS" [13]. The credit of rapid HTS goes to: (i) high-density arrays, micro-reaction wells, and (ii) biological response detection methods.

High-density array micro-reaction well plates ranging from 96-well plates to miniaturized 3456-well plates are available with typical working volumes ranging from 1 to 10 μl of total volume. However, efforts are being made for further miniaturization of plates [13] to develop mega-dense arrays (>10,000 wells/plate) [14]. Although there are few difficulties associated with ultra-high-density plates, nevertheless, it is possible to perform 100,000 assays per day using ultra-highthroughput screening (uHTS) [15].

Biological response detection techniques such as fluorescence, luminescence, and atomic absorption spectroscopy have been established, which makes the process robust in the identification of active compounds. These direct and indirect detection methods have been developed based on: (i) direct measurement of absorbance and (ii) indirect measurement through enzymatic or chemical reactions coupled with pH indicators and chelators. These methods establish a quantitative relationship between biological response and target metabolite concentration [16]. Apart from the pharmacological aspect, HTS is equally beneficial in the evaluation of toxicological aspects of the chemical entities such as (i) genotoxicity, (ii) carcinogenicity, and (iii) immunotoxicity [15].

Plants have an abundance of potentially diverse chemical entities in the form of complex mixtures which are required to be evaluated in HTS for the discovery of new drugs against microbes. However, pure chemical entities from these complex mixtures need to be isolated and structurally characterized before proceeding for target-specific evaluation. Bioassay (*in vitro*)-guided HTS of these plant extracts aids in the identification and isolation of bioactive compounds (**Figure 1**) [17].

Similarly, vHTS is a bioethical approach consisting of a wide variety of *in silico* simulation approaches to explore chemical libraries and identify which chemical

entity has the potential to display *in vitro* and/or *in vivo* drug-like properties in HTS. However, there are chances of false-negative and false-positive results [4].

### **3. Methods involved in HTS for drug discovery toward infectious diseases**

#### **3.1 Classification of HTS**

HTS methods for anti-infective drug discovery may be biological (cell-based or whole organism), biochemical (enzymes/receptors), and virtual (computer-based). Hence, the HTS methods may be classified as summarized in **Figure 2**. The HTS assay approach for the identification of "lead" molecule may vary depending on the target; however, the assay protocol must be (i) sensitive to low potency molecules, (ii) reproducible in biological response, (iii) accurate in terms of positive and negative control, and (iv) economically feasible. Therefore, these parameters should be optimized before proceeding with the assay of compounds in large numbers [18].

#### *3.1.1 Virtual high-throughput screening (computer simulation-guided selection)*

vHTS is an efficient approach to identify hits and lead compounds for an identified microbial target which are further optimized using medicinal chemistry approaches. The applications of vHTS can be further explored to virtually evaluate ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the identified lead chemical entities based on "Lipinski's rule." The shortlisted potential hit/lead molecules may then be evaluated *in vitro,* thus giving a meaningful rationale between computer simulations and practical experimentation. Where vHTS is a generalized term for different screening filters, it is categorized under two broad classes of virtual screening methods. These methods are (i) structure-based drug design (SBDD) and ligand-based drug design (LBDD) [19].

#### *<IP_ADDRESS> Structure-based drug design*

Advances in HT 'omics technologies and instrumental methods of analysis such as X-ray crystallography and nuclear magnetic resonance (NMR) have solved a large number of three-dimensional (3D) structures of target proteins involved in communicable and noncommunicable diseases. These structures with specific identification numbers and resolution are available for scientific research and education purposes in protein data bank (PDB) [20]. Therefore, understanding the biologically functional interacting pocket (druggable target site) within 3D structures of the target proteins is essential to proceed with SBDD. However, if this structural information is not completely reliable or any sequence of the structural information is missing, then homology modeling is performed to generate a homologous model of the target protein [21]. SBDD is further classified under two headings; (i) docking and scoring and (ii) *de novo* drug design.

Docking and scoring is an excellent approach to predict the binding affinity and pharmacodynamic status of small chemical entities (ligands) in the active site of the target macromolecule. Scoring is an energy function which estimates the free binding energy of protein-ligand interactions such as electrostatic and van der Waals forces. Docking may be performed using two theoretical strategies namely: (i) lock and key theory, and (ii) induced-fit theory. Earlier docking programs were run using the lock and key assumptions where both the target protein and the ligand were treated as rigid structures with docking affinity dependent on the shape of the

*High-Throughput Screening for Drug Discovery toward Infectious Diseases: Options and Challenges DOI: http://dx.doi.org/10.5772/intechopen.102936*

**Figure 2.** *Classification of HTS for anti-infective drug discovery.*

interacting structures. Hence, it is termed as a rigid docking program. However, the target proteins and ligands are never in their rigid conformational state; instead, they are flexible (induced-fit docking) and undertake complementary conformational changes. Therefore, optimizing the binding pocket enables it to accommodate ligands of various shapes and sizes. This approach reduces the dropping out chances of potential false negatives [22, 23].

*De novo* design is a method of drug design that involves six different strategies: (i) identifying site point within the target site and connecting them using chemical fragments, (ii) determination of desirable fragment location, (iii) positioning fragment within the target site and linking them with linkers or scaffolds, (iv) construction of ligand sequentially within the site using fragments, (v) whole molecule conformation and interaction studies similar to docking, and (vi) random connection methods [24].

#### *<IP_ADDRESS> Ligand-based drug design*

LBDD approach is applicable when nothing is known about the 3D structure of the target site and completely relies on the knowledge of previously established lead/drug molecules with known pharmacological/toxicological profiles and 2D/3D physicochemical descriptors. Therefore, LBDD is classified into two broad categories: (i) quantitative structure–activity relationship (QSAR) [25] and (ii) pharmacophore modeling [26]. However, scaffold hopping [27] and pseudo-receptor modeling [28] are also the strategies used in LBDD.

QSAR is a method for developing mathematical models to significantly correlate the pharmacological profile with the chemical structures within the data set using regression analysis. However, with technological advancement, the QSAR method has undergone dimensional transformations (2D and 3D). The process involves a collection of chemical data sets (in-house or external) to develop mathematical QSAR models. These models are then used to identify active compounds which are

sequentially evaluated and synthesized on various platforms, including docking, *in vitro,* and *in vivo* studies [25].

Scaffold hopping is also known as "lead hopping" as it starts with known active compounds which are modified using 1–4° chemical replacement in the known lead structure to generate a novel chemotype which is further evaluated using various platforms, including docking, *in vitro*, and *in vivo* studies [27]. In contrast, pseudoreceptor design is a method closely related to homology modeling of SBDD where presumed bioactive conformations of overlaid molecules are used to generate the target's pseudo-binding site map for further SBDD. Hence, this method is a bridge between LBDD and SBDD [28].

Pharmacophore fingerprinting is a method to identify a common "pharmacophore feature" among a set of active drug or lead molecules that may be used in SBDD and/or LBDD. The pharmacophore feature is an essential chemical portion of lead/drug molecules which is required for biological functions and may include hydrogen bond donors/acceptors, aromatic rings, hydrophilic/hydrophobic attachments, or any possible combinations. These features are enumerated in terms of three-point and four-point sets of varied pharmacophores to measure the distance in terms of bonds. Pharmacophore fingerprints thus generated are utilized for developing novel lead molecules in combination with SBDD (**Figure 3**) [26].


Evaluating the Effectiveness of the Strategies for Sustaining Nature-Based Tourism amid Global Health Crises: A Global Perspective

*Thembinkosi Keith Gumede, Antonia Thandi Nzama and Joyce Mnesi Mdiniso*


**4. Conclusions**

**Author Contributions:** Conceptualization, J.Q.; methodology, T.Y.; validation, Y.Z.; formal analysis, Y.L.; investigation, R.T.; resources, W.G.; data curation, J.J.; writing—original draft preparation, J.Q.; writing—review and editing, J.Q. and T.Y.; funding acquisition, J.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Key R&D and Promotion Projects in Henan Province<PHONE_NUMBER>12), the Doctoral Research Fund of North China University of Water Resources and Electric Power (40651), and the Key Project of Science and Technology Research of Henan Education Department (14A170006).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are contained within the article.

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


*4.3. CNN Parameter Adjustment*

We used the stochastic gradient descent method as the parameter adjustment optimization method. Although the convolutional neural network used in this article does not have many layers, its structure belongs to deep learning. Let θ be a parameter in the neural network, the negative conditional log-likelihood of the training data can be expressed by Equation (7):

$$J(\theta) = \frac{1}{m} \sum\_{i=1}^{m} L\left(\mathbf{x}^i, \mathbf{y}^i, \theta\right) \tag{7}$$

where *L* represents the loss function corresponding to the *i* − *th* input, then for these cost functions that you want to add, the gradient descent method needs to be calculated using Equation (8):

$$\nabla\_{\theta} J\_{\theta} = \frac{1}{m} \sum\_{i=1}^{m} \nabla\_{\theta} L\left(\mathbf{x}^{i}, y^{i}, \theta\right) \tag{8}$$

The calculation time complexity of this optimization process is *O*(*m*), where m is the amount of data in the training set. As training increases, this complexity will increase. The core of the stochastic gradient descent method is to perform a small-scale sample approximate estimation. A small batch of training (minibatch) was performed in each step, and the sample size at this time was *m*, and *m* << *m*, and then pass. The results obtained in this part were used to estimate the results of the entire sample so that the amount of calculation will be significantly reduced. The gradient estimation, Equation (9), is as follows:

$$\log = \frac{1}{m'} \nabla\_{\theta} \sum\_{i=1}^{m'} L\left(\mathbf{x}^i, \mathbf{y}^i, \theta\right) \tag{9}$$

Use the mini-batch described above to train and estimate the entire sample using the following gradient estimation algorithm as Equation (10):

$$
\theta - \mathfrak{a}\mathfrak{g} \to \theta
\tag{10}
$$

The small-batch gradient descent method solves the shortcomings of the gradient descent method's time complexity and unreliability of the gradient descent method in the parameter optimization process. Finally, we optimized the parameters of the classification layer and used the evaluation function as Equation (11):

$$f(\theta) = -\frac{1}{m} [\sum\_{i=1}^{m} \sum\_{j=1}^{n} 1\{y\_i = j\} \log(\frac{e^{\theta\_j^T} \mathbf{x}\_i}{\sum\_{k=1}^{n} e^{\theta\_k^T} \mathbf{x}\_i})] \tag{11}$$

The optimization process is the process of minimizing the evaluation function so that the parameter *θ* will reach the optimal value.


**Acknowledgments**

This work was supported in part by EC Framework 7 (FP7) project DAPHNE (www.fp7daphne.eu) − Developing aircraft photonic networks (grant ACP8-GA-2009-233709). We acknowledge funding from FCT and program POCTI/FEDER under the National Plan for Scientific Hardware Renewal with grant REEQ/1272/EEI/2005. D. Coelho also acknowledges support from FCT through a PhD grant.


*Article* **Effects of Taurine Depletion on Body Weight and Mouse Behavior during Development**

**Miho Watanabe 1 , Takashi Ito 2 and Atsuo Fukuda 1,\***

**Abstract:** Taurine (2-aminoethanesulfonic acid) plays an important role in various physiological functions and is abundant in the brain and skeletal muscle. Extracellular taurine is an endogenous agonist of gamma-aminobutyric acid type A and glycine receptors. Taurine actively accumulates in cells via the taurine transporter (TauT). Adult taurine-knockout (*TauT*−/−) mice exhibit lower body weights and exercise intolerance. To further examine the physiological role of taurine, we examined the effect of its depletion on mouse behavior, startle responses, muscular endurance, and body weight during development from postnatal day 0 (P0) until P60. In the elevated plus maze test, *TauT*−/− mice showed decreased anxiety-like behavior. In addition, *TauT*−/− mice did not show a startle response to startle stimuli, suggesting they have difficulty hearing. Wire-hang test revealed that muscular endurance was reduced in *TauT*−/− mice. Although a reduction of body weight was observed in *TauT*−/− mice during the developmental period, changes in body weight during 60% food restriction were similar to wild-type mice. Collectively, these results suggest that taurine has important roles in anxiety-like behavior, hearing, muscular endurance, and maintenance of body weight.

**Keywords:** taurine; taurine transporter; knockout mice; brain; skeletal muscle; body weight; behavior

#### **1. Introduction**

Taurine (2-aminoethanesulfonic acid) is one of the most ubiquitous and abundant amino acids in mammalian tissues [1,2], whereby it plays important roles in various physiological functions [3] such as mitochondrial translation [4], Ca2+ homeostasis [5], cell volume regulation [6], stability of membranes [7], and antioxidant effects [8]. Although the capacity to synthesize taurine is limited in most tissues [9], high levels of intracellular taurine are maintained by the taurine transporter (TauT/SLC6a6) on the membrane [10]. TauT is a Na+ -Cl− ion-dependent transporter expressed ubiquitously in mammalian tissues [10]. The development of TauT-knockout (*TauT*−/−) mice by two laboratories revealed the physiological functions of taurine [11,12]. *TauT*−/− mice exhibit shortened lifespan; cardiomyopathy; aging-dependent cardiac dysfunction; hepatitis; and renal, visual, auditory, and olfactory dysfunction [13–15]. Transcriptome microarray analysis of *TauT*−/− mice revealed that pro-fibrotic genes, such as S100 calcium-binding protein A4 (S100A4), actin alpha 2, smooth muscle (ACTA2), and connective tissue growth factor (CTGF), were increased in *TauT*−/− mice hearts [16]. Based on the transcriptome-pathway analysis, the genes involved in the "organization of extracellular matrix," such as galectin 3 (LGALS3), are enriched in old *TauT*−/− mice hearts, suggesting the contribution of these genes to fibrosis [16].

Taurine is present in high concentrations in the brain [17–19].  Taurine is required for the development of the central nervous system [20–22]. Our prior reports demonstrated that ambient taurine tonically activates gamma-aminobutyric acid type A

**Citation:** Watanabe, M.; Ito, T.; Fukuda, A. Effects of Taurine Depletion on Body Weight and Mouse Behavior during Development. *Metabolites* **2022**, *12*, 631. https://doi.org/10.3390/ metabo12070631

Academic Editors: Walter Wahli and David J. Beale

Received: 22 April 2022 Accepted: 7 July 2022 Published: 9 July 2022

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

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

(GABAA) receptors and slows radial migration in the developing cerebral cortex [23]. In addition, taurine regulates the temporal specification of excitatory glutamatergic neuronal progenitors in the developing cortex [24]. In immature neurons, taurine inhibits the K+ -Cl− cotransporter KCC2, which maintains low intracellular Cl− levels critical for mediating fast-hyperpolarizing synaptic inhibition by GABAA and glycine receptors via the withno-lysine (WNK) protein kinase signaling pathway [25]. However, the effects of taurine depletion on adult mouse behaviors, such as locomotor activity, anxiety-like behavior, and startle response, have not yet been reported.

Taurine also occurs in particularly high concentrations in skeletal muscle [11]. *TauT*−/− mice show a more than 98% reduction in muscle taurine content [12,26]. In addition, *TauT*−/− mice exhibit exercise intolerance in both treadmills and forced swimming tests [26,27], as well as muscular structural defects, such as myofibril derangement and the appearance of autophagic bodies [12,14]. *TauT*−/− mice exhibit accelerated skeletal muscle aging and growth, and differentiation factor 15 (GDF15), which is a member of the transforming growth factor-β superfamily, was elevated during aging in *TauT*−/− mice [28]. A previous report found that adult *TauT*−/− mice fed a normal diet had lower body weights and visceral fat, although the body weights of *TauT*−/− mice fed a diet containing 60% fat increased to a level comparable to that of wild-type (WT) mice [29]. *TauT*−/− mice showed lower fasting blood glucose and were more tolerant against glucose injection [29]. Metabolism of fatty acids was suppressed by the downregulation of genes and enzymes involved in fatty acids β-oxidation [27]. Taurine reduced obesity in WT mice fed a high-fat diet, suppressed increases in adipocyte size, body fat, and body weight [30] and improved insulin sensitivity, and increased energy expenditure and adaptive thermogenesis by elevating the browning of white adipose tissue [31]. Body weight and height of infants at birth were significantly higher in the high taurine intake pregnant women compared to infants in low taurine intake pregnant women [32]. However, changes in body weight of *TauT*−/− mice during development remain unclear.

In this study, to further examine the physiological role of taurine, we used *TauT*−/− mice to investigate the effects of taurine deficiency on body weight changes during development and in adult mice during food restriction. In addition, we measured muscular endurance. Furthermore, to examine the effects of taurine deficiency on behavior, we performed several behavioral analyses.

#### **2. Results**

## *2.1. TauT*−*/*− *Mice Showed Decreased Anxiety-Like Behavior*

We performed a behavioral analysis of *TauT*−/− mice. To assay general locomotor activity levels and anxiety, we performed an open field test. Percentages of time spent in the center (WT, 9.3 ± 3.1%, *n* = 12; *TauT*+/−, 7.4 ± 2.0%, *n* = 12; *TauT*−/−, 4.7 ± 1.4%, *n* = 8) and time spent in the corner (WT, 59.2 ± 5.7%; *TauT*+/−, 61.9 ± 3.7%; *TauT*−/−, 60.4 ± 4.7%) were similar between genotypes (Figure 1A,B). Moreover, no significant difference in the total distance moved was found between genotypes (WT, 3354.1 ± 234.1 cm; *TauT*+/−, 3690.5 ± 214.9 cm; *TauT*−/− mice, 3891.4 ± 582.9 cm; Figure 1C). From these results, *TauT*+/− and *TauT*−/− mice both showed normal locomotor activity. To examine anxiety-like behavior, we performed an elevated plus maze test. The percentage of time spent in the open arm was increased in *TauT*−/− mice compared with WT and *TauT*+/− mice, although there were no significant differences between groups (WT, 13.5 ± 2.3%, *n* = 13; *TauT*+/−, 14.7 ± 4.4%, *n* = 11; *TauT*−/−, 28.6 ± 10.5%, *n* = 11; Figure 1D). The percentage of time spent in the closed arm was significantly decreased in *TauT*−/− mice compared with WT and *TauT*+/− mice (WT, 78.4 ± 3.3%; *TauT*+/−, 77.8 ± 5.8%; *TauT*−/−, 52.8 ± 9.0%). In addition, the percentage of time spent in the center was significantly increased in *TauT*−/− mice compared with WT mice and showed an increased trend compared with WT mice (*p* = 0.06; WT, 8.1 ± 1.4%; *TauT*+/−, 7.4 ± 1.6%; *TauT*−/−, 18.6 ± 3.1%). No significant difference in total distance moved was observed between genotypes (WT, 1329.7 ± 58.2 cm; *TauT*+/−, 1274.9 ± 61.1 cm; *TauT*−/−,

1117.1 ± 67.0 cm; Figure 1E). These results suggest that *TauT*−/− mice showed decreased anxiety-like behavior and had difficulty making decisions underlying approach/avoidance conflict and risk assessment. *TauT*−/−, 1117.1 ± 67.0 cm; Figure 1E). These results suggest that *TauT*−/− mice showed decreased anxiety-like behavior and had difficulty making decisions underlying approach/avoidance conflict and risk assessment.

time spent in the center was significantly increased in *TauT*−/− mice compared with WT mice and showed an increased trend compared with WT mice (*p* = 0.06; WT, 8.1% ± 1.4%; *TauT*+/-, 7.4% ± 1.6%; *TauT*−/−, 18.6% ± 3.1%). No significant difference in total distance moved was observed between genotypes (WT, 1329.7 ± 58.2 cm; *TauT*+/-, 1274.9 ± 61.1 cm;

*Metabolites* **2022**, *12*, x FOR PEER REVIEW 3 of 13

**Figure 1.** (**A**–**C**) In the open field test, mice were placed in the center of the open field apparatus and their movement was tracked for 10 min. The percentage of time spent in the center (**A**), percentage of time spent in the corner (**B**), and total distance moved in an open field (**C**) were measured for WT, *TauT*+/-, and *TauT*−/− mice. (**D**,**E**) In the elevated plus maze test, mice were placed in the center of the elevated plus maze and allowed to explore for 5 min. The percentage of time spent in the open arms, closed arms, and center (\* *p* < 0.05, n.s. = not significant by Kruskal–Wallis test) (**D**) and total distance moved (**E**) in the elevated plus maze were measured. Error bars represent S.E.M. **Figure 1.** (**A**–**C**) In the open field test, mice were placed in the center of the open field apparatus and their movement was tracked for 10 min. The percentage of time spent in the center (**A**), percentage of time spent in the corner (**B**), and total distance moved in an open field (**C**) were measured for WT, *TauT*+/−, and *TauT*−/− mice. (**D**,**E**) In the elevated plus maze test, mice were placed in the center of the elevated plus maze and allowed to explore for 5 min. The percentage of time spent in the open arms, closed arms, and center (\* *p* < 0.05, n.s. = not significant by Kruskal–Wallis test) (**D**) and total distance moved (**E**) in the elevated plus maze were measured. Error bars represent S.E.M.

#### *2.2. TauT−/− Mice Showed Difficulty Hearing 2.2. TauT*−*/*− *Mice Showed Difficulty Hearing*

difficulty hearing.

Next, we performed the startle response test and checked the dependence of startle responses on startle stimulus intensity (60 to 120-dB pulse, WT *n* = 6, *TauT*−/− *n* = 6). WT mice showed a startle response to startle pulses between 90 and 120 dB (Table 1). In total, 50% of WT mice showed a startle response to a startle pulse of 90 dB. All WT mice showed startle responses to startle pulses between 100 and 120 dB. However, no *TauT*−/− mice exhibited a startle response to any startle pulse. From these results, *TauT*−/− mice likely have Next, we performed the startle response test and checked the dependence of startle responses on startle stimulus intensity (60 to 120-dB pulse, WT *n* = 6, *TauT*−/− *n* = 6). WT mice showed a startle response to startle pulses between 90 and 120 dB (Table 1). In total, 50% of WT mice showed a startle response to a startle pulse of 90 dB. All WT mice showed startle responses to startle pulses between 100 and 120 dB. However, no *TauT*−/− mice exhibited a startle response to any startle pulse. From these results, *TauT*−/− mice likely have difficulty hearing.

**Table 1.** Startle response test. Numbers of mice showing a startle response to the startle pulse. **Table 1.** Startle response test. Numbers of mice showing a startle response to the startle pulse.

#### *2.3. Muscular Endurance Was Reduced in TauT*−*/*− *Mice 2.3. Muscular Endurance Was Reduced in TauT−/− Mice*

A previous report suggested that exercise capacity (as determined by a treadmill test) was reduced in *TauT*−/− mice [27]. Therefore, to measure muscular endurance, a wire-hang test was performed. Each mouse was placed on a wire mesh and then turned upside down. The latency to fall was measured. Latency to fall was reduced in *TauT*−/− mice compared with WT mice (WT, 552.6 ± 32.4 s, *n* = 7; *TauT*−/−, 462.6 ± 32.6 s, *n* = 6; Figure 2). These results suggest that muscular endurance was reduced in *TauT*−/− mice. A previous report suggested that exercise capacity (as determined by a treadmill test) was reduced in *TauT*−/− mice [27]. Therefore, to measure muscular endurance, a wire-hang test was performed. Each mouse was placed on a wire mesh and then turned upside down. The latency to fall was measured. Latency to fall was reduced in *TauT*−/− mice compared with WT mice (WT, 552.6 ± 32.4 s, *n* = 7; *TauT*−/−, 462.6 ± 32.6 s, *n* = 6; Figure 2). These results suggest that muscular endurance was reduced in *TauT*−/− mice.

**Figure 2.** Muscular endurance was reduced in *TauT*−/− mice. Wire-hang test was performed. Average latency to fall from the wire mesh of two trials in WT and *TauT*−/− mice. \* *p* < 0.05 by Mann-Whitney test. Error bars represent S.E.M. Closed circles represent animals. **Figure 2.** Muscular endurance was reduced in *TauT*−/− mice. Wire-hang test was performed. Average latency to fall from the wire mesh of two trials in WT and *TauT*−/− mice. \* *p* < 0.05 by Mann-Whitney test. Error bars represent S.E.M. Closed circles represent animals.

#### *2.4. Body Weight Gain Was Reduced in TauT−/− Mice during Development 2.4. Body Weight Gain Was Reduced in TauT*−*/*− *Mice during Development*

Adult *TauT*−/− mice showed reduced body weights [29], so we examined changes in body weight during development from postnatal day 0 (P0) until P60. The body weight of each mouse was measured every 2 days. At birth, there were no differences in body weights between genotypes for either male (WT, 1.39 ± 0.05 g, *n* = 15; *TauT*+/-, 1.41 ± 0.03 g, *n* = 39; *TauT*−/−, 1.35 ± 0.02 g, *n* = 13) or female (WT, 1.37 ± 0.03 g, *n* = 23; *TauT*+/-, 1.38 ± 0.02 g, *n* = 39; *TauT*−/−, 1.38 ± 0.03 g, *n* = 16) mice (Figure 3A,B). However, body weights of male *TauT*−/− mice were significantly lower compared with male WT and *TauT*+/- mice between P4 onward (WT, *n* = 12; *TauT*+/-, *n* = 32; *TauT*−/−, *n* = 12) (Figure 3C). In addition, a reduction in body weights of female *TauT*−/− mice was observed between P10 onward (WT, *n* = 16; *TauT*+/-, *n* = 32; *TauT*−/−, *n* = 15) (Figure 3D). The difference in body weight seems to become apparent around P20 when mice start self-feeding. Notably, the difference in body weights between WT and *TauT*−/− mice was greater in male mice than in female mice. Adult *TauT*−/− mice showed reduced body weights [29], so we examined changes in body weight during development from postnatal day 0 (P0) until P60. The body weight of each mouse was measured every 2 days. At birth, there were no differences in body weights between genotypes for either male (WT, 1.39 ± 0.05 g, *n* = 15; *TauT*+/−, 1.41 ± 0.03 g, *n* = 39; *TauT*−/−, 1.35 ± 0.02 g, *n* = 13) or female (WT, 1.37 ± 0.03 g, *n* = 23; *TauT*+/−, 1.38 ± 0.02 g, *n* = 39; *TauT*−/−, 1.38 ± 0.03 g, *n* = 16) mice (Figure 3A,B). However, body weights of male *TauT*−/− mice were significantly lower compared with male WT and *TauT*+/− mice between P4 onward (WT, *n* = 12; *TauT*+/−, *n* = 32; *TauT*−/−, *n* = 12) (Figure 3C). In addition, a reduction in body weights of female *TauT*−/− mice was observed between P10 onward (WT, *n* = 16; *TauT*+/−, *n* = 32; *TauT*−/−, *n* = 15) (Figure 3D). The difference in body weight seems to become apparent around P20 when mice start selffeeding. Notably, the difference in body weights between WT and *TauT*−/− mice was greater in male mice than in female mice.

**Figure 3.** *TauT*−/− mice exhibited lower body weights during development. (**A**) Body weights of male WT, *TauT*+/-, and *TauT*−/− pups at P0. Closed circles represent animals. (**B**) Body weights of female WT, *TauT*+/-, and *TauT*−/− pups at P0. (**C**) Changes in body weights of male WT, *TauT*+/-, and *TauT*−/− mice during development. (**D**) Changes in body weights of female WT, *TauT*+/-, and *TauT−/−* mice during development. Error bars represent S.E.M. \* *p* < 0.05 *TauT*−/− vs. WT, \*\* *p* < 0.01 *TauT*−/− vs. WT, † *p* < 0.05 *TauT*−/− vs. WT and *TauT*+/-, ‡ *p* < 0.01 *TauT*−/− vs. WT and *TauT*+/-, †† *p* < 0.05 *TauT*−/− vs. *TauT*+/-, ‡‡ *p* < 0.01 *TauT*−/− vs. *TauT*+/-, and a *p* < 0.05 *TauT*+/- vs. WT by Kruskal-Wallis test. *2.5. Body Weight Changes during 60% Food Restriction Were Similar in WT and TauT−/− Mice*  **Figure 3.** *TauT*−/− mice exhibited lower body weights during development. (**A**) Body weights of male WT, *TauT*+/−, and *TauT*−/− pups at P0. Closed circles represent animals. (**B**) Body weights of female WT, *TauT*+/−, and *TauT*−/− pups at P0. (**C**) Changes in body weights of male WT, *TauT*+/−, and *TauT*−/− mice during development. (**D**) Changes in body weights of female WT, *TauT*+/−, and *TauT*−/− mice during development. Error bars represent S.E.M. \* *p* < 0.05 *TauT*−/− vs. WT, \*\* *p* < 0.01 *TauT*−/− vs. WT, † *p* < 0.05 *TauT*−/− vs. WT and *TauT*+/−, ‡ *p* < 0.01 *TauT*−/− vs. WT and *TauT*+/−, †† *p* < 0.05 *TauT*−/− vs. *TauT*+/−, ‡‡ *p* < 0.01 *TauT*−/− vs. *TauT*+/−, and a *p* < 0.05 *TauT*+/− vs. WT by Kruskal-Wallis test.

#### A previous report showed that the weight of visceral fat was significantly less in *2.5. Body Weight Changes during 60% Food Restriction Were Similar in WT and TauT*−*/*− *Mice*

*TauT*−/− mice compared with WT mice; however, when fed a high-fat diet, *TauT*−/− mice exhibited body weights comparable to WT mice and dramatically increased abdominal fat after 16 weeks [29]. Therefore, we examined the effect of a 60% food restriction. Mice were given a food pellet equal to 60% of daily food intake before the onset of the dark phase for 2 weeks. Subsequently, mice were given food *ad libitum* for 2 weeks. Body weights were measured every day. Body weights were markedly decreased during the first 5 days after starting 60% food restriction, then gradually decreased in both WT and *TauT*−/− mice (WT, *n* = 7; *TauT*−/−, *n* = 7; Figure 4A,B). After WT and *TauT*−/− mice were given food *ad libitum*; their body weights increased in both WT and *TauT*−/− mice. Indeed, the remarkable weight loss observed during the first 5 days after starting 60% food restriction gradually recovered in both WT and *TauT*−/− mice (Figure 4C). On the first day, mice were given food *ad libitum*; the weight gain was significantly lower in *TauT*−/− mice (2.9 ± 0.1 g) compared with WT mice (4.2 ± 0.2 g). Thus, *TauT*−/− mice showed an apparent disturbance of body weight recovery after food restriction compared with WT mice, although their body weight reduction during food restriction was comparable. A previous report showed that the weight of visceral fat was significantly less in *TauT*−/− mice compared with WT mice; however, when fed a high-fat diet, *TauT*−/− mice exhibited body weights comparable to WT mice and dramatically increased abdominal fat after 16 weeks [29]. Therefore, we examined the effect of a 60% food restriction. Mice were given a food pellet equal to 60% of daily food intake before the onset of the dark phase for 2 weeks. Subsequently, mice were given food *ad libitum* for 2 weeks. Body weights were measured every day. Body weights were markedly decreased during the first 5 days after starting 60% food restriction, then gradually decreased in both WT and *TauT*−/− mice (WT, *n* = 7; *TauT*−/−, *n* = 7; Figure 4A,B). After WT and *TauT*−/− mice were given food *ad libitum*; their body weights increased in both WT and *TauT*−/− mice. Indeed, the remarkable weight loss observed during the first 5 days after starting 60% food restriction gradually recovered in both WT and *TauT*−/− mice (Figure 4C). On the first day, mice were given food *ad libitum*; the weight gain was significantly lower in *TauT*−/− mice (2.9 ± 0.1 g) compared with WT mice (4.2 ± 0.2 g). Thus, *TauT*−/− mice showed an apparent disturbance of body weight recovery after food restriction compared with WT mice, although their body weight reduction during food restriction was comparable.

**Figure 4.** Changes in body weights following 60% food restriction in WT and *TauT*−/− mice. (**A**) Changes in body weight after 60% food restriction and recovery (food *ad libitum*) in WT mice and *TauT*−/− mice. (**B**) Body weight changes are shown as a percentage of the initial body weight before 60% food restriction. (**C**) Body weight loss or gain after 60% food restriction and recovery (food *ad*  **Figure 4.** Changes in body weights following 60% food restriction in WT and *TauT*−/− mice. (**A**) Changes in body weight after 60% food restriction and recovery (food *ad libitum*) in WT mice and *TauT*−/− mice. (**B**) Body weight changes are shown as a percentage of the initial body weight before 60% food restriction. (**C**) Body weight loss or gain after 60% food restriction and recovery (food *ad libitum*) in WT mice and *TauT*−/− mice. Error bars represent S.E.M. \* *p* < 0.05, \*\* *p* < 0.01 by Kruskal-Wallis test.

#### *2.6. Comprehensive Analysis of Protein Kinases and Target Proteins*

Metabolic processes are commonly regulated by a series of phosphorylation events by kinases. Therefore, we performed an active site-directed competition binding assay to quantitatively measure interactions between kinases and target proteins to compare differences between WT and *TauT*−/− mice. Nineteen proteins showed significant changes in expression levels between WT and *TauT*−/− mice. Moreover, three proteins exhibited significant changes in the phosphorylation of analyzed sites (Table S1). The most prominent protein alteration was phosphorylation of signal transducer and activator of transcription 3 (STAT3) at tyrosine 705, which was increased by 9449% in the brains of *TauT*−/− mice compared with WT mice.

#### **3. Discussion**

We have shown that taurine deficiency in mice decreased anxiety-like behaviors, abolished startle responses, and reduced muscular endurance. Furthermore, taurine deficiency caused lower body weight gain during development. Our findings demonstrate the importance of taurine in brain function, muscular endurance, and energy metabolism.

Our results show that *TauT*−/− mice exhibited decreased anxiety-like behaviors, as indicated by reduced time in the closed arm of the elevated plus maze test and increased time spent in the center square. Time spent exploring the center square has been suggested to reflect decision-making underlying approach-avoidance conflict and risk assessment. Although *TauT*−/− mice exhibit visual dysfunction due to retinal degeneration [11] and hearing loss [13], the elevated plus maze test is based on mouse exploratory patterns that avoid open spaces and are motivated by thigmotaxis; the tendency to be close to vertical surfaces is far more reliant on tactile inputs compared with vision [33].  Taurine is present at high concentrations in the brain [11], whereby it acts as a partial agonist of GABAA [34] and glycine receptors [35]. Although the physiological roles of taurine in the brain are still not fully understood, taurine is required for normal nervous system development [22]. In the developing brain, the taurine concentration is 3–4 times higher than present in the adult brain [18]. After the first postnatal week, taurine concentrations are downregulated [18]. Kittens from taurine-deficient mothers exhibit smaller brain weights and abnormal morphologies in the visual cortex [36] and cerebellum [37]. Ambient taurine in the mouse developing fetal cerebral cortex tonically activates GABAA receptors and slows radial migration [23]. In addition, taurine increases the proliferation of neural stem cells [24,38]. Taurine modulates the switch of GABA responses from depolarizing to hyperpolarizing by suppressing KCC2 in the embryonic brain via WNK kinase signaling [25]. In the adult brain, taurine also activates extrasynaptic GABAA receptors in the thalamus [39]. *TauT*−/− mice show impaired GABAergic inhibition in the striatum [40]. Therefore, taurine deficiency in the brain may cause structural deficits or an imbalance in inhibitory and excitatory neurotransmission in neuronal circuits controlling anxiety behavior, thereby causing abnormal behavior in *TauT*−/− mice.

*TauT*−/− mice did not show startle responses to startle stimuli. Taurine is present at high concentrations in the organ of Corti [41] and inferior colliculus [42]. Since taurine concentrations are relatively high in glia [43], *TauT* knockout in glia cells may exacerbate degeneration of auditory nerve fibers, resulting in hearing loss [13]. Six-month-old *TauT*−/− mice reportedly show 20-dB higher auditory brainstem response thresholds due to accelerated losses of outer and inner hair cells [13,44]. Taurine reduces neuronal activity via glycine receptors in the rat inferior colliculus [45]. In addition, taurine serves as a neuromodulator to enhance GABAergic and glycinergic transmission in the rat anteroventral cochlear nucleus [46]. Therefore, taurine may regulate neurotransmission of the central auditory system and its deficiency appears to cause difficulty hearing. The auditory brainstem response should be measured to precisely show *TauT*−/− mice exhibit hearing loss. Further experiment is needed at this point.

*TauT*−/− mice exhibited reduced body weights during the developmental period, although no difference was observed at birth. *TauT*−/− mice reportedly show lower body weights and less abdominal fat at 3 months, although both food and water intake were unchanged [12,29]. *TauT*−/− mice also exhibit lower weights of heart and tibial anterior muscle [12], as well as lower fasting blood glucose levels and tolerance against glucose injection [29]. Skeletal muscle lactate content is higher in *TauT*−/− mice than in WT mice [15], suggesting that the glycolytic pathway may be accelerated in *TauT*−/− mice. Acceleration of glycolysis can enhance glucose disposal from blood. In addition, several genes involved in the oxidation of fatty acids are downregulated in *TauT*−/− mice [15], suggesting that their fatty acid utilization is reduced. Accordingly, glucose disposal may be enhanced in *TauT*−/− mice as a compensatory mechanism. Therefore, imbalances in energy production and expenditure likely cause a reduction in body weight during development. Plasma insulin levels were lower in *TauT*−/− mice compared with WT mice due to decreased numbers of beta cells in the pancreas [29]. Insulin promotes the conversion of glucose into fat. Therefore, lower insulin levels may reduce the conversion of glucose into fat, which may cause reductions in fat and body weight in *TauT*−/− mice during development. In our comprehensive analysis of protein kinases and target proteins, STAT3 phosphorylation was significantly increased in *TauT*−/− mouse brains. Leptin, a hormone synthesized and released from adipose tissue, plays a central role in the long-term control of body weight, acting mainly in the central nervous system to induce satiety, energy expenditure, and glycolysis through the STAT3 pathway [47,48]. Leptin binding to the long isoform of leptin receptor (LepRb) results in its dimerization, leading to the formation of the LepRb/Janus kinase 2 (JAK2) complex. The activated JAK2 phosphorylates itself and also Tyr985, Tyr1077, and Tyr1138 in LepRb. STAT3 binds to phospho-Tyr1077 in LepRb and are subsequently phosphorylated. Active STAT3 dimers then translocate to the nucleus and activate the transcription of their target genes, which mediate leptin's anorexigenic effect. Therefore, there is a possibility that enhanced leptin signaling causes body weight and fat reduction in *TauT*−/− mice during development. Taurine depletion did not affect body weight loss during 60% food restriction. Significant body weight reduction in *TauT*−/− mice compared to WT mice was still observed during the food restriction and recovery period. *TauT*−/− mice exhibited a significant reduction in body weight gain compared to WT mice on the first day of *ad libitum* food after food restriction. Phosphorylated STAT3 activates anorexigenic neurons that express proopiomelanocortin while inhibiting orexigenic neurons that express Agouti-related protein, thereby inhibiting food intake and increasing energy expenditure [47]. Combined with a previous report of *TauT*−/− mice showing reduced abdominal fat weights [29], the present results show that extremely high levels of STAT3 phosphorylation may activate leptin's anorexigenic effect, thereby reducing food intake in *TauT*−/− mice even on the first day of *ad libitum* food after food restriction and disturb body weight recovery. The difference in body weights between WT and *TauT*−/− mice was greater in male mice compared with female mice. The mechanism of these sex differences is unclear, but a previous paper showed that taurine supplementation increased the level of testosterone in male rats [49], that increased skeletal muscle. Therefore, there is a possibility that taurine depletion may reduce testosterone levels, which leads to a reduction of muscle mass, and results in body weight loss, especially in male mice. Further studies are needed to show the sex differences in weight regulation by taurine.

Taurine contents in skeletal muscle are high [11,12]. *TauT*−/− mice reportedly show reduced swimming endurance times [12], as well as decreased maximum running speed and endurance duration in treadmill running tests [26,27], suggesting they exhibit exercise intolerance. Notably, taurine supplementation prolonged the time to exhaustion during treadmill running [50]. *TauT*−/− mice show decreased myofiber size and myofiber disruption [12]. We previously performed LC-MS-based metabolome analysis and suggested that organic osmolytes such as betaine, glycerophosphocholine, and amino acids were increased in the heart muscle of *TauT*−/− mice by compensating mechanisms for taurine depletion [51], although the expression of related gene expressions, such as betaine/GABA transporter-1 (BGT-1; Slc6a12), carnitine transporter Slc22a4 and Slc22a5 are not changed [52]. The mRNA expressions of heat shock protein 700 (Hsp70), the amino acid transporters (Slc38a2; ATA2), DnaJ (Hsp40) homolog, subfamily B, member 1 (Dnajb1), tumor necrosis factor receptor superfamily, member 12a (Tnfrsf12a), and S100 calciumbinding proteins (S100A4, S100A9), which are upregulated by osmotic stress, are elevated in both heart and skeletal muscle of the *TauT*−/− mice [15], suggesting that dysfunction of osmoregulatory system impairs the tibial anterior muscle volume regulation. Taurine depletion reduced muscular endurance in this study, supporting previous findings of an important role for taurine in the maintenance of skeletal muscle function. Furthermore, STAT3 signaling plays important roles in regulating skeletal muscle mass, repair, and diseases [53]. Chronic activation of the interleukin 6/JAK/STAT3 signaling pathway is involved in muscle wasting. Activated STAT3 promotes skeletal muscle atrophy in muscle diseases, such as muscular dystrophy, cancer, and sepsis [54,55]. Therefore, very high levels of STAT3 phosphorylation may cause skeletal muscle abnormalities, thereby decreasing muscle endurance in *TauT*−/− mice.

The limitation of this study is that we used whole-body *TauT* knockout mice. Since taurine is ubiquitous and is abundant free amino acid in the heart, retina, skeletal muscle, brain, and leukocytes [1,2], whole body *TauT* knockout causes various effects. Therefore, to demonstrate the functional role of taurine in specific tissue precisely, we need to generate tissue-specific *TauT* knockout mice. If we knockout *TauT* in a specific region of the brain, we can demonstrate the role of taurine in the behavior, excluding the effects of vision dysfunction and hearing loss. If we generate the time-specific *TauT* knockout mice, we can demonstrate the role of taurine at specific time points during development and in the adult mice. Further studies are needed to determine the precise role of taurine using cell/tissueand time-specific *TauT* knockout mice. We have shown the effects of taurine depletion on anxiety-like behavior, startle response, muscular endurance, and body weight during development. However, the detailed mechanism by which taurine depletion causes such effects remains unclear. We have shown that *TauT*−/− mice did not show startle responses. This result suggests that *TauT*−/− mice might have hearing difficulty. However, we need to perform the auditory brainstem response test to confirm hearing difficulty in *TauT*−/− mice and to locate hearing deficits along the auditory nerve pathway. Because *TauT*−/− mice showed degeneration in the inner ear and auditory nerve, it is speculated that taurine has a role in the regulating survival of cells related to auditory function. Since taurine also acts as a partial agonist of GABAA and glycine receptors, taurine might have a role in modulating the neurotransmission in the auditory nerve tract. Further studies are needed to show the underlying mechanisms of our results.

In conclusion, the present study suggests that taurine plays important roles in the maintenance of mouse behavior, hearing, body weight, and skeletal muscle function.

#### **4. Materials and Methods**

#### *4.1. Animals*

TauT-KO and littermate mice (C57BL/6 background) were used. The generation of TauT KO-mice was previously described [12]. Heterozygous TauT-KO male and female mice were mated, and homozygous TauT-KO (*TauT*−/−, male *n* = 51, female *n* = 17), heterozygous TauT-KO (*TauT*+/−, male *n* = 62, female *n* = 39), and wild-type (WT, male *n* = 60, female *n* = 24) mice were used for experiments. Genotyping for TauT-KO mice was carried out by PCR of mouse tail DNA using the following primers: 5 0 -GGTGTCACTAAGACGTGAGTTG-30 , 50 -CAGACAGCACAAAGTCGATCT-30 , and 5 0 -TGCTAAAGCGCATGCTCCAGACTG-30 . Mice were kept in a 12-h light/dark cycle (lights on at 07:00 a.m.) with access to food and water *ad libitum* unless otherwise noted. All experimental procedures were approved by the institutional animal care and use committee of Hamamatsu University School of Medicine. With regard to the ethical use of animals for experimentation, all efforts were made to minimize the number of animals used and their suffering.

#### *4.2. Measurements of Body Weight and Food Restriction*

Body weights of male and female mice aged P0–P60 were measured once every 2 days between 16:00 and 19:00 h. For the food-restriction experiment, daily food consumption of WT mice was measured for 10 days and average food consumption per gram of body weight was calculated. After 7 days of individual housing for acclimation, mice were given a food pellet equal to 60% of their average daily food intake. Body weights were measured between 16:00 and 19:00 h for 14 days during 60% food restriction. After food restriction, mice were allowed free access to food, and body weights were measured for an additional 14 days to observe recovery.

#### *4.3. Behavioral Analysis*

For the open field test, the size of the open field box was 42 × 42 × 40 cm. Mice were placed in the center of the chamber at the beginning of each test. The movement of each mouse was tracked for 10 min. Locomotor activity was recorded and analyzed using a video tracking system (SMART 3.0; Panlab, Cornellà de Llobregat, Spain). For the elevated plus maze test, we used an elevated plus maze apparatus comprising a central part (6 × 6 cm), two opposing open arms (40 × 6 cm), and two opposing closed arms (40 × 6 × 15 cm). The apparatus was raised 40 cm above the floor. Mice were placed in the center of the apparatus facing the closed arm and allowed to explore the maze for 5 min freely. Time spent and distance traveled in the center, open arms, and closed arms were measured using a video tracking system. For the acoustic startle response test, a startle box system (Panlab, Cornellà de Llobregat, Spain) containing a sound generator and accelerometer was used to record the amplitude of each mouse's startle response. Mice were placed into a plexiglass cylinder and set on a weight-transducing platform in the sound-attenuating chamber. A white background noise (70 dB) was generated throughout the experiment. The acoustic startle response test began with 5 min of acclimatization. Mice were randomly exposed to startle stimuli (from 60 to 120 dB, 10-dB increments, 40-ms duration) with a variable (10–20 s) inter-stimulus interval to avoid habituation. Each sound intensity was presented six times. The peak amplitude of the startle response was measured.

#### *4.4. Kinexus Protein Microarray*

Brains from WT and *TauT*−/− mice were cut into small pieces and rinsed in ice-cold phosphate-buffered saline to remove the blood and other contamination on the surface. Next, brains were wrapped in tinfoil and snap-frozen in liquid nitrogen. Subsequently, brains were shipped in dry ice to Kinexus Bioinformatics Corporation (Vancouver, BC, Canada) for Kinexus KAM-880 Antibody Microarray, which includes 518 pan-specific (for expression levels of these phosphoproteins) and 359 phosphorylation-site-specific antibodies (for phosphorylation). Briefly, 50 g of cell lysate from each sample was covalently labeled with a fluorescent dye. Free dye molecules were then removed at the completion of labeling reactions by gel filtration. After blocking non-specific binding sites, an incubation chamber was mounted onto the microarray to permit the loading of WT and *TauT*−/− samples side by side on the same chip and to prevent mixing of the samples. Following sample incubation, unbound proteins were washed away. Each array produced a pair of 16-bit images, which were captured with a Perkin-Elmer ScanArray Reader laser array scanner (Waltham, MA, USA). Signal quantification was performed with ImaGene 8.0 from BioDiscovery (El Segundo, CA, USA), using the predetermined settings for spot segmentation and background correction. The resultant changes in spot intensity are expressed as the percentages of change for *TauT*−/− mice compared with WT mice [% change from control (% CFC) = [(globally normalized signal intensity of *TauT*−/− sample /globally normalized signal intensity of WT sample) × 100] − 100]. Changes of 50% CFC were considered significant changes. Globally normalized signal intensity is the background-corrected intensity value.

#### *4.5. Statistical Analysis*

Data are presented as mean ± standard error of the mean (SEM). Comparisons between the two groups were made using the Mann–Whitney test. Multiple comparisons were made using Kruskal–Wallis one-way ANOVA followed by Dunn's multiple comparison test. Statistical significance was defined as *p* < 0.05. All statistical analyses were performed using GraphPad Prism (version 9.2.0; GraphPad Software, San Diego, CA, USA).

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/metabo12070631/s1, Table S1: Effects of taurine depletion on protein expression and phosphorylation by protein microarray analysis.

**Author Contributions:** Conceptualization, M.W. and A.F.; methodology, M.W., T.I., and A.F.; validation, M.W. and A.F.; investigation, M.W.; data curation, M.W.; writing–original draft preparation, M.W. and A.F.; writing—review and editing, M.W. and A.F.; visualization, M.W. and A.F.; supervision, A.F.; project administration, A.F.; funding acquisition, A.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by Grants-in-Aid for Scientific Research (B) (#21H02661 to A.F.), Grants-in-Aid for Transformative Research Area "Inducing lifelong plasticity (iPlasticity) by brain rejuvenation: elucidation and manipulation of critical period mechanisms" (#21H05687 to A.F.), Smoking Research Foundation (to A.F.), The Salt Science Research Foundation (to A.F.), Grants-in-Aid for Scientific Research (C) (#20K06924 to M.W.), and The Japan Epilepsy Research Foundation (to M.W.).

**Institutional Review Board Statement:** The animal study protocol was approved by the Institutional Review Board of Hamamatsu University School of Medicine (protocol code 2-53 and 2021.4.27).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data are contained within the article.

**Acknowledgments:** We thank Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript (accessed on 21 April 2022).

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

#### **References**