Effects of Bushen-Yizhi formula on age-related inflammation and oxidative stress in senescence-accelerated mice

The present study aimed to investigate the possible effects and underlying molecular mechanism of Bushen-Yizhi formula (BSYZ), a traditional Chinese medicine, on age-related degeneration of brain physiology in senescence-accelerated mouse prone 8 (SAMP8) mice. SAMP8 mice (age, 6 months) were administered BSYZ (1.46, 2.92 and 5.84 g/kg/day) for 30 days. Morris water maze and step-down tests demonstrated that BSYZ significantly improved memory impairments in SAMP8 mice. In addition, BSYZ significantly enhanced the expression levels of peroxisome proliferator-activated receptor-γ and B-cell lymphoma extra-large, and downregulated the expression levels of inflammatory mediators, glial fibrillary acidic protein, cyclooxygenase-2, nuclear factor-κB and interleukin-1β in the brain compared with untreated SAMP8 mice. Furthermore, BSYZ reversed disordered superoxide dismutase activity, malondialdehyde content and glutathione peroxidase activity, and ameliorated apoptosis and histological alterations. The present study indicated that BSYZ may attenuate cognitive impairment in SAMP8 mice, and modulate inflammation, oxidative stress and neuronal apoptosis. These results suggested that BSYZ may have the potential to be further developed into a therapeutic agent for protection against age-related neurodegenerative diseases.

Introduction
Age-related brain health decline is an important risk factor for neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), which are characterized by cognitive impairment, behavioral alterations and motor dysfunction. Inflammation contributes to cognitive decline, and abnormalities in brain structure and metabolism (1). Astrocytes serve an important role in modulating inflammation of the central nervous system. Astrocytosis in the brain may be an early phenomenon in AD development (2,3), and increased expression of glial fibrillary acidic protein (GFAP) by astrocytes has been reported to be associated with an AD-like pathology (4,5). Reactive astrocytes surrounding senile plaques may be responsible for the ongoing inflammatory process in AD via the release of cytokines and other toxic products (6), including interleukin (IL)-1β (7,8) and IL-6 (9). Cyclooxygenase (COX)-2 is a rate-limiting enzyme involved in the production of prostaglandins, which is also involved in inflammatory mechanisms. The increase of neuronal COX-2 signaling in the hippocampus may be an indicator of dementia progression in early AD (10). Furthermore, the activation of COX-2 may be induced by IL-1β and other cytokines (11,12). These inflammatory mediators may finally activate the nuclear factor (NF)-κB signaling pathway (13). Conversely, agonists of peroxisome proliferator-activated receptor (PPAR)-γ reduced COX-2 protein expression, NF-κB activation and oxidative stress (14).
Previous studies have suggested that oxidative stress is a prominent feature in patients with mild cognitive impairment (MCI) and AD (15)(16)(17), which exhibit high levels of the product of lipid peroxidation [malondialdehyde (MDA)] and low activities of antioxidants [superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px)]. In addition, oxidative stress is associated with the development of neuronal death and neural dysfunction in AD and PD, which are two common age-related neurodegenerative diseases (18). In addition, patients with PD exhibited higher levels of inflammatory markers compared with control individuals (19,20). Therefore, inhibiting inflammation and oxidative stress may be an important strategy for the treatment of age-related neurodegenerative diseases.

Effects of Bushen-Yizhi formula on age-related inflammation and oxidative stress in senescence-accelerated mice
Bushen-Yizhi formula (BSYZ) is a Chinese medical compound, which acts via numerous mechanisms. Our previous studies have demonstrated that BSYZ may attenuate cognitive impairment in AD-like animal models, and affect the modulation of numerous targets (21,22). The present study aimed to investigate the effects of BSYZ on learning and memory abilities in senescence-accelerated mouse prone 8 (SAMP8) mice, and examined the underlying molecular mechanisms involved in age-related alterations, including inflammation, oxidative stress and neuronal apoptosis, in the brains of mice.

Materials and methods
Preparation of BSYZ extracts. BSYZ extracts were provided by the School of Chinese Materia Medica, Guangzhou University of Chinese Medicine (Guangzhou, China), as described previously (22). The doses of BSYZ extracts were expressed as the weight of original raw herbs in grams per kilogram of body weight.
Animals and housing. Male SAMP8 mice (age, 3 months; weight, 35±5 g; n=48), and age-matched senescence-accelerated mouse resistant 1 (SAMR1) mice (weight, 35±5 g; n=12) were purchased from the Animal Research Center of Peking University (Beijing, China). All mice were housed at the Experimental Animal Center of Guangzhou University of Chinese Medicine under a controlled temperature (20±2˚C), a relative humidity of 55±2% and a 12-h light/dark cycle with ad libitum access to food and water. Experimental protocols were approved by the Animal Ethics Committee of Guangzhou University of Chinese Medicine, and experiments were performed in compliance with the Guide for the Care and Use of Laboratory Animals (https://grants.nih.gov/grants/olaw/guidefor-the-care-and-use-of-laboratory-animals.pdf).
Behavioral tests. After 30 days of drug administration, mice were acclimated to the environment of the behavioral test room for 3 days. Morris water maze test and step-down test were performed to investigate the learning and memory abilities of mice.
Morris water maze test. The equipment for the Morris water maze test consisted of a black circular pool (diameter, 120 cm; height, 40 cm) and a camera monitor linked to video-tracking software Behavior Sys (Guangzhou Feidi Biological Technology Co., Ltd., Guangzhou, China). The test was performed as described by Chen et al (24) with minor modifications. Briefly, the test consisted of two phases: Hidden-platform training and spatial probe trial. During hidden-platform training, the platform (diameter, 8 cm) was located in one of four equal quadrants of the pool and submerged 1 cm below the surface of the water. The water was 30 cm in depth, 22-26˚C and made opaque with non-toxic white paint. Mice were given four trials a day (120 sec each) for 5 days. The time to find the platform (the escape latency) was recorded. If the mouse failed to find the platform within 120 sec, the escape latency was recorded as 120 sec, and the mouse was directed to the platform. Mice were allowed to stay on the platform for 20 sec after finding or being guided to the platform. At the end of the daily trial, mice were dried and returned to their home cages. On the sixth day, memory was assessed via a spatial probe trial where the platform was removed. Each mouse was given only one trial for 120 sec from the starting point opposite to the platform location. The number of mice crossing the previous platform position and time spent in the target quadrant were recorded.
Step-down test. The apparatus for the step-down test consisted of a plastic testing box (12x12x18 cm) and a rubber platform placed in a corner of the box (Guangzhou Feidi Biological Technology Co., Ltd.). The step-down test was conducted as described by Zhu et al (25). The number of errors and the latency were recorded using video tracking software (Guangzhou Feidi Biological Technology Co. Ltd. China) to measure memory learning and retention.
Brain sections and tissue preparation. Following performance of the behavioral tests, 3 mice per group were used for preparation of brain sections, according to a previously described protocol (22). Brain coronal sections were taken for immunohistochemistry and histological examination. The remaining mice were anesthetized (sodium pentobarbital, 50 mg/kg body weight) and decapitated; the brains were rapidly removed, divided into two parts (the hippocampus and cortex) on ice and stored at -80˚C. The cortexes and hippocampi were used for biochemical analysis and reverse transcription-quantitative polymerase chain reaction (RT-qPCR), respectively.
Immunohistochemistry. Immunohistochemical staining was performed as previously described (22

Biochemical analysis.
To determine MDA content, SOD and GSH-Px activities, the cortexes of mice were homogenized with 0.9% saline (10 ml/g sample weight) on ice and centrifuged at 3,000 x g for 10 min at 4˚C. The supernatant was used for analysis according to the kits protocols (A003-1, A001-1-1 and A005 for MDA, SOD and GSH-Px, respectively; Nanjing Jiancheng Bioengineering Institute, Nanjing, China,). Absorbance (532, 550 and 420 nm wavelength for MDA, SOD and GSH-Px, respectively) was measured using a universal microplate spectrophotometer (Bio-Rad Laboratories, Inc., Hercules, CA, USA).
Statistical analysis. SPSS version 17.0 software (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. Differences in escape latency in the Morris water maze test were analyzed by a repeated measures analysis of variance, whereas the remaining data was analyzed by one-way analysis of variance or a nonparametric test (Kruskal-Wallis test) where appropriate. Data are expressed as the mean ± standard error (experiments were repeated at least three times, except the behavioral tests). The least significant difference post hoc test was used to establish significance between individual groups. P<0.05 was considered to indicate a statistically significant difference.

Results
Effects of BSYZ on learning and memory of SAMP8 mice in the Morris water maze test. Spatial learning and memory in the SAMP8 mice was determined using the Morris water maze test. In the hidden-platform training phase, there were significant differences in escape latency between the groups (F=4.612, P=0.003); however, there no significant difference was observed among training days (F=0.164, P=0.957) or in the interactive effect between group and training day (F=1.549, P=0.086). Post hoc analysis revealed that the escape latency in the SAMP8 group was significantly greater than the SAMR1 group (day 1-2, P<0.05; day 3-5, P<0.01). Following treatment with low dose BSYZ, the escape latency on day 5 was significantly reduced compared with the SAMP8 group (P<0.05). No significant difference in escape latency was observed between the M-BSYZ/H-BSYZ and SAMP8 groups (Fig. 1A).
In the spatial probe trial phase, there were significant differences in the times crossing the platform location (Kruskal-Wallis Test: χ 2 =21.706, P<0.001) and the time spent in the target quadrant (F=2.879, P=0.032) among the groups. The time crossing the platform in the SAMP8 group was significantly reduced compared with the SAMR1 group (P<0.01; Fig. 1B). A similar result was observed for time spent in the target quadrant between SAMP8 and SAMR1 groups (P<0.01; Fig. 1C). Compared with the SAMP8 mice, in the L-BSYZ treatment group, the time spent in the target quadrant was significantly increased ( Fig. 1C; P<0.05); however, the time crossing the platform between these two groups was not significantly different (Fig. 1B).
Effects of BSYZ on learning and memory of SAMP8 mice in the step-down test. Another method of assessing learning is measuring passive avoidance using a step-down test. There were significant differences in the number of errors made among the groups (F=3.313, P=0.018). The number of errors in the SAMP8 group was significantly increased compared with the SAMR1 group (P<0.01; Fig. 2A). Compared with the SAMP8 group, administration of BSYZ at low and middle doses significantly reduced the number of errors (P<0.05; Fig. 2A). No significant difference was observed in latency between the groups (F=2.185, P=0.085; Fig. 2B).
Effects of BSYZ on GFAP expression in the brains of SAMP8 mice. Immunohistochemical staining of GFAP was performed in brain sections from the mice (Fig. 3A). There were significant differences in GFAP expression in the hippocampus (F=62.220, P<0.001) and cortex (F=3.724, P=0.042) among the groups. GFAP expression in the SAMP8 group was significantly increased in the hippocampus and cortex compared with the SAMR1 group (P<0.01; Fig. 3B). BSYZ treatment significantly reduced GFAP expression in the brain compared with the SAMP8 group (hippocampus: All P<0.01; cortex: P<0.05 for L-BSYZ and M-BSYZ groups; Fig. 3B).
Effects of BSYZ on COX-2 expression in brains of SAMP8 mice. Immunohistochemical staining of COX-2 was performed in brain sections from the mice (Fig. 4A). There were significant differences in COX-2 expression in the hippocampus (F=4.413, P=0.026) and cortex (F=14.490, P<0.001) among the groups. COX-2-positive staining in the SAMP8  group was significantly enhanced in the hippocampus and cortex compared with the SAMR1 group (P<0.05 and P<0.01, respectively; Fig. 4B). Following treatment with BSYZ, COX-2 expression was significantly decreased in the brain compared with in the SAMP8 group (hippocampus: P<0.01, P<0.05 and P<0.05 for L-BSYZ, M-BSYZ and H-BSYZ groups, respectively; cortex: All P<0.01; Fig. 4B).

Effects of BSYZ on PPAR-γ expression in brains of SAMP8
mice. Immunohistochemical staining of PPAR-γ was performed in brain sections from the mice (Fig. 5A). No significant differences in PPAR-γ expression were observed in the hippocampus (F=1.911, P=0.185); however, there was a significant difference in cortex expression (F=6.004, P=0.010) among the groups. PPAR-γ expression in the SAMP8 group was significantly reduced in the cortex compared with the SAMR1 group (P<0.01; Fig. 5B). BSYZ treatment significantly increased PPAR-γ expression in the cortex compared with the SAMP8 group (P<0.01, P<0.05 and P<0.05 for L-BSYZ, M-BSYZ and H-BSYZ groups, respectively; Fig. 5B).

Effects of BSYZ on NF-κB expression in brains of SAMP8
mice. Immunohistochemical staining of NF-κB was performed in brain sections from the mice (Fig. 6A). No significant differences in NF-κB expression were observed in the hippocampus (F=2.471, P=0.112); however, there was a significant difference in cortex expression (F=31.556, P<0.001) among the groups. NF-κB expression in the SAMP8 group was significantly increased in the hippocampus and in the cortex compared with the SAMR1 group (P<0.05 and P<0.01, respectively; Fig. 6B). However, BSYZ treatment significantly decreased NF-κB expression in the cortex compared with the SAMP8 group (P<0.01 for all BSYZ groups; Fig. 6B).
Effects of BSYZ on IL1B and IL6 mRNA expression in brains of SAMP8 mice. The mRNA expression levels of genes encoding the inflammatory cytokines IL-1β and IL-6, IL1B and IL6, were measured by RT-qPCR. There was a significant difference in IL1B mRNA expression levels among the groups (F=4.892, P=0.011). The expression levels of IL1B in the SAMP8 group were significantly higher compared with the SAMR1 group (P<0.05;   Fig. 7B). However, IL6 was significantly increased in SAMP8 group compared with the SAMR1 group (P<0.05; Fig. 7B).

Effects of BSYZ on oxidative stress in SAMP8 mice.
To assess the effects of BSYZ on oxidative stress of SAMP8 mice, SOD and GSH-Px activities, and MDA content were measured in mice brains (Fig. 8). No significant difference in SOD activity was observed (F=1.901, P=0.163); however, there were significant differences in MDA content (F=5.458, P=0.006) and GSH-Px activity (F=5.618, P=0.006) among the groups. Post hoc test demonstrated that MDA content in the SAMP8 group was significantly greater compared with the SAMR1 group (P<0.01; Fig. 8B). Conversely, the activities of SOD and GSH-Px in the SAMP8 mice were significantly reduced compared with the SAMR1 group (P<0.05 and P<0.01, respectively; Fig. 8A and C). However, compared with the SAMP8 group, BSYZ treatment The results of TUNEL staining in the brain tissue sections of mice are presented in Fig. 10. There were significant differences in the number of TUNEL-positive cells in the Effects of BSYZ on histological alterations in the brains of SAMP8 mice. As presented in Fig. 11A, irregular arrangement of neurons in the hippocampus and unhealthy neurons (appearing as indistinct, darker or shrunken, lacking a clear cell boundary, and possessing a small darkened nucleus) were observed in brain tissue of SAMP8 mice. There were significant differences in the number of unhealthy neurons in the hippocampus (F=24.233, P<0.001) and cortex (F=17.947, P<0.001) among the groups. In addition, the number of unhealthy neurons in the SAMP8 group was significantly greater than in the SAMR1 group (P<0.01; Fig. 11B). However, administration of BSYZ significantly decreased the number of unhealthy neurons (hippocampus: P<0.01, P<0.01 and P<0.05 for L-BSYZ, M-BSYZ and H-BSYZ groups, respectively; cortex: P<0.01 for L-BSYZ and M-BSYZ groups; Fig. 11B). Chromatolysis, a decrease in Nissl-stained cells and shrinkage of cells was observed in the brains of SAMP8 mice (Fig. 12A). There were significant differences in the number of Nissl-positive neurons in the hippocampus (F=13.428, P<0.001) and cortex (F=8.273, P=0.003) among the groups. The number of Nissl-positive neurons in the SAMP8 group was significantly reduced compared with the SAMR1 group (P<0.01; Fig. 12B). However, administration of BSYZ significantly increased the number of Nissl-positive neurons (hippocampus: P<0.01 for L-BSYZ group; cortex: P<0.05 for L-BSYZ group; Fig. 12B) compared with the SAMP8 group.

Discussion
The present study demonstrated that BSYZ treatment exerted a beneficial effect on learning and memory abilities in SAMP8 mice, which was consistent with our previous studies in other AD-like animal models (21,22). In addition, BSYZ treatment demonstrated anti-inflammatory and antioxidative effects, and decreased neuronal apoptosis in SAMP8 mice.
SAMP8 mice harbor AD-like cognitive and behavioral alterations, and a neuropathological phenotype (27). In the present study, SAMP8 mice demonstrated significant  impairment of spatial learning and memory in a Morris water maze test, in addition to deficiency of passive avoidance response in a step-down test, compared with SAMR1 mice. These results were similar to other studies (28,29). Furthermore,  administration of BSYZ improved cognitive impairment in SAMP8 mice. Low dose BSYZ exhibited an improved cognition-enhancing effect compared with the higher doses; however, a further dose-effect study is warranted.
Inflammation is a risk factor in the course of neurodegenerative diseases, including dementia. It has been suggested that plasma levels of inflammatory proteins are increased prior to the clinical onset of dementia, AD and vascular dementia (30). Increased peripheral levels of inflammatory markers are also associated with a modest increase in the risk of all types of dementia (31).
Astrocytes are involved in the neuroinflammatory response and become reactive in response to the majority of pathological situations in the brain, including axotomy, ischemia, infection and neurodegenerative diseases (13). Astrocyte reactivity was originally characterized by morphological alterations, including hypertrophy and process remodeling, and the overexpression of the intermediate filament GFAP (13). It has been reported that compared with in non-demented individuals, significantly greater GFAP levels were observed in brain samples from individuals diagnosed with AD, mixed dementia and vascular-mediated dementia (32). In addition, age-related increases in cellular hypertrophy of GFAP-positive cells were observed in 6-and 10-month old SAMP8 mice (33). Another study detected higher levels of GFAP in the brains of SAMP8 mice compared with SAMR1 mice via immunofluorescence staining and western blotting (34). Therefore, the present study detected GFAP expression in brain tissue sections from SAMP8 mice and investigated the effects of BSYZ on astrocyte activation. Consistent with previous reports, hypertrophic astrocytes and increased GFAP expression were observed in SAMP8 mice compared with SAMRI mice, which were reduced following treatment with BSYZ. These findings suggested that the effects of BSYZ on inflammation may be mediated, at least partially, by reducing astrocyte activation in the brain.
Astrocytes host a complex network of signaling pathways, providing an abundance of potential molecular targets (35), including the NF-κB signaling pathway (13). Following injection of oligomers of β-amyloid protein into the cortex of rats, inflammatory markers, including COX-2, IL-1β and TNF-α were expressed in reactive astrocytes, and GFAP and COX-2 proteins co-localized with NF-κB (36). To investigate whether BSYZ had effects on the regulation of inflammatory markers, the present study measured COX-2, NF-κB and IL1B expression in the brain tissue of senescence-accelerated mice. In addition, other inflammatory markers, including IL6 (37) and PPAR-γ, (38) which are associated with cognitive function and are used as therapeutic targets, were detected. The results from a previous study suggested that PPAR-γ agonists affected modulation of the inflammatory response by reducing COX-2 protein expression and activating MAPKs and NF-κB in the hippocampus of rats exposed to cerebral ischemia/reperfusion (14). In the present study, the levels of proinflammatory markers in SAMP8 mice were higher than in SAMRI control mice. BSYZ treatment downregulated the expression levels of COX-2 in the hippocampus and cortex of mice, and significantly reduced the NF-κB expression in the cortex. Conversely, BSYZ significantly increased PPAR-γ expression in the cortex compared with untreated SAMP8 rats. In addition, BSYZ treatment significantly downregulated IL1B; however, no significant differences were observed in the mRNA expression levels of IL6. These results suggested that BSYZ may exert an anti-inflammatory effect by regulating numerous targets. However, further research is required to support this theory.
Alterations in astrocyte function are associated with oxidative stress. It has previously been reported that the expression of certain oxidative stress markers is increased in aged astrocytes (39). Oxidative damage is associated with aging and age-related diseases. In the serum of patients with MCI and AD, the activity of primary enzymatic antioxidant defenses (SOD and GSH-Px) were decreased and production of the MDA lipid peroxidation marker were increased, compared with the age-matched control group (40). Similar alterations in oxidative stress markers were observed in SAMP8 mice (41). Therefore, to assess the effects of BSYZ on oxidative stress, the present study determined the SOD, MDA and GSH-Px levels in the brains of SAMP8 mice. Results revealed that Figure 11. Effects of BSYZ on H&E staining of brain tissue. (A) Images of H&E staining in cortex, DG, CA3 and CA1 tissues of SAMRI, SAMP8, low dose BSYZ (1.46 g/kg), medium dose BSYZ (2.92 g/kg) and high dose BSYZ (5.84 g/kg) mice. Black arrows indicate unhealthy neurons. Scale bar: 50 µm. (B) Percentage of positively stained unhealthy neurons. Data are presented as the mean ± standard error (n=3/group). ** P<0.01 vs. SAMR1 group; # P<0.05 and ## P<0.01 vs. SAMP8 group. BSYZ, Bushen-Yizhi formula; SAMP8, senescence-accelerated mouse prone 8; SAMRI, senescence-accelerated mouse resistant 1; H&E, hematoxylin and eosin; DG, dentate gyrus. oxidative damage in the cortex of SAMP8 mice was attenuated following treatment with BSYZ. It has been reported that PPAR-γ agonists may reduce oxidative stress (14). The ameliorative effects of BSYZ on oxidative stress were consistent with the effects of BSYZ on increasing PPAR-γ expression in the cortex.
It has previously been reported that PPAR-γ overexpression prevents apoptosis by upregulating the anti-apoptotic Bcl-2 family proteins, including Bcl-xL (42). SAMP8 mice demonstrated that with age, there was a significant increase in the relative expression of pancreatic genes involved in inflammation, oxidative stress and apoptosis (43). Therefore, the present study detected neuronal apoptosis in the brains of SAMP8 mice and protein expression of Bcl-xL. The results demonstrated that the brains of SAMP8 mice exhibited histological alterations and reduced Bcl-xL expression. Following treatment with BSYZ for 30 days, the number of neuronal apoptotic cells was decreased and the expression levels of Bcl-xL were enhanced. These results suggested that BSYZ may exert an anti-apoptotic effect.
In conclusion, the results of the present study indicated that treatment with BSYZ improved cognitive function in SAMP8 mice, and the underlying molecular mechanism may be associated with anti-inflammatory, antioxidative and anti-apoptotic effects. With these multi-target effects, BSYZ may be a potential drug in treating dementia.

Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.


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MLSTar: automatic multilocus sequence typing of bacterial genomes in R

Multilocus sequence typing (MLST) is a standard tool in population genetics and bacterial epidemiology that assesses the genetic variation present in a reduced number of housekeeping genes (typically seven) along the genome. This methodology assigns arbitrary integer identifiers to genetic variations at these loci which allows us to efficiently compare bacterial isolates using allele-based methods. Now, the increasing availability of whole-genome sequences for hundreds to thousands of strains from the same bacterial species has allowed us to apply and extend MLST schemes by automatic extraction of allele information from the genomes. The PubMLST database is the most comprehensive resource of described schemes available for a wide variety of species. Here we present MLSTar as the first R package that allows us to (i) connect with the PubMLST database to select a target scheme, (ii) screen a desired set of genomes to assign alleles and sequence types, and (iii) interact with other widely used R packages to analyze and produce graphical representations of the data. We applied MLSTar to analyze more than 2,500 bacterial genomes from different species, showing great accuracy, and comparable performance with previously published command-line tools. MLSTar can be freely downloaded from http://github.com/iferres/MLSTar.

INTRODUCTION
Multilocus sequence typing (MLST) was introduced in 1998 as a portable tool for studying epidemiological dynamics and population structure of bacterial pathogens based on PCR amplification and capillary sequencing of housekeeping gene fragments (Maiden et al., 1998). In most MLST schemes, seven loci are indexed with arbitrary and unique allele numbers that are combined into an allelic profile or sequence type (ST) to efficiently summarize genetic variability along the genome. Rapidly, MLST demonstrated enhanced reproducibility and convenience in comparison with previous methods such as multilocus enzyme electrophoresis or pulsed-field gel electrophoresis, allowing us to perform global epidemiology and surveillance studies (Urwin & Maiden, 2003). For example, MLST has been applied to elucidate the global epidemiology of Burkholderia multivorans in cystic fibrosis patients (Baldwin et al., 2008) or to understand the dissemination of antibiotic-resistant enterobacteria (Castanheira et al., 2011).
However, as MLST started to be massively applied two main drawbacks were uncovered: (i) the impossibility of establishing a single universal MLST scheme applicable to all bacteria; and (ii) the lack of high resolution of seven-locus MLST schemes required for some purposes.
These problems pushed the development of improved alternatives to the original methodology. The extended MLST approach which is based on the analysis of longer gene fragments (Chen et al., 2011) or increased number of loci (Dingle et al., 2008;Crisafulli et al., 2013) proved to improve resolution, and the scheme based on 53 ribosomal protein genes (rMLST) was proposed as an universal approach since these loci are conserved in all bacteria (Jolley et al., 2012). Beyond these improvements, the advent of high-throughput sequencing and the increasing availability of hundreds to thousands whole-genome sequences (WGS) for many bacterial pathogens caused a paradigmatic change in clinical microbiology, making it possible to use nearly complete genomic sequences to enhance typing resolution. This revolution allowed the transition from standard MLST schemes testing a handful of genes to core genome approaches that scaled to hundreds of loci common to a set of bacterial genomes (Maiden et al., 2013).
The generation of this massive amount of genetic information required the accompanying development of database resources to effectively organize and store typing schemes and allele definitions. Rapidly, the PubMLST database (http://pubmlst.org) turned into the most comprehensive and standard resource storing today schemes and allelic definitions for more than 100 microorganisms. Subsequently, the shift to WGS motivated the development of the Bacterial Isolate Genome Sequence Database (BIGSdb) (Jolley & Maiden, 2010), which now encompasses all the software functionalities used for the PubMLST. Also, many tools for automatic MLST analysis from WGS have been developed using web servers like MLST-OGE (Larsen et al., 2012) or EnteroBase (http://enterobase.warwick.ac.uk), pay-walled tools like BioNumerics or SeqSphere+, and open source tools like mlst (http://github.com/tseemann/mlst) or MLSTcheck (Page, Taylor & Keane, 2016). Here, we present MLSTar as the first tool for automatic MLST of bacterial genomes written in R (R Development Core Team, 2008), allowing us to expand the application of MLST tools within this very popular and useful environment for data analysis and visualization.

Implementation
MLSTar is written in R and contains all data processing steps and command line parameters to call external dependencies wrapped in the package. MLSTar depends on BLAST+ (Camacho et al., 2009) that is used as sequence search engine, and must be installed locally. MLSTar is designed to work on Unix-based operating systems and is distributed as an open source software (MIT license) stored in GitHub (http://github. com/iferres/MLSTar). MLSTar contains four main functions that (i) takes genome assemblies or predicted genes in FASTA format from any number of strains, (ii) performs sequence typing using a previously selected scheme from PubMLST, and (iii) applies standard phylogenetic approaches to analyze the data. An overview of the overall workflow has been outlined in Fig. 1.

Interaction with PubMLST
First step in MLSTar workflow involves to interact with the PubMLST database to select a target scheme. This interaction requires Internet connection because is performed using the RESTful web application programming interface provided by PubMLST. The listPubmlst_orgs() function allows us to list the names of all microorganisms that have any scheme stored in PubMLST. Then, as some microorganisms have more than one scheme (i.e., one classical seven-loci and one core genome scheme), the listPubmlst_schemes() function lists the available schemes for any selected species. Additionally, MLSTar is not restricted only to the MLST definitions present in PubMLST since schemes stored in other databases can be manually downloaded and analyzed with MLSTar. Calling and storing alleles and sequence types MLSTar make allele and ST calls from FASTA files containing closed genomes or contigs using BLAST+ blastn comparisons implemented by the doMLST() function. Parallelization is available as internally implemented in R by the parallel package. Also, the doMLST() function can be run at the same time for different schemes using internal R functions like lapply(). Results are stored in a S3 class object named mlst that contains two data.frame objects: one containing allele and ST assignments for the analyzed genomes (unknown alleles or STs are labeled as "u"), and the other storing known allele profiles for the selected scheme. If required, nucleotide sequences for known or novel alleles can be written as multi FASTA files for downstream analyses.

Post analysis
Allele profiles are frequently used to reconstruct phylogenetic relationships among strains. Function plot.mlst() directly takes the mlst class object to compute distances assuming no relationships between allele numbers, so each locus difference is treated equally. Then, identical isolates have a distance of 0; those with no alleles in common have a distance of 1 and, for example, in a seven-loci scheme two strains with five differences would have a distance of 0.71 (5/7). The resulting distance matrix is used to build a minimum spanning tree using igraph (Csardi & Nepusz, 2006) that returns an object of class igraph or a neighbor-joining tree as implemented in APE package (Paradis, Claude & Strimmer, 2004) that returns an object of class phylo. The package also contains a specific method defined as plot.mlst that recognizes the mlst class object and plots the results using the generic plot() function. Additionally, a better resolution analysis based on the variability of the underlying sequences using more sophisticated Maximum-Likelihood or Bayesian phylogenies, can be achieved externally by aligning the allele sequences that are automatically retrieved by MLSTar.

RESULTS AND DISCUSSION
Comparison with capillary sequencing data Multilocus sequence typing analysis based on capillary sequencing has been considered as the gold standard. Hence, we used a previously reported dataset (Page et al., 2017) consisting in 72 Salmonella samples originally tested by capillary sequencing and deposited in the EnteroBase (Alikhan et al., 2018), that were posteriorly whole-genome sequenced. This dataset covers a wide host range and isolation dates of Salmonella strains comprising 32 different STs (Table S1). In average, MLSTar assignments at ST level matched in 92% of cases when compared with capillary sequencing. Additionally, ST calls for five samples that were distinct between capillary sequencing and genomederived inferences using several software tools (Page et al., 2017), were also discordant in the same way when using MLSTar. This is expected since capillary sequencing is not error free (Liu et al., 2012), in spite of being considered as the gold standard. By the contrary, the result for sample 139K matched between capillary sequencing and MLSTar but most other software tools, except stringMLST (

Comparison against BIGSdb
We retrieved 2,726 genomes from the BIGSdb belonging to 10 species most of which are very well-known pathogens (Table S2). For these datasets, reference allele, and ST assignments based on the corresponding standard MLST schemes were extracted from the BIGSdb and compared with results obtained running MLSTar. The concordance at allele and ST levels is shown in Table 1, measured as the percentage of identical assignments between BIGSdb and MLSTar. In average, assignments were 97.9% (SD = 1.95) and 95.6% (SD = 2.5) coincident for alleles and STs, respectively. These results evidence a very good performance of MLSTar in comparison with the reference assignments from the BIGSdb. Additionally, we tested MLSTar using the ribosomal MLST scheme (Jolley et al., 2012) over the same 354 genomes belonging to Staphylococcus aureus and Streptococcus agalactiae. This scheme was conceived as an universal approach for discrimination of bacterial species. Accordingly, the automatic phylogenetic analysis implemented in MLSTar was able to discriminate both species using ribosomal alleles (Fig. 2).

Comparison with MLST schemes of close species
The PubMLST database stores schemes for 10 different species within the genus Campylobacter, hence we used this case as negative control to test the specificity of MLSTar. We chose the 172-C. jejuni/coli dataset from BIGSdb and 150 randomly selected C. fetus genomes from a previously published study (Iraola et al., 2017) to run MLSTar against the schemes defined for the remaining Campylobacter species, in order to detect potential false positive calls when analyzing closely related taxa. False positives at both allele and ST levels were not detected neither for C. jejuni/coli nor for C. fetus against the rest (Table S3), indicating that MLSTar is highly specific when working with genetically related bacteria.

Comparison of variable coverage depths and number of genomes
Variable depths of sequencing coverage have been shown to affect the accuracy of different softwares to achieve confident ST calls. In general, most softwares require over than 10Â to ensure optimal performance (Page et al., 2017). Here, we tested MLSTar by sampling reads at gradual depths from 10 genomes (representing different STs) from the Salmonella dataset and measured the percentage of correctly assigned STs. Figure 3A shows that MLSTar produce good-enough results when sequencing depth is greater than 10Â, and its performance is comparable to similar tools such as MLSTcheck and mlst.
Considering that nowadays bacterial genome sequencing experiments typically ensure at least 30Â of coverage depth, our results evidence that MLSTar is appropriate for analyzing WGS with average or even slightly lower coverage depths. Additionally, we used a random set of genomes (n = 400) from the BIGSdb dataset to compare the running time between MLSTar, MLSTcheck, and mlst softwares in a single AMD Opteron 2.1 GHz processor, by gradually increasing the number of analyzed genomes from 2 to 400 (Fig. 3B). These results showed that MLSTar is 26-fold faster than MLSTcheck but is threefold slower than mlst (Table S4).

CONCLUSION
The advent of WGS has now allowed to type bacterial strains directly from their whole genomes avoiding to repeat tedious PCR amplifications and fragment capillary sequencing for multiple loci. Today MLST is a valid tool which is frequently used as a firstglimpse approach to explore genetic diversity and structure within huge bacterial population sequencing projects. This incessant availability of genomic information has motivated a constant effort to develop efficient analytical tools from multilocus typing data (Page et al., 2017). Here, we developed a new software package called MLSTar that expands the possibilities of performing allele-based genetic characterization within the R environment. We demonstrate that MLSTar has comparable performance with previously validated software tools and can be applied to analyze hundreds of genomes in a reasonable time.


s2fieldsofstudy: Biology Medicine

text:
Tanezumab: Therapy targeting nerve growth factor in pain pathogenesis

In recent years, nerve growth factor (NGF) and the NGF receptor have become potential therapeutic targets in the treatment of acute and chronic pain states. NGF is a neurotrophin involved in regulating the function of sensory and sympathetic neurons during development. Numerous pain states have been linked to elevated levels of NGF and its role in increasing the perception of pain. Tanezumab, a recombinant humanized monoclonal antibody (IgG), was developed to target NGF, binding both circulating and local tissue NGF preventing interaction with the tropomyosin-related kinase-A and p75 receptors. Recent clinical studies with tanezumab in different patient populations to date, including osteoarthritis, low back pain, and diabetic peripheral neuropathy, demonstrate efficacy with few side effects, including transient arthralgias, paresthesias, hypoesthesia, and rarely, osteonecrosis. Anti-NGF antibodies are a novel therapy in pain management and have shown promise in the treatment of certain pain conditions, which at present are poorly treated. Tanezumab offers an exciting new class of analgesics that has the potential to change the treatment of pain.

Introduction
Pain, both acute and chronic, remains a challenge for scientists and clinicians with regard to understanding its pathogenesis and efficacious treatments. In this regard, chronic pain is prevalent among Americans, affecting roughly 100 million people. [1] This condition has a significant impact on individual patients' quality of life, as it is strongly associated with disability and poor self-rated health. In addition, patients suffering from chronic inflammatory and neuropathic pain have decreased productivity and increase health-care costs, which contribute significantly to the overall economic burden on society. In 2011, the Institute of Medicine estimated losses of up to $635 billion dollars each year for medical treatment of pain-related conditions and lost economic productivity. [1] Hence, treatments are being developed with the aim to improve functional status and to reduce suffering, thereby decreasing health care visits and costs. The current best practice involves multimodal analgesic therapy, including both pharmacologic and nonpharmacologic modalities, to optimize patient outcomes and to minimize adverse effects. Current therapies include, but are not limited to, nonsteroidal anti-inflammatory drugs (NSAIDs), neuropathic medications, antidepressants, opioids, and targeted spinal injections. [2] Each modality has a unique risk profile that necessitates thoughtful planning before instituting each specific therapy. For instance, the use of NSAIDs is limited in patients due to renal, cardiovascular and gastrointestinal side effects. [3,4] The high incidence of cognitive dysfunction, respiratory depression, and addiction in patients taking opioids mandates the development of new therapeutic targets. [5,6] Patient selection and predictors of success for spinal injections continue to be a subject of much debate with regard to efficacy and potential risks. [7] In recent years, basic science and clinical research advancements have helped better understand the pathophysiology of pain. Some current areas of research have focused on compounds that attenuate glial activation (e.g., minocycline and methylxanthine derivatives); drugs that inhibit proinflammatory cytokine production (e.g., cytokine inhibitors and antagonists to toll-like receptor 4 activation); and anti-inflammatory agents that reduce inflammation. [8] One of the more promising specific targets that have evolved from this research is nerve growth factor (NGF) and its receptor. [5] Tanezumab is a recombinant humanized monoclonal antibody (IgG) that was developed by Pfizer to target NGF for the treatment of several pain conditions. [5,6] Figure 1 shows the molecular formula of tanezumab. The Fc mutation limits antibody dependent cell mediated toxicity and complement activation. The drug is an antibody that has high sensitivity and specificity for NGF. [6] Its favorable pharmacokinetic profile allows it to bind both circulating and local tissue NGF, thereby preventing interaction with the tropomyosin-related kinase-A (TrK-A) and p75 receptors. Tanezumab is a large protein and hence does not cross the blood-brain barrier. Its plasma half-life is 22-25 days. [7,8] Anti-NGF therapy appears to be antihyperalgesic (normalizing a decreased nociceptive threshold) as apposed to an analgesic (increasing normal and sensitized nociceptive thresholds). [8] Pharmacological Properties NGF is a neurotrophin involved in regulating the function of sensory and sympathetic neurons during development. In adults, it serves as a modulator of nociception and is found to be elevated in chronic pain conditions leading to increased perception of pain [11] [ Figure 2]. These large tightly bound homodimer protein molecules bind to a family of receptors called tropomyosin-related kinase (TrK) with high affinity and to the p75 receptor with low affinity. [11,12] The most notable of the receptors is TrK-A, which regulates ion channels and molecules that are critical in the signaling of pain. NGF is elevated in inflammatory conditions due to its release by mast cells, macrophages, and lymphocytes. TrK-A is found in high quantities in nerve fibers in the dorsal root ganglion and has been shown in animal studies to propagate chronic pain. [12] On NGF binding TrK-A, the complex is taken up peripherally and transported to the cell soma where it activates transcription factors affecting gene expression. Both in rodents and humans, cutaneous administration of NGF had led to hyperalgesia within 1-3 h. The rapid nociceptor sensitization of cutaneous receptors shows NGF plays a pivotal role in both acute nociceptive responses and in chronic pain. [13,14]

Clinical Indications
The Biological Investigational New Drug application for tanezumab was submitted to the Food and Drug Administration (FDA) in April 2004 for indications related to the treatment of moderate to severe chronic pain. [3] Clinical trials (Phase I and II) demonstrated the efficacy of tanezumab in treating the osteoarthritic pain of the hip and knee joint as well as chronic low back pain. [15][16][17][18] Due to reports of osteonecrosis and increased need for total joint replacements, the FDA placed the drug on clinical hold for on-going investigations related to osteoarthritis (OA) on June 22, 2010. On December 23, 2010, the drug was put on hold for all clinical investigations except for cancer. These events were investigated by an independent, expert adjudication committee, which did not find evidence indicating the aforementioned claims. [5,19] Increased risk for rapidly progressive OA was Figure 2: Role of nerve growth factor in modulating pain signals to the central nervous system Figure 1: Molecular formula of tanezumab noted in patients who received concomitant therapy with anti-NGF agents and NSAIDs. [20] The Arthritis Advisory Committee meeting held on March 12, 2012 reviewed the results and endorsed the continued clinical development of tanezumab. On August 28, 2012, the partial clinical hold on tanezumab was lifted. [5] Table 1 lists the current indications investigated in the literature. Tanezumab studies included placebo-controlled and active-controlled (comparing to naproxen and oxycodone) studies. Table demonstrates the different doses of tanezumab used for the treatment of patients with OA: 2.5 mg, 5 mg, 10 mg in Phase II trials and 10 µg/kg, 25 µg/kg, 50 µg/kg, 100 µg/kg, and 200 µg/kg in Phase III trials. The doses used for chronic low back pain ranged from 5 mg to 20 mg every 8 weeks or a single shot of intravenous 200 µg/kg. [18,21,22] Tanezumab demonstrated superiority in the efficacy of pain control compared to placebo in these patients. [17,18,[21][22][23][24][25] Tanezumab compared to NSAIDs and opioids showed greater efficacy for Western Ontario and McMaster Universities osteoarthritis Index (WOMAC) pain, WOMAC function and Patient's Global Assessment in patients with hip or knee OA. [23,25] While comparing the average low back pain scores and Roland Morris Disability Questionnaire responses, patients receiving tanezumab reported significantly better efficacy in pain control and functional improvement. [21,22] Other investigations are exploring the use of tanezumab to provide relief for neuropathic pain. Bramson et al. showed effective pain reduction in patients with diabetic peripheral neuropathy when they received 20 mg subcutaneous injections every 8 weeks. [26] For doses ranging between 50 mcg/kg and 200 mcg/kg given intravenously to patients with post-herpetic neuralgia, there was no significant benefit demonstrated in the same study. [26] The differing NGF involvement in these two disease processes may be responsible for the difference in the effectiveness of the drug to treat each condition.
Patients suffering from OA and chronic low back pain reported greater pain reduction and improved functionality when compared to placebo and NSAIDs. The initial safety and tolerability profile compared favorably with current therapies. The adverse effects associated with cyclooxygenase-2 inhibitors and NSAIDs were not demonstrated in patients on tanezumab therapy. Neither does the novel drug appear to have any effects on the respiratory or central nervous system or raise concerns regarding abuse liability. [18] Adverse events (AEs) such as transient arthralgias, paresthesias, and hypoesthesia were noted in patients receiving tanezumab. [21,22] These AEs occurred more frequently in the tanezumab group when compared to placebo. However, the AEs were fewer compared to patients receiving opioids and mixed results were obtained when comparing to NSAIDs. [17,18,[21][22][23][24][25] Safety, determined by odds ratios of withdrawals from studies due to AEs, was better at the lower doses than higher doses. These results reveal that antibodies to NGF provide efficacy in OA and that general safety at the lower doses appears similar to placebo. The severe adverse effects of osteonecrosis and earlier than expected joint replacement were investigated as noted above. Although a causal relationship was not seen, additional data on both efficacy and safety of these antibodies are needed to define the optimal dose to maximize benefit to risk. Based on the findings, the pharmaceutical companies, and the FDA negotiated restarting clinical trials, provided an avoidance of chronic concomitant NSAID and anti-NGF therapy, careful dose escalation in chronic pain patients, and discontinuing in patients who do not show improvement with initial doses. Patients with progressive OA are not to be included in further investigations. [5] Conclusion Acute and chronic pain states remain challenging to treat, yet there are several classes of drugs which are efficacious treatments with good safety profiles. Persistent chronic pain is a long-term condition which impacts the affected individuals by interfering with their quality of life both at home and at work and has significant societal consequences as well. Current therapies include NSAIDs, opioids, and neuropathic agents, but they may result in poorly tolerated side effects. Anti-NGF antibodies are a novel therapy in pain management and have shown promise in the treatment of conditions that are currently poorly treated.
To use anti-NGF antibodies for different pain conditions, further investigations are needed to determine the role NGF plays in pain maintenance. Based on the current findings, it has shown to have some benefit on chronic low back pain and osteoarthritic pain relief and functional improvement, but additional data are necessary to determine efficacy and safety. Further investigations are required as the data are lacking for the utility of the drug in postherpetic neuralgia. Other potential indications include treatment for interstitial cystitis, prostatitis, and pancreatitis. Continued attention to safety issues associated with tanezumab will also help determine its utility. Tanezumab offers an exciting new class of analgesics that has the potential to change the treatment of pain.

Financial support and sponsorship
Nil.

Conflicts of interest
There are no conflicts of interest. Journal of Anaesthesiology Clinical Pharmacology | Volume 34 | Issue 1 | January-March 2018


extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy: Biology Engineering

text:
Construction of an organelle-like nanodevice via supramolecular self-assembly for robust biocatalysts

Background When using the microbial cell factories for green manufacturing, several important issues need to be addressed such as how to maintain the stability of biocatalysts used in the bioprocess and how to improve the synthetic efficiency of the biological system. One strategy widely used during natural evolution is the creation of organelles which can be used for regional control. This kind of compartmentalization strategy has inspired the design of artificial organelle-like nanodevice for synthetic biology and “green chemistry”. Results Mimicking the natural concept of functional compartments, here we show that the engineered thermostable ketohydroxyglutarate aldolase from Thermotoga maritima could be developed as a general platform for nanoreactor design via supramolecular self-assembly. An industrial biocatalyst-(+)-γ-lactamase was selected as a model catalyst and successful encapsulated in the nanoreactor with high copies. These nanomaterials could easily be synthesized by Escherichia coli by heterologous expression and subsequently self-assembles into the target organelle-like nanoreactors both in vivo and in vitro. By probing their structural characteristics via transmission electronic microscopy and their catalytic activity under diverse conditions, we proved that these nanoreactors could confer a significant benefit to the cargo proteins. The encapsulated protein exhibits significantly improved stability under conditions such as in the presence of organic solvent or proteases, and shows better substrate tolerance than free enzyme. Conclusions Our biodesign strategy provides new methods to develop new catalytically active protein-nanoreactors and could easily be applied into other biocatalysts. These artificial organelles could have widely application in sustainable catalysis, synthetic biology and could significantly improve the performance of microbial cell factories. Graphical Abstract Electronic supplementary material The online version of this article (10.1186/s12934-018-0873-3) contains supplementary material, which is available to authorized users.

Furthermore, encapsulins, protein cages with a diameter of about 30 nm, are involved in important processes like iron mineralization [5,10]. These intriguing creations make nature a master of the art and have initiated new efforts in bioinspired chemistry. Compared with most of other inorganic/organic nanostructures, proteinaceous nanostructures exhibit many superior properties and represent a unique combination of chemistry, biology and materials science [11]. Generally, they have robust self-assembly properties and are soluble in aqueous solvents. Moreover, they are biological origin which makes them inherently biocompatible and allows for precise control [11]. For instance, intensive studies have been performed on virus capsids, and these versatile nanoparticles have been widely used in nanobiotechnology nowadays. Douglas et al. designed a biomolecular catalyst for hydrogen production based on the self-assembling properties of bacteriophage P22 capsids [12]. Through a scaffold protein, the [NiFe]hydrogenase was successfully embedded within the viral protein coat, which provides a strong protection to the protein cargo and increase the catalytic efficiency [12]. Another example is ferritin, which has been used to encapsulate rhodium nanoclusters for organic synthesis [13]. Recently discovered Encapsulin from Thermotoga maritima was demonstrated that can be used as a targeted delivery system for therapeutic medicines [14]. Besides a growing number of these natural origin protein-based compartments, several artificial viruslike protein nanoparticles have been designed [15,16]. For example, a hyperstable 60-subunit protein dodecahedron was created by Baker et al., which was directly self-assembled from an engineered trimeric protein called ketohydroxyglutarate aldolase from T. maritima [15]. Later on, another two components, 120-subunit icosahedral protein nanocage was also created by the same group [16]. These impressive designs have attracted considerable attention, and repurposing these protein compartments for applications ranging from nanoreactor design to drug delivery is of great interest and is currently underway.
To manipulate these fascinating protein-microcompartments, three general strategies have been developed: (1) fusion of the cargo protein to a scaffold that directs encapsulation to the nanocage interior; (2) specific packaging directly by natural or artificial sequence, e.g. SpyTag/SpyCatcher; and (3) nonspecific packaging by self-assembly [11]. Generally, the last two strategies have obvious disadvantages: yields of the encapsulated protein products are low. This is due to the efficiency of the self-assembly and the limited interior space of the protein cage. On the other hand, genetic fusion is more challenging yet could be precisely designed. In this study, we report the use of the protein nanocage as a platform for nanoreactor design. We hypothesized that encapsulation of the enzyme in the interior of a protein nanocompartment might improve both enzyme activity and stability which was proved by a (+)-γ-lactamase as the model biocatalyst. Moreover, it is envisioned that the protein nanoreactor, which can encapsulate diverse biocatalysts via genetic fusions, might provide a robust and universal method for the encapsulation of highly active biocatalysts, which might have wide application in biocatalysis and "green manufacturing".

Preparation of recombinant proteins for nanoreactor construction
The engineered ketohydroxyglutarate aldolase from T. maritima can self-assemble into a virus-like dodecahedron particle with 60 subunits (Fig. 1) [15]. Based on RosettaDesign, the trimeric aldolase with newly optimized interfaces between each building blocks has been showed that it could self-assemble into dodecahedron protein assemblies both in vivo and in vitro without any treatments [15]. The nanoparticles are remarkably stable at high concentration of guanidine hydrochloride and high temperature (up to 80 °C) [15]. To expand the application of such a protein nanocage, we have herein used the dodecahedron to develop novel nanoreactors. A γ-lactamase mutant (designated as MhIHL-V54L hereafter) from Microbacterium hydrocarbonoxydans was selected as a model biocatalyst and placed in the interior of the nanocage. γ-Lactamase is a versatile enzyme that is widely used for the enzymatic resolution of racemic 2-azabicyclo [2.2.1] hept-5-en-3-one (Vince lactam) [17][18][19][20][21]. The chiral products are valuable synthons used for an increasing number of drug candidates, such as Ziagen (Abacavir) [19]. Past research has shown that the lactamase from M. hydrocarbonoxydans had a superior activity among all reported (+)-γ-lactamases [22]. However, one shortcoming of this biocatalyst is its thermolabile nature, which hinders further industrial application [22]. To construct a nanoreactor for this unstable lactamase ( Fig. 1), the lactamase was first cloned into pET28a, and a single mutation (Val54Leu) was introduced to improve its enantioselectivity, resulting in a pET-28a-MhIHL-V54L plasmid [22]. Next, the engineered ketohydroxyglutarate aldolase from T. maritima (designated as Enc-I3-01 hereafter) was cloned with a His 6 tag encoded at the N terminus to facilitate purification and a short linker encoded at the C terminus to separate the two domains (Additional file 1: Fig. S1). The synthesized Enc-I3-01 was then cloned into pET-28a-MhIHL-V54L vector by NcoI and NdeI, resulting in the pET28a-Enc-I3-01-MhIHL-V54L plasmid. After sequencing, the right plasmids were transformed into E. coli BL21(DE3) and induced by IPTG for heterologous expression. Recombinant proteins were then purified by Ni-NTA affinity chromatography. These enzymes were concentrated and further purified by a size-exclusion chromatography step. The purity of the obtained proteins was analyzed by SDS-PAGE. As shown in Fig. 2a, SDS-PAGE analysis revealed a single band with the correct apparent molecular weight. Size-exclusion chromatography showed that the fusion protein had an unusually short retention time, indicating that it might form a supramolecular structure (Additional file 1: Fig.  S2).

Bioactivity and structural characterization of nanocaged enzymes
To determine the bioactivity of the constructed nanoreactor, the encapsulated (+)-γ-lactamase and the free enzyme were then assayed with the Vince lactam as the substrate in the typical assay described in the methods section. The activity of each enzyme was determined by chiral HPLC. The results show that the mutated  free (+)-γ-lactamase exhibited high enantioselectivity (99%) and specifically hydrolyzed the (+)-γ-lactam. On the other hand, its fusion with the engineered ketohydroxyglutarate aldolase did not affect its bioactivity. The encapsulated (+)-γ-lactamase also showed high enantioselectivity and activity, indicating that encapsulation did not affect its function (Fig. 2b).
To prove that the fusion proteins could indeed be assembled into a dodecahedron nanoreactor, high-resolution transmission electron microscopy (TEM) was applied to probe the structure of the protein cage. TEM showed that the free (+)-γ-lactamase did not form any obvious 3D structure (Fig. 3a, Additional file 1: Fig. S3a). Conversely, the fusion protein had a typical virus-like structure (Fig. 3b, Additional file 1: Fig. S3b). The individual particles were homogenous in terms of size and shape, clearly indicating that they form the designed structure: a dodecahedron with a diameter of about 30 nm and an interior volume of approximately 3000 nm 3 [15]. Considered that only 60 subunits could form such kind of 30 nm dodecahedron structures, it could be deduced that 60 copies of the cargo lactamases theoretically encapsulated in a single cage [15]. Atomic force microscopy (AFM) analysis supported the same conclusion. While the free (+)-γ-lactamase had no obvious structure (Fig. 3c, Additional file 1: Fig.  S4a), the fusion protein formed nanoreactors of about 30 nm in height (Fig. 3d, Additional file 1: Fig. S4b), in agreement with the predicted size. We further used circular dichroism spectra to probe the globular protein secondary structure and used dynamic light scattering to probe the size of the nano-particles (Fig. 4a, b). As expected, the free and encapsulated enzymes produce very different spectra in CD analysis indicating these two proteins have different protein secondary structure. Notably, the fusion protein does not show strong secondary structure for a typical protein. This might due Fig. 3 Structures of free γ-lactamase and encapsulated γ-lactamase self-assembled in vitro. Representative images from TEM: a free γ-lactamase; b encapsulated γ-lactamase. Representative images from AFM: c free γ-lactamase; d encapsulated γ-lactamase to its special structure where high copies of enzymes were concentrated in the interior of the nanocage. The arrangement of the encapsulated cargo proteins in the nanocage might not be very orderly. On the other hand, the dynamic light scattering diameter of the encapsulated lactamase is in the range of 30-35 nm which further proved the fusion protein could form the designed structure. Taken together, these results confirm the (+)-γ-lactamase was successfully embedded in the interior of the nanocages, with 60 copies of the enzyme theoretically encapsulated in a single cage based on computer modeling (Additional file 1: Fig. S5).

Compare the properties of the free and encapsulated (+)-γ-lactamase
From a chemical perspective, encapsulation strategy could enhance the performance of biocatalyst. First, the coat protein can protect the cargo proteins from harsh environment. More important, it is possible that 60 copies of enzymes concentrated in a confined space might significantly improve the catalytic efficiency. Past research has shown that encapsulation enzymes in inorganic/organic material or immobilization on solid supports could confer several benefits to biocatalysts, such as enhancing stability as well as facilitating re-use [23]. Compared with other strategies where the enzymes are sequestered in or on a wide variety of nonbiological compartments, sequestered enzymes in biological compartments like ours might be a superior strategy since no further processing is required. To show that the protein nanocages could confer benefits to the enzymes, biochemical characterization of the free and encapsulated (+)-γ-lactamase were performed using Vince lactam as the substrate. First, Km and Kcat for each enzyme were measured and interpreted from the Lineweaver-Burk plots. The results showed that Michaelis-Menten parameters for both biocatalysts are very similar. They are 120.4 ± 7.2 mM and 20,088 ± 718 s −1 for the free MhIHL-V54L and 86 ± 2.6 mM and 12,830 ± 164.5 s −1 for the encapsulated MhIHL-V54L, respectively (Additional file 1: Fig. S6). These data indicate that protein coat insignificantly affects the performance of these biocatalysts, especially the transport of the substrate to MhIHL-V54L and the release of the product.
We then probed the optimal temperature for both the free and encapsulated (+)-γ-lactamase. Interestingly, we found that the optimal temperature for the free enzyme was 15 °C while the optimal temperature for the encapsulated enzyme was 30 °C (Additional file 1: Fig. S7). This shift indicated that the encapsulated (+)-γ-lactamase might have a higher thermostability. To examine the thermostability of these two enzymes, both catalysts were treated at different temperature for various times in the assay buffer without any substrate, and reactions were then initiated by adding the substrate at 20 °C for 20 min. Our data showed that the free lactamase already lost half of its activity at 35 °C after 5 h, and almost lost all its initial activity at 45 °C after 5 h, indicating that it is a very thermolabile enzyme (Fig. 5a). In contrast, the encapsulated enzyme maintained almost 100% of its initial activity at 35 °C for 5 h. It only started to lose its activity above 40 °C, and only lost 20% of its activity after 5 h. These results suggest that forming such dodecahedral nanocompartment could significantly improve the stability of the cargo enzyme. This improvement might due to the compact structure of the nanocage, which hinders thermal fluctuations of each enzymes. Moreover, all the enzymes are immobilized on the interior surface of the nanocage which further increase the stability of cargo proteins.
The impact of pH on the enzyme activity was also investigated. The encapsulated (+)-γ-lactamase has similar optimal pH to the free enzyme (Additional file 1: Fig.   Fig. 4 CD spectra and DLS measurements of free γ-lactamase and encapsulated γ-lactamase. a CD spectra of free and encapsulated γ-lactamase. b DLS measurement of hydrodynamic diameter of free and encapsulated γ-lactamase S8). However, pH stability studies showed that the encapsulated enzymes performed better at pH 4. It retained 80% of its initial activity compared with the unprotected enzyme, which completely lost its activity under such conditions (Fig. 5b). These results also suggest that the microcompartments could create a unique microenvironment regarding pH for the enzymes and confer pH stability to the enzymes.

Nanocaged enzymes are protected from polar organic solvents deactivation and proteolysis
Past research showed that enzymes encapsulated in silica, polymer or nanogel had better stability in polar organic solvents [23]. Enzymes encapsulated in protein nanocage might have a similar affect. To test the effect of the polar organic solvent, several organic solvents were selected to treat the model enzymes. 40% methanol, 30% N,Ndimethylformamide, 40% acetone, and 30% isopropanol were used to treat the free and the encapsulated lactamase at 4 °C for 10 min, respectively. The residual activity was then measured. As shown in Fig. 6a, the encapsulated (+)-γ-lactamase maintained over 99, 99, 80, and 50% of its initial activity, respectively. On the contrast, the free enzyme had a completely different performance and lost almost all initial activity. We guess that the nanocage might be able to create a small hydrophilic environment for the cargo protein which could contribute to the significantly increased stability in organic solvent. It is also possible that the nanocage could reserve some water that is necessary for the enzymes to carry out their biological functions in the interior of the cage. On the other hand, without this kind of protection, the essential water for free enzymes was easily stripped by solvents, which led to denaturation of the unprotected enzyme.
Other nanocages such as self-assembled DNA nanocages were previously found to protect the encapsulated enzymes against proteases [24]. We guess that the protein nanocage should have a similar function. As shown in Additional file 1: Fig. S9, a time-course experiment revealed that the encapsulated (+)-γ-lactamase was highly resistant to degradation performed by trypsin (1:10), and maintained more than 90% of its initial activity after treated with trypsin for 5 h (Additional file 1: Fig.  S9). In contrast, the free (+)-γ-lactamase only retained 40% of its initial activity at the same condition. We then increased the amount of trypsin (1:50) used in the assays. As showed in Fig. 6b, the encapsulated (+)-γ-lactamase still performed much better than the free one. The encapsulated lactamase retained 50% of its initial activity after 2 h. This is probably because the protein coat could also be degraded by proteases. Nevertheless, these results demonstrate that the protein nanocage protects the encapsulated enzymes from protease degradation.

Nanocaged enzymes have stronger tolerance against high substrate concentrations
Enzymes' tolerance ability against high substrate concentrations is an important issue in developing efficient biocatalysts in synthetic biology [25,26]. The advantage brought by this feature is that it could significantly reduce the product separation cost and facilitate the re-use of biocatalysts. Therefore, the substrate tolerance of the free and encapsulated (+)-γ-lactamases was investigated at three different concentrations of substrate (Fig. 7). Interestingly, when we used a lower concentration of substrate, the free (+)-γ-lactamase (only 0.1 μM biocatalysts) had a similar or better performance compared to the encapsulated enzyme. However, when we increased the concentration of the substrate to 0.8 M, free enzyme could only weakly hydrolyze the Vince lactam. On the other hand, this had little effect on the activity of the encapsulated (+)-γ-lactamase (Fig. 7). When we further increased the concentration to 1.0 M, the encapsulated (+)-γ-lactamase could also be inhibited; however, it could still slowly hydrolyze the (+)-γ-lactam and reached a 99% ee value after 3 h (Fig. 7). To further investigate the inhibitory effect of substrate, experiments were carried out individually by varying the concentration of substrate. As shown in Additional file 1: Fig. S10, the Lineweaver-Burk plot clearly showed that the reaction rate is inhibited by higher substrate concentrations for both the free and encapsulated lactamase. However, nanocaged enzymes have stronger tolerance against high substrate concentrations than the free enzymes. We propose two explanations for this phenomenon. First, the protein nanocage provides a specific environment for catalysis wherein it controls substrate access and concentration used in the nanocage, thus avoiding inhibition at high concentrations of substrate. Second, it is possible that covalent attachment to the protein shell stabilizes the structure of the encapsulated (+)-γ-lactamase, making it active even after 3 h at high concentration of substrate. These results are also consistent with the thermostability studies, further demonstrating that construction of an organelle like our self-assembling nanoreactor provides efficient access to robust biocatalysts.

Discussion
In natural evolution, one of the biggest differences between eukaryotes and prokaryotes is that eukaryotes have created diverse organelles for compartmentalization of metabolic pathway which is a hallmark of higher cells [27]. Prokaryotes, on the contrast, have evolved only some organelle like protein nanocompartments such as ferritin, encapsulin and the carboxysome [1,3]. Generally, these compartmentalization strategies could bring great advantages to the cells such as keeping the high Fig. 7 Comparison of the substrate tolerance of the free and encapsulated γ-lactamase fidelity of metabolic pathway, improving the efficiency of enzymatic reaction and reducing the toxicity of metabolites to cells [7]. Currently, prokaryotes like E. coli or yeast is the most widely used "microbial cell factories" for synthetic biology due to their clear genetic background, high gene expression level and simple culture conditions. Thus, the ability to create and redesign protein containers for new synthetic purposes in these systems present an unprecedented opportunity for creating nanofactories. Self-assembled proteins with cage-like architectures, such as carboxysome and virus capsids, exist widely in nature [1]. Diverse artificial protein nanocages have also been designed [15,16]. All these materials provide particularly powerful platforms for the development of novel nanoreactors and nanofactories.
Past researches have showed that couples of strategies have been described for the encapsulation of active enzymes. However, they could only be used for in vitro studies. For example, Ouyang et al. used nanogel to encapsulate enzymes which was shown that can improve the biocatalytic activity and stability of biocatalysts [23]. Zhao et al. developed a DNA assembling strategy for enzymes encapsulation and have a similar effect [24]. All these strategies can only be used after the enzymes were purified and cannot be applied for in vivo studies. On the contrast, compartments based on protein-building blocks could be developed into nanofactories both in vivo and in vitro in a precise fashion. Compared with other studies, the work presented here shows several advantages. (1) The cargo protein or biocatalysts could be directly fused with the coat protein, resulting in high copies of catalyst per nanoreactor (60 copies per nanocage). When using other protein nanocage as a nanoreactor, a specific tag must normally be employed to mediate encapsulation of the cargo protein, and copy numbers are low. Moreover, two or more separated proteins need to be prepared to accomplish the encapsulation which make the system complex and inaccurate. (2) Compared with other encapsulation by inorganic/organic nanomaterial, this strategy does not require downstream treatment that could destroy part of the cargo protein. (3) This strategy could significantly improve the stability of the cargo protein and make the biocatalyst superior to its native form. Moreover, it is envisioned that this system could also be used to encapsulate multiple enzymes and develop multistep enzyme cascades nanoreactor. All these make it a promising tool to develop new generation of nanofactories for synthetic biology.

Conclusion
In conclusion, we herein established an efficient and simple method to develop an organelle-like nanoreactor based on protein self-assembling. The engineered ketohydroxyglutarate aldolase from T. maritima could be developed as a universal nano-platform for diverse biocatalysts. The cargo biocatalyst of interest could be easily fused to the C terminus of the engineered aldolase and encapsulated into the microcompartments with high copy number. Using one model biocatalyst-(+)-γ-lactamases, we showed that these unique nanoparticles could protection the enzyme system and improve the catalytic performance. Compared with the free enzyme, the encapsulated lactamase exhibits similar biocatalytic behaviour as evidenced by a similar Km and Kcat, but significantly improved thermostability at high temperature and in the presence of proteases, organic solvent and high substrate load. Thus, our studies provide a general compartmentalization strategy for biocatalysis, synthetic biology and metabolic engineering. We believe that such uniform nanoreactor at nanoscale could find a large variety of application in "Green Chemistry" and "Microbial Cell Factories".

Chemicals and bacterial strains
Q5 DNA polymerase and T4 DNA ligases were purchased from NEB (New England Biolabs). Oligonucleotides of HPLC purity were synthesized by Sangon Biotech (China). The gene for the engineered ketohydroxyglutarate aldolase (Genebank Accession Number: WP_010865041.1) from T. maritima was synthesized by Synbio Technologies (China). The gene was cloned into the pUC57 vector and was subsequently used as a template for PCR reactions. All primers used in this study are listed in Additional file 1: Table S1. Gel Extraction Kits and Plasmid Mini Kits were purchased from OMEGA (USA). Other chemicals were purchased from commercial sources and were of analytical grade.

Cloning and protein purification
The gene sequence encoding for (+)-γ-lactamase from M. hydrocarbonoxydans (Genebank Accession Number: AKS37009) was amplified by PCR from the genomic DNA of M. hydrocarbonoxydans and DNA polymerase. The primers used are shown in Additional file 1: Table S1. Purified PCR products and pET-28a(+) plasmid were digested with NdeI and XhoI restriction endonucleases and ligated with T4 DNA ligase to yield an final expression plasmid (named as pET-28a-MhIHL). Mutagenesis of MhHL was achieved using specific primers listed in Additional file 1: Table S1. The yield plasmid was named as pET-28a-MhIHL-V54L. The engineered ketohydroxyglutarate aldolase from T. maritima was amplified by PCR. The purified products and plasmid pET-28a-MhIHL-V54L were then digested with NdeI and NcoI and ligated with T4 DNA ligase to construct the final expression plasmid (pET28a-Enc-I3-01-MhIHL-V54L) for the nanoreactor.
The DNA ligation products were then transformed into E. coli DH5α cells and individual transformants were cultured to prepare plasmids. After confirmed by DNA sequencing, the resulting expression constructs were used to transform E. coli BL21(DE3) cells and cultured in LB (lysogeny broth) medium at 37 °C with shaking overnight. 50 µg/mL of kanamycin were added to the medium to maintain the plasmid. From these cultures, 0.5-L baffled culture flasks, each containing 200 mL of LB media, were inoculated to OD 600 ≈ 0.01. The culture was then grown at 37 °C to an OD 600 of 0.7 and was placed in a refrigerator for 30 min to cool down. The cells were induced after adding 0.05 mM of IPTG (isopropyl thiogalactopyranoside) and shaken for overnight. All the cells were then harvested via centrifugation (6000 rpm for 10 min). The cell pellets were then suspended in Tris-HCl buffer and lysed by sonication. Supernatant of the lysate was separated by centrifugation and loaded on a Ni-NTA gravity flow column. After a washing step, recombinant proteins were eluted from the Ni-NTA column and combined for further size exclusion chromatography (Superdex 200 column, GE Healthcare). After loaded on the column, proteincontaining fractions were collected, concentrated and flash-frozen using liquid N 2 . The protein concentrations were determined by using a Thermo NanoDrop 2000.

Enzyme assays
0.5 M (rac)-γ-lactam was dissolved in a universal buffer (50 mM Tris, 50 mM boric acid, 33 mM citric acid and 50 mM Na 2 PO 4 , pH was adjusted by NaOH or HCl). for in vitro studies. In a typical assay, ≈ 0.1 μM biocatalysts was incubated with 0.5 M substrates in 500 μL assay buffer. The reaction was performed at 20 °C for 20 min and quenched by addition of TFA (0.1% (v/v)). 20 μL of the reaction solution was then extracted with ethyl acetate (1000 μL). The bioactivity of the free and encapsulated (+)-γ-lactamase was determined by chiral HPLC.

Analytical methods
10 μL of the ethyl acetate extract was then subjected to HPLC. The separation of racemic lactam was achieved by a Daicel Chiralpak AS-H column (Japan) with the following gradient:90% acetonitrile and 10% isopropanol (volume ratio, 1.1 mL/min). The UV absorbance used for detection was 230 nm. The ee and conversion (conv.) were calculated as follows: where S a and S b are represented for the final concentration of each γ-lactam; C 0 and C were the initial and final concentrations of γ-lactam in the reaction.

Negative-stain electron microscopy
TEM (Transmission electron microscopy) data was acquired on a Hitachi H-800 transmission electron microscope. One drop of purified MhIHL-V54L and Enc-I3-01-MhIHL-V54L at 1.33 mg/mL were adsorbed onto a copper grids. After a short adsorption time, the protein samples were stained with 3% uranyl acetate for 0.5 h. Samples were ready for TEM analysis after all the excess liquid was removed. Grids were optimized for data collection and final data collection was performed on a 120 kV JEM1200-EX transmission electron microscope.

Atomic force microscope
For atomic force microscopy, samples were prepared by dropping 5 μL of purified MhIHL-V54L and Enc-I3-01-MhIHL-V54L at 1.33 mg/mL onto freshly leaved mica for 10 min and drying under air. Images were acquired using a scanning probe microscope (Bioscope Catalyst ® AFM, Bruker) operated in tapping mode. Height data analysis was performed using the Nanoscope 1.5 software package.

Circular dichroism spectra and dynamic light scattering (DLS) measurements
CD analysis was conducted on a JASCO J-810 CD. The integration time was set to 4 s and the bandwidth was set to 1 nm. About 2.5 mL 1.33 mg/mL purified MhIHL-V54L and Enc-I3-01-MhIHL-V54L were prepared for CD assay. Ellipticity data of for both the free and encapsulated (+)-γ-lactamase was then continuously collected in the far-UV CD band of 190-260 nm. The spectrum of a buffer was selected as a blank and subtracted. For DLS measurements, purified protein (about 1.33 mg/mL) was measured using a SZ-100 Nanoparticle Analyzer (HOR-IBA). Both the free and encapsulated lactamases were measured at 25 °C. Measurements were taken in the universal buffer (pH 7.0).

Comparison of enzymatic properties
Biochemical characterization was performed using the standard assays as mentioned above. The kinetic parameters of free and encapsulated (+)-γ-lactamase were determined by using different concentration of Vince lactam (20-250 mM) while keeping buffer and enzyme concentrations constant, and done in triplicate. The reactions were performed in 500 μL of assay buffer (pH 7.0) at optimum temperature for 3 min and were quenched by addition of TFA. Lineweaver-Burk plot was used to determine the kinetic parameters. Standard reaction systems were used to determine the optimal temperature for the free and encapsulated (+)-γ-lactamases. The residual activity of the treated enzymes was then measured by HPLC. The control was the one without any heat treatment.
To determine the optimal pH for the free and encapsulated (+)-γ-lactamases, reactions with different initial pH values ranging from 3.0 to 12.0 were formulated with 0.1 μM enzymes and 0.5 M substrates. The reaction was performed at 20 °C for 5 min and quenched by addition of TFA. Resulting mixtures were then measured by HPLC as described before. The pH stability of the free and encapsulated enzymes was investigated by incubation of enzymes in 50 μL assay buffer with different pH values (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) for 30 min at 4 °C. The treated enzymes (final concentration: 0.1 μM) were then assayed with another 450 μL assay buffer (pH 7.0) containing 1 M substrates (final concentration:0.5 M) at 20 °C for 20 min. The reaction was then quenched by addition of TFA and then measured by HPLC. Control was the one without any pH treatment.
To test the effect of polar organic solvent, the free and the encapsulated (+)-γ-lactamases were treated with 40% methanol, 30% N,N-dimethylformamide, 40% acetone, 30% isopropanol, separately. The assays were performed at 4 °C for 10 min and the treated enzymes (0.1 μM) were then assayed with another 450 μL assay buffer (pH 7.0) containing 1 M substrates (final concentration:0.5 M) at 20 °C for 20 min. The reaction was then quenched by addition of TFA and analyzed by HPLC. To assess the proteolytic stability of the free and encapsulated (+)-γ-lactamases, assays were performed using 1 μg of purified enzymes and two different concentration of Trypsin. The assays were performed with 0.2 μg/ μl of trypsin (1:10) or 1 μg/μl of trypsin (1:50) in a 50 mM Tris-HCl buffer at pH 7.0 for different time (1-6 h) at 4 °C. The treated enzymes (final concentration: 0.1 μM) were then assayed with another 450 μL assay buffer containing 1 M substrates (final concentration: 0.5 M) at 20 °C for 20 min. The reaction was then quenched by addition of TFA and analyzed by HPLC as described before.
To assess the substrate tolerance ability of the free and encapsulated (+)-γ-lactamase, assays were performed at 3 different substrate concentrations. Generally, in a typical assay, 0.1 μM biocatalysts were incubated with 0.5, 0.8 or 1 M substrates in 500 μL assay buffer (pH 7.0). The reaction was incubated at 20 °C for different time (0, 5, 10, 15, 20, 40, 60, 80, 100, 120, 140, 160, 180 min, respectively) and quenched by TFA as described before. All the activity was analyzed by HPLC. All experiments were conducted with three sets of parallel. To investigate the inhibitory effect the substrate, experiments were performed by varying the concentration of the substrate. 0.1 μM biocatalysts were incubated with substrates in 500 μL assay buffer (pH 7.0). The reaction was incubated at 20 °C for 3 min and quenched by TFA. The Lineweaver-Burk double reciprocal plot was used to study the inhibitory effect of substrates.
Authors' contributions SZ and GZ designed the experiments. HL performed the experiments. HL, GZ and SZ analyzed the data and wrote the manuscript. All authors read and approved the final manuscript.