Clustering results of synthetic data.
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He graduated and obtained his Ph.D. in Applied Life Sciences from Tokyo University of Agriculture and Technology (Japan) in 2011. He was awarded Japanese government scholarship and he visited University of California at Davis (UCD) as an exchange student in 2010. After his graduation, he became a research fellow at the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) in Heidelberg (Germany). Dr. Ying acts as a reviewer of many scientific journals and has authored or co-authored over 25 scientific publications. 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Cluster analysis found applications in different fields ranging from the main task of data mining applications [2] such as scientific data exploration, spatial database applications, web analysis, marketing, medical diagnostics, computational biology, etc., to statistical data analysis that is used in many fields including machine learning, pattern recognition [3], image analysis [4], information retrieval [5], and bioinformatics [6]. There are different algorithms related to neural networks; the most popular are K-means, the self-organizing map (SOM), neural gas (NG), and growing neural gas (GNG) [7, 8].
The goal of this work is to present a comparison among neural gas (NG), growing neural gas (GNG), and robust growing neural gas (RGNG) approaches that are related to neural networks, as well as design a new simulation tool for the purpose of education and scientific research using unsupervised learning methods. Due to the difficulty in introducing these algorithms in literature, the three techniques have been presented using a simple graphical user interface (GUI) model. Alziarjawey et al. [9] introduced an application of Matlab GUI in the medical field using the ECG signal for heart rate monitoring and PQRST detection. They introduced another application by developing a software package based on GUI, which consists of two modules using many important methods derived from linear algebra [10]. Aljobouri et al. [11] designed an educational tool for biosignal processing and medical imaging using a GUI package. The user friendly package explained in this work can be used easily by: choosing any method, changing the predefined parameters for each algorithm and comparing the results. Hence, it can be used without any programming knowledge. The interested reader may find more technical details in our previous reports and publications [12, 13].
The current study is organized as follows: Section 2 provides the unsupervised clustering algorithms. Case studies are described in Section 3. Sections 4 and 5 present the experimental implementation on the synthetic dataset and clustering package design, respectively. Finally, Section 6 concludes the paper and introduces future work.
In this section, a review of the NG, GNG, and RGNG algorithms are presented. Because of the length and complexity of these algorithms, along with the mathematical model, flowcharts are designed for the three algorithms in this work in order to make it more understandable and easier to write the related codes.
The NG network algorithm is a simple artificial neural network algorithm for finding optimal data representations based on reference vectors (prototype vectors). It was first introduced in 1991 [14] and is based on Kohonen’s SOM [15]. Because of the dynamics of the reference vectors during the adaptation process, this algorithm was called “neural gas” that spread itself as a gas through the data space. NG is unlike other methods that consider distance as a rank like Euclidean distance, but it proposes a new way of calculating the influence of distance. Nearer prototypes in NG algorithm are more affected, but it does not depend directly on the influence of distance.
NG has been successfully applied to clustering [16], speech recognition [17], image processing [18], vector quantization, pattern recognition, topology representation, etc., [19, 20] especially where there is a problem arriving at vector quantization or data compression.
It adapts the reference vectors (prototype vectors) “
The NG algorithm is represented by the dependence of updating strengths for
for
The updating step of adjusting
where:
for
The NG algorithm is widely related to the structure of fuzzy clustering methods [23]. So, NG used the uncertainty of the relationship value
with
Martinetz et al. [22, 26] introduced this cost function and proved that the updating in the Hebb-like learning rule can be derived by a stochastic gradient descent on this function. By starting with a large value of
Due to the sequential learning scheme in NG algorithms and the use of the neighborhood dealing rule, NG became less sensitive to various initializations due to the sequential learning scheme and use of neighborhood cooperation rule with comparison to other clustering algorithms like k-means and FCM.
Before feeding the NG algorithm, there are some parameters that have to be defined:
Figure 1 shows the flowchart of the NG algorithm. Although the NG model has many advantages as mentioned earlier, it also has some limitations. It depends on decaying parameters that change over time; it is incapable of finding a network size and structure automatically and continue learning. Hence, based on the NG algorithm, the GNG algorithm was introduced by Fritzke [24, 25], which has an advantage over NG algorithms through its ability to modify the network topology by removing edges with its age variable. Moreover, during the growth process associated with the neighborhood updating rule, there is no need for the neighborhood sorting step [24, 25]. It has the ability to find a network size and structure automatically, and continue learning, adding units and connections, until a performance criterion is fulfilled.
The flowchart of an NG algorithm.
In the GNG algorithm, Fritzke [24, 27] proposed changing the unit numbers (mostly increased) during SOM network with a variable topological structure [24, 25]. This growth mechanism is combined with topology formation rules using the competitive Hebbian learning (CHL) [26] and the earlier proposed growing mechanism inherited from the growing cell structures [27] to form a new model.
The GNG algorithm needs only constant parameters; it is not required to set the amount of prototypes. The main idea behind the GNG is to start with a minimal network size and insert a few new neurons and connections respectively in a growing structure by using a vector quantization until the desired characteristics of the model is fulfilled (e.g., net size, time limit, predefined number of neurons inserted, quality or some performance measure). To determine where to insert new units, local error measures are gathered during the adaptation process. Each new unit is inserted near the unit that has accumulated the highest error, and a connection between the winner and the second nearest neuron is formed using the competitive Hebbian learning algorithm.
Before feeding the GNG algorithm, there are some parameters that have to be defined:
Each reference vector
The pre-specified maximum number of prototypes or neurons is set to grow as
Figure 2 presents the flowchart of the GNG algorithm. This figure shows that nonfunctional prototypes that do not win over long time intervals may be detected by tracing the changes of an age variable associated with each edge. Hence, the GNG algorithm has an advantage against the NG algorithm through its ability to modify the network topology by removing edges with their age variable (not being refreshed for a time interval α_max) and the resultant nonfunctional prototypes. In the GNG algorithm, the growth process associated with the neighborhood updating rule used is somewhat similar to the neighborhood, decreasing procedure in NG. However, unlike the NG algorithm, there is no need for the neighborhood sorting step.
The flowchart of the GNG algorithm.
Any robust algorithm should have the following features [28]:
It should achieve a good precision for the given model.
The performance of the given model may have few deviations from the assumptions made, but these deviations should not weaken the performance, except by a small degree.
The presence of large deviations from the model assumptions should not cause disaster.
If classical clustering methods are to be used as prototype based clustering algorithms, the major robustness problems are the sensitivity to initialization, the order of input vectors, and existence of many outliers, but each well executed regarding condition 1. Due to the growth scheme associated with the GNG algorithm, the algorithm faces the “dead nodes” problem. This occurs due to inappropriate initializations that led to some prototypes that may never win through the training process.
Even with initialization-insensitive clustering methods, good clustering results may not be obtained if the order of the input sequence is not chosen properly.
Even with the initialization insensitive clustering methods, good clustering results may not be obtained if the order of the input sequence is not chosen properly. As well as the introduced problem gets along with the sensitivity for initialization and the order of input vectors, there also another problem attributable to the existence of many outliers. This implies the GNG network may fail to differentiate the outliers from the inliers through the original prototype updating rule when many of outliers exist in a data set. These outliers can be regarded as input vectors that different from data points belonging to the ordinary clusters (inliers).
For these limitations of the GNG algorithm, a novel robust clustering algorithm was proposed [29] within the GNG structure, namely the robust growing neural gas (RGNG) network. RGNG possesses better robustness than the original GNG algorithm because of its succession properties. It also incorporates with it several robust strategies, such as outlier resistant scheme, adaptive modulation of learning rates, and cluster repulsion method.
Therefore, compared to the GNG network, the RGNG network is insensitive to initialization, input sequence ordering, the presence of outliers, and determination of the optimal number of clusters. The minimum description length (MDL) value was used with RGNG as the clustering validity index [30, 31]. The MDL value is used to find the optimal number of clusters and their center positions corresponding to the smallest MDL. This determined automatically the optimal number of clusters by searching the extreme value of the MDL measure through the network growing process.
Before feeding the RGNG algorithm, there are some parameters that have to be defined:
The maximum number of nodes may be set to increase the
Figure 3 presents the flowchart of the RGNG algorithm.
The flowchart of the RGNG algorithm.
In the presented work, the performance of the NG, GNG, and RGNG algorithms on synthetic data are described. The cases studies are carried out to compare the performance of the three approaches. The experimental results on a public synthetic dataset are presented in the next section. Comparison of different neural networks and the need for such performance parameters using statistical evaluations has been recently highlighted by a number of researchers.
There are four parameters that are used in this work to evaluate the performance of the proposed clustering technique. These performance measures are: classification rate (CR), average partition quality (PQ), minimum cluster number (MCN), and mean square error (MSE). A robust clustering technique should be less sensitive to parameter configurations and give better performance under the same parameter settings in all experiments.
In the following experiments, the parameters are fixed for each technique with typical values suggested in literature. The RGNG technique was set with the typical values provided by Qin and Suganthan [29]:
Each index of the performance measures is explained in the following sections.
This index refers to the classification rate (CR) for the whole dataset so that each data point is classified according to its nearest prototype. CR is based on using a majority voting classifier [32] by labeling all prototypes using a simple voting mechanism. According to the proposed technique, the numbers of prototypes are small, so the resulting CR will not be high.
This index refers to the average partition quality (PQ) measurement, which is averaged over all the independent runs in the experiments. PQ was defined by Hamerly and Elkan [33], as:
where:
The number of classes
The
The minimum cluster number (MCN) is the average number of detected clusters by the techniques. The MCN indexes the ability of the techniques to find the underlying natural clusters. During the training of the techniques and according to the MCN value, only the proposed RGNG approach can find the actual number of clusters successfully.
During the growing process, this value is defined as the number of natural clusters in which the algorithm places at least one prototype when the number of prototypes in the network reaches the actual number of clusters. Cluster numbers detected by NG and GNG during the growing process deviate from the actual value of clusters when the number of prototypes is the same as the actual number of clusters.
Mean square error (MSE) is another criterion used for evaluating the performance of the proposed clustering technique. The MSE value represents the mean distance between the current nearest prototypes’ positions resulting from the application of the techniques and the actual cluster centers.
The average MSE value in this experiment is higher for NG and GNG techniques than the RGNG technique. This indicates that the RGNG approach achieves the best accuracy with the strongest stability among the three approaches.
There are six different types of 2D synthetic datasets [29, 35] which are used in this work. They are snail, screw, ring, set3, set5, and set25 dataset. Figures 4–6 show the plots of NG, GNG, and RGNG clustering with three types of 2D synthetic datasets (screw, set5, and snail) as an example. The number of neurons are selected randomly, N = 7, 10, and 12.
Clustering with screw synthetic dataset for N = 7, by running NG, GNG, and RGNG techniques.
Clustering with set5 synthetic dataset for N = 10, by running NG, GNG, and RGNG techniques.
Clustering with snail synthetic dataset for N = 12, by running NG, GNG, and RGNG techniques.
These figures cannot clearly differentiate between each method. Hence, four parameters are used in this work to evaluate the performance of the proposed clustering techniques: CR, PQ, MCN, and MSE introduced in the previous section. For the best comparison with RGNG, MDL criterion is added to NG and GNG techniques. The training results of these techniques with synthetic data are shown in Table 1, where the number of neurons is chosen randomly as N = 7, 10, and 12.
Parameters | Number of neurons | NG | GNG | RGNG |
---|---|---|---|---|
CR | N = 7 | 0.8718 | 0.9686 | 0.9929 |
N = 10 | 0.8514 | 0.9786 | 0.9843 | |
N = 12 | 0.8010 | 0.9647 | 0.9759 | |
MCN | N = 7 | 9 | 8 | 7 |
N = 10 | 12 | 11 | 10 | |
N = 12 | 15 | 14 | 12 | |
PQ | N = 7 | 0.8990 | 0.9465 | 0.9869 |
N = 10 | 0.8531 | 0.9288 | 0.9841 | |
N = 12 | 08279 | 0.9043 | 0.9807 | |
MSE | N = 7 | 2.8032e+004 | 2.7608e+004 | 2.6493e+004 |
N = 10 | 2.7913e+004 | 2.7378e+004 | 2.6351e+004 | |
N = 12 | 2.7703e+004 | 2.6940e+004 | 2.6188e+004 |
Clustering results of synthetic data.
According to literature [29, 36], the clustering output results introduced in Table 1 clarified that RGNG approach is insensitive to different initializations and the presence of outliers. In these techniques, the number of neurons used is small, so the CR values registered in the table are not high. In all the three clustering techniques, the number of neurons was equal to the actual cluster number. RGNG can effectively locate the actual number of clusters compared to the other two methods; NG and GNG fail with higher cluster numbers in the synthetic case.
The registered values of the MCN show that the number of detected prototypes or clusters in the RGNG technique is less than the others; which means that its ability to group data in actual number of clusters is better than the other two techniques. For example, when N is set to 10, the MCN value for RGNG is 10, which is less than that for NG and GNG values. The MCN value for running RGNG is equal to the number of neurons, 10, and has the same rate when compared with other N values; while the MCN value of running NG and GNG deviated from the actual cluster number.
Regarding the PQ value, it is noticed that the RGNG approach possesses higher PQ values than the NG and GNG techniques. For example, when N is set to 12, the PQ value for RGNG is 0.9807, which is higher than that of NG and GNG values. These high values of PQ indicate that the RGNG technique has a better partitioning quality with respect to the others, and finds more representative clusters.
Moreover, the RGNG method can find all the natural clusters during the growing stage with the correct number of prototypes. Hence, the MSE values are lower, which indicates that the RGNG technique has better robustness. For example, when N is set to 7, the MSE value for RGNG is 2.6493e + 004, which is lower than that for NG and GNG values. NG and GNG techniques may not detect all the actual clusters; hence, they yield higher MSE values.
The MDL value is one of the popular information theory evaluation measures that are used as clustering validity indexes [37]. The MDL criterion gives the ability of finding the optimal number of clusters and their center positions, corresponding to the smallest MDL value.
The average MDL values during the growth stages are plotted versus the number of clusters or prototypes. Figure 7 shows the curves for the NG and GNG techniques combined with the MDL criterion, as well as the RGNG approach on a synthetic dataset for different number of neurons, which are selected randomly as N = 7, 10, and 12. Each detected the cluster number corresponding to the MDL value.
MDL values versus the number of clusters running the NG, GNG, and RGNG techniques on synthetic data, for: (a) N = 7; (b) N = 10; (c) N = 12.
In RGNG, the smallest MDL value was recorded on average with respect to NG and GNG combined with the MDL principle. For example, in Figure 7 (b), the smallest MDL value is 2.65 that is obtained from running RGNG when N is equal to 4. While in the same N = 4, higher MDL value of 2.77 is recorded from running NG and GNG. From the presented figures, it is concluded that the proposed RGNG approach is insensitive to different initializations and the presence of outliers and can successfully find the actual number of clusters.
The techniques introduced in this work are designed and implemented in a simple software package tool that allows users to interact with the clustering techniques and output data easily [13]. Figure 8 shows the main window with the most important features of the designed prototype-based clustering software package.
Selection data: The user can select any one type of data from the different synthetic 2D datasets in the pop-up menu. Ring data is a 2D synthetic data selected as an example in Figure 8.
Load data: The selected data are loaded and all information related to the selected data (“Dimension,” “Name,” and “Type of Data”) appear in the “info” window. The dimension of the selected “Ring” data is 400x2 double. The selected data is plotted on sketch1 inside the main clustering window of Figure 8.
Main window of the prototype-based clustering software package.
Figure 9 shows some of selected 2D synthetic datasets from the different datasets that were used in this work. Beside each plot, the information related to it is shown in the “info” window, in the left side of each plot.
3. Selection technique: The user can select one of the clustering techniques NG, GNG, or RGNG. The RGNG technique is selected as an example for the training in Figure 8 with Ring data and N = 18, which is selected randomly.
Different datasets with their information: (a) snail data; (b) screw data; (c) ring data; (d) Set5 data.
Before clicking on “Apply NG,” “Apply GNG,” or “Apply RGNG” button, the training parameters related to each technique must be defined. As explained in Section 3, the training parameters must be set carefully within the limited range. The number of neurons (N) as well as the other parameters related to the selected technique must be defined. Another example of using the RGNG technique with Set3 dataset is shown in Figure 10. RGNG training parameters are set as the typical values in literature:
4. MDL plot: This panel is related to plotting MDL values versus the number of neurons (N) running the RGNG, GNG, and NG combined with MDL criterion. This panel includes three main buttons: “No. of neurons (N),” “Technique selection for MDL value,” and “Apply MDL versus N” buttons, as shown in Figure 11.
RGNG clustering with Set3 data (N = 14).
Comparison of MDL values for N = 16.
After defining the number of neurons (N); one, two, or three of the training techniques have to be selected for comparing the MDL results. In the “Technique selection for MDL value” pop-up menu, there are seven selections—either show the result of each technique alone, two of them, or three of them for easy comparison. After clicking on the “Apply MDL versus N” button, the output results of MDL values are plotted with respect to the number of neurons (N) in Sketch2.
Figure 11 shows an example of the MDL plot, defining N = 16 and choosing “RGNG & GNG & NG” for comparing the results of the three techniques in Sketch2. For easy and best comparison between the MDL values of the three techniques, the output results sketch in the same figure.
A simple user friendly software package is designed and implemented as an automatic clustering model for any dataset to use as part of the neural network course. NG, GNG, and RGNG algorithms are performed in the same package using a MATLAB-based graphical user interface (GUI) tool. This visual tool lets the students/ researchers visualize the desired results using plots obtained with the click of a few buttons. The performance of these algorithms on 2D synthetic datasets is reported with respect to statistical estimations to explain the meaning of the output results. These results clarified that RGNG is better than NG and GNG when considering insensitivity to initialization as well as the presence of outliers. RGNG enhances GNG to be more robust toward noisy input dataset by using MDL criteria. Hence, RGNG solves the problem of finding the optimal number of clusters with respect to NG and GNG.
For future research directions, other unsupervised or supervised clustering algorithms may be used in the laboratory experiments. Another research direction is to apply the comparison among the three clustering algorithms to real multimodal datasets in medical applications. The package results could also be shared to websites using ASP .NET, which can give facility for users by sharing applications which requires no installation of MATLAB or any special program just a Web browser.
The history of tuberculosis (TB), the disease caused by Mycobacterium tuberculosis (Mtb), has a remarkable involvement in human history; particularly in the evolution of human society and in the development of many scientific disciplines. TB has a negative role in many pages of human history, taking lives of many renowned artists, politicians, as well as poor, wealthy, young, or adult individuals. After its apparent “resurgence” at the end of the last century with the concomitant arises of HIV/AIDS cases, TB has been mainly associated with poverty and immunosuppression. Mtb was the model microorganism that inspired Koch to develop his postulates which are a cornerstone of sciences such as Microbiology and Immunology. What Robert Koch never probably imagined was that over a century later, this disease was going to continue as the leading cause of death, mostly because of the increasing number of drug-resistant TB cases. Regarding TB treatment, it is discouraging to see how this lethal disease only started to be cured and controlled during 1950s with the discovery of the first chemotherapy. It is hard to imagine the labor of a clinician taking care of TB patients before 1950s without an available treatment option. However, this same scenario is similar to what many health providers face nowadays with the current spreading of drug-resistant cases, particularly multidrug resistant (MDR) and extensively drug resistant (XDR) TB cases. The majority of drug-resistant Mtb strains found in clinical settings emerge due to mutations in genes that are involved in the antibiotic mode of action (drug activator, drug target, etc.). These bacterial genes have important roles in bacterial metabolism and pathogenicity. Therefore, the study of drug-resistant Mtb strains has been evolving from the exploration of the associated genotype (i.e. specific resistance-conferring and compensatory mutation(s)) to the entire phenotypic impact that mutation events confer to the bacteria beyond the drug resistance feature. The study of the global drug-resistant phenotype in Mtb thorough comprehensive system biology approaches (such as genomics, proteomics, and lipidomics) is expected to reveal important aspects that will help TB researchers in the development of new anti-TB chemotherapies and overcome the current global challenges toward an effective TB control. This chapter will describe an overview of Mtb physiology and metabolic pathways as an important scaffold to understand the physiological changes that some Mtb strains (specific genotypes) undergo after acquiring resistance to the major anti-TB drugs: isoniazid (INH) and rifampicin (RIF) from a biochemical perspective.
Mtb physiology is a broad subject comprising the study of the function and activities of this bacterium and its parts. In this chapter, we will narrow the study of Mtb physiology to its major metabolic pathways and cell envelope, particularly in the context of drug resistance.
Mtb has the ability to use very variable carbon sources in vitro such as carbohydrates, alcohols, and lipids (including cholesterol and fatty acids) (reviewed in Ref. [1]). Similar to other representative species of the Actinomycetales order, Mtb possesses a predominant aerobic metabolism, with the genes encoding for enzymes of the main energetic metabolic pathways such as glycolysis, tricarboxylic acid (TCA) cycle, and pentose phosphate pathway. Despite the genetic evidence of a complete TCA cycle in Mtb [2], there is no sufficient biochemical evidence to show the presence of all enzymatic reactions of the TCA cycle in Mtb. In fact, some researchers propose that TCA cycle in Mtb is not complete because this organism lacks the alpha ketoglutarate dehydrogenase (α-KDH) enzyme [3, 4, 5]. Instead, alpha ketoglutarate decarboxylase (α-KGD and Rv1248c) and succinic semialdehyde dehydrogenase (GabD1/2, Rv0234, and Rv1731, respectively) are proposed as the enzymes that catalyze the step from alpha ketoglutarate (α-KG) to succinate in Mtb TCA cycle under normoxic conditions. Particularly, α-KGD catalyzes the production of succinic semialdehyde, which can then be converted to succinate by GabD1/2 [5]. Also, experimental evidence suggests that Mtb operates a reversed TCA cycle with the reduction of fumarate to succinate to maintain the membrane potential in the absence of oxygen [6].
Mtb also has the glyoxylate shunt which allows the bacteria to bypass some enzymes of the regular TCA cycle under specific metabolic conditions (hypoxia or growth on fatty acids as carbon source) [2]. Under anaerobic conditions, the enzyme isocitrate lyase (Icl) (which is proposed to be required for virulence [7]), together with the α-KG ferredoxin oxidoreductase are believed to complete the cycle effectively bypassing the conversions of α-ketoglutarate to succinate to fumarate [8].
Mtb has the ability to use enzymes in multiple metabolic pathways to prolong its survival, a feature that is known as metabolic plasticity. For instance, Icl not only participates in the glyoxylate shunt and the methyl citrate cycle but also protects Mtb from the oxidative stress generated by the treatment with isoniazid (INH), rifampicin (RIF), and streptomycin [9]. Another example is the dihydrolipoamide dehydrogenase (Lpd) that can act as the E3 component of the pyruvate dehydrogenase or can provide electrons to the dihydrolipoamide succinyltransferase (DlaT, previously known as SucB) or be part of the branched-chain keto acid dehydrogenase complex to metabolize branched-chain amino acids [9]. On the other hand, Mtb has pathways with redundant enzymes (that include a variety of isozymes) that can catalyze the same reaction, which guarantees that vital processes occur despite possible external or internal stresses. A good example of this is the fatty acid degradation or β-oxidation pathway, which suggest that Mtb not only has a high lipid catabolism activity, but also that this is crucial part of its own metabolism [1].
Lipid metabolism is a highly relevant physiologic process in Mtb, with more than 6% of the genome devoted to these reactions and almost 20% of the genome encoding for genes related to cell wall processes. Lipid metabolism is an important part of this chapter as some enzymes of the lipid biosynthetic pathway are the target of anti-TB drugs such as INH and ethionamide. Compared to Escherichia coli, Mtb possess five times more enzymes dedicated to lipid metabolism. Mtb lipid metabolism is more lipolytic than lipogenic, probably as a result of the wide variety and amount of lipid sources in the human host as well as in the bacterial envelope [2]. For this reason, the first part of this chapter will focus on fatty acid degradation with a subsequent description of recent findings regarding fatty acid synthesis. Fatty acid degradation is a key process in Mtb metabolism and can explain some of its metabolic plasticity, while fatty acid synthesis is crucial in the understanding mechanism of action of the previously mentioned anti-TB drugs.
Fatty acid catabolism in Mtb is a process of successive oxidations where the β-carbon of the fatty acid is oxidized to a carbonyl group (Figure 1). In this process, the main goal is the synthesis of acetyl-CoA and reduced cofactors (such as NADH, FADH2) that can fulfill energy requirements in the cell and also intermediates that can serve as substrate for anabolic processes [2]. Specifically, odd-chain fatty acids produce acetyl-CoA while even-chain fatty acids produce acetyl-CoA and propionyl-CoA in addition to acyl-CoA derivatives missing two carbon units [2, 10]. By studying the Mtb genome, Cole et al. found at least 35 genes encoding for enzymes that catalyze the first step of fatty acid degradation only. As shown in Figure 1, most reactions in this pathway can be carried out by several isozymes. Of these, EchA5 and FadB3 are essential for Mtb growth and considered possible drug targets [11].
β-Oxidation of fatty acids in Mtb. Greek nomenclature indicates the different oxidations that take place in the β-carbon. The enzymes show the different number of identified isozymes that participate in this cycle.
The complexity of Mtb lipids can be partially explained by the fact that Mtb has both fatty acid synthases (FAS), type I and II. Cole et al. described the main enzymes of FAS I and II at the genetic level and recent reviews have compiled previous biochemical work, all of which have generated a better understanding of the complex pathways responsible for mycolic acid synthesis in Mtb [12, 13, 14]. The characterized enzymes that participate in FAS I and FAS II are shown in Table 1. FAS I is found mainly in eukaryotes and all the reactions are performed by a single multidomain homodimeric enzyme Fas (Rv2524) that has a mass higher than 300 kDa [2, 14]. This enzyme has seven catalytic domains: acyltransferase, enoyl reductase, dehydratase, malonyl/palmitoyl transferase, acyl carrier protein, β ketoacyl reductase, and β ketoacyl synthase [12]. Fas (Rv2524) uses acetyl-CoA and malonyl-CoA as substrates for the synthesis of acyl-CoA derivatives of 16 and 18 carbon units which are in turn used for the synthesis of membrane phospholipids. FAS I route also produces an acyl-CoA derivative with 26 carbon units that becomes the short α-alkyl chain or α-branch of the mycolic acids. FAS I and II are connected by the synthesis of acyl-CoA derivatives with 20 carbon atoms that are used in the FAS II pathway as the starting molecule for the elongation of mycolic acids (reviewed in Ref. [14]).
Description | Gene | Rv number | Enzyme |
---|---|---|---|
FAS I | fas | 2524 | Fatty acid synthetase |
Transition FAS I to FAS II | fabD | 2243 | Malonyl-CoA ACP transacylase |
accD6 | 2247 | Acetyl/propionyl-CoA carboxylase (beta subunit) | |
acpM | 2244 | Acyl carrier protein | |
fabH | 0533 | β-Ketoacyl-ACP synthase III | |
FAS II | kasA/B | 2245/2246 | β-Ketoacyl-ACP synthase |
fab1 or MabA | 1483 | β-Ketoacyl-ACP reductase | |
hadA/B/C | 0635/0636/0637 | (3)-hydroxyacyl-ACP dehydratase subunit A/B/C | |
htdX | 0241 | 3-hydroxyacyl-thioester dehydratase | |
echA10/11 | 1142/1141 | Currently annotated as a enoyl-CoA hydratase, but proposed to be 2-trans-enoyl-ACP isomerase | |
inhA | 1484 | 2-trans-enoyl-ACP reductase | |
Modifications | |||
Desaturases | desA1/2/3 | 0824/1094/3229 | Acyl carrier protein desaturase |
Methyltransferases (methylation, oxygen function introduction and cyclopropanation) | mmaA1 | 0645c | Methoxymycolic acid synthase 1 |
mmaA2 | 0644c | Methoxymycolic acid synthase 2 (distal cyclopropane in α-MA, proximal cis-cyclopropane in keto-MA) | |
mmaA3 | 0643c | Methoxymycolic acid synthase 3 (oxygenated MA) | |
mmaA4 | 0642c | Methoxy mycolic acid synthase 4 (oxygenated MA) | |
cmaA1 | 3392c | Cyclopropane-fatty-acyl-phospholipid synthase 1 (distal position) | |
cmaA2 | 0503c | Cyclopropane-fatty-acyl-phospholipid synthase 2 (proximal position-specific in methoxy-MA) | |
Mycolic acid modification | pcaA (umaA2) | 0470c | Mycolic acid synthase (proximal cyclopropanation function α-MA) |
umaA | 0469 | Mycolic acid synthase | |
Clainsen-type condensation | accD4 | 3799c | Acyl-CoA carboxylase |
accD5 | 3280 | Acyl-CoA carboxylase | |
fadD32 | 3801 | Fatty-acid-AMP ligase | |
pks13 | 3800 | Polyketide synthase-13 | |
Mycolic acid processing | mmpL3 | 0206 | Transmembrane transport protein-3 |
Rv3802 | 3802 | Proposed to be a Mycolyltransferase I, recently shown to have phospholipase and thioesterase activity | |
cmrA | 2509 | Reductase | |
fbpA/fbpB/fbpC2 | 3804c/1886c/0129c | Fibronectin-binding protein ABC or antigen 85 complex |
Enzymes that participate in the FAS I and II pathways in Mtb.
There are important aspects to highlight regarding FAS I and II in Mtb. First, proteins FabD, AcpM, and FabH act in the transition between FAS I and FAS II, generating ACP derivatives (the substrate required for the FAS II pathway). Second, there are two known Claisen-type reactions occurring: one before the FAS II starts (responsible for the condensation of malonyl-ACP with acyl-CoA and catalyzed by FabH) and one shared with the polyketide synthase system (catalyzed by Pks13). The latter reaction generates a carbon-carbon bond between two activated fatty acids at the end of the mycolic acids synthesis. This second condensation takes the α-branch (produced through FAS I) and the longer meromycolate chain (produced through FAS I and II) to form a “pre-mature mycolic acid” [13, 14].
Regarding FAS II specifically, this pathway is involved in fatty acid elongation instead of de novo synthesis (contrary to what occurs in most bacteria, where FAS II has de novo synthesis capacity) [12]. Mtb needs to use both FAS I and II to generate its characteristic mycolic acids [13, 14]. Therefore, the study of mycolic acids synthesis is in fact a study of both FAS pathways in Mtb. In FAS II, there is one different enzyme for each specific step, allowing for various levels of regulation. Most of the core enzymes of FAS II are NADPH or NADH dependent and organized in different clusters distributed through the genome (Figure 2 and Table 1). FAS II can be further divided into type I and type II elongation (E1 FAS II and E2 FAS II, respectively). Here, both types are catalyzed by the core proteins InhA, MabA, HadABC, and FabD, and elongation can be done by either KasA (E1) or KasB (E2). Despite the sequence homology between the condensases KasA and KasB, they are predicted to participate in two different stages during the FAS II pathway: KasA may catalyze the first elongation steps (E1-FAS II) while KasB might be involved in the later steps (E2-FAS II), ultimately producing full-length mycolates with more than 40 carbon units [12, 13]. A representation of matured α-mycolic acid is depicted in Figure 2B.
(A) Major operons involved in mycolic acid synthesis in Mtb. (B) Structure of an alpha-MA, the color represent the source of the carbon chain by either FAS I (light gray) or FAS II (black). P, proximal; D, distal.
The meromycolate chain resulting from FAS II cycle can be “decorated” with chemical modifications such as cyclopropanations and methylations that are introduced before the second Claisen-type reaction occurs. These modifications can be at distal or proximal positions and are carried out by S-adenosyl-methionine (SAM)-dependent methyl transferases (Table 1). Unsaturations on the other hand, are proposed to occur differently under aerobic or anaerobic conditions. The method of double bond introduction in mycolic acid in Mtb, however, remains unclear. Under aerobic conditions, desaturases encoded by desA1, 2, and 3 and other candidates such as Rv1371 are believed to complete the double bond introductions at the distal position, before the Claisen-type condensation take place. Under anaerobic conditions, unsaturations are believed to take place during the FAS II cycle in the transition of the trans 2-enoyl intermediate to its 3-cis isomer in the distal position, resembling what FabM does in Streptococcus pneumoniae [13]. By sequence homology, this enzymatic reaction could be mediated by EchA10 and EchA11 in Mtb; however, there is not enough experimental evidence to support this hypothesis (Table 1). Finally, the oxygenated mycolic acids (keto and methoxymycolic acids) have a common precursor (hydroxymycolate) that is synthesized by the action of the SAM-dependent methoxymycolic acid synthase 4 (MmaA). The synthesis of methoxymycolic acid is additionally driven by the MmaA3 enzyme (Table 1) [12, 14].
After the modification in the meromycolate chain and the last condensation reaction occur, a mycolic acid (either α-, keto, or methoxymycolic acid) molecule is formed and can be attached to a trehalose molecule by the action of the Corynebacterineae mycolate reductase A, encoded by Rv2509 (also known CmrA) [15]. Once the mycolic acid is covalently linked with trehalose to form trehalose monomycolate (TMM), it is transported to the cell wall by the protein MmpL3 [16]. TMM is then the source of the mycolyl group for arabinogalactan and for other TMMs in the cell wall, generating trehalose dimycolate (TDM); in a reaction catalyzed by the fibronectin-binding proteins (Fbp) ABC ([17], reviewed in Refs. [14, 18]). Much of the understanding of the FAS I and II routes has been based on sequence homology with reference bacterial strains and mutation analysis using model organism such as Mycobacterium smegmatis and Mycobacterium phlei. Despite the vast knowledge about the mycolic acid synthesis pathway, many unanswered questions remain regarding components of the FAS II pathway that are under current research [13].
In general, reduction-oxidation (i.e. redox) reactions are highly relevant for Mtb, since they not only comprise the necessary defence mechanisms developed to combat the host response during the infection, but they are also part of its own bacterial metabolism [19]. Redox reactions could generate endogenous or exogenous stress for the bacteria. The endogenous redox stress is generated during aerobic or anaerobic respiration, where Mtb is exposed to reactive oxygen (ROI) and reactive nitrogen intermediates (RNI), generated when the bacterium uses oxygen and nitrogen as the final electron acceptor in the electron transport chain, respectively [20, 21]. RNI can be also generated when Mtb relies on glutamate metabolism for survival. During host-infection, Mtb can experience a wide range of oxygen levels that can drastically alter its metabolism going from hyperoxic stress (when is in aerosol droplets) to low oxygen tension (during the intracellular phase in alveolar macrophages) to finally hypoxic to anoxic stress (in granulomas). Additionally, inside the macrophage, Mtb is exposed to both ROI and RNI. Hydrogen peroxide (H2O2) and the superoxide radical (O−2) are the two most common ROI forms that are produced by macrophages and neutrophils to eliminate Mtb [22]. During hypoxic conditions, the alteration in redox homeostasis leads to a higher NADH/NAD+ ratio which generate superoxide radicals that disrupt the redox balance in the cell. Consequently, enzymes with heme and sulfur complexes (i.e. cytochrome C, aconitase) can be severely affected. Therefore, the ability of Mtb to survive the redox stress from the host determines its success during the infection process. This stress has an impact on the bacterial metabolic pathways as well as on the expression of virulence factors [20, 21].
Intracellular or exogenously originated reactive oxygen species (ROS) and RNI have the potential to damage lipids, DNA, and proteins by oxidation, peroxidation, and nitration reactions [23], which can result in protein inactivation, and alteration of both cell organization and signal transduction. Therefore, it is crucial to successfully maintain redox homeostasis to keep the integrity of the cell. Intracellularly, the changes in the redox and nutrient levels are sensed by WhiB proteins (WhiB1-7) while extracellularly different molecules such as nitric oxide (NO), carbon monoxide (CO), and H2O2. The reduced and oxidized forms of the nicotinamide adenine dinucleotide (NADH/NAD+) can work as sensors that induce a direct transcriptional response or indirectly alter transcription through a two-component regulatory system such as DosRS-DosRT [2, 21]. Moreover, different bacterial enzymes participate in the neutralization of the host-induced ROI and NOI such as superoxide dismutase (SodA), catalase-peroxidase (KatG), and the antioxidant complex formed by alkyl-hydroperoxidases (AhpC and AhpD), dihydrolipoamide acyltransferase (DlaT), and dehydrogenase (LpdC). Other enzymes in the redox metabolism include the peroxiredoxins (AhpE, TPx, Bcp, and BcpB) and thioredoxins (TrxA, B, and C).
Of these, KatG also plays a central role in Mtb resistance to INH. Mtb has only one single copy of katG with a coding sequence of 2223 base pairs (bp) generating a 704 amino acid protein with a molecular weight of approximately 80.6 kDa. KatG is presented as a dimeric haemoprotein that belongs to class I peroxidase superfamily, because of its high homology with yeast cytochrome C peroxidase [24]. KatG activates the prodrug INH, however its functions extends beyond this activation. This enzyme is in fact one of the most important catalase-peroxidases that help the bacterium overcome external and internal redox stress. KatG possesses a monofunctional catalase, broad-spectrum peroxidase, and peroxynitritase activity [25, 26]. The catalase-peroxidase activity is in the N-terminal domain of the protein that contains a heme-binding motif, however, the C-terminus is also required for its catalytic function [24, 27]. KatG activity has also been associated with virulent Mtb strains, which are able to infect for longer periods and cause increased pathology in the host [28, 29].
As discussed above, redox reactions play an important role in bacterial respiration. In the next section, details about the cellular respiration process in Mtb are discussed. The relation with this topic and this chapter is based on the association of drug resistance mutations in important genes such as katG. The mutations in these redox homeostasis genes possibly generate an alteration in the respiration complexes in Mtb as well.
Given the dynamic Mtb lifestyle, respiration in the bacterium should be highly adaptable. Specifically, during respiration, Mtb uses oxygen and other compounds (such as fumarate or nitrate) as the final electron acceptor depending on the specific bacterial metabolic status and the surrounding environment [30, 31]. The respiratory apparatus is responsible for generating ATP and reduced coenzymes (NADH and/or FADH2). Respiration is made possible by selected membrane-associated asymmetric complexes that allow for generation of proton motive force (PMF) and ATP, which are the major sources of energy in the cell. Different from other model organisms such as E. coli or Bacillus subtilis, Mtb obtains the majority of its ATP by the electron transport chain and the F1F0-ATP synthase machinery, with very little contributions from substrate level phosphorylation [31]. In fact, the ATP synthase is a recently successfully exploited target for developing anti-TB drugs of the drug class diarylquinolines such as the recently described clinical drug bedaquiline [31, 32]. Specifically, diarylquinolines interact with the transmembrane subunit C of the ATP synthase machinery [33]. This again emphasizes the importance of ATP synthase machinery in the respiration process in Mtb.
Most of the Mtb enzymes/complexes involved in aerobic respiration have been identified and are composed primarily of two NADH dehydrogenases (NDH-1 and NDH-2) and two terminal cytochrome oxidases (aa3-type cytochrome C oxidase and bd-type cytochrome oxidase). These enzymes participate in oxygen reduction and are coupled to generate the PMF that is used by the ATP synthase for the production of ATP. NDH-1 is encoded by the nuo operon (nuoA-N) and NDH-2 is present in two copies encoded by ndh and ndhA. Previous studies demonstrated that NDH-2 does not have a proton-translocating-function and is the main dehydrogenase in Mtb. NDH-2 reduces menaquinone to menaquinol that in turn can be oxidized by one of the terminal aa3-type cytochrome C oxidase and bd-type cytochrome oxidase complexes. Because the bd-type cytochrome oxidase (CytA-B) is not coupled to proton pumping, the direct oxidation of menaquinol by this oxidase is less energetically efficient compared to the aa3-type (CtaC-F). Instead, the oxidation of menaquinol can happen in a two-step process with the participation of the cytochrome bc1 complex (QcrA-C) and the terminal aa3-type cytochrome oxidase (CtaC-F) with a higher energy yield [31, 32].
Contrary to aerobic respiration, mediators in Mtb anaerobic respiration are poorly defined. However, in vitro hypoxic studies have allowed the identification of some important enzymes involved in this process. In a reduced-oxygen environment, the nitrate reductase (NarG-I), the nitrate transporter (NarK-2), and the NDH-2 dehydrogenase are upregulated. On the other hand, the ATP synthase subunits and the aa3-type cytochrome oxidase are downregulated. During a low oxygen tension, the bd-type cytochrome oxidase is believed to be more utilized since it has a higher affinity for oxygen. ATP synthase is still active although at a lower membrane potential not commonly seen in other organisms, underlining the importance of PMF in keeping the bacterium alive during this metabolic state. This could be a regular scenario for Mtb inside the granuloma driving low metabolic activity with low or no Mtb growth (dormancy) [22]. Also, in the absence of oxygen, Mtb uses a set of reductases (such as succinate/fumarate reductase and nitrate reductase), hydrogenases (coupling H2 oxidation to respiration, encoded by Rv0082 and Rv0087), and ferredoxins (such as the encoded by fdxA) that preserve the PMF for bacterial survival [30, 31]. Other changes have been detected in anaerobic adaptation, for instance, the E1 subunit of the pyruvate dehydrogenase is upregulated. Under anaerobic conditions, Mtb can stay alive but its growth is strongly reduced [31]. This theme is relevant because as it was previously described, there is a wide variety of oxygen tension in the Mtb interaction with the host.
Moving to another important aspect of Mtb physiology, the cell envelope of this bacterium has been the focus of research for many decades because of its distinct features, importance in bacterial pathogenicity, and the generation of the host immune response. The mycobacterial cell envelope is complex such that nutrients penetrate 10,000 times slower than they can do in the E. coli outer membrane [34]. Components of the cell envelope, particularly the enzymes that participate in their synthesis, have been recognized as possible drug targets. The understanding of the cell envelope is also required to design drugs that will be able to cross this impermeable barrier efficiently [35].
The Mtb envelope forms the interface between pathogen and host. From the outside to the inside, the Mtb cell envelope is composed of a layer of non-covalently linked glycolipids, proteins, carbohydrates, and some lipids (the capsule), a covalently linked peptidoglycan layer that contains carbohydrates and lipids (the cell wall), and a plasmatic membrane (phospholipid bilayer). In 1991, Minnikin proposed visualizing the lipid material in the Mtb envelope as two distinct membranes, analogous to a Gram-negative bacterium [36].
The most external layer of Mtb has been described as a “capsule” by some scientists. This layer contains mainly polysaccharides and a small amount of lipids (2–3%). The major capsular component in slow growing mycobacteria, including Mtb is a glucan composed of repeating units of ->4-(-
The Mtb cell wall has a covalently linked backbone with a collection of cell wall-associated lipids and polypeptides. The covalently linked molecules include peptidoglycan, arabinogalactan, and mycolic acids. In addition to the presence of the last two biomolecules, there are two important hallmarks of the Mtb cell wall. First, the muramic acid is N-acylated, instead of N-acetylated as regularly observed in most eubacteria. Second, there are unusual cross-links between two chains of peptidoglycan that include bonds of two residues of diaminopimelic acid in addition to the usual
Mtb has a great variety of lipids that can be clustered into at least six lipid categories with around 2512 lipid groups [41]. Mycolic acids are the major constituent of the cell envelope. They were first named by Stodola and colleagues in 1938, who also depicted essential groups of their chemical structure. Mycolic acid structure was further defined by Asselineau in 1950 [42]. These are α-alkyl, β-hydroxyl, long-chain fatty acids that can be primarily covalently attached as esters of arabinogalactan in the cell wall or as “free lipids” in the capsule associated to trehalose in the TMM or TDM structures [12, 43]. Specifically, mycolic acids form an ester bound to the 5-position of the arabinose residue of the arabinogalactan [41]. Mycolic acids can also bind to glucose [44]. The covalently attached mycolic acids can be obtained by saponification or methanolysis of the cell wall of the delipidated Mtb cells. Because mycolic acids are not soluble in methanol, they can be separated from moderately long-chain fatty acids with ether or chloroform solutions. Mycolic acids have one carbon chain bound to the hydroxyl group called the meromycolic chain and another (shorter) carbon chain that is bound to the α-carbon [35]. The synthesis of these molecules was previously discussed in Section 2.2.2 of this chapter.
Mycolic acids are not unique structures of the Mycobacterium genera, they can be present in Corynebacterium, Nocardia, and Rhodococcus. Mycolic acids from Mycobacterium are longer in carbon units (C70–C90) and have the largest meromycolic chain [39]. Additional modifications such as the introduction of cyclopropane rings in the meromycolate chain, unsaturations, ethylenic groups, and methyl branches are also observed. Both cis and trans double bounds as well as cyclopropane rings can be found in the same type of mycolate. Some mycolic acids have additional oxygen functionality that is one feature used to classify them. These functionalities are keto, methoxy, carboxy, and epoxy. Other types of mycolic acids lack of these oxygen groups, they are called α-mycolic acids and αʹ-mycolic acids. α and αʹ-mycolic acids differ in their chain length, αʹ-mycolic acids are shorter (usually of 60 carbon units) whereas α-mycolic acids contains more than 70 carbon units. α-mycolic acids represent more than 70% of the total mycolic acids found in Mtb, followed by keto and methoxy variants (15 and 10%). The cyclopropane structures in this fatty acids contribute not only to its cell wall structure, but also protect the bacteria from oxidizing agents such as H2O2 (reviewed in Ref. [14]).
Finally, the plasmatic membrane includes different types of phospholipids such as phosphatidylglycerol, phosphatidylethanolamine, phosphatidylinositol, and phosphatidylinositol mannosides (PIMs). PIMs are mainly located in the outer leaflet. Other important components are the highly immunogenic lipoglycan lipoarabinomannan (LAM) and lipomannan [39]. Due to the high abundance of LAM in the Mtb envelope, it has been tested as a biomarker for a point of care test with a wide range of sensitivity and specificity results in HIV-positive patients [45].
As is the case in other microorganisms, drug resistance in Mtb can be either intrinsic or acquired. Mtb cell wall structure and its low permeability are the major factors accounting for the high degree of intrinsic or natural tolerance to many antibiotics and other chemotherapeutic agents. Highly abundant mycolic acids in the cell wall reduce the cell permeability and create a crystalline-like structure after the cytosolic membrane. As seen in other mycobacterial species (especially in saprophytic species such as Mycobacterium chelonae), the more impermeable the cell wall, the more antimicrobial agents the mycobacteria can resist. Drugs such as sulphonamides, penicillin, tetracycline, and vancomycin are ineffective against Mtb. For vancomycin, this can explained because of its size and structure that do not allow its effective penetration through the Mtb “pseudo-outer membrane” [34]. However, recent findings demonstrated that Mycobacterium bovis and Mtb mutants lacking phthiocerol dimycocerosates are susceptible to glycopeptides such as vancomycin [46]. Additionally, the reduced number of porins in the Mtb “pseudo-outer membrane” possibly contributes to the intrinsic Mtb resistance against hydrophilic compounds. Among other intrinsic factors, Mtb possess β-lactamase enzymes (encoded by blaC and blaS) that make this bacterium naturally resistant to β-lactams [2, 47]. For acquired drug resistance, spontaneous mutations in chromosomal genes during a suboptimal drug therapy are the most common cause for drug resistance in Mtb. Efflux mechanisms are less common but also present in these bacteria [47]. These intrinsic and acquired mechanisms have synergistic effects and make TB treatment particularly cumbersome.
Although a combined therapy for TB is normally effective for most cases, TB cases resistant to a subsection or all anti-TB drugs have been reported in clinical settings. Because INH and RIF are the most widely anti-TB drugs used, there is a higher frequency of mono-resistance to any of these drugs or to both drugs (INH and RIF, known as multidrug-resistance TB or MDR-TB) among drug-resistant Mtb strains. The study of drug-resistant TB has been an ongoing process, mainly because the understanding of the mechanism of action of several first line drugs (such as INH and pyrazinamide) has been subject of intensive research and controversies [48]. The cumulative exposure of Mtb strains to suboptimal concentrations of anti-TB drugs in an intermittent manner creates most of the acquired drug-resistant TB cases. In this way, many TB patients lose the best options for effective treatment from a disease that was initially curable.
In 1951, the anti-TB properties of a new drug, INH, were reported. This was a critical event in TB history that was optimistically described as the “new treatment for the white scourge.” Unfortunately, the appearance of INH-resistant (INHr) cases emerged the same year INH was introduced in medical practice [49]. INH resistance is one of the most common forms of drug-resistant TB. The resistance mechanism to this drug is multigenic and can be divided into three categories: prevention of drug activation, alteration of the target, and differential expression of the target. In the first group, mutations in katG that prevent the activation of INH are present in the majority of resistant cases to this drug [50, 51]. Mtb strains with a full deletion of katG also fall into this category. KatG function was first correlated to INH resistance in 1953, when Middlebrook et al. discovered that INHr Mtb strains lacked catalase-peroxidase activity and were less virulent in guinea pigs [52]. The molecular validation of this observation was completed later by Zhang et al., restoring the sensitivity to INH in some Mtb resistant strains after the introduction of the katG gene from E. coli [53]. More than 60 years of chemotherapy with INH in TB cases has allowed the development of different Mtb genotypes of INH-resistant (INHr) profile and their associated phenotypes. Currently, there are more than 300 known mutations in the katG gene alone associated with a wide range of minimum inhibitory concentrations (0.2–256 mg/L) [51]. These mutations include missense mutations, insertions, deletions, truncations, and full gene deletion. Depending on the position and nature of the mutation, katG mutants have different degrees of catalase-peroxidase activity [47, 51]. The mutation rate for the generation of INH-resistant strains is around 3.2 × 10−7 mutations/cell division (after exposure to 1 mg/L INH) in vitro [54, 55, 56] and presumably one in 108–9 organisms in vivo [57].
In the category of alteration of the target and increased expression of the target, mutations in the inhA gene or its promoter are accounted. InhA is the most commonly validated target for INH [49, 58]. Currently, around 15 mutations in the inhA gene have been identified in Mtb strains with low-level resistance to INH. inhA mutations also drive resistance to ethionamide (ETH), since INH and ETH share this enzyme as target [51]. The most studied mutation is the S94A that results in the reduction of the enzyme affinity for NADH and a reduced ability of INH-NAD adduct to inhibit the enzyme. Additionally, mutations in the inhA promoter that increase InhA levels have been also identified. Therefore, both the reduction in enzymatic activity, specifically KatG and the overexpression of the target (InhA) serve as resistance mechanisms against INH. Other mechanisms of INH resistance include the accumulation of NADH (by redox alteration) that binds InhA and protects it from the inhibitory effect of the INH-NAD adduct. An additional resistance mechanism includes acetylation of the drug by the nat encoded arylamine N-acetyl-transferase which prevents INH activation by KatG [59, 60]. Finally, the drug efflux mechanisms include the participation of the protein EfpA, which is induced upon INH treatment [49]. It is important to describe that INH resistance in Mtb can be either low- or high-level when there is >1% of bacterial growth in the presence of 0.2 or 1 μg/mL of INH, respectively. Regularly, mutations in the inhA promoter are linked to low-level of INH resistance while mutations in katG are associated with high-level of INH resistance in Mtb [61].
Followed the discovery of INH, rifampicin (RIF) was discovered in 1963 and reduced the anti-TB treatment from 18 to 9 months [62, 63, 64]. Currently, a shorter combined therapy with higher doses of rifampicin or isoniazid is being evaluated [65]. The rationale behind the increase dose of rifampicin is that the currently used dose of RIF was proposed in 1971 with the basis of generating a cost-effective treatment that was non-toxic for TB patients, albeit a study of the maximum dose of the drug tolerated in human has never been performed [66]. Recent studies in animal models have shown that higher doses of this drug could be effective even in shorter regimes, reducing also the probability to generate resistant microorganisms to the drug [66, 67, 68].
RIF resistance in Mtb is simpler than INH resistance. Up to date, mutations in one gene, rpoB, that encodes for the RNA polymerase β subunit and the target of the drug, are present in most of the RIF resistant (RIFr) cases. There are only four in the rpoB gene (N-terminus, and clusters I–III) where most of these mutations are found. In fact, mutations in an 81 base pair (27 codons) in the central region of cluster I, also known as the RIF resistance-determining region (RRDR), harbors more than 96% of all mutations associated with RIF resistance. Similarly to what is described for INH resistance, these mutations can be single amino acid substitutions, deletions, and insertions [62]. These mutations mainly affect the binding pocket where the drug interacts with the subunit of the polymerase. The most common amino acid substitutions observed in clinical RIFr strains include S531L and H526Y [69].
Since there is a wider repertoire of INH resistance-conferring mutations compared with RIF resistance-conferring mutations (see Sections 3.1.1 and 3.1.2), a more variable phenotype in INHr strains compared to RIFr strains is expected. Additionally, the genetic lineage and background of each strain play an important role in the phenotype resultant after drug-resistance is acquired [69, 70, 71]. This is explained by the fact that compensatory mutations associated with some genetic backgrounds but not others may results in different competitive phenotypes. Our laboratory recently demonstrated that the same mutation causing INH resistance in two Mtb strains from different genetic lineages can result in different virulent phenotypes. Furthermore, these differences were associated with differences in protein levels of AhpC without any detectable mutation in the ahpC gene or its promoter. These Mtb strains were from different genetic lineages and exhibited a strongly different virulent profile in the mouse model of infection [72]. Therefore, following a “conservative approach,” comparing clinically relevant clonal or isogenic Mtb strains is crucial to understand the changes in Mtb physiology caused by drug resistance events. However, obtaining pure clonal pairs of Mtb derived from clinical settings is quite challenging.
Clonal Mtb pairs conceptually defines a pair or group of bacterial strains that share the same progenitor, but are generated after successive replication events with the possibility to develop one or more single nucleotide polymorphisms (SNPs) each time, possibly due to external pressure such as drug exposure, oxygen tension among other factors [73]. The development of more discriminative and high-throughput genetic tools has allowed a more accurate characterization of these clonal and isogenic strains. Isogenic and clonal strains are difficult to obtain from clinical cases due to the possibility of infection with different clones of Mtb, especially in high burden TB countries such as India and South Africa [74]. Furthermore, most settings with high burdens of TB do not routinely perform whole genome sequencing and are not equipped to carry a biobank of Mtb isolates. In the next sections, we will explore specific examples of Mtb strains that experienced compensatory physiological events after acquiring INH and/or RIF resistance comparing them to their clonal or isogenic parental strain.
We have used comparative shotgun proteomics of different Mtb cellular fractions to describe different aspects of the Mtb physiology in vitro and in vivo, including the effects of drug resistance-conferring mutations in the new bacterial phenotype [75, 76, 77, 78, 79, 80, 81, 82]. The advantage of evaluating differences in protein abundance at each cellular fraction allows confirming if any differences seen are due to a global redistribution of protein levels or if changes in protein abundance are instead associated with a specific compartmentalization of the protein. After the elucidation of the genome of many organisms, proteomics emerged as a powerful methodology that not only describes the sequence, structure, and function of the proteins, but also extends to the analysis of complex mixture of proteins using high-throughput techniques [83, 84]. Proteomics analyze mature proteins considering all the complex post-translational events that occur in the cell and that finally represent the bacterial phenotype. As it was stated by LaBaer in 2011, “proteins provide the verbs to biology” [85, 86], and proteomics allow for naming different biological events [87]. As the proteome of the cell variate parallel to internal metabolic variation and external cues, proteomics is considered the most direct scaffold to measure cell activity [86]. Mass spectrometry (MS)-based technologies are central components of the protein analysis. These methods include shotgun and targeted proteomics that have different modes for acquiring mass spectra. Shotgun proteomics, a term coined by John Yates III and his laboratory, offers an indirect measurement of proteins through peptides derived from their enzymatic digestion [84]. Shotgun proteomics, also known as discovery proteomics, uses liquid chromatography (LC) connected to tandem MS (MS/MS) for the identification of the protein components in the sample. The protein identification is based on the determination of the amino acid sequence which is achieved by comparing the experimental tandem mass spectra with the theoretical tandem mass spectra generated from an in silico digestion of a protein database.
Given the high frequency of katG mutations among INHr Mtb strains, this section will focus on the proteomics findings that were revealed in the study of an isogenic pair of the Beijing lineage after acquisition of drug resistance due to a katG mutation [80]. As the starting point, it should be noted that early studies revealed that INHr Mtb strains with katG mutations have different levels of the enzyme and a different degree of alteration of its catalase or peroxidase activities [88]. These mutations have also different impact in the virulence and fitness of the INHr bacterium. However, to our knowledge, the study described here is unique as it used clinical isogenic pairs of Mtb strains resulting from katG mutations and associated with an INHr profile.
Consistent with previous studies, the global proteomics study of the Beijing clinical pair through LC-MS/MS demonstrated that the INHr strain had significantly reduced levels of KatG in three of the four subcellular fractions evaluated compared with its isogenic INHs progenitor. The fact that the levels of this protein were reduced in the soluble fractions (cytosol and secreted proteins) and the bacterial membrane is a clear indication that this INHr strain lacks its ability to activate INH. An additional 45 proteins were found with altered abundance; these protein changes may be a potential compensatory mechanisms related to the reduced KatG levels and its consequent impact on mycobacterial physiology and fitness [80].
Among the 45 proteins identified, proteins related to intermediary metabolism and respiration represented majority of differentially abundant between INHr and INHs strains. Among them, enzymes from the tricarboxylic acid (TCA) cycle (SucC, SucD, Mdh, Acn, and AceE) were all decreased in the INHr strain. Proteins related to lipid biosynthesis and degradation pathways also represented important differences between the strains, with mainly higher levels in the INHr strain. The proteins Fas, FabG4, and FbpD of the lipid biosynthetic pathway were increased. In the β-oxidation pathway, the dehydrogenases FadE22 and FadE32 and the acetyl-CoA acyltransferase FadA2 were increased, but the crotonases EchA9 and EchA21 were decreased in the INHr strain. Proteins in the virulence and detoxification category such DnaK and GroES were also increased in the INHr strain as well as the hypothetical protein Rv2204c. Finally, the transcription regulation proteins Crp and PrrA were also higher in the INHr strain compared to the INHs parental strain [80].
Interestingly, the INHr Beijing strain had the katG mutation L101R (identified in the INHr by whole genome sequencing) [89]. However, this katG mutation was not very stable for the Beijing INHr strain, which after successive passes reversed to the wild type genotype and INHs phenotype. A previous report of an INHr reversion in Mtb was observed in a katG mutant in the absence of the drug pressure [90]. Based on these reports, it is possible that not only the resistant-conferring mutations can result in a distinctive phenotype but also that these mutations are not easily conserved in the Mtb genome after removing the pressure that originates them.
A previous proteomic analysis using non-clonal Mtb strains found five proteins overexpressed in the INHr strains comparing whole cell lysates. These proteins were found through two-dimensional (2D) gel electrophoresis and matrix-assisted laser desorption ionization time of flight-MS (MALDI-TOF) and include OpcA, FixB, RegX3, a probable oxidoreductase (Rv2971), and Wag31. Most of these proteins were involved in cellular metabolism, including redox metabolism (such as OpcA, Rv2971, and FixB) and there was one transcriptional regulatory protein (RegX3). These proteins are not related to any of the known INH-resistance mechanisms and were not observed in the previous clonal study. However, they still confirm the alteration of proteins involved in redox stress and energetic metabolism [91].
A recent virulence study of laboratory and clinical clonal pairs of Mtb from the T lineage and with different susceptibility profiles to INH also showed an important reduction of the KatG protein in the INHr strains. Associated with this KatG reduction, this study revealed a variable alkyl-peroxidase C (AhpC) response in the INHr strains which was dependant on the genetic background. Although both clinically and laboratory-derived INHr Mtb strains had reduced levels of KatG, western blot analysis with anti-AhpC demonstrated that the laboratory INHr strain had increased levels of AhpC while the clinical INHr strain had reduced levels of AhpC compared to their clonal parental strain, respectively. The difference observed in the AhpC levels was also translated in a non-significant reduction of the virulence in the laboratory INHr contrasting the strongly significantly reduced virulent profile for the clinical INHr strain [72]. A more robust proteomics study trough LC-MS/MS is being developed to reveal more insights about the proteomics differences among this clinical and laboratory-derived clonal pairs.
Phenotypic consequences of mutations in the rpoB gene associated with RIF resistance are understudied in Mtb. However in recent years, this theme has gained interest given the association of rpoB mutations with a variety of phenotypes in other microorganisms. For instance, in E. coli, rpoB mutations mimic the “stringent” response that is usually driven by ppGpp under stress conditions [92]. In B. subtilis and Streptomyces coelicolor, rpoB mutations are associated with an increased antibiotic production and increased production of other metabolites [93, 94, 95]. In Neisseria meningitidis and Staphylococcus aureus, rpoB mutations lead to a decrease permeability of the cell wall, which can be related to a subsequent increase in tolerance to certain antibiotics such as vancomycin [96, 97, 98]. Interestingly, after exposure to RIF, Mtb also appears to have an increased tolerance to ofloxacin, probably because of an increase activity of efflux pumps [99], although recent findings from our laboratory as well as others also suggest a potential role for cell permeability [75, 100]. In our study, isogenic Mtb pairs with two different rpoB mutations and representing two different genetic lineages (Beijing and Haarlem) showed an increased abundance of proteins involved polyketide synthesis. Proteomics findings were confirmed by an independent transcriptomics analysis of the strains grown intracellularly in in vitro macrophages. Both RIFr rpoB mutants revealed significant increased expression of multifunctional enzymes of the phenolpthiocerol synthesis type I polyketide synthase PpsE and C, which are involved in the biosynthesis of phthiocerol dimycocerosate (PDIM) and other lipids in Mtb [75]. We also observed a significantly increased abundance of the ABC transporter drrA, which has homology with other daunorubicin efflux pumps, but it is also implicated in export of PDIM across the cell membrane [101, 102]. Both increased abundance in lipids, as well as potential increase in efflux pump activity may result in accumulative reduction of cell permeability and may have important implications in subsequent acquisition of drug resistance.
A handful of proteomic studies focused on the comparison of drug susceptible (DS) versus multidrug-resistant strains (MDR) Mtb strains are available in the literature [70, 103, 104, 105, 106, 107]. Although these studies analyze clinical DS and MDR Mtb strains, the majority of them were comparing either non-related strains or strains with specific different genetic lineage. For instance, one study included H37Rv and H37Ra in the comparison and other compared DS Central-Asian (CAS)-2 with MDR East-African Indian (EAI)-3 strains [104]. The latter did not allow the study of the MDR phenotype under the same genetic background. There was one study that evaluated Mtb strains isolated from one single patient after many treatment failure episodes. Here, we will explore the findings specifically related to the DS and the first MDR strain isolated, since the next Mtb strains were also resistant to kanamycin. The MDR strain of the CAS 1-Delhi genotype had increased levels of 10 proteins through 2D electrophoresis and MALDI-TOF compared to its DS clonal pair. These proteins include chaperonin Hsp70, bacterioferritin BfrA, mycolyl-transferase FbpD, a component of the translational apparatus GatA, the phosphoserine aminotransferase SerC, Wag31, and the hypothetical proteins Rv1827, Rv2204c, Rv0543c, and Rv2004c [103]. Interestingly increased levels of FbpD and Rv2204c were also found in the INHr study of Beijing lineage [80]. Similarly, protein Wag31 was increased in a previous proteomic study monoresistant Mtb strains.
The proteomic analysis of non-genetically related Mtb strains revealed commonly increased levels of GroEL2, DlaT, ESAT-6, and conserved protein Rv3699 in the MDR strains compared to DS strains [70, 105, 106, 107]. Similar to the previous INHr proteomics studies mentioned above, some studies showed increased levels of FadA2, FabG4, BfrA, GroES, FixB, Rv2971, OpcA as well as lower levels of Mpt63 in MDR versus DS Mtb strains. However, there were contrasting levels of the proteins Mdh and SahH among the MDR studies and also discrepant tendencies of Fas in MDR strains compared to the INHr study of the Beijing genotype. On the other hand, there was one study that found increased levels of PpsC in a MDR strain compared to H37Rv as it was described in the RIF resistance proteomics study of isogenic pairs of Beijing and Haarlem genotype.
The analysis using non-genetically related strains provide valuable insights about the protein dynamics among DS and MDR Mtb strains. However, the fact that proteins such as the catalase-peroxidase KatG are increased in MDR strains without establishing the INH-resistance mechanism [104, 107], generates some questions such as: Is this protein increase because some genotypes express constitutively more KatG? According to this, it is not possible to conclude that there are actually INHr strains that have increased levels of KatG and highlight the necessity of study the drug resistance event under the same genetic background.
Among the different scientific disciplines supporting biological research, metabolomics is the study of chemically diverse groups of biomolecules including sugars, nucleotides, peptides, lipids, among others; using technologies such as MS and nuclear magnetic resonance (NMR). Lipidomics is a branch of metabolomics that specializes on the water-insoluble metabolites—lipids. These are diverse metabolites that are part of the major molecules in the cell (particularly, in the cell membrane) [108]. In Mtb, lipids are a very relevant group of molecules, since at it has been previously discussed, they are responsible for the intrinsic-drug-resistant nature against some antibiotics, and its synthesis has been the target of some anti-TB drugs (INH and ETH). Consistent with this idea, it is plausible to think that the study of the Mtb lipid is an important part of the description of drug-resistant Mtb strains.
Thus far, only one metabolomics study has been reported comparing katG mutant-INHr strains derived from a drug susceptible parental strain of the Haarlem genotype. Through 2D-gas chromatography-TOF MS, this study showed increased levels of saturated fatty acids (FA) in the INHr strains; particularly saturated FA with 16–20-carbon chain compared with its wild type that could be as a result of the oxidative stress which makes the bacteria rely on the β-oxidation of fatty acids as a carbon and energy source [109].
The lipidomics studies in RIF resistant Mtb have been focused on the RIF resistant strains that are both laboratory and clinically isolated. The laboratory-derived W-Beijing and CDC1551 were used as the parental DS strains and were exposed to 2 ug/mL RIF to select for the RIFr strains. In this way, three different rpoB mutants (S531L, Q513E, and H526Y for each Mtb strain) were studied. The analysis revealed reduced levels of di-acylated sulfoglycolipid (Ac2SGL) and mycobactins (including carboxymycobactins), while increased levels of PDIM compared to their DS parental strain. These compounds were identified among 172 features in the W-Beijing group and 102 features in the CDC1551 group analyzed by high performance liquid chromatography (HPLC) mass quadrupole-time-of-flight (QTOF) MS and suggesting a global remodeling of the cell wall after acquisition of RIF resistance [100]. This study supports previous findings from our group, which included a significant increase of diacylglycerol phosphocholines and PDIM precursors as observed by ultra-performance liquid chromatography (UPLC)-QTOF [75].
The systematic study of Mtb phenotype, its proteome and metabolome (including, but not limited to lipidome) permits a functional description of how Mtb adapts, and sometimes thrives, under intrinsic (i.e. host response) and extrinsic pressure (i.e. exposure to drugs). These types of studies help to resolve not only the features of drug-resistant strains, but also contribute to the discovery of the facile and specific detection of biomarkers of drug resistance and ultimately contribute to the discovery of new targets for these Mtb strains that are hard to eliminate and often result in poor clinical outcomes for those infected.
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