Tuberculosis: global approaches to a global disease

Increased susceptibility to pneumonia due to tumour necrosis factor inhibition and prospective immune system rescue via immunotherapy

Immunomodulators such as tumour necrosis factor (TNF) inhibitors are used to treat autoimmune conditions by reducing the magnitude of the innate immune response. Dampened innate responses pose an increased risk of new infections by opportunistic pathogens and reactivation of pre-existing latent infections. The alteration in immune response predisposes to increased severity of infections. TNF inhibitors are used to treat autoimmune conditions such as rheumatoid arthritis, juvenile arthritis, psoriatic arthritis, transplant recipients, and inflammatory bowel disease. The efficacies of immunomodulators are shown to be varied, even among those that target the same pathways. Monoclonal antibody-based TNF inhibitors have been shown to induce stronger immunosuppression when compared to their receptor-based counterparts. The variability in activity also translates to differences in risk for infection, moreover, parallel, or sequential use of immunosuppressive drugs and corticosteroids makes it difficult to accurately attribute the risk of infection to a single immunomodulatory drug. Among recipients of TNF inhibitors, Mycobacterium tuberculosis has been shown to be responsible for 12.5-59% of all infections; Pneumocystis jirovecii has been responsible for 20% of all non-viral infections; and Legionella pneumophila infections occur at 13-21 times the rate of the general population. This review will outline the mechanism of immune modulation caused by TNF inhibitors and how they predispose to infection with a focus on Mycobacterium tuberculosis, Legionella pneumophila, and Pneumocystis jirovecii. This review will then explore and evaluate how other immunomodulators and host-directed treatments influence these infections and the severity of the resulting infection to mitigate or treat TNF inhibitor-associated infections alongside antibiotics.

Characterization of Two New Multidrug-Resistant Strains of Mycobacterium smegmatis: Tools for Routine In Vitro Screening of Novel Anti-Mycobacterial Agents

Mycobacterium tuberculosis is a pathogen of global public health concern. This threat is exacerbated by the emergence of multidrug-resistant and extremely-drug-resistant strains of the pathogen. We have obtained two distinct clones of multidrug-resistant Mycobacterium smegmatis after gradual exposure of Mycobacterium smegmatis mc² 155 to increasing concentrations of erythromycin. The resulting resistant strains of Mycobacterium smegmatis exhibited robust viability in the presence of high concentrations of erythromycin and were found to be resistant to a wide range of other antimicrobials. They also displayed a unique growth phenotype in comparison to the parental drug-susceptible Mycobacterium smegmatis mc² 155, and a distinct colony morphology in the presence of cholesterol. We propose that these two multidrug-resistant clones of Mycobacterium smegmatis could be used as model organisms at the inceptive phase of routine in vitro screening of novel antimicrobial agents targeted against multidrug-resistant Mycobacterial tuberculosis.

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.

Experimental animal modelling for TB vaccine development

Research for a novel vaccine to prevent tuberculosis is an urgent medical need. The current vaccine, BCG, has demonstrated a non-homogenous efficacy in humans, but still is the gold standard to be improved upon. In general, the main indicator for testing the potency of new candidates in animal models is the reduction of the bacillary load in the lungs at the acute phase of the infection. Usually, this reduction is similar to that induced by BCG, although in some cases a weak but significant improvement can be detected, but none of candidates are able to prevent establishment of infection. The main characteristics of several laboratory animals are reviewed, reflecting that none are able to simulate the whole characteristics of human tuberculosis. As, so far, no surrogate of protection has been found, it is important to test new candidates in several models in order to generate convincing evidence of efficacy that might be better than that of BCG in humans. It is also important to investigate the use of "in silico" and "ex vivo" models to better understand experimental data and also to try to replace, or at least reduce and refine experimental models in animals.

Local Inflammation, Dissemination and Coalescence of Lesions Are Key for the Progression toward Active Tuberculosis: The Bubble Model

The evolution of a tuberculosis (TB) infection toward active disease is driven by a combination of factors mostly related to the host response. The equilibrium between control of the bacillary load and the pathology generated is crucial as regards preventing the growth and proliferation of TB lesions. In addition, some experimental evidence suggests an important role of both local endogenous reinfection and the coalescence of neighboring lesions. Herein we propose a mathematical model that captures the essence of these factors by defining three hypotheses: (i) lesions grow logistically due to the inflammatory reaction; (ii) new lesions can appear as a result of extracellular bacilli or infected macrophages that escape from older lesions; and (iii) lesions can merge when they are close enough. This model was implemented in Matlab to simulate the dynamics of several lesions in a 3D space. It was also fitted to available microscopy data from infected C3HeB/FeJ mice, an animal model of active TB that reacts against Mycobacterium tuberculosis with an exaggerated inflammatory response. The results of the simulations show the dynamics observed experimentally, namely an initial increase in the number of lesions followed by fluctuations, and an exponential increase in the mean area of the lesions. In addition, further analysis of experimental and simulation results show a strong coincidence of the area distributions of lesions at day 21, thereby highlighting the consistency of the model. Three simulation series removing each one of the hypothesis corroborate their essential role in the dynamics observed. These results demonstrate that three local factors, namely an exaggerated inflammatory response, an endogenous reinfection, and a coalescence of lesions, are needed in order to progress toward active TB. The failure of one of these factors stops induction of the disease. This mathematical model may be used as a basis for developing strategies to stop the progression of infection toward disease in human lungs.

Architectural plan of transcriptional regulation in Mycobacterium tuberculosis

Transcriptional regulation enables adaptation in bacteria. Typically, only a few transcriptional events are well understood, leaving many others unidentified. The recent genome-wide identification of transcription factor binding sites in Mycobacterium tuberculosis has changed this by deciphering a molecular road-map of transcriptional control, indicating active events and their immediate downstream effects.

Screening and identification of potential biomarkers and establishment of the diagnostic serum proteomic model for the Traditional Chinese Medicine Syndromes of tuberculosis

ETHNOPHARMACOLOGICAL RELEVANCE

Chemotherapy is the mainstay of modern tuberculosis (TB) control. Traditional Chinese Medicine (TCM) can enhance the effect of anti-TB drug, promote the absorption of the foci in the lung and reduce drug toxicity. In TCM, the determination of treatment is based on ZHENG (also called TCM syndrome). To establish a diagnostic model by using proteomics technology in order to identify potential biomarkers for TCM syndromes of TB.

MATERIALS AND METHODS

The surface-enhanced laser desorption ionization time of flight mass spectrometer (SELDI-TOF MS) combined with weak cation exchange (WCX) magnetic beads was used to screen serum samples from 71 cases of deficiency of lung yin syndrome (DLYS), 64 cases of fire (yang) excess yin deficiency syndrome (FEYDS) and 45 cases of deficiency of both qi and yin syndrome (DQYS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by reverse phase-high performance liquid chromatograph (RP-HPLC), identified by MALDI-TOF MS, LC-MS/MS and validated by ProteinChip Immunoassays.

RESULTS

A total of 74 discriminating m/z peaks (P<0.001) among three TCM syndromes of TB were detected. A diagnostic model for the TCM syndrome of TB based on the five biomarkers (3961.7, 4679.7, 5646.4, 8891.2 and 9416.7 m/z) was established which could discriminate DLYS, FEYDS and DQYS patients with an accuracy of 74.0%, 72.5%, and 96.7%, respectively. The candidate biomarker with m/z of 9416.7 was identified as a fragment of apolipoprotein C-III (apoC-III) by MALDI-TOF-MS and LC-MS/MS.

CONCLUSION

The TCM syndrome diagnostic model of TB could successfully distinguish the three TCM syndromes of TB patients. This provided a biological basis for the determination of treatment based on different TCM syndromes of TB. ApoC-III was identified as a potential biomarker for TCM syndromes of TB and revealed the biochemical basis and pathogenesis of TCM syndromes in TB.

Modeling the potential impact of host population survival on the evolution of M. tuberculosis latency

Tuberculosis (TB) is an infectious disease with a peculiar feature: Upon infection with the causative agent, Mycobacterium Tuberculosis (MTB), most hosts enter a latent state during which no transmission of MTB to new hosts occurs. Only a fraction of latently infected hosts develop TB disease and can potentially infect new hosts. At first glance, this seems like a waste of transmission potential and therefore an evolutionary suboptimal strategy for MTB. It might be that the human immune response keeps MTB in check in most hosts, thereby preventing it from achieving its evolutionary optimum. Another possible explanation is that long latency and progression to disease in only a fraction of hosts are evolutionary beneficial to MTB by allowing it to persist better in small host populations. Given that MTB has co-evolved with human hosts for millenia or longer, it likely encountered small host populations for a large share of its evolutionary history and had to evolve strategies of persistence. Here, we use a mathematical model to show that indeed, MTB persistence is optimal for an intermediate duration of latency and level of activation. The predicted optimal level of activation is above the observed value, suggesting that human co-evolution has lead to host immunity, which keeps MTB below its evolutionary optimum.

Mathematical models of mitochondrial aging and dynamics

Research on the role of mitochondria in aging and disease is rapidly growing. Furthermore, in recent years, it also became clear that mitochondria are dynamic structures undergoing constant and rapid cycles of fusion and fission. The involvement of mitochondria in multiple complex processes makes them a prime target for mathematical and computational modeling. This review consists of two parts. In the first (Section 2), we provide a detailed introduction to the underlying concepts of mathematical modeling to help the reader who is not so familiar with these techniques to judge the requirements and results that can be obtained through modeling. In the second part (Section 3), we review existing mathematical and computational models that investigate mitochondrial dynamics and the role of mitochondria for the aging process.

The discovery and identification of a candidate proteomic biomarker of active tuberculosis

BACKGROUND

Noninvasive and convenient biomarkers for early diagnosis of tuberculosis (TB) remain an urgent need. The aim of this study was to discover and identify potential biomarkers specific for TB.

METHODS

The surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) combined with weak cation exchange (WCX) magnetic beads was used to screen serum samples from 180 cases of TB and 211 control subjects. A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by reverse phase-high performance liquid chromatography (RP-HPLC), identified by MALDI-TOF MS, LC-MS/MS and validated using enzyme-linked immunosorbent assay (ELISA).

RESULTS

A total of 35 discriminating m/z peaks were detected that were related to TB (P < 0.01). The model of biomarkers based on the four biomarkers (2554.6, 4824.4, 5325.7, and 8606.8 Da) was established which could distinguish TB from controls with the sensitivity of 83.3% and the specificity of 84.2%. The candidate biomarker with m/z of 2554.6 Da was found to be up-regulated in TB patients, and was identified as a fragment of fibrinogen, alpha polypeptide isoform alpha-E preproprotein. Analysis in 22 patients with TB showed increased fibrinogen degradation product (FDP) (5,005 ± 1,297 vs. 4,010 ± 1,181 ng/mL, P < 0.05) and in 142 patients showed elevated plasma fibrinogen levels.

CONCLUSIONS

A diagnostic model for TB with high sensitivity and specificity was developed using mass spectrometry combined with magnetic beads. Fibrinogen was identified as a potential biomarker for TB and showed diagnostic values in clinical application.

Latent Tuberculosis: Models, Computational Efforts and the Pathogen's Regulatory Mechanisms during Dormancy

Latent tuberculosis is a clinical syndrome that occurs after an individual has been exposed to the Mycobacterium tuberculosis (Mtb) Bacillus, the infection has been established and an immune response has been generated to control the pathogen and force it into a quiescent state. Mtb can exit this quiescent state where it is unresponsive to treatment and elusive to the immune response, and enter a rapid replicating state, hence causing infection reactivation. It remains a gray area to understand how the pathogen causes a persistent infection and it is unclear whether the organism will be in a slow replicating state or a dormant non-replicating state. The ability of the pathogen to adapt to changing host immune response mechanisms, in which it is exposed to hypoxia, low pH, nitric oxide (NO), nutrient starvation, and several other anti-microbial effectors, is associated with a high metabolic plasticity that enables it to metabolize under these different conditions. Adaptive gene regulatory mechanisms are thought to coordinate how the pathogen changes their metabolic pathways through mechanisms that sense changes in oxygen tension and other stress factors, hence stimulating the pathogen to make necessary adjustments to ensure survival. Here, we review studies that give insights into latency/dormancy regulatory mechanisms that enable infection persistence and pathogen adaptation to different stress conditions. We highlight what mathematical and computational models can do and what they should do to enhance our current understanding of TB latency.

Mycobacterium tuberculosis Strains Are Differentially Recognized by TLRs with an Impact on the Immune Response

Tuberculosis associates with a wide spectrum of disease outcomes. The Beijing (Bj) lineage of Mycobacterium tuberculosis (Mtb) is suggested to be more virulent than other Mtb lineages and prone to elicit non-protective immune responses. However, highly heterogeneous immune responses were reported upon infection of innate immune cells with Bj strains or stimulation with their glycolipids. Using both in vitro and in vivo mouse models of infection, we here report that the molecular mechanism for this heterogeneity may be related to distinct TLR activations. Among this Mtb lineage, we found strains that preferentially activate TLR2, and others that also activate TLR4. Recognition of Mtb strains by TLR4 resulted in a distinct cytokine profile in vitro and in vivo, with specific production of type I IFN. We also uncover a novel protective role for TLR4 activation in vivo. Thus, our findings contribute to the knowledge of the molecular basis underlying how host innate immune cells handle different Mtb strains, in particular the intricate host-pathogen interaction with strains of the Mtb Bj lineage.

21st century natural product research and drug development and traditional medicines

Natural products and related structures are essential sources of new pharmaceuticals, because of the immense variety of functionally relevant secondary metabolites of microbial and plant species. Furthermore, the development of powerful analytical tools based upon genomics, proteomics, metabolomics, bioinformatics and other 21st century technologies are greatly expediting identification and characterization of these natural products. Here we discuss the synergistic and reciprocal benefits of linking these 'omics technologies with robust ethnobotanical and ethnomedical studies of traditional medicines, to provide critically needed improved medicines and treatments that are inexpensive, accessible, safe and reliable. However, careless application of modern technologies can challenge traditional knowledge and biodiversity that are the foundation of traditional medicines. To address such challenges while fulfilling the need for improved (and new) medicines, we encourage the development of Regional Centres of 'omics Technologies functionally linked with Regional Centres of Genetic Resources, especially in regions of the world where use of traditional medicines is prevalent and essential for health.

Lack of Mycobacterium tuberculosis-specific interleukin-17A-producing CD4+ T cells in active disease

Protective immunity to Mycobacterium tuberculosis (Mtb) is commonly ascribed to a Th1 profile; however, the involvement of Th17 cells remains to be clarified. Here, we characterized Mtb-specific CD4(+) T cells in blood and bronchoalveolar lavages (BALs) from untreated subjects with either active tuberculosis disease (TB) or latent Mtb infection (LTBI), considered as prototypic models of uncontrolled or controlled infection, respectively. The production of IL-17A, IFN-γ, TNF-α, and IL-2 by Mtb-specific CD4(+) T cells was assessed both directly ex vivo and following in vitro antigen-specific T-cell expansion. Unlike for extracellular bacteria, Mtb-specific CD4(+) T-cell responses lacked immediate ex vivo IL-17A effector function in both LTBI and TB individuals. Furthermore, Mtb-specific Th17 cells were absent in BALs, while extracellular bacteria-specific Th17 cells were identified in gut biopsies of healthy individuals. Interestingly, only Mtb-specific CD4(+) T cells from 50% of LTBI but not from TB subjects acquired the ability to produce IL-17A following Mtb-specific T-cell expansion. Finally, IL-17A acquisition by Mtb-specific CD4(+) T cells correlated with the coexpression of CXCR3 and CCR6, currently associated to Th1 or Th17 profiles, respectively. Our data demonstrate that Mtb-specific Th17 cells are selectively undetectable in peripheral blood and BALs from TB patients.

Single nucleotide polymorphisms in Mycobacterium tuberculosis and the need for a curated database

Recent advances in DNA sequencing have led to the discovery of thousands of single nucleotide polymorphisms (SNPs) in clinical isolates of Mycobacterium tuberculosis complex (MTBC). This genetic variation has changed our understanding of the differences and phylogenetic relationships between strains. Many of these mutations can serve as phylogenetic markers for strain classification, while others cause drug resistance. Moreover, SNPs can affect the bacterial phenotype in various ways, which may have an impact on the outcome of tuberculosis (TB) infection and disease. Despite the importance of SNPs for our understanding of the diversity of MTBC populations, the research community currently lacks a comprehensive, well-curated and user-friendly database dedicated to SNP data. First attempts to catalogue and annotate SNPs in MTBC have been made, but more work is needed. In this review, we discuss the biological and epidemiological relevance of SNPs in MTBC. We then review some of the analytical challenges involved in processing SNP data, and end with a list of features, which should be included in a new SNP database for MTBC.

Modeling innate immune response to early Mycobacterium infection

In the study of complex patterns in biology, mathematical and computational models are emerging as important tools. In addition to experimental approaches, these modeling tools have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to mycobacterial infection and tuberculous granuloma formation. We present an approach in which a computational model represents the interaction of the Mycobacterium infection with the innate immune system in zebrafish at a high level of abstraction. We use the Petri Net formalism to model the interaction between the key host elements involved in granuloma formation and infection dissemination. We define a qualitative model for the understanding and description of causal relations in this dynamic process. Complex processes involving cell-cell or cell-bacteria communication can be modeled at smaller scales and incorporated hierarchically into this main model; these are to be included in later elaborations. With the infection mechanism being defined on a higher level, lower-level processes influencing the host-pathogen interaction can be identified, modeled, and tested both quantitatively and qualitatively. This systems biology framework incorporates modeling to generate and test hypotheses, to perform virtual experiments, and to make experimentally verifiable predictions. Thereby it supports the unraveling of the mechanisms of tuberculosis infection.

SOCS proteins in infectious diseases of mammals

As for most biological processes, the immune response to microbial infections has to be tightly controlled to remain beneficial for the host. Inflammation is one of the major consequences of the host's immune response. For its orchestration, this process requires a fine-tuned interplay between interleukins, endothelial cells and various types of recruited immune cells. Suppressors of cytokine signalling (SOCS) proteins are crucially involved in the complex control of the inflammatory response through their actions on various signalling pathways including the JAK/STAT and NF-κB pathways. Due to their cytokine regulatory functions, they are frequent targets for exploitation by infectious agents trying to escape the host's immune response. This review article aims to summarize our current knowledge regarding SOCS family members in the different mammalian species studied so far, and to display their complex molecular interactions with microbial pathogens.

Differential arabinan capping of lipoarabinomannan modulates innate immune responses and impacts T helper cell differentiation

Toll-like receptors (TLRs) recognize pathogens by interacting with pathogen-associated molecular patterns, such as the phosphatidylinositol-based lipoglycans, lipomannan (LM) and lipoarabinomannan (LAM). Such structures are present in several pathogens, including Mycobacterium tuberculosis, being important for the initiation of immune responses. It is well established that the interaction of LM and LAM with TLR2 is a process dependent on the structure of the ligands. However, the implications of structural variations on TLR2 ligands for the development of T helper (Th) cell responses or in the context of in vivo responses are less studied. Herein, we used Corynebacterium glutamicum as a source of lipoglycan intermediates for host interaction studies. In this study, we have deleted a putative glycosyltransferase, NCgl2096, from C. glutamicum and found that it encodes for a novel α(1→2)arabinofuranosyltransferase, AftE. Biochemical analysis of the lipoglycans obtained in the presence (wild type) or absence of NCgl2096 showed that AftE is involved in the biosynthesis of singular arabinans of LAM. In its absence, the resulting molecule is a hypermannosylated (hLM) form of LAM. Both LAM and hLM were recognized by dendritic cells, mainly via TLR2, and triggered the production of several cytokines. hLM was a stronger stimulus for in vitro cytokine production and, as a result, a more potent inducer of Th17 responses. In vivo data confirmed hLM as a stronger inducer of cytokine responses and suggested the involvement of pattern recognition receptors other than TLR2 as sensors for lipoglycans.

The tuberculous granuloma: an unsuccessful host defence mechanism providing a safety shelter for the bacteria?

One of the main features of the immune response to M. Tuberculosis is the formation of an organized structure called granuloma. It consists mainly in the recruitment at the infectious stage of macrophages, highly differentiated cells such as multinucleated giant cells, epithelioid cells and Foamy cells, all these cells being surrounded by a rim of lymphocytes. Although in the first instance the granuloma acts to constrain the infection, some bacilli can actually survive inside these structures for a long time in a dormant state. For some reasons, which are still unclear, the bacilli will reactivate in 10% of the latently infected individuals, escape the granuloma and spread throughout the body, thus giving rise to clinical disease, and are finally disseminated throughout the environment. In this review we examine the process leading to the formation of the granulomatous structures and the different cell types that have been shown to be part of this inflammatory reaction. We also discuss the different in vivo and in vitro models available to study this fascinating immune structure.

NF-κB Signaling Dynamics Play a Key Role in Infection Control in Tuberculosis

The NF-κB signaling pathway is central to the body’s response to many pathogens. Mathematical models based on cell culture experiments have identified important molecular mechanisms controlling the dynamics of NF-κB signaling, but the dynamics of this pathway have never been studied in the context of an infection in a host. Here, we incorporate these dynamics into a virtual infection setting. We build a multi-scale model of the immune response to the pathogen Mycobacterium tuberculosis (Mtb) to explore the impact of NF-κB dynamics occurring across molecular, cellular, and tissue scales in the lung. NF-κB signaling is triggered via tumor necrosis factor-α (TNF) binding to receptors on macrophages; TNF has been shown to play a key role in infection dynamics in humans and multiple animal systems. Using our multi-scale model, we predict the impact of TNF-induced NF-κB-mediated responses on the outcome of infection at the level of a granuloma, an aggregate of immune cells and bacteria that forms in response to infection and is key to containment of infection and clinical latency. We show how the stability of mRNA transcripts corresponding to NF-κB-mediated responses significantly controls bacterial load in a granuloma, inflammation level in tissue, and granuloma size. Because we incorporate intracellular signaling pathways explicitly, our analysis also elucidates NF-κB-associated signaling molecules and processes that may be new targets for infection control.

Role of P27 -P55 operon from Mycobacterium tuberculosis in the resistance to toxic compounds

Background

The P27-P55 (lprG-Rv1410c) operon is crucial for the survival of Mycobacterium tuberculosis, the causative agent of human tuberculosis, during infection in mice. P55 encodes an efflux pump that has been shown to provide Mycobacterium smegmatis and Mycobacterium bovis BCG with resistance to several drugs, while P27 encodes a mannosylated glycoprotein previously described as an antigen that modulates the immune response against mycobacteria. The objective of this study was to determine the individual contribution of the proteins encoded in the P27-P55 operon to the resistance to toxic compounds and to the cell wall integrity of M. tuberculosis.

Method

In order to test the susceptibility of a mutant of M. tuberculosis H37Rv in the P27-P55 operon to malachite green, sodium dodecyl sulfate, ethidium bromide, and first-line antituberculosis drugs, this strain together with the wild type strain and a set of complemented strains were cultivated in the presence and in the absence of these drugs. In addition, the malachite green decolorization rate of each strain was obtained from decolorization curves of malachite green in PBS containing bacterial suspensions.

Results

The mutant strain decolorized malachite green faster than the wild type strain and was hypersensitive to both malachite green and ethidium bromide, and more susceptible to the first-line antituberculosis drugs: isoniazid and ethambutol. The pump inhibitor reserpine reversed M. tuberculosis resistance to ethidium bromide. These results suggest that P27-P55 functions through an efflux-pump like mechanism. In addition, deletion of the P27-P55 operon made M. tuberculosis susceptible to sodium dodecyl sulfate, suggesting that the lack of both proteins causes alterations in the cell wall permeability of the bacterium. Importantly, both P27 and P55 are required to restore the wild type phenotypes in the mutant.

Conclusions

The results clearly indicate that P27 and P55 are functionally connected in processes that involve the preservation of the cell wall and the transport of toxic compounds away from the cells.

Multiscale computational modeling reveals a critical role for TNF-α receptor 1 dynamics in tuberculosis granuloma formation

Multiple immune factors control host responses to Mycobacterium tuberculosis infection, including the formation of granulomas, which are aggregates of immune cells whose function may reflect success or failure of the host to contain infection. One such factor is TNF-α. TNF-α has been experimentally characterized to have the following activities in M. tuberculosis infection: macrophage activation, apoptosis, and chemokine and cytokine production. Availability of TNF-α within a granuloma has been proposed to play a critical role in immunity to M. tuberculosis. However, in vivo measurement of a TNF-α concentration gradient and activities within a granuloma are not experimentally feasible. Further, processes that control TNF-α concentration and activities in a granuloma remain unknown. We developed a multiscale computational model that includes molecular, cellular, and tissue scale events that occur during granuloma formation and maintenance in lung. We use our model to identify processes that regulate TNF-α concentration and cellular behaviors and thus influence the outcome of infection within a granuloma. Our model predicts that TNF-αR1 internalization kinetics play a critical role in infection control within a granuloma, controlling whether there is clearance of bacteria, excessive inflammation, containment of bacteria within a stable granuloma, or uncontrolled growth of bacteria. Our results suggest that there is an interplay between TNF-α and bacterial levels in a granuloma that is controlled by the combined effects of both molecular and cellular scale processes. Finally, our model elucidates processes involved in immunity to M. tuberculosis that may be new targets for therapy.

Systems biology of the gut: the interplay of food, microbiota and host at the mucosal interface

Our intestinal tract is colonized since birth by complex and subject-specific microbial communities that interact with the host. The human adult microbiota has recently been characterized by deep metagenomic sequencing and several hundreds of intestinal genomes have been characterized at the sequence level. Moreover, the transcriptional response of the host and selected microbes has been identified both in animal model systems and in human. Similarly, the transcriptional response of the host to different diets has been determined in humans, germ-free and gene knockout animals. These developments bring the intestinal tract in the realm of systems biology. An integrated, modular modelling framework that cross-links top-down and bottom-up approaches for the various levels of biological organization is paramount for the understanding of intestinal function.

Systems biology approaches to understanding mycobacterial survival mechanisms

The advent of high-throughput platforms for the interrogation of biological systems at the cellular and molecular level have allowed living cells to be observed and understood at a hitherto unprecedented level of detail and have enabled the construction of comprehensive, predictive in silico models. Here, we review the application of such high-throughput, systems-biological techniques to mycobacteria-specifically to the pernicious human pathogen Mycobacterium tuberculosis (MTb) and its ability to survive in human hosts. We discuss the development and application of transcriptomic, proteomic, regulomic, and metabolomic techniques for MTb as well as the development and application of genome-scale in silico models. Thus far, systems-biological approaches have largely focused on in vitro models of MTb growth; reliably extending these approaches to in vivo conditions relevant to infection is a significant challenge for the future that holds the ultimate promise of novel chemotherapeutic interventions.