Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis

While Tuberculosis (TB) infection remains a serious challenge worldwide, big data and “omic” approaches have greatly contributed to the understanding of the disease. Transcriptomics have been used to tackle a wide variety of queries including diagnosis, treatment evolution, latency and reactivation, novel target discovery, vaccine response or biomarkers of protection. Although a powerful tool, the elevated cost and difficulties in data interpretation may hinder transcriptomics complete potential. Technology evolution and collaborative efforts among multidisciplinary groups might be key in its exploitation. Here, we discuss the main fields explored in TB using transcriptomics, and identify the challenges that need to be addressed for a real implementation in TB diagnosis, prevention and therapy.

Changes in the Immune Phenotype and Gene Expression Profile Driven by a Novel Tuberculosis Nanovaccine: Short and Long-Term Post-immunization

Deciphering protection mechanisms against Mycobacterium tuberculosis (Mtb) remains a critical challenge for the development of new vaccines and therapies. We analyze the phenotypic and transcriptomic profile in lung of a novel tuberculosis (TB) nanoparticle-based boosting mucosal vaccine Nano-FP1, which combined to BCG priming conferred enhanced protection in mice challenged with low-dose Mtb. We analyzed the vaccine profile and efficacy at short (2 weeks), medium (7 weeks) and long term (11 weeks) post-vaccination, and compared it to ineffective Nano-FP2 vaccine. We observed several changes in the mouse lung environment by both nanovaccines, which are lost shortly after boosting. Additional boosting at long-term (14 weeks) recovered partially cell populations and transcriptomic profile, but not enough to enhance protection to infection. An increase in both total and resident memory CD4 and CD8 T cells, but no pro-inflammatory cytokine levels, were correlated with better protection. A unique gene expression pattern with differentially expressed genes revealed potential pathways associated to the immune defense against Mtb. Our findings provide an insight into the critical immune responses that need to be considered when assessing the effectiveness of a novel TB vaccine.

Identification of candidate host serum and saliva biomarkers for a better diagnosis of active and latent tuberculosis infection

In our work, we aim to identify new candidate host biomarkers to discriminate between active TB patients (n = 28), latent infection (LTBI; n = 27) and uninfected (NoTBI; n = 42) individuals. For that, active TB patients and their contacts were recruited that donated serum and saliva samples. A multiplex assay was performed to study the concentration of different cytokines, chemokines and growth factors. Proteins with significant differences between groups were selected and logistic regression and the area under the ROC curve (AUC) was used to assess the diagnostic accuracy. The best marker combinations that discriminate active TB from NoTBI contacts were [IP-10 + IL-7] in serum and [Fractalkine + IP-10 + IL-1α + VEGF] in saliva. Best discrimination between active TB and LTBI was achieved using [IP-10 + BCA-1] in serum (AUC = 0.83) and IP-10 in saliva (p = 0.0007; AUC = 0.78). The levels of TNFα (p = 0.003; AUC = 0.73) in serum and the combination of [Fractalkine+IL-12p40] (AUC = 0.83) in saliva, were able to differentiate between NoTBI and LTBI contacts. In conclusion, different individual and combined protein markers could help to discriminate between active TB and both uninfected and latently-infected contacts. The most promising ones include [IP-10 + IL-7], [IP-10 + BCA-1] and TNFα in serum and [Fractalkine + IP-10 + IL-1α + VEGF], IP-10 and [Fractalkine+IL-12p40] in saliva.

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected –LTBI- or uninfected –NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas.

Multi-parameter flow cytometry immunophenotyping distinguishes different stages of tuberculosis infection

OBJECTIVES

To identify new potential host biomarkers in blood to discriminate between active TB patients, uninfected (NoTBI) and latently infected contacts (LTBI).

METHODS

A blood cell count was performed to study parent leukocyte populations. Peripheral blood mononuclear cells (PBMCs) were isolated and a multi-parameter flow cytometry assay was conducted to study the distribution of basal and Mycobacterium tuberculosis (Mtb)-stimulated lymphocytes. Differences between groups and the area under the ROC curve (AUC) were investigated to assess the diagnostic accuracy.

RESULTS

Active TB patients presented higher Monocyte-to-lymphocyte and Neutrophil-to-lymphocyte ratios than LTBI and NoTBI contacts (p<0.0001; AUC>0.8). Lymphocyte subsets with differences (p >0.05; AUC >0.7) between active TB and both contact groups include the basal distribution of Th1/Th2 ratio, Th1-Th17, CD4⁺ Central Memory (T_CM) or MAIT cells. Expression of CD154 is increased in Mtb-activated CD4⁺ T_CM and Effector Memory T cells in active TB and LTBI compared to NoTBI. In CD4⁺T cells, expression of CD154 showed a higher accuracy than IFNγ to discriminate Mtb-specific activation.

CONCLUSIONS

We identified different cell subsets with potential use in tuberculosis diagnosis. Among them, distribution of CD4 T_CM cells and their expression of CD154 after Mtb-activation are the most promising candidates.

High-resolution quantitative proteomics applied to the study of the specific protein signature in the sputum and saliva of active tuberculosis patients and their infected and uninfected contacts

Our goal was to establish panels of protein biomarkers that are characteristic of patients with microbiologically confirmed pulmonary tuberculosis (TB) and their contacts, including latent TB-infected (LTBI) and uninfected patients. Since the first pathogen-host contact occurs in the oral and nasal passages the saliva and sputum were chosen as the biological fluids to be studied. Quantitative shotgun proteomics was performed using a LTQ-Orbitrap-Elite platform. For active TB patients, both fluids exhibited a specific accumulation of proteins that were related to complement activation, inflammation and modulation of immune response. In the saliva of TB patients, a decrease of in proteins related to glucose and lipid metabolism was detected. In contrast, the sputum of uninfected contacts presented a specific proteomic signature that was composed of proteins involved in the perception of bitter taste, defense against pathogens and innate immune response, suggesting that those are key events during the initial entry of the pathogen in the host. SIGNIFICANCE: This is the first study to compare the saliva and sputum from active TB patients and their contacts. Our findings strongly suggest that TB patients show not only an activation of processes that are related to complement activation and modulation of inflammation but also an imbalance in carbohydrate and lipid metabolism. In addition, those individuals who do not get infected after direct exposure to the pathogen display a typical proteomic signature in the sputum, which is a reflection of the secretion from the nasal and oral mucosa, the first immunological barriers that M. tuberculosis encounters in the host. Thus, this result indicates the importance of the processes related to the innate immune response in fighting the initial events of the infection.

Redetermination of 1-cyclo-hexyl-3-(2-furo-yl)thio-urea

The title compound, C₁₂H₁₆N₂O₂S, was synthesized from furoyl isothio­cyanate and cyclo­hexyl­amine in dry acetone, and the crystal structure redetermined. The thio­urea group is in the thio­amide form. The structure [Otazo-Sánchez et al. (2001 ▶). J. Chem. Soc. Perkin Trans. 2, pp. 2211–2218] has been redetermined in order to establish the intra- and inter­molecular inter­actions. The trans–cis geometry of the thio­urea group is stabilized by intra­molecular hydrogen bonding between the carbonyl and cis-thio­amide groups, resulting in a pseudo-S(6) planar ring which makes a dihedral angle of 3.24 (6)° with the 2-furoyl group and a torsion angle of −84.3 (2)° with the cyclo­hexyl group. There is also an intra­molecular hydrogen bond between the furan O atom and the other thio­amide H atom. In the crystal structure, mol­ecules are linked by inter­molecular N—H⋯O hydrogen bonds, forming chains along [010].

Counterphase dichoptic flicker is seen as its own second harmonic

If a patternless field is modulated sinusoidally in time so that the luminance change in one eye is in counterphase to that in the other, the resulting flicker appears faster than if the modulation to both eyes has the same phase. If observers set the frequency and the amplitude of a comparison in-phase field so that it matches a neighbouring counterphase field, modulated at, say, 2.5 x its threshold, they set the frequency to twice the counterphase frequency, and the amplitude to a value that is, for a given frequency, a constant ratio of the modulation of the counterphase field. Counterphase stimulation thus appears to cause an internal second-harmonic signal. However, it is not possible to cancel this by adding a second harmonic component to the stimulus.

Improved color fundamentals offer a new view on photometric additivity

New action spectra are derived for the foveal cones on the basis of three assumptions: (1) the Stiles-Burch (1959) colorimetric data are better than CIE 1931; (2) the long wavelength flanks of the dichromatic luminosity functions offer better anchorage than the confusion loci; and (3) the relative mutual heights per receptor can be derived from the Bezold-Brücke effect. Since these assumptions do not include photometric data, we could at the end ask how L lambda, M lambda and S lambda combine to V lambda. The best answer appeared to be V lambda = 0.68 L lambda + 0.34 M lambda -0.02 S lambda, reflecting both relative receptor population densities and a negative input of the S-system to luminance.

Interindividual receptor variability of normal colour observers: analysis of the 2-deg Stiles and Burch data

Examination of the interobserver variability among the ten observers of the 2-deg Stiles and Burch colorimetric study reveals an interesting pattern: the chromaticities of the matches made by different normal subjects fall approximately on straight lines and these lines appear to converge. Because of a geometric relationship that holds between the normal, anomalous, and dichromatic colour spaces, it can be argued that part of the variability must be due to actual differences in the shape or spectral position of the receptor sensitivities of different normal observers.

Evoked potential latencies as a function of contrast: a system analytical approach

That the latency of the pattern evoked potential (EP) increases as the stimulus contrast decreases can be understood as the result of, first, a low-pass filtering process of the (contrast) signal followed by, second, and nonlinear 'threshold' stage. We show here that by using this simple concept it is possible to estimate the shape of the 'unit step' response of the low-pass filter with the latency vs. contrast data. We show also that the step responses calculated from several subjects are in reasonable agreement if they are normalized with using the subjects' own contrast thresholds. Within experimental error, the response of a filter consisting of four low-pass first-order stages with a 10.5 Hz cut-off frequency gives a reasonable fit to our own data and to that of one other study (Musselwhite and Jeffreys, 1982).

The "silent substitution" method in visual research

The "silent substitution" method, which has become well-known mostly through the work of Rushton and his collaborators, can be traced back to experiments performed by M. Ishihara under Exner's supervision at the beginning of the century. Rushton provided a theoretical framework for the method with the enunciation of his "principle of univariance". In this paper we show how the "silent substitution" concept can be further generalized to any arbitrary number of photoreceptor classes by making use of well-established concepts of colorimetry. With this approach, which we have called "spectral compensation", one also gains a better insight into the possibilities and shortcomings of the technique. To illustrate this, we apply our approach to examine a number of published studies where use has been made of "silent substitution", with particular emphasis on the work of W. A. H. Rushton.

Contrast sensitivity of individual colour mechanisms of human vision

1. Contrast sensitivity functions of isolated colour mechanisms were measured at spatial frequencies from 0-2 to 32 c/deg. The contrast sensitivity vs. spatial-frequency functions of the red (pi5) and green (pi4) mechanisms are similar, while the blue (pi3) mechanism has lower absolute sensitivity and lower resolving power. Isolation of a single mechanism never increases its maximum sensitivity. 2. The shape of the contrast sensitivity function of a colour mechanism is established within the mechanism. Little if any inhibitory interaction takes place among colour mechanisms. 3. Differences that have been reported between the sensitivities of the red and green mechanisms, as well as the apparent "supersensitivity" of the isolated green mechanism, may be artifacts that result from the extrapolation procedures that were used to estimate the absolute sensitivities of the colour mechanisms.