Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection

Correlating transcription and protein expression profiles of immune biomarkers following lipopolysaccharide exposure in lung epithelial cells

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.

Plasma CXCL8 and MCP-1 as surrogate plasma biomarkers of latent tuberculosis infection among household contacts-A cross-sectional study

Early detection of latent tuberculosis infection (LTBI) is critical to TB elimination in the current WHO vision of End Tuberculosis Strategy. The study investigates whether detecting plasma cytokines could aid in diagnosing LTBI across household contacts (HHCs) positive for IGRA, HHCs negative for IGRA, and healthy controls. The plasma cytokines were measured using a commercial Bio-Plex Pro Human Cytokine 17-plex assay. Increased plasma CXCL8 and decreased MCP-1, TNF-α, and IFN-γ were associated with LTBI. Regression analysis showed that a combination of CXCL8 and MCP-1 increased the risk of LTBI among HHCs to 14-fold. Our study suggests that CXCL-8 and MCP-1 could serve as the surrogate biomarkers of LTBI, particularly in resource-limited settings. Further laboratory investigations are warranted before extrapolating CXCL8 and MCP-1 for their usefulness as surrogate biomarkers of LTBI in resource-limited settings.

Plasma CXCL8 and MCP-1 as biomarkers of latent tuberculosis infection

Background

Early detection of latent tuberculosis infection (LTBI) is critical to TB elimination in the current WHO vision of End Tuberculosis Strategy.

Methods

We investigated whether detecting plasma cytokines could aid in diagnosing LTBI across household contacts (HHCs) positive for IGRA, HHCs negative for IGRA, and healthy controls. We also measured the plasma cytokines using a commercial Bio-Plex Pro Human Cytokine 17-plex assay.

Results

Increased plasma CXCL8 and decreased MCP-1, TNF-α, and IFN-γ were associated with LTBI. Regression analysis showed that a combination of CXCL8 and MCP-1 increased the risk of LTBI among HHCs to 14-fold.

Conclusions

We postulated that CXCL8 and MCP-1 could be the surrogate biomarkers of LTBI, especially in resource-limited settings.

Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.

Latent tuberculosis and computational biology: A less-talked affair

Tuberculosis (TB) is a pervasive and devastating air-borne disease caused by the organisms belonging to the Mycobacterium tuberculosis (Mtb) complex. Currently, it is the global leader in infectious disease-related death in adults. The proclivity of TB to enter the latent state has become a significant impediment to the global effort to eradicate TB. Despite decades of research, latent tuberculosis (LTB) mechanisms remain poorly understood, making it difficult to develop efficient treatment methods. In this review, we seek to shed light on the current understanding of the mechanism of LTB, with an accentuation on the insights gained through computational biology. We have outlined various well-established computational biology components, such as omics, network-based techniques, mathematical modelling, artificial intelligence, and molecular docking, to disclose the crucial facets of LTB. Additionally, we highlighted important tools and software that may be used to conduct a variety of systems biology assessments. Finally, we conclude the article by addressing the possible future directions in this field, which might help a better understanding of LTB progression.

Activation of transcription factor CREB in human macrophages by Mycobacterium tuberculosis promotes bacterial survival, reduces NF-kB nuclear transit and limits phagolysosome fusion by reduced necroptotic signaling

Macrophages are a first line of defense against pathogens. However, certain invading microbes modify macrophage responses to promote their own survival and growth. Mycobacterium tuberculosis (M.tb) is a human-adapted intracellular pathogen that exploits macrophages as an intracellular niche. It was previously reported that M.tb rapidly activates cAMP Response Element Binding Protein (CREB), a transcription factor that regulates diverse cellular responses in macrophages. However, the mechanism(s) underlying CREB activation and its downstream roles in human macrophage responses to M.tb are largely unknown. Herein we determined that M.tb-induced CREB activation is dependent on signaling through MAPK p38 in human monocyte-derived macrophages (MDMs). Using a CREB-specific inhibitor, we determined that M.tb-induced CREB activation leads to expression of immediate early genes including COX2, MCL-1, CCL8 and c-FOS, as well as inhibition of NF-kB p65 nuclear localization. These early CREB-mediated signaling events predicted that CREB inhibition would lead to enhanced macrophage control of M.tb growth, which we observed over days in culture. CREB inhibition also led to phosphorylation of RIPK3 and MLKL, hallmarks of necroptosis. However, this was unaccompanied by cell death at the time points tested. Instead, bacterial control corresponded with increased colocalization of M.tb with the late endosome/lysosome marker LAMP-1. Increased phagolysosomal fusion detected during CREB inhibition was dependent on RIPK3-induced pMLKL, indicating that M.tb-induced CREB signaling limits phagolysosomal fusion through inhibition of the necroptotic signaling pathway. Altogether, our data show that M.tb induces CREB activation in human macrophages early post-infection to create an environment conducive to bacterial growth. Targeting certain aspects of the CREB-induced signaling pathway may represent an innovative approach for development of host-directed therapeutics to combat TB.

Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection

Globally, it is estimated that one-quarter of the world's population is latently infected with Mycobacterium tuberculosis (Mtb), also known as latent tuberculosis infection (LTBI). Recently, this condition has been referred to as tuberculosis infection (TBI), considering the dynamic spectrum of the infection, as 5-10% of the latently infected population will develop active TB (ATB). The chances of TBI development increase due to close contact with index TB patients. The emergence of multidrug-resistant TB (MDR-TB) and the risk of development of latent MDR-TB has further complicated the situation. Detection of TBI is challenging as the infected individual does not present symptoms. Currently, there is no gold standard for TBI diagnosis, and the only screening tests are tuberculin skin test (TST) and interferon gamma release assays (IGRAs). However, these tests have several limitations, including the inability to differentiate between ATB and TBI, false-positive results in BCG-vaccinated individuals (only for TST), false-negative results in children, elderly, and immunocompromised patients, and the inability to predict the progression to ATB, among others. Thus, new host markers and Mtb-specific antigens are being tested to develop new diagnostic methods. Besides screening, TBI therapy is a key intervention for TB control. However, the long-course treatment and associated side effects result in non-adherence to the treatment. Additionally, the latent MDR strains are not susceptible to the current TBI treatments, which add an additional challenge. This review discusses the current situation of TBI, as well as the challenges and efforts involved in its control.

Detection of biomarkers for filoviral infection with a silicon photonic resonator platform

This protocol describes the use of silicon photonic microring resonator sensors for detection of Ebola virus (EBOV) and Sudan virus (SUDV) soluble glycoprotein (sGP). This protocol encompasses biosensor functionalization of silicon microring resonator chips, detection of protein biomarkers in sera, preparing calibration standards for analytical validation, and quantification of the results from these experiments. This protocol is readily adaptable toward other analytes, including cytokines, chemokines, nucleic acids, and viruses. For complete details on the use and execution of this protocol, please refer to Qavi et al. (2022).

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

Immune responses in COVID-19 and tuberculosis coinfection: A scoping review

Background and aim

Patients with COVID-19 and tuberculosis coinfection are at an increased risk of severe disease and death. We therefore sought to evaluate the current evidence which assessed the immune response in COVID-19 and tuberculosis coinfection

Methods

We searched Pubmed/MEDLINE, EMBASE, Scopus, and Web of Science to identify articles published between 2020 and 2021. We included observational studies evaluating the immune response in patients with tuberculosis and COVID-19 compared to patients with COVID-19 alone.

Results

Four cross-sectional studies (372 participants) were identified. In patients with asymptomatic COVID-19 and latent tuberculosis (LTBI), increased cytokines, chemokines, growth factors and humoral responses were found. In addition, patients with symptomatic COVID-19 and LTBI had higher leukocytes counts and less inflammation. Regarding patients with COVID-19 and active tuberculosis (aTB), they exhibited decreased total lymphocyte counts, CD4 T cells specific against SARS-CoV-2 and responsiveness to SARS-CoV-2 antigens compared to patients with only COVID-19.

Conclusion

Although the evidence is limited, an apparent positive immunomodulation is observed in patients with COVID-19 and LTBI. On the other hand, patients with COVID-19 and aTB present a dysregulated immune response. Longitudinal studies are needed to confirm these findings and expand knowledge.

Feasibility of novel approaches to detect viable Mycobacterium tuberculosis within the spectrum of the tuberculosis disease

Tuberculosis (TB) is a global disease caused by Mycobacterium tuberculosis (Mtb) and is manifested as a continuum spectrum of infectious states. Both, the most common and clinically asymptomatic latent tuberculosis infection (LTBI), and the symptomatic disease, active tuberculosis (TB), are at opposite ends of the spectrum. Such binary classification is insufficient to describe the existing clinical heterogeneity, which includes incipient and subclinical TB. The absence of clinically TB-related symptoms and the extremely low bacterial burden are features shared by LTBI, incipient and subclinical TB states. In addition, diagnosis relies on cytokine release after antigenic T cell stimulation, yet several studies have shown that a high proportion of individuals with immunoreactivity never developed disease, suggesting that they were no longer infected. LTBI is estimated to affect to approximately one fourth of the human population and, according to WHO data, reactivation of LTBI is the main responsible of TB cases in developed countries. Assuming the drawbacks associated to the current diagnostic tests at this part of the disease spectrum, properly assessing individuals at real risk of developing TB is a major need. Further, it would help to efficiently design preventive treatment. This quest would be achievable if information about bacterial viability during human silent Mtb infection could be determined. Here, we have evaluated the feasibility of new approaches to detect viable bacilli across the full spectrum of TB disease. We focused on methods that specifically can measure host-independent parameters relying on the viability of Mtb either by its direct or indirect detection.

Biodetection Techniques for Quantification of Chemokines

Chemokines are a class of cytokine whose special properties, together with their involvement and relevant role in various diseases, make them a restricted group of biomarkers suitable for diagnosis and monitoring. Despite their importance, biodetection techniques dedicated to the selective determination of one or more chemokines are very scarce. For some years now, the critical diagnosis of inflammatory diseases by detecting both cytokine and chemokine biomarkers, has had a strong impact on the development of multiple detection platforms. However, it would be desirable to implement methodologies with a higher degree of selectivity for chemokines, in order to provide more precise information. In addition, better development of biosensor technology applied to this specific field would make it possible to address the main challenges of detection methods for several diseases with a high incidence in the population, avoiding high costs and low sensitivity. Taking this into account, this review aims to present the state of the art of chemokine biodetection techniques and emphasize the role of these systems in the prevention, monitoring and treatment of various diseases associated with chemokines as a starting point for future developments that are also analyzed throughout the article.

Individual Factors Associated With COVID-19 Infection: A Machine Learning Study

The fast, exponential increase of COVID-19 infections and their catastrophic effects on patients' health have required the development of tools that support health systems in the quick and efficient diagnosis and prognosis of this disease. In this context, the present study aims to identify the potential factors associated with COVID-19 infections, applying machine learning techniques, particularly random forest, chi-squared, xgboost, and rpart for feature selection; ROSE and SMOTE were used as resampling methods due to the existence of class imbalance. Similarly, machine and deep learning algorithms such as support vector machines, C4.5, random forest, rpart, and deep neural networks were explored during the train/test phase to select the best prediction model. The dataset used in this study contains clinical data, anthropometric measurements, and other health parameters related to smoking habits, alcohol consumption, quality of sleep, physical activity, and health status during confinement due to the pandemic associated with COVID-19. The results showed that the XGBoost model got the best features associated with COVID-19 infection, and random forest approximated the best predictive model with a balanced accuracy of 90.41% using SMOTE as a resampling technique. The model with the best performance provides a tool to help prevent contracting SARS-CoV-2 since the variables with the highest risk factor are detected, and some of them are, to a certain extent controllable.

Rapid detection of an Ebola biomarker with optical microring resonators

Ebola virus (EBOV) is a highly infectious pathogen, with a case mortality rate as high as 89%. Rapid therapeutic treatments and supportive measures can drastically improve patient outcome; however, the symptoms of EBOV disease (EVD) lack specificity from other endemic diseases. Given the high mortality and significant symptom overlap, there is a critical need for sensitive, rapid diagnostics for EVD. Facile diagnosis of EVD remains a challenge. Here, we describe a rapid and sensitive diagnostic for EVD through microring resonator sensors in conjunction with a unique biomarker of EBOV infection, soluble glycoprotein (sGP). Microring resonator sensors detected sGP in under 40 min with a limit of detection (LOD) as low as 1.00 ng/mL in serum. Furthermore, we validated our assay with the detection of sGP in serum from EBOV-infected non-human primates. Our results demonstrate the utility of a high-sensitivity diagnostic platform for detection of sGP for diagnosis of EVD.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Standardized Whole Blood Assay and Bead-Based Cytokine Profiling Reveal Commonalities and Diversity of the Response to Bacteria and TLR Ligands in Cattle

Recent developments in multiplex technologies enable the determination of a large nu\mber of soluble proteins such as cytokines in various biological samples. More than a one-by-one determination of the concentration of immune mediators, they permit the establishment of secretion profiles for a more accurate description of conditions related to infectious diseases or vaccination. Cytokine profiling has recently been made available for bovine species with the development of a Luminex^® technology-based 15-plex assay. Independently from the manufacturer, we evaluated the bovine cytokine/chemokine multiplex assay for limits of detection, recovery rate, and reproducibility. Furthermore, we assessed cytokine secretion in blood samples from 107 cows upon stimulation with heat-killed bacteria and TLR2/4 ligands compared to a null condition. Secretion patterns were analyzed either using the absolute concentration of cytokines or using their relative concentration with respect to the overall secretion level induced by each stimulus. Using Partial Least Square-Discriminant Analysis, we show that the 15-cytokine profile is different under Escherichia coli, Staphylococcus aureus, and Streptococcus uberis conditions, and that IFN-γ, IL-1β, and TNF-α contribute the most to differentiate these conditions. LPS and E. coli induced largely overlapping biological responses, but S. aureus and S. uberis were associated with distinct cytokine profiles than their respective TLR ligands. Finally, results based on adjusted or absolute cytokine levels yielded similar discriminative power, but led to different stimuli-related signatures.