Dominant Circulating Cell-free Mycobacterial Proteins in in-use Machining Fluid and their Antigenicity Potential

BACKGROUND

Occupational exposure to industrial Metalworking Fluid (MWF) colonized by Mycobacterium immunogenum (MI) has been associated with immune lung disease hypersensitivity pneumonitis (HP) in machinists. This warrants regular fluid monitoring for early detection of mycobacterial proteins, especially those with antigenic potential.

OBJECTIVE

To detect and identify dominant MI proteins and antigens directly from the field-drawn in-use MWF using an integrated immunoproteomic and immunoinformatic approach.

METHODS

An MI-positive MWF selected by DNA-based screening of several field-drawn MWF samples were cultured to isolate the colonizing strain and profiled for dominant circulating cell- free (ccf) MI proteins, including antigens using an integrated immunoproteomic (1D- and 2Dgel fractionation of seroreactivity proteins combined with shotgun proteomic analysis using LC-MS/ MS) and immunoinformatic strategy.

RESULTS

A new MI strain (MJY-27) was identified. The gel fractionated MI protein bands (1Dgel) or spots (2D-gel) seroreactive with anti-MI sera probes (Rabbit and Patient sera) yielded 86 MI proteins, 29 of which showed peptide abundance. T-cell epitope analysis revealed high (90-100%) binding frequency for HLA-I& II alleles for 13 of the 29 proteins. Their antigenicity analysis revealed the presence of 6 to 37 antigenic determinants. Interestingly, one of the identified candidates corresponded to an experimentally validated strong B- and T-cell antigen (AgD) from our laboratory culture-based studies.

CONCLUSION

This first report on dominant proteins, including putative antigens of M. immunogenum prevalent in field in-use MWF, is a significant step towards the overall goal of developing fluid monitoring for exposure and disease risk assessment for HP development in machining environments.

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens

BACKGROUND

The identification of tumor T cell antigens (TTCAs) is crucial for providing insights into their functional mechanisms and utilizing their potential in anticancer vaccines development. In this context, TTCAs are highly promising. Meanwhile, experimental technologies for discovering and characterizing new TTCAs are expensive and time-consuming. Although many machine learning (ML)-based models have been proposed for identifying new TTCAs, there is still a need to develop a robust model that can achieve higher rates of accuracy and precision.

RESULTS

In this study, we propose a new stacking ensemble learning-based framework, termed StackTTCA, for accurate and large-scale identification of TTCAs. Firstly, we constructed 156 different baseline models by using 12 different feature encoding schemes and 13 popular ML algorithms. Secondly, these baseline models were trained and employed to create a new probabilistic feature vector. Finally, the optimal probabilistic feature vector was determined based the feature selection strategy and then used for the construction of our stacked model. Comparative benchmarking experiments indicated that StackTTCA clearly outperformed several ML classifiers and the existing methods in terms of the independent test, with an accuracy of 0.932 and Matthew's correlation coefficient of 0.866.

CONCLUSIONS

In summary, the proposed stacking ensemble learning-based framework of StackTTCA could help to precisely and rapidly identify true TTCAs for follow-up experimental verification. In addition, we developed an online web server ( http://2pmlab.camt.cmu.ac.th/StackTTCA ) to maximize user convenience for high-throughput screening of novel TTCAs.

PSRTTCA: A new approach for improving the prediction and characterization of tumor T cell antigens using propensity score representation learning

Despite the arsenal of existing cancer therapies, the ongoing recurrence and new cases of cancer pose a serious health concern that necessitates the development of new and effective treatments. Cancer immunotherapy, which uses the body's immune system to combat cancer, is a promising treatment option. As a result, in silico methods for identifying and characterizing tumor T cell antigens (TTCAs) would be useful for better understanding their functional mechanisms. Although few computational methods for TTCA identification have been developed, their lack of model interpretability is a major drawback. Thus, developing computational methods for the effective identification and characterization of TTCAs is a critical endeavor. PSRTTCA, a new machine learning (ML)-based approach for improving the identification and characterization of TTCAs based on their primary sequences, is proposed in this study. Specifically, we introduce a new propensity score representation learning algorithm that allows one to generate various sets of propensity scores of amino acids, dipeptides, and g-gap dipeptides to be TTCAs. To enhance the predictive performance, optimal sets of variant propensity scores were determined and fed into the final meta-predictor (PSRTTCA). Benchmarking results revealed that PSRTTCA was a more precise and promising tool for the identification and characterization of TTCAs than conventional ML classifiers and existing methods. Furthermore, PSR-derived propensities of amino acids in becoming TTCAs are used to reveal the relationship between TTCAs and their informative physicochemical properties in order to provide insights into TTCA characteristics. Finally, a user-friendly online computational platform of PSRTTCA is publicly available at http://pmlabstack.pythonanywhere.com/PSRTTCA. The PSRTTCA predictor is anticipated to facilitate community-wide efforts in accelerating the discovery of novel TTCAs for cancer immunotherapy and other clinical applications.

Human leukocyte antigen (HLA)-binding epitopes dataset for the newly identified T-cell antigens of Mycobacterium immunogenum

The dataset described herein is related to our article entitled “T-cell antigens of Mycobacterium immunogenum (MI), an etiological agent of occupational hypersensitivity pneumonitis’’ (Chandra and Yadav, 2016) [1]. The data include in silico-predicted T-cell epitopes of the T-cell antigens AgA and AgD of MI predicted to bind to HLA-I or HLA-II alleles. Data on two reference T-cell antigens ESAT-6 and CFP-10 of Mycobacterium tuberculosis H37Rv are included for comparison. The data for each antigen include the predicted epitope׳s amino acid sequence, its first amino acid position, and its ability to bind HLA-I or HLA-II allele(s).

Detection of T- and B-cell Target Antigens of Fowlpox Virus Isolated from Backyard Chickens in India

With the aim of assessing the antigenic characteristics of a circulating pool of fowlpox virus (FPV) that exists in the backyard poultry system in India, one of the field isolates generated was characterized by in vitro immunologic techniques. FPV was isolated from clinically positive fowlpox cases (n  =  10) from the Jhargram (West Midnapur district) and Kakdwip (South 24 Pargana district) areas of West Bengal State, India. Initially, FPV-specific PCR was performed for confirmation of the samples. Isolation of FPV was done using embryonated chicken eggs and the choreoallantoic membrane route. Subsequently, FPV antigen was prepared from chicken embryo fibroblast cell culture-adapted field isolate. Biologic transmission of FPV was performed in Rhode Island red chickens experimentally to assess humoral and cell-mediated immune (CMI) responses. High level of anti-FPV antibodies were observed in test birds as assessed by indirect ELISA. Seroreactive polypeptides (B-cell antigens) of FPV antigen with molecular weights of 44.5, 66.5, 75, 90.5, and 99 kDa were detected by western blot analysis. Significant increases in CMI responses were observed in inoculated chickens as assessed by lymphocyte proliferation assay, cytotoxicity assay, and T-cell immunoblotting. The predominant T-cell antigen of FPV detected had a molecular weight of 66.5 kDa. The present study revealed the antigenic characteristics of FPV that exists in backyard poultry system in West Bengal for the first time, thus exploring the rationality of designing future T- and B-cell vaccines against fowlpox.