REPORT INTERNATIONAL: TB RICC BIOMARKER– JUNIOR INVESTIGATOR TRAINING

By: Gustiani Salim

Enhancing Laboratory Skills and Capacity in TB Biomarker Research

For the first time, RePORT International held a Junior Investigator Training program aimed at enhancing the capacity and laboratory skills of staff from RePORT International member countries. This training provides participants the opportunity to learn not only from expert scientists directly but also by using the latest advanced laboratory equipment and techniques that can be used as assays for research such as biomarkers, immune mechanisms, and transcriptomics, metabolomics, advanced host-pathogen interactions in high-throughput associated with tuberculosis (TB) dis-ease. From this program, participants are also expected to engage in relevant research, including RePORT International study projects and to build collaborative relationships between countries.

The Junior Investigator Training in 2024 was attended by five participants from three RePORT member countries: one person from Indonesia, two from India, and two from the Philippines. The training was conducted in three central laboratories located in three different countries where participants specifically learned techniques and laboratory methods that support research focusing on the search for host biomarkers associated with TB treatment failure. The three laboratories are:

1. SATVI Laboratory at the University of Cape Town, South Africa, directed by Prof. Thomas Scriba. Participants learn and receive hands-on training on multiplex quantitative PCR assay methodology (Fluidigm/Microfluidics). The training was held for two weeks from February 5 – 16, 2024.
2. RiCC Luminex Referral Laboratory, Salvador, Bahia, Brazil, directed by Dr. Bruno Andrade. Participants learn and receive hands-on training on protein quantification assays using Lu-minex technology. The training was held for two weeks from February 19 – March 1, 2024.
3. Rutgers – New Jersey Medical School (NJMS) Laboratory, Newark, New Jersey, United States of America, led by Dr. Padmini Salgame. Participants learn and receive hands-on training on the digital Nanostring platform. The training will be held for two weeks from May 6 – 17, 2024.

In addition to wet lab training, the trainees also received virtual training through online webinars by Dr. Evan Johnson, an informatics specialist. He provided training to the group in the use of R Studio – a software package intended for the analysis of complex biology and informatics data. This training serves as a foundation for analyzing the results that will be obtained from the three laboratory assays above.

Training at SATVI Laboratory, University of Cape Town, South Africa

The South African Tuberculosis Vaccine Initiative (SATVI), is a world leader in TB vaccine clinical research located within the Health Sciences Faculty at the University of Cape Town.

The research conducted at the SATVI laboratory mainly focuses on immunology in TB, TB vaccine development, biomarker development, and deepening understanding of pathogenesis and immunity related to risk and protection against TB. Specifically for this training program, SATVI focuses on providing material regarding gene expression studies aimed at finding and developing specific biomarkers for TB disease by performing microfluidic RT-qPCR using the Fluidigm Biomark© plat-form.

1. Gene Expression Study

What is Gene Expression Study

Gene expression is the determination of the pattern of genes expressed at the level of genetic transcription, under specific circumstances or in a specific cell. Gene expression works by measuring levels of mRNA, the intermediary between DNA and protein. It is well-known that to activate a gene, a cell must first copy the DNA sequence of that gene into a piece of mRNA known as a transcript. Thus, by determining which mRNA transcripts are present in a cell, it can be determined which genes are expressed in that cell. However, gene expression can also be analyzed by directly measuring protein levels. When studying gene expression, researchers usually investigate changes, increases, or decreases, in the expression of a particular gene. The investigation monitors the response of a gene to treatment with a compound or a drug of interest, under a defined set of conditions.

There are several laboratory methods that can be used to measure gene expression such as quantitative polymerase chain reaction (qPCR), DNA micro-array, and RNA-sequencing by measuring mRNA levels. For protein levels, techniques such as Western blot, ELISA, or bead-based immunoassay (Luminex) can be used.

Quantitative Polymerase Chain Reaction (qPCR) for Assessing Relative Quantification of Gene Expression

Quantitative Polymerase Chain Reaction (qPCR), also known as real-time PCR using the TaqMan primers/probes platform, is a commonly used assay when studying gene expression. This platform allows us to relatively quantify differences in the expression level of a specific target gene between different samples. The data output is expressed as a fold-change or fold-difference of expression levels, for example, the change in expression of a particular gene over a given time period in treated versus untreated samples.

One of the analysis methods for relative quantification of gene expression from qPCR data is known as the comparative CT or double delta CT (ΔΔΔΔCT) method. This method requires the quantification of two different genes: the target gene and the housekeeping gene as a reference gene. The reference gene is used to normalize the quantification of targets for differences in the amount of total nucleic acid added to each reaction. ΔΔΔΔCT analysis assumes that:

• There is equal primer efficiency between primer sets (i.e., within 5%),
• There is near 100% amplification efficacy of the reference and the target genes,
• The internal control genes are constantly expressed and aren’t affected by the treatment.

Calculating Gene Expression Using the ΔΔC_T Meth-od and Interpretation

In general, there are several main components required when performing the relative quantification ΔΔC_T method:

• Target gene: The gene of interest whose expression we are determining.
• Endogenous control gene: The housekeeping gene whose expression is regulated.
• Calibrator sample: The sample or group of samples used as a control.
• Test sample: The sample or group of samples treated or tested for differences.
• Expression fold change/Relative gene expression: The ratio of the target gene expression in the test sample over the calibrator sample.

The arithmetic formula needed to measure the amount of target, normalized to an endogenous control and relative to a calibrator, is given by:

RQ=2_⁻ΔΔ^CT

Where:

ΔΔC_T = ΔCT (test samples) − ΔC_T (calibrator samples)

ΔCT (test samples) = CT value (target gene in test) – CT value (endogenous control gene in test)

ΔC_T (calibrator samples) = CT value (target gene in calibrator) – C_T value (endogenous control gene in calibrator)

The value obtained from the calculation represents the fold change of the gene of interest in the test condition, relative to the control condition, after normalization to the housekeeping gene. For ex-ample:

• A fold change of 1 indicates that there is 100% as much gene expression in the test condition as in the control condition, meaning there is no change between the experimental group and the control group.
• A fold-change value above 1 indicates upregulation of the gene of interest relative to the control (e.g., a 1.2-fold change equals 120% gene expression relative to control, 5 equals 500%, 10 equals 1,000%, etc.).
• Values below 1 indicate gene downregulation relative to the control (e.g., a fold change of 0.5 equals 50% gene expression relative to control, meaning there is half as much expression as in the control, etc.).

Microfluidic RT-qPCR using the Fluidigm Biomark© platform for Gene Expression Study

In the SATVI laboratory, the trainess learn about gene expression using the Fluidigm Biomark© platform, an automated, high-performance PCR/qPCR system that utilizes microfluidics technology to process samples at nanoliter-scale volumes. This machine features Dynamic Array™ integrated fluid-ic circuits (IFCs) that allow us to test up to 96 individual cells against 96 genes in a single experiment. The Dynamic Array™ combines cDNA mate-rial from individual cells with reagents to create individual quantitative PCR (qPCR) reactions.

Before performing qRT-PCR on the Fluidigm system, it is necessary to conduct reverse transcription and specific target amplification using a thermocycler suitable for 96-well plates. The overall process for processing blood samples for gene expression is summarized in the following chart.

When running the samples, upon completion, the Fluidigm qPCR analysis software calculates and provides results displayed as a table, an image view diagram, or a heat map. The results table presents the numeric CT values for the different samples for each gene. The image view option graphically plots the fluorescence intensity as it increases during qPCR amplification. The heat map represents the results according to a color range, with each color tone indicating a CT value. In the heat map display, individual assays (X-axis) are plotted against individual samples (Y-axis). The software can also use a housekeeping gene included in the array to normalize the CT values, thereby correcting differences in the CT values due to slightly varying starting amounts of RNA—these are known as ΔΔCT values, which are then converted to fold change values. Furthermore, the resulting data are analyzed using R software for quality control, statistical analysis, and presentation purposes.