Gold Biotechnology stands at the threshold of operating for 40 years. To celebrate this milestone, we’re looking back at the amazing advances made in biotechnology during this stretch of time. Bit by bit, small discoveries, collaboration, and access made paths to huge victories that opened doors to new science and even bigger breakthroughs.

Where and when the next breakthroughs will come can feel daunting. But it becomes easier to envision future discoveries that solve critical problems when we take a moment, in this article, to look back on what we as a scientific community have accomplished, and the significance of those achievements.  

Article Table of Contents:

Mid to late 1980s: PCR

1990 – 2003: Human Genome Project

1998: RNAi (RNA Interference)

2005: NGS (Next Generation Sequencing)

2009: scRNA-Seq (Single-cell RNA Sequencing)

2012: CRISPR-Cas9 as a Gene Editing Tool (Clustered Regularly Interspaced Short Palindromic Repeats)

2020: AI and Biology (AlphaFold and Beyond)

2020: mRNA Vaccines

References

 

Mid to late 1980s: PCR

Before PCR, researchers used the primer extension method to sequence DNA. By comparison, primer extension was a laborious and inefficient linear process that resulted in lower yields of DNA. This is because only a single primer was used rather than both primers (one to complement either strand). Your original strand of DNA would be denatured. A primer would pair up with one strand and produce the complementary strand. One DNA fragment could only yield one copied strand of DNA.

Kary Mullis’ groundbreaking idea was to use primers for both sides. The math was there. His lab notebooks paved the foundation. In 1985 Randall Saiki, Mullis, and colleagues published the work using both primers to exponentially amplify DNA.

PCR changed the game because now we weren’t just reading nucleotide letters, we were copying DNA, and at more useful quantities.

Initially, however, the process of PCR was tedious because it used DNA polymerase from E. coli. After every denaturation cycle, you lost your enzyme. The introduction of thermal-stable Taq DNA polymerase (isolated from a thermophilic microbe living in one of Yellowstone National Park’s thermal pools) and a thermal-cycler automated the process, and the rest was history.

1990 – 2003: Human Genome Project

Imagine taking your car to the mechanic and as they’re diagnosing the problem they say “Well, I don’t know what 90% of these parts do, but I think the issue is…” This response wouldn’t leave you very confident.

Similarly, when it came to human health, we faced the issue of not understanding what is going wrong in different diseases because we didn’t have knowledge of the vast majority of cellular parts. This was one of the arguments presented in the 1980s to petition for mapping the human genome. And the arguments were presented at an important time in bioscience.

While the technology wasn’t all there, it was in sight. The idea of mapping the human genome suddenly wasn’t science fiction. Sanger sequencing showed us that we were capable of end-to-end sequencing. Smaller genomes such as brewer’s yeast Saccharomyces cerevisiae and the roundworm Caenorhabditis elegans had been sequenced. Another important technology was now really at our fingertips: computers.

The Human Genome Project took 13 years to get about 90% complete. It was the biggest global and multidisciplinary collaboration project in biotechnology that involved not just biologists, but chemists, physicists, engineers and more.

This endeavor forced scientists to invent new technology in order to sequence faster and cheaper. It also forced a culture of access to science because it required data sharing and collaboration.

Since then, we have come so far. What cost $2.7 billion dollars (Gyles, 2008) and took 13 years to sequence now costs only a few hundred dollars and can be finished in an afternoon.

1998: RNAi (RNA Interference)

Biotechnology was moving fast. The Human Genome Project was under way, and scientists were identifying genes quicker than ever before.

An analogy would be like an author putting words on paper. But we still needed to organize the plot, or in this case, we needed to understand the function of the genes we were decoding.

At the time, using antisense RNA and homologous recombination were the options. But they were very slow, time-consuming, and led to inconsistent results.

Quite by accident, RNA interference was discovered. While studying gene expression in plants and roundworms, researchers encountered a phenomenon when introducing RNA molecules designed to affect genes. The introduction sometimes shut that gene off.

But the reason why wasn’t exactly clear until 1998 when Andrew Fire and Craig Mello showed that the cause was due to double-stranded RNA formed by the introduced RNA binding to the matching messenger RNA (mRNA) directed the selective destruction of that mRNA. The degradation of that mRNA meant the corresponding protein wasn’t getting produced. This process was coined RNA interference, or RNAi.

Why this was such an important breakthrough is because RNAi gave us a fast way to turn off a targeted gene without changing DNA. It allowed us to discover the function of a gene through a process of elimination approach. We could see the change that occurred when a gene was silenced and begin to understand that gene’s role.

Presently, there are RNAi-based therapies approved for certain rare genetic disorders, and this technology is still being examined for other applications.

2005: NGS (Next-Generation Sequencing)

Another shift in scale happened in 2005 with the evolution of first-generation sequencing into next-generation sequencing (NGS).

The Human Genome Project and spin-off genome projects revealed a major bottleneck. Sanger sequencing (the first-generation sequencing approach for the Human Genome Project) was accurate, but it was limited to only one DNA fragment at a time.

Next-generation sequencing resulted from the convergence in several technologies. Improvements in DNA amplification made it possible to create millions of copies of DNA fragments very quickly. Advances in fluorescent chemistry and imaging allowed fragments to be read simultaneously. Computing power and bioinformatics gave us a way to process and assemble an enormous amount of sequenced data, as well as store information at a lower cost.

Together, these innovations and others brought forth the ability to do high-throughput or massively parallel sequencing. Researchers went from one fragment at a time to sequencing millions of fragments in a single run.

Like PCR, NGS isn’t just a milestone in biotechnology. It has become a widely-used technology allowing us to do whole-genome sequencing, RNA sequencing, tumor sequencing, microbiome research and so much more.

2009: scRNA-Seq (Single-cell RNA Sequencing)

PCR allowed us to copy DNA on a large scale. Genome sequencing gave us the blueprints to life. RNAi gave us insight into the role of different genes. But we needed to zoom in even closer.

What we were missing is the difference between one cell and a population of cells. At the individual level, nothing is homogenous.

Continued advances in biotechnology gave us the ability to isolate a single cell. We also had the technology to quickly produce cDNA libraries. These little pieces of the puzzle were what led to published use of single-cell RNA sequencing (scRNA-Seq) in 2009 by Dr. Fuchou Tang.

Rather than relying on averages, we can now read the genes expressed in a single cell. Related technologies provide single cell read-outs on proteins, important posttranslational modifications, and more.

Out of this came the discoveries of new cell types like ionocytes in human lungs (Montoro et al., 2018). It shows us which genes are turned on or off in a certain cell at a particular stage. It can even help scientists see spatially where an individual cell is within a tumor sample.

2012: CRISPR-Cas as Gene Editing Tools (Clustered Regularly Interspaced Short Palindromic Repeats)

CRISPR, or clustered regularly interspaced short palindromic repeats has quite a history. The repeated DNA sequences were first described by Ishino et al. in E. coli in 1987. At the time, sequencing these fragments took months, and their biological importance was unknown.

But in the 1990s, Francisco Mojica discovered something that would lead researchers to investigate CRISPR more. Historically, these repeats were found in prokaryotes. But Mojica found something very similar within a species of archaea, an entirely different domain. The presence of these repeats was more widespread than originally known, and that signaled potential biological importance.

Mojica eventually hypothesized that CRISPR could function as a type of bacterial immune system.

The pieces of the CRISPR puzzle, including the role of CRISPR-associated (Cas) proteins, continued to come together.

Then, in 2012, Emmanuelle Charpentier, Jennifer Doudna and their colleagues demonstrated that CRISPR-Cas9 could be programmed to target and cut a chosen DNA sequence, transforming a naturally occurring microbial defense system into a powerful gene-editing tool.

We now had a tool that would not only let us knock out genes, but also introduce new genetic material, create disease models, and learn how genes influence biological processes. While other gene-editing technologies had been developed for decades, the precision, versatility, and speed of CRISPR-Cas was a substantial leap forward compared to alternative systems.

Researchers are now exploring gene editing to prevent or cure diseases. In fact, CRISPR-based treatments have been approved for sickle cell disease and beta thalassemia.

 

2020: AI and Biology (AlphaFold and Beyond)

The central dogma of molecular biology starts with DNA. DNA is transcribed into RNA. RNA is translated into chains of amino acids, which get folded and packaged as proteins. These proteins are the taskmasters of our bodies. They interact with each other, build on each other and carry out incredible biological functions.

So far, we had the ability to quickly sequence DNA, and from that, we can determine the RNA sequence and the amino acid sequence of a given gene. But what we didn’t know is how to take that amino-acid sequence and accurately predict how it would fold into a protein.

Think of every amino acid sequence on a ball-joint that can rotate, fold, or bind to each other in virtually any direction. Biophysicist Cyrus Levinthal (1969) estimated that an average amino acid string for a given protein could have about 10³⁰⁰ possible ways it could fold. Of course, there are physical and chemical restrictions at play that limit the potential folding pathways of a protein. Nevertheless, the possibilities are still astronomical.

The good news is researchers learned that the way a protein folds is not random. After denaturing a protein, when it underwent renaturation, it folded back into the same shape. This told us that protein folding is encoded within the protein sequence (Marcu et al., 2022).

Knowing or predicting a protein’s shape is very important because its shape gives us insight into its function. Or it can help us guess how it might interact with another protein or molecule, or reveal key binding sites that may be useful in drug discovery.

But how a protein folds isn’t just based on the sequence of amino acids. Environment, distance, hydrophobicity and hydrophilicity, stereochemistry, kinetics, and so many other factors influence how a protein folds. The rules and variables compound, making this too big even for computers to reliably solve.

Instead of punching in all the rules and calculations needed, DeepMind’s AlphaFold used machine learning and neural networks along with approximately 170,000 known protein structures from the Protein Data Bank (PDB) to learn the patterns involved in protein folding.

AlphaFold still had to prove that its predictions reflected what proteins actually looked like.

To do this, it was entered in the Critical Assessment of Structure Prediction’s biennial challenge (CASP).

DeepMind’s first AlphaFold system’s performance in CASP13’s 2018 contest took the lead that year. However, it hadn’t yet solved the problem. Its predictions were still not quite accurate enough.

At CASP14 in 2020, however, nearly two-thirds of its predictions were comparable to the known protein test set. That is, CASP has a set of protein structures that are not known to the public and challenges contest entrants to reliably predict the shape of these proteins. They are then compared and assessed.

AlphaFold2’s results were good enough for the protein problem to be deemed solved.

But there was another breakthrough that AlphaFold2 gave us when it became accessible to other researchers. DeepMind released the source code along with creating the AlphaFold Protein Structure Database, which gave researchers access to more than 200 million predicted protein structures.

 

2020: mRNA Vaccines

Within biotechnology, researchers had long been studying mRNA and the potential for an mRNA vaccine. An early challenge to the concept of an mRNA vaccine was delivery. You can’t simply inject someone with mRNA because it would get degraded before an immune response happened.

In order to develop a real concept for an mRNA vaccine, it needed a delivery system.

As time went by, nanotechnology caught up, and researchers found potential in lipid nanoparticles. They could envelop the mRNA within these nanoparticles, which the cell would engulf through endocytosis.

Early exploration of an mRNA vaccine began with Ebola, but nothing was approved. It wasn’t until the Covid-19 pandemic, that researchers globally had to find a way to quickly produce a safe vaccine.

But the challenge to traditional vaccine development and production is speed. Traditional vaccines need to be grown in cells, harvested, isolated, tested, and then packaged if they worked. Meanwhile, the entire world was nearly shut down.

mRNA vaccines are fundamentally different from protein- or virus-based vaccines in a way that accelerates their development. All that is needed is an mRNA sequence and the fatty droplets to bubble wrap them in.

Pfizer was the first to receive FDA emergency authorization for an mRNA vaccine in December 2020, with Moderna’s vaccine following soon after. Both vaccines were later fully approved by the FDA in 2021 and 2022 respectively.

The development of a working mRNA vaccine was a breakthrough in biotechnology for several reasons:

• It is a quick way to produce a vaccine. To put it simply, all you need is to synthesize a sequence rather than growing up cells.
• It can quickly be adapted. Certain viruses mutate quickly. As long as we have the sequence for the new strain, we can generate a new vaccine to match the current strain.
• A patient receives only mRNA rather than a dead, weak or related virus. The sequence is only for a portion of that virus, which helps the immune system respond.
• We bypass virulence challenges where the virus kills cultures before we can work toward developing a vaccine the traditional way.
• mRNA vaccines also have potential for personalized medicines, especially within oncology where specialized treatments could be produced to help a patient’s body recognize cancer cells.

While there appears to be a growing list of benefits, mRNA vaccines do have limitations that make traditional vaccines sometimes more appropriate. For instance, stability. While we can envelop mRNA into lipid nanoparticles, it has to stay very cold to prevent mRNA degradation, which may not be practical in disaster areas or rural areas without modern hospitals.

Another limitation is that an mRNA vaccine can only instruct the body to produce proteins. They can’t be used against pathogens where the immune target is a polysaccharide (sugar).

Though traditional vaccines will still be part of our toolkit for a long time, mRNA vaccine technology is a revolution in health science.

This timeline shows us how science has leveled up, with each discovery unlocking the door to the next level. Technology alone couldn’t get us there. Being curious, exploring, publishing, keying into fine details, and sharing knowledge are also essential to moving forward.

As these examples illustrate, scientific discovery is the ultimate collaborative endeavor. When looking back, we know that your personal stories are interwoven into these larger scientific revolutions, and we have been honored to be part of the last 40 years of discoveries. Here is to many more.

 

 

References

Akintunde, O., Tucker, T., & Carabetta, V. J. (2023). The evolution of next-generation sequencing technologies. arXiv 2023. arXiv preprint arXiv:2305.08724.

Beyrer, C. (2021, October 6). The long history of mRNA vaccines. Johns Hopkins Bloomberg School of Public Health. https://publichealth.jhu.edu/2021/the-long-history-of-mrna-vaccines

Cara, E. (2023). The human genome project turns 20: here’s how it altered the world.

Gostimskaya, I. (2022). CRISPR–cas9: A history of its discovery and ethical considerations of its use in genome editing. Biochemistry (Moscow), 87(8), 777-788.

Gyles, C. (2008). The DNA revolution. The Canadian Veterinary Journal, 49(8), 745.

Jackson, Nicholas AC, Kent E. Kester, Danilo Casimiro, Sanjay Gurunathan, and Frank DeRosa. "The promise of mRNA vaccines: a biotech and industrial perspective." npj Vaccines 5, no. 1 (2020): 11.

Levinthal, C. (1969). How to fold graciously. In Mössbauer spectroscopy in biological systems: Proceedings of a meeting held at Allerton House, Monticello, Illinois (pp. 22–24). University of Illinois.

Marcu, Ş. B., Tăbîrcă, S., & Tangney, M. (2022). An overview of Alphafold's breakthrough. Frontiers in artificial intelligence, 5, 875587.

Montoro, D. T., Haber, A. L., Biton, M., Vinarsky, V., Lin, B., Birket, S. E., ... & Rajagopal, J. (2018). A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature, 560(7718), 319-324.

Saiki, R. K., Scharf, S., Faloona, F., Mullis, K. B., Horn, G. T., Erlich, H. A., & Arnheim, N. (1985). Enzymatic amplification of β-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science, 230(4732), 1350-1354.

Sen, G. L., & Blau, H. M. (2006). A brief history of RNAi: the silence of the genes. The FASEB journal, 20(9), 1293-1299.

Smithsonian Archives. (n.d.). Sia ru009577, history of the polymerase chain reaction Videohistory collection, 1992-1993 | Smithsonian Institution Archives. History of the Polymerase Chain Reaction Videohistory Collection, 1992-1993 Collection Overview. https://siarchives.si.edu/collections/siris_arc_217745

Wang, S., Sun, S. T., Zhang, X. Y., Ding, H. R., Yuan, Y., He, J. J., ... & Li, Y. B. (2023). The evolution of single-cell RNA sequencing technology and application: progress and perspectives. International Journal of Molecular Sciences, 24(3), 2943.

Zhu, H., Zhang, H., Xu, Y., Laššáková, S., Korabečná, M., & Neužil, P. (2020). PCR past, present and future. Biotechniques, 69(4), 317-325.

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Katharine Martin

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