India’s bioinformaticists are the heroes of the coming biotech revolution: Raymond McCauley
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Mumbai: Raymond McCauley is chair, Digital Biology at Singularity University—a Silicon Valley think tank devoted to training leaders in exponential technologies. He is also part of the team that developed next generation DNA sequencing at Illumina, where he worked in bioinformatics, cancer sequencing, and personal genomics. In an email interview, McCauley—who is a speaker at the two-day SingularityU India Summit that begins on 7 April—spoke about the explosion in genomics research, gene editing tools like CRISPR-CAS9 and the consequences of bioinformatics and genetic testing on health and life insurance. Edited excerpts:
The explosion in genomics research along with the widespread adoption of DNA sequencing is accelerating the growth of the genomics market across the globe. Have we reached an inflection point?
As the price continues to drop for DNA sequencing, and all of the associated tools and services, we are seeing a larger market for those services really take off. The research market is strong, as always, and many new medical applications are driving new growth. And we’re actually seeing an explosion of consumer genetics—people can use their genes to look at ancestry, optimize nutrition and athletic training, and even pick a good tasting wine.
Gene sequencing and gene editing (CRISPR CAS-9) are playing huge roles in the push toward personalized medicine and healthcare. Provide us with some applications of genomics.
Now that we are able to read and write DNA fairly cheaply, with even more advances to come, we can use this to help personalize medical care. Consider these examples. Pharmacogenomics uses genetic information to determine what drugs and dosages work for each person. This pays off quickly because taking the wrong drug, or a drug that actually causes an adverse reaction, is an expensive, and sometimes tragic, mistake. And it’s now cheap to read DNA. We all have hereditary tendencies for things like heart disease, neurological disease, and cancer locked away in our genes. Knowing your individual risks with chronic disease prediction, means you can be better prepared to prevent actually getting sick.
In the field of cancer diagnostics, new liquid biopsies can look at free-floating DNA in a simple blood sample, and sometimes identify cancer before it would show up on a body scan. Moreover, by genetically engineering a person’s bone marrow cells, we can basically reprogram their immune system, and their circulatory system. Some new cancer treatments are based on this. Further, looking at the DNA of the collection of microbes in your gut can help with digestive disorders, weight loss, even understanding mood changes.
What are the consequences of bioinformatics and genetic testing on health and life insurance?
Being able to predict health and lifespan with genomic tools pretty much breaks insurance. It turns the information asymmetry, where actuaries know more about how long you’ll live, on its head. Because they can only predict how long people live on average. There’s a chance, though, for these companies to use this technology to help their clients make better decisions, and get access to better treatments, and live longer.
Could you point out some of the opportunities and consequences of, what you describe as, “drag & drop” genetic editing?
The first and biggest changes that come here are in industrial microbiology, where we reprogram single-celled creatures to manufacture things for us, like plastics and biofuels and even complex products like medicine and computers—and also to mine and recycle these materials. It also makes it easier to do engineering and enhancement on plants and animals, for agriculture, and also on humans, for medicine and enhancement, then finally, on the environment itself. Think about using this technology to produce mosquitoes that can’t pass along malaria, or to reverse greenhouse gas increases and ocean acidification.
What steps are being taken to address some of the challenges arising out of fields like bioinformatics and bioengineering like privacy and legal hurdles?
Some enlightened polities are passing laws to prevent discrimination by the government or employers based on genetic information, and to give individuals some control over who can access their information. This is particularly important for insurance, and who is allowed to access genetic databases built for different purposes.
But, policy-wise, it’s still the early days. Should law enforcement be allowed to scan databases built for research or health practitioners to go fishing to find someone who may have been at the scene of a crime? Should a sports team test their prospective athletes for hidden heart disease genes before signing a multi-million dollar contract? Do people have a right to ‘not’ know about health problems or secrets in their family tree, and what if siblings disagree about this? Many questions remain to be resolved. And it’s likely that different groups and nations will come up with different best answers.
How can a country like India take advantage of genomics research?
India has more trained bioinformaticists than any other country on earth. These people, who are trained to use computers in the life sciences, and systems biologists, and biostatisticians, are the heroes of the coming biotech revolution. They keep it moving forward. Professionals here are uniquely positioned to both contribute to pushing this technology forward, and to make a huge amount of money, for themselves and the economy, while doing it. India also has a pharmaceutical industry that serves as an example to the rest of the world—where the entrepreneurs and the policymakers have done much figured out how to balance making money with the needs of the people to receive affordable health care.
Glossary for laypersons
1. Genome sequencing
Getting a read-out of an individual organism’s entire DNA sequence. For humans, this is about 3.2 billion bases, spread across 23 chromosomes. Useful for research, and for personalized medicine. This is how we read the blueprint of life.
2. De novo sequencing
Looking at a species genome for the first time. It’s always more difficult to sequence the first member of a species. This is harder, because we read DNA in short sections, and we have to figure out how all the sections connect.
Looking at an individual’s DNA, when you’ve already got examples of that species DNA sequence, called a reference sequence, to compare it to. Sometimes, individual genes or groups of genes are targeted and sequenced, without looking at the entire genome.
4. Exome sequencing
Looking at just the genes, or portion of the DNA that codes for proteins (about 1% in humans) or the portion that is thought to be involved in regulating genes (about 10% in humans).
5. RNA sequencing
The DNA is the read-only memory of the cell, and when new proteins need to be made, the cell copies the appropriate genes’ DNA into RNA. All cells, from bone cells to brain cells, have basically the same DNA, but they don’t always turn on, or express, the same genes. By sequencing the RNA, we get a snapshot of what proteins the different kinds of cells express at different times. We call this gene expression, or sometimes RNAseq, for short. Useful for research, and responsible for about half the research sequencing done.
6. Methylation sequencing
One way of understanding gene expression, sometimes referred to as the epigenome. There’s a level of gene regulation in DNA, where special stretches of DNA are methylated (have a special chemical group added to them), to keep nearby genes turned off. Useful for research, especially in understanding cancer, which sometimes sneakily figures out ways to change this and turn growth genes on.
7. CHIP sequencing
A special research method for finding which proteins grab which stretches of DNA, so we can understand better how the cellular machinery works together. Not done on a computer chip, but this is an acronym for Chromatin Immuno-Precipitation.
8. MicroRNA sequencing
Another research method for understanding gene expression, by looking at short pieces of RNA that float around the cell and turn off groups of genes.
9. Microbiome sequencing
Looking at the DNA in entire groups of tiny organisms, like all of the microbes in your gut, or all of the microbes in an environmental sample. It’s like a population census for bacteria. New uses for this are being found in agriculture and biomanufacturing. Sometimes called metagenomics.
10. Barcode sequencing
Looking at a short stretch of DNA, to identify a species, like scanning a printed barcode in a market. Used for diagnosing infections, environmental research, and even protecting endangered species.