Genomics and Medical Informatics Lecture Notes

Introduction

• Genomics and medical informatics focus on big data analysis in research and medicine.

• Genomics and medical informatics are part of the Bachelor of Medical Sciences Program.

• The lecture will define genomics and transcriptomics and explain how they apply to health and medicine.

Central Dogma of Molecular Biology

• DNA: The basic molecule of life and the blueprint that makes us who we are.

  • Replication: The process by which DNA is made and passed on from one generation to the next.

• RNA: Another genetic component with various functions.

  • Transcription: The process of DNA being converted into RNA.

• Proteins: Carry out various biological processes including growth, metabolism, signaling, and survival.

• Central Dogma: This model shapes our understanding of how genes and genetic materials are passed on and how they function.

Definitions

• Genome: The complete set of DNA that we possess.

• Transcriptome: The complete set of RNA that is made from the genome.

• Genomics: The study of the genome and its functions.

• Transcriptomics: The study of the transcriptome and its various functions.

• The human genome contains three billion base pairs.

• 80% of the genome DNA gets made into RNA, and only 2% of the human genome gets made into proteins.

• Genomics and transcriptomics are studied to understand the cause and treatment of complex human diseases, as well as to develop novel diagnostic tools and novel treatments.

Genome Complexity

• As genome size increases, the number of genes also increases.

• Genes can be either protein-coding (red) or non-protein-coding (blue).

• Only 2% of the human genome encodes for protein-coding genes, which equates to roughly 20,000 genes.

• Non-protein-coding genes increase proportionally with the complexity of the genome, while protein-coding genes plateau.

• Humans share the same number of protein-coding genes as some complex animals and even plants.

• Non-protein-coding genes differentiate humans from other organisms.

• Genomics and transcriptomics aim to understand the roles of both protein and non-protein-coding genes.

Genomics Timeline

• The timeline of genetic discoveries ranges from Gregor Mendel's concept of inheritance to the Human Genome Project.

• The Human Genome Project, launched in 1990, aimed to map all three billion DNA sequences in the human genome and was completed in 2003.

Human Genome Project

• Showed that humans share 99.9% of their genome.

• Differences between individuals are based on how DNA is regulated.

Genomics Studies

• The structure and function of the human genome, including the purpose of non-coding genes, are being studied.

• Epigenetics: How our environment can shape our genetic code.

• Changes in the genome, such as mutations and variants, can lead to diseases like cancer and can be used diagnostically for genetic testing.

• Curative treatments can be developed by modifying a person's genome to treat genetic diseases.

Transcriptomics

• Multicellular organisms, like humans, produce a wide array of RNA species.

• Messenger RNA (mRNA) gets encoded into proteins.

• There is a diverse range of RNA species, each with distinct functions.

• Non-coding RNAs have a critical role in regulating transcription and translation and are equally important in protein production.

Medical Informatics

• Medical informatics studies how to use and apply big data (like genomics and transcriptome data) in healthcare.

• The ability to acquire, manage, and interpret data is crucial.

• Emerging technologies and techniques, such as coding, programming, and machine learning, are applied to the understanding and treatment of human diseases.

Genome Sequencing

• A genome is all the genes plus some extra that make up an organism.

• Genes are made up of DNA, and DNA is made up of long paired strands of A's, T's, C's, and G's.

• The first human genome was sequenced in two decades and cost over 3 billion. But very soon, it will be possible to know the sequence of letters that make up your own personal genome all in a matter of minutes and for less than the cost of a pretty nice birthday present.

• Knowing the sequence of the billions of letters that make up your genome is the goal of genome sequencing.

• To get all that information out of that tiny space, scientists first have to break the long string of DNA down into smaller pieces. Each of these pieces is then separated in space and sequenced individually.

• DNA Binding: DNA binds to other DNA if the sequences are the exact opposite of each other, A's bind to T's and T's bind to A's G's bind to C's and C's to G's. If the A T G C sequence of two pieces of DNA are exact opposites, they stick together because the genome pieces are so very small, we need some way to increase the signal we can detect from each of the individual letters.

• Scientists use enzymes to make thousands of copies of each genome piece.

• A batch of special letters each with a distinct color, a mixture of these special colored letters and enzymes are then added to the genome, we're trying to read at each spot on the genome.

• Seeing the order of the colors allows us to read the sequence.

• The sequences of each of these millions of pieces of DNA are stitched together using computer programs to create a complete sequence of the entire genome.

• Interpreting the genes of the genome is the part scientists are still working on while not every difference is consequential. The sum of these differences is responsible for differences in how we look, what we like, how we act and even how likely we are to get sick or respond to specific medicines.

Applications of Genomics, Transcriptomics, and Medical Informatics

• These fields are applied to answer important clinical challenges and questions, particularly in cancer research.

Categories

• Diagnosis: Identifying or confirming a disease.

• Prognosis: Determining how severe the disease is and its outcome.

• Prediction: Forecasting whether a certain treatment is effective or not.

• Discovery: Acquiring new knowledge and understanding about the biology and processes of the disease.

Diagnosis

• Transcription profiles are used to determine whether an individual has a particular disease or type of cancer.

• Cancer Type ID: A test used in the clinic that looks at the expression of 92 different genes to differentiate between 50 tumor types or cancer types based on genetic data, with up to 87%.

• Diagnosing a patient correctly is essential for effective treatment and developing a personalized treatment plan.

Prognosis

• Transcriptome data is used to estimate how severe the disease is.

• MammoPrint Test: A breast cancer risk test that looks at the expression of 70 genes to predict whether the patient will have a high or low chance of recurrence.

• Breast cancer is highly metastatic and can spread to many different organs besides the primary site.

Disease Prediction

• Genomic data is used in genome-wide association studies (GWAS).

• The genome of a group of healthy individuals is compared to the genome of a group of people with the disease.

• Single Nucleotide Polymorphisms (SNPs): Genetic variations in DNA are analyzed to see whether they are associated with the disease.

• Genetic variants are used to link to an increased risk of developing the disease.

Treatment Prediction

• Transcriptome data is used to predict how well a cancer patient will respond to treatment.

• In chronic myeloid leukemia (CML), 98% of patients show a fusion or mutation in the BCR-ABL gene.

• Chinese inhibitors that target this mutation can be used to assess whether the treatment is successful.

• The blood of these patients is monitored for the presence or expression of this BCR-ABL gene fusion.

• A decrease in the levels of this mutant gene expression indicates successful inhibition and a decrease in the cancer itself.

Cancer Transformation

• The study of genomics and transcripts has transformed what we know of cancer.

• Melanoma Example:

  • 50% of melanomas show a mutation in the B-RAF protein.

  • The effects of the B-RAF mutation are studied by comparing the genetic profile of normal versus tumor samples and B-RAF normal versus B-RAF mutant samples.

  • B-RAF mutant melanomas show activation of the MAP kinase pathway.

  • Inhibitors that target this pathway are designed and used in the clinic to treat melanoma patients.

  • The transcriptome of 37 melanoma patients treated with MAP inhibitors is analyzed.

• Before treatment, all 37 patients show some degree of expression of the MAP pathway.

• Following treatment, most patients show a decrease in MAP pathway gene expression, indicating that the treatment is working.

• However, when patients are tracked for longer term, it is found that most patients fail the treatment.

• After one year of treatment, their MAP pathway levels return back to normal, suggesting that the treatment itself has now failed and is no longer effective at blocking this particular signaling.

• Patient response can be monitored by looking at the transcription signature, and using this signature, the best treatment strategies for these patients can be determined.

Summary

• The genome and the transcriptome are studied to better understand the cause of complex human diseases and to develop novel treatments for these diseases.

• Genomics and transcriptomics are applied for disease diagnosis, prognosis, prediction, and discovery.

• All of these applications depend on the ability to manage, analyze, and interpret the vast amount of genomic and transcriptomic data.

• Developing skills in medical informatics is crucial, including being able to understand and use bio-statistical computing and programming knowledge to handle and manipulate the data so that it can be applied to address important clinical questions.