Immuno-biotechnology and Bioinformatics in Community Colleges

Immuno-biotechnology is one of the fastest growing areas in the field of biotechnology. Digital World Biology’s Biotech-Careers.org database of nearly 7000 biotechnology employers has nearly 700 organizations that are involved with immunology in some way. With the advent of advanced DNA sequencing, and other technologies, immuno-biotechnology has significantly increased the use of computing technologies to decipher the meaning of large datasets and predict interactions between immune receptors (antibodies / T-Cell receptors / MHC) and their targets.

The use of new technologies like immune-profiling - where large numbers of immune receptors are sequenced en masse - and targeted cancer therapies - where researchers create, engineer, and grow modified T cells to attack tumors - are leading to job growth and demands for new skills and knowledge in biomanufacturing, quality systems, immuno-bioinformatics, and cancer biology. In response to these new demands, Shoreline Community College (Shoreline, WA) has begun developing an immuno-biotechnology certificate. Part of this certificate includes a five-week course (30 hours hands-on computer lab) on immuno-bioinformatics.

The immuno-bioinformatics course includes exercises in immune profiling, vaccine development, and operating bioinformatics programs using a command line interface. In immune profiling, students explore T-cell receptor datasets from early stage breast cancer samples using Adaptive Biotechnologies’ (Seattle, WA) immunoSEQ Analyzer public server to learn how T-cells differ between normal tissue, blood, and tumors. Next, they use the IEDB (Immune Epitope Database) in conjunction with Molecule World (Digital World Biology) to predict antigens from sequences and verify the results to learn the differences between continuous and discontinuous epitopes that are recognized by T-cell receptors and antibodies. Finally, to get hands-on experience with bioinformatics programs, students will use cloud computing (CyVerse) and igBLAST (NCBI) to explore data from an immune profiling experiment.

Transcript

1. Immuno-bioinformatics in Community Colleges SFAF 2019 Todd M Smith Ph.D., Sandra Porter Ph.D, Dina Kovarik, Ph.D Digital World Biology, Shoreline Community College 11-30 aa
2. Digital World Biology, LLC Agenda Introduce Digital World Biology Immuno-Bioinformatics Scaling Bioinformatics Instruction Model / Collaborations / New Projects
3. Digital World Biology, LLC Poster 75
4. Digital World Biology, LLC Digital World Biology Resources for teaching modern biology Web Sites Courses Games Molecule World™ Apps
5. Digital World Biology, LLC Courses • General Bioinformatics • Learn about standard tools, connect genotype to phenotype • Molecular structures, databases, genome browsers, blast • Students and instructors • Immuno-bioinformatics • Share progress - what, where, how • First classes 2020, Shoreline Community College, WA
6. Digital World Biology, LLC Shoreline Community College 2017 received an ATE award to develop immuno-biotechnology curriculum • Community college students • Kits for high schools • New Courses: Case Studies in Drug Development, Cancer Biology, Quality Systems, and Advanced Bioinformatics DUE 1700441 https://www.bio-link.org/state/washington https://www.bio-link.org/program/shoreline- community-college
7. Digital World Biology, LLC NSF Advanced Technological Education •Established by Congress in 1992; Annual budget ~$60M •Promotes education in high--technology fields that drive our nation's economy •Supports curriculum and professional development for college faculty and secondary school teachers to develop career pathways, and other activities •Encourages partnerships between academic institutions and industry
8. Digital World Biology, LLC Why Immuno-biotech? Washington State Data from https://www.biotech-careers.org/ 10%-20% of all biotechnology 30% of WA biotechnology
9. Digital World Biology, LLC Teaching Bioinformatics Context Local interest, instructive Relevance Current topics, impactful Accessibility Data >> programming, user interfaces Infrastructure Resource intensive, computers, data Cost Free is best, public data
10. Digital World Biology, LLC Acknowledgements DUE 1700441 DUE 1764225 DUE 1501207 DUE 1400721 IIP 1315426 Adaptive Biotechnologies • Cindy Reichel • Hugh Arnold Juno Therapeutics • Eric Olson CyVerse • Jason Williams • Andreas Madlung IEDB • David Koelle • Kerry Laing General Advice • John Fonner • AR Galaxy, VDJ Server Icons made by { } from www.flaticon.com Eucalyp geotatah Freepik SimpleIcon
11. Digital World Biology, LLC Immuno-bioinformatics Course Immunoprofiling Diagnostics Biomarker Discovery Antigen Discovery Vaccines Biomarkers Core Bioinformatics Integral Foundational See how it woks
12. Digital World Biology, LLC Immunoprofiling
13. Digital World Biology, LLC Immunoprofiling https://digitalworldbiology.com/blog/immunoprofiling-how-it-works Combinatoric process with sloppy joining and mutation (antibodies) => millions of possible sequences “single drop of blood, we find only 10 iNKT cells, 10 million red blood cells, 100,000 white blood cells and 5000 conventional T cells*” Dr. Lili Yang - https://digitalworldbiology.com/blog/recap-isb-2019-future-health-immunotherapy
14. Digital World Biology, LLC Immunoprofiling General Worlflow Blood or other tissue Collect Samples Purify Cells PCR amplify CDR3 regions (V, J specific primers) Sequence the DNA Isolate mRNA or DNA cDNA RNA DNA Bioinformatics Compare Annotate Discover 1 2 3 4 5 6 Carlson CS, et. al., Using synthetic templates to design an unbiased multiplex PCR assay. Nat Commun. 2013;4:2680. https://digitalworldbiology.com/blog/what-immunoprofiling https://digitalworldbiology.com/blog/immunoprofiling-how-it-works
15. Digital World Biology, LLC Options –TNTC: h.ps://omictools.com/ rep-seq-category lists 52 tools for various aspects of anFgen receptor analysis – tend to be single concept focused, so many are required for a complete analysis - Dec 2017 IMGT ARGalaxy MiXCR (basespace) Raw Tools VDJ Server ???
16. Digital World Biology, LLC ImmunoSeq Analyzer Adaptive Biotechnologies (Seattle, WA) Public Server +
17. Digital World Biology, LLC Immunoprofiling Exercises 10 assertions in the abstract • Breast tumors have ~2.5-fold greater density of T cells when compared to normal breast tissue • Tumors have higher clonality than normal tissue • Both tumor and normal breast contain high- abundance “enriched” sequences that are absent or low abundance in blood • Enriched T-cell sequences are typically unique to each patient • Many T-cells overlap between tissues from the same patient, including approximately 50% of T- cells between tumor and normal breast …
18. Digital World Biology, LLC Tumors have higher clonality Breast Blood Tumor
19. Digital World Biology, LLC 50% of the TCR sequences are shared between tumor & normal in same patient
20. Digital World Biology, LLC Sequences and Rearrangements DNA level - visualize V(D)J + junction indels Amino acid level Explore common/rare
21. Digital World Biology, LLC Antigen Discovery Immune Epitope Database - IEDB Molecule World™
22. Digital World Biology, LLC Prediction and Verification 5FHC = Protective antibodies bound to ebola virus
23. Digital World Biology, LLC Prediction and Verification
24. Digital World Biology, LLC Core Bioinformatics in CyVerse • Cloud Computing • Basic unix commands • FASTQ vs FASTA • IgBLAST => tab delimited file • Interpret output Assessing an effective undergraduate module teaching applied bioinformatics to biology students Andreas Madlung*
 University of Puget Sound, Department of Biology, Tacoma, Washington
25. Digital World Biology, LLC Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Model Virtual Machine Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Instance Dataset(s) IgBLAST Reference data Tools (FASTA, FASTQ, QC, Filters) UNIX Building the VM How is a Bioinformatician Like Odysseus https://digitalworldbiology.com/blog/how-bioinformatician-odysseus
26. Digital World Biology, LLC IgBLAST and Data Where do immunoprofiles come from? igblastn -germline_db_V database/ighv-ref.fasta - germline_db_D database/ighd-ref.fasta -germline_db_J database/ighj-ref.fasta -organism human -query SRR4431764_2.fasta -auxiliary_data optional_file/ human_gl.aux -show_translation -outfmt 19 > SRR4431764_2.fasta.counts &
27. Digital World Biology, LLC Analyzing the Data
28. Digital World Biology, LLC Student Benefits Bioinformatics - Many applications Algorithms / Databases / Software Supports discovery applications Integral component of certain diagnostics Use unix commands to view files and file contents Perform immune profiling with a command line program to understand underlying principals Transform tab delimited data to pivot tables and analyze results Use scientific software to predict epitopes Use molecular visualization software to compare predicted epitopes to epitopes identified from molecular models Antigen Discovery - Research Vaccine development Biomarker discovery Biotechnology Applications Skills Taught Use DNA sequencing data to reproduce published results Use large-scale data analyses to compare immune responses between different samples Identify common and rare receptors by DNA and amino acid sequences to identify potential biomarkers Immune Profiling - Diagnostics / Biomarkers Cancer - TILs (Tumor Infiltrating Lymphocytes) MRD (Minimal Residual Disease) Infection / Allergy 11-30 aa
29. Digital World Biology, LLC Dissemination
30. Digital World Biology, LLC Scaling Data Science Education Context Seattle / immunology, other areas / agriculture, cancer, bioenergy, public funding Relevance Immunology, climate, microbiomes, antibiotic resistance Accessibility Web browsers >> installed software Infrastructure Cloud computing (CyVerse), Docker images, optimized datasets (balance time / learning) Cost Free is best, public resources, public data Leverage immense infrastructure of public/private investments in tools and data