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Overview of Our Research

What is Computational Medicine

Scientists started using computers to catalogue and make sense of biological data nearly 40 years ago. Over time, these activities gave rise to three distinct fields of science: bioinformatics, computational biology, and computational medicine.

  • Bioinformatics is an overloaded term that is used incorrectly much of the time. Originally coined in the 1970’s to describe “the study of informatic processes in biotic systems” what is represented by the term evolved over time eventually stabilizing in the late ’90’s. Bioinformatics deals with information sciences and technologies that make it easier to collect, manage and process diverse, and complex datasets.
  • Computational Biology is fundamentally different. Computational Biology is a multi-disciplinary field that develops methods, and applies modeling and simulation to study biological, behavioral, and social systems. By analogy, Bioinformatics is the art of making telescopes whereas Computational Biology is the science of astronomy.
  • Computational Medicine is different yet again. Computational Medicine is a multi-disciplinary field that focuses on developing and using quantitative approaches to study the events underlying the onset and progression of disease and design new diagnostics and new therapeutics. To practice in Computational Medicine requires knowledge from computer science, mathematics, statistics, biochemistry, chemistry, chemical engineering, biomedical engineering, biophysics, molecular biology, genetics, ecology, evolution, anatomy, and from sub-fields of those disciplines.

Our History

The Computational Medicine Center (CMC) at Jefferson was founded in early 2010 by Dr. Isidore Rigoutsos, who is also its current Director. Dr Rigoutsos has a computer science background and a long career at IBM.

The Computational Medicine Center

Research in the CMC focuses on several categories of short regulatory non-coding RNA. The molecules that we study range in length from 18 to about 70 nucleotides (nts). Specifically, we focus on microRNA, microRNA isoforms (isomiRs), transfer RNA, tRNA-derived fragments (tRFs), PIWI-associated RNAs, cyclic phosphate-containing RNAs (cP-RNAs), and ribosomal RNA-derived fragments (rRFs). Work by us and others has shown for many of these molecules that they control the behavior of cells and tissues, and shape many of the mechanistic events that underlie the onset and progression of disease. Additionally, the existence and abundance of those molecules, depends on the person’s individual characteristics such as age, gender and population. To arrive at these conclusion, CMC uses a variety of computational tools and Next Generation Sequenced data. As we do so, we often design our own tools and methods.

Even though our research findings are highly inter-connected, we divided our work in four areas to make understanding easier:

Computational Medicine Research at CMC

DNA is the template that is used to make RNA. RNA molecules can belong to two categories based on whether they “code” for proteins or not. The molecules in the latter category are referred to as “non-coding”.

The CMC’s work challenges an assumption. For decades, the practice of Medicine has been predicated on the assumption that our DNA is the main driver of the trajectory that we follow when disease strikes. Comparisons between the DNA of many individuals with ‘disease X’ and the DNA of many healthy persons uncovered telltale differences (“mutations”) that were eventually associated with either propensity for or the presence of disease X. Over time, mutations have been leveraged as diagnostic markers, prognostic markers, or therapeutic targets in various diseases. CMC researchers have shown that while this model is accurate, it is markedly incomplete. Let us briefly explain why this is the case by using isomiRs and tRFs as an example.

Both isomiRs and tRFs are produced from parental precursor molecules that are relatively short (~70 nts). For many years, it had been assumed that transcription of the DNA that encodes a miRNA precursor or a tRNA precursor gave rise to a single RNA product, the “mature miRNA” and the “mature tRNA,” respectively. The advent of deep sequencing revealed a more complicated picture with each parental precursor molecule producing “clouds” of miRNA isoforms and of tRNA fragments that co-exist in the cell. For a long time, miRNA isoforms and tRNA fragments were dismissed as inconsequential. However, work by Jefferson’s CMC showed that this is not the case. In particular, through many publications, CMC scientists showed that:

  1. different isoforms of the same mature miRNA can have different functions;
  2. different tRFs from the same mature tRNA can have different functions;
  3. across individuals, the identities and abundances of the molecules comprising the “isomiR clouds” or the “tRF clouds” differ in people who differ by sex, race/ethnicity, and population origin; and,
  4. within the same individual, the identities and abundances of the molecules comprising the “isomiR clouds” or the “tRF clouds” differ by tissue, tissue state, and disease type.

These four observations are very important because they state that the same DNA sequence will give rise to different groups of functional RNAs with different relative abundances in different people, in different tissues, and in different disease (sub-)types. By now, the CMC team has generated evidence for these observations by analyzing the transcriptomes of nearly 12,000 people from two large public efforts, the “1000 Genomes Project” and “The Cancer Genome Atlas.” Moreover, the Jefferson CMC team identified

  1. thousands of novel DNA segments that are primate-specific and produce short RNA regulators;
  2. three novel categories of important short RNA regulators with numerous members.

Across the various categories of interest, the CMC team has identified more than 50,000 novel regulatory molecules.

These findings represent a paradigm-shift for the following reasons:

  • These newly-discovered molecules orchestrate biology that has never been studied before and is important for human disease. A large portion of these molecules are specific to primates and their actions cannot be captured by mouse models.
  • Many of the newly-discovered molecules are effect biology that is specific to the tissue in which these regulators are found. This makes them high-priority targets for understanding a given tissue’s transition from health to disease. However, research efforts to date have largely ignored tissue-specific regulators.
  • The CMC’s work showed that people who are different (by sex, population group, or race/ethnicity) produce different RNA regulators from the same piece of DNA. In other words, in the absence of mutations, the same piece of DNA will generate multiple distinct products thereby predisposing different people differently in a given disease setting.
  • By analyzing publicly available and in-house data, we have shown that these molecules are linked to disparities by race (triple negative breast cancer, prostate cancer, glaucoma, normal samples) and by sex (bladder cancer, kidney renal clear cell carcinoma, lung adenocarcinoma, Parkinson’s disease, normal samples).
  • The results show that the community greatly underestimated the number of regulatory molecules that matter in a cell. The CMC’s work showed that more than 85% of the molecules that we now know to be functionally important in a cell have never been studied.
  • The CMC team showed that the commercially available qRT-PCR assays cannot quantify accurately these molecules. This raises important questions about the results that have been generated and published over the years using these assays.
  • CMC has designed methods for the specific identification of those molecules, like cP-RNA-seq.

What Do The CMC Findings Mean Practically?

The CMC’s findings have direct implications for the practice of medicine and the delivery of healthcare. The findings represent tremendous new opportunities for breaking new ground, deepening our understanding of disease, understanding disease disparities, reshaping basic and clinical research, and improving healthcare delivery for decades to come. At the same time, the new findings are beginning to compel the research community to go back to the drawing board. This is because, as the CMC’s work has shown, all studies to date have examined only a very small fraction of the events that underlie homeostasis and disease.

The CMC team has also been generating evidence that big advances can be realized by leveraging these molecules and their properties. For example, several of the things that the CMC team has shown so far include the following (selected from a longer list):

  • isomiRs can effectively distinguish among 32 different cancers and can be used as biomarkers (2017) (read more) [1];
  • isomiRs and tRFs can serve as blood-based biomarkers for
    • Parkinson’s disease (2019) [2]
    • primary open-angle glaucoma (unpublished)
    and likely other conditions.
  • isomiRs and tRFs are associated with metastasis and survival in uveal melanoma (2019) [3];
  • primate-specific ncRNA can predict the survival of patients with colon cancer (2017) (read more) [4];
  • tRFs implicate repetitive sequences (“junk DNA”) from multiple types of repeats in gene regulation for more than two dozen cancers (2019) [5];
  • cP-RNAs are associated with age in the mouse genome (2019) (read more) [6];
  • rRFs depend on the gender and population of the person (2020) (read more) [7].

In summary, the CMC’s work focused on segments of human DNA that the community has been studying for decades and showed that they produce regulatory molecules in a manner that depends on who we are (sex, population, race/ethnicity), as well as on the tissue at hand, and on the disease of interest. These findings by the CMC are fundamental in nature because they involve molecules that shape cell properties in homeostasis and disease. Read more about the CMC’s research by visiting research highlights.

Joining the Computational Medicine Center

To join the computational medicine center please apply to Jefferson and contact us. For all other applicants please look at our employment opportunities.

Jefferson offers several classes related to computational medicine.

  • GC558: Introduction to UNIX and Programming in C
  • GC559: Introduction to R programming
  • GC560: Data Visualization
  • GC561: Data Structures and Algorithms
  • GC562: Computational Genomics
  • GC563: Computational Transcriptomics
  • GC564: Data Mining and Knowledge Discovery

Recognition

CMC has been selected as an IBM case study and has won awards, for example the W.M. Keck foundation.

Support

To support our research efforts, consider a donation.

References

  1. Telonis, AG, Magee, R, Loher, P, Chervoneva, I, Londin, E, Rigoutsos, I. Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types. Nucleic Acids Res. 2017;45 (6):2973-2985. doi: 10.1093/nar/gkx082. PubMed PMID:28206648 PubMed Central PMC5389567.
  2. Magee, R, Londin, E, Rigoutsos, I. tRNA-derived fragments as sex-dependent circulating candidate biomarkers for Parkinson’s disease. Parkinsonism Relat Disor Aug 2019 May 25;65:203-209. doi: 10.1016/j.parkreldis.2019.05.035. PubMed PMID:31402278.
  3. Londin, E, Magee, R, Shields, CL, Lally, SE, Sato, T, Rigoutsos, I. IsomiRs and tRNA-derived Fragments are Associated with Metastasis and Patient Survival in Uveal Melanoma. Pigment Cell Melanoma Res. 2019; :. doi: 10.1111/pcmr.12810. PubMed PMID:31283110.
  4. Rigoutsos, I, Lee, SK, Nam, SY, Anfossi, S, Pasculli, B, Pichler, M, Jing, Y, Rodriguez-Aguayo, C, Telonis, AG, Rossi, S, Ivan, C, Catela Ivkovic, T, Fabris, L, Clark, PM, Ling, H, Shimizu, M, Redis, RS, Shah, MY, Zhang, X, Okugawa, Y, Jung, EJ, Tsirigos, A, Huang, L, Ferdin, J, Gafà, R, Spizzo, R, Nicoloso, MS, Paranjape, AN, Shariati, M, Tiron, A, Yeh, JJ, Teruel-Montoya, R, Xiao, L, Melo, SA, Menter, D, Jiang, ZQ, Flores, ER, Negrini, M, Goel, A, Bar-Eli, M, Mani, SA, Liu, CG, Lopez-Berestein, G, Berindan-Neagoe, I, Esteller, M, Kopetz, S, Lanza, G, Calin, GA. N-BLR, a primate-specific non-coding transcript leads to colorectal cancer invasion and migration. Genome Biol. 2017;18 (1):98. doi: 10.1186/s13059-017-1224-0. PubMed PMID:28535802 PubMed Central PMC5442648.
  5. Telonis, AG, Loher, P, Magee, R, Pliatsika, V, Londin, E, Kirino, Y, Rigoutsos, I. tRNA Fragments Show Intertwining with mRNAs of Specific Repeat Content and Have Links to Disparities. Cancer Res. 2019 Apr 17. doi: 10.1158/0008-5472.CAN-19-0789. PubMed PMID:30996049.
  6. Shigematsu, M, Morichika, K, Kawamura, T, Honda, S, Kirino, Y. Genome-wide identification of short 2′,3′-cyclic phosphate-containing RNAs and their regulation in aging. PLoS Genet. 2019 Nov 13;15(11):e1008469. doi: 10.1371/journal.pgen.1008469. PubMed PMID:31721758.
  7. Cherlin, T, Magee, R, Jing, Y, Pliatsika, V, Loher, P, Rigoutsos, I. Ribosomal RNA fragmentation into short RNAs (rRFs) is modulated in a sex- and population of origin-specific manner. BMC Biol 18, 38 (2020). doi: 10.1186/s12915-020-0763-0. PubMed PMID:32279660.

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