As a member of Dr. Sebti's lab, my research focused on unraveling the biology of mutant (mt) KRAS-driven cancers, specifically investigating why certain tumors exhibit dependency—or “addiction”—to oncogenic mt KRAS, while others are less reliant on its activity. By employing integrative analyses that combined CRISPR/Cas9 cancer cell sensitivity data with gene expression profiles of mt KRAS human cancer cell lines, I identified a novel 30-gene transcriptome signature, “KDS30.” This signature not only predicts mt KRAS addiction but also correlates with poor patient prognosis and drug response, providing valuable insights for therapeutic strategies in KRAS-driven cancers.

This work has been published in Tyc KM et al 2023 iScience. PMID: 36852277.

Provisional patent application: "Novel KRAS addiction signature identifies EGFR-driven signaling network associated with drug response and poor prognosis in pancreatic cancer patients (63/437,861)."

The three-dimensional (3D) organization of the genome plays a critical role in regulating gene expression and recombination in eukaryotic cells. Using advanced sequencing technologies, particularly Hi-C data, we can now study chromatin loops and topologically associating domains (TADs), key structural elements that facilitate interactions between distant regulatory elements. Disruptions in these structures can lead to disease states, including cancer.

In the context of cancer genomics, patient-derived xenograft (PDX) mouse models are being used to study the alterations in 3D genome organization, specifically changes in TAD boundaries. While PDX models are commonly used to study disease progression, evaluating Hi-C data processing strategies for these models has been limited. We established a bioinformatics pipeline to process high-throughput Hi-C data from PDX models and assessed various strategies for mouse read removal and Hi-C data processing. Our findings suggest that removing mouse reads offers minimal benefits, providing valuable insights for researchers analyzing chromatin architecture in cancer in terms of compute and storage resources.

This work has been published in Dozmorov MG*, Tyc KM* et al 2021, Gigascience. PMID: 33880552.

While at Rutgers University at the Department of Genetics, our study focused on uncovering the genetic causes of aneuploid conceptions, where we combined computational analysis with experimental validation to identify key genetic risk factors. Using whole exome sequencing (WES) data from patients undergoing in vitro fertilization (IVF) procedures, we sought to discover novel disease-causing genes and their associated variants that contribute to female infertility. This highly collaborative effort involved close partnership with experimental cell biology labs, where the candidate genes identified through computational analysis were functionally validated both in vitro and in vivo, including the generation of knockout (KO) mouse models.

Through rigorous variant calling and gene prioritization, I analyzed genomes of sub-fertile female patients, revealing genetic markers linked to increased risk of aneuploid egg production—a leading cause of infertility. Notably, our findings implicated mutations in microtubule-related pathways in reproductive dysfunction, identifying CEP120 as a potential biomarker (PMID: 32772081). In addition, we recently identified novel genetic determinants of reproductive aging linked to egg aneuploidy by analyzing maternal exomes. Our findings demonstrated that variants in kinesin genes, particularly KIF18A, contribute to increased aneuploidy and accelerated reproductive aging, offering new avenues for personalized fertility medicine (currently accessible on medRxiv).

Moreover, our analysis uncovered mutations in microRNAs and their 3’ UTR binding sites, suggesting disruptions in maternal transcript clearance that may impede zygotic genome activation during early embryonic development. These molecular defects likely contribute to failed embryogenesis, implantation failures, or early miscarriages, providing new insights into the underlying mechanisms of infertility. This interdisciplinary approach, integrating computational predictions with experimental validation, highlighted novel pathways and biomarkers that hold promise for improving fertility treatments.

Representative work:

• Tyc KM*, Yakoubi El W*, Bag A, Tao X, Landis J, Zhan Y, Treff N, Scott RT Jr., Schindler K, Xing J. (Hum Reprod. 2020) Exome sequencing links variants in centrosomal genes to maternally-derived aneuploid conception risk. 

• Tyc KM, McCoy RC, Schindler K, Xing J. (PNAS 2020) Mathematical modeling of human oocyte aneuploidy.

• Tyc KM*, Wong A*, Scott RT Jr, Tao X, Schindler K, Xing J. (Lab Invest. 2021) Analysis of DNA variants in miRNAs and miRNA 3'UTR binding sites in female infertility patients. 

* equal contribution

During a pivotal period in my scientific career, I immersed myself in experimental biology, gaining hands-on expertise in C. elegans research. From growing the worms and meticulously scanning them under the microscope to searching for males—a rare event occurring in less than 0.1% of the population due to spontaneous non-disjunction of the sex chromosome, often requiring sifting through thousands of worms to find just a few—I learned every aspect of the experimental workflow. I conducted genotyping experiments, extracted RNA/proteins and measured gene expression using qRT-PCR or Northern blotting techniques, and analyzed protein levels with Western blots. This deep experimental knowledge provided the foundation for my subsequent transition into bioinformatics, where I now specialize.

One of my significant contributions to science was investigating the functional role of endogenous double-stranded RNAs (dsRNA) in gene regulation during C. elegans development. Adenosine deaminases acting on RNA (ADARs) are enzymes that bind to dsRNA regions and edit adenosines to inosines. While editing by ADARs had no known function, our integrative study of RNA-seq and small RNA-seq data revealed that these editing events correlate with the suppression of specific small RNA populations. Supported by genetic studies, we demonstrated that C. elegans use ADARs to prevent abnormal processing of dsRNA, thereby regulating genes with matching sequences. This groundbreaking research uncovered a novel role for ADARs in developmentally controlling the processing of endogenous dsRNAs, adding a new layer of understanding to gene regulation in C. elegans.

This work has been published in: Reich DP, Tyc KM, Bass BL. C. elegans ADARs antagonize silencing of cellular dsRNAs by the antiviral RNAi pathway. Genes & development. 2018; 32(3-4):271-282. PMID: 29483152, PMCID: PMC5859968.

In another integrative study, I explored the role of the conserved intron-binding protein EMB-4 and its interaction with two critical nuclear small RNA pathways, CSR-1 and HRDE-1, in C. elegans. These pathways regulate the germline transcriptome and fertility. Using RIP-seq, RNA-seq, and small RNA-seq, we discovered that EMB-4 differentially affects the small RNAs and transcriptomes associated with these pathways. Specifically, EMB-4 binds HRDE-1 targets in both exonic and intronic regions but interacts only with the intronic sequences of CSR-1 targets. This led us to propose a model in which EMB-4 distinguishes between CSR-1 and HRDE-1 pathways through differential transcript binding, thereby providing a molecular mechanism that fine-tunes gene regulation.

This work has been published in: Tyc KM*, Nabih A*, Wu MZ*, Wedeles CJ, Sobotka JA, Claycomb JM. The Conserved Intron Binding Protein EMB-4 Plays Differential Roles in Germline Small RNA Pathways of C. elegans. Developmental cell. 2017; 42(3):256-270.e6. PMID: 28787592. (* equal contribution)

These experiences shaped my transition to bioinformatics, where I apply computational tools to delve deeper into the molecular mechanisms I previously studied in the lab. My journey from math to the bench to bioinformatics allows me to integrate experimental and computational approaches, contributing unique insights into the regulation of gene expression and small RNA pathways.

While at Humboldt University in Berlin, my research focused on developing mathematical models to study host-pathogen interactions, particularly in the opportunistic human pathogen Candida albicans, the most common Candida species to infect immunocompromised patients. A key factor in C. albicans pathogenicity is its ability to adapt to a wide range of environmental conditions. During host infection, C. albicans can transition between yeast and hyphal states, a process central to its colonizing success and disease progression. To investigate this adaptability, I applied evolutionary game theory to study how this phenotypic flexibility optimizes the fungus’s ability to infect the host (Tyc et al., 2014; Tyc et al., 2016). In addition,gGiven that fever is one of the host’s primary defenses against infection, I also developed an ordinary differential equations model to explore the activation of the fungal heat shock stress response (Leach, Tyc et al., 2012). This model helped uncover the mechanisms by which C. albicans copes with thermal stress during host invasion. 

Both mathematical models were experimentally validated through close collaboration with molecular cell biologists, allowing me to integrate computational insights with experimental data. This collaborative effort gave me specialized expertise in leading mathematical modeling projects that require parameter estimation and designing experiments to validate these models.

By combining mathematical modeling with experimental biology, I have enhanced our understanding of fungal pathogenicity mechanisms, contributing valuable insights into how C. albicans interacts with its host. These interdisciplinary approaches hold promise for informing new therapeutic strategies to combat fungal infections in vulnerable populations.

This doctoral research of mine resulted in:

• Tyc KM, Herwald SE, Hogan JA, Pierce JV, Klipp E, Kumamoto CA. The game theory of Candida albicans colonization dynamics reveals host status-responsive gene expression. BMC systems biology. 2016; 10:20. PMID: 26927448, PMCID: PMC4772284

• Barros de Andrade E Sousa LC, Kühn C, Tyc KM, Klipp E. Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies. Frontiers in physiology. 2016; 6:398. PMID: 26779031, PMCID: PMC4701919

• Tyc KM, Kühn C, Wilson D, Klipp E. Assessing the advantage of morphological changes in Candida albicans: a game theoretical study. Frontiers in microbiology. 2014; 5:41. PMID: 24567730, PMCID: PMC3915147

• Leach MD*, Tyc KM*, Brown AJ, Klipp E. Modelling the regulation of thermal adaptation in Candida albicans, a major fungal pathogen of humans. PloS one. 2012; 7(3):e32467. PMID: 22448221, PMCID: PMC3308945

* equal contribution