December 13

Tong Tong
Advisors: Lindsay Farrer & Xiaoling Zhang
Title: Blood Mitochondrial DNA Heteroplasmy Level Are Associated with Amyloid and Tau

Abstract:
Mitochondrial dysfunction plays an important role in Alzheimer’s disease (AD) pathogenesis. Mitochondrial heteroplasmy is the coexistence of wildtype and mutant mitochondrial DNAs (mtDNA), which could lead to deteriorated mitochondria when the proportion of mutants reaches a certain threshold. However, the relationship between the heteroplasmy proportion and AD risk is unclear. We investigated the association of heteroplasmy with AD and AD-related pathological traits in CSF biomarker and whole genome sequence data in blood samples from 1,566 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We developed a pipeline – MitoH3 – to efficiently and accurately call mtDNA heteroplasmic variants. The association of heteroplasmy with AD, age, gender, mtDNA copy number (mtDNA-CN) adjusted for cell type composition was assessed using negative binomial regression models. The number of heteroplasmic sites positively associated with age adjusted for all other covariates (β=0.016, P=5.32×10-4). We also observed associations of heteroplasmy with the ratio of β-amyloid 42/ β-amyloid 40 in females (β=0.117, P=0.018) and total tau level in males (β=-0.091, P=0.021) using linear regression analysis. These findings suggested that blood mtDNA heteroplasmy level contributes to abnormal AD proteins measured in CSF, but not directly to clinical symptoms of AD.

 
Lina Kroehling
Advisor: Stefano Monti
Title: High-Resolution Characterization of Age-Specific Cell Type Composition Changes and Signaling Events in HPV-Negative HNSCC Tumors

Abstract:
This study is aimed at testing the hypothesis that increased patient age fosters conditions that are favorable for head and neck squamous cell carcinomas of the oral cavity (HNSCC) development. There is a well-documented increase in cancer incidence with age observed across diverse cancer types, where elderly patients exhibit diminished survival outcomes compared to their younger counterparts. While the convergence of aging and cancer hallmarks offers valuable insights, further work is needed to elucidate the age-specific mechanisms influencing HNSCC, beyond the shared characteristics of these biological processes.

To this end, we have assembled a high-quality human single-cell RNA-sequencing HNSCC atlas profiling more than 230,000 cells across more than 50 patients with ages ranging between 18 and 89, which provides a resource to investigate age-associated changes in the disease heterogeneity. For this library, we integrated six publicly available single-cell RNAseq datasets from 54 HPV-negative patients to create the atlas. Cells were clustered, classified, characterized by gene set enrichment analysis, both in the epithelial cell compartment and in the tumor microenvironment (TME). Differential cell type proportion analysis was performed to identify cell types enriched in young (<49 years) or old (>70 years) patients. Cell-cell communication analysis was performed to identify signaling events occurring between different populations, and how these and specific ligand-receptor pairs differed in patients of different ages. CNV analyses were performed to identify cancer subclones and assess level of variation across tumors. Interestingly, we identified distinct cell populations and signaling events that associate with age, and we were able to compare, validate and recapitulate these observations in the 4MOSC1 OSCC isograft tumor model in old and young mice. Additional notable findings include a differential enrichment for cytotoxic CD8 T cells over dysfunctional CD8 T cells in young patients when compared to old, suggesting younger patients have a more effective immune TME. We also found an enrichment for cancer-associated fibroblast populations in old patients (myCAFs) that send collagen signals to malignant epithelial cells, a result we have recapitulated using the 4MOSC1 model, and hypothesize may play a role in ECM stiffening, EMT, and metastasis. Further analyses are ongoing, and we plan to validate the hypotheses generated, specifically the presence of differential abundance of specific cell populations, and age-specific ligand-receptor signaling events that lead to tumor growth.

 
November 29

Zhaorong Li
Advisor: Juan Fuxman Bass
Title: Investigating how viral transcription regulators (vTR) alter host biological pathways and its mechanism

Abstract:
When a cell is infected by virus, the virus replicate itself within the cell. In some viruses the viral genome encodes viral transcription regulators (vTR) to facilitate virus activity within cells, and these viral transcription regulators have been demonstrated to be able to interact with host genome and alter host biological pathways. To study which biological pathways are altered by viral transcription regulators, we use RNA-Seq samples from in-vitro vTR transfected cell lines and from in-vivo virus infection tissues to study what biological pathways are altered by vTR the mechanisms of the alterations.

 
November 15

Shruthi Bandyadka
Advisors: Kim McCall &  Josh Campbell
Title: Image Analysis Methods for Drosophila Cell Death and Clearance.

Abstract:
Are circulating phagocytes recruited to the ovary during starvation-stress? Does blocking glial phagocytosis cause neurodegeneration due to increased immune signaling? To answer these distinct questions, we developed two quantitative workflows to analyze images of fluorescently labelled tissues in the fly abdomen and whole head. Using methods like deep learning-based image segmentation and spatial statistics, we processed hundreds of images from confocal or 3D 2-photon microscopy. In this talk, I will present the two methods I developed, the findings from their application, and the main challenges to studying these distinct cell death events in the fly ovary and brain.

 
Aubrey Odom
Advisor: W. Evan Johnson
Title: Metagenomic Profiling Pipelines Improve Taxonomic Classification for 16S Amplicon Sequencing Data

Abstract:
Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools.

 
November 1

Kelley Anderson
Advisors: Jennifer Beane and Marc Lenburg
Title: Molecular Subtyping of Lung Adenocarcinoma Premalignant Lesions Identifies Features Associated with Aggressive Disease

Abstract:
Lung cancer is the leading cause of cancer-related death. Adenocarcinoma (LUAD) is the most common form of lung cancer. Atypical adenomatous hyperplasia and adenocarcinoma in situ are the only known precursors in the sequence of LUAD pathogenesis, and the molecular features of aggressive premalignant lesions (PMLs) that would benefit from early interventions are poorly characterized. We hypothesized that transcriptomic changes in subsets of PMLs are linked to distinct genomic and clinicopathologic features of malignant disease. To test this hypothesis, we performed exome sequencing and bulk RNA sequencing of laser capture microdissected tissue from tumor margins that included PMLs, tumor, and adjacent normal tissues. We discovered de novo subtypes of LUAD PMLs based on gene co-expression. One subtype had gene expression alterations similar to aggressive invasive LUAD. This molecular adenomatous PML subtype was associated with increased accumulation of known cancer driver mutations, and further characterized by altered expression of immune-related pathways. Molecular signatures measured in adenomatous PML may thus enhance our understanding of pathway dysregulation and mutational heterogeneity occurring during LUAD carcinogenesis, and implicate immunotherapeutic strategies to prevent their progression to cancer.

 
October 18

Michael Silverstein
Advisors: Daniel Segrè and Jennifer Bhatnagar
Title: Soil Microbiome Engineering for Climate Change Mitigation

Abstract:
Climate change continues to threaten the stability of the biosphere, increasing the demand for mitigation strategies. One exciting opportunity is soil microbiome engineering, i.e., the use of a microbial inoculum to induce enduring, stable modifications to a natural soil microbial community and the ecosystem functions it regulates. While environmental microbiome engineering has existed for at least a century, the properties underlying this process and strategies for maximizing its efficacy are yet to be uncovered. Here, I will discuss (i) a recent experiment which suggests how more complex environments, like forests, may be more susceptible to microbiome engineering than simple ones and (ii) my plans to use directed evolution to design optimal microbial inocula for boosting the carbon use efficiency of natural soil microbial communities.

 
Rebekah Miller
Advisor: Trevor Siggers
Title: Profiling COF Recruitment and Transcription Regulation

Abstract:
Every cell in our bodies has the same basic genetic material, but individual cells are highly specialized to perform a vast array of different functions. These different functions arise as a result of expressing different sets of genes in different cell types. As such, regulating which genes are transcribed is critical to establishing and maintaining specialized cell types. Transcription is regulated by transcription factors (TFs) and transcription cofactors (COFs) and the interactions between them. TFs bind to specific DNA sequences found in regulatory regions known as enhancers and recruit specific COFs to these loci. The COFs then establish and maintain epigenetic modifications, such as histone acetylation and methylation marks; these marks affect the transcription of genes associated with that enhancer. My work focuses on identifying the rules governing the interactions between TFs and COFs using two complementary approaches. The first approach is a novel protein binding microarray-based approach called CoRec to determine COF recruitment preferences in a cell-type specific manner. The second approach is a meta-analysis of publicly available ChIP-seq datasets to examine how the genomic localization of COFs and TFs affects the epigenome and ultimately regulates gene transcription.

 
October 4

Devlin Moyer
Advisors: Juan Fuxman-Bass and Daniel Segrè
Title: Metabolic Models of Cancer Cells, or Maybe Any Cells

Abstract:
Dysregulated metabolism has been recognized as one of the hallmarks of cancer, to the point that many chemotherapies have been designed to target the unique metabolic requirements of cancer cells. However, there is extensive heterogeneity in the response to treatment amongst cancer patients — even between patients with the same type of cancer. Direct measurement of metabolic fluxes is expensive and time consuming and thus challenging to apply to large cohorts of patients. I am trying to circumvent this limitation by incorporating transcriptomics data from healthy and cancerous cells into mathematical models of the metabolic networks inside those cells to simulate cancer-associated metabolic disruptions. These models could be used to group patients based on simulated metabolic phenotypes, simulate the effects of administering various real and hypothetical treatments, and identify common and patient-specific metabolic vulnerabilities that could be exploited by novel treatment approaches. However, I have encountered a number of fundamental issues with common approaches to working with these kinds of metabolic models that have gotten in the way of exploring new ways to incorporate transcriptomics data into these models. Finding novel solutions to these problems could have much farther-reaching impacts than I initially expected this project to have, as similar techniques are also used in contexts as varied as pharmaceutical production, agriculture, and mitigating climate change.

 
Muzamil Khan
Advisor: Stefano Monti
Title: Investigating the Cellular Heterogeneity of Carcinogen-Induced Murine Oral Tumors Via Targeted Therapy

Abstract:
Head and neck cancer is the 8th most common cancer in the United States, of which oral cancers (OC) arising in the oral cavity, such as the tongue, esophagus, etc., are the most prevalent. OC can be caused by one of two factors: infection with the human papillomavirus (HPV), also known as HPV+, that drives them to malignancy, or HPV- OCs. Because of their complex mechanisms, such as tumor heterogeneity and plasticity, the latter are difficult to treat. Treatment of HPV- OCs is limited, with only a handful of drug therapies available for certain subtypes of OCs. Small molecule inhibitors are gaining popularity due to their effectiveness in reducing tumor growth via epigenetic transformation. One such drug, E7386, developed by Eisai Inc., targets the Wnt/b-cat/CBP complex, blocking cellular proliferation and aggressive cancer traits. In this study, we are treating 4-NQO carcinogen-induced oral tumors in mice with E7386 and studying the cellular heterogeneity using single-cell RNA sequencing. Cellular compartments indicative of tumors, such as the epithelium and their microenvironment (Immune, stroma, and so on), are annotated and stratified across treatment groups as healthy, tumor-only, and treatment with E7386 with two different concentrations (25 mg/kg and 50 mg/kg). Our initial analysis so far on the epithelial and immune compartments shows that there are cellular groups that change upon treatment to E7386 and become control-like. Additionally, we found a distinct subpopulation in the epithelium related to AP-1 activity commonly known as the stress program that shows a strong correlation with the b-cat/CBP activity indicating a reduction in stress response. Our collective analysis reveals cellular sub-groups indicative of cancer programs are changing in response to E7386 treatment.

 
September 20

Anastasia Leshchyk
Advisors: Stefano Monti & Paola Sebastiani
Title: A Bayesian Network-based Approach for Multi-omics Integration to Reveal Underlying Mechanisms of Healthy Aging.

Abstract:
Previous research on long-lived individuals showed that centenarians significantly delay the onset of disability and aging-related diseases such as Alzheimer’s, dementia, and cardiovascular diseases to the very end of their lives compared to the general population. Genetic studies of centenarians and healthy agers showed that carriers of the APOE e2 allele had increased odds of reaching longevity compared to the non-e2 allele carriers. In addition, the APOE e2 allele is characterized by distinct serum proteomics and metabolomics profiles that could be useful to understand the mechanism of propagation of the genetic effect of APOE to molecular level and eventually to phenotypes. We are developing a novel network-based approach of multi-layer data integration to identify shared molecular profiles among the subjects with familial longevity that lead to compression of morbidity, disability, and mortality. This Bayesian network-based approach, which integrates proteomics, metabolomics, genetics, and multiple phenotype data, will help to decipher the risk factors and pathways contributing to the prolonged subjects’ health span.

 
Vanessa Li
Advisor: Stefano Monti
Title: Cell Type Deconvolution Using Whole Blood Transcriptomic Data Revealed Blood Cell Type Proportion Changes Across Different Age Groups

Abstract:
As people age, their blood immune cell type proportion changes, reflecting a systematic change in their pathogen exposure and immune functionality. There are computational tools that can perform cell type deconvolution analysis to estimate cell type proportion from bulk RNAseq data. In this project, we analyzed the whole blood RNAseq data from 1,348 participants in Long Life Family Study (LLFS) using Cibersort and the cell signature matrix lm22.

We identified the proportion estimates of 22 blood cell types. We then grouped participants into different age groups and compared with previous findings in a centenarians single-cell analysis of 66 participants. In both analyses, the proportions of naive CD4 T-cells, memory CD4 T-cells and naive B-cells decreased in extreme old people compared to younger people, while CD8 T-cells, natural killer cells and monocytes were observed to increase. These findings suggested a higher overall exposure of pathogens in older people compared to younger people. Further investigation about the analysis of cell type diversity changes across different ages is still on-going.