Identification of Essential Genes in Hepatocellular Carcinomas using CRISPR Screening

Hepatocellular carcinoma (HCC) is an aggressive subtype of liver cancer with a poor prognosis. Currently, prognosis for HCC patients remains poor as few therapies are available. The clinical need for more effective HCC treatments remains unmet partially because HCC is genetically heterogeneous and HCC driver genes amenable to targeted therapy are largely unknown. Mutations in the TP53 gene are found in ~30% of HCC patients and confer poor prognosis to patients. Identifying genes whose depletion can inhibit HCC growth, and determining the mechanisms involved, will aid the development of targeted therapies for HCC patients. Therefore, the first half of this thesis focuses on identifying genes that are required for cell growth in HCC independent of p53 status. We performed a kinome-wide CRISPR screen to identify genes required for cell growth in three HCC cell lines: HepG2 (p53 wild-type), Huh7 (p53-mutant) and Hep3B (p53-null) cells. The kinome screen identified 31 genes that were required for cell growth in 3 HCC cell lines independent of TP53 status. Among the 31 genes, 8 genes were highly expressed in HCC compared to normal tissue and increased expression was associated with poor survival in HCC patients. We focused on TRRAP, a co-factor for histone acetyltransferases. TRRAP function has not been previously characterized in HCC. CRISPR/Cas9 mediated depletion of TRRAP reduced cell growth and colony formation in all three cell lines. Moreover, depletion of TRRAP reduced its histone acetyltransferase co-factors KAT2A and KAT5 at the protein level with no change at the mRNA level. I found that depletion of KAT5, but not KAT2A, reduced cell growth. Notably, inhibition of proteasome- and lysosome-mediated degradation failed to rescue protein levels of KAT2A and KAT5 in the absence of TRRAP. Moreover, tumor initiation in an HCC mouse model failed after CRISPR/Cas9 depletion of TRRAP due to clearance via macrophages and HCC cells depleted of TRRAP and KAT5 failed to grow as subcutaneous xenografts in vivo. RNA-seq and bioinformatic analysis of HCC patient samples revealed that TRRAP positively regulates expression of genes that are involved in mitotic progression. In HCC, this subset of genes is clinically relevant as they are overexpressed compared to normal tissue and high expression confers poor survival to patients. I identified TOP2A as one of the mitotic gene targets of the TRRAP/KAT5 complex whose inhibition greatly reduces proliferation of HCC cells. Given that this was the first time the TRRAP/KAT5 complex has been identified as a therapeutic target in HCC, the second half of this thesis focuses on identifying the mechanism via which depletion of this complex inhibits proliferation of HCC cells. I discovered that depletion of TRRAP, KAT5 and TOP2A reduced proliferation of HCC cells by inducing senescence. Typically, senescence is an irreversible state of cell cycle arrest at G1 that is due to activation of p53/p21 expression, phosphorylation of RB, and DNA damage. Surprisingly, induction of senescence after loss of TRRAP, KAT5 and TOP2A arrested cells during G2/M and senescence was independent of p53, p21, RB and DNA damage. In summary, this thesis identifies TRRAP as a potential oncogene in HCC. I identified a network of genes regulated by TRRAP and its-cofactor KAT5 that promote mitotic progression. Moreover, I demonstrated that disruption of TRRAP/KAT5 and its downstream target gene TOP2A result in senescence of HCC cells independent of p53 status. Taken together, this work suggests that targeting the TRRAP/KAT5 complex and its network of target genes is a potential therapeutic strategy for HCC patients

The Role of the Novel Lupus Antigen, Acheron, in Moderating Life and Death Decisions

Programmed cell death (PCD) is a major regulatory mechanism employed during development and homeostasis. The term PCD was coined to describe the death of the intersegmental muscles (ISMs) of moths at the end of metamorphosis. The timing of ISM death in the Tobacco Hawkmoth, Manduca sexta, is regulated by a fall in the titer of the steroid molting hormone 20-hydroxyecdysone (20E) late on day 17of pupal-adult development. This triggers the release of the peptide hormone, Eclosion Hormone (EH), which mediates its effects via the secondary messenger cGMP. It has been previously demonstrated that ISM death requires de novo gene expression. One induced gene in the ISMs encodes the novel protein Acheron. However, Acheron’s role in PCD is unknown. Acheron is a novel member of the Lupus-Antigen family of RNA binding proteins. In humans, Acheron is expressed in many tissues including the myoepithelial cells in mammary ducts. Analysis of the mammary gland revealed that Acheron mRNA levels were elevated in some basal-like breast cancers in women. Ectopic expression of Acheron in human MDA-MB-231 breast cancer cells results in dramatic elevations in proliferation, angiogenesis and metastasis. Moreover, Acheron expressing MDA-MB-231 cells in mouse xenographs resulted in tumors that were five times larger than control cell tumors. These data suggests that Acheron enhances the growth of some human breast cancers. This thesis describes two primary studies. The first tested the hypothesis that Acheron functions as a survival protein for cells in vitro. MDA-MB-231 cells engineered to express Acheron were challenged with various death-inducing treatments, which act via different signaling pathways, to determine if Acheron expression confers survival. Acheron protects cells from apoptosis induced by nutrient withdrawal, proteosome inhibition, heat stress, mitochondrial toxins, inhibiting cellular respiration, DNA damage, and oxidative stress. The second study tested the hypothesis that Acheron is phosphorylated by a cGMP-dependent kinase in the ISMs when the cells initiate death following adult eclosion. Using a non-radioactive in-vitro kinase assay I observed that Acheron is phosphorylated via a cGMP-dependent kinase, presumed via kinase binding motif predictions to be Protein Kinase G. Furthermore I show that phosphorylation is coupled to Acheron degradation

Depletion of TRRAP induces p53-independent senescence in liver cancer by downregulating mitotic genes

Hepatocellular carcinoma (HCC) is an aggressive subtype of liver cancer with few effective treatments and the underlying mechanisms that drive HCC pathogenesis remain poorly characterized. Identifying genes and pathways essential for HCC cell growth will aid the development of new targeted therapies for HCC. Using a kinome CRISPR screen in three human HCC cell lines, we identified transformation/transcription domain-associated protein (TRRAP) as an essential gene for HCC cell proliferation. TRRAP has been implicated in oncogenic transformation, but how it functions in cancer cell proliferation is not established. Here, we show that depletion of TRRAP or its co-factor, histone acetyltransferase KAT5, inhibits HCC cell growth via induction of p53- and p21-independent senescence. Integrated cancer genomics analyses using patient data and RNA-sequencing identified mitotic genes as key TRRAP/KAT5 targets in HCC, and subsequent cell cycle analyses revealed that TRRAP- and KAT5-depleted cells are arrested at G2/M phase. Depletion of TOP2A, a mitotic gene and TRRAP/KAT5 target, was sufficient to recapitulate the senescent phenotype of TRRAP/KAT5 knockdown. CONCLUSION: Our results uncover a role for TRRAP/KAT5 in promoting HCC cell proliferation via activation of mitotic genes. Targeting the TRRAP/KAT5 complex is a potential therapeutic strategy for HCC

CRISPR/Cas9-mediated genome editing induces exon skipping by alternative splicing or exon deletion

CRISPR is widely used to disrupt gene function by inducing small insertions and deletions. Here, we show that some single-guide RNAs (sgRNAs) can induce exon skipping or large genomic deletions that delete exons. For example, CRISPR-mediated editing of β-catenin exon 3, which encodes an autoinhibitory domain, induces partial skipping of the in-frame exon and nuclear accumulation of β-catenin. A single sgRNA can induce small insertions or deletions that partially alter splicing or unexpected larger deletions that remove exons. Exon skipping adds to the unexpected outcomes that must be accounted for, and perhaps taken advantage of, in CRISPR experiments. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1237-8) contains supplementary material, which is available to authorized users

Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions

Wetland methane (CH4) emissions (FCH4) are important in global carbon budgets and climate change assessments. Currently, FCH4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent FCH4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that FCH4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between FCH4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between FCH4 and temperature, suggesting larger FCH4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments. Wetland methane emissions contribute to global warming, and are oversimplified in climate models. Here the authors use eddy covariance measurements from 48 global sites to demonstrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes.Peer reviewe

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0):Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).</p

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe