Biology subjects

Lai, I.

Publications and source records attributed to Lai, I..

6 recordsLinked to original sources

bioRxiv2022

An Investigation of the Effects of Nitrate vs. Ammonium on Plants Using Metabolic Modeling

As metabolism is a very complex process, it is essential to use computational modeling to better understand the changes in metabolism at a systems level. In this paper, we used a computational model that simulates the metabolism of mature plants to test how the use of nitrate and ammonium as nitrogen sources affects plant metabolism. Our model predicted and showed the possible changes in metabolism in different plant tissues in response to the use of different nitrogen sources. Our methodology produces predictions of metabolic fluxes in plants and improves our understanding of how plants react to changes in conditions. This research can potentially give insights into how we can improve crop yield by identifying metabolic processes that are important in plant growth and in the adaptation to different nitrogen sources. In future research work, we can apply the existing model to explore the effects of different nutrient availability under different conditions (e.g. well-watered vs drought) to optimize nutrient availability under particular environmental conditions. Experimental researchers, plant breeders, and farmers can use the knowledge gained from the modeling work to further our understanding of plant metabolism and apply the knowledge to improve plant growth in the lab and the field.

plant biology↗

bioRxiv2022

MYC Overexpression Drives Immune Evasion in Human Cancer that is Reversible Through Restoration of Pro-Inflammatory Macrophages

Cancers evade immune surveillance that in some, but not in many, cases can be reversed through immune checkpoint therapy. Here we report that the MYC oncogene suppresses immune surveillance, activates immune checkpoint expression, and predicts responsiveness to immune checkpoint inhibition. First, when MYC is genomically amplified and overexpressed in 33 different human cancers, this increases immune checkpoint expression, drives immune checkpoint therapeutic resistance, and is associated with both Th2-like immune profile, and reduced CD8 T cell infiltration. Second, experimentally, MYC-driven tumors suppress pro-inflammatory antigen-presenting macrophages with increased CD40 and MHCII expression, which in turn impedes T cell response. This MYC-driven suppression of macrophages can be reversed by combined but not individual blockade of PDL1 and CTLA4. Third, the depletion of macrophages abrogated the anti-neoplastic effects of PDL1 and CTLA4 blockade. Hence, MYC is a predictor of immune checkpoint responsiveness and a key driver of immune evasion through the suppression of pro-inflammatory macrophages. The immune evasion by MYC can be overcome by combined PDL1 and CTLA4 blockade. Statement of SignificanceMYC is the most commonly activated oncogene in human cancers. In this study, we identify macrophage-mediated immune evasion as a major therapeutic vulnerability of MYC-driven cancers. Our results have implications for developing effective immunotherapies for MYC-driven human cancers and also for prioritizing patients with MYC-driven tumors for combination immunotherapy.

cancer biology↗

bioRxiv2022

Inducible lncRNA transgenic mice reveal continual role of HOTAIR in promoting breast cancer metastasis

HOTAIR is a 2.2 kb long noncoding RNA (lncRNA) whose dysregulation has been linked to oncogenesis, defects in pattern formation during early development, and irregularities during the process of epithelial-to-mesenchymal transition (EMT). However, the oncogenic transformation determined by HOTAIR in vivo and its impact on chromatin dynamics are incompletely understood. Here we generate a transgenic mouse model with doxycycline-inducible expression of human HOTAIR in the context of the MMTV-PyMT breast cancer-prone background to systematically interrogate the cellular mechanisms by which human HOTAIR lncRNA acts to promote breast cancer progression. We show that sustained high levels of HOTAIR over time increased breast metastatic capacity and invasiveness in breast cancer cells, promoting migration and subsequent metastasis to the lung. Subsequent withdrawal of HOTAIR overexpression reverted the metastatic phenotype, indicating oncogenic lncRNA addiction. Furthermore, HOTAIR overexpression altered both the cellular transcriptome and chromatin accessibility landscape of multiple metastasis-associated genes and promoted epithelial to mesenchymal transition. These alterations are abrogated within several cell cycles after HOTAIR expression is reverted to basal levels, indicating an erasable lncRNA-associated epigenetic memory. These results suggest that a continual role for HOTAIR in programming a metastatic gene regulatory program. Targeting HOTAIR lncRNA may potentially serve as a therapeutic strategy to ameliorate breast cancer progression.

cancer biology↗

bioRxiv2022

Bi-steric mTORC1-selective Inhibitors activate 4EBP1 reversing MYC-induced tumorigenesis and synergize with immunotherapy

The MYC oncogene is causally involved in the pathogenesis of most types of human cancer but it remains therapeutically untargeted. The mTORC1 protein complex regulates cap-dependent translation through 4EBP1 and S6K and thereby, downstream MYC protein expression. However, to date, agents such as rapalogs that selectively target mTORC1 (as compared to mTORC2) fail to reactivate 4EBP1 and thus, to block MYC in vivo. In contrast, agents that nonselectively inhibit both protein complexes of the mTOR pathway, mTORC1 and mTORC2, can activate 4EBP1, but often suffer from a lack of tolerability including in vivo hepatotoxicity and immunosuppression. Here, we report the anti-tumor activity of bi-steric mTORC1-selective inhibitors, including Revolution Medicines clinical candidate RMC-5552, that potently and selectively target mTORC1 over mTORC2. In an autochthonous transgenic mouse model of MYC-amplified and MYC-driven hepatocellular carcinoma (HCC), representative bi-steric mTORC1-selective inhibitors suppress translation initiation via activation of 4EBP1, thereby suppressing MYC protein expression and blocking tumor growth. Furthermore, in human HCC samples, the low levels of 4EBP1 and MYC is correlated with immune reactivation. Immunohistochemistry, CIBERSORT, and CODEX reveal that selective mTORC1 inhibition results in activation of both CD4+ T cell- and NKp46+ NK cell-mediated immune surveillance. Moreover, bi-steric mTORC1-selective inhibitors synergize with -PD-1 to induce sustained tumor regression, with immune cell degranulation and release of perforins and granzyme B. These agents also exhibit anti-tumor activity in human patient-derived xenografts of HCC, colorectal cancer, head and neck cancer, and ovarian cancer harboring genomic amplifications in MYC. We infer that selective mTORC1 inhibition is a potential therapeutic strategy to drive effective MYC inactivation in cancer, and the consequent restoration of immune surveillance against neoplasia.

cancer biology↗

bioRxiv2021

Immunogenic SARS-CoV2 Epitopes Defined by Mass Spectrometry

SARS-CoV-2 infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID19, the disease caused by SARS-CoV-2, T cell responses are important in mediating recovery and immune-protection. The identification of immunogenic epitopes that can elicit a meaningful T cell response can be elusive. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes; however, our previous studies find that immunodominant SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing SARS-CoV-2. We postulated that immunogenic epitopes for SARS-CoV-2 are best defined by directly analyzing peptides eluted from the peptide-MHC complex and then validating immunogenicity empirically by determining if such peptides can elicit T cells recognizing SARS-CoV-2 antigen-expressing cells. Using a tandem mass spectrometry approach, we identified epitopes of SARS-CoV-2 derived not only from structural but also non-structural genes in regions highly conserved among SARS-CoV-2 strains including recently recognized variants. We report here, for the first time, several novel SARS-CoV-2 epitopes from membrane glycol-protein (MGP) and non-structure protein-13 (NSP13) defined by mass-spectrometric analysis of MHC-eluted peptides, provide empiric evidence for their immunogenicity to induce T cell response. Significance StatementCurrent state of the art uses putative epitope peptides based on in silico prediction algorithms to evaluate the T cell response among COVID-19 patients. However, none of these peptides have been tested for immunogenicity, i.e. the ability to elicit a T cell response capable of recognizing endogenously presented peptide. In this study, we used MHC immune-precipitation, acid elution and tandem mass spectrometry to define the SARS-CoV-2 immunopeptidome for membrane glycol-protein and the non-structural protein. Furthermore, taking advantage of a highly robust endogenous T cell (ETC) workflow, we verify the immunogenicity of these MS-defined peptides by in vitro generation of MGP and NSP13 peptide-specific T cells and confirm T cell recognition of MGP or NSP13 endogenously expressing cell lines.

immunology↗

bioRxiv2021

In Silico Defined SARS-CoV2 Epitopes May Not Predict Immunogenicity to COVID19

SARS-CoV-2 infections elicit both humoral and cellular immune responses. For the prevention and treatment of COVID19, the disease caused by SARS-CoV-2, it has become increasingly apparent that T cell responses are equally, if not more important than humoral responses in mediating recovery and immune-protection. One of the major challenges in developing T cell-based therapies for infectious and malignant diseases has been the identification of immunogenic epitopes that can elicit a meaningful T cell response. Traditionally, this has been achieved using sophisticated in silico methods to predict putative epitopes deduced from binding affinities and consensus data. Our studies find that, in contrast to current dogma, immunodominant SARS-CoV-2 peptides defined by such in silico methods often fail to elicit T cell responses recognizing naturally presented SARS-CoV-2 epitopes.

immunology↗