Biology subjects

Landis, M.

Publications and source records attributed to Landis, M..

6 recordsLinked to original sources

bioRxiv2026

Mitophagy Inhibition Promotes Survival and Mitochondrial Function in MYC-driven HCC

Hepatocellular carcinomas (HCC) are genetically heterogeneous cancers frequently characterized by MYC gene amplification or hyperactivating {beta}-catenin (CTNNB1) mutations. Analysis of TCGA transcriptomics revealed that MYC-driven HCC tumors have decreased expression of mtDNA-encoded genes, but increased expression of nuclear-encoded mitochondrial genes. To investigate this apparent discrepancy, we generated MYC- and CTNNB1-driven murine HCCs, all of which displayed aberrant mitochondrial metabolism. Notably, MYC-driven tumors exhibited significant reductions in OXPHOS and TCA cycle activity that correlated with increased ROS levels, as well as elevated mitochondrial turnover through mitochondrial fission and mitophagy. MYC induces the expression of nuclear respiratory factor 1 (NRF1), which regulates DRP1 and other genes to promote receptor-mediated mitophagy. Knocking out DRP1 reduced mitophagy and ROS levels and promoted survival of HCC-bearing mice. These results identify elevated mitochondrial turnover as a potential therapeutic target in MYC-driven HCC. SignificanceHepatocellular carcinoma can arise from multiple oncogenes, making targeted therapy more difficult. Here we show that tumors with MYC amplification lose mitochondrial function via fission and mitophagy upregulation. Targeting mitochondrial quality control results in increased survival suggesting a therapeutic window in MYC-driven HCC.

cancer biology↗

bioRxiv2025

Opposing effects of systemic and pancreas-specific inhibition of BCKDK on pancreatic carcinogenesis

Branched-chain amino acid (BCAA) metabolism is perturbed in patients with pancreatic cancer, but the contribution of systemic or pancreas-intrinsic BCAA catabolism to pancreatic carcinogenesis is unclear. We show here that pancreas-specific loss of DBT, the E2 subunit of the branched-chain keto-acid dehydrogenase (BCKDH) complex required for BCAA oxidation, strikingly exacerbates premalignant pancreatic intraepithelial neoplasia (PanIN) lesions in KC (p48-Cre;KrasLSL-G12D/+) mice. However, deletion of upstream enzyme BCAT2 neither phenocopied nor rescued loss of DBT in KC mice, ruling out involvement of both upstream and downstream metabolites as mediators of PanIN promotion. Instead, we observed that DBT deficiency led to loss of the kinase BCKDK, a negative regulator of the BCKDH complex, and that, remarkably, pancreas-specific loss of BCKDK phenocopied DBT deficiency in accelerating PanIN formation. These data thus support a model in which pancreas BCKDK restrains tumorigenesis. In contrast, systemic treatment of KC mice with the BCKDK inhibitor BT2, which inhibits BCKDH phosphorylation across many tissues except the pancreas, reduced PanIN formation and preserved normal acinar area. Together the data reveal the promotion of BCAA catabolism systemically, but not within the pancreas, as a promising intervention strategy to suppress tumor initiation.

cancer biology↗

bioRxiv2023

Deep learning approaches to viral phylogeography are fast and as robust as likelihood methods to model misspecification

Analysis of phylogenetic trees has become an essential tool in epidemiology. Likelihood-based methods fit models to phylogenies to draw inferences about the phylodynamics and history of viral transmission. However, these methods are computationally expensive, which limits the complexity and realism of phylodynamic models and makes them ill-suited for informing policy decisions in real-time during rapidly developing outbreaks. Likelihood-free methods using deep learning are pushing the boundaries of inference beyond these constraints. In this paper, we extend, compare and contrast a recently developed deep learning method for likelihood-free inference from trees. We trained multiple deep neural networks using phylogenies from simulated outbreaks that spread among five locations and found they achieve close to the same levels of accuracy as Bayesian inference under the true simulation model. We compared robustness to model misspecification of a trained neural network to that of a Bayesian method. We found that both models had comparable performance, converging on similar biases. We also implemented a method of uncertainty quantification called conformalized quantile regression which we demonstrate has similar patterns of sensitivity to model misspecification as Bayesian highest posterior intervals (HPI) and greatly overlap with HPIs, but have lower precision (more conservative). Finally, we trained and tested a neural network against phylogeographic data from a recent study of the SARS-Cov-2 pandemic in Europe and obtained similar estimates of region-specific epidemiological parameters and the location of the common ancestor in Europe. Along with being as accurate and robust as likelihood-based methods, our trained neural networks are on average over 3 orders of magnitude faster. Our results support the notion that neural networks can be trained with simulated data to accurately mimic the good and bad statistical properties of the likelihood functions of generative phylogenetic models.

evolutionary biology↗

bioRxiv2022

ACSS2 Regulates HIF-2α Degradation through the E3-Ubiquitin Ligase MUL1 in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is an aggressive kidney cancer driven by VHL loss and aberrant HIF-2 signaling. Acetate metabolism may contribute to this axis by ACSS2-dependent acetylation of HIF-2 and may provide opportunities to intervention. Here we tested the effects of pharmacological and genetic manipulation of ACSS2 on HIF-2, ccRCC cells, and tumors. ACSS2 inhibition led to HIF-2 degradation and suppressed ccRCC growth in vitro, in vivo, and in primary cell cultures of ccRCC patient tumors. This treatment resulted in reduced glucose and cholesterol metabolism, mitochondrial biogenesis and altered cristae deformation, that are consistent with loss of HIF-2. Mechanistically, HIF-2 protein levels are regulated through proteolytic degradation and we found, in parallel to VHL, HIF-2 stability was dependent on ACSS2 activity to prevent direct interaction with the E3 ligase MUL1. These findings highlight ACSS2 as a critical upstream regulator of HIF-2 that may be exploited to overcome resistance to HIF-2 inhibitor therapies. STATEMENT OF SIGNIFICANCEWe have unveiled ACSS2 as a critical upstream regulator of HIF-2 in ccRCC. Targeting ACSS2 potently promotes HIF-2 degradation via MUL1 to effectively deplete mitochondrial activity and block ccRCC primary tumor models and growth models resistant to HIF-2 inhibitor therapy.

cancer biology↗

bioRxiv2022

Alterations to the broad-spectrum formin inhibitor SMIFH2 improve potency

SMIFH2 is a small molecule inhibitor of the formin family of cytoskeletal regulators that was originally identified in a screen for suppression of actin polymerization induced by the mouse formin Diaphanous 1 (mDia1). Despite widespread use of this compound, it is unknown whether SMIFH2 inhibits all human formins. Additionally, the nature of protein/inhibitor interactions remains elusive. We assayed SMIFH2 against human formins representing six of the seven mammalian classes and found inhibitory activity against all formins tested. We synthesized a panel of SMIFH2 derivatives and found that, while many alterations disrupt SMIFH2 activity, substitution of an electron-donating methoxy group in place of the bromine along with halogenation of the furan ring increases potency by approximately five-fold. Similar to SMIFH2, the active derivatives are also pan-inhibitors for the formins tested. This result suggests that while potency can be improved, the goal of distinguishing between highly conserved FH2 domains may not be achievable using the SMIFH2 scaffold.

biochemistry↗

bioRxiv2017

Punctuated evolution shaped modern vertebrate diversity

The relative importance of different modes of evolution in shaping phenotypic diversity remains a hotly debated question. Fossil data suggest that stasis may be a common mode of evolution, while modern data suggest very fast rates of evolution. One way to reconcile these observations is to imagine that evolution is punctuated, rather than gradual, on geological time scales. To test this hypothesis, we developed a novel maximum likelihood framework for fitting Levy processes to comparative morphological data. This class of stochastic processes includes both a gradual and punctuated component. We found that a plurality of modern vertebrate clades examined are best fit by punctuated processes over models of gradual change, gradual stasis, and adaptive radiation. When we compare our results to theoretical expectations of the rate and speed of regime shifts for models that detail fitness landscape dynamics, we find that our quantitative results are broadly compatible with both microevolutionary models and with observations from the fossil record.

evolutionary biology↗