Biology subjects

Liotta, L.

Publications and source records attributed to Liotta, L..

3 recordsLinked to original sources

bioRxiv2024

Application of Ensemble Machine Learning to Metabolomic Data Identifies Metabolites Associated with Macrophage Polarization

Towards developing quantitative models of anti-tumor activities of macrophages, we evaluated the effects of cytokines, tumor exosomes, and polarization states of macrophages in a tumor microenvironemnt using a system of differential equations. We modeled the non-linear dynamics of macrophage polarization states (M0/M1/M2), tumor cell killing by macrophages, and evasion of macrophage mediated killing by tumor originated extracellular vesicle decoys. Solving these coupled differential equations using numerical approaches, showed that the rate of macrophage polarization into the M1 state is the critical determinant of anti-tumor activity mediated by M1 polarized macrophages. To determine what metabolomic factors correlate with the polarization of naive macrophage into anti-tumor M1 or pro-tumor M2 phenotypes, we performed LC/MS-based untargeted metabolomic analysis. Statistical analysis using Python-Scikit-learn was performed on the metabolomic data from naive, M1 or M2 polarized murine macrophages followed by multiple feature selection methods. Application of ensemble machine learning methods to both secreted and cell associated metabolites revealed novel molecules of fatty acid metabolism to be the main mediators of polarization. Integration of ensemble machine learning feature-ranking tools into our analysis of metabolomic data identified new potential targets in macrophage metabolism for enhancing anti-tumor activities.

bioinformatics↗

bioRxiv2021

Biological networks with singular Jacobians: their origins and adaptation criteria

This work is focused on Ordinary Differential Equations(ODE)-based models of biochemical systems that possess a singular Jacobian manifesting in non-hyperbolic equilibria. We show that there are several classes of systems that exhibit this behavior: a)systems with monomial-type interaction terms and b)systems with linear or nonlinear conservation laws. While models derived from mass-action principles often present with linear conservation laws stemming from the underlying biologic rationale, nonlinear conservation laws are more subtle and harder to detect. Nevertheless, in both situations the corresponding ODE system will contain non-hyperbolic equilibria. While having a potentially more complex dynamics and falling outside of the scope of existing theoretical frameworks, this class of systems can still exhibit adapting behavior associated with certain nodes and inputs. We derive a generalized adaptation condition that extends to singular systems and is compatible with both single-input/single-output and multiple-input/multiple-output settings. The approach explored herein, based on the notion of Moore-Penrose pseudoinverse, is tested on several synthetic systems that are shown to exhibit homeostatic behavior but are not covered by existing methods. These results highlight the role of the network structure and modeling assumptions when understanding system response to input and can be helpful in discovering intrinsic relationships between the nodes.

systems biology↗

bioRxiv2020

Development of a novel hybrid alphavirus-SARS-CoV-2 particle for rapid in vitro screening and quantification of neutralization antibodies, antiviral drugs, and viral mutations

Timely development of vaccines and antiviral drugs is critical to control the COVID-19 pandemic 1-6. Current methods for quantifying vaccine-induced neutralizing antibodies involve the use of pseudoviruses, such as the SARS-CoV-2 spike protein (S) pseudotyped lentivirus7-14. However, these pseudoviruses contain structural proteins foreign to SARS-CoV-2, and require days to infect and express reporter genes15. Here we describe the development of a new hybrid alphavirus-SARS-CoV-2 (Ha-CoV-2) particle for rapid and accurate quantification of neutralization antibodies and viral variants. Ha-CoV-2 is a non-replicating SARS-CoV-2 virus-like particle, composed of SARS-CoV-2 structural proteins (S, M, N, and E) and a RNA genome derived from a fast expressing alphavirus vector 16. We demonstrated that Ha-CoV-2 can rapidly and robustly express reporter genes in target cells within 3-6 hours. We further validated Ha-CoV-2 for rapid quantification of neutralization antibodies, viral variants, and antiviral drugs. In addition, as a proof-of-concept, we assembled and compared the relative infectivity of a panel of 10 Ha-CoV-2 variant isolates (D614G, P.1, B.1.1.207, B.1.351, B.1.1.7, B.1.429, B.1.258, B.1.494, B.1.2, B.1.1298), and demonstrated that these variants in general are 2-10 fold more infectious. Furthermore, we quantified the anti-serum from an infected and vaccinated individual; the one dose vaccination with Moderna mRNA-1273 has greatly increased the anti-serum titer for approximately 6 fold. The post-vaccination serum has also demonstrated various neutralizing activities against all 9 variants tested. These results demonstrated that Ha-CoV-2 can be used as a robust platform for rapid quantification of neutralizing antibodies against SARS-CoV-2 and its variants.

microbiology↗