Biology subjects

Foroughmand-Araabi, M.-H.

Publications and source records attributed to Foroughmand-Araabi, M.-H..

4 recordsLinked to original sources

bioRxiv2024

In silico genomic surveillance by CoVerage predicts and characterizes SARS-CoV-2 Variants of Interest

Rapidly evolving viral pathogens such as SARS-CoV-2 continuously accumulate amino acid changes, some of which affect transmissibility, virulence or improve the virus ability to escape host immunity. Since the beginning of the pandemic and establishment of SARS-CoV-2 as a human pathogen, multiple lineages with concerning phenotypic alterations, so called Variants of Concern (VOCs), have emerged and risen to predominance. To optimize public health management and to ensure the continued efficacy of vaccines, the early detection of such variants of interest is essential. Therefore, large-scale viral genomic surveillance programs have been initiated worldwide, with data being deposited in public repositories in a timely manner. However, technologies for their continuous interpretation are currently lacking. Here, we describe the CoVerage system (www.sarscoverage.org) for viral genomic surveillance, which continuously predicts and characterizes novel and emerging potential Variants of Interest (pVOIs) from country-wise lineage frequency dynamics together with their antigenic and evolutionary alterations utilizing the GISAID viral genome resource. In a comprehensive assessment of VOIs, VOCs and VUMs identified, we demonstrate how CoVerage can be used to swiftly identify and characterize such variants, with a lead time of almost three months relative to them reaching their maximal abundances. CoVerage can facilitate the timely identification and assessment of future SARS-CoV-2 variants relevant for public health.

bioinformatics↗

bioRxiv2023

Weighted Centroid Trees: A general approach for summarizing phylogenies in tumor mutation tree inference

Tumor mutation trees are the primary tools to model the evolution of cancer. Not only some tumor phylogeny inference methods may produce a set of trees having potential and parallel evolutionary histories, but also mutation trees from different patients may also exhibit similar evolutionary processes. When a set of correlated mutation trees is available, compressing the data into a single best-fit tree, exhibiting the shared evolutionary processes, is definitely of great importance and can be beneficial in many applications. In this study, we present a general setup to study and analyse the problem of finding a best-fit (centroid) tree to a given set of trees and we use our general setup to analyse mutation trees as our main motivation. For this let{varepsilon} :[T] n [->] [R]nxn be an embedding of labeled rooted trees into the space of real square matrices and also let L be a norm on this space. We introduce the nearest mapped tree problem as the problem of finding a closest tree to a given matrix A with respect to{varepsilon} and L, i.e., a tree T *(A) for which L({varepsilon}(T *(A)) - A) is minimized. Within this setup, our potential candidates for the embedding are adjacency, ancestry, and distance matrices of trees, where we consider the cases of L1 and L2 norms in our analysis. We show that the function d(T1, T2) = L({varepsilon}(T1) -{varepsilon} (T2)) defines a family of dissimilarity measures, covering previously studied parent-child and ancestor-descendent metrics. Also, we show that the nearest mapped tree problem is polynomial-time solvable for the adjacency matrix embedding and is[N][P] -hard for the ancestry and the distance embeddings. The weighted centroid tree problem for a given set of trees of size k is naturally defined as a nearest mapped tree solution to a weighted sum of the corresponding matrix set. In this article we consider uniform weighted-sums for which all weights are equal, where in particular, the (classical) centroid tree is defined to be a solution when all weights are chosen to be equal to 1/k (i.e., the mean case). Similarly, the{omega} -weighted centroid tree is a solution when all weights are equal to{omega} /k. To show the generality of our setup, we prove that the solution-set of the centroid tree problem for the adjacency and the ancestry matrices are identical to the solution-set of the consensus tree problem for parent-child and ancestor-descendent distances already handled by the algorithms GraPhyC(2018) and TuELiP(2023), respectively. Next, to tackle this problem for some new cases, we provide integer linear programs to handle the nearest mapped tree problem for the ancestry and the distance embeddings, giving rise to solutions of the weighted centroid tree problem in these cases. To show the effectiveness of this approach, we provide an algorithm, WAncILP2, to solvethe 2-weighted centroid tree problem for the case of the ancestry matrix and we justify the importance of the weighted setup by showing the pioneering performance of WAncILP2 both in a comprehensive simulation analysis as well as on a real breast cancer dataset, in which, by finding the centroids as representatives of data clusters, we provide supporting evidence for the fact that some common aspects of these centroids can be considered as suitable candidates for reliable evolutionary information in relation to the original data. metrics.

bioinformatics↗

bioRxiv2022

Delayed dynamics of migratory response to CTLA-4 blockade reveals a mechanistic view on potential T cells reinvigoration following immune checkpoint blockade

Cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death protein 1 (PD-1) receptors, two clinically relevant targets for immunotherapy of cancer, are negative regulators of in immune cell activation and migration. However, optimizing therapeutic outcomes still requires fundamental research to reach a comprehensive insight into the coherent function of immune regulators. Here, we investigated the statistical dynamics of T cells migration as a measure of the functional response to these pathways in an experimental setup of immune checkpoint blockade. For this purpose, we used a previously developed 3-dimensional organotypic culture of patient-derived tumor spheroids. Experiment-based dynamical modeling remarked distinct characteristics of the receptors regulation followed through with the modification of their proportions in the immune modulation. We demonstrated that time-delayed kinetics of PD-1 activation just overrides its relatively more efficient cell-level function which potentially makes an operative contribution to the functional dominance of CTLA-4 in the tumor microenvironment. Simulation results showed good agreement with data for tumor cells reduction and active immune cells count observed in each experiment. These analyses propose a new mechanistic view on relative immunogenicity of PD-1 and CTLA-4 inhibitors manifested in literature and point the possible inherent obstacles in checkpoint inhibition-based immunotherapy of cancer to address in the future. SignificanceEx vivo monitoring of temporal response to PD-1 and CTLA-4 in the closure of T cell movement dynamics and elucidating their feasible commitment to the kinetic constraints at cell-level resolution. Delayed dynamics of migratory response to CTLA-4 inhibition revealed a mechanistic view on potential T cell reinvigoration following immune checkpoint blockade.

cancer biology↗

bioRxiv2021

Scelestial: fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm

Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms - BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit - on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial.

bioinformatics↗