scholarly journals Topology identifies emerging adaptive mutations in SARS-CoV-2

2021 ◽  
Author(s):  
Michael Bleher ◽  
Lukas Hahn ◽  
Juan Ángel Patiño-Galindo ◽  
Mathieu Carrière ◽  
Ulrich Bauer ◽  
...  
Keyword(s):  
Immune Escape ◽  
Early Stage ◽  
Persistent Homology ◽  
Viral Evolution ◽  
Topological Data Analysis ◽  
Adaptive Mutations ◽  
Topological Measure ◽  
Inference Techniques ◽  
Early Occurrence ◽  
New Mutations

The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. As the virus spreads and evolves it acquires new mutations that could have important public health consequences, including higher transmissibility, morbidity, mortality, and immune evasion, among others. Ideally, we would like to quickly identify new mutations that could confer adaptive advantages to the evolving virus by leveraging the large number of SARS-CoV-2 genomes. One way of identifying adaptive mutations is by looking at convergent mutations, mutations in the same genomic position that occur independently. The large number of currently available genomes, more than a million at this moment, however precludes the efficient use of phylogeny-based techniques. Here, we establish a fast and scalable Topological Data Analysis approach for the early warning and surveillance of emerging adaptive mutations of the coronavirus SARS-CoV-2 in the ongoing COVID-19 pandemic. Our method relies on a novel topological tool for the analysis of viral genome datasets based on persistent homology. It systematically identifies convergent events in viral evolution merely by their topological footprint and thus overcomes limitations of current phylogenetic inference techniques. This allows for an unbiased and rapid analysis of large viral datasets. We introduce a new topological measure for convergent evolution and apply it to the complete GISAID dataset as of February 2021, comprising 303,651 high-quality SARS-CoV-2 isolates taken from patients all over the world since the beginning of the pandemic. A complete list of mutations showing topological signals of convergence is compiled. We find that topologically salient mutations on the receptor-binding domain appear in several variants of concern and are linked with an increase in infectivity and immune escape. Moreover, for many adaptive mutations the topological signal precedes an increase in prevalence. We demonstrate the capability of our method to effectively identify emerging adaptive mutations at an early stage. By localizing topological signals in the dataset, we are able to extract geo-temporal information about the early occurrence of emerging adaptive mutations. The identification of these mutations can help to develop an alert system to monitor mutations of concern and guide experimentalists to focus the study of specific circulating variants.

scholarly journals Classification of apatite structures via topological data analysis: a framework for a ‘Materials Barcode’ representation of structure maps

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Scott Broderick ◽  
Ruhil Dongol ◽  
Tianmu Zhang ◽  
Krishna Rajan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Crystal Chemistry ◽  
Persistent Homology ◽  
Hierarchical Classification ◽  
Topological Data Analysis ◽  
Learning Tool ◽  
Coordination Polyhedra ◽  
Machine Learning Tool ◽  
Topological Data

AbstractThis paper introduces the use of topological data analysis (TDA) as an unsupervised machine learning tool to uncover classification criteria in complex inorganic crystal chemistries. Using the apatite chemistry as a template, we track through the use of persistent homology the topological connectivity of input crystal chemistry descriptors on defining similarity between different stoichiometries of apatites. It is shown that TDA automatically identifies a hierarchical classification scheme within apatites based on the commonality of the number of discrete coordination polyhedra that constitute the structural building units common among the compounds. This information is presented in the form of a visualization scheme of a barcode of homology classifications, where the persistence of similarity between compounds is tracked. Unlike traditional perspectives of structure maps, this new “Materials Barcode” schema serves as an automated exploratory machine learning tool that can uncover structural associations from crystal chemistry databases, as well as to achieve a more nuanced insight into what defines similarity among homologous compounds.

New mutations raise specter of ‘immune escape’

Science ◽  
2021 ◽  
Vol 371 (6527) ◽  
pp. 329-330 ◽  
Author(s):  
Kai Kupferschmidt
Keyword(s):  
Immune Escape ◽  
New Mutations

scholarly journals Targeting Acute Myeloid Leukemia Using the RevCAR Platform: A Programmable, Switchable and Combinatorial Strategy

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4785
Author(s):  
Enrico Kittel-Boselli ◽  
Karla Elizabeth González Soto ◽  
Liliana Rodrigues Loureiro ◽  
Anja Hoffmann ◽  
Ralf Bergmann ◽  
...  
Keyword(s):  
T Cells ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Immune Escape ◽  
Early Stage ◽  
Logic Gates ◽  
Boolean Logic ◽  
In Vivo Models ◽  
Peptide Epitope ◽  
Acute Myeloid

Clinical translation of novel immunotherapeutic strategies such as chimeric antigen receptor (CAR) T-cells in acute myeloid leukemia (AML) is still at an early stage. Major challenges include immune escape and disease relapse demanding for further improvements in CAR design. To overcome such hurdles, we have invented the switchable, flexible and programmable adaptor Reverse (Rev) CAR platform. This consists of T-cells engineered with RevCARs that are primarily inactive as they express an extracellular short peptide epitope incapable of recognizing surface antigens. RevCAR T-cells can be redirected to tumor antigens and controlled by bispecific antibodies cross-linking RevCAR T- and tumor cells resulting in tumor lysis. Remarkably, the RevCAR platform enables combinatorial tumor targeting following Boolean logic gates. We herein show for the first time the applicability of the RevCAR platform to target myeloid malignancies like AML. Applying in vitro and in vivo models, we have proven that AML cell lines as well as patient-derived AML blasts were efficiently killed by redirected RevCAR T-cells targeting CD33 and CD123 in a flexible manner. Furthermore, by targeting both antigens, a Boolean AND gate logic targeting could be achieved using the RevCAR platform. These accomplishments pave the way towards an improved and personalized immunotherapy for AML patients.

scholarly journals Epigenetic Regulation of Immunotherapy Response in Triple-Negative Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 4139
Author(s):  
Pere Llinàs-Arias ◽  
Sandra Íñiguez-Muñoz ◽  
Kelly McCann ◽  
Leonie Voorwerk ◽  
Javier I. J. Orozco ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Immune Escape ◽  
Checkpoint Inhibitors ◽  
Early Stage ◽  
Regulatory Elements ◽  
Her2 Overexpression ◽  
Breast Cancers ◽  
Cancer Subtypes

Triple-negative breast cancer (TNBC) is defined by the absence of estrogen receptor and progesterone receptor and human epidermal growth factor receptor 2 (HER2) overexpression. This malignancy, representing 15–20% of breast cancers, is a clinical challenge due to the lack of targeted treatments, higher intrinsic aggressiveness, and worse outcomes than other breast cancer subtypes. Immune checkpoint inhibitors have shown promising efficacy for early-stage and advanced TNBC, but this seems limited to a subgroup of patients. Understanding the underlying mechanisms that determine immunotherapy efficiency is essential to identifying which TNBC patients will respond to immunotherapy-based treatments and help to develop new therapeutic strategies. Emerging evidence supports that epigenetic alterations, including aberrant chromatin architecture conformation and the modulation of gene regulatory elements, are critical mechanisms for immune escape. These alterations are particularly interesting since they can be reverted through the inhibition of epigenetic regulators. For that reason, several recent studies suggest that the combination of epigenetic drugs and immunotherapeutic agents can boost anticancer immune responses. In this review, we focused on the contribution of epigenetics to the crosstalk between immune and cancer cells, its relevance on immunotherapy response in TNBC, and the potential benefits of combined treatments.

scholarly journals RR:C19 Editorial: Immune Escape and Viral Evolution

2021 ◽  
Author(s):  
Gina Borgo ◽  
Yash S. Huilgol ◽  
Michael Cronce ◽  
Stefano M. Bertozzi
Keyword(s):  
Immune Escape ◽  
Viral Evolution

scholarly journals Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

2020 ◽  
Author(s):  
Jacob Billings ◽  
Manish Saggar ◽  
Shella Keilholz ◽  
Giovanni Petri
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Persistent Homology ◽  
Brain Regions ◽  
Topological Data Analysis ◽  
Time Varying ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Common Time ◽  
Brain Imaging Data

Functional connectivity (FC) and its time-varying analogue (TVFC) leverage brain imaging data to interpret brain function as patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brain’s spontaneous self-organization. One tool from TDA, “persistent homology”, observes the occurrence and the persistence of n-dimensional holes presented in the metric space over a dataset. The occurrence of n-dimensional holes within the TVFC point cloud may denote conserved and preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional TVFC metrics at the task of segmenting brain states that differ across a common time-series of experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment volunteer performance during experimentally defined tasks, and generalize better across volunteers. Finally, we present empirical and theoretical observations that interpret brain function as a topological space defined by cyclic and interlinked motifs among distributed brain regions, especially, the attention networks.

scholarly journals Topological data analysis for true step detection in periodic piecewise constant signals

2018 ◽  
Vol 474 (2218) ◽  
pp. 20180027 ◽  
Author(s):  
Firas A. Khasawneh ◽  
Elizabeth Munch
Keyword(s):  
Data Analysis ◽  
Persistent Homology ◽  
Topological Data Analysis ◽  
Future Research ◽  
Accurate Identification ◽  
Piecewise Constant ◽  
Higher Dimensional ◽  
Powerful Approach ◽  
Hadamard Transforms ◽  
Topological Data

This paper introduces a simple yet powerful approach based on topological data analysis for detecting true steps in a periodic, piecewise constant (PWC) signal. The signal is a two-state square wave with randomly varying in-between-pulse spacing, subject to spurious steps at the rising or falling edges which we call digital ringing. We use persistent homology to derive mathematical guarantees for the resulting change detection which enables accurate identification and counting of the true pulses. The approach is tested using both synthetic and experimental data obtained using an engine lathe instrumented with a laser tachometer. The described algorithm enables accurate and automatic calculations of the spindle speed without any choice of parameters. The results are compared with the frequency and sequency methods of the Fourier and Walsh–Hadamard transforms, respectively. Both our approach and the Fourier analysis yield comparable results for pulses with regular spacing and digital ringing while the latter causes large errors using the Walsh–Hadamard method. Further, the described approach significantly outperforms the frequency/sequency analyses when the spacing between the peaks is varied. We discuss generalizing the approach to higher dimensional PWC signals, although using this extension remains an interesting question for future research.

scholarly journals Topological Data Analysis as a Morphometric Method: Using Persistent Homology to Demarcate a Leaf Morphospace

2018 ◽  
Vol 9 ◽  
Author(s):  
Mao Li ◽  
Hong An ◽  
Ruthie Angelovici ◽  
Clement Bagaza ◽  
Albert Batushansky ◽  
...  
Keyword(s):  
Data Analysis ◽  
Persistent Homology ◽  
Topological Data Analysis ◽  
Morphometric Method ◽  
Topological Data

scholarly journals Antigenic cooperation among intrahost HCV variants organized into a complex network of cross-immunoreactivity

2015 ◽  
Vol 112 (21) ◽  
pp. 6653-6658 ◽  
Author(s):  
Pavel Skums ◽  
Leonid Bunimovich ◽  
Yury Khudyakov
Keyword(s):  
Immune Escape ◽  
Complex Dynamics ◽  
Viral Evolution ◽  
Hcv Infection ◽  
Viral Population ◽  
Viral Adaptation ◽  
Chronic Hcv Infection ◽  
Potential Strategy ◽  
Chronic Hcv

Hepatitis C virus (HCV) has the propensity to cause chronic infection. Continuous immune escape has been proposed as a mechanism of intrahost viral evolution contributing to HCV persistence. Although the pronounced genetic diversity of intrahost HCV populations supports this hypothesis, recent observations of long-term persistence of individual HCV variants, negative selection increase, and complex dynamics of viral subpopulations during infection as well as broad cross-immunoreactivity (CR) among variants are inconsistent with the immune-escape hypothesis. Here, we present a mathematical model of intrahost viral population dynamics under the condition of a complex CR network (CRN) of viral variants and examine the contribution of CR to establishing persistent HCV infection. The model suggests a mechanism of viral adaptation by antigenic cooperation (AC), with immune responses against one variant protecting other variants. AC reduces the capacity of the host’s immune system to neutralize certain viral variants. CRN structure determines specific roles for each viral variant in host adaptation, with variants eliciting broad-CR antibodies facilitating persistence of other variants immunoreacting with these antibodies. The proposed mechanism is supported by empirical observations of intrahost HCV evolution. Interference with AC is a potential strategy for interruption and prevention of chronic HCV infection.

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