Clonal Selection Theory Research Articles

Theories of immune recognition: Is anybody right?

The clonal selection theory (CST) is the centrepiece of the current paradigm used to explain immune recognition and memory. Throughout the past decades, the original CST had been expanded and modified to explain new experimental evidences since its original publication by Burnet. This gave origin to new paradigms that govern experimental immunology nowadays, such as the associative recognition of antigen model and the stranger/danger signal model. However, these new theories also do not fully explain experimental findings such as natural autoimmune immunoglobulins, idiotypic networks, low and high dose tolerance, and dual-receptor T and B cells. To make sense of these empirical data, some authors have been trying to change the paradigm of immune cognition using a systemic approach, analogies with brain processing and concepts from second-order cybernetics. In the present paper, we review the CST and some of the theories/hypotheses derived from it, focusing on immune recognition. We point out their main weaknesses and highlight arguments made by their opponents and believers. We conclude that, until now, none of the proposed theories can fully explain the totality of immune phenomena and that a theory of everything is needed in immunology.

Genomic alterations in the stepwise progression from normal mucosa to metastasizing oral squamous cell carcinoma.

The aim of this study was to investigate the genomic changes that occur in the development from dysplasia, cancer and to regional metastases in patients with oral cavity squamous cell carcinoma (OSCC). We included OSCC patients with lymph node metastases at diagnosis, treated with primary surgery at Rigshospitalet, University of Copenhagen in the period 2007-2014. The resected tumor specimens were evaluated by a pathologist, who marked areas of morphologically normal tissue and dysplasia surrounding the cancer, two areas from the cancer tissue, and one area within the lymph node metastases. From these areas a punch biopsy was taken, and DNA from each sample was extracted and sequenced using Illumina's TSO500 HT cancer panel. From 51 OSCC patients, 255 samples were included, comprising a wide variety of genomic alterations. Substantial intratumor heterogeneity was found. The most commonly mutated gene was TP53, mutated in 65% of all samples. Only two patients had no TP53 mutation in any samples. We found that morphologically normal appearing mucosa as well as surrounding dysplasia also contained malignant mutations, supporting the theory of field cancerization. There was a significant lower average tumor mutational burden (TMB) in the lymph node metastases compared to the primary tumors, supporting the theory of clonal selection. Substantial inter- and intratumor genomic heterogeneity was found. Mutation of TP53 was the most common and was present in all but two patients. Our data strongly supports the theory of clonal selection and the theory of field cancerization.

A monoclonal Trd chain supports the development of the complete set of functional γδ T cell lineages

The clonal selection theory describes key features of adaptive immune responses of B and Tcells. For αβTcells and B cells, antigen recognition and selection principles are known at a detailed molecular level. The precise role of the antigen receptor in γδ Tcells remains less well understood. To better understand the role of the γδ Tcell receptor (TCR), we generate an orthotopic TCRδ transgenic mouse model. We demonstrate a multi-layered functionality of γδ TCRs and diverse roles of CDR3δ-mediated selection during γδTcell development. Whereas epithelial populations using Vγ5 or Vγ7 chains are almost unaffected in their biology in the presence of the transgenic TCRδ chain, pairing with Vγ1 positively selects γδ Tcell subpopulations with distinct programs in several organs, thereby distorting the repertoire. In conclusion, our data support dictation of developmental tropism together with adaptive-like recognition principles in a single antigen receptor.

Immune systems in computer science

A simplified description of the immune system is as follows: this is an organic system intended for protecting the host organism from the threats posed to it from pathogens and toxic substances. The architecture of the immune system is such that a series of defensive layers protect the host. Once the pathogen makes it inside the host, it must contend with the innate and adaptive immune system. These two immunological sub-systems are comprised of several types of cells and molecules produced by specialized organs and processes to address the self non-self problem. Artificial Immune Systems (AIS) are computational models, based on natural immune systems. They tend to solve specific problems in computer science by resembling the natural mechanisms. These systems are more widely applied within problem domains like clustering, pattern recognition, classification, optimization, and machine learning. Modern AIS are inspired by one of three sub-fields: clonal selection, negative selection, and immune network algorithms. In computer science, the clonal selection pattern can be used for pattern matching and optimization. The negative selection algorithm was designed for change detection, novelty detection, and intrusion detection. Immune network algorithms are inspired by the immune network theory of the acquired immune system and it is an upgrade of the clonal selection theory. The objective of the immune network process is to prepare a repertoire of discrete pattern detectors for a given problem domain, where better performing solutions suppress low affinity solutions within the same network. This is an interactive process of exposing the pattern to external information to which it responds. This article explains the biological background, the mechanisms of AIS, and presents their real-world applications. It presents an overview of those important applications of AIS for solving problems from problem domains like data analysis, anomaly detection, intrusion detection, and others.

The Australasian Cell Death Society (ACDS): celebrating 50 years of Australasian cell death research

The Australasian Cell Death Society (ACDS): celebrating 50 years of Australasian cell death research

Dynamics of B cell repertoires and emergence of cross-reactive responses in patients with different severities of COVID-19.

SummaryIndividuals with the 2019 coronavirus disease (COVID-19) show varying severity of the disease, ranging from asymptomatic to requiring intensive care. Although monoclonal antibodies specific to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been identified, we still lack an understanding of the overall landscape of B cell receptor (BCR) repertoires in individuals with COVID-19. We use high-throughput sequencing of bulk and plasma B cells collected at multiple time points during infection to characterize signatures of the B cell response to SARS-CoV-2 in 19 individuals. Using principled statistical approaches, we associate differential features of BCRs with different disease severity. We identify 38 significantly expanded clonal lineages shared among individuals as candidates for responses specific to SARS-CoV-2. Using single-cell sequencing, we verify the reactivity of BCRs shared among individuals to SARS-CoV-2 epitopes. Moreover, we identify the natural emergence of a BCR with cross-reactivity to SARS-CoV-1 and SARS-CoV-2 in some individuals. Our results provide insights important for development of rational therapies and vaccines against COVID-19.

A novel deep learning-based feature selection model for improving the static analysis of vulnerability detection

The automatic detection of software vulnerabilities is considered a complex and common research problem. It is possible to detect several security vulnerabilities using static analysis (SA) tools, but comparatively high false-positive rates are observed in this case. Existing solutions to this problem depend on human experts to identify functionality, and as a result, several vulnerabilities are often overlooked. This paper introduces a novel approach for effectively and reliably finding vulnerabilities in open-source software programs. In this paper, we are motivated to examine the potential of the clonal selection theory. A novel deep learning-based vulnerability detection model is proposed to define features using the clustering theory of the clonal selection algorithm. To our knowledge, this is the first time we have used deep-learned long-lived team-hacker features to process memories of sequential features and mapping from the entire history of previous inputs to target vectors in theory. With an immune-based feature selection model, the proposed approach aimed to improve static analyses' detection abilities. A real-world SA dataset is used based on three open-source PHP applications. Comparisons are conducted based on using a classification model for all features to measure the proposed feature selection methods' classification improvement. The results demonstrated that the proposed method got significant enhancements, which occurred in the classification accuracy also in the true positive rate.

Hybrid Cuckoo Search with Clonal Selection for Triclustering Gene Expression Data of Breast Cancer

Triclustering techniques are applied to analyze three-dimensional gene expression microarray data to retrieve group of genes under the tested samples over certain time points based on a similarity measure. The real-life three-dimensional dataset chosen is estrogen induced breast cancer dataset. In such datasets, identifying the variations in mining the gene expressions across samples and time points is a difficult task. In this work, the hybrid cuckoo search with clonal selection is applied to extract co-expressed genes over samples and times with triclustering solution. The cuckoo eggs are procreated based on the clonal selection theory. Clonal selection generates the variability required for the solution without any substantial modification. The findings are then used to determine the biological significance of genes in the resulting cluster using gene ontology, functional annotation, and transcription factor binding sites. The proposed work identifies the top genes SRY, SOX5, PAX4, NKX61, SP1, OCT, CDP, POU3F2 and PAX4 present in the tricluster which are associated with the breast cancer. To learn the performance of the proposed work, the experiment results are analyzed with conventional cuckoo search. The hybrid cuckoo search with clonal selection outperforms both the conventional cuckoo search and the other existing triclustering algorithms.

Adaptation of the clonal selection algorithm to the real-time coordinated Volt/VAr control through a software-in-the-loop strategy

With the advent of smart grid technologies, modern Distribution Management Systems (DMSs) enable the real-time operation of advanced automation functions such as Coordinated Volt/VAr Control (CVVC). Typically, control rule-based techniques have been proposed to efficiently carry out this important function in Distribution Networks (DNs). In this paper, a novel adaptation of the clonal selection theory of immune systems (CLONALG - Clonal Selection Algorithm) is proposed for real-time optimal solution of CVVC problem in radial DNs composed of multiple control equipment. To do this, a Software-in-the-Loop (SIL) strategy is developed, through the use of the Real-Time Digital Simulator (RTDS) and Matlab® program. Implementation details of the referred strategy and the corresponding adaptation of the CLONALG-based CVVC are discussed in this paper. Simulations results demonstrated that the proposed adaptation of the CLONALG technique is suitable to solve the CVVC problem in a maximum time of less than 10 min, in accordance with the Brazilian electric utilities’ need. On the other hand, the developed SIL strategy proved to be effective to analyzing the time domain behavior of CVVC algorithms, designed to operate in real time in a modern DMS.

Going rogue: how autoantibodies become pathogenic

In 1972, reflecting on his theory of clonal selection, Sir Macfarlane Burnet noted similarities between the development of the “rogue” autoreactive lymphocyte clones that cause autoimmunity and the outgrowth of mutated cancer cells.1 This link between autoimmunity and cancer is well recognized in the clinic; autoimmunity often increases a patient’s risk of lymphoma, and resultant lymphomas are often of autoreactive antigen receptor specificity.2 The nature of this relationship between autoimmune lymphocyte clones and lymphoma is unclear.

T follicular regulatory cells: Guardians of the germinal centre?

It is a central tenet of the clonal selection theory, that lymphocyte repertoires are tolerized to self‐antigens during their ontogeny. Germinal centres are the sites in secondary lymphoid tissues where B cells undergo affinity maturation and class‐switching to produce high‐affinity antibodies. This process is crucial, both in our ability to mount protective humoral responses to infections and to vaccinations, but it is also involved in untoward reactions to self‐antigens, which underlie autoimmunity. The process of affinity maturation poses a significant challenge to tolerance, as the random nature of somatic hypermutation can introduce novel reactivities. Therefore, it has been a long‐standing idea that mechanisms must exist which limit the emergence of autoreactivity at the germinal centre level. One of these mechanisms is the requirement for linked recognition, which imposes on B cells a dependence on centrally tolerant T follicular helper cells. However, as linked recognition can be bypassed by adduct formation of autoantigenic complexes, it has been an appealing notion that there should be an additional layer of dominant mechanisms regulating emergence of autoreactive specificities. About a decade ago, this notion was addressed by the discovery of a novel subset of T regulatory cells localizing to the germinal centre and regulating germinal centre B‐cell responses. Here, we detail the progress that has been made towards characterizing this T follicular regulatory cell subset and understanding the functions of these ‘guardians of the germinal centre’.

Univariate Financial Time Series Prediction using Clonal Selection Algorithm

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

A Hybrid Artificial Immune System for Mobile Robot Navigation in Unknown Environments

To solve the problem of obstacle avoidance for mobile robot in an unknown environment, an algorithm is proposed in this article which uses both the idiotypic network theory and clonal selection theory of the artificial immune system. While the former is used for navigation in general, the latter comes into effect during local minima situations. Also, a modified version of Farmer’s equation for Jerne’s idiotypic network model has been used to determine the antibody concentrations. The simulation results for navigation using the proposed algorithm are presented. The results proved that our mechanism is capable of successfully avoiding various types of obstacles including the local minima traps. A comparative study between the proposed algorithm and various other algorithms is also presented. Finally, experimental validation of the simulation results for the proposed algorithm is carried out using a physical robot.

Experimenting Dynamic Clonal Selection (DCS) for Parallel Multiple Interest Topics of User Profile Adaptation in Content Based Filtering

User profile for information filtering is an inspiring issue through distinctive profiling features plus profiling desires. The profile of the user should be accomplished of constant learning and be unable to remember past interest. Forgetting on the past interest is necessary for maintaining present demonstration of the user’s profiling. Profile that does not forget the past interest will eventually become drenched with not relevant features of user’s interest. The dynamic nature of the user profiling offers the use of Artificial Immune Systems (AIS). The clonal selection theory in natural immune system has gain the insight to researchers to create an algorithm that evolve the repertoire by means of selection, cloning and mutation. This paper discusses the experimentation of dynamic clonal selection (DCS) algorithm in adapting user profile for content-based filtering. The experiment focused on the scenario of multiple interest of topics in user profiling for content-based filtering. The result shows that the algorithm of DCS is applicable with classification over multiple topics of interest in which changing interest of topics are tracked in data over time with a form of maintaining and enhancing range of the repertoire.

Monthly Rainfall Prediction Model of Peninsular Malaysia Using Clonal Selection Algorithm

Nowadays, various algorithms inspired by natural processes have been extensively applied in solving engineering problems. This study proposed Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, as an algorithm to predict future rainfall. This proposed algorithm is another alternative technique as compared to the commonly used Statistical, Stochastic and Artificial Neural Network techniques traditionally use in Hydrology. Rainfall prediction is pertinent in order to solve many hydrological problems. The proposed Clonal Selection Algorithm (CSA) is one of the main algorithms in AIS, which inspired on Clonal selection theory in the immune system of human body that includes selection, hyper mutation, and receptor editing processes. This study proposed algorithm is utilised to predict future monthly rainfall in Peninsular Malaysia. The collected data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters used in this analysis are humidity, wind speed, temperature and pressure at monthly interval. Four (4) meteorological stations involved are Chuping (north), Subang Jaya(west), Senai (south) and Kota Bharu (west) represented peninsular Malaysia. Based on the results at testing stage, it is found that the trend and peaks of the hydrographs from generated data are approximately similar to the actual historical data. The highest similarity percentage obtained is 91%. The high values of similarity percentage obtained between simulated and actual rainfall data in this study, reinforced the hypothesis that CSA is suitable to be used for prediction of continuous time series data such as monthly rainfall data which highly variable in nature. As a conclusion, the results showed that the proposed Clonal Selection Algorithm is acceptable and stable at all stations.

Complete receptor editing operation based on quantum clonal selection algorithm for optimization problems

Clonal selection mechanism, which is the theoretical foundation of clonal selection algorithm (CSA) and its variants, was proposed for explaining the essential features of adaptive immune responses: adequate diversity, discrimination of self and nonself, and sustaining immunologic memory. On the basis of the clonal selection theory, only the high‐affinity immune cells are chosen to proliferate. Those cells with low affinity must be efficiently eliminated. However, the ability of receptor editing to salvage low‐affinity immune cells from deletion by changing their receptor specificity realized the clonal selection process anew. By combining clonal selection theory and receptor editing, a quantum clonal selection algorithm based on complete receptor editing operation is proposed for the traveling salesman problem (TSP) and the holes‐machining‐path‐planning (HMPP) problem. Two receptor editing operators (inversion and deletion) work together to improve the performance of CSA. Furthermore, in order to overcome the drawback of asexual proliferation during the immune maturation process, a quantum interference crossover based on complete receptor editing operation is used. The effectiveness of the improved algorithm is evaluated on optimization problems including TSP and HMPP problems. The experimental results are also compared with those of other methods based on clonal selection theory. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Modifying clonal selection theory with a probabilistic cell.

SummaryProblem‐solving strategies in immunology currently utilize a series of ad hoc, qualitative variations on a foundation of Burnet's formulation of clonal selection theory. These modifications, including versions of two‐signal theory, describe how signals regulate lymphocytes to make important decisions governing self‐tolerance and changes to their effector and memory states. These theories are useful but are proving inadequate to explain the observable genesis and control of heterogeneity in cell types, the nonlinear passage of cell fate trajectories and how the input from multiple environmental signals can be integrated at different times and strengths. Here, I argue for a paradigm change to place immune theory on a firmer philosophical and quantitative foundation to resolve these difficulties. This change rejects the notion of identical cell subsets and substitutes the concept of a cell as comprised of autonomous functional mechanical components subject to stochastic variations in construction and operation. The theory aims to explain immunity in terms of cell population dynamics, dictated by the operation of cell machinery, such as randomizing elements, division counters, and fate timers. The effect of communicating signals alone and in combination within this system is determined with a cellular calculus. A series of models developed with these principles can resolve logical cell fate and signaling paradoxes and offer a reinterpretation for how self‐non‐self discrimination and immune response class are controlled.

Comparison of Burnet's clonal selection theory with tumor cell-clone development.

Increasing evidence has shown that Darwin's theory of evolution provides vital insights into the emergence and etiology of different types of cancer. On a microscopic scale, cancer stem cells meet the conditions for the Darwinian process of natural selection. In particular, cancer stem cells undergo cell reproduction characterized by the emergence of heritable variability that promotes replication and cell survival.Methods: Evidence from previous studies was gathered to compare Burnet's clonal selection theory with the tumor evolution theory.Results: The findings show that the Darwinian theory offers a general framework for understanding fundamental aspects of cancer. As fundamental theoretical frameworks, Burnet's clonal selection theory and the tumor evolution theory can be used to explain cancer cell evolution and identify the beneficial adaptations that contribute to cell survival in tissue landscapes and tissue ecosystems.Conclusions: In conclusion, this study shows that both Burnet's clonal selection theory and the tumor evolution theory postulate that cancer cells in tissue ecosystems evolve through reiterative processes, such as clonal expansion, clonal selection, and genetic diversification. Therefore, both theories provide insights into the complexities and dynamics of cancer, including its development and progression. Finally, we take into account the occurrence of biologic variation in both tumor cells and lymphocytes. It is important to note that the presence of lymphocyte variations appears to be advantageous in the framework of tumor defense but also dangerous within the framework of autoimmune disease development.

Reverse Transcriptase Mechanism of Somatic Hypermutation: 60 Years of Clonal Selection Theory.

The evidence for the reverse transcriptase mechanism of somatic hypermutation is substantial and multifactorial. In this 60th anniversary year of the publication of Sir MacFarlane Burnet’s Clonal Selection Theory, the evidence is briefly reviewed and updated.

The Evolutionary Origin of the Immune System, Neonatal Tolerance, Mhc Restriction

Immunology has made tremendous progress during more than half century after Burnet's clonal selection theory was published, but there are still more questions than answers. What is the function of the immune system, given that invertebrates have lived without one for millions of years, although they are also susceptible to infections and tumors? On the other hand, the emergence of the immune system in evolution did not deliver higher animals from either infections or cancer. The concept of linked functions is an attempt at answering these and other related questions. The concept assumes that the evolutionary origin of the immune system is related to a primary nonimmune function rather than to self/ nonself recognition. However, the mechanisms used to fulfill this function proved to be a suitable basis for immune recognition, which, according to the concept, occurs at the level of receptor-bearing immune cells rather than receptors themselves. Since cross-reactivity is a common phenomenon, it is assumed that specific combinations of antigenic determinants, rather than determinants per se, serve as recognition criteria, antigen processing and MCH-II restriction being necessary steps of the immune recognition of these combinations. The new views on adaptive immunity suggest new approaches to preventing graft rejection and treating chronic infections and malignant tumors.