Biology subjects

Innan, H.

Publications and source records attributed to Innan, H..

4 recordsLinked to original sources

bioRxiv2018

Muller’s ratchet as a mechanism of frailty and multimorbidity

Mutation accumulation has been proposed as a cause of senescence. In this process, both constitutional and recurrent mutations accumulate gradually and differentially among differentiating cells, tissues and organs, in relation to stage and age, analogous to Mullers ratchet in asexually reproducing organisms. Latent and cascading deleterious effects of mutations might initiate steady \"accumulation of deficits\" in cells, leading to cellular senescence, and functional decline of tissues and organs, and ultimately manifest as frailties and disease. We investigated a few of these aspects in cell populations through modeling and simulation using the Moran birth-death process, under varied scenarios of mutation accumulation. Our results agree with the principle of Mullers ratchet. The ratchet speed in a given tissue depends on the population size of cells, mutation rate, and selection coefficient. Additionally, deleterious mutations seem to rapidly accumulate particularly early in the life-course, during which the rate of cell division is high, thereby exerting a greater effect on cellular senescence. The speed of the ratchet afterward varies greatly between cells nested in tissues and tissues within organs due to heterogeneity in the life span and turnover rate of specific cell types. Importantly, the ratchet accelerates with age, resulting in a synergistic fitness decay in cell populations. We extend Fishers average excess concept and rank order scale to interpret differential phenotypic effects of mutation load in a given tissue. We conclude that classical evolutionary genetic models could explain partially, the origins of frailty, subclinical conditions, morbidity and health consequences of senescence.\n\nSignificanceFrailty is defined as physiological and functional decline of organs and organ systems, due to deficit accumulation from stochastic damages within the organism with advanced age. Equivalently, with age, both constitutional and somatic mutations accumulate gradually and differentially among cells, cell lineages, tissues, and organs. Since most mutations are deleterious, accumulation of random and recurrent mutations could create a \"load,\" on the genome and contextually express in the epigenome and phenotype spaces. Here we extend Mullers ratchet principle to explain frailty and multi-morbidity using the Moran model and simulations. Our results agree with the Mullers ratchet principle. We emphasize the need for considering cumulative effects of the entire spectrum of mutations for explaining the origin of frailty, sub-clinical conditions, and morbidity.

genetics↗

bioRxiv2017

The coalescent for prokaryotes with homologous recombination from external source

The coalescent process for prokaryote species is theoretically considered. Prokaryotes undergo homologous recombination not only with other individuals within the same species (intra-specific recombination) but also with other species (inter-specific recombination). This work particularly focuses the latter because the former has been well incorporated in the framework of the coalescent. We here developed a simulation framework for generating patterns of SNPs (single nucleotide polymorphisms) allowing integration of external DNA out of the focal species, and a simulator named msPro was developed. We found that the joint work of intra- and inter-specific recombination creates a complex pattern of SNPs. The direct effect of inter-specific recombination is to increase the amount of polymorphism. Because inter-specific recombination is very rare in general, it creates a regions with an exceptionally high level of polymorphisms. Following an inter-specific recombination event, intra-specific recombination chop the integrated foreign DNA into small pieces, making a complicated pattern of SNPs that looks as if foreign DNAs were integrated multiple times. This work with the msPro simulator would be useful to understand and evaluate the relative contribution of intra- and inter specific recombination to creating complicated patterns of SNPs in prokaryotes.

evolutionary biology↗

bioRxiv2017

Simulation Framework for Generating Intratumor Heterogeneity Patterns in a Cancer Cell Population

As cancer cell populations evolve, they accumulate a number of somatic mutations, resulting in heterogeneous subclones in the final tumor. Understanding the mechanisms that produce intratumor heterogeneity (ITH) is important for selecting the best treatment. Although some studies have involved ITH simulations, their model settings differed substantially. Thus, only limited conditions were explored in each. Herein, we developed a general framework for simulating ITH patterns and a simulator (tumopp). Tumopp offers many setting options so that simulations can be carried out under various settings. Setting options include how the cell division rate is determined, how daughter cells are placed, and how driver mutations are treated. Furthermore, to account for the cell cycle, we introduced a gamma function for the waiting time involved in cell division. Tumopp also allows simulations in a hexagonal lattice, in addition to a regular lattice that has been used in previous simulation studies. A hexagonal lattice produces a more biologically reasonable space than a regular lattice. Using tumopp, we investigated how model settings affect the growth curve and ITH pattern. It was found that, even under neutrality (with no driver mutations), tumopp produced dramatically variable patterns of ITH and tumor morphology, from tumors in which cells with different genetic background are well intermixed to irregular shapes of tumors with a cluster of closely related cells. This result suggests a caveat in analyzing ITH data with simulations with limited settings, and tumopp will be useful to explore ITH patterns in various conditions.\n\nAuthor SummaryUnderstanding the mechanisms that produce intratumor heterogeneity (ITH) is important for selecting the best treatment. Despite a growing body of data and tools for analyzing ITH, the spatial structure and its evolution are poorly understood because of the lack of well established theoretical framework. Herein, we provide a general framework for simulating ITH patterns, under which a simulator (tumopp) is developed. Tumopp offers many setting options so that simulations can be carried out under various settings. Simulations using tumopp demonstrate that dramatically variable patterns of ITH and tumor morphology can be produced depending on the model setting. The present work provides a guideline for future simulation studies of cancer cell populations.

cancer biology↗

bioRxiv2017

Allele Frequency Spectrum in a Cancer Cell Population

A cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provided a theoretical framework of the stochastic process in a cancer cell population and obtained near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell division and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic are very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.

cancer biology↗