A theory for the tissue specificity of BRCA1/2 related and other hereditary cancers

Women who inherit a defective BRCA1 or BRCA2 gene have risks for breast and ovarian cancer that are so high and seem so selective that many mutation carriers choose to have prophylactic surgery. There has been much conjecture to explain such apparently striking tissue specificity. All these suggestions share the assumption that some disabled function of normal tumor suppressor genes leads to a tissue specific cancer response. Here the idea is proposed and tested that major determinants of where BRCA1/2 hereditary cancers occur are related to tissue specificity of the cancer pathogen, the agent that causes chronic inflammation or the carcinogen. The target tissue may have receptors for the pathogen, become selectively exposed to an inflammatory process or to a carcinogen such as during digestion, metabolism or elimination. An innate genomic deficit in a tumor suppressor gene impairs normal responses to these extrinsic challenges and exacerbates the susceptibility to disease in organ targets. This hypothesis also fits data for several tumor suppressors beyond BRCA1/2. A major advantage of this model is that it suggests there may be some options in addition to prophylactic surgery

Time Series Data Mining Algorithms for Identifying Short RNA in Arabidopsis thaliana

The class of molecules called short RNAs (sRNAs) are known to play a key role in gene regulation. Th are typically sequences of nucleotides between 21-25 nucleotides in length. They are known to play a key role in gene regulation. The identification, clustering and classification of sRNA has recently become the focus of much research activity. The basic problem involves detecting regions of interest on the chromosome where the pattern of candidate matches is somehow unusual. Currently, there are no published algorithms for detecting regions of interest, and the unpublished methods that we are aware of involve bespoke rule based systems designed for a specific organism. Work in this very new field has understandably focused on the outcomes rather than the methods used to obtain the results. In this paper we propose two generic approaches that place the specific biological problem in the wider context of time series data mining problems. Both methods are based on treating the occurrences on a chromosome, or “hit count” data, as a time series, then running a sliding window along a chromosome and measuring unusualness. This formulation means we can treat finding unusual areas of candidate RNA activity as a variety of time series anomaly detection problem. The first set of approaches is model based. We specify a null hypothesis distribution for not being a sRNA, then estimate the p-values along the chromosome. The second approach is instance based. We identify some typical shapes from known sRNA, then use dynamic time warping and fourier trans-form based distance to measure how closely the candidate series matches. We demonstrate that these methods can find known sRNA on Arabidopsis thaliana chromosomes and illustrate the benefits of the added information provided by these algorithms

Effects of an Unusual Poison Identify a Lifespan Role for Topoisomerase 2 in Saccharomyces Cerevisiae

A progressive loss of genome maintenance has been implicated as both a cause and consequence of aging. Here we present evidence supporting the hypothesis that an age-associated decay in genome maintenance promotes aging in Saccharomyces cerevisiae (yeast) due to an inability to sense or repair DNA damage by topoisomerase 2 (yTop2). We describe the characterization of LS1, identified in a high throughput screen for small molecules that shorten the replicative lifespan of yeast. LS1 accelerates aging without affecting proliferative growth or viability. Genetic and biochemical criteria reveal LS1 to be a weak Top2 poison. Top2 poisons induce the accumulation of covalent Top2-linked DNA double strand breaks that, if left unrepaired, lead to genome instability and death. LS1 is toxic to cells deficient in homologous recombination, suggesting that the damage it induces is normally mitigated by genome maintenance systems. The essential roles of yTop2 in proliferating cells may come with a fitness trade-off in older cells that are less able to sense or repair yTop2-mediated DNA damage. Consistent with this idea, cells live longer when yTop2 expression levels are reduced. These results identify intrinsic yTop2-mediated DNA damage as a potentially manageable cause of aging

Self-Reference, Biologic and the Structure of Reproduction

This paper concentrates on relationships of formal systems with biology. The paper is based on previous papers by the author. We have freely used texts of those papers where the formulations are of use, and we have extended the concepts and discussions herein considerably beyond the earlier work. We concentrate on formal systems not only for the sake of showing how there is a fundamental mathematical structure to biology, but also to consider and reconsider philosophical and phenomenological points of view in relation to natural science and mathematics. The relationship with phenomenology comes about in the questions that arise about the nature of the observer in relation to the observed that arise in philosophy, but also in science in the very act of determining the context and models upon which it shall be based.We examine the schema behind the reproduction of DNA. The DNA molecule consists of two interwound strands, the Watson Strand (W) and the Crick Strand (C). The two strands are bonded to each other via a backbone of base-pairings and these bonds can be broken by certain enzymes present in the cell. In reproduction of DNA the bonds between the two strands are broken and the two strands then acquire the needed complementary base molecules from the cellular environment to reconstitute each a separate copy of the DNA. At this level the situation can be described by a symbolism like this. DNA = -------> --------> = = DNA DNA. Here E stands for the environment of the cell. The first arrow denotes the separation of the DNA into the two strands. The second arrow denotes the action between the bare strands and the environment that leads to the production of the two DNA molecules. The paper considers and compares many formalisms for self-replication, including aspects of quantum formalism and the Temperley-Lieb algebra.Comment: LaTeX document, 71 pages, 33 figures. arXiv admin note: substantial text overlap with arXiv:quant-ph/020400

Electrical Conductance in Biological Molecules

Nucleic acids and proteins are not only biologically important polymers: They have recently been recognized as novel functional materials surpassing in many aspects the conventional ones. Although Herculean efforts have been undertaken to unravel fine functioning mechanisms of the biopolymers in question, there is still much more to be done. This particular paper presents the topic of biomolecular charge transport, with a particular focus on charge transfer/transport in DNA and protein molecules. Here the experimentally revealed details, as well as the presently available theories, of charge transfer/transport along these biopolymers are critically reviewed and analyzed. A summary of the active research in this field is also given, along with a number of practical recommendations.Comment: v2: This paper has been withdrawn by the authors due to a serious complaints from one author whose work we cite. v3: After clarifying the issue we are herewith republishing our paper

Modeling meiotic recombination hotspots using deep learning

La recombinaison méiotique joue un rôle essentiel dans la ségrégation des chromosomes pendant la méiose et dans la création de nouvelles combinaisons du matériel génétique des espèces. Ses effets cause une déviation du principe de l'assortiment indépendant de Mendel; cependant, les mécanismes moléculaires impliqués restent partiellement incompris jusqu'à aujourd'hui. Il s'agit d'un processus hautement régulé et de nombreuses protéines sont impliquées dans son contrôle, dirigeant la recombinaison méiotique dans des régions génomiques de 1 à 2 kilobases appelées « hotspots ». Au cours des dernières années, l'apprentissage profond a été appliqué avec succès à la classification des séquences génomiques. Dans ce travail, nous appliquons l'apprentissage profond aux séquences d'ADN humain afin de prédire si une région spécifique d'ADN est un hotspot de recombinaison méiotique ou non. Nous avons appliqué des réseaux de neurones convolutifs sur un ensemble de données décrivant les hotspots de quatre individus non-apparentés, atteignant une exactitude de plus de 88 % avec une précision et un rappel supérieur à 90 % pour les meilleurs modèles. Nous explorons l'impact de différentes tailles de séquences d'entrée, les stratégies de séparation des jeux d'entraînement/validation et l’utilité de montrer au modèle les coordonnées génomiques de la séquence d'entrée. Nous avons exploré différentes manières de construire les motifs appris par le réseau et comment ils peuvent être liés aux méthodes classiques de construction de matrices position-poids, et nous avons pu déduire des connaissances biologiques pertinentes découvertes par le réseau. Nous avons également développé un outil pour visualiser les différents modèles afin d'aider à interpréter les différents aspects du modèle. Dans l'ensemble, nos travaux montrent la capacité des méthodes d'apprentissage profond à étudier la recombinaison méiotique à partir de données génomiques.Meiotic recombination plays a critical role in the proper segregation of chromosomes during meiosis and in forming new combinations of genetic material within sexually-reproducing species. For a long time, its side effects were observed as a deviation from the Mendel’s principle of independent assortment; however, its molecular mechanisms remain only partially understood until today. We know that it is a highly regulated process and that many molecules are involved in this tight control, resulting in directing meiotic recombination into 1-2 kilobase genomic pairs regions called hotspots. During the past few years, deep learning was successfully applied to the classification of genomic sequences. In this work, we apply deep learning to DNA sequences in order to predict if a specific stretch of DNA is a meiotic recombination hotspot or not. We applied convolution neural networks on a dataset describing the hotspots of four unrelated male individuals, achieving an accuracy of over 88% with precision and recall above 90% for the best models. We explored the impact of different input sequence lengths, train/validation split strategies and showing the model the genomic coordinates of the input sequence. We explored different ways to construct the learnt motifs by the network and how they can relate to the classical methods of constructing position-weight-matrices, and we were able to infer relevant biological knowledge uncovered by the network. We also developed a tool for visualizing the different models output in order to help digest the different aspects of the model. Overall, our work shows the ability for deep learning methods to study meiotic recombination from genomic data

Structural, mechanical and thermodynamic properties of a coarse-grained DNA model

We explore in detail the structural, mechanical and thermodynamic properties of a coarse-grained model of DNA similar to that introduced in Thomas E. Ouldridge, Ard A. Louis, Jonathan P.K. Doye, Phys. Rev. Lett. 104 178101 (2010). Effective interactions are used to represent chain connectivity, excluded volume, base stacking and hydrogen bonding, naturally reproducing a range of DNA behaviour. We quantify the relation to experiment of the thermodynamics of single-stranded stacking, duplex hybridization and hairpin formation, as well as structural properties such as the persistence length of single strands and duplexes, and the torsional and stretching stiffness of double helices. We also explore the model's representation of more complex motifs involving dangling ends, bulged bases and internal loops, and the effect of stacking and fraying on the thermodynamics of the duplex formation transition.Comment: 25 pages, 16 figure

A Computational Approach to Estimating Nondisjunction Frequency in Saccharomyces cerevisiae.

Errors segregating homologous chromosomes during meiosis result in aneuploid gametes and are the largest contributing factor to birth defects and spontaneous abortions in humans. Saccharomyces cerevisiae has long served as a model organism for studying the gene network supporting normal chromosome segregation. Measuring homolog nondisjunction frequencies is laborious, and involves dissecting thousands of tetrads to detect missegregation of individually marked chromosomes. Here we describe a computational method (TetFit) to estimate the relative contributions of meiosis I nondisjunction and random-spore death to spore inviability in wild type and mutant strains. These values are based on finding the best-fit distribution of 4, 3, 2, 1, and 0 viable-spore tetrads to an observed distribution. Using TetFit, we found that meiosis I nondisjunction is an intrinsic component of spore inviability in wild-type strains. We show proof-of-principle that the calculated average meiosis I nondisjunction frequency determined by TetFit closely matches empirically determined values in mutant strains. Using these published data sets, TetFit uncovered two classes of mutants: Class A mutants skew toward increased nondisjunction death, and include those with known defects in establishing pairing, recombination, and/or synapsis of homologous chromosomes. Class B mutants skew toward random spore death, and include those with defects in sister-chromatid cohesion and centromere function. Epistasis analysis using TetFit is facilitated by the low numbers of tetrads (as few as 200) required to compare the contributions to spore death in different mutant backgrounds. TetFit analysis does not require any special strain construction, and can be applied to previously observed tetrad distributions