Defective Histone Transition during Spermiogenesis in Heterozygous SEGREGATION DISTORTER Males of DROSOPHILA MELANOGASTER

Males of Drosophila melanogaster that are heterozygous for the segregation distorter (SD) chromosome produce a gross excess of SD-bearing offspring because most of the non-SD-bearing sperm are dysfunctional. These dysfunctional sperm exhibit abnormalities in chromatin condensation and compaction during spermiogenesis. Use of the fluorescent dye sulfoflavine, which is specific for basic proteins, has now revealed that the dysfunctional sperm are also defective in the normal transition from somatic to spermatid-specific histones.     

Genetic Dissection of Segregation Distortion II. Mechanism of Suppression of Distortion by Certain Inversions

In(2L+2R)Cy and In(2LR)Pm² are inversion-bearing chromosomes, the former carrying a paracentric inversion in each arm and the latter carrying a long pericentric. Both chromosomes produce normal segregation ratios when present in heterozygous males with certain segregation distorter chromosomes. The apparent suppression of distortion by these chromosomes was long attributed to a failure of synapsis, but this hypothesis has fallen out of favor recently because a large number of chromosome aberrations, particularly translocations and inversions, suppress distortion even though their breakpoints fall into no recognizable pattern. Although failure of synapsis does not appear to be the mechanism of suppression of distortion, what is responsible for the suppression remains unknown. In this paper it is shown that In(2L+2R)Cy and In(2LR)Pm² suppress segregation distortion because they carry Rsp, a component of the segregation distorter system that renders a chromosome insensitive to distortion. Both chromosomes induce "suicide" of chromosomes carrying Sd Rsp⁺.     

Distinguishing excess mutations and increased cell death based on variant allele frequencies

Tumors often harbor orders of magnitude more mutations than healthy tissues. The increased number of mutations may be due to an elevated mutation rate or frequent cell death and correspondingly rapid cell turnover, or a combination of the two. It is difficult to disentangle these two mechanisms based on widely available bulk sequencing data, where sequences from individual cells are intermixed and, thus, the cell lineage tree of the tumor cannot be resolved. Here we present a method that can simultaneously estimate the cell turnover rate and the rate of mutations from bulk sequencing data. Our method works by simulating tumor growth and finding the parameters with which the observed data can be reproduced with maximum likelihood. Applying this method to a real tumor sample, we find that both the mutation rate and the frequency of death may be high.     

A method for the inference of cytokine interaction networks

Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.     

Distribution of transposable elements in prokaryotes

We consider models for the distribution of the number of elements per host genome for families of transposable elements (TEs). The hosts are assumed to be prokaryotes. These models assume a constant rate of infection of uninfected hosts by TEs, replicative transposition within each host, and a reduction of the fitness of a host dependent on the number of TEs it contains. No provision was made for the deletion of individual TEs within a host or for recombination, since both are relatively rare events in prokaryotes. These models mostly assume that the TE performs no function for the host, and that the reduction in fitness with increased copy number is due to effects such as the impairment of beneficial genes by transposition or homologous recombination. We also consider a model in which the TEs can convey a selective advantage to the host. The equilibrium distributions of copy number are determined for these models, and are of a variety of classical types. Relevant parameters of the models are estimated using data on the distribution of insertion sequences in natural isolates of Escherichia coli.     

Bladder tissue characterization using probe‐based Raman spectroscopy: Evaluation of tissue heterogeneity and influence on the model prediction

Existing approaches for early‐stage bladder tumor diagnosis largely depend on invasive and time‐consuming procedures, resulting in hospitalization, bleeding, bladder perforation, infection and other health risks for the patient. The reduction of current risk factors, while maintaining or even improving the diagnostic precision, is an underlying factor in clinical instrumentation research. For example, for clinic surveillance of patients with a history of noninvasive bladder tumors real‐time tumor diagnosis can enable immediate laser‐based removal of tumors using flexible cystoscopes in the outpatient clinic. Therefore, novel diagnostic modalities are required that can provide real‐time in vivo tumor diagnosis. Raman spectroscopy provides biochemical information of tissue samples ex vivo and in vivo and without the need for complicated sample preparation and staining procedures. For the past decade there has been a rise in applications to diagnose and characterize early cancer in different organs, such as in head and neck, colon and stomach, but also different pathologies, for example, inflammation and atherosclerotic plaques. Bladder pathology has also been studied but only with little attention to aspects that can influence the diagnosis, such as tissue heterogeneity, data preprocessing and model development. The present study presents a clinical investigative study on bladder biopsies to characterize the tumor grading ex vivo, using a compact fiber probe‐based imaging Raman system, as a crucial step towards in vivo Raman endoscopy. Furthermore, this study presents an evaluation of the tissue heterogeneity of highly fluorescent bladder tissues, and the multivariate statistical analysis for discrimination between nontumor tissue, and low‐ and high‐grade tumor.