Identification of Constrained Cancer Driver Genes Based on Mutation Timing

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.     

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.     

A previously unobserved conformation for the human Pex5p receptor suggests roles for intrinsic flexibility and rigid domain motions in ligand binding

Background  

The C-terminal tetratricopeptide (TPR) repeat domain of Pex5p recognises proteins carrying a peroxisomal targeting signal type 1 (PTS1) tripeptide in their C-terminus. Previously, structural data have been obtained from the TPR domain of Pex5p in both the liganded and unliganded states, indicating a conformational change taking place upon cargo protein binding. Such a conformational change would be expected to play a major role both during PTS1 protein recognition as well as in cargo release into the peroxisomal lumen. However, little information is available on the factors that may regulate such structural changes.     

NGC: lossless and lossy compression of aligned high-throughput sequencing data

A major challenge of current high-throughput sequencing experiments is not only the generation of the sequencing data itself but also their processing, storage and transmission. The enormous size of these data motivates the development of data compression algorithms usable for the implementation of the various storage policies that are applied to the produced intermediate and final result files. In this article, we present NGC, a tool for the compression of mapped short read data stored in the wide-spread SAM format. NGC enables lossless and lossy compression and introduces the following two novel ideas: first, we present a way to reduce the number of required code words by exploiting common features of reads mapped to the same genomic positions; second, we present a highly configurable way for the quantization of per-base quality values, which takes their influence on downstream analyses into account. NGC, evaluated with several real-world data sets, saves 33–66% of disc space using lossless and up to 98% disc space using lossy compression. By applying two popular variant and genotype prediction tools to the decompressed data, we could show that the lossy compression modes preserve >99% of all called variants while outperforming comparable methods in some configurations.     

Taenia solium Cysticercosis in the Democratic Republic of Congo: How Does Pork Trade Affect the Transmission of the Parasite?

Background

Taenia solium, a zoonotic parasite that is endemic in most developing countries where pork is consumed, is recognised as the main cause of acquired epilepsy in these regions. T. solium has been reported in almost all of the neighboring countries of Democratic Republic of Congo (DRC) but data on the current prevalence of the disease in the country itself are lacking. This study, focusing on porcine cysticercosis (CC), makes part of a first initiative to assess whether cysticercosis is indeed actually present in DRC.

Methods

An epidemiological study on porcine CC was conducted (1) on urban markets of Kinshasa where pork is sold and (2) in villages in Bas-Congo province where pigs are traditionally reared. Tongue inspection and ELISA for the detection of circulating antigen of the larval stage of T. solium were used to assess the prevalence of active CC in both study sites.

Findings

The overall prevalence of pigs with active cysticercosis did not significantly differ between the market and the village study sites (38.8 [CI95%: 34–43] versus 41.2% [CI95%: 33–49], respectively). However, tongue cysticercosis was only found in the village study site together with a significantly higher intensity of infection (detected by ELISA).

Interpretation

Pigs reared at village level are sold for consumption on Kinshasa markets, but it seems that highly infected animals are excluded at a certain level in the pig trade chain. Indeed, preliminary informal surveys on common practices conducted in parallel revealed that pig farmers and/or buyers select the low infected animals and exclude those who are positive by tongue inspection at village level. This study provides the only recent evidence of CC presence in DRC and gives the first estimates to fill an important gap on the African taeniasis/cysticercosis distribution map.     

Plant volatiles providing additional evidences to the occurence of a wild-growing population of Calamintha vardarensis (Greuter et Burdet) Šilić outside of its natural habitat

The essential‐oil profile of a Calamintha species, wild‐growing in the urban settings of the city of Ni? (South Serbia) and botanically tentatively identified as C. vardarensis (endemic species native to FYR Macedonia and East Serbia), has been statistically (multivariate statistical analyses) compared with those of other Calamintha species, including two previously investigated C. vardarensis populations, as a means of corroboration of the surprising occurence of this Calamintha population outside of its natural distributional range. Agglomerative hierarchical clustering reveals a close link of C. vardarensis from Ni? (with neo‐menthol (40.0%), menthone (21.8%), and pulegone (27.2%) as its major oil contributors) and C. vardarensis from FYR Macedonia.     

Geranium macrorrhizum L. (Geraniaceae) essential oil: a potent agent against Bacillus subtilis

The volatile hydrodistilled compounds from aerial parts and rhizomes of the ethnopharmacologically highly valued plant species Geranium macrorrhizum L. were screened for their antimicrobial activity in disc‐diffusion and microdilution assays. The assays pointed out to a very high and selective activity of the oils against Bacillus subtilis with minimum inhibitory concentrations (MIC) of 0.4–1.0 μg/ml. This prompted us to perform detailed compositional analyses of the oils. GC and GC/MS analyses allowed the identification of 283 constituents. The oils consisted mainly of sesquiterpenoids, the main ones being germacrone (49.7% in the oil from aerial parts) and δ‐guaiene (49.2% in rhizome oil). Significant qualitative and quantitative compositional differences in the oils from the two plant parts were observed. Further antimicrobial testing enabled us to determine that germacrone, the major constituent of the oil from aerial parts, was not the sole agent responsible for the observed activity.     

Reinforcement Learning on Slow Features of High-Dimensional Input Streams

Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA) network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.     

Models of RNA virus evolution and their roles in vaccine design

Viruses are fast evolving pathogens that continuously adapt to the highly variable environments they live and reproduce in. Strategies devoted to inhibit virus replication and to control their spread among hosts need to cope with these extremely heterogeneous populations and with their potential to avoid medical interventions. Computational techniques such as phylogenetic methods have broadened our picture of viral evolution both in time and space, and mathematical modeling has contributed substantially to our progress in unraveling the dynamics of virus replication, fitness, and virulence. Integration of multiple computational and mathematical approaches with experimental data can help to predict the behavior of viral pathogens and to anticipate their escape dynamics. This piece of information plays a critical role in some aspects of vaccine development, such as viral strain selection for vaccinations or rational attenuation of viruses. Here we review several aspects of viral evolution that can be addressed quantitatively, and we discuss computational methods that have the potential to improve vaccine design.