Germline ancestry influences the evolutionary disease course in lung adenocarcinomas

Evolutionary Applications published by John Wiley & Sons Ltd Precision medicine relies on targeting specific somatic alterations present in a patient's tumor. However, the extent to which germline ancestry may influence the somatic burden of disease has received little attention. We estimated the genetic ancestry of non-small-cell lung cancer (NSCLC) patients and performed an in-depth analysis of the influence of genetic ancestry on the evolutionary disease course. Compared with European Americans (EA), African Americans (AA) with lung adenocarcinoma (LUAD) were found to be significantly younger and smoke significantly less. However, LUADs from AAs exhibited a significantly higher somatic mutation burden, with a more pronounced tobacco carcinogen footprint and increased frequencies of alterations affecting cancer genes. Conversely, no significant differences were observed between lung squamous cell carcinomas (LUSC) from EAs and AAs. Our results suggest germline ancestry influences the somatic evolution of LUAD but not LUSC

Loss of USP28 and SPINT2 expression promotes cancer cell survival after whole genome doubling

Background Whole genome doubling is a frequent event during cancer evolution and shapes the cancer genome due to the occurrence of chromosomal instability. Yet, erroneously arising human tetraploid cells usually do not proliferate due to p53 activation that leads to CDKN1A expression, cell cycle arrest, senescence and/or apoptosis. Methods To uncover the barriers that block the proliferation of tetraploids, we performed a RNAi mediated genome-wide screen in a human colorectal cancer cell line (HCT116). Results We identified 140 genes whose depletion improved the survival of tetraploid cells and characterized in depth two of them: SPINT2 and USP28. We found that SPINT2 is a general regulator of CDKN1A transcription via histone acetylation. Using mass spectrometry and immunoprecipitation, we found that USP28 interacts with NuMA1 and affects centrosome clustering. Tetraploid cells accumulate DNA damage and loss of USP28 reduces checkpoint activation, thus facilitating their proliferation. Conclusions Our results indicate three aspects that contribute to the survival of tetraploid cells: (i) increased mitogenic signaling and reduced expression of cell cycle inhibitors, (ii) the ability to establish functional bipolar spindles and (iii) reduced DNA damage signaling

DNA replication stress mediates APOBEC3 family mutagenesis in breast cancer

BACKGROUND: The APOBEC3 family of cytidine deaminases mutate the cancer genome in a range of cancer types. Although many studies have documented the downstream effects of APOBEC3 activity through next-generation sequencing, less is known about their upstream regulation. In this study, we sought to identify a molecular basis for APOBEC3 expression and activation. RESULTS: HER2 amplification and PTEN loss promote DNA replication stress and APOBEC3B activity in vitro and correlate with APOBEC3 mutagenesis in vivo. HER2-enriched breast carcinomas display evidence of elevated levels of replication stress-associated DNA damage in vivo. Chemical and cytotoxic induction of replication stress, through aphidicolin, gemcitabine, camptothecin or hydroxyurea exposure, activates transcription of APOBEC3B via an ATR/Chk1-dependent pathway in vitro. APOBEC3B activation can be attenuated through repression of oncogenic signalling, small molecule inhibition of receptor tyrosine kinase signalling and alleviation of replication stress through nucleoside supplementation. CONCLUSION: These data link oncogene, loss of tumour suppressor gene and drug-induced replication stress with APOBEC3B activity, providing new insights into how cytidine deaminase-induced mutagenesis might be activated in tumourigenesis and limited therapeutically

GibbsST: a Gibbs sampling method for motif discovery with enhanced resistance to local optima

BACKGROUND: Computational discovery of transcription factor binding sites (TFBS) is a challenging but important problem of bioinformatics. In this study, improvement of a Gibbs sampling based technique for TFBS discovery is attempted through an approach that is widely known, but which has never been investigated before: reduction of the effect of local optima. RESULTS: To alleviate the vulnerability of Gibbs sampling to local optima trapping, we propose to combine a thermodynamic method, called simulated tempering, with Gibbs sampling. The resultant algorithm, GibbsST, is then validated using synthetic data and actual promoter sequences extracted from Saccharomyces cerevisiae. It is noteworthy that the marked improvement of the efficiency presented in this paper is attributable solely to the improvement of the search method. CONCLUSION: Simulated tempering is a powerful solution for local optima problems found in pattern discovery. Extended application of simulated tempering for various bioinformatic problems is promising as a robust solution against local optima problems

ELECTROENCEPHALOGRAPHY (EEG) AND ITS USE IN MOTOR LEARNING AND CONTROL

Electroencephalography (EEG) is a non-invasive technique of measuring electric currents generated from active brain regions and is a useful tool for researchers interested in motor control. The study of motor learning and control seeks to understand the way the brain understands, plans and executes movement both physical and imagined. Thus, the purpose of this study was to better understand the ways in which electroencephalography can be used to measure regions of the brain involved with motor control and learning. For this purpose, two independent studies were completed using EEG to monitor brain activity during both executed and imagined actions. The first study sought to understand the cognitive demand of altering a running gait and provides EEG evidence of motor learning. 13 young healthy runners participated in a 6-week in-field gait-retraining program that altered running gait by increasing step rate (steps per minute) by 5-10%. EEG was collected while participants ran on a treadmill with their original gait as a baseline measurement. After the baseline collection, participants ran for one minute at the same speed with a 5-10% step rate increase while EEG was collected. Participants then participated in a 6-week in-field gait-retraining program in which they received bandwith feedback while running in order to learn the new gait. After completing the 6-week training protocol, participants returned to the lab for post training EEG collection while running with the new step rate. Power spectral density plots were generated to measure frequency band power in all gait-retraining phases. Results in the right prefrontal cortex showed a significant increase in beta (13-30 Hz) while initially running with the new gait compared to the baseline step rate. Previous work suggests the right prefrontal cortex is involved with the inhibition of a previously learned behavior and thus, our results suggest an increase in cognitive load to inhibit the previous full stride motion. After training, this increase in beta over the right prefrontal cortex decreased, suggesting motor adaptations had occurred as a result of motor learning. These results give promising evidence for a new method of ensuring permanent changes in performance that will benefit rehabilitation and athletic performance training programs. The second study in this project sought to understand differences in right and left-handers as they mentally simulate movement. 24 right and left-handed individuals (12 right-handers, 12 left-handers) were shown pictures of individual hands on a screen while EEG was collected. Previous research has shown than while solving this task, participants mentally rotate a mental representation of their own hand to determine the handedness of the image. Event-related potential results showed that right-handers had an earlier and greater activation in the parietal regions than left-handers, whereas left-handers had a later and greater activation in the motor related brain regions compared to right-handers. These results suggest differing strategies while mentally solving motor related tasks between right and left-handers. We speculate this is a result of left-handers' need to adapt to a majorly right-hand dominant environment. Both these studies show the benefits of using EEG to understand the motor system in physically executed and imagined actions

Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty

Background: Accurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, a comparative study of pairwise statistical significance with database statistical significance was conducted. In this paper, we extend the earlier work on pairwise statistical significance by incorporating with it the use of multiple parameter sets. Results: Results for a knowledge discovery application of homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives better coverage than using a single parameter set, at least at some error levels. Further, the results of pairwise statistical significance using multiple parameter sets are shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH. Using non-zero parameter set change penalty values give better performance than zero penalty. Conclusion: The fact that the homology detection performance does not degrade when using multiple parameter sets is a strong evidence for the validity of the assumption that the alignment score distribution follows an extreme value distribution even when using multiple parameter sets. Parameter set change penalty is a useful parameter for alignment using multiple parameter sets. Pairwise statistical significance using multiple parameter sets can be effectively used to determine the relatedness of a (or a few) pair(s) of sequences without performing a time-consuming database search

LRIG1 regulates cadherin-dependent contact inhibition directing epithelial homeostasis and pre-invasive squamous cell carcinoma development.

Epidermal growth factor receptor (EGFR) pathway activation is a frequent event in human carcinomas. Mutations in EGFR itself are, however, rare, and the mechanisms regulating EGFR activation remain elusive. Leucine-rich immunoglobulin repeats-1 (LRIG1), an inhibitor of EGFR activity, is one of four genes identified that predict patient survival across solid tumour types including breast, lung, melanoma, glioma, and bladder. We show that deletion of Lrig1 is sufficient to promote murine airway hyperplasia through loss of contact inhibition and that re-expression of LRIG1 in human lung cancer cells inhibits tumourigenesis. LRIG1 regulation of contact inhibition occurs via ternary complex formation with EGFR and E-cadherin with downstream modulation of EGFR activity. We find that LRIG1 LOH is frequent across cancers and its loss is an early event in the development of human squamous carcinomas. Our findings imply that the early stages of squamous carcinoma development are driven by a change in amplitude of EGFR signalling governed by the loss of contact inhibition

Local sequence alignments statistics: deviations from Gumbel statistics in the rare-event tail

<p>Abstract</p> <p>Background</p> <p>The optimal score for ungapped local alignments of infinitely long random sequences is known to follow a Gumbel extreme value distribution. Less is known about the important case, where gaps are allowed. For this case, the distribution is only known empirically in the high-probability region, which is biologically less relevant.</p> <p>Results</p> <p>We provide a method to obtain numerically the biologically relevant rare-event tail of the distribution. The method, which has been outlined in an earlier work, is based on generating the sequences with a parametrized probability distribution, which is biased with respect to the original biological one, in the framework of Metropolis Coupled Markov Chain Monte Carlo. Here, we first present the approach in detail and evaluate the convergence of the algorithm by considering a simple test case. In the earlier work, the method was just applied to one single example case. Therefore, we consider here a large set of parameters:</p> <p>We study the distributions for protein alignment with different substitution matrices (BLOSUM62 and PAM250) and affine gap costs with different parameter values. In the logarithmic phase (large gap costs) it was previously assumed that the Gumbel form still holds, hence the Gumbel distribution is usually used when evaluating p-values in databases. Here we show that for all cases, provided that the sequences are not too long (<it>L </it>> 400), a "modified" Gumbel distribution, i.e. a Gumbel distribution with an additional Gaussian factor is suitable to describe the data. We also provide a "scaling analysis" of the parameters used in the modified Gumbel distribution. Furthermore, via a comparison with BLAST parameters, we show that significance estimations change considerably when using the true distributions as presented here. Finally, we study also the distribution of the sum statistics of the <it>k </it>best alignments.</p> <p>Conclusion</p> <p>Our results show that the statistics of gapped and ungapped local alignments deviates significantly from Gumbel in the rare-event tail. We provide a Gaussian correction to the distribution and an analysis of its scaling behavior for several different scoring parameter sets, which are commonly used to search protein data bases. The case of sum statistics of <it>k </it>best alignments is included.</p

Potassium Starvation in Yeast: Mechanisms of Homeostasis Revealed by Mathematical Modeling

The intrinsic ability of cells to adapt to a wide range of environmental conditions is a fundamental process required for survival. Potassium is the most abundant cation in living cells and is required for essential cellular processes, including the regulation of cell volume, pH and protein synthesis. Yeast cells can grow from low micromolar to molar potassium concentrations and utilize sophisticated control mechanisms to keep the internal potassium concentration in a viable range. We developed a mathematical model for Saccharomyces cerevisiae to explore the complex interplay between biophysical forces and molecular regulation facilitating potassium homeostasis. By using a novel inference method (“the reverse tracking algorithm”) we predicted and then verified experimentally that the main regulators under conditions of potassium starvation are proton fluxes responding to changes of potassium concentrations. In contrast to the prevailing view, we show that regulation of the main potassium transport systems (Trk1,2 and Nha1) in the plasma membrane is not sufficient to achieve homeostasis