Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks

This paper proposes a new method to reverse engineer gene regulatory networks from experimental data. The modeling framework used is time-discrete deterministic dynamical systems, with a finite set of states for each of the variables. The simplest examples of such models are Boolean networks, in which variables have only two possible states. The use of a larger number of possible states allows a finer discretization of experimental data and more than one possible mode of action for the variables, depending on threshold values. Furthermore, with a suitable choice of state set, one can employ powerful tools from computational algebra, that underlie the reverse-engineering algorithm, avoiding costly enumeration strategies. To perform well, the algorithm requires wildtype together with perturbation time courses. This makes it suitable for small to meso-scale networks rather than networks on a genome-wide scale. The complexity of the algorithm is quadratic in the number of variables and cubic in the number of time points. The algorithm is validated on a recently published Boolean network model of segment polarity development in Drosophila melanogaster.Comment: 28 pages, 5 EPS figures, uses elsart.cl

Accelerated search for biomolecular network models to interpret high-throughput experimental data

<p>Abstract</p> <p>Background</p> <p>The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.</p> <p>Results</p> <p>Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.</p> <p>Conclusion</p> <p>Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.</p

РЕЗУЛЬТАТЫ ПРИМЕНЕНИЯ МОДИФИЦИРОВАННОЙ РЕВЕРСИВНОЙ ПЛАСТИКИ ЛОСКУТОМ ЛЕВОЙ ПОДКЛЮЧИЧНОЙ АРТЕРИИ У ПАЦИЕНТОВ С КОАРКТАЦИЕЙ АОРТЫ В СОЧЕТАНИИ С ГИПОПЛАЗИЕЙ ДИСТАЛЬНОГО ОТДЕЛА ДУГИ АОРТЫ

The purpose: This study evaluates long-term results of modified method of reverse subclavian flap aortoplasty, of infants with coarctation of the aorta combined with hypoplasia of the distal aortic arch.Materials and methods. 64 patients under the age of 1 year, correction of aortic coarctation with hypoplasia of the distal arch department was performed. Patients were divided into 2 groups: the 1st group included patients, whose correction was made by modified method of reverse subclavian flap aortoplasty of the left subclavian artery (n=32); the 2nd one included patients, whose correction was made by the method of extended anastomosis (n=32). Average diameter of aorta at the level of distal aortic arch was 5.1±0.1and 5.5±1.8 (р=0.51), peak gradient at the level of isthmus was 46.6±31.03 and 48.4±32.01(р=0.7). All patients underwent CAT scanning of aorta together with opacification, and Z score of aortic arch was calculated to clarify the level of hypoplasia. Results. Four-year cumulative survival rate was 95.9% in the 1st group and 95.6% in the 2nd group. In the distant period, isthmus diameter in the 2nd group was significantly different from the one in the 1st group, and was 0.98±0.4 mm and 1.2±0.86 mm correspondingly (р=0.003). For patients from the 1st group, freedom from recoarctation in distant period was 98.2%, while it was 96.3% for patients from the 2nd group, and it was not fairly different from statistical point of view. While comparing the long-term results, statistically significant prevalence of hypertension in the 2nd group (р=0,0034) was observed; pressure gradient in the 1st group at the level of isthmus was 12.2 ±1.06 and in the 2nd group it was 15.5±1.89 (р=0.002); cases of hypertrophy of myocardium of the left ventriculus -10 (40%) in comparison with the 1st group, where they were 2 (8%) (р=0,003).Conclusions. Modified method of reverse subclavian flap aortoplasty of the subclavian artery exhibits results, which can be compared to those of the method of extended anastomosis.Цель. Оценить отдаленные результаты модифицированной методики реверсивной пластики у младенцев с коарктацией аорты в сочетании с гипоплазией дистального отдела дуги.Материалы и методы. 64 пациентам в возрасте до 1 года, была выполнена коррекция коарктации аорты с гипоплазией дистального отдела дуги. Пациенты были разделены на 2 группы: I – пациенты, коррекция которым выполнялась модифицированным методом реверсивной пластики лоскутом левой подключичной артерии (n=32); II – пациенты, коррекция которым выполнялась методом расширенного анастомоза (n=32). Средний диаметр аорты на уровне дистального отдела дуги аорты составил 5,1±0,1и 5,5±1,8 (р=0,51), пиковый градиент на уровне перешейка 46,6±31,03 и 48,4±32,01(р=0,7). Для уточнения степени гипоплазии выполнялись МСКТ аорты с контрастированием и расчет показателя Z score дуги аорты.Результаты. Четырехлетняя кумулятивная выживаемость составила 95,9 % для I группы и 95,6 % – для II группы. В отдаленном периоде диаметр перешейка во II группе достоверно отличался в сравнении с I группой 0,98±0,4 мм и 1,2±0,86 мм соответственно (р=0,003). Свобода от рекоарктации в отдаленном периоде составила 98,2 % для пациентов I группы и 96,3 % для пациентов II группы и статистически достоверно не различалась. При сравнении отдаленных результатов отмечалось статистически значимое преобладание артериальной гипертензии во II группе (р=0,0034); градиента давления на уровне перешейка в I группе – 12,2 ±1,06 и во II – 15,5±1,89 (р=0,002); гипертрофия миокарда левого желудочка, во II группе – 10 (40%) случаев, в сравнении с I группой – 2 (8%) (р=0,003).Выводы. Модифицированная методика реверсивной пластики лоскутом подключичной артерии ассоциируется с лучшими морфо-функциональными результатами в отдаленном периоде

Transcatheter surgery of residual right ventricular outflow tract stenosis

Over the past 40 years, various types of prostheses have been developed for right ventricular outflow tract reconstruction. However, conduit stenosis and insufficiency due to valve degeneration occur frequently, decreasing the lifetime of patients. Transcatheter stenting of conduits does not always give favorable results and can lead to severe pulmonary regurgitation. The novel method of percutaneous pulmonary valve implantation is a good alternative to the surgical intervention according to data on long-term survival and quality of life

G = MAT: Linking Transcription Factor Expression and DNA Binding Data

Transcription factors are proteins that bind to motifs on the DNA and thus affect gene expression regulation. The qualitative description of the corresponding processes is therefore important for a better understanding of essential biological mechanisms. However, wet lab experiments targeted at the discovery of the regulatory interplay between transcription factors and binding sites are expensive. We propose a new, purely computational method for finding putative associations between transcription factors and motifs. This method is based on a linear model that combines sequence information with expression data. We present various methods for model parameter estimation and show, via experiments on simulated data, that these methods are reliable. Finally, we examine the performance of this model on biological data and conclude that it can indeed be used to discover meaningful associations. The developed software is available as a web tool and Scilab source code at http://biit.cs.ut.ee/gmat/

Inferring gene regression networks with model trees

<p>Abstract</p> <p>Background</p> <p>Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.</p> <p>Results</p> <p>We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>, is experimentally tested on two well-known data sets: <it>Saccharomyces Cerevisiae </it>and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.</p> <p>Conclusions</p> <p>R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>.</p

Роль неоадъювантной гормонотерапии в лечении рака молочной железы: что мы знаем на данный момент?

Breast cancer is the most common malignant disease in the world. One of the methods of treating breast cancer is neoadjuvant chemotherapy. Neoadjuvant chemotherapy (NCT) is now the standard of care for locally advanced breast cancer. Patients with HER2-positive and triple-negative breast cancer subtypes benefi t the most from NCT, with a 50–60 % chance of achieving pCR, while patients with hormone-sensitive, HER2-negative breast cancer subtypes have an average chance of achieving pCR of 10–20 %. For patients with locally advanced, hormone-sensitive Her2neu-negative breast cancer, neoadjuvant hormone therapy contributes to a tumor downstaging and an increasing rate of organ-preserving surgery. However, neoadjuvant hormone therapy is still not used routinely. There are a limited number of clinical guidelines that describe the choice of the optimal drugs, the optimal duration of hormone therapy and the criteria for selecting patients for preoperative hormone therapy. This is the first literature review in Russia that includes a systematization of the evidence regarding the effectiveness of neoadjuvant hormone therapy, a comparison of hormone therapy with neoadjuvant chemotherapy, comparison of hormonal drug groups, optimal duration of hormone therapy, attempts to combine hormone therapy with drugs of the group of selective CDK4/6 cyclin-dependent kinase inhibitors and phosphatidylinositol 3-kinase inhibitors for women with locally advanced hormone-sensitive Her2neu-negative breast cancer. The possibilities of using modern commercial multigene panels to assess the feasibility of identifying the cohort of patients for whom neoadjuvant hormone therapy would be most effective are also considered.Рак молочной железы – самое распространенное злокачественное заболевание в мире. Один из методов лечения рака молочной железы – неоадъювантная химиотерапия. В настоящее время неоадъювантная химиотерапия (НХТ) – это стандарт лечения местнораспространенного рака молочной железы. Наибольшие преимущества от НХТ получают пациентки с HER2-положительным и трижды негативным подтипами рака молочной железы, у которых вероятность достижения pCR равна 50–60 %, в то время как вероятность достижения pCR у гормончувствительных, HER2-негативных подтипов рака молочной железы равна в среднем 10–20 %. Для пациенток с местнораспространенным гормончувствительным Her2neu-негативным раком молочной железы неоадъювантная гормонотерапия терапия способствует уменьшению стадии опухоли и увеличению частоты органосохраняющих операций. Однако неоадъювантная гормонотерапия по-прежнему не используется рутинно. Существует ограниченное количество клинических рекомендаций, в которых описан выбор оптимальной группы лекарственных препаратов, оптимальная продолжительность гормонотерапии и критерии отбора пациентов для предоперационной гормонотерапии. Это первый литературный обзор в России, который включает в себя систематизацию фактических данных, касающихся эффективности неоадъювантной гормонотерапии, сравнения гормонотерапии с химиотерапией в неоадъювантном режиме, сравнения групп гормональных препаратов, оптимальной длительности гормонотерапии, попыток комбинации гормонотерапии с препаратами группы селективных ингибиторов циклинзависимых киназ CDK4/6 и ингибиторов фосфатидилинозитол-3-киназы для женщин с местнораспространенным гормончувствительным Her2neu-негативным раком молочной железы. Также рассмотрены возможности применения современных коммерческих мультигенных панелей для оценки возможности определения когорты пациентов, для которой неоадъювантная гормонотерапия будет наиболее эффективна