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1

Genome-wide identification and characterisation of ammonium transporter gene family in barley

Tanwar Umesh K., Stolarska Ewelina, Paluch-Lubawa Ewelina, Rudy Elżbieta, Sobieszczuk-Nowicka Ewa

Journal of Water and Land Development

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2023

|

no. 58

220--233

EN

Nitrogen (N) is an essential macronutrient for the growth and development of plants, but excessive use of nitrogen fertiliser in agriculture can result in environmental pollution. As a preferred nitrogen form, ammonium (NH4+) is absorbed from the soil by the plants through ammonium transporters (AMTs). Therefore, it is important to explore AMTs to improve the efficiency of plant N utilisation. Here, we performed a comprehensive genome-wide analysis to identify and characterise the AMT genes in barley (HvAMTs), which is a very important cereal crop. A total of seven AMT genes were identified in barley and further divided into two subfamilies (AMT1 and AMT2) based on phylogenetic analysis. All HvAMT genes were distributed on five chromosomes with only one tandem duplication. HvAMTs might play an important role in plant growth, development, and various stress responses, as indicated by cis-regulatory elements, miRNAs, and protein interaction analysis. Further, we analysed the expression pattern of HvAMTs in various developmental plant tissues, which indicated that AMT1 subfamily members might play a major role in the uptake of NH4+ from the soil through the roots in barley. Altogether, these findings might be helpful to improve the barley crop with improved nitrogen use efficiency, which is not only of great significance to the crop but also for land and water as it will reduce N fertiliser pollution in the surrounding ecosystem.

2

Machine learning for analysis of gene expression data in fast- and slow-progressing amyotrophic lateral sclerosis murine models

Iadanza Ernesto, Fabbri Rachele, Goretti Francesco, Nardo Giovanni, Niccolai Elena, Bendotti Caterina, Amedei Amedeo

Biocybernetics and Biomedical Engineering

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2022

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Vol. 42, no. 1

273--284

EN

Amyotrophic lateral sclerosis is a fatal motor neuron disease characterised by degenerative changes in both upper and lower motor neurons. Current treatment options in the general cohort of ALS patients have only a minimal impact on survival. Only two approved medications are available today, just addressing the management of symptoms and supporting the respiration. In this work, gene expression data from genetically modified murine motor neurons have been analysed with machine learning techniques, with the scope of distinguishing between mice developing a fast progression of the disease, and mice showing a slower progression. Results showed high accuracy (above 80%) in all tasks, with peaks of accuracy for specific ones – such as distinguishing between fast and slow progression. In the above mentioned task the best performing algorithm reached an accuracy of 100%. This research group is currently working on three more investigations on data from mice, using similar approaches and methodology, focusing on thoracic and lumbar metabolomic data as well as microbiome data. We believe that, based on the findings in the murine models, machine learning could be used to discover ALS progression markers in humans by looking at features related to the immune response. This could pave the path for the discovery of druggable targets and disease biomarkers for homogeneous ALS patient subgroups.

3

Cancer gene recognition from microarray data with manta ray based enhanced ANFIS technique

Mishra Purnendu, Bhoi Nilamani

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 3

916--932

EN

Recognizing the cancer genes from the microarray dataset is considered as the most essential research topic in bioinformatics and computational biology domain. Microarray dataset represents the state of each cell at the molecular level which is identified as the important diagnostic tool in medical field. Analyzing the microarray data may provide a huge support for cancer gene classification. Therefore recently a number of artificial intelligence and machine learning techniques are developed which utilize the microarray data for distinguishing the cancer and non-cancer cells. But still now these techniques does not achieved a satisfactory performance. Therefore, an efficient technique that provides a crisp output for cancer classification is required. To overcome such defect, an enhanced ANFIS (EANFIS) method is used in this proposed architecture for classifying the cancer genes. The convergence time of ANFIS gets increased during learning process, therefore to avoid such issue the Manta ray foraging optimization (MaFO) algorithm is hybrid along with ANFIS which improves the overall classification performance. The data given as an input to the classification process is pre-processed at the initial phase using the Ensemble Kalman Filter (EnKF) technique. After pre-processing, the genes having similar properties are clustered using an adaptive density-based spatial clustering with noise (ADBSCAN) clustering technique. Finally, the performance of proposed enhanced ANFIS is evaluated using the precision, accuracy, f-measure, recall, sensitivity, and specificity metrics. Further, the clustering based performance evaluation is also carried out using the cluster index metrics. Finally, the comparison with the state-of-the-art techniques is also performed to show the effectiveness of proposed approach.

4

May metallic biomaterials used for orthopaedic implants promote carcinogenesis? Preliminary transcriptomic research on human chondrocytes

Walkowiak-Przybyło Magdalena

Engineering of Biomaterials

|

2020

|

Vol. 23, no. 157

15--19

EN

The aim of this research was to assess the risk of carcinogenesis induced by the metallic materials intended for orthopaedic implants. The report is an analytical summary of changes in the expression of cancer-related genes in human chondrocytes of normal and neoplastic phenotype. Cq values (quantification cycle values) obtained from qRT-PCR reactions (quantitative real-time polymerase chain reactions) were used to count Fc values (fold change values) for each gene. Differences in Fc values obtained for primary and cancer cells grown on the surface of medical steel AISI316L and titanium-aluminum-vanadium alloy Ti6Al4V were then analyzed by t-Student test. The results indicate that for cancer cells grown on the surfaces of both examined materials the fold change greater than 2, usually considered essential, was found for LUM gene involved in sarcoma induction. For FOS gene, also involved in sarcoma induction, the Fc value was also very close to 2 in the primary cells exposed to Ti6Al4V alloy. The remaining observed changes were rather subtle, although they cannot be omitted from further studies because differences in gene expression in primary and tumor cells grown on the same biomaterial were statistically significant in several cases. The compilation of qRT-PCR experiments carried out on primary and cancer cells in parallel allowed to identify possible future contraindications for patients with a genetic predisposition to cancer or with cancer history.

5

Plant-derived rhamnogalacturonan-I’s modulate proinflammatory cytokine gene expression in neutrophils stimulated by E. coli LPS and P. gingivalis bacteria

Folkert J., Mieszkowska A., Burke B., Addison O., Gurzawska K.

Engineering of Biomaterials

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2018

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Vol. 21, no. 144

2--7

EN

Titanium dental implants often induce the foreign body immune response. The duration of the inflammatory process determines the initial stability and biocompatibility of the implant. The challenge for bone tissue engineering is to develop implant biocompatible and bioactive surface coatings that regulate the inflammatory response and enhance osseointegration. Pectins, plant-derived polysaccharides, have been shown to be potential candidates for surface coating due to their possible roles in improving osseointegration and bone healing. The aim of this study was to evaluate in vitro the effect of plant-derived pectin rhamnogalacturonan-I (RG-I) nanocoating on pro- and anti-inflammatory human polymorphonuclear leucocytes (PMN) responses to E. coli LPS or P. gingivalis bacteria. In this study unmodified RG-I and structurally modified RG-I from potato were examined. All in vitro studies were performed on tissue culture polystyrene surfaces (TCPS) or titanium (Ti) discs coated with unmodified and modified RG-Is. Changes in PMN gene expression occurred on both surfaces. The presence of RG-Is down-regulated proinflammatory genes, IL1B, IL8, TNFA. Our results clearly showed that pectin RG-I nanocoating decreased the level of proinflammatory genes expression in stimulated PMN and may therefore be considered as a potential candidate for modulation of the inflammatory response elicited by insertion of implants into living tissue.

6

A multi-layered incremental feature selection algorithm for adjuvant chemotherapy effectiveness/futileness assessment in non-small cell lung cancer

Naftchali R. E., Abadeh M. S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 3

477--488

EN

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; and is one of the leading causes of death in the world. Surgery combined with chemotherapy is the recommended treatment for NSCLC. Since chemotherapy is an expensive treatment for either medical staff or patients suffering from pain, this study attempts to construct an intelligent predictive model to predict the adjuvant chemotherapy (ACT) effectiveness/ futileness in the patients, in order to help futile cases for unnecessary applications. There is a 2-step method: preprocessing and predicting. First a purposefully preprocessing tech-nique: chi-square test, SVM-RFE and correlation matrix, were employed in NSCLC gene expression dataset as a novel multi-layered feature selection method to defeat the curse of dimension and detect the chemotherapy target genes from tens of thousands features, based on which the patients can be classified into two groups, with NB classifier at second step. 10-Fold cross-validation was found with accuracy of 68.93% for 2 genes, TGFA (205015_s_at) and SEMA6C (208100_x_at), which is preferable compared to earlier studies, even though more than 2 input features are employed for the prediction. According to the results found in this study, one can concludes that the multi-layered feature selection approach has increased the classification accuracy in terms of finding the fitted patient for receiving ACT by reducing the number of features and has significant power to be used in medical datasets with small train samples and large number of features.

7

MicroRNA expression prediction: Regression from regulatory elements

Oğul H., Tuncer M. E.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

89--94

EN

MicroRNAs are known as important actors in post-transcriptional regulation and relevant biological processes. Their expression levels do not only provide information about their own activities but also implicitly explain the behaviors of their targets, thus, in turn, the circuitry of underlying gene regulatory network. In this study, we consider the problem of estimating the expression of a newly discovered microRNA with known promoter sequence in a certain condition where the expression values of some known microRNAs are available. To this end, we offer a regression model to be learnt from the expression levels of other microRNAs obtained through a microarray experiment. To our knowledge, this is the first study that evaluates the predictability of microRNA expression from the regulatory elements found in its promoter sequence. The results obtained through the experiments on real microarray data justify the applicability of the framework in practice.

8

Strategiczny projekt badawczy Narodowego Centrum Badań i Rozwoju pt. " Technologie wspomagające rozwój bezpiecznej energetyki jądrowej" Zadanie nr 6. Rozwój metod zapewnienia bezpieczeństwa jądrowego i ochrony radiologicznej dla bieżących i przyszłych potrzeb energetyki jądrowej. Cel 2: Rozwój metod dozymetrii biologicznej oraz biofizycznych markerów i indykatorów wpływu promieniowania na organizmy żywe. (Etapy 6, 7, 8, 9, i 10)

Brzóska K., Kowalska M., Kruszewski M., Lankoff A., Sommer S.

Postępy Techniki Jądrowej

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2015

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z. 2

42--46

PL

Dozymetria biologiczna pozwala odczytać dawkę pochłoniętą promieniowania jonizującego w organizmie i jest niezbędnym elementem systemu ochrony radiologicznej. Aby zapewnić większą wiarygodność uzyskanych wyników metoda analizy dicentryków zostanie akredytowana w 2 laboratoriach, a w jednym istniejąca akredytacja zostanie rozszerzona o biodozymetrię mieszanego promieniowania gamma i neutronowego. Jedną ze strategii dostosowania dozymetrii biologicznej do scenariusza masowego zdarzenia jest łączenie laboratoriów we współdziałającą sieć. Założenia takiej sieci zostały opracowane i opisane. W trakcie projektu wypróbowano nowe, obiecujące metody: analizę ekspresji genów (m.in. FDXR, GADD45A) na poziomie mRNA z wykorzystaniem techniki PCR oraz metody oparte na PCC (przedwczesnej kondensacji chromosomów), w szczególności RICA (The Rapid Interphase Chromosome Assay). Obie metody okazały się skutecznymi metodami dozymetrii biologicznej.

EN

Biological dosimetry allows to read the absorbed dose of ionizing radiation in the body and is an essential element of the system of radiological protection. To ensure greater reliability of the results obtained by dicentric assay method will be accredited in 2 laboratories, and one existing accreditation will be extended to biodosimetry of mixed gamma and neutron radiation. One of the strategies to adapt biological dosimetry to the mass events scenario is to combine laboratories in cooperating network. Assumptions of such a network have been developed and described. The new methods of biological dosimetry have been investigated: analysis of gene expression (including FDXR, GADD45A) at the mRNA level using PCR method and methods based on the PCC (premature chromosome condensation), in particular RICA (The Interphase Chromosome Rapid Assay). Both methods have proven to be effective methods of biological dosimetry.

9

Analiza zmian w ekspresji genów cyklu komórkowego w komórkach osteoblastów hodowanych na powierzchniach stopów tytanu

Walkowiak-Przybyło M., Komorowski P., Jakubowski W., Klimek L., Walkowiak B.

Engineering of Biomaterials

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2012

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Vol. 15, no. 116-117 spec. iss.

46--48

10

Evaluation of how low frequency magnetic field 50 Hz affect living cells

Sztafrowski D., Wróblewski Z., Łukaszewicz M.

Przegląd Elektrotechniczny

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2011

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R. 87, nr 2

250-254

EN

The mechanism of ELF-MF impact on the metabolic processes occurring in cells of the living organisms is discussed. Existing research suggests that biological membranes may be composite antenna for stimulation by an electromagnetic field. To further elucidate this mechanism the use of fluorescent probes is suggested.

PL

W referacie przedstawiono postulowany mechanizm oddziaływania ELF-MF na procesy metaboliczne zachodzące w komórkach organizmów żywych. Błony biologiczne mogą być anteną zbiorczą dla bodźca, jakim jest pole elektromagnetyczne. Postuluje się wykorzystanie sond fluorescencyjnych do dalszych badań.

11

Benzo[a]pyrene and cyclopenta[c]phenanthrene suppress expression of p53 in head kidney of rainbow trout

Brzuzan P., Woźny M., Góra M., Łuczyński M. K., Jabłońska A.

Environmental Biotechnology

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2011

|

Vol. 7, No. 1

41-45

EN

Although p53, a protein of important tumor suppressive function, has been extensively studied in mammals, relatively little is known about the p53 pathways in lower vertebrates. Particularly, limited information exists on possible influences of environmental contaminants on the expression of the p53 gene in fish. In the current study, we assessed the effects of benzo[a]pyrene (B[a]P; potent tumor promoter) and cyclopenta[c]phenanthrene (CP[c]Ph; clastogenic agent) exposure on a 24h profile of p53 gene expression in head kidney of juvenile rainbow trout (Oncorhynchus mykiss). To analyze the p53 transcription rate, we developed protocol for the examination of both mRNA and heterogeneous nuclear (hn) RNA of the gene, using Real-Time RTPCR approach. The results show that both compounds are capable of suppressing p53 transcriptional activity within 12h of the treatment. Our finding supports the idea that structurally different PAHs may influence cell physiologic functions controlled by p53 in fish, in part, by down-regulating its RNA expression levels.

12

An Extension of TSP-family Algorithms for Microarray Classification

Czajkowski M., Krętowski M.

Zeszyty Naukowe Politechniki Białostockiej. Informatyka

|

2009

|

Z. 4

31-45

EN

Classification of microarray data and generation of simple and efficient decision rules may be successfully performed with Top Scoring Pair algorithms. TSP-family methods are based on pairwise comparisons of gene expression values. This paper presents a new method, referred as Linked TSP that extends previous approaches kˇTSP and Weight kˇTSP algorithms by linking top pairwise mRNA comparisons of gene expressions in different classes. Opposite to existing TSP-family classifiers, the proposed approach creates decision rules involving single genes that most frequently appeared in top scoring pairs. Motivation of this paper is to improve classification accuracy results and to extract simple, readily interpretable rules providing biological insight as to how classification is performed. Experimental validation was performed on several human microarray datasets and obtained results are promising.

PL

Klasyfikacja danych mikromacierzowych a także późniejsza interpretacja reguł decyzyjnych może być skutecznie przeprowadzona za pomocą metod z rodziny Top Scoring Pair, polegających na analizie par genow o przeciwstawych poziomach ekspresji w róźnych klasach. W poniższym artykule zaprezentowano nową metodę: Linked TSP, ktora rozszerza działanie klasyfikatorów k-TSP i Weight k-TSP. W przeciwieństwie do algorytmow z rodziny TSP proponowane rozwiązanie tworzy reguły decyzyjne zbudowane z pojedynczych genów, co znacznie ułatwia ich późniejszą interpretację medyczną. W algorytmie wykorzystywane są pary genow uzyskane z algorytmow TSP z których następnie, wybierane są pojedyncze, najczęściej powtarzające się geny. Testy algorytmu Linked TSP przeprowadzone zostająy na rzeczywistych zbiorach danych pacjentow a uzyskane wyniki są obiecujące.

13

Decision tree approach to microarray data analysis

Grześ M., Krętowski M.

Biocybernetics and Biomedical Engineering

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2007

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Vol. 27, no. 3

29-42

EN

The classification of gene expression data is still new, difficult and also an interesting field of endeavour. There is a demand for powerful approaches to this problem, which is one of the ultimate goals of modern biological research. Two different techniques for inducing decision trees are discussed and evaluated on well-known and publicly available gene expression datasets. Empirical results are presented.

14

Krótkie cząsteczki kwasów nukleinowych jako terapeutyki

Sipa K.

Laboratorium - Przegląd Ogólnopolski

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2004

|

nr 4

38--42

PL

Pół wieku po opisaniu przez Watsona i Cricka struktury DNA nasze rozumienie procesów leżących u podstaw życia jest coraz pełniejsze, ale wciąż dalekie od kompletnego. Uniwersalne zasady przepływu informacji genetycznej w komórce są uzupełniane przez odkrycia rzucające światło na procesy regulacji ekspresji genów, w których doniosłą rolę pełnią cząsteczki RNA – drugiego, starszego ewolucyjnie kwasu nukleinowego.

15

Wprowadzenie do technologii mikromacierzy DNA

Skommer J.

LAB Laboratoria, Aparatura, Badania

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2004

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R. 9, nr 1

18-20

PL

Pierwotnie badania wzoru ekspresji genów polegały na praco- i czasochłonnej analizie pojedynczych genów. Wprowadzenie technologii mikromacierzy DNA umożliwiło badanie całego genomu danego organizmu na chipie wielkości szkiełka mikroskopowego.

16

Learning Rough Set Classifiers from Gene Expressions and Clinical Data

Midelfart H., Komorowski J., Norsett K., Yadetie F., Sandvik A.K., Laegreid A.

Fundamenta Informaticae

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2002

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Vol. 53, nr 2

155-183

EN

Biological research is currently undergoing a revolution. With the advent of microarray technology the behavior of thousands of genes can be measured simultaneously. This capability opens a wide range of research opportunities in biology, but the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance. One application of microarray technology is cancer studies where supervised learning may be used for predicting tumor subtypes and clinical parameters. We present a general Rough Set approach for classification of tumor samples analyzed with microarrays. This approach is tested on a data set of gastric tumors, and we develop classifiers for six clinical parameters. One major obstacle in training classifiers from microarray data is that the number of objects is much smaller that the number of attributes. We therefore introduce a feature selection method based on bootstrapping for selecting genes that discriminate significantly between the classes, and study the performance of this method. Moreover, the efficacy of several learning and discretization methods implemented in the ROSETTA system [18] is examined. Their performance is compared to that of linear and quadratic discrimination analysis. The classifiers are also biologically validated. One of the best classifiers is selected for each clinical parameter, and the connection between the genes used in these classifiers and the parameters are compared to the establish knowledge in the biomedical literature.