Browsing by Subject "bioinformatics"

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  • Sarin, Heikki V.; Gudelj, Ivan; Honkanen, Jarno; Ihalainen, Johanna K.; Vuorela, Arja; Lee, Joseph H.; Jin, Zhenzhen; Terwilliger, Joseph D.; Isola, Ville; Ahtiainen, Juha P.; Häkkinen, Keijo; Juric, Julija; Lauc, Gordan; Kristiansson, Kati; Hulmi, Juha J.; Perola, Markus (2019)
    Exercise and exercise-induced weight loss have a beneficial effect on overall health, including positive effects on molecular pathways associated with immune function, especially in overweight individuals. The main aim of our study was to assess how energy deprivation (i.e., "semi-starvation") leading to substantial fat mass loss affects the immune system and immunosuppression in previously normal weight individuals. Thus, to address this hypothesis, we applied a high-throughput systems biology approach to better characterize potential key pathways associated with immune system modulation during intensive weight loss and subsequent weight regain. We examined 42 healthy female physique athletes (age 27.5 +/- 4.0 years, body mass index 23.4 +/- 1.7 kg/m(2)) volunteered into either a diet group (n = 25) or a control group (n = 17). For the diet group, the energy intake was reduced and exercise levels were increased to induce loss of fat mass that was subsequently regained during a recovery period. The control group was instructed to maintain their typical lifestyle, exercise levels, and energy intake at a constant level. For quantification of systems biology markers, fasting blood samples were drawn at three time points: baseline (PRE), at the end of the weight loss period (MID 21.1 +/- 3.1 weeks after PRE), and at the end of the weight regain period (POST 18.4 +/- 2.9 weeks after MID). In contrast to the control group, the diet group showed significant (false discovery rate
  • Wang, Yinyin (Helsingin yliopisto, 2021)
    Traditional Chinese medicine (TCM) has obvious efficacy on disease treatments and is a valuable source for novel drug discovery. However, the underlying mechanism of the pharmacological effects of TCM remains unknown because TCM is a complex system with multiple herbs and ingredients coming together as a prescription. Therefore, it is urgent to apply computational tools to TCM to understand the underlying mechanism of TCM theories at the molecular level and use advanced network algorithms to explore potential effective ingredients and illustrate the principles of TCM in system biological aspects. In this thesis, we aim to understand the underlying mechanism of actions in complex TCM systems at the molecular level by bioinformatics and computational tools. In study Ⅰ, a machine learning framework was developed to predict the meridians of the herbs and ingredients. Finally, we achieved high accuracy of the meridians prediction for herbs and ingredients, suggesting an association between meridians and the molecular features of ingredients and herbs, especially the most important features for machine learning models. Secondly, we proposed a novel network approach to study the TCM formulae by quantifying the degree of interactions of pairwise herb pairs in study Ⅱ using five network distance methods, including the closest, shortest, central, kernel, as well as separation. We demonstrated that the distance of top herb pairs is shorter than that of random herb pairs, suggesting a strong interaction in the human interactome. In addition, center methods at the ingredient level outperformed the other methods. It hints to us that the central ingredients play an important role in the herbs. Thirdly, we explored the associations between herbs or ingredients and their important biological characteristics in study III, such as properties, meridians, structures, or targets via clusters from community analysis of the multipartite network. We found that herbal medicines among the same clusters tend to be more similar in the properties, meridians. Similarly, ingredients from the same cluster are more similar in structure and protein target. In summary, this thesis intends to build a bridge between the TCM system and modern medicinal systems using computational tools, including the machine learning model for meridian theory, network modelling for TCM formulae, as well as multipartite network analysis for herbal medicines and their ingredients. We demonstrated that applying novel computational approaches on the integrated high-throughput omics would provide insights for TCM and accelerate the novel drug discovery as well as repurposing from TCM.
  • Liu, Chengyu; Lehtonen, Rainer; Hautaniemi, Sampsa (2018)
    Identification of intracellular pathways that play key roles in cancer progression and drug resistance is a prerequisite for developing targeted cancer treatments. The era of personalized medicine calls for computational methods that can function with one sample or very small set of samples. Developing such methods is challenging because standard statistical approaches pose several limiting assumptions, such as number of samples, that prevent their application when n approaches to one. We have developed a novel pathway analysis method called PerPAS to estimate pathway activity at a single sample level by integrating pathway topology and transcriptomics data. In addition, PerPAS is able to identify altered pathways between cancer and control samples as well as to identify key nodes that contribute to the pathway activity. In our case study using breast cancer data, we show that PerPAS can identify highly altered pathways that are associated with patient survival. PerPAS identified four pathways that were associated with patient survival and were successfully validated in three independent breast cancer cohorts. In comparison to two other pathway analysis methods that function at a single sample level, PerPAS had superior performance in both synthetic and breast cancer expression datasets. PerPAS is a free R package (
  • Cowley, Mark J.; Pinese, Mark; Kassahn, Karin S.; Waddell, Nic; Pearson, John V.; Grimmond, Sean M.; Biankin, Andrew V.; Hautaniemi, Sampsa; Wu, Jianmin (2012)
  • Tamminen, Sakari; Deibel, Eric (Routledge, 2018)
    This book addresses the unprecedented convergence between the digital and the corporeal in the life sciences and turns to Foucault’s biopolitics in order to understand how life is being turned into a technological object. It examines a wide range of bioscientific knowledge practices that allow life to be known through codes that can be shared (copied), owned (claimed, and managed) and optimised (remade through codes based on standard language and biotech engineering visions). The book’s approach is captured in the title, which refers to 'the biopolitical'. The authors argue that through discussions of political theories of sovereignty and related geopolitical conceptions of nature and society, we can understand how crucially important it is that life is constantly unsettling and disrupting the established and familiar ordering of the material world and the related ways of thinking and acting politically. The biopolitical dynamics involved are conceptualised as the 'metacode of life', which refers to the shifting configurations of living materiality and the merging of conventional boundaries between the natural and artificial, the living and non-living. The result is a globalising world in which the need for an alternative has become a core part of its political and legal instability, and the authors identify a number of possible alternative platforms to understand life and the living as framed by the 'metacodes' of life. This book will appeal to scholars of science and technology studies, as well as scholars of the sociology, philosophy, and anthropology of science, who are seeking to understand social and technical heterogeneity as a characteristic of the life sciences.
  • Eskelin, Katri; Varjosalo, Markku; Ravantti, Janne; Makinen, Kristiina (2019)
    Nicotiana benthamiana is an important model plant for plant-microbe interaction studies. Here, we compared ribosome profiles and riboproteomes of healthy and infected N. benthamiana plants. We affinity purified ribosomes from transgenic leaves expressing a FLAG-tagged ribosomal large subunit protein RPL18B of Arabidopsis thaliana. Purifications were prepared from healthy plants and plants that had been infiltrated with Agrobacterium tumefaciens carrying infectious cDNA of Potato virus A (PVA) or firefly luciferase gene, referred to here as PVA- or Agrobacterium-infected plants, respectively. Plants encode a number of paralogous ribosomal proteins (r-proteins). The N. benthamiana riboproteome revealed approximately 6600 r-protein hits representing 424 distinct r-proteins that were members of 71 of the expected 81 r-protein families. Data are available via ProteomeXchange with identifier PXD011602. The data indicated that N. benthamiana ribosomes are heterogeneous in their r-protein composition. In PVA-infected plants, the number of identified r-protein paralogues was lower than in Agrobacterium-infected or healthy plants. A. tumefaciens proteins did not associate with ribosomes, whereas ribosomes from PVA-infected plants co-purified with viral cylindrical inclusion protein and helper component proteinase, reinforcing their possible role in protein synthesis during virus infection. In addition, viral NIa protease-VPg, RNA polymerase NIb and coat protein were occasionally detected. Infection did not affect the proportions of ribosomal subunits or the monosome to polysome ratio, suggesting that no overall alteration in translational activity took place on infection with these pathogens. The riboproteomic data of healthy and pathogen-infected N. benthamiana will be useful for studies on the specific use of r-protein paralogues to control translation in infected plants.
  • Karinen, Sirkku Helena; Saarinen, Silva; Lehtonen, Rainer Juhani; Rastas, Pasi; Vahteristo, Pia Marita; Aaltonen, Lauri Antti; Hautaniemi, Sampsa (2012)
  • Zhou, Fang; Toivonen, Hannu; King, Ross D. (2014)
  • Lahti, Leo; Salojärvi, Jarkko; Salonen, Anne; Scheffer, Marten; Vos de, Willem M. (2014)
  • Ritala, Joel F.; Lyne, Sean B.; Sajanti, Antti; Girard, Romuald; Koskimäki, Janne (2022)
    The role of neurotrophins in neuronal plasticity has recently become a strong focus in neuroregeneration research field to elucidate the biological mechanisms by which these molecules modulate synapses, modify the response to injury, and alter the adaptation response. Intriguingly, the prior studies highlight the role of p75 neurotrophin receptor (p75(NTR)) in various injuries and diseases such as central nervous system injuries, Alzheimer's disease and amyotrophic lateral sclerosis. More comprehensive elucidation of the mechanisms, and therapies targeting these molecular signaling networks may allow for neuronal tissue regeneration following an injury. Due to a diverse role of the p75(NTR) q in biology, the body of evidence comprising its biological role is diffusely spread out over numerous fields. This review condenses the main evidence of p75(NTR) for clinical applications and presents new findings from published literature how data mining approach combined with bioinformatic analyses can be utilized to gain new hypotheses in a molecular and network level.
  • Budowle, Bruce; Connell, Nancy D.; Bielecka-Oder, Anna; Colwell, Rita R.; Corbett, Cindi R; Fletcher, Jacqueline; Forsman, Mats; Kadavy, Dana R; Markotic, Alemka; Morse, Stephen A; Murch, Randall S; Sajantila, Antti; Schemes, Sarah E; Ternus, Krista L; Turner, Stephen D; Minot, Samuel (2014)
    High throughput sequencing (HTS) generates large amounts of high quality sequence data for microbial genomics. The value of HTS for microbial forensics is the speed at which evidence can be collected and the power to characterize microbial-related evidence to solve biocrimes and bioterrorist events. As HTS technologies continue to improve, they provide increasingly powerful sets of tools to support the entire field of microbial forensics. Accurate, credible results allow analysis and interpretation, significantly influencing the course and/or focus of an investigation, and can impact the response of the government to an attack having individual, political, economic or military consequences. Interpretation of the results of microbial forensic analyses relies on understanding the performance and limitations of HTS methods, including analytical processes, assays and data interpretation. The utility of HTS must be defined carefully within established operating conditions and tolerances. Validation is essential in the development and implementation of microbial forensics methods used for formulating investigative leads attribution. HTS strategies vary, requiring guiding principles for HTS system validation. Three initial aspects of HTS, irrespective of chemistry, instrumentation or software are: 1) sample preparation, 2) sequencing, and 3) data analysis. Criteria that should be considered for HTS validation for microbial forensics are presented here. Validation should be defined in terms of specific application and the criteria described here comprise a foundation for investigators to establish, validate and implement HTS as a tool in microbial forensics, enhancing public safety and national security.