Biology subjects

Dikicioglu, D.

Publications and source records attributed to Dikicioglu, D..

7 recordsLinked to original sources

bioRxiv2022

Automated liquid-handling operations for robust, resilient, and efficient bio-based laboratory practices

Increase in the adoption of liquid handling devices (LHD) can facilitate experimental activities. Initially adopted by businesses and industry-based laboratories, the practice has also moved to academic environments, where a wide range of non-standard/non-typical experiments can be performed. Current protocols or laboratory analyses require researchers to transfer liquids for the purpose of dilution, mixing, or inoculation, among other operations. LHD can render laboratories more efficient by performing more experiments per unit of time, by making operations robust and resilient against external factors and unforeseen events such as the COVID-19 pandemic, and by remote operation. The present work reviews literature that reported the adoption and utilisation of LHD available in the market and presents examples of their practical use. Applications demonstrate the critical role of automation in research development and its ability to reduce human intervention in the experimental workflow. Ultimately, this work will provide guidance to academic researchers to determine which LHD can fulfil their needs and how to exploit their use in both conventional and non-conventional applications. Furthermore, the breadth of applications and the scarcity of academic institutions involved in research and development that utilise these devices highlights an important area of opportunity for shift in technology to maximize research outcomes.

bioengineering↗

bioRxiv2020

Formal model of Parkinson's disease neurons unveils possible causality links in the pathophysiology of the disease

Parkinsons Disease is the second most common neurodegenerative disease after Alzheimers disease. Despite extensive research, the initial cause of the disease is still unknown, although substantial advances were made in understanding of its genetics and the cognate neurophysiological mechanisms. Determining the causality relationships and the chronological steps pertaining to Parkinsons Disease is essential for the discovery of novel drug targets. We developed a systematic in silico model based on available data, which puts the possible sequence of events occurring in a neuron during disease onset into light. This is the first ever attempt, to our knowledge, to model comprehensively the primary modifications in the molecular pathways that manifest in compromised neurons from the commencement of the disease to the consequences of its progression. We showed that our proposed disease pathway was relevant for unveiling yet incomplete knowledge on calcium homeostasis in mitochondria, ROS production and -synuclein misfolding. Graphical abstract HighlightsO_LIVarying calcium concentration in aging dopaminergic neurons triggers disease onset. C_LIO_LIROS production in the mitochondria potentially causes iron accumulation. C_LIO_LIIron homeostasis dysregulation is linked to -synuclein aggregation. C_LI

systems biology↗

bioRxiv2019

Metabolic response to Parkinson’s disease recapitulated by the haploinsufficient phenotype of diploid yeast cells hemizygous for the adrenodoxin reductase gene

Adrenodoxin reductase, a widely conserved mitochondrial P450 protein, catalyses essential steps in steroid hormone biosynthesis and is highly expressed in the adrenal cortex. The yeast adrenodoxin reductase homolog, Arh1p, is involved in cytoplasmic and mitochondrial iron homeostasis and is required for activity of enzymes containing an Fe-S cluster. In this paper, we investigated the response of yeast to the loss of a single copy of ARH1, an oxidoreductase of the mitochondrial inner membrane, which is among the few mitochondrial proteins that is essential for viability in yeast. The phenotypic, transcriptional, proteomic, and metabolic landscape indicated that Saccharomyces cerevisiae successfully adapted to this loss, displaying an apparently dosage-insensitive cellular response. However, a considered investigation of transcriptional regulation in ARH1-impaired yeast highlighted that a significant hierarchical reorganisation occurred, involving the iron assimilation and tyrosine biosynthetic processes. The interconnected roles of the iron and tyrosine pathways, coupled with oxidative processes, are of interest beyond yeast since they are involved in dopaminergic neurodegeneration associated with Parkinsons disease. The identification of similar responses in yeast suggest that this simple eukaryote could have potential as a model system for investigating the regulatory mechanisms leading to the initiation and progression of early disease responses in humans.

systems biology↗

bioRxiv2018

Understanding the killing mechanism of action by virus-infected yeasts

Killer yeasts are microorganisms, which can produce and secrete proteinaceous toxins, a characteristic gained via infection by a virus. These toxins are able to kill sensitive cells of the same or a related species. From a biotechnological perspective, killer yeasts have been considered as beneficial due to their antifungal/antimicrobial activity, but also regarded as problematic for large-scale fermentation processes, whereby those yeasts would kill species off starter cultures and lead to stuck fermentations. Here, we propose a mechanistic model of the toxin-binding kinetics pertaining to the killer population coupled with the toxin-induced death kinetics of the sensitive population to study toxic action in silico. Our deterministic model explains how killer Saccharomyces cerevisiae cells distress and consequently kill the sensitive members of the species, accounting for the K1, K2 and K28 toxin mode of action at high or low concentrations. The dynamic model captured the transient toxic activity starting from the introduction of killer cells into the culture at the time of inoculation through to induced cell death, and allowed us to gain novel insight on these mechanisms. The kinetics of K1/K2 activity via its primary pathway of toxicity was 5.5 times faster than its activity at low concentration inducing the apoptotic pathway in sensitive cells. Conversely, we showed that the primary pathway for K28 was approximately 3 times slower than its equivalent apoptotic pathway, indicating the particular relevance of K28 in biotechnological applications where the toxin concentration is rarely above those limits to trigger the primary pathway of killer activity.

systems biology↗

bioRxiv2018

Saccharomyces cerevisiae adapted to grow in the presence of low-dose rapamycin exhibit altered amino acid metabolism

Rapamycin is a potent inhibitor of the highly conserved TOR kinase, the nutrient-sensitive controller of growth and aging. It has been utilised as a chemotherapeutic agent due to its anti-proliferative properties and as an immunosuppressive drug, and is also known to extend lifespan in a range of eukaryotes from yeast to mammals. However, the mechanisms through which eukaryotic cells adapt to sustained exposure to rapamycin have not yet been thoroughly investigated. Here, S. cerevisiae response to long-term rapamycin exposure was investigated by identifying the physiological, transcriptomic and metabolic differences observed for yeast populations inoculated into low-dose rapamycin-containing environment. The effect of oxygen availability and acidity of extracellular environment on this response was further deliberated by controlling or monitoring the dissolved oxygen level and pH of the culture. Yeast populations grown in the presence of rapamycin reached higher cell densities complemented by an increase in their chronological lifespan, and these physiological adaptations were associated with a rewiring of the amino acid metabolism, particularly that of arginine. The ability to synthesise amino acids emerges as the key factor leading to the major mechanistic differences between mammalian and microbial TOR signalling pathways in relation to nutrient recognition. Furthermore, oxygen levels and extracellular acidity of the culture were observed to conjointly affect yeast populations, virtually acting as coupled physiological effectors; cells were best adapted when maximal oxygenation of the culture was maintained in slightly acidic pH, any deviation necessitated more extensive readjustment to additional stress factors.

biochemistry↗

bioRxiv2017

Organisation of the transcriptional regulation of genes involved in protein transactions in yeast

The topological analyses of many large-scale molecular interaction networks often provide only limited insights into network function or evolution. In this paper, we argue that the functional heterogeneity of network components, rather than network size, is the main factor limiting the utility of topological analysis of large cellular networks. We have analysed large epistatic, functional, and transcriptional regulatory networks of genes that were attributed to the following biological process groupings: protein transactions, gene expression, cell cycle, and small molecule metabolism. Control analyses were performed on networks of randomly selected genes. We identified novel biological features emerging from the analysis of functionally homogenous biological networks irrespective of their size. In particular, direct regulation by transcription as an underrepresented feature of protein transactions. The analysis also demonstrated that the regulation of the genes involved in protein transactions at the transcriptional level was orchestrated by only a small number of regulators. Quantitative proteomic analysis of nuclear- and chromatin-enriched sub-cellular fractions of yeast provided supportive evidence for the conclusions generated by network analyses.

systems biology↗

bioRxiv2017

Estimating global enzyme abundance levels from cofactor requirements: a model-based analysis of the iron metabolism in yeast

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. Unfortunately such data are only available on a fraction of the enzyme and transporter pools, and this leaves us distant from generating the full picture. We here propose a strategy to quantify metabolic protein requirements for cofactor-bearing enzymes and transporters through constraint-based modelling. In this work, we constructed the first eukaryotic genome-scale metabolic model with a comprehensive iron metabolism and investigated partial functional impairment of the genes involved in iron-sulphur (Fe-S) cluster maturation, which revealed extensive rewiring of the fluxes in silico, despite only marginal phenotypic dissimilarities. The yeast metabolism at steady state was determined to employ a constant iron-recruiting enzyme turnover at a rate of 3.02 x 10-11 mM / (g biomass)-1 h-1. Furthermore, we employed this model to identify and study a mutualistic relationship between Streptomyces coelicolor and Saccharomyces cerevisiae, which involves iron and folate sharing economies between the two microbes.

systems biology↗