Molecular Components Of Cells Research Articles

Systemic cellular migration: The forces driving the directed locomotion movement of cells.

Directional motility is an essential property of cells. Despite its enormous relevance in many fundamental physiological and pathological processes, how cells control their locomotion movements remains an unresolved question. Here, we have addressed the systemic processes driving the directed locomotion of cells. Specifically, we have performed an exhaustive study analyzing the trajectories of 700 individual cells belonging to three different species (Amoeba proteus, Metamoeba leningradensis, and Amoeba borokensis) in four different scenarios: in absence of stimuli, under an electric field (galvanotaxis), in a chemotactic gradient (chemotaxis), and under simultaneous galvanotactic and chemotactic stimuli. All movements were analyzed using advanced quantitative tools. The results show that the trajectories are mainly characterized by coherent integrative responses that operate at the global cellular scale. These systemic migratory movements depend on the cooperative nonlinear interaction of most, if not all, molecular components of cells.

Detection of Stage-wise Biomarkers in Lung Adenocarcinoma Using Multiplex Analysis

Introduction: Lung cancer is the leading cancer in terms of morbidity and mortality rate. Its prevalence has been steadily increasing over the world in recent years. An integrated study is unavoidable to analyse the cascading interrelationships between molecular cell components at multiple levels resulting in hidden biological events in cancer. Methods: Multiplex network modeling is a unique methodology that could be used as an integrative method for dealing with diverse interactions. Here, we have employed a multiplex framework to model the lung adenocarcinoma (LUAD) network by incorporating co-expression correlations, methylation relations, and protein physical binding interactions as network layers. Hub nodes identified from the multiplex network utilizing centrality measures, including degree, eigenvector, and random walk with a random jump technique, are considered as biomarker genes. These stage-wise biomarker genes identified for LUAD are investigated using GO enrichment analysis, pathway analysis, and literature evidence to determine their significance in tumor progression. Results: The study has identified a set of stage-specific biomarkers in LUAD. The 31 genes identified from the results of multiple centrality analysis can be targeted as novel diagnostic biomarkers in LUAD. Multiple signaling pathways identified here may be considered as potential targets of interest. Conclusion: Based on the analysis results, patients may be identified by their stage of cancer progression, which can aid in treatment decision-making.

Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework

Complexity of cascading interrelations between molecular cell components at different levels from genome to metabolome ordains a massive difficulty in comprehending biological happenings. However, considering these complications in the systematic modelings will result in realistic and reliable outputs. The multilayer networks approach is a relatively innovative concept that could be applied for multiple omics datasets as an integrative methodology to overcome heterogeneity difficulties. Herein, we employed the multilayer framework to rehabilitate colon adenocarcinoma network by observing co-expression correlations, regulatory relations, and physical binding interactions. Hub nodes in this three-layer network were selected using a heterogeneous random walk with random jump procedure. We exploited local composite modules around the hub nodes having high overlay with cancer-specific pathways, and investigated their genes showing a different expressional pattern in the tumor progression. These genes were examined for survival effects on the patient’s lifespan, and those with significant impacts were selected as potential candidate biomarkers. Results suggest that identified genes indicate noteworthy importance in the carcinogenesis of the colon.

Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks.

Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.

YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut.

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software.

YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software.

YeATS - a tool suite for analyzing RNA-seq derived transcriptome identifies a highly transcribed putative extensin in heartwood/sapwood transition zone in black walnut

The transcriptome provides a functional footprint of the genome by enumerating the molecular components of cells and tissues. The field of transcript discovery has been revolutionized through high-throughput mRNA sequencing (RNA-seq). Here, we present a methodology that replicates and improves existing methodologies, and implements a workflow for error estimation and correction followed by genome annotation and transcript abundance estimation for RNA-seq derived transcriptome sequences (YeATS - Yet Another Tool Suite for analyzing RNA-seq derived transcriptome). A unique feature of YeATS is the upfront determination of the errors in the sequencing or transcript assembly process by analyzing open reading frames of transcripts. YeATS identifies transcripts that have not been merged, result in broken open reading frames or contain long repeats as erroneous transcripts. We present the YeATS workflow using a representative sample of the transcriptome from the tissue at the heartwood/sapwood transition zone in black walnut. A novel feature of the transcriptome that emerged from our analysis was the identification of a highly abundant transcript that had no known homologous genes (GenBank accession: KT023102). The amino acid composition of the longest open reading frame of this gene classifies this as a putative extensin. Also, we corroborated the transcriptional abundance of proline-rich proteins, dehydrins, senescence-associated proteins, and the DNAJ family of chaperone proteins. Thus, YeATS presents a workflow for analyzing RNA-seq data with several innovative features that differentiate it from existing software.

Next-generation biomarkers based on 100-parameter functional super-resolution microscopy TIS

Functional super-resolution (fSR) microscopy is based on the automated toponome imaging system (TIS). fSR-TIS provides insight into the myriad of different cellular functionalities by direct imaging of large subcellular protein networks in morphologically intact cells and tissues, referred to as the toponome. By cyclical fluorescence imaging of at least 100 molecular cell components, fSR-TIS overcomes the spectral limitations of fluorescence microscopy, which is the essential condition for the detection of protein network structures in situ/in vivo. The resulting data sets precisely discriminate between cell types, subcellular structures, cell states and diseases (fSR). With up to 16 bits per protein, the power of combinatorial molecular discrimination (PCMD) is at least 2(100) per subcellular data point. It provides the dimensionality necessary to uncover thousands of distinct protein clusters including their subcellular hierarchies controlling protein network topology and function in the one cell or tissue section. Here we review the technology and findings showing that functional protein networks of the cell surface in different cancers encompass the same hierarchical and spatial coding principle, but express cancer-specific toponome codes within that scheme (referred to as TIS codes). Findings suggest that TIS codes, extracted from large-scale toponome data, have the potential to be next-generation biomarkers because of their cell type and disease specificity. This is functionally substantiated by the observation that blocking toponome-specific lead proteins results in disassembly of molecular networks and loss of function.

Detection and quantification of poliovirus infection using FTIR spectroscopy and cell culture

BackgroundIn a globalized word, prevention of infectious diseases is a major challenge. Rapid detection of viable virus particles in water and other environmental samples is essential to public health risk assessment, homeland security and environmental protection. Current virus detection methods, especially assessing viral infectivity, are complex and time-consuming, making point-of-care detection a challenge. Faster, more sensitive, highly specific methods are needed to quantify potentially hazardous viral pathogens and to determine if suspected materials contain viable viral particles. Fourier transform infrared (FTIR) spectroscopy combined with cellular-based sensing, may offer a precise way to detect specific viruses. This approach utilizes infrared light to monitor changes in molecular components of cells by tracking changes in absorbance patterns produced following virus infection. In this work poliovirus (PV1) was used to evaluate the utility of FTIR spectroscopy with cell culture for rapid detection of infective virus particles.ResultsBuffalo green monkey kidney (BGMK) cells infected with different virus titers were studied at 1 - 12 hours post-infection (h.p.i.). A partial least squares (PLS) regression method was used to analyze and model cellular responses to different infection titers and times post-infection. The model performs best at 8 h.p.i., resulting in an estimated root mean square error of cross validation (RMSECV) of 17 plaque forming units (PFU)/ml when using low titers of infection of 10 and 100 PFU/ml. Higher titers, from 103 to 106 PFU/ml, could also be reliably detected.ConclusionsThis approach to poliovirus detection and quantification using FTIR spectroscopy and cell culture could potentially be extended to compare biochemical cell responses to infection with different viruses. This virus detection method could feasibly be adapted to an automated scheme for use in areas such as water safety monitoring and medical diagnostics.

The origins and evolution of freeze-etch electron microscopy

The introduction of the Balzers freeze-fracture machine by Moor in 1961 had a much greater impact on the advancement of electron microscopy than he could have imagined. Devised originally to circumvent the dangers of classical thin-section techniques, as well as to provide unique en face views of cell membranes, freeze-fracturing proved to be crucial for developing modern concepts of how biological membranes are organized and proved that membranes are bilayers of lipids within which proteins float and self-assemble. Later, when freeze-fracturing was combined with methods for freezing cells that avoided the fixation and cryoprotection steps that Moor still had to use to prepare the samples for his original invention, it became a means for capturing membrane dynamics on the millisecond time-scale, thus allowing a deeper understanding of the functions of biological membranes in living cells as well as their static ultrastructure. Finally, the realization that unfixed, non-cryoprotected samples could be deeply vacuum-etched or even freeze-dried after freeze-fracturing opened up a whole new way to image all the other molecular components of cells besides their membranes and also provided a powerful means to image the interactions of all the cytoplasmic components with the various membranes of the cell. The purpose of this review is to outline the history of these technical developments, to describe how they are being used in electron microscopy today and to suggest how they can be improved in order to further their utility for biological electron microscopy in the future.

Quantitative Assessment of Specificity in Immunoelectron Microscopy

In immunoelectron microscopy (immuno-EM) on ultrathin sections, gold particles are used for localization of molecular components of cells. These particles are countable, and quantitative methods have been established to estimate and evaluate the density and distribution of "raw" gold particle counts from a single uncontrolled labeling experiment. However, these raw counts are composed of two distinct elements: particles that are specific (specific labeling) and particles that are not (nonspecific labeling) for the target component. So far, approaches for assessment of specific labeling and for correction of raw gold particle counts to reveal specific labeling densities and distributions have not attracted much attention. Here, we discuss experimental strategies for determining specificity in immuno-EM, and we present methods for quantitative assessment of (1) the probability that an observed gold particle is specific for the target, (2) the density of specific labeling, and (3) the distribution of specific labeling over a series of compartments. These methods should be of general utility for researchers investigating the distribution of cellular components using on-section immunogold labeling.

Noise in biological circuits

Noise biology focuses on the sources, processing, and biological consequences of the inherent stochastic fluctuations in molecular transitions or interactions that control cellular behavior. These fluctuations are especially pronounced in small systems where the magnitudes of the fluctuations approach or exceed the mean value of the molecular population. Noise biology is an essential component of nanomedicine where the communication of information is across a boundary that separates small synthetic and biological systems that are bound by their size to reside in environments of large fluctuations. Here we review the fundamentals of the computational, analytical, and experimental approaches to noise biology. We review results that show that the competition between the benefits of low noise and those of low population has resulted in the evolution of genetic system architectures that produce an uneven distribution of stochasticity across the molecular components of cells and, in some cases, use noise to drive biological function. We review the exact and approximate approaches to gene circuit noise analysis and simulation, and review many of the key experimental results obtained using flow cytometry and time-lapse fluorescent microscopy. In addition, we consider the probative value of noise with a discussion of using measured noise properties to elucidate the structure and function of the underlying gene circuit. We conclude with a discussion of the frontiers of and significant future challenges for noise biology.

Functional architecture of the cell nucleus: Towards comprehensive toponome reference maps of apoptosis

We have recently described the MELC/TIS fluorescence robot technology that is capable of colocalizing at least a hundred different molecular cell components in one cell. The technology reveals new hierarchical properties of protein network organisation, referred to as the toponome, in which topologically confined protein clusters are interlocked within the structural framework of the cell. In this study we have applied MELC/TIS to construct a three-dimensional toponome map of the cell nucleus of a single human hepatocyte undergoing apoptosis. The map reveals six different spatially separated toponome domains in the nuclear interior of one apoptotic cell. In the drive to decipher the apoptosis-specific molecular network on the single cell level, the present toponome map is a first milestone towards the construction of much larger maps addressing hundreds of molecular cell components across the stages of apoptosis.

Cytomics in characterizing Toponomes: Towards the biological code of the cell

Cytomics in characterizing Toponomes: Towards the biological code of the cell

Effect of hyperoxia and metformin on vascular responses to vasoactive compounds in rats.

Exposure of cells to oxygen concentrations higher than normal (hyperoxia) damages the molecular components of cells, resulting in cellular dysfunction and death. Metformin, a biguanide molecule used for treating non-insulin-dependent diabetes, been shown to lower blood pressure. The aim of this study was to investigate the possible effects of hyperoxia and metformin on the vascular responses of thoracic aorta to vasoactive compounds, using an in vitro rat model. In the hyperoxia-control (HC) group, the response to acetylcholine was completely abolished, but metformin treatment before (MH) or after (HM) exposure to 100% oxygen restored the response to acetylcholine to near-control values. In aortas from HC, MH, or HM groups, no significant differences were found in pD2 values to the endothelium-dependent vasodilator sodium nitroprussiate. In aortic strips from metformin-treated rats, the pD2 values for noradrenaline in the presence of endothelium were significantly smaller than those in the normal control group. The maximal contractile responses to KCl were not significantly different among all experimental groups. The results of the present study show that in hyperoxia-exposed rats, metformin treatment reverses the abolished vascular relaxation to AChe.

Concepts, potentials and methods for constructing transgenic cells and animals.

The spectre of methods to stably alter the genetic constitution of cells and animals is currently being extended by methods of unprecedented precision. Whereas classical transgene constructs are used to randomly insert additional sequences into the genome, todays targeting vectors allow the modification of a selected endogenous gene in a precisely predetermined way. This has opened new ways to experimentally investigate the molecular components of cells and of complex in vivo systems (Hanahan 1989). With regard to toxicology, genetically modified cells and animals will contribute to a better understanding of the primary events and of the mechanisms leading to the manifestation of damage by toxic agents (Liggitt and Reddington 1992). This in turn will eventually lead to refined in vitro and in vitro systems to monitor for toxic effects.