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Following publication of the original article [1], we have been notified that the tagging of one of the author names was done incorrectly in the XML version of the paper. The online and pdf versions of this paper are not affected by the change. Original and corrected tagging can be seen below. The original article has been corrected. [ABSTRACT FROM AUTHOR]

Background: Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration., Results: Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems., Conclusion: Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved. Results: In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells. Conclusions: In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods. [ABSTRACT FROM AUTHOR]

Background: Glycobiology pertains to the study of carbohydrate sugar chains, or glycans, in a particular cell or organism. Many computational approaches have been proposed for analyzing these complex glycan structures, which are chains of monosaccharides. The monosaccharides are linked to one another by glycosidic bonds, which can take on a variety of comformations, thus forming branches and resulting in complex tree structures. The q-gram method is one of these recent methods used to understand glycan function based on the classification of their tree structures. This q-gram method assumes that for a certain q, different q-grams share no similarity among themselves. That is, that if two structures have completely different components, then they are completely different. However, from a biological standpoint, this is not the case. In this paper, we propose a weighted q-gram method to measure the similarity among glycans by incorporating the similarity of the geometric structures, monosaccharides and glycosidic bonds among q-grams. In contrast to the traditional q-gram method, our weighted q-gram method admits similarity among q-grams for a certain q. Thus our new kernels for glycan structure were developed and then applied in SVMs to classify glycans. Results: Two glycan datasets were used to compare the weighted q-gram method and the original q-gram method. The results show that the incorporation of q-gram similarity improves the classification performance for all of the important glycan classes tested. Conclusion: The results in this paper indicate that similarity among q-grams obtained from geometric structure, monosaccharides and glycosidic linkage contributes to the glycan function classification. This is a big step towards the understanding of glycan function based on their complex structures. [ABSTRACT FROM AUTHOR]

Background: With the advent of new high-throughput electron microscopy techniques such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion-beam scanning electron microscopy (FIB-SEM) biomedical scientists can study sub-cellular structural mechanisms of heart disease at high resolution and high volume. Among several key components that determine healthy contractile function in cardiomyocytes are Z-disks or Z-lines, which are located at the lateral borders of the sarcomere, the fundamental unit of striated muscle. Z-disks play the important role of anchoring contractile proteins within the cell that make the heartbeat. Changes to their organization can affect the force with which the cardiomyocyte contracts and may also affect signaling pathways that regulate cardiomyocyte health and function. Compared to other components in the cell, such as mitochondria, Z-disks appear as very thin linear structures in microscopy data with limited difference in contrast to the remaining components of the cell.Methods: In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including "pre-processing", "segmentation" and "refinement". We represent a simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre-process the dataset, and well-known "Sobel operators" are used in the segmentation module.Results: We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have.Conclusions: Our validation results demonstrate the robustness and reliability of our algorithm and model both in terms of validation metrics and in terms of a comparison with a 3D visualisation of Z-disks obtained using immunofluorescence based confocal imaging. [ABSTRACT FROM AUTHOR]

Background: Methods based on site-specific recombinases are widely used in studying gene activities in vivo and in vitro. In these studies, constitutively active or inducible variants of these recombinases are expressed under the control of either lineage-specific or ubiquitous promoters. However, there is a need for more advanced schemes that combine these features with possibilities to choose a time point from which lineage tracing starts in an autonomous fashion. For example, the key mammalian germline gatekeeper gene Oct4 (Pou5f1) is expressed in the peri-implantation epiblast which gives rise to all cells within embryos. Thus the above techniques are hardly applicable to Oct4 tracing past the epiblast stage, and the establishment of genetic tools addressing such a limitation is a highly relevant pursuit. Methods: The CRISPR/Cas9 tool was used to manipulate the genome of mouse embryonic stem cells (ESCs), and various cell culture technics—to maintain and differentiate ESCs to neural cell, lentivirus-based reprogramming technique—to generate induced pluripotent stem cells (iPSCs). Results: In this paper, we have developed a two-component genetic system (referred to as O4S) that allows tracing Oct4 gene activity past the epiblast stage of development. The first component represents a knock-in of an ubiquitous promoter-driven inducible Cre, serving as a stop signal for downstream tdTomato. Upon activation of Cre activity with 4-hydroxytamoxifen (4-OHT) at any given time point, the recombinase excises a stop signal and poses the second component of the system—the FlpO recombinase, knocked into 3'UTR of Oct4, to be expressed upon activation of the latter gene. Oct4-driven expression of FlpO, in turn, triggers the tdTomato expression and thus, permanently marks Oct4+ cells and their progeny. We have validated the O4S system in cultured ESCs and shown that it is capable, for example, to timely capture an activation of Oct4 gene during the reprogramming of somatic cells into iPSCs. Conclusions: The developed O4S system can be used to detect Oct4 activation event, both permanent and transient, in somatic cell types outside the germline. The approach can be equally adjusted to other genes, provided the first component of the system is placed under transcriptional control of these genes, thus, making it a valuable tool for cell fate mapping in mice. [ABSTRACT FROM AUTHOR]

Background: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology., Results: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations., Conclusion: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Keywords: Pancreatic ductal adenocarcinoma; PanNET; Single-cell sequencing; Tumor heterogeneity EN Pancreatic ductal adenocarcinoma PanNET Single-cell sequencing Tumor heterogeneity 1 3 3 07/09/20 20200708 NES 200708 Background Multi-omics sequencing techniques have been applied to dissect inter- and intra-tumoral heterogeneity. Single-cell sequencing is pivotal for exploring tumor heterogeneity and dissecting the tumor-related mechanism in detail. In a recent paper published in I Cell Research i [[1]], Peng et al. applied single-cell RNA sequencing to 24 primary PDAC tumors and 11 control pancreases. [Extracted from the article]

Background: Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. Results: This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. Conclusions: As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-orpora.sourceforge.net/CRAFT/index.shtml. [ABSTRACT FROM AUTHOR]

Background: In biomedical ontologies, mereological relations have always been subject to special interest due to their high relevance in structural descriptions of anatomical entities, cells, and biomolecules. This paper investigates two important subrelations of has_proper_part, viz. the relation has_grain, which relates a collective entity to its multiply occurring uniform parts (e.g., water molecules in a portion of water), and the relation has_component, which relates a compound to its constituents (e.g., molecules to the atoms they consist of). Method: We distinguish between four kinds of complex entities and characterize them in first order logic. We then discuss whether similar characterizations could be given in description logics, and finally apply the results to mixtures. Results: At first sight, collectives and compounds seem to be disjoint categories. Their disjointness, however, relies on agreement about what are uniform entities, and thus on the granularity of description. For instance, the distinction between isomeric subtypes of a molecule can be important in one use case but might be neglected in another one. We demonstrate that, as implemented in the BioTop domain upper level ontology, equivalence or subsumption between different descriptions of same or similar entities cannot be achieved. Using OWL-DL, we propose a new design pattern that avoids primitive subrelations at the expense of more complex descriptions and thus supports the needed inferences. [ABSTRACT FROM AUTHOR]

Background: RNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence size increases. This makes the case for parallelization. Parallelization can be achieved by way of networked platforms (clusters, grids, etc) as well as using modern day multi-core chips. Methods: In this paper, we exploit the parallelism capabilities of the IBM Cell Broadband Engine to parallelize an existing Dynamic Programming (DP) algorithm for RNA secondary structure prediction. We design three different implementation strategies that exploit the inherent data, code and/or hybrid parallelism, referred to as C-Par, D-Par and H-Par, and analyze their performances. Our approach attempts to introduce parallelism in critical sections of the algorithm. We ran our experiments on SONY Play Station 3 (PS3), which is based on the IBM Cell chip. Results: Our results suggest that introducing parallelism in DP algorithm allows it to easily handle longer sequences which otherwise would consume a large amount of time in single core computers. The results further demonstrate the speed-up gain achieved in exploiting the inherent parallelism in the problem and also elicits the advantages of using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA. Conclusion: The speed-up performance reported here is promising, especially when sequence length is long. To the best of our literature survey, the work reported in this paper is probably the first-of-its-kind to utilize the IBM Cell Broadband Engine (a heterogeneous multi-core chip) to implement a DP. The results also encourage using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA to predict its secondary structure. [ABSTRACT FROM AUTHOR]

Background: Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. Results: In this paper, we present an approach based on a new learning framework, semisupervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. Conclusion: Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%. [ABSTRACT FROM AUTHOR]

During interphase, each cell contains a single centrosome that acts as a microtubule organizing center for cellular functions in interphase and in mitosis. Centrosome amplification during the S phase of the cell cycle is a tightly regulated process to ensure that each daughter cell receives the proper complement of the genome. The controls that ensure that centrosomes are duplicated exactly once in the cell cycle are not well understood. In solid tumors and hematological malignancies, centrosome abnormalities resulting in aneuploidy is observed in the majority of cancers. These phenotypes are also observed in cancers induced by viruses, including adult T cell lymphoma which is caused by the human T cell lymphotrophic virus Type 1 (HTLV-1). Several reports have indicated that the HTLV-1 transactivator, Tax, is directly responsible for the centrosomal abnormalities observed in ATL cells. A recent paper in Nature Cell Biology by Ching et al. has shed some new light into how Tax may be inducing centrosome abnormalities. The authors demonstrated that 30% of ATL cells contained more than two centrosomes and expression of Tax alone induced supernumerary centrosomes. A cellular coiled-coil protein, Tax1BP2, was shown to interact with Tax and disruption of this interaction led to failure of Tax to induce centrosome amplification. Additionally, down-regulation of Tax1BP2 led to centrosome amplification. These results suggest that Tax1BP2 may be an important block to centrosome re-duplication that is observed in normal cells. Presently, a specific cellular protein that prevents centrosome re-duplication has not been identified. This paper has provided further insight into how Tax induces centrosome abnormalities that lead to ATL. Lastly, additional work on Tax1BP2 will also provide insight into how the cell suppresses centrosome re-duplication during the cell cycle and the role that Tax1BP2 plays in this important cellular pathway. [ABSTRACT FROM AUTHOR]

Background: Astaxanthin (ATX) is a carotenoid pigment with pleiotropic pharmacological properties that is seen as a possible drug for treating cerebral ischemic injury and subarachnoid hemorrhage. Na+-K+-2Cl- co-transporter-1 (NKCC1), an intrinsic membrane protein expressed by many cell types, is activated by various insults, leading to the formation of cell swelling and brain edema. We previously established that ATX attenuated brain edema and improved neurological outcomes by modulating NKCC1 expression after traumatic brain injury in mice. This paper explored the molecular mechanism of ATX-mediated inhibition of NKCC1 utilizing an in vitro astrocyte stretch injury model.Results: Stretch injury in cultured astrocytes lowered cell viability time-dependently, which was substantially reducing by pretreating with ATX (50 μmol/L). Stretch injury increased Bax level and cleaved caspase-3 activity, and decreased Bcl-2 level and pro-caspase 3 activity, resulting in the apoptosis of astrocytes. Additionally, stretch injury substantially raised the gene and protein expressions of interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α and prompted the expression and nuclear translocation of NF-κB. Pretreatment with ATX remarkably prevented the trauma-induced initiation of NF-κB, expressions of pro-inflammatory cytokines, and cell apoptosis. Moreover, stretch injury markedly elevated the gene and protein expression of NKCC1, which was partly blocked by co-treatment with ATX (50 µmol/L) or an NF-κB inhibitor (PDTC, 10 µmol/L). Cleaved caspase-3 activity was partially reduced by PDTC (10 µmol/L) or an NKCC1 inhibitor (bumetanide, 50 µmol/L).Conclusions: ATX attenuates apoptosis after stretch injury in cultured astrocytes by inhibiting NKCC1 expression, and it acts by reducing the expression of NF-κB-mediated pro-inflammatory factors. [ABSTRACT FROM AUTHOR]

Background: Encephalitis caused by dengue virus (DENV) is considered a manifestation of severe dengue. Tanreqing injection (TRQ) is a well-known Chinese patented medicine, which has been used to treat brain-related disorders by inhibiting inflammation. Nevertheless, the effects of TRQ on DENV encephalitis have not been studied. The aim of this study was to evaluate the effects of TRQ on DENV encephalitis and to explore its potential mechanisms. Methods: The cytotoxicity of TRQ was examined by MTT assay, and the anti-DENV activities of TRQ in BHK-21 baby hamster kidney fibroblast were evaluated through CCK-8 and plaque assays. The expression levels of NO, IL1B/IL-1β, TNFα and IL6 were measured by qRT‒PCR and ELISA in the BV2 murine microglial cell line. The inhibitory effects of TRQ on NLRP3 inflammasome activation in BV2 cells were examined by Western blotting, qRT‒PCR and ELISA. The effects of TRQ on HT22 mouse hippocampal neuronal cells were examined by CCK-8 assay, morphology observation and flow cytometry. Moreover, a DENV-infected ICR suckling mouse model was developed to investigate the protective role of TRQ in vivo. Results: TRQ decreased the release of NO, IL6, TNFα and IL1B from BV2 cells and inhibited the activation of NLRP3. The presence of the NLRP3 agonist nigericin reversed the anti-inflammatory activities of TRQ. Furthermore, TRQ inhibited the death of HT22 cells by decreasing IL1B in DENV-infected BV2 cells. In addition, TRQ significantly attenuated weight loss, reduced clinical scores and extended the survival in DENV-infected ICR suckling mice. Critically, TRQ ameliorated pathological changes in ICR suckling mice brain by inhibiting microglia and NLRP3 activation and decreasing the production of inflammatory factors and the number of dead neurons. Conclusion: TRQ exerts potent inhibitory effects on dengue encephalitis in vitro and in vivo by reducing DENV-2-induced microglial activation and subsequently decreasing the inflammatory response, thereby protecting neurons. These findings demonstrate the potential of TRQ in the treatment of dengue encephalitis. [ABSTRACT FROM AUTHOR]

Background: The placement of liners near the pulp area is essential for therapeutic effects and maintaining pulp health while stimulating the formation of tertiary dentin. This in vitro study aimed to evaluate the calcium release, pH, biocompatibility, solubility, and bioactivity of three resin-modified calcium hydroxide cavity liners. Methods: The disc specimens of each cavity liner were prepared using polyethylene molds of 7 mm in diameter and 2 mm in height (n = 10). Three light-cure liners evaluated include Ultra-Blend Plus (UB), Base-it (BI), and Master Dent (MD). The samples were then immersed in flasks containing 10 mL of distilled water. Calcium ion release, pH, and solubility were evaluated in two weeks of incubation. The cytotoxicity of extracts adjacent to the specimens was evaluated by MTT assay using NIH/3T3 cells after 1, 3, and 7 days of incubation. The ability to induce the nucleation of calcium phosphates (CaPs) after 28-day immersion in a simulated body fluid was investigated by SEM-EDX analysis. Statistical analysis was performed using ANOVA, Kruskal-Wallis, and repeated measures tests at the significant level of 0.05. Results: There was a significant difference in the release of calcium ions among the three liners investigated on days 1, 7, and 14 (p < 0.05). UB liners exhibited a significantly higher amount of calcium release than the other two liners, followed by BI, and MD. On day 1, there was no significant difference in the average pH among the three liners. However, after day 7, the MD liner showed a significant decrease in pH compared to the other two liners. BI liner demonstrated the highest level of biocompatibility, followed by the MD and UB liners. UB showed a high calcium release, solubility with no alkalizing activity, and the formation of more mature Ca-rich apatite deposits than the other two liners. Conclusion: Based on the results of this study, the cavity liner material's performance is material dependent. It can impact ion release, biocompatibility, and bioactivity which are important factors to consider in clinical practice. Further studies are needed to investigate the long-term effects of different liner materials on oral tissues. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Background: Multiscale approaches for integrating submodels of various levels of biological organization into a single model became the major tool of systems biology. In this paper, we have constructed and simulated a set of multiscale models of spatially distributed microbial communities and study an influence of unevenly distributed environmental factors on the genetic diversity and evolution of the community members. Results: Haploid Evolutionary Constructor software http://evol-constructor.bionet.nsc.ru/ was expanded by adding the tool for the spatial modeling of a microbial community (1D, 2D and 3D versions). A set of the models of spatially distributed communities was built to demonstrate that the spatial distribution of cells affects both intensity of selection and evolution rate. Conclusion: In spatially heterogeneous communities, the change in the direction of the environmental flow might be reflected in local irregular population dynamics, while the genetic structure of populations (frequencies of the alleles) remains stable. Furthermore, in spatially heterogeneous communities, the chemotaxis might dramatically affect the evolution of community members. [ABSTRACT FROM AUTHOR]

Background Constraint-based metabolic models and flux balance analysis (FBA) have been extensively used in the last years to investigate the behavior of cells and also as basis for different industrial applications. In this context, this work provides a validation of a small-sized FBA model of the yeast Pichia pastoris. Our main objective is testing how accurate is the hypothesis of maximum growth to predict the behavior of P. pastoris in a range of experimental environments. Results A constraint-based model of P. pastoris was previously validated using metabolic flux analysis (MFA). In this paper we have verified the model ability to predict the cells behavior in different conditions without introducing measurements, experimental parameters, or any additional constraint, just by assuming that cells will make the best use of the available resources to maximize its growth. In particular, we have tested FBA model ability to: (a) predict growth yields over single substrates (glucose, glycerol, and methanol); (b) predict growth rate, substrate uptakes, respiration rates, and by-product formation in scenarios where different substrates are available (glucose, glycerol, methanol, or mixes of methanol and glycerol); (c) predict the different behaviors of P. pastoris cultures in aerobic and hypoxic conditions for each single substrate. In every case, experimental data from literature are used as validation. Conclusions We conclude that our predictions based on growth maximisation are reasonably accurate, but still far from perfect. The deviations are significant in scenarios where P. pastoris grows on methanol, suggesting that the hypothesis of maximum growth could be not dominating in these situations. However, predictions are much better when glycerol or glucose are used as substrates. In these scenarios, even if our FBA model is small and imposes a strong assumption regarding how cells will regulate their metabolic fluxes, it provides reasonably good predictions in terms of growth, substrate preference, product formation, and respiration rates. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]

Tumours do not exist in isolation from the organism; their growth, proliferation, motility, and immunosuppressive response are intricately connected to the tumour's microenvironment. As tumour cells and the microenvironment coevolve, an inflammatory microenvironment ensues, propelling the phenomenon of inflammation-cancer transformation—an idea proposed by modern medicine. This review aims to encapsulate the array of representative factors within the tumour's inflammatory microenvironment, such as interleukins (IL-6, IL-10, IL-17, IL-1β), transforming growth factor-beta (TGF-β), interferon-gamma (IFN-γ), tumour necrosis factor-alpha (TNF-α), vascular endothelial growth factor (VEGF), and matrix metalloproteinases (MMPs). Moreover, drawing upon research in traditional Chinese medicine (TCM) and pharmacology, we explore the delicate interplay between these factors and tumour-associated inflammatory cells: tumour-associated macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), tumour-associated neutrophils (TANs) and dendritic cells (DCs). By analyzing the tumour-promoting effects of these entities, we delve into the connotations of Academician Tong Xiao-lin's novel model of "state-target differentiation" and its application in the diagnosis and treatment of tumours. Our aim is to enhance the precision and targeting of tumour treatment in clinical practice. Delving deeper into our understanding of tumour pathogenesis through the lens of modern medicine, we discern the key etiology and pathogenesis throughout the entire developmental stage of tumours, unveiling the evolutionary patterns of Chinese Medicine (CM) states: heat state → phlegm state → stagnation state → deficiency state. Building upon this foundation, we devised a state-regulating formula. Simultaneously, drawing on pharmacological research in traditional Chinese medicine (TCM), we meticulously identified a range of targeted drugs that effectively modulate the aforementioned tumour-related mediators. This comprehensive strategy—a harmonious integration of state identification, target recognition, and simultaneous regulation—aims to elevate clinical efficacy. The fusion of TCM with Western medicine in tumour treatment introduces novel dimensions to the precise and refined application of TCM in clinical practice. [ABSTRACT FROM AUTHOR]

It had been long assumed that almost all insertions of mobile DNA elements occurred during germ-cell development rather than in somatic-cell development, but solid evidence for transposition in somatic cells is now accumulating. To add to this evidence, a recent paper in Mobile DNA reports the somatic transposition of a site-specific retrotransposon, R2, into its insertion site in 28S ribosomal DNA in Drosophila embryos.

According to the hypothesis explored in this paper, native aggregation is genetically controlled (programmed) reversible aggregation that occurs when interacting proteins form new temporary structures through highly specific interactions. It is assumed that Anfinsen's dogma may be extended to protein aggregation: composition and amino acid sequence determine not only the secondary and tertiary structure of single protein, but also the structure of protein aggregates (associates). Cell function is considered as a transition between two states (two states model), the resting state and state of activity (this applies to the cell as a whole and to its individual structures). In the resting state, the key proteins are found in the following inactive forms: natively unfolded and globular. When the cell is activated, secondary structures appear in natively unfolded proteins (including unfolded regions in other proteins), and globular proteins begin to melt and their secondary structures become available for interaction with the secondary structures of other proteins. These temporary secondary structures provide a means for highly specific interactions between proteins. As a result, native aggregation creates temporary structures necessary for cell activity."One of the principal objects of theoretical research in any department of knowledge is to find the point of view from which the subject appears in its greatest simplicity."Josiah Willard Gibbs (1839-1903).

Background: Gene expression profiles and protein dynamics in single cells have a large cell-to-cell variability due to intracellular noise. Intracellular fluctuations originate from two sources: intrinsic noise due to the probabilistic nature of biochemical reactions and extrinsic noise due to randomized interactions of the cell with other cellular systems or its environment. Presently, there is no systematic parameterization and modeling scheme to simulate cellular response at the single cell level in the presence of extrinsic noise. Results: In this paper, we propose a novel statistical ensemble method to simulate the distribution of heterogeneous cellular responses in single cells. We capture the effects of extrinsic noise by randomizing values of the model parameters. In this context, a statistical ensemble is a large number of system replicates, each with randomly sampled model parameters from biologically feasible intervals. We apply this statistical ensemble approach to the well-studied NF-κB signaling system. We predict several characteristic dynamic features of NF-κB response distributions; one of them is the dosage-dependent distribution of the first translocation time of NF-κB. Conclusion: The distributions of heterogeneous cellular responses that our statistical ensemble formulation generates reveal the effect of different cellular conditions, e.g., effects due to wild type versus mutant cells or between different dosages of external stimulants. Distributions generated in the presence of extrinsic noise yield valuable insight into underlying regulatory mechanisms, which are sometimes otherwise hidden. [ABSTRACT FROM AUTHOR]

Gene expression profiles can show significant changes when genetically diseased cells are compared with nondiseased cells. Biological networks are often used to identify active subnetworks (ASNs) of the diseases from the expression profiles to understand the reason behind the observed changes. Current methodologies for discovering ASNs mostly use undirected PPI networks and node centric approaches. This can limit their ability to find the meaningful ASNs when using integrated networks having comprehensive information than the traditional proteinprotein interaction networks. Using appropriate scoring functions to assess both genes and their interactions may allow the discovery of better ASNs. In this paper, we present CASNet, which aims to identify better ASNs using (i) integrated interaction networks (mixed graphs), (ii) directions of regulations of genes, and (iii) combined node and edge scores. We simplify and extend previous methodologies to incorporate edge evaluations and lessen their sensitivity to significance thresholds. We formulate our objective functions using mixed integer programming (MIP) and show that optimal solutions may be obtained. We compare the ASNs obtained by CASNet and similar other approaches to show that CASNet can often discover more meaningful and stable regulatory ASNs. Our analysis of a breast cancer dataset finds that the positive feedback loops across 7 genes, AR, ESR1, MYC, E2F2, PGR, BCL2 and CCND1 are conserved across the basal/triple negative subtypes in multiple datasets that could potentially explain the aggressive nature of this cancer subtype. Furthermore, comparison of the basal subtype of breast cancer and the mesenchymal subtype of glioblastoma ASNs shows that an ASN in the vicinity of IL6 is conserved across the two subtypes. This result suggests that subtypes of different cancers can show molecular similarities indicating that the therapeutic approaches in different types of cancers may be shared. [ABSTRACT FROM AUTHOR]

Background: The normal functioning of a living cell is characterized by complex interaction networks involving many different types of molecules. Associations detected between diseases and perturbations in well-defined pathways within such interaction networks have the potential to illuminate the molecular mechanisms underlying disease progression and response to treatment. Results: In this paper, we present a computational method that compares expression profiles of genes in cancer samples to samples from normal tissues in order to detect perturbations of pre-defined pathways in the cancer. In contrast to many previous methods, our scoring function approach explicitly takes into account the interactions between the gene products in a pathway. Moreover, we compute the sub-pathway that has the highest score, as opposed to merely computing the score for the entire pathway. We use a permutation test to assess the statistical significance of the most perturbed sub-pathway. We apply our method to 20 pathways in the Netpath database and to the Global Cancer Map of gene expression in 18 cancers. We demonstrate that our method yields more sensitive results than alternatives that do not consider interactions or measure the perturbation of a pathway as a whole. We perform a sensitivity analysis to show that our approach is robust to modest changes in the input data. Our method confirms numerous well-known connections between pathways and cancers. Conclusions: Our results indicate that integrating differential gene expression with the interaction structure in a pathway is a powerful approach for detecting links between a cancer and the pathways perturbed in it. Our results also suggest that even well-studied pathways may be perturbed only partially in any given cancer. Further analysis of cancer-specific sub-pathways may shed new light on the similarities and differences between cancers. [ABSTRACT FROM AUTHOR]

Background: The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means. Results: This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA). Conclusions: Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments. [ABSTRACT FROM AUTHOR]

Background: Protein complexes are important entities to organize various biological processes in the cell, like signal transduction, gene expression, and molecular transmission. In most cases, proteins perform their intrinsic tasks in association with their specific interacting partners, forming protein complexes. Therefore, an enriched catalog of protein complexes in a cell could accelerate further research to elucidate the mechanisms underlying many biological processes. However, known complexes are still limited. Thus, it is a challenging problem to computationally predict protein complexes from protein-protein interaction networks, and other genome-wide data sets. Methods: Macropol et al. proposed a protein complex prediction algorithm, called RRW, which repeatedly expands a current cluster of proteins according to the stationary vector of a random walk with restarts with the cluster whose proteins are equally weighted. In the cluster expansion, all the proteins within the cluster have equal influences on determination of newly added protein to the cluster. In this paper, we extend the RRW algorithm by introducing a random walk with restarts with a cluster of proteins, each of which is weighted by the sum of the strengths of supporting evidence for the direct physical interactions involving the protein. The resulting algorithm is called NWE (Node-Weighted Expansion of clusters of proteins). Those interaction data are obtained from the WIPHI database. Results: We have validated the biological significance of the results using curated complexes in the CYC2008 database, and compared our method to RRW and MCL (Markov Clustering), a popular clustering-based method, and found that our algorithm outperforms the other algorithms. Conclusions: It turned out that it is an effective approach in protein complex prediction to expand a cluster of proteins, each of which is weighted by the sum of the strengths of supporting evidence for the direct physical interactions involving the protein. [ABSTRACT FROM AUTHOR]

Background: Folding and intermingling of chromosomes has the potential of bringing close to each other loci that are very distant genomically or even on different chromosomes. On the other hand, genomic rearrangements also play a major role in the reorganisation of loci proximities. Whether the same loci are involved in both mechanisms has been studied in the case of somatic rearrangements, but never from an evolutionary standpoint. Results: In this paper, we analysed the correlation between two datasets: (i) whole-genome chromatin contact data obtained in human cells using the Hi-C protocol; and (ii) a set of breakpoint regions resulting from evolutionary rearrangements which occurred since the split of the human and mouse lineages. Surprisingly, we found that two loci distant in the human genome but adjacent in the mouse genome are significantly more often observed in close proximity in the human nucleus than expected. Importantly, we show that this result holds for loci located on the same chromosome regardless of the genomic distance separating them, and the signal is stronger in gene-rich and open-chromatin regions. Conclusions: These findings strongly suggest that part of the 3D organisation of chromosomes may be conserved across very large evolutionary distances. To characterise this phenomenon, we propose to use the notion of spatial synteny which generalises the notion of genomic synteny to the 3D case. [ABSTRACT FROM AUTHOR]

Background: CrMYC2 is an early jasmonate-responsive bHLH transcription factor involved in the regulation of the expression of the genes of the terpenic indole alkaloid biosynthesis pathway in Catharanthus roseus. In this paper, we identified the amino acid domains necessary for the nuclear targeting of CrMYC2. Findings: We examined the intracellular localization of whole CrMYC2 and of various deletion mutants, all fused with GFP, using a transient expression assay in onion epidermal cells. Sequence analysis of this protein revealed the presence of four putative basic nuclear localization signals (NLS). Assays showed that none of the predicted NLS is active alone. Further functional dissection of CrMYC2 showed that the nuclear targeting of this transcription factor involves the cooperation of three domains located in the C-terminal region of the protein. The first two domains are located at amino acid residues 454-510 and 510-562 and contain basic classical monopartite NLSs; these regions are referred to as NLS3 (KRPRKR) and NLS4 (EAERQRREK), respectively. The third domain, between residues 617 and 652, is rich in basic amino acids that are well conserved in other phylogenetically related bHLH transcription factors. Our data revealed that these three domains are inactive when isolated but act cooperatively to target CrMYC2 to the nucleus. Conclusions: This study identified three amino acid domains that act in cooperation to target the CrMYC2 transcription factor to the nucleus. Further fine structure/function analysis of these amino acid domains will allow the identification of new NLS domains and will allow the investigation of the related molecular mechanisms involved in the nuclear targeting of the CrMYC2 bHLH transcription factor. [ABSTRACT FROM AUTHOR]

Background: Genomic methylation patterns are established during gametogenesis, and perpetuated in somatic cells by faithful maintenance methylation. There have been previous indications that genomic methylation patterns may be less stable in embryonic stem (ES) cells than in differentiated somatic cells, but it is not known whether different mechanisms of de novo and maintenance methylation operate in pluripotent stem cells compared with differentiating somatic cells. Results: In this paper, we show that ablation of the DNA methyltransferase regulator DNMT3L (DNA methyltransferase 3-like) in mouse ES cells renders them essentially incapable of de novo methylation of newly integrated retroviral DNA. We also show that ES cells lacking DNMT3L lose DNA methylation over time in culture, suggesting that DNA methylation in ES cells is the result of dynamic loss and gain of DNA methylation. We found that wild-type female ES cells lose DNA methylation at a much faster rate than do male ES cells; this defect could not be attributed to sex-specific differences in expression of DNMT3L or of any DNA methyltransferase. We also found that human ES and induced pluripotent stem cell lines showed marked but variable loss of methylation that could not be attributed to sex chromosome constitution or time in culture. Conclusions: These data indicate that DNA methylation in pluripotent stem cells is much more dynamic and error-prone than is maintenance methylation in differentiated cells. DNA methylation requires DNMT3L in stem cells, but DNMT3L is not expressed in differentiating somatic cells. Error-prone maintenance methylation will introduce unpredictable phenotypic variation into clonal populations of pluripotent stem cells, and this variation is likely to be much more pronounced in cultured female cells. This epigenetic variability has obvious negative implications for the clinical applications of stem cells. [ABSTRACT FROM AUTHOR]

Background: Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding. Methods: In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots. Results: The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels. Conclusions: The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots. [ABSTRACT FROM AUTHOR]

Comet assay and micronucleus (MN) test are widely applied in genotoxicity testing and biomonitoring. While comet assay permits to measure direct DNA-strand breaking capacity of a tested agent MN test allows estimating the induced amount of chromosome and/or genome mutations. The potential of these two methods can be enhanced by the combination with fluorescence in situ hybridization (FISH) techniques. FISH plus comet assay allows the recognition of targets of DNA damage and repairing directly. FISH combined with MN test is able to characterize the occurrence of different chromosomes in MN and to identify potential chromosomal targets of mutagenic substances. Thus, combination of FISH with the comet assay or MN test proved to be promising techniques for evaluation of the distribution of DNA and chromosome damage in the entire genome of individual cells. FISH technique also permits to study comet and MN formation, necessary for correct application of these methods. This paper reviews the relevant literature on advantages and limitations of Comet-FISH and MN-FISH assays application in genetic toxicology. [ABSTRACT FROM AUTHOR]

Background: Brain, heart and skeletal muscle share similar properties of excitable tissue, featuring both discrete behavior (all-or-nothing response to electrical activation) and continuous behavior (recovery to rest follows a temporal path, determined by multiple competing ion flows). Classical mathematical models of excitable cells involve complex systems of nonlinear differential equations. Such models not only impair formal analysis but also impose high computational demands on simulations, especially in large-scale 2-D and 3-D cell networks. In this paper, we show that by choosing Hybrid Automata as the modeling formalism, it is possible to construct a more abstract model of excitable cells that preserves the properties of interest while reducing the computational effort, thereby admitting the possibility of formal analysis and efficient simulation. Results: We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior. Conclusions: The CellExcite simulation framework for multicellular HA arrays exhibits significantly improved computational efficiency in large-scale simulations, thus opening the possibility for formal analysis based on HA theory. A demo of CellExcite is available at http://www.cs.sunysb.edu/∼eha/. [ABSTRACT FROM AUTHOR]

Background: Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model. Results: In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable. Conclusion: Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network. [ABSTRACT FROM AUTHOR]

Background: Primary hepatocyte cultures are a valuable tool for the understanding of cellular and molecular phenomena occurring during malaria liver stage. This paper describes an improved perfusion/dissociation procedure to isolate hepatocytes from mouse liver that is suitable for malaria studies and allows reproducible preparation of primary hepatocytes with consistent cell yields and controlled purity. Results: This protocol is a detailed description of a technique to isolate and culture mouse hepatocytes and represents an improvement over previous descriptions of hepatocyte isolation for malaria studies, regarding three technical aspects: (1) dissociation reagents choice; (2) cell separation gradient and (3) cell purity control. Cell dissociation was optimized for a specific collagenase digestion media. The cell dissociation step was improved by using a three-layer discontinuous gradient. A cell purity check was introduced to monitor the expression of CD95 on hepatocytes using flow cytometry methods. Conclusion: The procedure described allows reproducible recovery of one to three million hepatocytes per preparation with cell purity of about 90% as determined by FACS analysis. Completion of the protocol is usually achieved in about four hours per preparation and pooling is suggested for multiple preparations of larger number of cells. [ABSTRACT FROM AUTHOR]

Background: Huntington's disease is an inherited neurodegenerative disorder that is caused by the expansion of an N-terminal polyQ stretch in the huntingtin protein. In order to investigate the hypothesis that huntingtin was already involved in development of the nervous system in the last common ancestor of chordates, we isolated and characterised the huntingtin homologue from the amphioxus Branchiostoma floridae. In the present paper the amphioxus general term must be referred to Branchiostoma floridae. Results: In this report, we show that the exon-intron organization of the amphioxus huntingtin gene is highly conserved with that of other vertebrates species. The AmphiHtt protein has two glutamine residues in the position of the typical vertebrate polyQ tract. Sequence conservation is greater along the entire length of the protein than in a previously identified Ciona huntingtin. The first three N-terminal HEAT repeats are highly conserved in vertebrates and amphioxus, although exon rearrangement has occurred in this region. AmphiHtt expression is detectable by in situ hybridization starting from the early neurula stage, where it is found in cells of the neural plate. At later stages, it is retained in the neural compartment but also it appears in limited and well-defined groups of non-neural cells. At subsequent larval stages, AmphiHtt expression is detected in the neural tube, with the strongest signal being present in the most anterior part. Conclusion: The cloning of amphioxus huntingtin allows to infer that the polyQ in huntingtin was already present 540 million years ago and provides a further element for the study of huntingtin function and its evolution along the deuterostome branch. [ABSTRACT FROM AUTHOR]

Background: Previously, we have shown that deviations from the average transcription profile of a group of functionally related genes can be epigenetically transmitted to daughter cells, thereby implicating nuclear programming as the cause. As a first step in further characterizing this phenomenon it was necessary to determine to what extent such deviations occur in non-tumorigenic tissues derived from normal individuals. To this end, a microarray database derived from 90 human donors aged between 22 to 87 years was used to study deviations from the average transcription profile of the proteasome genes. Results: Increase in donor age was found to correlate with a decrease in deviations from the general transcription profile with this decline being gender-specific. The age-related index declined at a faster rate for males although it started from a higher level. Additionally, transcription profiles from similar tissues were more alike than those from different tissues, indicating that deviations arise during differentiation. Conclusion: These findings suggest that aging and differentiation are related to epigenetic changes that alter the transcription profile of proteasomal genes. Since alterations in the structure and function of the proteasome are unlikely, such changes appear to occur without concomitant change in gene function. These findings, if confirmed, may have a significant impact on our understanding of the aging process. [ABSTRACT FROM AUTHOR]

Background: Bistability, the capacity to achieve two distinct stable steady states in response to a set of external stimuli, arises within biological systems ranging from the ? phage switch in bacteria to cellular signal transduction pathways in mammalian cells. On the other hand, more and more experimental evidence in the form of bimodal population distribution has indicated that noise plays a very important role in the switching of bistable systems. However, the physiological mechanism underling noise-induced switching behaviors remains to be fully understood. Results: In this paper, we investigate the effect of noises on switching in single and coupled genetic toggle switch systems in Escherichia coli. In the case of the single toggle switch, we show that the multiplicative noises resulting from stochastic fluctuations in degradation rates can induce switching. In the case of the toggle switches interfaced by a quorum-sensing signaling pathway, we find that stochastic fluctuations in degradation rates inside cells, i.e., intracellular noises, can induce synchronized switching, whereas the extracellular noise additive to the common medium can not only entrain all the individual systems to switch in a synchronous manner but also enhance this ordering behavior efficiently, leading a robust collective rhythm in this interacting system. Conclusion: These insights on the effect of noises would be beneficial to understanding the basic mechanism of how living systems optimally facilitate to function under various fluctuated environments. [ABSTRACT FROM AUTHOR]

Background: Conventional cell culture studies have been performed on 2D surfaces, resulting in flat, extended cell growth. More relevant studies are desired to better mimic 3D in vivo tissue growth. Such realistic environments should be the aim of any cell growth study, requiring new methods for culturing cells in vitro. Cell biology is also tending toward miniaturization for increased efficiency and specificity. This paper discusses the application of a self-assembling peptide-derived hydrogel for use as a 3D cell culture scaffold at the microscale. Results: Phenylalanine derivative hydrogel formation was seen to occur in multiple dispersion media. Cells were immobilized in situ within microchambers designed for cell analysis. Use of the highly biocompatible hydrogel components and simplistic procedures significantly reduced the cytotoxic effects seen with alternate 3D culture materials and microstructure loading methods. Cells were easily immobilized, sustained and removed from microchambers. Differences in growth morphology were seen in the cultured cells, owing to the 3-dimentional character of the gel structure. Degradation improved the removal of hydrogel from the microstructures, permitting reuse of the analysis platforms. Conclusion: Self-assembling diphenylalanine derivative hydrogel provided a method to dramatically reduce the typical difficulties of microculture formation. Effective generation of patterned 3D cultures will lead to improved cell study results by better modeling in vivo growth environments and increasing efficiency and specificity of cell studies. Use of simplified growth scaffolds such as peptide-derived hydrogel should be seen as highly advantageous and will likely become more commonplace in cell culture methodology. [ABSTRACT FROM AUTHOR]

Background: Quantitative simultaneous monitoring of the expression levels of thousands of genes under various experimental conditions is now possible using microarray experiments. However, there are still gaps toward whole-genome functional annotation of genes using the gene expression data. Results: In this paper, we propose a novel technique called Fuzzy Nearest Clusters for genomewide functional annotation of unclassified genes. The technique consists of two steps: an initial hierarchical clustering step to detect homogeneous co-expressed gene subgroups or clusters in each possibly heterogeneous functional class; followed by a classification step to predict the functional roles of the unclassified genes based on their corresponding similarities to the detected functional clusters. Conclusion: Our experimental results with yeast gene expression data showed that the proposed method can accurately predict the genes' functions, even those with multiple functional roles, and the prediction performance is most independent of the underlying heterogeneity of the complex functional classes, as compared to the other conventional gene function prediction approaches. [ABSTRACT FROM AUTHOR]

Background: The embryonic and larval peripheral nervous system of Drosophila melanogaster is extensively studied as a very powerful model of developmental biology. One main advantage of this system is the ability to study the origin and development of individual sensory cells. However, there remain several discrepancies regarding the organization of sensory organs in each abdominal segment A1-A7. Description: We have constructed a web site called FlyPNS (for Fly Peripheral Nervous System) that consolidates a wide range of published and unpublished information about the embryonic and larval sensory organs. It communicates (1) a PNS pattern that solves the discrepancies that have been found in the recent literature, (2) the correspondence between the different nomenclatures that have been used so far, (3) a comprehensive description of each sensory organ, and (4) a list of both published and unpublished markers to reliably identify each PNS cell. Conclusions: The FlyPNS database integrates disparate data and nomenclature and thus helps understanding the conflicting observations that have been published recently. Furthermore, it is designed to provide assistance in the identification and study of individual sensory cells. We think it will be a useful resource for any researcher with interest in Drosophila sensory organs. [ABSTRACT FROM AUTHOR]

Background: Prodigiosin produced by Serratia marcescens is a promising drug owing to its reported characteristics of having antifungal, immunosuppressive and antiproliferative activity. From an industrial point of view the necessity to obtain a suitable medium to simultaneously enhance the growth of Serratia marcescens and the pigment production was the aim of this work. The usage of individual fatty acid as substrate in industries would be cost-effective in the long run and this paved the way for us to try the effect of different fatty acid-containing seeds and oils of peanut, sesame and coconut as source of substrate. Results: The addition of sugars only showed slight enhancement of prodigiosin production in nutrient broth but not in fatty acid containing seed medium. The powdered peanut broth had supported better growth of Serratia marcescens and higher yield of prodigiosin when compared with the existing nutrient broth and peptone glycerol broth. A block in prodigiosin production was seen above 30°C in nutrient broth, but the fatty acid seed medium used by us supported prodigiosin production upto 42°C though the yields were lower than what was obtained at 28°C. From the results, the fatty acid form of carbon source has a role to play in enhanced cell growth and prodigiosin production. Conclusion: We conclude by reporting that the powdered and sieved peanut seed of different quality grades were consistent in yielding a fourty fold increase in prodigiosin production over the existing media. A literature survey on the composition of the different media components in nutrient broth, peptone glycerol broth and the fatty acid containing seeds and oils enabled us to propose that the saturated form of fatty acid has a role to play in enhanced cell growth and prodigiosin production. This work has also enabled us to report that the temperature related block of prodigiosin biosynthesis varies with different media and the powdered peanut broth supports prodigiosin production at higher temperatures. The medium suggested in this work is best suitable from an industrial point of view in being economically feasible, in terms of the higher prodigiosin yield and the extraction of prodigiosin described in this paper is simple with minimal wastage. [ABSTRACT FROM AUTHOR]

Background: Many investigations have reported that advantageous mutations occurred more frequently under selective conditions than those under non-selective conditions. This phenomenon is referred to as adaptive mutation. Their characteristics are that adaptive mutations are directed and growth-independent. The idea of directed adaptive mutation had been objected by some reports, however, the idea of growth-independent adaptive mutation has been held till today. Results: In this paper, we have observed that under leucine starvation conditions, leu+ revertants accumulated as a function of time; leu- to leu+ reverse mutation rates and frequencies were higher than those under non starvation conditions; and no divided cells could be monitored by the penicillin method. These results were similar to the time-dependent manner of adaptive mutation from previous reports. However, leucine concentration determinate experiments revealed that certain traces of leucine, which leaked from the E. coli cells, was almost always present in the culture. More numbers of leu+ revertants appeared when the similar cultures were dropped in small areas on the selective plates than when spread on the whole selective plates. These results have shown that mutations under leucine starving conditions are growth-dependent. Fluctuation analysis of leu+ revertants indicated that leu-leu+ mutation occurred spontaneously and randomly. In addition, the spectra of leuB gene in the revertants proved that mutations under selective conditions were not specific or directed. Conclusions: The above investigations led to the conclusion (1) that the occurrence of leu+ mutations under starvation conditions was growth-dependent. The occurence mutations was also similar to that under non-starvation conditions (2). Under starvation conditions the mutation rates were higher, and was not constant during the long process. [ABSTRACT FROM AUTHOR]

Background: Ethylene is a widely distributed alkene product which is formed enzymatically (e.g., in plants) or by photochemical reactions (e.g., in the upper oceanic layers from dissolved organic carbon). This gaseous compound was recently found to induce in cells from the marine sponge Suberites domuncula, an increase in intracellular Ca2+ level ([Ca2+]i) and an upregulation of the expression of two genes, the potential ethylene-responsive gene, SDERR, and a Ca2+/calmodulin-dependent protein kinase. Results: Here we describe for the first time, that besides sponge cells, mammalian cell lines (mouse NIH-3T3 and human HeLa and SaOS-2 cells) respond to ethylene, generated by ethephon, with an immediate and strong, transient increase in [Ca2+]i level, as demonstrated using Fura-2 imaging method. A rise of [Ca2+]i level was also found following exposure to ethylene gas of cells kept under pressure (SaOS-2 cells). The upregulation of [Ca2+]i was associated with an increase in the level of the cell cycle-associated Ki-67 antigen. In addition, we show that the effect of ethephon addition to S. domuncula cells depends on the presence of calcium in the extracellular milieu. Conclusion: The results presented in this paper indicate that ethylene, previously known to act as a mediator (hormone) in plants only, deserves also attention as a potential signaling molecule in higher vertebrates. Further studies are necessary to clarify the specificity and physiological significance of the effects induced by ethylene in mammalian cells. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via the original article. [ABSTRACT FROM AUTHOR]