Regulating quiescence: new insights into hematopoietic stem cell biology

Blood-forming hematopoietic stem cells (HSCs) ensure production of all mature blood cells during homeostatic and regenerative hematopoiesis. Proliferation, cell cycle regulation, and quiescence are key processes involved in this function, and in a recent issue of Cancer Cell, show that HSC quiescence is actively regulated by specific molecular mechanisms that appear to distinguish normal HSC maintenance from HSC responses to hematologic injury.     

Comparative genomics of yeast species: new insights into their biology

The genomes of two hemiascomycetous yeasts (Saccharomyces cerevisiae and Candida albicans) and one archiascomycete (Schizosaccharomyces pombe) have been completely sequenced and the genes have been annotated. In addition, the genomes of 13 more Hemiascomycetes have been partially sequenced. The amount of data thus obtained provides information on the evolutionary relationships between yeast species. In addition, the differential genetic characteristics of the microorganisms explain a number of distinctive biological traits. Gene order conservation is observed between phylogenetically close species and is lost in distantly related species, probably due to rearrangements of short regions of DNA. However, gene function is much more conserved along evolution. Compared to S. cerevisiae and S. pombe, C. albicans has a larger number of specific genes, i.e., genes not found in other organisms, a fact that can account for the biological characteristics of this pathogenic dimorphic yeast which is able to colonize a large variety of environments.     

A novel approach for pilot error detection using Dynamic Bayesian Networks

In the last decade Dynamic Bayesian Networks (DBNs) have become one type of the most attractive probabilistic modelling framework extensions of Bayesian Networks (BNs) for working under uncertainties from a temporal perspective. Despite this popularity not many researchers have attempted to study the use of these networks in anomaly detection or the implications of data anomalies on the outcome of such models. An abnormal change in the modelled environment’s data at a given time, will cause a trailing chain effect on data of all related environment variables in current and consecutive time slices. Albeit this effect fades with time, it still can have an ill effect on the outcome of such models. In this paper we propose an algorithm for pilot error detection, using DBNs as the modelling framework for learning and detecting anomalous data. We base our experiments on the actions of an aircraft pilot, and a flight simulator is created for running the experiments. The proposed anomaly detection algorithm has achieved good results in detecting pilot errors and effects on the whole system.     

Speed of fetal growth: a new methodological approach

We suggest a new method to calculate fetal growth velocity. Fetal growth curves are divided in small linear units, and corresponding slopes calculated as growth rates. An example is detailed to set out statistical contingencies for methodological reliability. Specific interest of the method is discussed.     

Systems biology analysis reveals new insights into invasive lung cancer

Background

Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development.

Results

In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth.

Conclusions

Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients.

     

On the limitations of standard statistical modeling in biological systems: A full Bayesian approach for biology

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.     

Leishmania-macrophage interactions: insights into the redox biology

Leishmaniasis is a neglected tropical disease that affects about 350 million individuals worldwide. The protozoan parasite has a relatively simple life cycle with two principal stages: the flagellated mobile promastigote living in the gut of the sandfly vector and the intracellular amastigote within phagolysosomal vesicles of the vertebrate host macrophage. This review presents a state-of-the-art overview of the redox biology at the parasite-macrophage interface. Although Leishmania species are susceptible in vitro to exogenous superoxide radical, hydrogen peroxide, nitric oxide, and peroxynitrite, they manage to survive the endogenous oxidative burst during phagocytosis and the subsequent elevated nitric oxide production in the macrophage. The parasite adopts various defense mechanisms to cope with oxidative stress: the lipophosphoglycan membrane decreases superoxide radical production by inhibiting NADPH oxidase assembly and the parasite also protects itself by expressing antioxidant enzymes and proteins. Some of these enzymes could be considered potential drug targets because they are not expressed in mammals. In respect to antileishmanial therapy, the effects of current drugs on parasite-macrophage redox biology and its involvement in the development of drug resistance and treatment failure are presented.     

New insights into the phylogeny of fig pollinators using Bayesian analyses

The interaction between figs and fig pollinators is one of the most species-specific mutualisms. Recently, phylogenies of both partners based on molecular data provided insights into a wide spectrum of co-evolutionary questions. However, for the phylogeny of fig pollinators, there are some discrepancies between different studies and left some relationships unresolved, especially for deep nodes. The phylogenetic uncertainties of pollinators prohibit our further understanding of the history of the mutualism. Here, we present phylogenetic analyses of a larger COI sequence dataset that includes previously published datasets and our sequences from 20 species using Bayesian method and maximum parsimony. The analyses using different methods share similar topologies. Bayesian analyses provide high level of confidence for most internal nodes in terms of posterior probability. This study also clarifies some discrepancies between previous studies. After rooting with Tetrapus, other pollinators split into two clades. Wiebesia and Blastophaga are at basal positions in respective clade. Ceratosolen is not monophyletic because Kradibia and Liporrhopalum fall inside this group. Three subgenera of Ceratosolen: subgen. Ceratosolen, subgen. Rothropus, and subgen. Strepitus are not supported. Therefore, Ceratosolen is suggested to be re-divided into three groups. Urostigma pollinators (including Dolichoris and Blastophaga psenes) are clustered together. The monophylies of Wiebesia, Blastophaga, Dolichoris are not supported in this analysis. This study also provides a new framework for re-evaluating character evolution and re-inspecting the definition of some genera.     

A systems biology approach to learning autophagy

With its relevance to our understanding of eukaryotic cell function in the normal and disease state, autophagy is an important topic in modern cell biology; yet, few textbooks discuss autophagy beyond a two- or three-sentence summary. Here, we report an undergraduate/graduate class lesson for the in-depth presentation of autophagy using an active learning approach. By our method, students will work in small groups to solve problems and interpret an actual data set describing genes involved in autophagy. The problem-solving exercises and data set analysis will instill within the students a much greater understanding of the autophagy pathway than can be achieved by simple rote memorization of lecture materials; furthermore, the students will gain a general appreciation of the process by which data are interpreted and eventually formed into an understanding of a given pathway. As the data sets used in these class lessons are largely genomic and complementary in content, students will also understand first-hand the advantage of an integrative or systems biology study: No single data set can be used to define the pathway in full-the information from multiple complementary studies must be integrated in order to recapitulate our present understanding of the pathways mediating autophagy. In total, our teaching methodology offers an effective presentation of autophagy as well as a general template for the discussion of nearly any signaling pathway within the eukaryotic kingdom.     

Plant systems biology: insights,advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this  review, we discuss the basics of systems biology including the various ‘omics’ approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.     

Scaling, growth and cyclicity in biology: a new computational approach

Background

The Phenomenological Universalities approach has been developed by P.P. Delsanto and collaborators during the past 2–3 years. It represents a new tool for the analysis of experimental datasets and cross-fertilization among different fields, from physics/engineering to medicine and social sciences. In fact, it allows similarities to be detected among datasets in totally different fields and acts upon them as a magnifying glass, enabling all the available information to be extracted in a simple way. In nonlinear problems it allows the nonscaling invariance to be retrieved by means of suitable redefined fractal-dimensioned variables.

Results

The main goal of the present contribution is to extend the applicability of the new approach to the study of problems of growth with cyclicity, which are of particular relevance in the fields of biology and medicine.

Conclusion

As an example of its implementation, the method is applied to the analysis of human growth curves. The excellent quality of the results (R ² = 0.988) demonstrates the usefulness and reliability of the approach.     

Teaching systems biology: an active-learning approach

With genomics well established in modern molecular biology, recent studies have sought to further the discipline by integrating complementary methodologies into a holistic depiction of the molecular mechanisms underpinning cell function. This genomic subdiscipline, loosely termed "systems biology," presents the biology educator with both opportunities and obstacles: The benefit of exposing students to this cutting-edge scientific methodology is manifest, yet how does one convey the breadth and advantage of systems biology while still engaging the student? Here, I describe an active-learning approach to the presentation of systems biology. In graduate classes at the University of Michigan, Ann Arbor, I divided students into small groups and asked each group to interpret a sample data set (e.g., microarray data, two-hybrid data, homology-search results) describing a hypothetical signaling pathway. Mimicking realistic experimental results, each data set revealed a portion of this pathway; however, students were only able to reconstruct the full pathway by integrating all data sets, thereby exemplifying the utility in a systems biology approach. Student response to this cooperative exercise was extremely positive. In total, this approach provides an effective introduction to systems biology appropriate for students at both the undergraduate and graduate levels.     

ErbB receptors: new insights on mechanisms and biology

The ErbB family of four receptor tyrosine kinases occupies a central role in a wide variety of biological processes from neuronal development to breast cancer. New information continues to expand their biologic significance and to unravel the molecular mechanisms that underlie the signaling capacity of these receptors. Here, we review several aspects of ErbB receptor physiology for which new and significant information is available. These include ligand-dependent receptor dimerization and kinase activation, which is a prerequisite for all subsequent growth factor-dependent cell responses. We also address novel roles of receptor fragments in signaling, trafficking to intracellular sites, such as the nucleus, and ErbB roles in non-cancer disease processes, including schizophrenia, chronic renal disease, hypertension, and the cellular entry of infectious pathogens.