Systems analysis of host-parasite interactions.

Parasitic diseases caused by protozoan pathogens lead to hundreds of thousands of deaths per year in addition to substantial suffering and socioeconomic decline for millions of people worldwide. The lack of effective vaccines coupled with the widespread emergence of drug-resistant parasites necessitates that the research community take an active role in understanding host-parasite infection biology in order to develop improved therapeutics. Recent advances in next-generation sequencing and the rapid development of publicly accessible genomic databases for many human pathogens have facilitated the application of systems biology to the study of host-parasite interactions. Over the past decade, these technologies have led to the discovery of many important biological processes governing parasitic disease. The integration and interpretation of high-throughput -omic data will undoubtedly generate extraordinary insight into host-parasite interaction networks essential to navigate the intricacies of these complex systems. As systems analysis continues to build the foundation for our understanding of host-parasite biology, this will provide the framework necessary to drive drug discovery research forward and accelerate the development of new antiparasitic therapies

A Qualitative Examination of Graduate Advising Relationships: The Advisor Perspective

Nineteen counseling psychology faculty members were interviewed regarding their advising relationships with doctoral students. Advisors informally learned to advise from their experiences with their advisor and their advisees and defined their role as supporting and advocating for advisees as they navigated their doctoral program. Advisors identified personal satisfaction as a benefit and time demands as a cost of advising. Good advising relationships were facilitated by advisees’ positive personal or professional characteristics, mutual respect, open communication, similarity in career path between advisor and advisee, and lack of conflict. Difficult relationships were affected by advisees’ negative personal or professional characteristics, lack of respect, research struggles, communication problems, advisors feeling ineffective working with advisees, disruption or rupture of the relationship, and conflict avoidance. Implications for research and training are discussed

Synthesis and Characterization of Mixed Methyl/Allyl Monolayers on Si(111)

The formation of mixed methyl/allyl monolayers has been accomplished through a two-step halogenation/alkylation reaction on Si(111) surfaces. The total coverage of alkylated Si, the surface recombination velocities, and the degree of surface oxidation as a function of time have been investigated using X-ray photoelectron spectroscopy, Fourier-transform infrared spectroscopy, and microwave conductivity measurements. The total coverage of alkyl groups, the rate of oxidation, and the surface recombination velocities of Si(111) terminated by mixed monolayers were found to be close to those observed for CH_3−Si(111) surfaces. Hence, the mixed-monolayer surfaces retained the beneficial properties of CH_3−Si(111) surfaces while allowing for convenient secondary surface functionalization

Filling the gap between experimentalists and modelers by determining a mammalian cell\u27s metabolic capabilities based on transcriptomic data

Large-scale omics experiments are now standard in many biological studies, and many methods exist to interpret these data. One emerging approach uses genome-scale metabolic models (GEMs) for model-guided data analysis, since they provide cellular context to these large data sets by establishing a mechanistic link from genotype to phenotype. GEMs include all reactions in an organism, but not all enzymes are active in each tissue, cell line or culture condition. Therefore, algorithms have been developed to build context-specific models that recapitulate the metabolism of specific cell types under specific conditions, based on omics data measurements1. While these context-specific models improve the ability to predict genotype-phenotype relationships, the physiological accuracy and relevance of these models are often overlooked, due to gaps in our knowledge of context-specific metabolism functionalities. Indeed, many cell types have unique metabolic functions they natively accomplish. However, since these functions are often poorly defined for specific cell types, it can be difficult to evaluate a cell’s metabolic activities in an unbiased fashion within a modeling context. To overcome this, we curated a list of previously published metabolic tasks2,3 and obtained a collection of 210 tasks covering 7 major metabolic activities of a cell (energy generation, nucleotide, carbohydrates, amino acid, lipid, vitamin & cofactor and glycan metabolism). Using published genome-scale metabolic models for human and CHO cells, we identified all metabolic genes that are used for each metabolic function. Thus, by using these lists of genes to analyze omics data (e.g., RNA-Seq), one can estimate the metabolic capabilities of a cell without modeling. Please click Additional Files below to see the full abstract

Combinatorial synthesis and high-throughput photopotential and photocurrent screening of mixed-metal oxides for photoelectrochemical water splitting

A high-throughput method has been developed using a commercial piezoelectric inkjet printer for synthesis and characterization of mixed-metal oxide photoelectrode materials for water splitting. The printer was used to deposit metal nitrate solutions onto a conductive glass substrate. The deposited metal nitrate solutions were then pyrolyzed to yield mixed-metal oxides that contained up to eight distinct metals. The stoichiometry of the metal oxides was controlled quantitatively, allowing for the creation of vast libraries of novel materials. Automated methods were developed to measure the open-circuit potentials (Eoc), short-circuit photocurrent densities (Jsc), and current density vs. applied potential (J–E) behavior under visible light irradiation. The high-throughput measurement of Eoc is particularly significant because open-circuit potential measurements allow the interfacial energetics to be probed regardless of whether the band edges of the materials of concern are above, close to, or below the values needed to sustain water electrolysis under standard conditions. The Eoc measurements allow high-throughput compilation of a suite of data that can be associated with the composition of the various materials in the library, to thereby aid in the development of additional screens and to form a basis for development of theoretical guidance in the prediction of additional potentially promising photoelectrode compositions

A proof for loop-law constraints in stoichiometric metabolic networks

Background: Constraint-based modeling is increasingly employed for metabolic network analysis. Its underlying assumption is that natural metabolic phenotypes can be predicted by adding physicochemical constraints to remove unrealistic metabolic flux solutions. The loopless-COBRA approach provides an additional constraint that eliminates thermodynamically infeasible internal cycles (or loops) from the space of solutions. This allows the prediction of flux solutions that are more consistent with experimental data. However, it is not clear if this approach over-constrains the models by removing non-loop solutions as well. Results: Here we apply Gordan’s theorem from linear algebra to prove for the first time that the constraints added in loopless-COBRA do not over-constrain the problem beyond the elimination of the loops themselves. Conclusions: The loopless-COBRA constraints can be reliably applied. Furthermore, this proof may be adapted to evaluate the theoretical soundness for other methods in constraint-based modeling

The evolution of genome-scale models of cancer metabolism

The importance of metabolism in cancer is becoming increasingly apparent with the identification of metabolic enzyme mutations and the growing awareness of the influence of metabolism on signaling, epigenetic markers, and transcription. However, the complexity of these processes has challenged our ability to make sense of the metabolic changes in cancer. Fortunately, constraint-based modeling, a systems biology approach, now enables one to study the entirety of cancer metabolism and simulate basic phenotypes. With the newness of this field, there has been a rapid evolution of both the scope of these models and their applications. Here we review the various constraint-based models built for cancer metabolism and how their predictions are shedding new light on basic cancer phenotypes, elucidating pathway differences between tumors, and dicovering putative anti-cancer targets. As the field continues to evolve, the scope of these genome-scale cancer models must expand beyond central metabolism to address questions related to the diverse processes contributing to tumor development and metastasis

The ASD Living Biology: from cell proliferation to clinical phenotype.

Autism spectrum disorder (ASD) has captured the attention of scientists, clinicians and the lay public because of its uncertain origins and striking and unexplained clinical heterogeneity. Here we review genetic, genomic, cellular, postmortem, animal model, and cell model evidence that shows ASD begins in the womb. This evidence leads to a new theory that ASD is a multistage, progressive disorder of brain development, spanning nearly all of prenatal life. ASD can begin as early as the 1st and 2nd trimester with disruption of cell proliferation and differentiation. It continues with disruption of neural migration, laminar disorganization, altered neuron maturation and neurite outgrowth, disruption of synaptogenesis and reduced neural network functioning. Among the most commonly reported high-confidence ASD (hcASD) genes, 94% express during prenatal life and affect these fetal processes in neocortex, amygdala, hippocampus, striatum and cerebellum. A majority of hcASD genes are pleiotropic, and affect proliferation/differentiation and/or synapse development. Proliferation and subsequent fetal stages can also be disrupted by maternal immune activation in the 1st trimester. Commonly implicated pathways, PI3K/AKT and RAS/ERK, are also pleiotropic and affect multiple fetal processes from proliferation through synapse and neural functional development. In different ASD individuals, variation in how and when these pleiotropic pathways are dysregulated, will lead to different, even opposing effects, producing prenatal as well as later neural and clinical heterogeneity. Thus, the pathogenesis of ASD is not set at one point in time and does not reside in one process, but rather is a cascade of prenatal pathogenic processes in the vast majority of ASD toddlers. Despite this new knowledge and theory that ASD biology begins in the womb, current research methods have not provided individualized information: What are the fetal processes and early-age molecular and cellular differences that underlie ASD in each individual child? Without such individualized knowledge, rapid advances in biological-based diagnostic, prognostic, and precision medicine treatments cannot occur. Missing, therefore, is what we call ASD Living Biology. This is a conceptual and paradigm shift towards a focus on the abnormal prenatal processes underlying ASD within each living individual. The concept emphasizes the specific need for foundational knowledge of a living child's development from abnormal prenatal beginnings to early clinical stages. The ASD Living Biology paradigm seeks this knowledge by linking genetic and in vitro prenatal molecular, cellular and neural measurements with in vivo post-natal molecular, neural and clinical presentation and progression in each ASD child. We review the first such study, which confirms the multistage fetal nature of ASD and provides the first in vitro fetal-stage explanation for in vivo early brain overgrowth. Within-child ASD Living Biology is a novel research concept we coin here that advocates the integration of in vitro prenatal and in vivo early post-natal information to generate individualized and group-level explanations, clinically useful prognoses, and precision medicine approaches that are truly beneficial for the individual infant and toddler with ASD