Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Protistan microbial observatory in the Cariaco Basin,Caribbean. I. Pyrosequencing vs Sanger insights into species richness

Microbial diversity and distribution are topics of intensive research. In two companion papers in this issue, we describe the results of the Cariaco Microbial Observatory (Caribbean Sea, Venezuela). The Basin contains the largest body of marine anoxic water, and presents an opportunity to study protistan communities across biogeochemical gradients. In the first paper, we survey 18S ribosomal RNA (rRNA) gene sequence diversity using both Sanger- and pyrosequencing-based approaches, employing multiple PCR primers, and state-of-the-art statistical analyses to estimate microbial richness missed by the survey. Sampling the Basin at three stations, in two seasons, and at four depths with distinct biogeochemical regimes, we obtained the largest, and arguably the least biased collection of over 6000 nearly full-length protistan rRNA gene sequences from a given oceanographic regime to date, and over 80 000 pyrosequencing tags. These represent all major and many minor protistan taxa, at frequencies globally similar between the two sequence collections. This large data set provided, via the recently developed parametric modeling, the first statistically sound prediction of the total size of protistan richness in a large and varied environment, such as the Cariaco Basin: over 36 000 species, defined as almost full-length 18S rRNA gene sequence clusters sharing over 99% sequence homology. This richness is a small fraction of the grand total of known protists (over 100 000–500 000 species), suggesting a degree of protistan endemism.     

Multiline varieties and disease control

Summary A general model for the evolution of pathogen populations on mixtures or multilines is developed. This model is used to extend previous analyses of the effects of the widespread cultivation of multilines on the evolution of virulence in obligate parasites to mixtures of lines carrying different numbers of resistance genes. It is concluded that the composition of an equilibrium pathogen population growing on a multiline may vary within wide limits and the prinicipal determinant of its composition is the number of components in the multiline and the resistance genes they carry. Other factors of importance are (i) the relative contribution made by each host class (with different numbers of resistance genes) to the pathogen spore pool each generation; (ii) the levels of stabilizing selection against unnecessary virulence genes; and (iii) the way in which unnecessary genes for virulence combine to reduce pathogen fitness. Conditions for the fixation of avirulent biotypes in the pathogen population and the evolution of a pathogen superrace are given for multilines of various compositions.Paper No. 9246 of the Journal Series of the North Carolina Agricultural Research Service, Raleigh, North Carolina. This investigation was supported in part by NIH Research Grant No. GM 11546 from the National Institute of General Medical Sciences     

Linkage analysis for 18 enzyme loci in Pinus rigida Mill.

Summary Estimates of recombination frequency among enzyme loci of pitch pine revealed two new linkages, Mdh3:Pgm2 (=0.01) and Pep1:Mdh4 (=0.38), and confirmed two previously established linkages. Tighter linkage (=0.30) was ruled out for nearly all gene pairs examined. In general, the Bayesian approach used in this study to test for linkage performed better than alternative methods.This work was supported by the School of Natural Resources, College of Agricultural and Life Sciences, University of Wisconsin, Madison, WI, and by McIntyre-Stennis, project no. 142-C385     

Multiline varieties and disease control

Summary Existing theoretical models have led to conflicting predictions concerning the likely effect of the widespread use of dirty crop multilines on the evolution of virulence in pathogen populations. Here we attempt to clarify these problems by extending existing models to include selection against unnecessary genes for virulence at two different stages in the life cycle of the pathogen. The results of these studies indicate that the stage of the life cycle at which selection occurs can significantly influence the evolution of virulence in pathogen populations growing on multiline varieties.     

A trait-based approach to assess niche overlap and functional distinctiveness between non-indigenous and native species

Our understanding of the community assembly processes acting on non-indigenous species (NIS), as well as the relationship with native species is limited, especially in marine ecosystems. To overcome this knowledge gap we here develop a trait-based approach based on the functional distinctiveness metric to assess niche overlap between NIS and native species, using high-resolution data on benthic invertebrate communities in the Baltic Sea. Our results show that NIS retain a certain degree of similarity with native species, but display one or a few singular unique traits (e.g., bioturbation ability). Furthermore, we demonstrate that community assembly processes, including both environmental filtering and limiting similarity affect NIS establishment, but that their effects may be highly context dependent, as illustrated by pronounced spatial patterns in distinctiveness. Finally, our trait-based approach provides a generic framework applicable to other areas and organisms, to better understand and address biological invasions.     

FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.