An evaluation of semi‐automated methods for collecting ecosystem‐level data in temperate marine systems

Historically, marine ecologists have lacked efficient tools that are capable of capturing detailed species distribution data over large areas. Emerging technologies such as high‐resolution imaging and associated machine‐learning image‐scoring software are providing new tools to map species over large areas in the ocean. Here, we combine a novel diver propulsion vehicle (DPV) imaging system with free‐to‐use machine‐learning software to semi‐automatically generate dense and widespread abundance records of a habitat‐forming algae over ~5,000 m² of temperate reef. We employ replicable spatial techniques to test the effectiveness of traditional diver‐based sampling, and better understand the distribution and spatial arrangement of one key algal species. We found that the effectiveness of a traditional survey depended on the level of spatial structuring, and generally 10–20 transects (50 × 1 m) were required to obtain reliable results. This represents 2–20 times greater replication than have been collected in previous studies. Furthermore, we demonstrate the usefulness of fine‐resolution distribution modeling for understanding patterns in canopy algae cover at multiple spatial scales, and discuss applications to other marine habitats. Our analyses demonstrate that semi‐automated methods of data gathering and processing provide more accurate results than traditional methods for describing habitat structure at seascape scales, and therefore represent vastly improved techniques for understanding and managing marine seascapes.     

The value of animal models in predicting genetic susceptibility to complex diseases such as rheumatoid arthritis

For a long time, genetic studies of complex diseases were most successfully conducted in animal models. However, the field of genetics is now rapidly evolving, and human genetics has also started to produce strong candidate genes for complex diseases. This raises the question of how to continue gene-finding attempts in animals and how to use animal models to enhance our understanding of gene function. In this review we summarize the uses and advantages of animal studies in identification of disease susceptibility genes, focusing on rheumatoid arthritis. We are convinced that animal genetics will remain a valuable tool for the identification and investigation of pathways that lead to disease, well into the future.     

A human gene similar to Drosophila melanogaster peanut maps to the DiGeorge syndrome region of 22q11

A Drosophila-related expressed sequence tag (DRES) with sequence similarity to the peanut gene has previously been localized to human chromosome 22q11. We have isolated the cDNA corresponding to this DRES and show that it is a novel member of the family of septin genes, which encode proteins with GTPase activity thought to interact during cytokinesis. The predicted protein has P-loop nucleotide binding and GTPase motifs. The gene, which we call PNUTL1, maps to the region of 22q11.2 frequently deleted in DiGeorge and velo-cardio-facial syndromes and is particularly highly expressed in the brain. The mouse homologue, Pnutl1, maps to MMU16 adding to the growing number of genes from the DiGeorge syndrome region that map to this chromosome.     

Integrated gut/liver microphysiological systems elucidates inflammatory inter‐tissue crosstalk

A capability for analyzing complex cellular communication among tissues is important in drug discovery and development, and in vitro technologies for doing so are required for human applications. A prominent instance is communication between the gut and the liver, whereby perturbations of one tissue can influence behavior of the other. Here, we present a study on human gut‐liver tissue interactions under normal and inflammatory contexts, via an integrative multi‐organ platform comprising human liver (hepatocytes and Kupffer cells), and intestinal (enterocytes, goblet cells, and dendritic cells) models. Our results demonstrated long‐term (>2 weeks) maintenance of intestinal (e.g., barrier integrity) and hepatic (e.g., albumin) functions in baseline interaction. Gene expression data comparing liver in interaction with gut, versus isolation, revealed modulation of bile acid metabolism. Intestinal FGF19 secretion and associated inhibition of hepatic CYP7A1 expression provided evidence of physiologically relevant gut‐liver crosstalk. Moreover, significant non‐linear modulation of cytokine responses was observed under inflammatory gut‐liver interaction; for example, production of CXCR3 ligands (CXCL9,10,11) was synergistically enhanced. RNA‐seq analysis revealed significant upregulation of IFNα/β/γ signaling during inflammatory gut‐liver crosstalk, with these pathways implicated in the synergistic CXCR3 chemokine production. Exacerbated inflammatory response in gut‐liver interaction also negatively affected tissue‐specific functions (e.g., liver metabolism). These findings illustrate how an integrated multi‐tissue platform can generate insights useful for understanding complex pathophysiological processes such as inflammatory organ crosstalk. Biotechnol. Bioeng. 2017;114: 2648–2659. © 2017 Wiley Periodicals, Inc.