Landmarking the brain for geometric morphometric analysis: An error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes. © 2014 Chollet et al

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Stochastic changes over time and not founder effects drive cage effects in microbial community assembly in a mouse model

Maternal transmission and cage effects are powerful confounding factors in microbiome studies. To assess the consequences of cage microenvironment on the mouse gut microbiome, two groups of germ-free (GF) wild-type (WT) mice, one gavaged with a microbiota harvested from adult WT mice and another allowed to acquire the microbiome from the cage microenvironment, were monitored using Illumina 16S rRNA sequencing over a period of 8 weeks. Our results revealed that cage effects in WT mice moved from GF to specific pathogen free (SPF) conditions take several weeks to develop and are not eliminated by the initial gavage treatment. Initial gavage influenced, but did not eliminate a successional pattern in which Proteobacteria became less abundant over time. An analysis in which 16S rRNA sequences are mapped to the closest sequenced whole genome suggests that the functional potential of microbial genomes changes significantly over time shifting from an emphasis on pathogenesis and motility early in community assembly to metabolic processes at later time points. Functionally, mice allowed to naturally acquire a microbial community from their cage, but not mice gavaged with a common biome, exhibit a cage effect in Dextran Sulfate Sodium-induced inflammation. Our results argue that while there are long-term effects of the founding community, these effects are mitigated by cage microenvironment and successional community assembly over time, which must both be explicitly considered in the interpretation of microbiome mouse experiments