| 1. |
- Klionsky, Daniel J., et al.
(author)
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Guidelines for the use and interpretation of assays for monitoring autophagy
- 2012
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In: Autophagy. - : Informa UK Limited. - 1554-8635 .- 1554-8627. ; 8:4, s. 445-544
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Research review (peer-reviewed)abstract
- In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.
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| 2. |
- Elsik, Christine G., et al.
(author)
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The Genome Sequence of Taurine Cattle : A Window to Ruminant Biology and Evolution
- 2009
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 324:5926, s. 522-528
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Journal article (peer-reviewed)abstract
- To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.
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| 3. |
- Gross, Sean M., et al.
(author)
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A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses
- 2022
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In: Communications Biology. - : Springer Nature. - 2399-3642. ; 5:1
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Journal article (peer-reviewed)abstract
- The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.
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| 4. |
- Larsson, Erik, 1975, et al.
(author)
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Do two mutually exclusive gene modules define the phenotypic diversity of mammalian smooth muscle?
- 2008
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In: Molecular genetics and genomics : MGG. - : Springer Science and Business Media LLC. - 1617-4615 .- 1617-4623. ; 280:2, s. 127-37
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Journal article (peer-reviewed)abstract
- Smooth muscle cells (SMCs) are key components of all hollow organs, where they perform contractile, synthetic and other functions. Unlike other muscle cells, SMCs are not terminally differentiated, but exhibit considerable phenotypic variation. Such variation is manifested both across disease states such as asthma and atherosclerosis, and physiological states such as pregnancy and wound healing. While there has been considerable investigation into the diversity of SMCs at the level of morphology and individual biomarkers, less is known about the diversity of SMCs at the level of the transcriptome. To explore this question, we performed an extensive statistical analysis that integrates 200 transcriptional profiles obtained in different SMC phenotypes and reference tissues. Our results point towards a non-trivial hypothesis: that transcriptional variation in different SMC phenotypes is characterized by coordinated differential expression of two mutually exclusive (anti-correlating) gene modules. The first of these modules (C) encodes 19 co-transcribed cell cycle associated genes, whereas the other module (E) encodes 41 co-transcribed extra-cellular matrix components. We propose that the positioning of smooth muscle cells along the C/E axis constitutes an important determinant of SMC phenotypes. In conclusion, our study introduces a new approach to assess phenotypic variation in smooth muscle cells, and is relevant as an example of how integrative bioinformatics analysis can shed light on not only terminal differentiated states but also subtler details in phenotypic variability. It also raises the broader question whether coordinated expression of gene modules is a common mechanism underlying phenotypic variability in mammalian cells.
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