Emily Greenfest-Allen

Ontogeny of erythroid gene expression

Erythroid ontogeny is characterized by overlapping waves of primitive and definitive erythroid lineages that share many morphologic features during terminal maturation but have marked differences in cell size and globin expression. In the present study, we compared global gene expression in primitive, fetal definitive, and adult definitive erythroid cells at morphologically equivalent stages of maturation purified from embryonic, fetal, and adult mice. Surprisingly, most transcriptional complexity in erythroid precursors is already present by the proerythroblast stage. Transcript levels are markedly modulated during terminal erythroid maturation, but housekeeping genes are not preferentially lost. Although primitive and definitive erythroid lineages share a large set of nonhousekeeping genes, annotation of lineage-restricted genes shows that alternate gene usage occurs within shared functional categories, as exemplified by the selective expression of aquaporins 3 and 8 in primitive erythroblasts and aquaporins 1 and 9 in adult definitive erythroblasts. Consistent with the known functions of Aqp3 and Aqp8 as H2O2 transporters, primitive, but not definitive, erythroblasts preferentially accumulate reactive oxygen species after exogenous H2O2 exposure. We have created a user-friendly Web site (http://www.cbil.upenn.edu/ErythronDB) to make these global expression data readily accessible and amenable to complex search strategies by the scientific community.

Stat and interferon genes identified by network analysis differentially regulate primitive and definitive erythropoiesis

BackgroundHematopoietic ontogeny is characterized by overlapping waves of primitive, fetal definitive, and adult definitive erythroid lineages. Our aim is to identify differences in the transcriptional control of these distinct erythroid cell maturation pathways by inferring and analyzing gene-interaction networks from lineage-specific expression datasets. Inferred networks are strongly connected and do not fit a scale-free model, making it difficult to identify essential regulators using the hub-essentiality standard.ResultsWe employed a semi-supervised machine learning approach to integrate measures of network topology with expression data to score gene essentiality. The algorithm was trained and tested on the adult and fetal definitive erythroid lineages. When applied to the primitive erythroid lineage, 144 high scoring transcription factors were found to be differentially expressed between the primitive and adult definitive erythroid lineages, including all expressed STAT-family members. Differential responses of primitive and definitive erythroblasts to a Stat3 inhibitor and IFNγ in vitro supported the results of the computational analysis. Further investigation of the original expression data revealed a striking signature of Stat1-related genes in the adult definitive erythroid network. Among the potential pathways known to utilize Stat1, interferon (IFN) signaling-related genes were expressed almost exclusively within the adult definitive erythroid network.ConclusionsIn vitro results support the computational prediction that differential regulation and downstream effectors of STAT signaling are key factors that distinguish the transcriptional control of primitive and definitive erythroid cell maturation.

NIA GENETICS OF ALZHEIMER’S DISEASE DATA STORAGE SITE (NIAGADS): ALZHEIMER’S GENOMICS DATABASE

Functional Expression Regulating N-terminal domain of Kv4.2. The p.F11 residue plays a crucial role in the binding to the potassium channel-Interacting protein (KChIP). Mutations in this residue are known to disrupt KChIP binding, trafficking, and functional modulation of the Kv4.2 channel (Kunjilwar et al., 2013). Conclusions:The genetic data suggest that Kv4.2, the molecular partner of DPP6, is intolerant to mutations. Together, our results as well as the specific protein function, warrant further investigation of this multimeric protein complex in the pathogenesis of neurodegenerative brain diseases.

NIA GENETICS OF ALZHEIMER’S DISEASE DATA STORAGE SITE (NIAGADS): UPDATE 2018

Ellen Wijsman, Margaret A. Pericak-Vance, Richard Mayeux and Alzheimer’s Disease Sequencing Project, Columbia University, New York, NY, USA; John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; University of Washington, Seattle, WA, USA; Mother and Teacher Pontifical Catholic University, Santiago, Dominican Republic; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Boston University, Boston, MA, USA; University of Miami, Miami, FL, USA; Case Western Reserve University, Cleveland, OH, USA; Erasmus University Medical Center, Rotterdam, Netherlands; Washington University, Saint Louis,MO,USA; BostonUniversity Alzheimer’s Disease Center, Boston, MA, USA; Baylor College of Medicine, Houston, TX, USA. Contact e-mail: bnv2103@cumc.columbia.edu

iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks

Weighted-gene correlation network analysis (WGCNA) is frequently used to identify highly co-expressed clusters of genes (modules) within whole-transcriptome datasets. However, transcriptome-scale networks tend to be highly connected, making it challenging for the hierarchical clustering underlying the WGCNA-based classification to discriminate coherently expressed gene sets without significant information loss from either a priori filtering of the expression dataset or a posteriori pruning of the cluster dendrogram. Here we present iterativeWGCNA, a Python-wrapped extension for the WGCNA R software package that improves the robustness of detected modules and minimizes information loss. The method works by pruning poorly fitting genes from estimated modules and then re-running WGCNA to refine gene clusters. After refining, pruned genes are assembled into a new expression dataset to isolate overlapping modules and the process repeated. In doing so, iterativeWGCNA provides an unsupervised, non-biased filtering to generate a robust, comprehensive network-based classification of whole-transcriptome expression datasets.

Demographic disequilibrium in living nautiloids (Nautilus and Allonautilus): Canary in the coal mines?

Averaged demographic data from previously unfished populations of Nautilus and Allonautilus (Cephalopoda) provide a baseline to determine if a population is undisturbed and in “equilibrium” or is in “disequilibrium” as a result of fishery pressure. Data are available for previously undisturbed local nautiloid populations in Papua New Guinea, Australia, Indonesia, Fiji, Palau, American Samoa, New Caledonia and Vanuatu (total n = 2,669 live-caught, tagged and released animals). The data show that unfished populations average ~75% males and ~74% mature animals. By contrast, unpublished, anecdotal and historical records since 1900 from the heavily fished central Philippines have shown a persistent decline in trap yields and a change in demographics of N. pompilius. By 1979, a sample of fished live-caught animals (n = 353) comprised only ~28% males and ~27% mature animals. Continued uncontrolled trapping caused collapse of the fishery and the shell industry has moved elsewhere, including Indonesia. In addition, we show that estimated rates of population decline are offered by unpublished tag-release records in unfished Palau. These data show that patterns of trap yields and demographic differences between fished and unfished populations in relative age class and sex ratios can indicate disequilibria wrought by fisheries pressure that can render local populations inviable. Given adequate samples (n ≥100 live-caught animals), a threshold of <50% males and mature animals in fished populations should signal the need to initiate curative conservation initiatives. The current trajectory of uncontrolled nautiloid fisheries can only mean trouble and possibly extinction of local populations of this ancient, iconic molluscan lineage.

NIAGADS: The NIA Genetics of Alzheimer's Disease Data Storage Site

Work in the past 10 years has greatly expanded our understanding of Alzheimers disease (AD) genetics, with more than 20 susceptibility genetic loci discovered in large genome‐wide association and sequencing studies. The NIA Genetics of Alzheimers Disease Data Storage Site (NIAGADS) is funded by National Institute on Aging to serve as a one‐stop portal for the community to access research data and findings generated from AD genetics projects. In this article, we describe the mission of NIAGADS, available resources, and data sets including the Alzheimers Disease Sequencing Project, and how researchers may apply for access to genetic data.