Relatedness, Industrial Branching and Technological Cohesion in US Metropolitan Areas

Essletzbichler J. Relatedness, industrial branching and technological cohesion in US metropolitan areas, Regional Studies. Work by evolutionary economic geographers on the role of industry relatedness for regional economic development is extended into a number of methodological and empirical directions. First, relatedness is measured as the intensity of input-output linkages between industries. Second, this measure is employed to examine industry evolution in 360 US metropolitan areas. Third, an employment-weighted measure of metropolitan technological cohesion is developed. The results confirm that technological relatedness is positively related to metropolitan industry portfolio membership and industry entry and negatively related to industry exit. The decomposition of technological cohesion indicates that the selection of related incumbent industries complements industry entry and exit as the main drivers of change in metropolitan technological cohesion. © 2013 Copyright Regional Studies Association

The top 1% in US metropolitan areas

Increases in U.S. income inequality are driven primarily by rapidly rising incomes of the top 1%. At the national scale, rising inequality is associated with negative consequences for economic growth and stability, a range of social problems and declining social mobility. To date, there is no or little work on the geography of the top 1% and their impact on the cities they inhabit. Using individual income data from the U.S. Census, the paper offers the first detailed analysis of the spatial distribution of the top 1% in the United States. The paper makes use of the range of socio-demographic variables attached to individual records to illustrate that the large majority of the top 1% lives in large cities and that women and ethnic minorities are largely excluded from membership in the top 1%. The widening gap between incomes at the top and bottom will thus lead to increasing gender and ethnic income inequalities. Exploratory analysis of the impact of the top 1% on the bottom 99% suggests that cities with large shares of the top 1% are characterized by higher levels of skill polarization, higher labour force participation rates and lower unemployment rates for those with little formal education and higher median incomes for the better educated. However, the paper shows that higher incomes are outstripped by higher housing costs indicating that any potential advantage trickling down from the top 1% to the bottom 99% is eroded by higher living costs. Preliminary analysis also suggests that cities with a higher share of the top 1% tend to be more segregated with potential implications for the supply, quality, access to and distribution of public local services

Nucleic acid detection with CRISPR-Cas13a/C2c2

Rapid, inexpensive, and sensitive nucleic acid detection may aid point-of-care pathogen detection, genotyping, and disease monitoring. The RNA-guided, RNA-targeting clustered regularly interspaced short palindromic repeats (CRISPR) effector Cas13a (previously known as C2c2) exhibits a "collateral effect" of promiscuous ribonuclease activity upon target recognition. We combine the collateral effect of Cas13a with isothermal amplification to establish a CRISPR-based diagnostic (CRISPR-Dx), providing rapid DNA or RNA detection with attomolar sensitivity and single-base mismatch specificity. We use this Cas13a-based molecular detection platform, termed Specific High-Sensitivity Enzymatic Reporter UnLOCKing (SHERLOCK), to detect specific strains of Zika and Dengue virus, distinguish pathogenic bacteria, genotype human DNA, and identify mutations in cell-free tumor DNA. Furthermore, SHERLOCK reaction reagents can be lyophilized for cold-chain independence and long-term storage and be readily reconstituted on paper for field applications.United States. Air Force Office of Scientific Research (Grant FA9550-14-1-0060)Defense Threat Reduction Agency (DTRA) (Grant HDTRA1-14-1-0006)National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706)National Institutes of Health (U.S.) (Grant 1R01-MH110049

RNA targeting with CRISPR–Cas13

RNA has important and diverse roles in biology, but molecular tools to manipulate and measure it are limited. For example, RNA interference1-3 can efficiently knockdown RNAs, but it is prone to off-target effects4, and visualizing RNAs typically relies on the introduction of exogenous tags5. Here we demonstrate that the class 2 type VI6,7 RNA-guided RNA-targeting CRISPR-Cas effector Cas13a8(previously known as C2c2) can be engineered for mammalian cell RNA knockdown and binding. After initial screening of 15 orthologues, we identified Cas13a from Leptotrichia wadei (LwaCas13a) as the most effective in an interference assay in Escherichia coli. LwaCas13a can be heterologously expressed in mammalian and plant cells for targeted knockdown of either reporter or endogenous transcripts with comparable levels of knockdown as RNA interference and improved specificity. Catalytically inactive LwaCas13a maintains targeted RNA binding activity, which we leveraged for programmable tracking of transcripts in live cells. Our results establish CRISPR-Cas13a as a flexible platform for studying RNA in mammalian cells and therapeutic development.National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706)National Institute of Mental Health (U.S.) (Grant 1R01-MH110049

The regulation of monoamine oxidase: a gene expression by distinct variable number tandem repeats

The monoamine oxidase A (MAOA) uVNTR (upstream variable number tandem repeat) is one of the most often cited examples of a gene by environment interaction (GxE) in relation to behavioral traits. However, MAOA possesses a second VNTR, 500 bp upstream of the uVNTR, which is termed d- or distal VNTR. Furthermore, genomic analysis indicates that there are a minimum of two transcriptional start sites (TSSs) for MAOA, one of which encompasses the uVNTR within the 5′ untranslated region of one of the isoforms. Through expression analysis in semi-haploid HAP1 cell lines genetically engineered in order to knockout (KO) either the uVNTR, dVNTR, or both VNTRs, we assessed the effect of the two MAOA VNTRs, either alone or in combination, on gene expression directed from the different TSSs. Complementing our functional analysis, we determined the haplotype variation of these VNTRs in the general population. The expression of the two MAOA isoforms was differentially modulated by the two VNTRs located in the promoter region. The most extensively studied uVNTR, previously considered a positive regulator of the MAOA gene, did not modulate the expression of what it is considered the canonical isoform, while we found that the dVNTR positively regulated this isoform in our model. In contrast, both the uVNTR and the dVNTR were found to act as negative regulators of the second less abundant MAOA isoform. The haplotype analysis for these two VNTRs demonstrated a bias against the presence of one of the potential variants. The uVNTR and dVNTR differentially affect expression of distinct MAOA isoforms, and thus, their combined profiling offers new insights into gene-regulation, GxE interaction, and ultimately MAOA-driven behavior

Applications of CRISPR–Cas systems in neuroscience

Genome-editing tools, and in particular those based on CRISPR-Cas (clustered regularly interspaced short palindromic repeat (CRISPR)-CRISPR-associated protein) systems, are accelerating the pace of biological research and enabling targeted genetic interrogation in almost any organism and cell type. These tools have opened the door to the development of new model systems for studying the complexity of the nervous system, including animal models and stem cell-derived in vitro models. Precise and efficient gene editing using CRISPR-Cas systems has the potential to advance both basic and translational neuroscience research.National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706)National Institute of Mental Health (U.S.) (Grant 1R01-MH110049)National Institute of Diabetes and Digestive and Kidney Diseases (U.S.) (Grant 5R01DK097768-03