Isoelectric focusing studies of A2/1957 influenza neuraminidase and its subunits

Purified A2/1957 influenza neuraminidase (mucopolysaccharide N-acetylneuraminylhydrolase, EC 3.2.1.18) and its subunits were examined by isoelectric focusing (`electrofocusing') in sucrose gradients. Native neuraminidase contained enzymically active components with isoelectric points (pI) of about 5.2, 5.35, 5.5, 5.8, 6.2 and 6.5. The major components were at about pI 5.5 and 5.8. Neuraminidase was dissociated into subunits, whose sulfhydryl groups were blocked with iodo[14C]acetamide. 80% of isotope label incorporated was present in a single size of subunits with a molecular weight (Mr) of 51 000 as determined by sodium dodecyl sulfate acrylamide gel electrophoresis. The pI of denatured subunits was about 3.6 to 4.4. 14C-labelled peptides of tryptically digested neuraminidase had predominantly acidic isoelectric points. Results are consistent that the pI of native neuraminidase is about 1.5-2 pH units higher than the pI of its structural subunits, suggesting that side chain carboxyl groups are conformationally masked in the native enzyme, and that isoelectric heterogeneity of neuraminidase may result from conformation-dependent variations in the acid-base dissociation of these groups.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/33847/1/0000105.pd

Effects of leas and mecury on the blood proteome of children

Heavy metal exposure in children has been associated with a variety of physiological and neurological problems. The goal of this study was to utilize proteomics to enhance the understanding of biochemical interactions responsible for the health problems related to lead and mercury exposure at concentrations well below CDC guidelines. Blood plasma and serum samples from 34 children were depleted of their most abundant proteins using antibody-based affinity columns and analyzed using two different methods, LC-MS/MS and 2-D electrophoresis coupled with MALDI-TOF/MS and tandem mass spectrometry. Apolipoprotein E demonstrated an inverse significant association with lead concentrations (average being one microgram/deciliter) as deduced from LC-MS/MS and 2-D electrophoresis and confirmed by Western blot analysis. This coincides with prior findings that Apolipoprotein E genotype moderates neurobehavioral effects in individuals exposed to lead. Fifteen other proteins were identified by LC-MS/MS as proteins of interest exhibiting expressional differences in the presence of environmental lead and mercury. Brooks Gump is currently at Syracuse University

Evidence of precursors of defective measles virus

Cytoplasmic extracts of Vero cells infected with wild strain Edmonston measles virus were found to contain at least two distinct nucleocapsid species. The two most prominent species of nucleocapsids sedimented at 200S and 110S and contained RNA of molecular weight 6.0×10 6 and 0.6×10 6 daltons respectively. Both species of nucleocapsids had a density of 1.31 g/cm 3 in CsCl. A third species sedimenting at 170S was not present in all experiments and was not characterized in detail. Infection of cells with undiluted-passage virus usually resulted in production of mostly 110S nucleocapsids while both 110S and 200S species were found when diluted-passage virus was used. These results suggest that measles virus may produce distinct classes of defective virus which contain segments of RNA representing as little as 10% of the complete viral genome.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47527/1/430_2005_Article_BF02121716.pd

Genomic characterization of the Yersinia genus

Comparative Yersinia genomics identifies features responsible for the colonization of specific host habitats and the horizontal transfer of virulence determinants

Genotyping of Bacillus cereus Strains by Microarray-Based Resequencing

The ability to distinguish microbial pathogens from closely related but nonpathogenic strains is key to understanding the population biology of these organisms. In this regard, Bacillus anthracis, the bacterium that causes inhalational anthrax, is of interest because it is closely related and often difficult to distinguish from other members of the B. cereus group that can cause diverse diseases. We employed custom-designed resequencing arrays (RAs) based on the genome sequence of Bacillus anthracis to generate 422 kb of genomic sequence from a panel of 41 Bacillus cereus sensu lato strains. Here we show that RAs represent a “one reaction” genotyping technology with the ability to discriminate between highly similar B. anthracis isolates and more divergent strains of the B. cereus s.l. Clade 1. Our data show that RAs can be an efficient genotyping technology for pre-screening the genetic diversity of large strain collections to selected the best candidates for whole genome sequencing

Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Quantifying sources of variability in infancy research using the infant-directed-speech preference

Psychological scientists have become increasingly concerned with issues related to methodology and replicability, and infancy researchers in particular face specific challenges related to replicability: For example, high-powered studies are difficult to conduct, testing conditions vary across labs, and different labs have access to different infant populations. Addressing these concerns, we report on a large-scale, multisite study aimed at (a) assessing the overall replicability of a single theoretically important phenomenon and (b) examining methodological, cultural, and developmental moderators. We focus on infants’ preference for infant-directed speech (IDS) over adult-directed speech (ADS). Stimuli of mothers speaking to their infants and to an adult in North American English were created using seminaturalistic laboratory-based audio recordings. Infants’ relative preference for IDS and ADS was assessed across 67 laboratories in North America, Europe, Australia, and Asia using the three common methods for measuring infants’ discrimination (head-turn preference, central fixation, and eye tracking). The overall meta-analytic effect size (Cohen’s d) was 0.35, 95% confidence interval = [0.29, 0.42], which was reliably above zero but smaller than the meta-analytic mean computed from previous literature (0.67). The IDS preference was significantly stronger in older children, in those children for whom the stimuli matched their native language and dialect, and in data from labs using the head-turn preference procedure. Together, these findings replicate the IDS preference but suggest that its magnitude is modulated by development, native-language experience, and testing procedure. (This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 798658.

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense