Neurospora proteome 2000

The filamentous fungus, Neurospora crassa, has an eminent history as a central organism in the elucidation of the tenets of classical and biochemical genetics. Of particular significance are the experiments of George Beadle and Edward Tatum in the 1940s with N. crassa that led to the one gene-one enzyme hypothesis (Beadle and Tatum 1941 Proc. Natl. Acad. Sci. USA 27:499 506). In six decades, over 1,000 genes have been mapped and characterized (Perkins, Radford and Sachs 2000 The Neurospora Compendium: Chromosomal Loci. Academic Press; Perkins 2000 Fungal Genet. Newsl., this volume), but that leaves perhaps 10,000 or more genes not yet identified by classical genetics. High-throughput, automated partial sequencing of cDNA libraries to generate expressed sequence tags (ESTs) allows for the rapid identification and characterization of preferentially expressed genes in different tissues, as well as the discovery of novel genes (Adams et al. 1991 Science252:1651-1656; Okubo et al. 1992 Nature Genet. 1:173-179)

A provisional UniGene clone set based on ESTs from Neurospora crassa

We have constructed a list of N. crassa cDNA clones for which partial sequences exist, toward the goal of maximizing the number of genes represented while avoiding redundancy. This effort employed GenBank sequences from the combined N. crassa EST projects at the University of New Mexico, the University of Oklahoma and Dartmouth College (27,557 ESTs; Nelson et al. 1997 Fungal Genet. Biol.21:348-363; Zhu et al. 2001 Genetics 157: 1057-1065). The current list, subject to ongoing revision, includes 2842 clones and is available at the web site of the Neurospora Genome Project (NGP) at the University of New Mexico (http://www.unm.edu/~ngp/), along with details of its construction. Each cDNA clone in the list represents a unique gene. We have also assembled a UniGene set of cDNA clones for that portion of the UniGene set that is represented in libraries constructed by the NGP at UNM. This UniGene library is comprised of 1786 clones distributed in 20 96-well dishes, and it is available through the Fungal Genetics Stock Center

X-Ray and UV Orbital Phase Dependence in LMC X-3

The black-hole binary LMC X-3 is known to be variable on time scales of days to years. We investigate X-ray and ultraviolet variability in the system as a function of the 1.7 day binary phase using a 6.4 day observation with the Rossi X-ray Timing Explorer (RXTE) from December 1998. An abrupt 14% flux decrease, lasting nearly an entire orbit, is followed by a return to previous flux levels. This behavior occurs twice, at nearly the same binary phase, but it is not present in consecutive orbits. When the X-ray flux is at lower intensity, a periodic amplitude modulation of 7% is evident in data folded modulo the orbital period. The higher intensity data show weaker correlation with phase. This is the first report of X-ray variability at the orbital period of LMC X-3. Archival RXTE observations of LMC X--3 during a high flux state in December 1996 show similar phase dependence. An ultraviolet light curve obtained with the High Speed Photometer aboard the Hubble Space Telescope shows orbital modulation consistent with that in the optical, caused by the ellipsoidal variation of the spatially deformed companion. The X-ray spectrum of LMC X-3 can be acceptably represented by a phenomenological disk-black-body plus a power law. Changes in the spectrum of LMC X-3 during our observations are compatible with earlier observations during which variations in the 2-10 keV flux are tracked closely by the disk geometry spectral model parameter.Comment: 11 pages, 7 figures, ApJ in pres

Viral vectors for gene modification of plants as chem/bio sensors.

Chemical or biological sensors that are specific, sensitive, and robust allowing intelligence gathering for verification of nuclear non-proliferation treaty compliance and detouring production of weapons of mass destruction are sorely needed. Although much progress has been made in the area of biosensors, improvements in sensor lifetime, robustness, and device packaging are required before these devices become widely used. Current chemical and biological detection and identification techniques require less-than-covert sample collection followed by transport to a laboratory for analysis. In addition to being expensive and time consuming, results can often be inconclusive due to compromised sample integrity during collection and transport. We report here a demonstration of a plant based sensor technology which utilizes mature and seedling plants as chemical sensors. One can envision genetically modifying native plants at a site of interest that can report the presence of specific toxins or chemicals. In this one year project we used a developed inducible expression system to show the feasibility of plant sensors. The vector was designed as a safe, non-infectious vector which could be used to invade, replicate, and introduce foreign genes into mature host plants that then allow the plant to sense chem/bio agents. The genes introduced through the vector included a reporter gene that encodes for green fluorescent protein (GFP) and a gene that encodes for a mammalian receptor that recognizes a chemical agent. Specifically, GFP was induced by the presence of 17-{beta}-Estradiol (estrogen). Detection of fluorescence indicated the presence of the target chemical agent. Since the sensor is a plant, costly device packaging development or manufacturing of the sensor were not required. Additionally, the biological recognition and reporting elements are maintained in a living, natural environment and therefore do not suffer from lifetime disadvantages typical of most biosensing platforms. Detection of the chem/bio agent reporter (GFP) can be detected only at a specific wavelength

Long-term CD4+ lymphocyte response following HAART initiation in a U.S. Military prospective cohort

<p>Abstract</p> <p>Background</p> <p>Among HIV-infected persons initiating highly active antiretroviral therapy (HAART), early CD4+ lymphocyte count increases are well described. However, whether CD4+ levels continue to increase or plateau after 4-6 years is controversial.</p> <p>Methods</p> <p>To address this question and identify other determinants of CD4+ response, we analyzed data for 1,846 persons from a prospective HIV military cohort study who initiated HAART, who had post-HAART CD4+ measurements, and for whom HIV seroconversion (SC) date was estimated.</p> <p>Results</p> <p>CD4+ count at HAART initiation was ≤ 200 cells/mm³for 23%, 201-349 for 31%, 350-499 for 27%, and ≥500 for 19%. The first 6 months post-HAART, the greatest CD4+ increases (93-151 cells) occurred, with lesser increases (22-36 cells/year) through the first four years. Although CD4+ changes for the entire cohort were relatively flat thereafter, HIV viral load (VL) suppressors showed continued increases of 12-16 cells/year. In multivariate analysis adjusting for baseline CD4+ and post-HAART time interval, CD4+ responses were poorer in those with: longer time from HIV SC to HAART start, lower pre-HAART CD4+ nadir, higher pre-HAART VL, and clinical AIDS before HAART (P < 0.05).</p> <p>Conclusions</p> <p>Small but positive long-term increases in CD4+ count in virally suppressed patients were observed. CD4+ response to HAART is influenced by multiple factors including duration of preceding HIV infection, and optimized if treatment is started with virally suppressive therapy as early as possible.</p

Rational Redesign of Glucose Oxidase for Improved Catalytic Function and Stability

Glucose oxidase (GOx) is an enzymatic workhorse used in the food and wine industries to combat microbial contamination, to produce wines with lowered alcohol content, as the recognition element in amperometric glucose sensors, and as an anodic catalyst in biofuel cells. It is naturally produced by several species of fungi, and genetic variants are known to differ considerably in both stability and activity. Two of the more widely studied glucose oxidases come from the species Aspergillus niger (A. niger) and Penicillium amagasakiense (P. amag.), which have both had their respective genes isolated and sequenced. GOx from A. niger is known to be more stable than GOx from P. amag., while GOx from P. amag. has a six-fold superior substrate affinity (KM) and nearly four-fold greater catalytic rate (kcat). Here we sought to combine genetic elements from these two varieties to produce an enzyme displaying both superior catalytic capacity and stability. A comparison of the genes from the two organisms revealed 17 residues that differ between their active sites and cofactor binding regions. Fifteen of these residues in a parental A. niger GOx were altered to either mirror the corresponding residues in P. amag. GOx, or mutated into all possible amino acids via saturation mutagenesis. Ultimately, four mutants were identified with significantly improved catalytic activity. A single point mutation from threonine to serine at amino acid 132 (mutant T132S, numbering includes leader peptide) led to a three-fold improvement in kcat at the expense of a 3% loss of substrate affinity (increase in apparent KM for glucose) resulting in a specify constant (kcat/KM) of 23.8 (mM−1 · s−1) compared to 8.39 for the parental (A. niger) GOx and 170 for the P. amag. GOx. Three other mutant enzymes were also identified that had improvements in overall catalysis: V42Y, and the double mutants T132S/T56V and T132S/V42Y, with specificity constants of 31.5, 32.2, and 31.8 mM−1 · s−1, respectively. The thermal stability of these mutants was also measured and showed moderate improvement over the parental strain

Addressing challenges in the production and analysis of illumina sequencing data

Advances in DNA sequencing technologies have made it possible to generate large amounts of sequence data very rapidly and at substantially lower cost than capillary sequencing. These new technologies have specific characteristics and limitations that require either consideration during project design, or which must be addressed during data analysis. Specialist skills, both at the laboratory and the computational stages of project design and analysis, are crucial to the generation of high quality data from these new platforms. The Illumina sequencers (including the Genome Analyzers I/II/IIe/IIx and the new HiScan and HiSeq) represent a widely used platform providing parallel readout of several hundred million immobilized sequences using fluorescent-dye reversible-terminator chemistry. Sequencing library quality, sample handling, instrument settings and sequencing chemistry have a strong impact on sequencing run quality. The presence of adapter chimeras and adapter sequences at the end of short-insert molecules, as well as increased error rates and short read lengths complicate many computational analyses. We discuss here some of the factors that influence the frequency and severity of these problems and provide solutions for circumventing these. Further, we present a set of general principles for good analysis practice that enable problems with sequencing runs to be identified and dealt with

Measuring self-regulation in everyday life: reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one’s goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test–retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures’ construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks

NSAIDs Modulate CDKN2A, TP53, and DNA Content Risk for Progression to Esophageal Adenocarcinoma

BACKGROUND: Somatic genetic CDKN2A, TP53, and DNA content abnormalities are common in many human cancers and their precursors, including esophageal adenocarcinoma (EA) and Barrett's esophagus (BE), conditions for which aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) have been proposed as possible chemopreventive agents; however, little is known about the ability of a biomarker panel to predict progression to cancer nor how NSAID use may modulate progression. We aimed to evaluate somatic genetic abnormalities with NSAIDs as predictors of EA in a prospective cohort study of patients with BE. METHODS AND FINDINGS: Esophageal biopsies from 243 patients with BE were evaluated at baseline for TP53 and CDKN2A (p16) alterations, tetraploidy, and aneuploidy using sequencing; loss of heterozygosity (LOH); methylation-specific PCR; and flow cytometry. At 10 y, all abnormalities, except CDKN2A mutation and methylation, contributed to EA risk significantly by univariate analysis, ranging from 17p LOH (relative risk [RR] = 10.6; 95% confidence interval [CI] 5.2–21.3, p < 0.001) to 9p LOH (RR = 2.6; 95% CI 1.1–6.0, p = 0.03). A panel of abnormalities including 17p LOH, DNA content tetraploidy and aneuploidy, and 9p LOH was the best predictor of EA (RR = 38.7; 95% CI 10.8–138.5, p < 0.001). Patients with no baseline abnormality had a 12% 10-y cumulative EA incidence, whereas patients with 17p LOH, DNA content abnormalities, and 9p LOH had at least a 79.1% 10-y EA incidence. In patients with zero, one, two, or three baseline panel abnormalities, there was a significant trend toward EA risk reduction among NSAID users compared to nonusers (p = 0.01). The strongest protective effect was seen in participants with multiple genetic abnormalities, with NSAID nonusers having an observed 10-y EA risk of 79%, compared to 30% for NSAID users (p < 0.001). CONCLUSIONS: A combination of 17p LOH, 9p LOH, and DNA content abnormalities provided better EA risk prediction than any single TP53, CDKN2A, or DNA content lesion alone. NSAIDs are associated with reduced EA risk, especially in patients with multiple high-risk molecular abnormalities