Can fossil fuel energy be recovered and used without any CO2 emissions to the atmosphere?

Reviews in Environmental Science and Bio/Technology - The world’s energy system is still dominated by fossil fuels. While there is a rapid reduction in the cost of renewable energy and the...     

Metabolic engineering of <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> for linalool production

Objectives

To engineer the yeast Saccharomyces cerevisiae for the heterologous production of linalool.

Results

Expression of linalool synthase gene from Lavandula angustifolia enabled heterologous production of linalool in S. cerevisiae. Downregulation of ERG9 gene, that encodes squalene synthase, by replacing its native promoter with the repressible MET3 promoter in the presence of methionine resulted in accumulation of 78 µg linalool l^?1 in the culture medium. This was more than twice that produced by the control strain. The highest linalool titer was obtained by combined repression of ERG9 and overexpression of tHMG1. The yeast strain harboring both modifications produced 95 μg linalool l^?1.

Conclusions

Although overexpression of tHMG1 and downregulation of ERG9 enhanced linalool titers threefold in the engineered yeast strain, alleviating linalool toxicity is necessary for further improvement of linalool biosynthesis in yeast.

     

HIPPI: highly accurate protein family classification with ensembles of HMMs

Background

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics.

Results

We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy.

Conclusion

HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp.

     

Anchoring quartet-based phylogenetic distances and applications to species tree reconstruction

Background

Inferring species trees from gene trees using the coalescent-based summary methods has been the subject of much attention, yet new scalable and accurate methods are needed.

Results

We introduce DISTIQUE, a new statistically consistent summary method for inferring species trees from gene trees under the coalescent model. We generalize our results to arbitrary phylogenetic inference problems; we show that two arbitrarily chosen leaves, called anchors, can be used to estimate relative distances between all other pairs of leaves by inferring relevant quartet trees. This results in a family of distance-based tree inference methods, with running times ranging between quadratic to quartic in the number of leaves.

Conclusions

We show in simulated studies that DISTIQUE has comparable accuracy to leading coalescent-based summary methods and reduced running times.

     

Mafb and c-Maf Have Prenatal Compensatory and Postnatal Antagonistic Roles in Cortical Interneuron Fate and Function

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Detection of methicillin-resistance (mec-A) gene inStaphylococcus aureus strains by PCR and determination of antibiotic susceptibility

Methicillin-ResistantStaphylococcus aureus (MSRA) has become a frequent cause of serious infections. Extended hospitalization and antibiotic therapy have been identified as additional risk factors for MRSA carrier and infection. The aim of this study was to determine the incidence of MRSA infections in the hospitals affiliated to Hamedan University of Medical Sciences. SeventyS. aureus clinical strains were isolated from patients from June 2005 to June 2006 and examined by PCR and conventional microbiological tests. Then, the antibiotic susceptibility to methicillin/oxacillin and other antibiotics were performed by Disc Diffusion Agar (DDA). The results of this study showed that methicillin resistance gene was detected in 35 (50%) and 22 (31.4%) cases by PCR and DDA, respectively. The results of antibiotic susceptibility assays also showed there were high resistance MRSA strains to penicilin (100%), cloxacillin (91.4%), tetracycline (74.2%), cotrimoxazole (68.5%), erythromycin (68.5%) and less resistance to rifampin (11.4). Two MRSA also had decreased susceptibility to vancomycin. But the strains of Methicillin-SensitiveS. aureus (MSSA) showed high sensitivity to all antibiotics profiles except to penicillin (complete resistance). As a conclusion, the resistance to methicillin/oxacillin ofS. aureus in Hamedan hospitals has reached to 50% and they show multidrug resistance.     

In vivo protein-protein and protein-DNA crosslinking for genomewide binding microarray

Chromatin immunoprecipitation (ChrIP or ChIP) has commonly been used to map protein-DNA interaction sites at specific genomic loci through use of formaldehyde-induced crosslinking. However, formaldehyde alone has proved inadequate for crosslinking of certain proteins such as the yeast histone deacetylase Rpd3. We report here a modified crosslinking procedure that includes a protein-protein crosslinking agent in addition to formaldehyde. Using this double crosslinking method, we have successfully mapped Rpd3 binding sites in vivo. We also describe the use of ChrIP in combination with DNA microarrays (ChrIP-array) to determine the pattern of Rpd3 binding genomewide. This approach couples the versatility of ChrIP with that of microarrays to identify binding patterns that would otherwise be hidden in a gene-by-gene survey.     

Quantitative Ultrasound Spectroscopic Imaging for Characterization of Disease Extent in Prostate Cancer Patients

Three-dimensional quantitative ultrasound spectroscopic imaging of prostate was investigated clinically for the noninvasive detection and extent characterization of disease in cancer patients and compared to whole-mount, whole-gland histopathology of radical prostatectomy specimens. Fifteen patients with prostate cancer underwent a volumetric transrectal ultrasound scan before radical prostatectomy. Conventional-frequency (~ 5 MHz) ultrasound images and radiofrequency data were collected from patients. Normalized power spectra were used as the basis of quantitative ultrasound spectroscopy. Specifically, color-coded parametric maps of 0-MHz intercept, midband fit, and spectral slope were computed and used to characterize prostate tissue in ultrasound images. Areas of cancer were identified in whole-mount histopathology specimens, and disease extent was correlated to that estimated from quantitative ultrasound parametric images. Midband fit and 0-MHz intercept parameters were found to be best associated with the presence of disease as located on histopathology whole-mount sections. Obtained results indicated a correlation between disease extent estimated noninvasively based on midband fit parametric images and that identified histopathologically on prostatectomy specimens, with an r² value of 0.71 (P < .0001). The 0-MHz intercept parameter demonstrated a lower level of correlation with histopathology. Spectral slope parametric maps offered no discrimination of disease. Multiple regression analysis produced a hybrid disease characterization model (r² = 0.764, P < .05), implying that the midband fit biomarker had the greatest correlation with the histopathologic extent of disease. This work demonstrates that quantitative ultrasound spectroscopic imaging can be used for detecting prostate cancer and characterizing disease extent noninvasively, with corresponding gross three-dimensional histopathologic correlation.