Improved ethanol yield and reduced Minimum Ethanol Selling Price (MESP) by modifying low severity dilute acid pretreatment with deacetylation and mechanical refining: 1) Experimental

ABSTRACT: BACKGROUND: Historically, acid pretreatment technology for the production of bio-ethanol from corn stover has required severe conditions to overcome biomass recalcitrance. However, the high usage of acid and steam at severe pretreatment conditions hinders the economic feasibility of the ethanol production from biomass. In addition, the amount of acetate and furfural produced during harsh pretreatment is in the range that strongly inhibits cell growth and impedes ethanol fermentation. The current work addresses these issues through pretreatment with lower acid concentrations and temperatures incorporated with deacetylation and mechanical refining. RESULTS: The results showed that deacetylation with 0.1 M NaOH before acid pretreatment improved the monomeric xylose yield in pretreatment by up to 20 % while keeping the furfural yield under 2 %. Deacetylation also improved the glucose yield by 10 % and the xylose yield by 20 % during low solids enzymatic hydrolysis. Mechanical refining using a PFI mill further improved sugar yields during both low- and high-solids enzymatic hydrolysis. Mechanical refining also allowed enzyme loadings to be reduced while maintaining high yields. Deacetylation and mechanical refining are shown to assist in achieving 90 % cellulose yield in high-solids (20 %) enzymatic hydrolysis. When fermentations were performed under pH control to evaluate the effect of deacetylation and mechanical refining on the ethanol yields, glucose and xylose utilizations over 90 % and ethanol yields over 90 % were achieved. Overall ethanol yields were calculated based on experimental results for the base case and modified cases. One modified case that integrated deacetylation, mechanical refining, and washing was estimated to produce 88 gallons of ethanol per ton of biomass. CONCLUSION: The current work developed a novel bio-ethanol process that features pretreatment with lower acid concentrations and temperatures incorporated with deacetylation and mechanical refining. The new process shows improved overall ethanol yields compared to traditional dilute acid pretreatment. The experimental results from this work support the techno-economic analysis and calculation of Minimum Ethanol Selling Price (MESP) detailed in our companion paper.     

Use of 16S rRNA Gene for Identification of a Broad Range of Clinically Relevant Bacterial Pathogens

According to World Health Organization statistics of 2011, infectious diseases remain in the top five causes of mortality worldwide. However, despite sophisticated research tools for microbial detection, rapid and accurate molecular diagnostics for identification of infection in humans have not been extensively adopted. Time-consuming culture-based methods remain to the forefront of clinical microbial detection. The 16S rRNA gene, a molecular marker for identification of bacterial species, is ubiquitous to members of this domain and, thanks to ever-expanding databases of sequence information, a useful tool for bacterial identification. In this study, we assembled an extensive repository of clinical isolates (n = 617), representing 30 medically important pathogenic species and originally identified using traditional culture-based or non-16S molecular methods. This strain repository was used to systematically evaluate the ability of 16S rRNA for species level identification. To enable the most accurate species level classification based on the paucity of sequence data accumulated in public databases, we built a Naïve Bayes classifier representing a diverse set of high-quality sequences from medically important bacterial organisms. We show that for species identification, a model-based approach is superior to an alignment based method. Overall, between 16S gene based and clinical identities, our study shows a genus-level concordance rate of 96% and a species-level concordance rate of 87.5%. We point to multiple cases of probable clinical misidentification with traditional culture based identification across a wide range of gram-negative rods and gram-positive cocci as well as common gram-negative cocci.     

Potassium channel blocker crafted by α-hairpinin scaffold engineering

The α-Hairpinins are a family of plant defense peptides with a common fold presenting two short α-helices stabilized by two invariant S–S-bridges. We have shown previously that substitution of just two amino acid residues in a wheat α-hairpinin Tk-AMP-X2 leads to Tk-hefu-2 that features specific affinity to voltage-gated potassium channels K_V1.3. Here, we utilize a combined molecular modeling approach based on molecular dynamics simulations and protein surface topography technique to improve the affinity of Tk-hefu-2 to K_V1.3 while preserving its specificity. An important advance of this work compared with our previous studies is transition from the analysis of various physicochemical properties of an isolated toxin molecule to its consideration in complex with its target, a membrane-bound ion channel. As a result, a panel of computationally designed Tk-hefu-2 derivatives was synthesized and tested against K_V1.3. The most active mutant Tk-hefu-10 showed a half-maximal inhibitory concentration of ～150 nM being >10 times more active than Tk-hefu-2 and >200 times more active than the original Tk-hefu. We conclude that α-hairpinins provide an attractive disulfide-stabilized scaffold for the rational design of ion channel inhibitors. Furthermore, the success rate can be considerably increased by the proposed “target-based” iterative strategy of molecular design.     

Screening drug effects in patient‐derived cancer cells links organoid responses to genome alterations

Cancer drug screening in patient‐derived cells holds great promise for personalized oncology and drug discovery but lacks standardization. Whether cells are cultured as conventional monolayer or advanced, matrix‐dependent organoid cultures influences drug effects and thereby drug selection and clinical success. To precisely compare drug profiles in differently cultured primary cells, we developed DeathPro, an automated microscopy‐based assay to resolve drug‐induced cell death and proliferation inhibition. Using DeathPro, we screened cells from ovarian cancer patients in monolayer or organoid culture with clinically relevant drugs. Drug‐induced growth arrest and efficacy of cytostatic drugs differed between the two culture systems. Interestingly, drug effects in organoids were more diverse and had lower therapeutic potential. Genomic analysis revealed novel links between drug sensitivity and DNA repair deficiency in organoids that were undetectable in monolayers. Thus, our results highlight the dependency of cytostatic drugs and pharmacogenomic associations on culture systems, and guide culture selection for drug tests.     

Weighted averaging partial least squares regression (WA-PLS): an improved method for reconstructing environmental variables from species assemblages

Weighted averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages. Based on the concepts of niche-space partitioning and ecological optima of species (indicator values), it performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units). Partial least squares regression is a linear method for multivariate calibration that is popular in chemometrics as a robust alternative to principal component regression. It successively selects linear components so as to maximize predictive power. In this paper the ideas of the two methods are combined. It is shown that the weighted averaging method is a form of partial least squares regression applied to transformed data that uses the first PLS-component only. The new combined method, ast squares, consists of using further components, namely as many as are useful in terms of predictive power. The further components utilize the residual structure in the species data to improve the species parameters (optima) in the final weighted averaging predictor. Simulations show that the new method can give 70% reduction in prediction error in data sets with low noise, but only a small reduction in noisy data sets. In three real data sets of diatom assemblages collected for the reconstruction of acidity and salinity, the reduction in prediction error was zero, 19% and 32%.     

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F₂ mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users. Tova F. Fuller, Anatole Ghazalpour contributed equally to this work.     

Inferring Landscape-Scale Land-Use Impacts on Rivers Using Data from Mesocosm Experiments and Artificial Neural Networks

Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard metrics, such as substratum composition, do not necessarily relate causally to ecological impacts. Consequently, the absence of a significant relationship between a hypothesised driver and a dependent variable does not necessarily indicate the absence of a causal relationship. We conducted a gradient survey to identify impacts of catchment-scale grazing by domestic livestock on river macroinvertebrate communities. A standard correlative approach showed that community structure was strongly related to the upstream catchment area under grazing. We then used data from a stream mesocosm experiment that independently quantified the impacts of nutrients and fine sediments on macroinvertebrate communities to train artificial neural networks (ANNs) to assess the relative influence of nutrients and fine sediments on the survey sites from their community composition. The ANNs developed to predict nutrient impacts did not find a relationship between nutrients and catchment area under grazing, suggesting that nutrients were not an important factor mediating grazing impacts on community composition, or that these ANNs had no generality or insufficient power at the landscape-scale. In contrast, ANNs trained to predict the impacts of fine sediments indicated a significant relationship between fine sediments and catchment area under grazing. Macroinvertebrate communities at sites with a high proportion of land under grazing were thus more similar to those resulting from high fine sediments in a mesocosm experiment than to those resulting from high nutrients. Our study confirms that 1) fine sediment is an important mediator of land-use impacts on river macroinvertebrate communities, 2) ANNs can successfully identify subtle effects and separate the effects of correlated variables, and 3) data from small-scale experiments can generate relationships that help explain landscape-scale patterns.