Dynamic Metabolic Flux Analysis of Shewanella oneidensis MR-1 Central Metabolisms [abstract]

Only abstract of poster available.Track II: Transportation and BiofuelsShewanella oneidensis MR-1 have received significant attention because of their versatile carbon metabolisms and potential to engage in bioremediation of toxic metal compounds and microbial fuel cell applications. In active growth phase using lactate as the carbon source, MR-1 shows a dynamic metabolism. MR-1 produces a significant amount of pyruvate and acetate when the lactate is excess in the medium. When the energy favorable carbon source (lactate) is depleted, MR-1 will utilize the waste product (pyruvate and acetate) for their growth. In response to the switch of carbon sources during the growth, the central metabolism (TCA cycle, glyoxylate shunt and futile pathways) of MR-1 also changes. To describe this dynamic metabolism, we combine the enzyme kinetic modeling with the isotopomer analysis to quantitatively understand the regulation of metabolic network and profile the flux distribution as the function of the time. Using MATLAB based ODE tool box, we can solve the dynamic metabolism and predict the growth/extracellular metabolites production. Meanwhile, we may improve the model predictions using the constraints from the labeling information. The isotopomer information can also provide us the insight into the regulation of central metabolic pathways during MR-1 growth. Such isotopomer assisted dynamic flux model may be potentially used in other biological systems including the biofuel producers or microbial communities

Study of the First Isolated Fungus Capable of Heavy Crude Oil Biodesulfurization [abstract]

Track II: Transportation and BiofuelsOnly abstract of poster available.To meet stringent emission standards stipulated by regulatory agencies, the oil industry is required to bring down the sulfur content of fuels. Oil supplies 38% of the worldwide energy, and as the light oil is limited and meanwhile the energy demand is increasing, it is a must to use heavy crude oil and therefore desulfurize it to meet environmental standards. As it is not feasible to desulfurize all the sulfur containing compounds of heavy crude oil by the existing methods (such as hydro-desulfurization) we have focused on biodesulfurization of heavy crude oil. We have isolated the first native fungus which has been identified as Stachybotrys sp. and is able to remove sulfur and nitrogen from heavy crude oil selectively at 30 °C. This fungus is able to desulfurize 76% and 64.8% of the sulfur content of heavy crude oil of Soroush oil field and Kuhemond oil field (with the initial sulfur contents of 5 wt % and 7.6 wt %, respectively) in 72 and 144 h, respectively. We have studied the impacts of several parameters on heavy crude oil biodesulfurization efficiency of our fungus strain such as initial pH of the medium, water−oil ratio, and number of spores in the suspension used for inoculation. This fungus strain has been isolated as a part of the heavy crude oil biodesulfurization project initiated by Petroleum Engineering Development Company (PEDEC), a subsidiary of National Iranian Oil Company

Field-to-farm gate greenhouse gas emissions from corn stover production in the Midwestern U.S.

Measured field data were used to compare two allocation methods on life cycle greenhouse gas emissions from corn (Zea mays L.) stover production in the Midwest U.S. We used publicly-available crop yield, nitrogen fertilizer, and direct soil nitrous oxide emissions data from the USDA-ARS Resilient Economic Agricultural Practices research program. Field data were aggregated from 9 locations across 26 site-years for 3 stover harvest rates (no removal; moderate removal e 3.1Mg ha-1; high removal e 7.2Mg ha-1) and 2 tillage practices (conventional; reduced/no-till). Net carbon uptake by crops was computed from measured plant carbon content. Monte Carlo simulations sampled input distributions to assess variability in farm-to-gate GHG emissions. The base analysis assumed no change in soil organic carbon stocks. In all cases, net CO2 uptake during crop growth and soil-respired CO2 dominated system emissions. Emissions were most sensitive to co-product accounting method, with system expansion emissions ~15% lower than mass allocation. Regardless of accounting method, lowest emissions occurred for a moderate removal rate under reduced/no-till management. The absence of correlations between N fertilization rate and stover removal rate or soil N2O emissions in this study challenges the use of such assumptions typically employed in life cycle assessments Storage of all carbon retained on the field as SOC could reduce emissions by an additional 15%. Our results highlight how variability in GHG emissions due to location and weather can overshadow the impact of farm management practices on field-to-farm gate emissions

Pathogenic Huntingtin Repeat Expansions in Patients with Frontotemporal Dementia and Amyotrophic Lateral Sclerosis.

We examined the role of repeat expansions in the pathogenesis of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS) by analyzing whole-genome sequence data from 2,442 FTD/ALS patients, 2,599 Lewy body dementia (LBD) patients, and 3,158 neurologically healthy subjects. Pathogenic expansions (range, 40-64 CAG repeats) in the huntingtin (HTT) gene were found in three (0.12%) patients diagnosed with pure FTD/ALS syndromes but were not present in the LBD or healthy cohorts. We replicated our findings in an independent collection of 3,674 FTD/ALS patients. Postmortem evaluations of two patients revealed the classical TDP-43 pathology of FTD/ALS, as well as huntingtin-positive, ubiquitin-positive aggregates in the frontal cortex. The neostriatal atrophy that pathologically defines Huntington's disease was absent in both cases. Our findings reveal an etiological relationship between HTT repeat expansions and FTD/ALS syndromes and indicate that genetic screening of FTD/ALS patients for HTT repeat expansions should be considered

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Genetic basis of a cognitive complexity metric

Relational complexity (RC) is a metric reflecting capacity limitation in relational processing. It plays a crucial role in higher cognitive processes and is an endophenotype for several disorders. However, the genetic underpinnings of complex relational processing have not been investigated. Using the classical twin model, we estimated the heritability of RC and genetic overlap with intelligence (IQ), reasoning, and working memory in a twin and sibling sample aged 15-29 years (N = 787). Further, in an exploratory search for genetic loci contributing to RC, we examined associated genetic markers and genes in our Discovery sample and selected loci for replication in four independent samples (ALSPAC, LBC1936, NTR, NCNG), followed by meta-analysis (N>6500) at the single marker level. Twin modelling showed RC is highly heritable (67%), has considerable genetic overlap with IQ (59%), and is a major component of genetic covariation between reasoning and working memory (72%). At the molecular level, we found preliminary support for four single-marker loci (one in the gene DGKB), and at a gene-based level for the NPS gene, having influence on cognition. These results indicate that genetic sources influencing relational processing are a key component of the genetic architecture of broader cognitive abilities. Further, they suggest a genetic cascade, whereby genetic factors influencing capacity limitation in relational processing have a flow-on effect to more complex cognitive traits, including reasoning and working memory, and ultimately, IQ

Genome sequencing analysis identifies new loci associated with Lewy body dementia and provides insights into its genetic architecture

The genetic basis of Lewy body dementia (LBD) is not well understood. Here, we performed whole-genome sequencing in large cohorts of LBD cases and neurologically healthy controls to study the genetic architecture of this understudied form of dementia, and to generate a resource for the scientific community. Genome-wide association analysis identified five independent risk loci, whereas genome-wide gene-aggregation tests implicated mutations in the gene GBA. Genetic risk scores demonstrate that LBD shares risk profiles and pathways with Alzheimer's disease and Parkinson's disease, providing a deeper molecular understanding of the complex genetic architecture of this age-related neurodegenerative condition

Field-to-farm gate greenhouse gas emissions from corn stover production in the Midwestern U.S.

Measured field data were used to compare two allocation methods on life cycle greenhouse gas emissions from corn (Zea mays L.) stover production in the Midwest U.S. We used publicly-available crop yield, nitrogen fertilizer, and direct soil nitrous oxide emissions data from the USDA-ARS Resilient Economic Agricultural Practices research program. Field data were aggregated from 9 locations across 26 site-years for 3 stover harvest rates (no removal; moderate removal e 3.1Mg ha-1; high removal e 7.2Mg ha-1) and 2 tillage practices (conventional; reduced/no-till). Net carbon uptake by crops was computed from measured plant carbon content. Monte Carlo simulations sampled input distributions to assess variability in farm-to-gate GHG emissions. The base analysis assumed no change in soil organic carbon stocks. In all cases, net CO2 uptake during crop growth and soil-respired CO2 dominated system emissions. Emissions were most sensitive to co-product accounting method, with system expansion emissions ~15% lower than mass allocation. Regardless of accounting method, lowest emissions occurred for a moderate removal rate under reduced/no-till management. The absence of correlations between N fertilization rate and stover removal rate or soil N2O emissions in this study challenges the use of such assumptions typically employed in life cycle assessments Storage of all carbon retained on the field as SOC could reduce emissions by an additional 15%. Our results highlight how variability in GHG emissions due to location and weather can overshadow the impact of farm management practices on field-to-farm gate emissions

Mutation of WIF1: a potential novel cause of a Nail-Patella-like disorder.

PurposeNail-Patella syndrome is a dominantly inherited genetic disorder characterized by abnormalities of the nails, knees, elbows, and pelvis. Nail abnormalities are the most constant feature of Nail-Patella syndrome. Pathogenic mutations in a single gene, LMX1B, a mesenchymal determinant of dorsal-ventral patterning, explain approximately 95% of Nail-Patella syndrome cases. However, 5% of cases remain unexplained.MethodsHere, we present exome sequencing and analysis of four generations of a family with a dominantly inherited Nail-Patella-like disorder (nail dysplasia with some features of Nail-Patella syndrome) who tested negative for LMX1B mutation.ResultsWe identify a loss-of-function mutation in WIF1 (NM_007191 p.W15*), which is involved in mesoderm segmentation, as the suspected cause of the Nail-Patella-like disorder observed in this family.ConclusionsMutation of WIF1 is a potential novel cause of a Nail-Patella-like disorder. Testing of additional patients negative for LMX1B mutation is needed to confirm this finding and further clarify the phenotype.Genet Med advance online publication 06 April 2017