Phosphoenolpyruvate Carboxykinase as the Sole Anaplerotic Enzyme in Saccharomyces cerevisiae

Pyruvate carboxylase is the sole anaplerotic enzyme in glucose-grown cultures of wild-type Saccharomyces cerevisiae. Pyruvate carboxylase-negative (Pyc–) S. cerevisiae strains cannot grow on glucose unless media are supplemented with C4 compounds, such as aspartic acid. In several succinate-producing prokaryotes, phosphoenolpyruvate carboxykinase (PEPCK) fulfills this anaplerotic role. However, the S. cerevisiae PEPCK encoded by PCK1 is repressed by glucose and is considered to have a purely decarboxylating and gluconeogenic function. This study investigates whether and under which conditions PEPCK can replace the anaplerotic function of pyruvate carboxylase in S. cerevisiae. Pyc– S. cerevisiae strains constitutively overexpressing the PEPCK either from S. cerevisiae or from Actinobacillus succinogenes did not grow on glucose as the sole carbon source. However, evolutionary engineering yielded mutants able to grow on glucose as the sole carbon source at a maximum specific growth rate of ca. 0.14 h–1, one-half that of the (pyruvate carboxylase-positive) reference strain grown under the same conditions. Growth was dependent on high carbon dioxide concentrations, indicating that the reaction catalyzed by PEPCK operates near thermodynamic equilibrium. Analysis and reverse engineering of two independently evolved strains showed that single point mutations in pyruvate kinase, which competes with PEPCK for phosphoenolpyruvate, were sufficient to enable the use of PEPCK as the sole anaplerotic enzyme. The PEPCK reaction produces one ATP per carboxylation event, whereas the original route through pyruvate kinase and pyruvate carboxylase is ATP neutral. This increased ATP yield may prove crucial for engineering of efficient and low-cost anaerobic production of C4 dicarboxylic acids in S. cerevisiae

Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands

To understand diverse natural language commands, virtual assistants today are trained with numerous labor-intensive, manually annotated sentences. This paper presents a methodology and the Genie toolkit that can handle new compound commands with significantly less manual effort. We advocate formalizing the capability of virtual assistants with a Virtual Assistant Programming Language (VAPL) and using a neural semantic parser to translate natural language into VAPL code. Genie needs only a small realistic set of input sentences for validating the neural model. Developers write templates to synthesize data; Genie uses crowdsourced paraphrases and data augmentation, along with the synthesized data, to train a semantic parser. We also propose design principles that make VAPL languages amenable to natural language translation. We apply these principles to revise ThingTalk, the language used by the Almond virtual assistant. We use Genie to build the first semantic parser that can support compound virtual assistants commands with unquoted free-form parameters. Genie achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's generality by showing a 19% and 31% improvement over the previous state of the art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201

Liver Enzymes and the Development of Posttransplantation Diabetes Mellitus in Renal Transplant Recipients

BACKGROUND: Posttransplantation diabetes mellitus (PTDM) is common in renal transplant recipients (RTR), increasing the risk of graft failure, cardiovascular disease, and mortality. Early detection of a high risk for PTDM is warranted. Because liver function and liver fat are involved, we investigated whether serum liver markers are associated with future PTDM in RTR. METHODS: Between 2001 and 2003, 606 RTR with a functioning allograft beyond the first year after transplantation were included of which 500 participants (56% men; age, 50 ± 12 years) were free of diabetes at baseline and had liver enzyme values (1 missing) available. Serum concentrations of alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), alkaline phosphatase were measured at baseline at 6.0 (6.2-11.5) years posttransplantation. PTDM cases were recorded until April 2012. RESULTS: During median follow-up for 9.6 years (interquartile range [IQR], 6.2-10.2) beyond baseline, 76 (15.2%) patients developed PTDM. Comparing the highest to the lower tertiles, higher liver enzyme activities were significantly related to incident PTDM for ALT (hazard ratio [HR], 2.22; IQR, 1.42-3.48), for GGT (HR, 2.93; IQR, 1.87-4.61), and for alkaline phosphatase (HR, 1.78; IQR, 1.13-2.80). The associations of ALT and GGT with development of PTDM were independent of potential confounders and risk factors, including age, sex, renal function, medication use, lifestyle factors, adiposity, presence of the metabolic syndrome, fasting glucose, HbA1c, proinsulin, and cytomegalovirus status. CONCLUSIONS: Markers for liver function and liver fat in the subclinical range are potential markers for future PTDM, independent of other known risk factors. This may allow for early detection and management of PTDM development

Real-Time Ventilation Measurements from Mechanically Ventilated Livestock Buildings for Emission Rate Estimations

A six-state USDA-IFAFS funded research project (Aerial Pollutant Emissions from Confined Animal Buildings, APECAB) was conducted with the purpose of determining hydrogen sulfide, ammonia, PM10, and odor emission rates from selected swine and poultry housing systems. An important aspect of emission studies is to be able to measure the mass flow rate of air through the housing system. For this research project, the decision was made to study only fan ventilated buildings due to the difficulty in estimating mass flow rates through naturally ventilated buildings. This paper highlights the various techniques used throughout the study to determine mass flow rate through fan ventilated swine and poultry housing systems