Webb-Robertson, Bobbie-Jo M | Engineering Papers

DOE OSTI2024

Corn stover variability drives differences in bisabolene production by engineered Rhodotorula toruloides

Microbial conversion of lignocellulosic biomass represents an alternative route for production of biofuels and bioproducts. While researchers have mostly focused on engineering strains such as Rhodotorula toruloides for better bisabolene production as a sustainable aviation fuel, less is known about the impact of the feedstock heterogeneity on bisabolene production. Critical material attributes like feedstock composition, nutritional content, and inhibitory compounds can all influence bioconversion. Further, the given feedstocks can have a marked influence on selection of suitable pretreatment and hydrolysis technologies, optimizing the fermentation conditions, and possibly even modifying the microorganism's metabolic pathways, to better utilize the available feedstock. Here, this work aimed to examine and understand how variations in corn stover batches, anatomical fractions, and storage conditions impact the efficiency of bisabolene production by R. toruloides. All of these represent different facets of feedstock heterogeneity. Deacetylation, mechanical refining, and enzymatic hydrolysis of these variable feedstocks served as the basis of this research. The resulting hydrolysates were converted to bisabolene via fermentation, a sustainable aviation fuel precursor, using an engineered R. toruloides strain. This study showed that different sources of feedstock heterogeneity can influence microbial growth and product titer in counterintuitive ways, as revealed through global analysis of protein expression. The maximum bisabolene produced by R. toruloides was on the stalk fraction of corn stover hydrolysate (8.89 ± 0.47 g/L). Further, proteomics analysis comparing the protein expression between the anatomic fractions showed that proteins relating to carbohydrate metabolism, energy production, and conversion as well as inorganic ion transport metabolism were either significantly upregulated or downregulated. Specifically, downregulation of proteins related to the iron–sulfur cluster in stalk fraction suggests a coordinated response by R. toruloides to maintain overall metabolic balance, and this was corroborated by the concentration of iron in the feedstocks.

09 BIOMASS FUELS↗

DOE OSTI2024

New Onset Type 1 Diabetes Urine Metabolomics

Gas chromatography-mass spectrometry-based metabolomics to identify molecular signatures of Type 1 Diabetes (T1D) in urine. We utilize three cohorts in different stages post-diagnosis: (1) new onset, (2) within one year of diagnosis and (3) after 6 years of diagnosis. There were 91 metabolites identified in all three datasets with complete data represented in each cohort dataset. Cohort 1 (CNMC): 32 T1D cases; 32 healthy controls siblings Cohort 2 (BDCD): 27 T1D cases; 27 healthy controls siblings Cohort 3 (IUSOM): 12 T1D cases; 20 healthy controls Cases and controls matched on sex and age

Bramer, Lisa↗

DOE OSTI2024

Data for The utility of transfer learning to improve the performance of deep learning in axon segmentation

The utility of transfer learning to improve the performance of deep learning in axon segmentation Data Data: All the input and labeled volumes tf-logs: Tensorflow logs, view with command "tensorboard --logdir [name of folder]" Model Weights: model_weights: the argument list under variable combo indicate 1) no oversampling, 2) no rotation, 3) no learn scheduler, and 4) flipping on all three dimensions, and the additional values indicate 5) elastic deformation percentage, 6) rotate deformation percentage, 7) layer setting , 8) learning rate, and 9) training/validation/test data division suffix (leave '' if not using suffix). Results: Output from inference segment_total_results_validation_final: All validation results and calculations segment_total_results: All test results and calculations Authors The modified code was created for a paper by: Marjolein Oostrom, Michael A. Muniak, Rogene Eichler West, Sarah Akers, Paritosh Pande, Moses Obiri, Wei Wang, Kasey Bowyer, Zhuhao Wu, Lisa Bramer, Tianyi Mao, Bobbie Jo Webb-Robertson The work is adapted from Github TrailMap, which was created by Albert Pun and Drew Friedmann Acknowledgments MO, RMEW, SA, MO, LB, BJWR were supported by the Laboratory Directed Research and Development at Pacific Northwest National Laboratory (PNNL), a Department of Energy facility operated by Battelle under contract DE-AC05-76RLO01830. WW, KB, and ZW were supported in part by a NIH/BRAIN Initiative Grant RF1MH128969. MAM and TM were supported by two NIH/BRAIN Initiative Grants R01NS104944, RF1MH120119 and NIH R01NS081071. This research is affiliated with the Pacific northwest bioMedical Innovation Co-laboratory (PMedIC) collaboration between OHSU and PNNL.

Oostrom, Marjolein T↗

DOE OSTI2023

DAISY Complement Protein ML-Ready Data

A total of 172 children from the DAISY study with multiple plasma samples collected over time, with up to 23 years of follow-up, were characterized via proteomics analysis. Of the children there were 40 controls and 132 cases. All 132 cases had measurements across time relative to IA. Sampling was not consistent for all children. There were 47 of the children who had samples taken and evaluated prior to IA (Pre-IA), and 131 children had measurements at or after IA, but prior to diagnosis of clinical T1D (Post-IA). The control children were frequency matched on HLA genotypes and age and sex with an observed lower frequency of first degree relatives within the control group versus the cases For machine learning the children that will develop islet autoantibodies the 40 control and 47 Pre-IA children were down-selected to a single sample time point. For the 40 control children this was the earliest sample collected and for the 47 Pre-IA children it was a random selection of the first or second time point prior to the detection of autoantibodies to assure the age distributions were not significantly different.

machine learning, proteomics, Type 1 Diabetes, com↗

DOE OSTI2023

DAISY Complement Protein ML-Ready Data

A total of 172 children from the DAISY study with multiple plasma samples collected over time, with up to 23 years of follow-up, were characterized via proteomics analysis. Of the children there were 40 controls and 132 cases. All 132 cases had measurements across time relative to IA. Sampling was not consistent for all children. There were 47 of the children who had samples taken and evaluated prior to IA (Pre-IA), and 131 children had measurements at or after IA, but prior to diagnosis of clinical T1D (Post-IA). The control children were frequency matched on HLA genotypes and age and sex with an observed lower frequency of first degree relatives within the control group versus the cases For machine learning the children that will develop islet autoantibodies the 40 control and 47 Pre-IA children were down-selected to a single sample time point. For the 40 control children this was the earliest sample collected and for the 47 Pre-IA children it was a random selection of the first or second time point prior to the detection of autoantibodies to assure the age distributions were not significantly different.

Webb-Robertson, Bobbie-Jo M↗

DOE OSTI2023

Human Islet Research Network (HIRN): Alternative Splicing Events

Inclusion levels of alternative splicing (AS) events of five different varieties (i.e. skipped exon (SE), retained intron (RI), alternative 5’ splice site (A5SS), alternative 3’ splice site (A3SS), and mutually exclusive exons (MXE)) were measured in human blood samples from two separate cohorts of patients. Cohort 1 (Training Cohort): 12 healthy controls; 12 new onset type 1 diabetic (T1D) cases cases and controls matched on biological sex, age, and body mass index (BMI) 180 million reads Cohort 2 (Testing Cohort): 12 healthy controls; 12 new onset type 1 diabetic (T1D) cases cases and controls matched on biological sex and age. BMI not recorded. 150 million reads

Webb-Robertson, Bobbie-Jo M↗

DOE OSTI2023

Alternative Splicing Events

Inclusion levels of alternative splicing (AS) events of five different varieties (i.e. skipped exon (SE), retained intron (RI), alternative 5’ splice site (A5SS), alternative 3’ splice site (A3SS), and mutually exclusive exons (MXE)) were measured in human blood samples from two separate cohorts of patients. Cohort 1 (Training Cohort): 12 healthy controls; 12 new onset type 1 diabetic (T1D) cases cases and controls matched on biological sex, age, and body mass index (BMI) 180 million reads Cohort 2 (Testing Cohort): 12 healthy controls; 12 new onset type 1 diabetic (T1D) cases cases and controls matched on biological sex and age. BMI not recorded. 150 million reads

Webb-Robertson, Bobbie-Jo M↗