STUDENT #1 SYNCHRONIZATION AND ISOLATION OF SWITCHGRASS FOR - - PowerPoint PPT Presentation
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n STUDENT #1 SYNCHRONIZATION AND ISOLATION OF SWITCHGRASS FOR INTERECOTYPIC HYBRID DEVELOPMENT Laxman Adhikari and Yanqi Wu Department of Plant and
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
To know the flowering behavior of upland and lowland
To determine the effects of synchronization and isolation
To identify male sterile genotypes in interecotypic hybrid.
1.
Earlier flowering upland plants were trimmed.
2.
Crossing pairs were isolated.
3.
Hybrid seeds were collected.
4.
Hybrid seeds’ genetic origins were identified using SSR markers.
5.
Male sterile lines were identified using pollen stainability and pollen germination.
Fig 1. (a) Isolated synchronized upland and lowland plants, (b) Panicles from two ecotypes crossing each
a b c d
Fig 2. (A) F1 upland progenies and their genetic origin, and (B) lowland F1 progenies and their genetic origin.
10 20 30 40 50 60 70 80 90 100 C1 C2 C3 C4 C5 C6 C7 C8 C9 Hybrid % Cross ID (upland progenies) Hybrid % Selfed % Contaminated %
A
10 20 30 40 50 60 70 80 90 100 RC1 RC2 RC3 RC4 RC6 RC7 RC8 Hybrid % Reciprocal cross ID (lowland progenies)
Hybrid % Selfed % Contaminated %
B
Proper synchronization of reciprocal parents yielded 58 to
Improper synchronization yielded relatively higher selfed
The F1 genotypes C-8-17 and RC-3-3 were detected as
The information from this study will be valuable in the
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
Prakash Bhoi, Research Engineer
Biosystems & Agricultural Engineering Department Oklahoma State University, Stillwater, OK 74078
To develop an equilibrium based process model of a wet
To study the effect of important variables on the removal
RadFrac
Oil in Model syngas Model syngas Oil out
Thermodynamic property methods:
Solvent: Soybean oil Packing media: 6 mm Raschig ring Raschig Ring Characteristics Values Density, Kg/m3 900 Surface are, m2/m3 900 Packing factor, 1/m 2300 Void fraction, % 89
20 40 60 80 100 20 30 40 50 60 70 Tar Removal Efficiency, % Solvent Temperature, °C
Peng-Robinson
L/G = 0.52 L/G = 0.45 L/G = 0.37
Both property models (Peng-Robinson and RK-Soave)
Packed bed height significantly increases tar removal
Solvent temperatures above 40°C significantly reduce tar
An increase in liquid-to-gas (L/G) ratio substantially
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
MB Couger, Noha H. Youssef, Audra S. Liggenstoffer, and Mostafa Elshahed Oklahoma State University Stillwater, Oklahoma.
Establish a high quality, well annotated, genome
Identify the unique salient features of the genome and
Identify enzymatic components of the genome that allows
10x Coverage 100x Coverage 35GB 16,347 Models Average Gene Length 1.6KB 100MB 3.5KB n50 22,000 Models N50 1080
Genome size 100.95 MB Number of Contigs 32,574 Protein Coding 20.60% Non- coding intergenic 73.60% Non-coding introns 5.10% rRNA 0.67% 5.8S 183 (30,763 bp) 18S 272 (168,110 bp) 28S 366 (457,301 bp) tRNA 0.06% 770 (58,292 bp) Number of Genes 16,347 Number of Genes with transcripts 14,009 Average Gene Length 1623 Number of Intron 35,697 Introns/gene 2.18 Average Intronlength 163 GC content 17.00% Protein Coding 26.80% Intergenic 14.80% Intron 8.10% SSR Repeats 4.90% TE repeats 3.31%
0.0 0.2 0.4 PC1 PC2
20
20
GH1 GH2 GH3 GH5 GH6 GH8 GH9 GH10 GH11 GH13 GH16GH18 GH26 GH28 GH31 GH43 GH45 GH47 GH48 GH61
0.0 0.2 0.4
Orpinomyces P_anse A_oryz M_gris M_ther P_chry T_rees N_cras P_plac R_oryz S_punc B_dend A_mac C_obs A_ther C_ther R_alba F_succ M_circ C_phyt
Bacterial Homolog 247 Rumen Homolog 141 Eukaryotic Homolog 110
Analysis of the Genome of Orpinomyces C1A reveals a
Anaerobic fungi contain a uniquely evolved enzymatic
This unique system combined with the invasiveness of
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
Miguel A. Gonzalez Borja, Daniel E. Resasco
School of Chemical, Biological & Materials Engineering University of Oklahoma
To develop bio-oil upgrading strategies that maximize the
To evaluate the performance of water-resistance catalyst for
To understand the reaction pathways for the alkylation of
phenolic compounds with 2-isopropanol.
MULTI-STAGE PYROLYSIS
T increase FRACTION I
Light Oxygenates
FRACTION II
Sugar derived
FRACTION III
Phenolics
ALKYLATION
CHALLENGES
APPROACH
liquid-liquid interphase
presence of water. HYDROPHOBIC ZEOLITE IN A BIPHASIC LIQUID PHASE REACTOR
2-Propanol m-Cresol
0.0 0.1 0.2 0.3 0.4 0.5 0.6 5 10 15 20 25 m-Cresol Conversion (mol/l) Time (hours)
Regular Zeolite / Monophasic Hydrophobic Zeolite / Monophasic Regular Zeolite / Biphasic Hydrophobic Zeolite / Biphasic
HYDROPHOBIC ZEOLITE PERFORMANCE
Alkylation reactions between light oxygenates and
Hydrophobic zeolites that remain at the oil-water interphase
posses improved stability for alkylation reactions in the presence
2-Propanol can be incorporated into the aromatic ring of
phenolics via alkylation or via etherification. Ethers can in turn convert to the alkylated product via trans-alkylation.
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
F . L i n a, C . M a n i s s e r i b, A . F a g e r s t r o m d, B . W i l l i a m s c, D . M . C h i n i q u y b , c, M . L . P e c k a, P . S a h a a, M . V e g a - S a n c h e z b , c, J . U . F a n g e l d, W . T . W i l l a t s d, H . V . S c h e l l e r b, P . C . R o n a l d b , c, L . E . B a r t l e y a , b , c
a D e p a r t m e n t o f M i c r o b i o l o g y a n d P l a n t B i o l o g y , U n i v e r s i t y o f O k l a h o m a , N o r m a n , O K 7 3 0 1 9 b J o i n t B i o E n e r g y I n s t i t u t e , E m e r y v i l l e , C A 9 4 6 0 8 a n d L a w r e n c e B e r k e l e y N a t i o n a l L a b o r a t o r y ,
B e r k e l e y , C A 9 4 7 2 0
c D e p a r t m e n t o f P l a n t P a t h o l o g y a n d T h e G e n o m e C e n t e r , U n i v e r s i t y o f C a l i f o r n i a , D a v i s , C A
9 5 6 1 6
d D e p a r t m e n t o f P l a n t a n d E n v i r o n m e n t a l S c i e n c e s , U n i v e r s i t y o f C o p e n h a g e n , D e n m a r k
Focus on grass-specific cell wall biosynthesis. Develop a correlation based method to identify cell wall
Improve grass cell walls as a feedstock for biofuel
0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.05 0.15 0.25 0.35 Correlation between a GT77 gene and Xylose Gene expression of GT77 Xylose (ug/ug cell wall residue)
Quantitative PCR results of 74 grass diverged genes
16 cell wall components and 18 cell wall epitopes
30 samples from different developmental stages
The cutoff is set as FDR<0.05 and |correlation coefficient|>0.6 Gene Cell Wall Component Gini correlation coefficient Pearson correlation coefficient Reference AT4-OsPMT p-coumarate 0.776 0.669 Withers, 2012 GT8-GAUT8-1 L5-galactan 0.716 0.633 GT8-GAUT8-1 GalA 0.787 0.742 GT8-GAUT1-1 L5-galactan 0.746 0.760 GT17-C-1 coumarate 0.699 0.609 GT17-C-1 Xyl 0.709 0.660 GT17-C-1 L10-xylan 0.732 0.680 GT77-4 Xyl 0.705 0.742 GT77-4 L10-xylan 0.740 0.733
It is feasible to identify cell wall synthesis genes using the
This analysis reveals the putative functions in cell wall
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
University of Oklahoma
Fast Pyrolysis Bio-oil Upgrading : Oxygen Removal Improving Carbon Retention
Cut 1: Light oxygenates: Acetic acid, Acetol, Acetaldehyde, Water Cut 2: Sugar derived compounds: Furfurals Cut 3: Lignin derived compounds: C6-C8 Phenolics
Acetone Iso- propanol
H2
To Alkylation* With Cut 3 Aldol Condensation
C8-C13 Oxygenates
To Alkylation* With Cut 1 : Hydro- Deoxygenation (HDO) C10-C13 Phenolics
To Gasoline
pool
O
+
0.1g Pt-Fe/SiO2
OH
0.06g H-Beta
+
OH
0.06g H-Beta
O
4 : 1 (molar) 2 : 2 : 1 (molar)
OH
OH OH OH
Alkylate Yield : 2.5%
An alternative bio-oil upgrading straegy is proposed: combination
Relative low reaction temperature needed. The dehydration product, propylene, is the true alkylating agent. Pt-Fe/SiO2 was found to be a selective hydrogenation catalyst.
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
Taiwo Omotoso Steven Crossley
School of Chemical, Biological and Materials Engineering University of Oklahoma
Investigate mechanism of methoxy group conversion Minimize catalyst deactivation Catalytically remove oxygen from liquid bio oil
+ *CH3 + CH4 + H2O methoxybenzene Sequential pathway Concerted pathway
O CH3 OH
Surface OH groups Ru Interface Surface defects H
Model compound, Methoxybenzene
TiO2
= Ru
PRODUCT DISTRIBUTION Primary products Secondary products CALCINATION PRETREATMENT Calcine under air at 400 °C Calcine under air at 500 °C
Product Distribution Calcination Temperature Effects
Catalyst Ru wt% Particle size(nm) Ru/TiO2(400) 3.66 6.9 Ru/TiO2(500) 3.86 >10
Ru/TiO2 is an active catalyst for deoxygenation of model
A
Calcination temperature plays an important role in the activity
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
A s h o k k u m a r M . S h a r m a a, A j a y K u m a r a, S u n d a r M a d i h a l l y b, R o b W h i t e l e y b, R a y m o n d L . H u h n k e a
a B i o s y s t e m s a n d A g r i c u l t u r a l E n g i n e e r i n g D e p a r t m e n t b C h e m i c a l E n g i n e e r i n g D e p a r t m e n t
O k l a h o m a S t a t e U n i v e r s i t y , S t i l l w a t e r , O K 7 4 0 7 8 U S A
To develop a reaction kinetics-based gasification model
1) Gibbs Equilibrium Model: 2) Reaction-Kinetics Model:
Inputs Biomass flowrate Air flowrate Gasifier temperature Gasifier pressure Outputs Gas composition Gas yield Inputs Biomass flowrate Air flowrate Gasifier temperature Gasifier pressure Gasification reactions Reaction rates (r values) Rate constants (k values) Residence time (τ) Outputs Gas composition Gas yield
Fig.1 – Experimental and predicted CO yield with varying ER Fig.2 - Experimental and predicted H2 yield with varying ER
0.00 0.20 0.40 0.60 0.80 0.29 0.32 0.40 CO, kg/kg biomass Equivalence ratio (ER) Experiment Reaction-kinetics Gibbs Eq. 0.00 0.01 0.02 0.03 0.04 0.05 0.29 0.32 0.40 H2, kg/kg biomass Equivalence ratio (ER) Experiment Reaction-kinetics Gibbs Eq.
As compared to experimental results:
Gibbs equilibrium-based gasification model predicted CO and H2
yields 78% and 180% higher, respectively.
Reaction kinetics based gasification model predicted CO and H2
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
University of Oklahoma
Understand zeolite performance characteristics
Apply known model zeolite chemistries to vapor-phase
Increase total carbon retention in liquid product,
Develop more robust upgrade strategies and pathways for
Glass Plug Biomass Catalyst Wool
Separate Reactor 500°C
GC/MS
Pyroprobe 500°C
~0.5 mg (typ.) ~5mg
Catalytic
Glass Beads
Non-Catalytic
Separate reactor allows us to:
Vary reactor conditions (temperature,
residence time) independent of pyrolysis conditions
Measure catalyst deactivation with
multiple pulses
Autosampler: very high throughput (15
pulses/day)
Feedstock: Pyrolysis temp: Catalyst: Oak sawdust 500C HZSM5, 5mg
Increased temperature slows catalyst deactivation Zeolite chemistry produces aromatic hydrocarbons from
Separating the catalysis from the pyrolysis decouples
Rapid screening allows for evaluation of performance of
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
Tyler Weirick 1, Babu Z. Fathepure 2, Ramamurthy Mahalingam 3, Rakesh Kaundal 1*
1National Institute for Microbial Forensics & Food and
Agricultural Biosecurity, 1,3Dept of Biochemistry & Molecular Biology; 2Dept of Microbiology & Molecular Genetics
Better understand processes related to lignin. Improve machine learning techniques for protein
Discover novel lignin-related enzymes.
Obtain high quality datasets Reduce datasets to 40% sequence similarity Split datasets into independent and training datasets. Do 5-fold testing on training datasets to find optimum kernel
Generate models with training datasets and to independent
Classify all Swiss-Prot proteins not in dataset to further test
Classify true datasets; metagenomes, NCBI unknowns, etc.
50 classes identified, 37 suitable for SVM classification. 5-Fold testing excellent.
Maximum Matthews Correlation Coefficient (MCC): 1.0
Independent Training acceptable.
Max MCC ~0.7
Negative Set testing poor (maybe). Sequences predicted from metagenome and NCBI unknowns.
High chance of correctly predicting lignin related
False positives likely very high. False positives can likely be reduced with modifications to
O k l a h o m a N S F E P S C o R B i o e n e r g y R e s e a r c h a n d E d u c a t i o n
The University of Oklahoma
Characterize the enzymatic activity of a cellulosomal
Examine the physiological function of this inhibitor Examine the role of this inhibitor in regulating
Enzymatic activity test
Physiological characterization
Analysis to cellulosomal composition
This is the first study to uncover the physiological
The presence of this protease inhibitor allows C.