Methodology

Herbicide resistance is the heritable ability of weeds to survive and reproduce in the presence of herbicide doses that are lethal to the wild type of the species. Herbicide resistance was first discovered in Senecio vulgaris in 1968, and ever since the phenomenon of herbicide resistance has become one of the most crucial challenges to agriculture, due to 187 weed species developing resistance to one or more herbicide chemicals (Pieterse 2010). Glyphosate is one of the most abundantly used herbicides in the world and has ideal characteristics including potency against an extensive variety of species (monocots and dicots), less harmful activity against animals than other herbicides (the target enzyme is not found in animals), rapid microbial degeneration, and low cost (Duke and Powles 2008).. However, resistance to this particular multipurpose herbicide has also evolved in several weed species, which has become a growing concern for farmers and weed scientists (Anders & Huber, 2010). 

Two mechanisms that confer resistance and have contributed to glyphosate resistance in weeds are target-site and non-target site resistance. In California’s Central Valley, an introduced species of the Asteraceae family known as Erigeron bonarensis (hairy fleabane) is known to exhibit resistance to glyphosate, however the exact molecular mechanism underlying the resistance is still obscure. The characterization of herbicide resistance in E. bonariensis will identify the presence of genetic changes conferring resistance in the weed species (Anders, Pyl & Huber, 2015). The herbicide glyphosate targets the 5-enolpyruvulshikimate-3-phosphate synthase (EPSPS) gene, inhibiting its normal activity and causing plant death by preventing the synthesis of aromatic amino acids.  Previous research on a species closely related to fleabane, horseweed (E. canadensis), suggests that the resistance mechanism in this species may be due to transcriptional upregulation of the EPSPS gene and/or ATP-binding cassette (ABC) transporter genes responsible for sequestering glyphosate in the leaf mesophyll cell vacuoles, to prevent its translocation around the plant and subsequent toxicity.  Although much molecular work has been performed on horseweed, very little has been performed on fleabane, which has a more limited geographical distribution as a problematic agricultural weed: it is particularly troublesome in California’s Central Valley.  Population genetic work performed on California fleabane indicates that glyphosate resistance has evolved multiple times in the Valley, and could have different genetic bases in different populations (Okada and Jasieniuk 2014). 

Seeds from 10 Central Valley fleabane populations were collected in Spring 2016, and grown in the Biology Greenhouse at Fresno State in Summer 2017, alongside plants from known glyphosate-resistant and glyphosate-sensitive control populations.  Each population was characterized for resistance via glyphosate spraying at a 1X and 2X field rate (see Methods below), and tissue was collected from each plant just before and after glyphosate exposure. As of late October 2017, RNA extraction from this tissue for four populations has been completed successfully, with quality and quantity of RNA evaluated via Qubit quantitation and gel electrophoresis. cDNA synthesis for one population has also been completed.  Primers from horseweed were used to PCR-amplify and direct-sequence candidate genes in several fleabane samples, and the sequences were used to design short (50-150 bp) fleabane-specific qPCR primers for the M10 ABC transporter gene and the housekeeping gene actin, using Primer3 software.  These primers are currently being extensively tested for amplification and specificity with standard PCR and gel electrophoresis prior to qPCR. The sampling method will utilize seeds from 10 different populations of Erigeron bonariensis, collected from the San Joaquin Valley in 2016, based on previous fleabane collection locality data. In addition, seeds from two control populations (one known glyphosate resistant population and one known glyphosate sensitive population) will be included in the experiment. Seeds will be germinated in a growth chamber set at 20/15C (day/night temperature) on Petri plates following a standard germination process. At the 2-3 leaf stage, 18 seedlings per population will be transplanted into 4-inch pots, and three treatments of 6 plants each per population will be randomly assembled and sprayed at 0X, 1X, or 2X the field rate of glyphosate at the 5-8 leaf stage. A complete randomized block design will be set up after spraying, to minimize spatial effects in the greenhouse (Baldwin & Goldman, 2012). Above-ground biomass will be harvested at 35 days after spraying, dried at 60C, and weighed, and ANOVA will be used to calculate statistical differences in biomass after spraying between treatments, to characterize each population for glyphosate resistance. One medium-aged leaf (~2 grams of tissue) will be harvested twice from each individual plant, once prior to the glyphosate spraying time point, and once twenty-four hours after glyphosate spraying occurs. Following RNA isolation using the Qiagen Plant RNeasy Mini Kit, genomic DNA will be removed with DNase (FisherSci) and RNA will be quantified using Qubit® dsDNA HS Assay Kits. First-strand cDNA will be reverse-transcribed from 100µg of total RNA using M-MLV reverse transcriptase. Quantitative real-time PCRs (qPCRs) will be performed on this cDNA for the candidate genes EPSPS, ABC M10, ABC M11, and the housekeeping gene actin as a control.  qPCR will be performed using Thermo Scientific ABsolute SYBR® Green Master Mix on an Eppendorf® Mastercycler® RealPlex thermocycler (Applied Biosystems). The expression levels of the E. bonariensis actin gene will be used as an internal standard to normalize small differences in cDNA template amounts. Relative transcript levels of the genes of interest will be calculated as a ratio to the actin gene transcripts, and relative expression of each gene of interest before and after glyphosate treatment will be calculated using the ΔΔ Ct method (implemented in Microsoft Excel). All real-time qPCR will be performed on four biological replications (e.g., four plants per population which received the same treatment) for appropriate statistical power.

PCR Product for cDNA

First lane is ladder

2nd lane is sample 1V4 for actin gene, 3rd lane is sample S1V4 for actin gene, 4th lane is sample 1V7 for actin gene & 5th lane is sample S1V7 for actin gene

6th lane is 1V4 for M10 gene, 7th lane is sample S1V4 for M10 gene, 8th lane is Sample 1v7 for M10 gene and 9th lane is sample S1V7 for M10 gene, 10th and 11th lanes are negative control for both the genes.

Where 1V4 means unsprayed fleabane sample from population 1 of central valley and S1V4 means Sprayed fleabane sample from population 1 of central valley. Same thing goes for other samples too.

Lane 1: 3V5 where RFU Value from Qubit is 180 ng/ul
Lane 2: S3V5 where RFU Value from Qubit is 178 ng/ul
Lane3: 4V12 where RFU Value from Qubit is  172 ng/ul
Lane 4: S4V12 where RFU Value from Qubit is TOO High
Lane5: 3V11 where RFU Value from Qubit is 180 ng/ul
Lane 6: S3V11 where RFU Value from Qubit is 176 ng/ul
Lane 7: 4V16 where RFU Value from Qubit is TOO High
Lane8: S4V16 where RFU Value from Qubit is Too High
Lane 9: 6V8 where RFU Value from Qubit is Too High 
Lane 10: S6V8 where RFU Value from Qubit is 170ng/ul
Lane 11: 5V16 where RFU Value from Qubit is Too High
Lane12: S5V16 where RFU Value from Qubit is Too High
Where ng/ul  nanogram per microliter and 3V5 means unsprayed sample of fleabane from population 3 of central valley and S3V5 means Sprayed with herbicide fleabane from population 3 of central valley.
Same thing goes for other samples 4 means population 4 and 5 means population 5 and V indicates central valley

Lane 1: 1v6  where RFU Value from Qubit is 144ng/Ul
Lane 2: S1V6 where RFU Value from Qubit is 128ng/ul
Lane3: 1V15 where RFU Value from Qubit is  178 ng/ul
Lane 4: S1V15 where RFU Value from Qubit is 112 ng/ul
Lane5: 2V1 where RFU Value from Qubit is  9.76 ng/ul
Lane 6: S2V1 where RFU Value from Qubit is 16.7 ng/ul 
Lane 7: 1V11 where RFU Value from Qubit is 180ng/ul
Lane8: S1V11 where RFU Value from Qubit is 43.2ng/ul
Lane 9: 2V12 where RFU Value from Qubit is  42.2 ng/ul
Lane 10: S2V12 where RFU Value from Qubit is 108 ng/ul
Lane 11: 2V15 where RFU Value from Qubit is 108 ng/ul
Lane12: S2V15 where RFU Value from Qubit is 142 ng/ul
Where ng/ul is nanogram per microliter

References

Beckie, H. J., & Tardif, F. J. (2012). Herbicide cross resistance in weeds. Crop Protection, 35, 15-28.

Okada, M., & Jasieniuk, M. (2014). Inheritance of glyphosate resistance in hairy fleabane (Conyza bonariensis) from California. Weed science, 62(2), 258-266.

Anders, S., Pyl, P. T., & Huber, W. (2015). HTSeq—a Python framework to work with high-throughput sequencing data.

Bioinformatics, 31(2), 166-169.

Anders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome biology, 11(10), R106.

Duke, S. O., & Powles, S. B. (2008). Glyphosate: a once-in-a-century herbicide. Pest management science, 64(4), 319-325. 

Pieterse, P. J. (2010). Herbicide resistance in weeds–a threat to effective chemical weed control in South Africa. South African Journal of Plant and Soil, 27(1), 66-73.