Progress report for GNE24-312
Project Information
Wheat is a staple crop globally and one of the five most valuable crops in Pennsylvania. Wheat is milled into flour to produce staple foods like bread and baked products. Studies investigated the microbial diversity of wheat and bread products including sourdough. However, the changes of microbial diversity from wheat to flour to bread remains less understood. I propose to investigate the influence of wheat varieties, farming methods, and processing techniques in a sourdough model system. By tracking biodiversity changes from farm-to-fermentation, I seek to use this ancient knowledge to modernize wheat-based products.
Ancient and common wheat will be grown under organic or conventional farming. Amplicon sequencing of wheat berries will determine diversity and abundance of bacteria and fungi. Whole wheat and refined grain flours will be produced from the different wheat and farming methods. Flour from ancient v. modern wheat, organic v. conventional, and whole v. refined will be sequenced and utilized to create sourdough starters. Comparative physical and chemical analysis of starters will include organic acids, pH, and free amino acids. Mature sourdoughs will be sequenced. Outcomes from this study could provide insights into preservation and resilience of microbial communities from farm-to-fermentation.
This proposed work aligns with our current USDA NIFA funded project (#2023-67017-40251) that characterizes sourdough microbiomes to enhance quality and nutrition of breads. The proposed study seeks to conserve biodiversity in locally produced staple crops, including ancient wheat, which offers opportunities for market expansion. This research supports local farmers and consumers, promoting sustainable agriculture within Pennsylvania.
Objective 1: Compare microbial diversity between common wheat (T. aestivum) and ancient wheat (T. monococcum) farmed conventionally and organically in Pennsylvania. I hypothesize that ancient and common wheat will have significantly different microbial diversity. I also hypothesize that organically farmed grains will have higher alpha diversity. Overall, I hypothesize that ancient wheat grown organically will have the highest alpha diversity. Beta diversity is expected to be high between samples. This objective will result in tables with relative abundance of bacteria and fungi and results of statistical analyses of microbial diversity.
Objective 2:
(2a) Assess microbial diversity in whole-grain and refined grain flours derived from common wheat (T. aestivum) and ancient wheat (T. monococcum) farmed organically and conventionally in Pennsylvania.
(2b) Utilize flours from 2a to develop and characterize sourdough starter cultures and evaluate their functional outcomes. I hypothesize that whole wheat flour will have a higher alpha diversity than refined grain flour. I also hypothesize that ancient wheat grown organically and milled into whole wheat flour will produce sourdough starters with the lowest pH, highest organic acids, and highest microbial alpha diversity. Beta diversity is expected to be high between samples. This objective will result in tables with relative abundance of bacteria and fungi, statistical analyses of microbial diversity and proximate analysis measurements, and graphs illustrating sourdough proximate analysis results.
The purpose of this project is to compare microbial communities between ancient and modern wheat grown using organic and conventional farming methods. This purpose is motivated by the question, "How do wheat varieties, production, and processing influence wheat biodiversity?”. To accomplish this, I will use our previously developed sourdough model system1,2. While previous studies have explored aspects of wheat cultivation and sourdough microbiome assembly independently, how different wheat varieties and farming practices impact biodiversity (defined as richness and abundance of microbial communities) from farm-to-fermentation is unknown.
To study biodiversity differences in wheat varieties, we will use amplicon sequencing of bacterial and fungal populations to compare modern domesticated wheat, T. aestivum, to ancient, domesticated wheat, T. monococcum. We will investigate the effect of farming practices (organic vs. conventional) on biodiversity of modern and ancient wheat. Next, I will compare whole vs. refined grain of each respective wheat variety and farming practice. I will investigate how biodiversity changes from different types of wheat, farming practices, and processing parameters to the final flour that will be made into sourdough starters. Comparative physical and chemical analysis of sourdough starters will be measured to track how biodiversity influences quality parameters. This study aims to understand biodiversity changes from farm-to-fermentation (or grain-to-gain) and how the microbial communities impact quality of sourdough.
Wheat, a staple crop worldwide, plays a significant role in global nutrition. Wheat is one of the three main cereal cash crops in the world, providing more food energy than any other crop3. Wheat stands among the top 5 most valuable crops cultivated in Pennsylvania, with a focus on winter wheat4. Common wheat, known as T. aestivum, is the most widely cultivated wheat species in the world3. Ancient grains have had a resurgence of interest due to consumer demand for functional foods. For example, individuals affected by gluten intolerances who need to avoid gluten have found alternatives in ancient grain products3. Einkorn (T. monococcum) is an ancient wheat and one of the three founder crops in the development of agriculture; however, it is not widely used anymore3. After wheat has been grown and harvested, the grain can then be milled to leave the endosperm, producing white flour, or used in whole (endosperm, bran and germ), producing whole grain flour. When refined by removing the bran and germ, the endosperm is mostly carbohydrate and lacks nutrients3,5.
Wheat flour is used to produce staple baked goods including bread. Commercial bread relies on a single yeast strain, Saccharomyces cerevisiae, to leaven flour and water. In contrast, sourdough relies on naturally occurring yeast and bacteria commonly found in wheat flour, water, and the environment. These microorganisms, known as the ‘sourdough microbiome’, function to ferment flour and water. Similar to interest in ancient wheat, there has been a resurgence in sourdough due to its superior taste, perceived health benefits, and suitability for individuals with gluten intolerances6.
Microbial communities in sourdough assemble from the interaction between ingredients (type of flour, water, salt) and technological parameters (temperature, dough hydration, dough yield, fermentation time, percent sourdough inoculum, etc.)7. Microbial communities in cereal grains depend on factors including climatic conditions (temperature and rainfall), insects or fungal contamination and agricultural practices (tillage, use of insecticides and fungicides)7. Moving from crop microbiota to flour microbiota, previous studies have demonstrated that type of flour plays a limited role in shaping diversity and abundance of microbial communities. However, the use of white flour or whole-grain flour shows significant differences, affecting the assembly of sourdough microbiota. The microbiome of raw ingredients such as flour can impact sourdough quality. For example, microbes from flour produce organic acids and other metabolites that affect the sensory, technological, safety, nutritional and functional properties of cereal based foods, including bread
This research is significant for Pennsylvania farmers as it could provide insights into how ancient wheat and farming practices can be leveraged to enhance the quality of grain-based products. In addition, this study could boost the organic market and ancient grain market in the state. In 2023, winter wheat production in Pennsylvania amounted to $1.2 million, yielding 17.5 million bushels9. By examining microbial biodiversity from wheat to sourdough formation, this study aims to enhance our understanding of microbial preservation in farm-to-fork systems. This knowledge can support sustainable organic farming practices and the production of ancient wheat varieties, meeting consumer preferences for modernized grain-based products. This research benefits Pennsylvania's agricultural landscape by equipping farmers with insights to cultivate new wheat varieties for enhanced food products10.
Research
Wheat Sample Collection and Processing:
Common (T. aestivum) and ancient (T. monococcum) wheat varieties are being obtained from an on-going collaboration with the PSU Small Grain Variety Testing Program (see letter from Carrijo) and local Pennsylvania growers and millers The Testing Program features a grain rotation system that includes corn, soy, and wheat. Dr. Carrijo’s network of Pennsylvania wheat growers and millers is providing common and ancient grains, as well as flours, for this study. Recent regional evaluations of ancient grains like einkorn have shown them to be more adaptable, genetically diverse, and require less fertilizer input than wheat10. Einkorn also offers a better nutritional composition than wheat and has potential for market growth in food.
Conventionally farmed wheat varieties will use the herbicide glyphosate; a widely used herbicide that controls broadleaf weeds and grasses. Glyphosate has toxic effects that are broad-spectrum, not only killing weeds and grasses, but killing many microorganisms including bacteria, fungi, and protists11. Conventionally farmed wheat varieties will also use fungicides which are often used to control the growth of fungi that can cause plant diseases. It would be of interest to monitor fungal distribution on wheat since fungicides are prohibited for use in organic production systems.
Conventionally farmed common soft red wheat has been obtained from a previous trial through the PSU Small Grain Variety Testing Program. This variety (FS746) is a widely cultivated Pennsylvania wheat with average field management practices for wheat farming systems in Pennsylvania currently. This management includes a spring herbicide and fungicide applications, nitrogen fertilizer split applications, and a plant growth regulator at heading. These practices represent standard wheat farming system in Pennsylvania.
Organic ancient einkorn grains and flours will be obtained from local Pennsylvania growers and millers.
DNA Extraction:
One gram of wheat berries will be used for DNA extraction using the Qiagen DNeasy Plant Pro and Plant Kit and amplified with barcoded primers for multiplexed sequencing of bacteria (primers 515f/806r) and fungi (ITS1f/ ITS2)2. We will sequence multiple DNA extractions and PCR negative controls to detect potential contamination. Samples will be sequenced using the Illumina NovaSeq 6000 platform (800 M paired end reads; 250 bp). Multiplexing samples (up to 96 per run) will allow for reasonable throughput with good depth of microbiome sequencing, with more than 150K sequences per sample.
Bioinformatics:
Raw reads will be analyzed with the R package containing the DADA2 v3.14 pipeline following the standard protocol for 16S rRNA v4 region amplicon sequence reads (ASVs)13. Low-quality sequence reads were filtered, low-quality bases will be trimmed, calculation of error rates will be performed, and ASVs will be deduced from the remaining sequence reads. Paired-end sequence variants will be merged into contigs where contigs <251 bp or >253 bp will be removed. Chimeras will be detected and removed, and remaining ASVs will be assigned taxonomy using the Silva database v13214 for bacteria and the UNITE database15 for fungi. ASVs assigned to mitochondria or chloroplasts will be filtered out from the bacterial taxa and reads unassigned at the phylum or class levels.
Statistics and Visualization:
Beta diversity will be estimated based on Bray–Curtis distances using rarefied bacterial and fungal ASVs as a measure of how similar or different wheat microbiomes are between samples. Bray–Curtis dissimilarity will be calculated based on occurrence data or abundance. To visualize inter-sample distances, a principal coordinate analysis (PCoA) ordination plot will be generated for bacterial and fungal communities. Hierarchical clustering analysis (HCA) of fungal and bacterial ASVs will be constructed based on Bray–Curtis dissimilarities. Alpha diversity will be calculated using Chao1 and Shannon index. Taxonomic composition for mapped reads above 1% relative abundance will be plotted using the R package ggplot2.
Objective (2a) Assess microbial diversity in whole-grain and refined grain flours derived from common wheat (T. aestivum) and ancient wheat (T. monococcum) farmed organically and conventionally in Pennsylvania.
Flour Milling:
Eight flour samples will be milled, each replicated three times, comprising both whole wheat and refined grain flours from organically and conventionally farmed ancient and common wheat varieties. These include: ancient wheat, organically farmed, whole wheat flour (AOW); ancient wheat, organically farmed, refined grain flour (AOR); ancient wheat, conventionally farmed, whole wheat flour (ACW); ancient wheat, conventionally farmed, refined grain flour (ACR); common wheat, organically farmed, whole wheat flour (COW); common wheat, organically farmed, refined grain flour (COR); common wheat, conventionally farmed, whole wheat flour (CCW); and common wheat, conventionally farmed, refined grain flour (CCR). For whole wheat flour, the entire wheat berry from the crop will be milled. For refined grain flour, only the endosperm will be milled. The bran and germ will be removed by using a 60-mesh sifting screen. Wheat will be milled using a MockMill100 stone grain mill (Ancient Grains). DNA extraction, sequencing, bioinformatics, and statistics and visualization will be performed on the flours as stated above in Objective 1. Milling will only be conducted for grains collected directly from farms where pre-milled flours were unavailable.
Objective (2b) Utilize flours from 2a to develop and characterize sourdough starter cultures and evaluate their functional outcomes.
Sourdough Preparation:
Sourdough starters will be propagated into active phase by combining a 1:1 w/w mixture of autoclaved deionized water (dH2O) and each flour sample (AOW, AOR, ACW, ACR, COW, COR, CCW, CCR) in loosely capped 50 mL conical tubes for 24 hours. DNA extraction, sequencing, and bioinformatics will be performed on the sourdough starters as stated above in Objective 1.
Proximate Analysis:
To measure pH, 10 grams of each sourdough starter will be weighed and combined with 100 mL of deionized water in a sanitized commercial food blender (Ninja) and transferred to a glass beaker for mixing on a magnetic stir plate. The pH will be recorded while mixing using an FiveEasy Plus FP20 pH meter (Mettler-Toledo).
Lactic and acetic acid will be extracted according to a previously established method16. Three independent extractions of sourdough starter will be performed. Standards of lactic acid (VWR) and acetic acid (Millipore Sigma) will be prepared at concentrations of 100, 50, 25, 12.5, 6.25, and 3.125, and 50 mM butyric acid will be used as an internal standard. Standards and filtered sourdough samples are then injected into a Dionex ICS-5000+ high performance liquid chromatography (HPLC) system (Thermo Scientific) equipped with an Aminex HPX-86H column (300 × 7.8 mm; Bio-Rad Laboratories) kept at 50◦C. The analytical conditions used are as follows: 20 μL injection volume, 0.45 mL/min flow rate, and 5 mM H2SO4 as an eluent. Lactic and acetic acid will be identified within each chromatogram based on retention time. Concentrations of lactic and acetic acid are calculated based on peak area in comparison to standard curves generated with 0–100 mM of lactic acid and acetic acid standards, respectively. The molar ratio between lactic and acetic acid will be used to calculate the fermentation quotient16.
Free amino acids will be measured in sourdough starters with the ninhydrin chemical test according to previously established methods17 using a BioTek Epoch 2 Microplate Reader (BioTek). Free amino acid concentration will be determined using a standard curve of L-leucine (Thermo Fisher).
Statistics and Visualization
To visualize correlation between measured functional properties of sourdoughs, Shannon alpha-diversity metric will be used as it considers both species richness and evenness. Pearson correlation analyses for functional properties of sourdough with specific taxa will be conducted by combining sample variables, abundance, and taxonomic rank at the genus level using the psmelt function in the phyloseq package and visualized using the corrplot package in R.
Functional properties of sourdough will be compared using one- way analysis of variance and Tukey’s honestly significant difference post hoc test. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) will be used to evaluate overarching similarities in sourdough for each flour. HCA will be performed based on pairwise Euclidean distances followed by Ward’s minimum variance clustering (hclust, method = ward.d2) using the ggplot2 package FactoMineR. The strength of associations between microbial diversity and quality outcomes will be calculated by Pearson correlation analysis using the corrplot package in R version 0.92.
Education & Outreach Activities and Participation Summary
Participation Summary:
I am committed to outreach and education throughout my academic journey. I’ve led and participated in workshops and short courses centered on food microbiology, food mycology, international agriculture, and food security. The last two summers, I served as a mentor to undergraduate students participating in the USDA REEU (Research and Extension Experience for Undergraduates) and PSU SROP (Student Research Opportunities Program). My role was to guide undergraduate students in developing research projects, help with experimental design, and assist in effective communication of their findings.
To share findings from this work with current and future scientists, I will engage with K-12 students and educators by collaborating with the K-12 Engagement Network at Penn State. I aim to provide students with resources on the critical role of biodiversity in agriculture, specifically focusing on microbial communities in wheat cultivation and food production. During the presentation, I will incorporate interactive activities and hands-on demonstrations to make the concepts of microbial biodiversity understandable for students. For the interactive activity, students will use microscopes to look at microorganisms found in agriculture crops, including wheat, as well as food products, including bread. Through the presentation and activity, students will explore the ‘unseen’ organisms in agriculture that influence our food systems.
At Ag Progress Days, Pennsylvania's largest outdoor agricultural exposition hosted annually by Penn State's College of Agricultural Sciences, I intend to create an educational video tailored to fit into the Agronomic Crops section. This video will serve as a valuable resource for attendees, providing insights into our ongoing research project comparing microbial communities in ancient and modern wheat varieties under various farming methods. Drawing from our study's purpose of understanding how wheat varieties, production practices, and processing influence microbial communities, the video will discuss the relationship of microbes in crops and food. We will highlight the key findings of our research on agricultural and food systems in Pennsylvania. The video will include what wheat varieties and farming practices promoted biodiversity and/or increased the functional parameters of staple grain-based product. The video will highlight the potential benefits of incorporating organic farming systems and ancient wheat varieties to improve microbial resilience and food product outcomes. By presenting this educational video at Ag Progress Days, we aim to reach a diverse audience of farmers, agronomists, agricultural researchers, and industry professionals. Additionally, we will leverage Ag Progress Days' on-demand educational video platform to ensure the accessibility and longevity of our outreach efforts.