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Item A “solid” solution for wheat stem sawfly (Hymenoptera: Cephidae) resistance: Genetics, breeding and development of solid stem wheat(Wiley, 2023-06) Bathini, Akshara; Mendu, Lavanya; Pratap Singh, Nagendra; Cook, Jason; Weaver, David; Sherman, Jamie; Hager, Megan; Mondal, Suchismita; Mendu, VenugopalWheat (Triticum spp. L) production needs to be improved to meet the needs of a global population of >9 billion people by 2050. Increasing the productivity of the crop under conditions of abiotic and biotic stress to achieve food security continues to be a challenging proposition. Wheat stem sawfly (WSS) (Cephus cinctus Norton) has been considered as a serious pest of wheat since the late 19th century, causing devastating losses of wheat productivity in the Northern Great Plains of United States and regions of Canada. Developing resistant varieties of wheat that show consistent agronomic performances in varying environments is an effective strategy to manage WSS infestations. To achieve this goal, it is necessary to understand the underlying mechanisms of WSS infestation, damage, subsequent response of the host plant, and resulting yield losses. The review focuses on genetics, breeding, and development of solid stem (SS)-mediated WSS resistance in wheat since it has been the most effective method of genetic resistance in reducing wheat yield losses. Furthermore, the knowledge gaps that need to be addressed to develop an effective resistant cultivar against WSS are also discussed.Item Experimental guidance for discovering genetic networks through hypothesis reduction on time series(Public Library of Science, 2022-10) Cummins, Breschine; Motta, Francis C.; Moseley, Robert C.; Deckard, Anastasia; Campione, Sophia; Gedeon, Tomáš; Mischaikow, Konstantin; Haase, Steven B.Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small “core” network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent dynamics pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data.Item Screening of Additive Formulations Enables Off-Chip Drop Reverse Transcription Quantitative Polymerase Chain Reaction of Single Influenza A Virus Genomes(American Chemical Society, 2021-03) Loveday, Emma Kate; Zath, Geoffrey K.; Bikos, Dimitri A.; Jay, Zackary J.; Chang, Connie B.The miniaturization of polymerase chain reaction (PCR) using drop-based microfluidics allows for amplification of single nucleic acids in aqueous picoliter-sized drops. Accurate data collection during PCR requires that drops remain stable to coalescence during thermocycling and drop contents are retained. Following systematic testing of known PCR additives, we identified an optimized formulation of 1% w/v Tween-20, 0.8 μg/μL bovine serum albumin, 1 M betaine in the aqueous phase, and 3 wt % (w/w) of the polyethylene glycol-perfluoropolyether2 surfactant in the oil phase of 50 μm diameter drops that maintains drop stability and prevents dye transport. This formulation enables a method we call off-chip drop reverse transcription quantitative PCR (OCD RT-qPCR) in which drops are thermocycled in a qPCR machine and sampled at various cycle numbers “off-chip”, or outside of a microfluidic chip. qPCR amplification curves constructed from hundreds of individual drops using OCD RT-qPCR and imaged using epifluorescence microscopy correlate with amplification curves of ≈300,000 drops thermocycled using a qPCR machine. To demonstrate the utility of OCD RT-qPCR, influenza A virus (IAV) RNA was detected down to a single viral genome copy per drop, or 0.320 cpd. This work was extended to perform multiplexed detection of IAV M gene RNA and cellular β-actin DNA in drops, and direct amplification of IAV genomes from infected cells without a separate RNA extraction step. The optimized additive formulation and the OCD-qPCR method allow for drop-based RT-qPCR without complex devices and demonstrate the ability to quantify individual or rare nucleic acid species within drops with minimal processing.