Up Regulation of Heat Shock Protein 70B (HSP70B) and \u3cem\u3eSSA1\u3c/em\u3e in \u3cem\u3eChlamydomonas Reinhardtii\u3c/em\u3e via HSP70A-RBCS2 and PSAD Promoter

Fabrication of effective algae cultivation systems adjacent to coal-fired power plants to fixate waste CO2 would represent a sizable step towards achieving a carbon neutral energy cycle. However, emission gas would elevate the algal cultivation system temperature and decreases its pH without expensive preprocessing. Increased temperature and acidity constitutes a profound stress on the algae. Although stressed algae produce heat shock proteins (HSPs) that promote protein folding and protect against stress, the ordinary biological response is insufficient to protect against coal flue gas. Experimental upregulation of HSPs could make algae respond to the stress caused by high temperatures and low pH at an elevated level. However, no work has been done to determine whether HSPs can be experimentally upregulated in algae. Here, the Chlamydomonas reinhardtii algal strain was selected because it has a sequenced genome and singular cell structure ideal for genetic modifications. Two genetic modification methods: transformation with plasmids pCB720/pCB740, and cloned pchlamiRNA3/pchlamiRNA3int with yeast HSP gene SSA1 were evaluated. pCB720/pCB740 up regulate algae production of native HSP, HSP70B. pCB720 transformation success was observed but statistically, data varied. pchlamiRNA3/pchlamiRNA3int were cloned with SSA1. Chlorophyll content measured growth indirectly. Quantitative HSP detection could be done using RT-PCR

Optimizing the Use of a Liquid Handling Robot to Conduct a High Throughput Forward Chemical Genetics Screen of \u3cem\u3eArabidopsis thaliana\u3c/em\u3e

Chemical genetics is increasingly being employed to decode traits in plants that may be recalcitrant to traditional genetics due to gene redundancy or lethality. However, the probability of a synthetic small molecule being bioactive is low; therefore, thousands of molecules must be tested in order to find those of interest. Liquid handling robotics systems are designed to handle large numbers of samples, increasing the speed with which a chemical library can be screened in addition to minimizing/standardizing error. To achieve a high-throughput forward chemical genetics screen of a library of 50,000 small molecules on Arabidopsis thaliana (Arabidopsis), protocols using a bench-top multichannel liquid handling robot were developed that require minimal technician involvement. With these protocols, 3,271 small molecules were discovered that caused visible phenotypic alterations. 1,563 compounds induced short roots, 1,148 compounds altered coloration, 383 compounds caused root hair and other, non-categorized, alterations, and 177 compounds inhibited germination

LEVERAGING CHEMICAL AND COMPUTATIONAL BIOLOGY TO PROBE THE CELLULOSE SYNTHASE COMPLEX

Cellular expansion in plants is a complex process driven by the constraint of internal cellular turgor pressure by an expansible cell wall. The main structural element of the cell wall is cellulose. Cellulose is vital to plant fitness and the protein complex that creates it is an excellent target for small molecule inhibition to create herbicides. In the following thesis many small molecules (SMs) from a diverse library were screened in search of new cellulose biosynthesis inhibitors (CBI). Loss of cellular expansion was the primary phenotype used to search for putative CBIs. As such, this was approached in a forward chemical genetics manner. Reverse chemical genetics would require one of the variants of the proteins responsible for cellulose biosynthesis, a CELLULOSE SYNTHASE (CESA) variant, to be expressed and capable of screening. Unfortunately, it is a very large protein and quite recalcitrant to in vitro assay. To advance the forward genetics paradigm this thesis explores two main pieces of technology: (1) the capacity to increase high throughput screening using robotics and in parallel, (2) the use of in silico methodologies to reduce false positive rates and streamline mechanism of action discovery. Within in silico modeling and drug discovery a major goal is to allow the interpretation of how a SM might act with a protein, an enzyme, or as an inhibitor of a protein-protein interaction. One approach is to screen multiple SMs against a target of interest upon/within the protein surface and estimate binding energies as well as top ranking SM confirmations. As noted above, it is technically implausible to isolate variants of CESA for a reverse chemical genetics in vitro assay. Due to this, it was my goal to perform a reverse in silico chemical genetics screen to drive efficient bench top biology via the information derived from computational refinements. Forward chemical genetic screening and virtual screening form a circle of continuous refinement and reiteration, one leading to the next, in either direction. In this circle virtual screening can be used before or after bench top biology. If virtual screening is performed before benchtop biology, these results can aid in the purchasing of portions of libraries that enable benchtop biology to have a higher hit percentage. In a reverse fashion, virtual screening can be done after benchtop biology has determined the SM protein interaction. This approach can help elucidate the mechanism of action of the SM against the protein. In addition to elucidating mechanism of action, virtual screening after experimental validation can afford the search for chemical space that allows the identification of additional molecules/variations of the hit molecule that might have higher activity. Within this body of work forward chemical genetics was applied to Arabidopsis thaliana (referred to herein as a proper name Arabidopsis) to investigate cellular expansion via the screening of 50,000 SMs with a liquid handling robot for suppression of seedling expansion. The use of liquid handling robotics to aliquot and screen these compounds in working concentrations emerged as a stand-alone publication but is generally integrated into the ‘screening’ portion of the discovery pipeline. Exploiting the rapid radical (root) cell expansion observed in plant seedling development allowed for the use of 96 well plates to observe the influence of individual chemicals on expansion. Light microscopy was used to score whole plates of 80 chemicals at a time. Results from the initial screen for expansion inhibition identified roughly 3,000 of the 50,000 screened SMs as bioactive at 100 µM for a 6% hit rate. Phenotypic effects of SMs on Arabidopsis were placed in one of eight categories based on phenotypic aberrations: (1) normal growth, (2) stunted roots, (3) severely stunted roots, (4) bleached, (5) colored root hairs, (6) other, (7) incomplete germination, and (8) no germination. Two of the eight categories, stunted root and severely stunted root, were of interest as they were the first line of evidence that the SM could potentially be a CBI. One SM was identified as a CBI and named fluopipamine which forms the focal point for much of the thesis. Other compounds were identified as probable CBIs but could not be characterized in as much detail. Lines of evidence including (1) etiolation prevention, (2) ectopic lignification, (3) ectopic lignification at or below 100 µM, (4) decreases in radiolabeled glucose uptake, (5) loss of anisotropic cellular growth, (6) decrease cellulose synthase complex accumulation and movement in the plasma membrane, (7) bred resistance verified with a cleaved polymorphic sequence assays, (8) cross resistance to a known Arabidopsis mutant, and (9) in silico docking supported fluopipamine as a cellulose synthase 1 (CESA1) antagonist. In hopes of casting a larger net over chemical space an inhouse method was developed to create a pairwise similarity matrix based on SM structures being converted into bit vectors. Initially, the DUDE database containing roughly 22,000 SMs and 102 of their protein targets was used as a truth set. This matrix of SMs from the DUDE database was clustered via Markov Clustering and the resultant clusters were assessed for quality. Quality of clusters was crudely measured due to grouping SMs based on protein target. The purpose of this approach was to identify optimal parameters within a data truth set so that when this method is applied to new SMs, they would optimally cluster based on protein target. Scripts were written that allow for SM extraction from the clustered results based on a list of anchor SMs. For example, in reverse chemical genetics screens SMs could form clusters that are centered around being associated with the same protein target by shared SM structural similarity. Additionally, a reverse ligand and structural based virtual screening approach was taken to probe all 111 million PubChem compounds in search of putative CBIs. Three SMs: quinoxyphen, flupoxam, and fluopipamine, were screened against all PubChem Compound across four different fingerprint types and the top percentage of Dice similarity comparisons were retained. This resulted in roughly 75,000 SMs of high similarity to either flupoxam, quinoxyphen, or fluopipamine. Roughly 53,000 SMs obey Lipinski’s rule of five and roughly 1,600 are lead like. Modeling was performed across roughly 72,000 SMs against a wild type and 6 mutant CESA1 proteins using AutoDockGPU. This results in SMs that have equal or better binding affinity than known CBIs. For example, 42 SMs were lead like with better binding affinity than fluopipamine in CESA1 model G1009S. This work is an example of how in silico, in vitro, and in vivo biology can be combined to yield insight into how a SM interacts with a protein target. This body of work also explores how active compounds can be used to generate lists of SMs that could have high affinity in vivo with protein targets of interest. It is imperative that the future of biology, due to the vast amount of data present within an organism, enlist the help of computational biologists

Bacterial Spermosphere Inoculants Alter <i>N. benthamiana</i>-Plant Physiology and Host Bacterial Microbiome

In this study, we investigated the interplay between the spermosphere inoculum, host plant physiology, and endophytic compartment (EC) microbial community. Using 16S ribosomal RNA gene sequencing of root, stem, and leaf endophytic compartment communities, we established a baseline microbiome for Nicotiana sp. Phenotypic differences were observed due to the addition of some bacterial inoculants, correlated with endogenous auxin loads using transgenic plants expressing the auxin reporter pB-GFP::P87. When applied as spermosphere inoculants, select bacteria were found to create reproducible variation within the root EC microbiome and, more systematically, the host plant physiology. Our findings support the assertion that the spermosphere of plants is a zone that can influence the EC microbiome when applied in a greenhouse setting

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models.

BackgroundA mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic.ResultsHere we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus.ConclusionsThese results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics