Use of RT-PCR in conjunction with a respiratory pathogen assay to concurrently determine the prevalence of bacteria and SARS-CoV-2 from the nasopharynx of outpatients.

Introduction: COVID-19 has emerged as a highly contagious and debilitating disease caused by the SARS-CoV-2 virus and has claimed the lives of over 7.7 million people worldwide. Bacterial co-infections are one of many co-morbidities that have been suggested to impact the outcome of COVID-19 in patients. The goals of this study are to elucidate the presence of bacteria in the nasopharynx of SARS-CoV-2 positive and negative patients and to describe demographic categories that may be associated with the detection of these organisms during one of the initial waves of the COVID-19 pandemic. Methods: To this end, we investigated SARS-CoV-2 and bacterial co-detection from outpatient RT-PCR testing in Texas. Results: The results indicate that Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae, Moraxella catarrhalis, and Haemophilus influenzae were the most frequently detected bacteria in both SARS-CoV-2 positive and SARS-CoV-2 negative patients and that these bacteria were present in these two patient populations at similar proportions. We also detected Staphylococcus aureus in a significantly larger proportion of males relative to females and people under 65 years of age relative to those 65 and over. Finally, we observed that SARS-CoV-2 was more commonly detected in Hispanics compared to non-Hispanics; however, low disclosure rates make volunteer bias a concern when interpreting the effects of demographic variables. Discussion: This study describes the bacteria present in the nasopharynx of SARS-CoV-2 positive and negative patients, highlights associations between patient demographics and SARS-CoV-2 as well as bacterial co-detection. In addition, this study highlights RT-PCR based molecular testing as a tool to detect bacteria simultaneously when SARS-CoV-2 tests are performed.

Metabolic Profile of the Cellulolytic Industrial Actinomycete Thermobifida fusca.

Actinomycetes have a long history of being the source of numerous valuable natural products and medicinals. To expedite product discovery and optimization of biochemical production, high-throughput technologies can now be used to screen the library of compounds present (or produced) at a given time in an organism. This not only facilitates chemical product screening, but also provides a comprehensive methodology to the study cellular metabolic networks to inform cellular engineering. Here, we present some of the first metabolomic data of the industrial cellulolytic actinomycete Thermobifida fusca generated using LC-MS/MS. The underlying objective of conducting global metabolite profiling was to gain better insight on the innate capabilities of T. fusca, with a long-term goal of facilitating T. fusca-based bioprocesses. The T. fusca metabolome was characterized for growth on two cellulose-relevant carbon sources, cellobiose and Avicel. Furthermore, the comprehensive list of measured metabolites was computationally integrated into a metabolic model of T. fusca, to study metabolic shifts in the network flux associated with carbohydrate and amino acid metabolism.

Lack of Overt Genome Reduction in the Bryostatin-Producing Bryozoan Symbiont "Candidatus Endobugula sertula".

The uncultured bacterial symbiont "Candidatus Endobugula sertula" is known to produce cytotoxic compounds called bryostatins, which protect the larvae of its host, Bugula neritina The symbiont has never been successfully cultured, and it was thought that its genome might be significantly reduced. Here, we took a shotgun metagenomics and metatranscriptomics approach to assemble and characterize the genome of "Ca Endobugula sertula." We found that it had specific metabolic deficiencies in the biosynthesis of certain amino acids but few other signs of genome degradation, such as small size, abundant pseudogenes, and low coding density. We also identified homologs to genes associated with insect pathogenesis in other gammaproteobacteria, and these genes may be involved in host-symbiont interactions and vertical transmission. Metatranscriptomics revealed that these genes were highly expressed in a reproductive host, along with bry genes for the biosynthesis of bryostatins. We identified two new putative bry genes fragmented from the main bry operon, accounting for previously missing enzymatic functions in the pathway. We also determined that a gene previously assigned to the pathway, bryS, is not expressed in reproductive tissue, suggesting that it is not involved in the production of bryostatins. Our findings suggest that "Ca Endobugula sertula" may be able to live outside the host if its metabolic deficiencies are alleviated by medium components, which is consistent with recent findings that it may be possible for "Ca Endobugula sertula" to be transmitted horizontally. IMPORTANCE: The bryostatins are potent protein kinase C activators that have been evaluated in clinical trials for a number of indications, including cancer and Alzheimer's disease. There is, therefore, considerable interest in securing a renewable supply of these compounds, which is currently only possible through aquaculture of Bugula neritina and total chemical synthesis. However, these approaches are labor-intensive and low-yielding and thus preclude the use of bryostatins as a viable therapeutic agent. Our genome assembly and transcriptome analysis for "Ca Endobugula sertula" shed light on the metabolism of this symbiont, potentially aiding isolation and culturing efforts. Our identification of additional bry genes may also facilitate efforts to express the complete pathway heterologously.

Proteomics-based metabolic modeling and characterization of the cellulolytic bacterium Thermobifida fusca.

BACKGROUND: Thermobifida fusca is a cellulolytic bacterium with potential to be used as a platform organism for sustainable industrial production of biofuels, pharmaceutical ingredients and other bioprocesses due to its capability of potential to convert plant biomass to value-added chemicals. To best develop T. fusca as a bioprocess organism, it is important to understand its native cellular processes. In the current study, we characterize the metabolic network of T. fusca through reconstruction of a genome-scale metabolic model and proteomics data. The overall goal of this study was to use multiple metabolic models generated by different methods and comparison to experimental data to gain a high-confidence understanding of the T. fusca metabolic network. RESULTS: We report the generation of three versions of a metabolic model of Thermobifida fusca sp. XY developed using three different approaches (automated, semi-automated, and proteomics-derived). The model closest to in vivo growth was the proteomics-derived model that consists of 975 reactions involving 1382 metabolites and account for 316 EC numbers (296 genes). The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h. In vivo activity of the DXP pathway for terpenoid biosynthesis was also confirmed using real-time PCR. CONCLUSIONS: iTfu296 provides a platform to understand and explore the metabolic capabilities of the actinomycete T. fusca for the potential use in bioprocess industries for the production of biofuel and pharmaceutical ingredients. By comparing different model reconstruction methods, the use of high-throughput proteomics data as a starting point proved to be the most accurate to in vivo growth.

Evolutionary engineering for industrial microbiology.

Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.

Identification of metabolic changes in genetically unstable stem cells by using model analysis of gene expression.

Stem-cell research seeks to address many different questions related to fundamental stem-cell function with the ultimate goal of being able to control and utilize stem cells for a broad range of therapeutic needs. While a large amount of work is focused on discovering and controlling differentiation mechanisms in stem cells, an equally interesting and important area of work is to understand the basics of stem-cell propagation and self-renewal. With high-throughput genomics and transcriptomic information on hand, it is becoming possible to address some of the detailed mechanistic processes occurring in stem cells, though interpretation of these data is often difficult. In this work, stem cells with genetic abnormalities were compared to genetically normal stem cells using gene-expression array data integrated with a large-scale metabolic model to help interpret changes in metabolism resulting in the identification of several metabolic pathways that were different in the normal and abnormal cells.

A genome-scale metabolic model of Cryptosporidium hominis.

The apicomplexan Cryptosporidium is a protozoan parasite of humans and other mammals. Cryptosporidium species cause acute gastroenteritis and diarrheal disease in healthy humans and animals, and cause life-threatening infection in immunocompromised individuals such as people with AIDS. The parasite has a one-host life cycle and commonly invades intestinal epithelial cells. The current genome annotation of C. hominis, the most serious human pathogen, predicts 3884 genes of which ca. 1581 have predicted functional annotations. Using a combination of bioinformatics analysis, biochemical evidence, and high-throughput data, we have constructed a genome-scale metabolic model of C. hominis. The model is comprised of 213 gene-associated enzymes involved in 540 reactions among the major metabolic pathways and provides a link between the genotype and the phenotype of the organism, making it possible to study and predict behavior based upon genome content. This model was also used to analyze the two life stages of the parasite by integrating the stage-specific proteomic data for oocyst and sporozoite stages. Overall, this model provides a computational framework to systematically study and analyze various functional behaviors of C. hominis with respect to its life cycle and pathogenicity.