9S1R Nullomer Peptide Induces Mitochondrial Pathology, Metabolic Suppression, and Enhanced Immune Cell Infiltration, in Triple-Negative Breast Cancer Mouse Model

Nullomers are the shortest strings of absent amino acid (aa) sequences in a species or group of species. Primes are those nullomers that have not been detected in the genome of any species. 9S1R is a 5-aa peptide prime sequence attached to 5-arginine aa, used to treat triple negative breast cancer (TNBC) in an in vivo mouse model. This unique peptide, administered with a trehalose carrier (9S1R-NulloPT), offers enhanced solubility and exhibits distinct anti-cancer effects against TNBC. In our study, we investigated the effect of 9S1R-NulloPT on tumor growth, metabolism, metastatic burden, tumor immune-microenvironment (TME), and transcriptome of aggressive mouse TNBC tumors. Notably, treated mice had smaller tumors in the initial phase of the treatment, as compared to untreated control, and diminished in vivo and ex vivo bioluminescence at later-stages - indicative of metabolically quiescent, dying tumors. The treatment also caused changes in TME with increased infiltration of immune cells and altered tumor transcriptome, with 365 upregulated genes and 710 downregulated genes. Consistent with in vitro data, downregulated genes were enriched in cellular metabolic processes (179), specifically mitochondrial TCA cycle/oxidative phosphorylation (44), and translation machinery/ribosome biogenesis (45). The upregulated genes were associated with the developmental (13), ECM organization (12) and focal adhesion pathways (7). In conclusion, our study demonstrates that 9S1R-NulloPT effectively reduced tumor growth during its initial phase, altering the TME and tumor transcriptome. The treatment induced mitochondrial pathology which led to a metabolic deceleration in tumors, aligning with in vitro observations

In silico comparative function prediction of enzymes, applied to fatty acid metabolism in microalgae: Final Report

This is a confidential report on the design and implementation of in silico methods for large-scale comparative analysis of desaturase genes from all branches of life, with the goal of defining methods for predicting function, specificity, and regiospecificity. The work included a systematic, multi-criteria comparison of candidate proteins, integrating sequences, annotations, structures and experimental results.The biotechnological context for this work is the search for production of polyunsaturated fatty acids of nutritional interest at industrial scale by cultivating green algae directly, rather than relying on commercial fishing to recover fatty acids concentrated through the food chain. Ultimately, the cultivated algae may be domesticated strains of naturally occurring species, or engineered strains developed by genetic engineering or synthetic biology.For confidentiality reasons the full content of this report cannot be disseminated at this time

Final Report

This is a confidential report on the design and implementation of in silico methods for large-scale comparative analysis of desaturase genes from all branches of life, with the goal of defining methods for predicting function, specificity, and regiospecificity. The work included a systematic, multi-criteria comparison of candidate proteins, integrating sequences, annotations, structures and experimental results.The biotechnological context for this work is the search for production of polyunsaturated fatty acids of nutritional interest at industrial scale by cultivating green algae directly, rather than relying on commercial fishing to recover fatty acids concentrated through the food chain. Ultimately, the cultivated algae may be domesticated strains of naturally occurring species, or engineered strains developed by genetic engineering or synthetic biology.For confidentiality reasons the full content of this report cannot be disseminated at this time

Final Report

This is a confidential report on the design and implementation of in silico methods for large-scale comparative analysis of desaturase genes from all branches of life, with the goal of defining methods for predicting function, specificity, and regiospecificity. The work included a systematic, multi-criteria comparison of candidate proteins, integrating sequences, annotations, structures and experimental results.The biotechnological context for this work is the search for production of polyunsaturated fatty acids of nutritional interest at industrial scale by cultivating green algae directly, rather than relying on commercial fishing to recover fatty acids concentrated through the food chain. Ultimately, the cultivated algae may be domesticated strains of naturally occurring species, or engineered strains developed by genetic engineering or synthetic biology.For confidentiality reasons the full content of this report cannot be disseminated at this time

In silico comparative function prediction of enzymes, applied to fatty acid metabolism in microalgae: Final Report

This is a confidential report on the design and implementation of in silico methods for large-scale comparative analysis of desaturase genes from all branches of life, with the goal of defining methods for predicting function, specificity, and regiospecificity. The work included a systematic, multi-criteria comparison of candidate proteins, integrating sequences, annotations, structures and experimental results.The biotechnological context for this work is the search for production of polyunsaturated fatty acids of nutritional interest at industrial scale by cultivating green algae directly, rather than relying on commercial fishing to recover fatty acids concentrated through the food chain. Ultimately, the cultivated algae may be domesticated strains of naturally occurring species, or engineered strains developed by genetic engineering or synthetic biology.For confidentiality reasons the full content of this report cannot be disseminated at this time

COVID-19 Hospitalizations and Deaths Predicted by SARS-CoV-2 Levels in Boise, Idaho Wastewater

The viral load of COVID-19 in untreated wastewater from Idaho\u27s capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers

Computational screening for new inhibitors of M. tuberculosis mycolyltransferases antigen 85 group of proteins as potential drug targets

The group of antigen 85 proteins of Mycobacterium tuberculosis is responsible for converting trehalose monomycolate to trehalose dimycolate, which contributes to cell wall stability. Here, we have used a serial enrichment approach to identify new potential inhibitors by searching the libraries of compounds using both 2D atom pair descriptors and binary fingerprints followed by molecular docking. Three different docking softwares AutoDock, GOLD, and LigandFit were used for docking calculations. In addition, we applied the criteria of selecting compounds with binding efficiency close to the starting known inhibitor and showing potential to form hydrogen bonds with the active site amino acid residues. The starting inhibitor was ethyl-3-phenoxybenzyl-butylphosphonate, which had IC50 value of 2.0 μM in mycolyltransferase inhibition assay. Our search from more than 34 million compounds from public libraries yielded 49 compounds. Subsequently, selection was restricted to compounds conforming to the Lipinski rule of five and exhibiting hydrogen bonding to any of the amino acid residues in the active site pocket of all three proteins of antigen 85A, 85B, and 85C. Finally, we selected those ligands which were ranked top in the table with other known decoys in all the docking results. The compound NIH415032 from tuberculosis antimicrobial acquisition and coordinating facility was further examined using molecular dynamics simulations for 10 ns. These results showed that the binding is stable, although some of the hydrogen bond atom pairs varied through the course of simulation. The NIH415032 has antitubercular properties with IC90 at 20 μg/ml (53.023 μM). These results will be helpful to the medicinal chemists for developing new antitubercular molecules for testin

Computational screening for new inhibitors of <i>M. tuberculosis</i> mycolyltransferases antigen 85 group of proteins as potential drug targets

<div><p>The group of antigen 85 proteins of <i>Mycobacterium tuberculosis</i> is responsible for converting trehalose monomycolate to trehalose dimycolate, which contributes to cell wall stability. Here, we have used a serial enrichment approach to identify new potential inhibitors by searching the libraries of compounds using both 2D atom pair descriptors and binary fingerprints followed by molecular docking. Three different docking softwares AutoDock, GOLD, and LigandFit were used for docking calculations. In addition, we applied the criteria of selecting compounds with binding efficiency close to the starting known inhibitor and showing potential to form hydrogen bonds with the active site amino acid residues. The starting inhibitor was ethyl-3-phenoxybenzyl-butylphosphonate, which had IC₅₀ value of 2.0 μM in mycolyltransferase inhibition assay. Our search from more than 34 million compounds from public libraries yielded 49 compounds. Subsequently, selection was restricted to compounds conforming to the Lipinski rule of five and exhibiting hydrogen bonding to any of the amino acid residues in the active site pocket of all three proteins of antigen 85A, 85B, and 85C. Finally, we selected those ligands which were ranked top in the table with other known decoys in all the docking results. The compound NIH415032 from tuberculosis antimicrobial acquisition and coordinating facility was further examined using molecular dynamics simulations for 10 ns. These results showed that the binding is stable, although some of the hydrogen bond atom pairs varied through the course of simulation. The NIH415032 has antitubercular properties with IC₉₀ at 20 μg/ml (53.023 μM). These results will be helpful to the medicinal chemists for developing new antitubercular molecules for testing.</p> </div