Development of teixobactin analogues containing hydrophobic, non-proteogenic amino acids that are highly potent against multidrug-resistant bacteria and biofilms.

Teixobactin is a cyclic undecadepsipeptide that has shown excellent potency against multidrug-resistant pathogens, such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE). In this article, we present the design, synthesis, and antibacterial evaluations of 16 different teixobactin analogues. These simplified analogues contain commercially available hydrophobic, non-proteogenic amino acid residues instead of synthetically challenging expensive L-allo-enduracididine amino acid residue at position 10 together with different combinations of arginines at positions 3, 4 and 9. The new teixobactin analogues showed potent antibacterial activity against a broad panel of Gram-positive bacteria, including MRSA and VRE strains. Our work also presents the first demonstration of the potent antibiofilm activity of teixobactin analogoues against Staphylococcus species associated with serious chronic infections. Our results suggest that the use of hydrophobic, non-proteogenic amino acids at position 10 in combination with arginine at positions 3, 4 and 9 holds the key to synthesising a new generation of highly potent teixobactin analogues to tackle resistant bacterial infections and biofilms.

Relationships between SARS-CoV-2 in Wastewater and COVID-19 Clinical Cases and Hospitalizations, with and without Normalization against Indicators of Human Waste.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater has been used to track community infections of coronavirus disease-2019 (COVID-19), providing critical information for public health interventions. Since levels in wastewater are dependent upon human inputs, we hypothesize that tracking infections can be improved by normalizing wastewater concentrations against indicators of human waste [Pepper Mild Mottle Virus (PMMoV), ß-2 Microglobulin (B2M), and fecal coliform]. In this study, we analyzed SARS-CoV-2 and indicators of human waste in wastewater from two sewersheds of different scales: a University campus and a wastewater treatment plant. Wastewater data were combined with complementary COVID-19 case tracking to evaluate the efficiency of wastewater surveillance for forecasting new COVID-19 cases and, for the larger scale, hospitalizations. Results show that the normalization of SARS-CoV-2 levels by PMMoV and B2M resulted in improved correlations with COVID-19 cases for campus data using volcano second generation (V2G)-qPCR chemistry (r s = 0.69 without normalization, r s = 0.73 with normalization). Mixed results were obtained for normalization by PMMoV for samples collected at the community scale. Overall benefits from normalizing with measures of human waste depend upon qPCR chemistry and improves with smaller sewershed scale. We recommend further studies that evaluate the efficacy of additional normalization targets.