DD-S067
DD-S067 is an antibacterial peptide. DD-S067 exhibits multiple antibacterial mechanisms, including disrupting both the outer and inner bacterial membranes, and inducing ROS that trigger lipid peroxidation. DD-S067 inhibits the electron transport chain. DD-S067 demonstrates potent antibacterial activity, achieving a GM value of 4.1 μM against 27 MDR bacteria. DD-S067 exhibits significant protective effects in a CRAB-induced septic shock mouse model.
For research use only. We do not sell to patients.
- Formula: C70H118N24O12
- Molecular Weight:1487.84
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Storage:
Please store the product under the recommended conditions in the Certificate of Analysis.
Biological Activity
Description
Chemical Information
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Molecular Weight 1487.84
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Formula C70H118N24O12
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Sequence
d-{Lys-Trp-Arg-Val-Lys-Leu-Arg-Ala-Tyr-Leu-Arg-NH2}
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Sequence Shortening
d-{KWRVKLRAYLR-NH2}
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Shipping
Room temperature in continental US; may vary elsewhere.
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Storage
Please store the product under the recommended conditions in the Certificate of Analysis.
Protocols
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Research Protocol for Infectious Diseases
Infectious-disease experiments test how pathogens interact with host barriers, innate immune receptors, inflammatory signaling, pathogen replication, and tissue injury; pattern-recognition receptors such as TLRs, RIG-I-like receptors, NOD-like receptors, and inflammasomes detect microbial molecules and activate NF-κB, interferon, and cytokine responses. The central hypothesis is that infection severity reflects the balance between pathogen burden and host response: protective inflammation restricts pathogen growth, whereas excessive or mislocalized inflammation contributes to tissue damage and disease phenotype. Unresolved questions include which host pathways are protective versus pathogenic, why some infection models fail to translate to human disease, and which combined readouts best predict clinically relevant infection outcomes.
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ROS/oxidative-stress fluorescent staining
ROS/oxidative-stress fluorescent staining uses cell-permeant fluorogenic probes that become fluorescent after oxidation inside cells or tissues; commonly used examples include DCFH-DA/DCFDA for broad cellular oxidant detection, DHE for superoxide-related signal detection, MitoSOX for mitochondrial superoxide-related signal detection, and CellROX probes for oxidative-stress-associated fluorescence readouts. The assay detects probe oxidation rather than a single ROS species unless the probe and analysis method have been chemically validated for that species. DCFH-DA enters cells, is deacetylated by intracellular esterases to DCFH, and produces fluorescent DCF after oxidation, so the readout is used as an operational measure of total cellular oxidative stress rather than a species-specific ROS measurement. DHE and MitoSOX can report superoxide-related oxidation, but red fluorescence alone can include non-specific ethidium-like oxidation products; HPLC or optimized spectral approaches are
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Ferroptosis Solutions
Ferroptosis is an iron-dependent, non-apoptotic form of regulated cell death characterized by lethal lipid peroxidation and sensitivity to suppression by iron chelators or lipophilic radical-trapping antioxidants. The core pathway links cystine uptake through system Xc−, glutathione availability, GPX4-dependent detoxification of phospholipid hydroperoxides, iron-dependent oxidative reactions, and polyunsaturated-phospholipid metabolism into a cell-death program that is biochemically and morphologically distinct from apoptosis, necrosis, and autophagy. The ferroptosis pathway is experimentally linked to phenotype through chemical and genetic perturbation. Erastin induces ferroptosis by inhibiting cystine uptake through system Xc− and weakening antioxidant defenses, while GPX4 inhibition or depletion causes lipid peroxide accumulation and ferroptotic cancer-cell death. ACSL4 and oxidizable arachidonoyl- or adrenoyl-containing phosphatidylethanolamines shape ferroptosis sensitivity by con
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Bacterial live/dead nucleic-acid viability staining
The LIVE/DEAD bacterial viability staining method is based on differential permeability of nucleic-acid-binding fluorescent dyes, most commonly SYTO 9 and propidium iodide (PI), which enables discrimination of bacterial populations with intact versus compromised cytoplasmic membranes. SYTO 9 penetrates both intact and damaged bacterial membranes and binds nucleic acids to produce green fluorescence, whereas propidium iodide penetrates only cells with compromised membranes and fluoresces red while also reducing SYTO 9 signal through competitive binding and fluorescence interactions. The resulting fluorescence pattern is interpreted as a proxy for membrane integrity, which is widely used as an indicator of bacterial viability in microscopy, flow cytometry, and spectroscopic platforms. However, mechanistic studies show that SYTO 9 and PI interactions involve displacement and fluorescence resonance energy transfer effects, which can influence signal interpretation depending on dye ratios a
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How to Choose the Right Model Animal
Choosing the right model animal is a validity-driven decision in which the species, strain, sex, age, genetic background, disease-induction method, outcome measures, and welfare burden must match the scientific question rather than laboratory tradition or convenience. A model should be selected by judging face validity, construct validity, and predictive validity: whether it resembles the human phenotype, whether it reproduces relevant mechanisms, and whether results are likely to predict human biology or treatment response. Animal studies often fail to translate because of species differences, weak disease resemblance, poor experimental design, inadequate reporting, publication bias, and underuse of randomization, blinding, and sample-size justification. Unresolved questions include how to rank competing models objectively, how much human-disease complexity must be reproduced for a given objective, and when non-animal systems such as organoids, ex vivo tissue, or computational models
Purity & Documentation
References
Calculators
Concentration (start) × Volume (start) = Concentration (final) × Volume (final)