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Lipids

Lipids are a diverse class of organic compounds that are essential for many biological processes.
They include fats, oils, waxes, and related compounds.
Lipids play crucial roles in energy storage, cell membrane structure, and signaling pathways.
Researching lipids is crucial for understanding human health, as imbalances in lipid metabolism can contribute to conditions like heart disease, diabetes, and neurological disorders.
PubCompre.ai's AI-powered platform helps optimize lipid research protocols, enabling precise comparisons across literature, preprints, and patents.
This unlocks new insights and identifies the best methods for your specific lipid-related needs, empowering you to advance the future of this vital field.

Most cited protocols related to «Lipids»

1
PhenoScanner: Comprehensive Genotype-Phenotype Database
Cited 438 times
PhenoScanner consists of a Perl interface (with R command line tool) that connects to a MySQL database. To develop the initial database, we collated 137 genotype–phenotype association datasets, including results for anthropometric traits, blood pressure, lipids, cardiometabolic diseases, renal function measures, glycemic traits, inflammatory diseases, psychiatric diseases and smoking phenotypes (Supplementary Table). We also included the NHGRI-EBI GWAS catalog, NHLBI GRASP (Leslie et al., 2014 (link)) and dbGaP catalogues of associations. To ensure consistent formatting, we aligned alleles to the plus strand, added or updated chromosome positions to build 37 using dbSNP (release 138) (Sherry et al., 2001 (link)) and liftOver (https://genome.ucsc.edu/cgi-bin/hgLiftOver), and updated old rsIDs to dbSNP release 141 (Supplementary Data). Linkage disequilibrium (LD) measures between neighbouring variants in the autosomal chromosomes were calculated using the phased haplotypes from European samples in 1000 Genomes phase 3 (N = 503) (1000 Genomes Project Consortium et al., 2012 (link)). Variants with minor allele frequencies <0.5% were removed along with multiallelic variants and large indels ( ≥ 5 bases). For each remaining variant, we calculated D′ and r2 for variants within 500 kb in either direction, and kept LD statistics for pairs of variants with r2≥0.6 . LD statistics based on the CEU population from Hapmap 2 release 24 (Frazer et al., 2007 (link)) are also available (Supplementary Data).
The user may enter either one variant into the text box on the website or upload up to 50 variants in a text file. The Perl interface annotates the variant alleles using dbSNP, identifies proxies of the specified variants (if requested) in the database according to a user-specified pairwise r2 threshold, and queries the catalogue of genotype–phenotype associations for the specified variants and their proxies. Association results are collated and presented with respect to the same effect and non-effect alleles for each variant. The associations with proxies are aligned according to the effect and non-effect alleles of the corresponding primary variant of interest for added ease of interpretation. The output is a file of associations, which is made available to download. There is also a P value filter option that only retains results with study-specific P values less than the selected threshold.
Staley J.R., Blackshaw J., Kamat M.A., Ellis S., Surendran P., Sun B.B., Paul D.S., Freitag D., Burgess S., Danesh J., Young R, & Butterworth A.S. (2016). PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics, 32(20), 3207-3209.
Full text: Click here
Publication 2016
Alleles Blood Pressure Chromosomes Europeans Genome Genome-Wide Association Study Grasp Haplotypes HapMap INDEL Mutation Inflammation Kidney Lipids Mental Disorders Phenotype
2
Genetic Variants Prioritization for Lipid Traits
Cited 349 times
We genotyped 196,710 genetic variants prioritized on the basis of prior GWAS for cardiovascular and metabolic phenotypes using the Illumina iSelect Metabochip8 genotyping array. To design the Metabochip, we used our previous GWAS of ~100,000 individuals4 (link) to prioritize 5,023 SNPs for HDL cholesterol, 5,055 for LDL cholesterol, 5,056 for triglycerides, and 938 for total cholesterol. These independent SNPs represent most loci with P < .005 in our original GWAS for HDL cholesterol, LDL cholesterol and triglycerides and with P < .0005 for total cholesterol. An additional 28,923 SNPs were selected for fine-mapping of 65 previously identified lipid loci. The Metabochip also included 50,459 SNPs prioritized based on GWAS of non-lipid traits and 93,308 SNPs selected for fine-mapping of loci associated with non-lipid traits (5 of these loci were associated with blood lipids by the analyses described here).
Willer C.J., Schmidt E.M., Sengupta S., Peloso G.M., Gustafsson S., Kanoni S., Ganna A., Chen J., Buchkovich M.L., Mora S., Beckmann J.S., Bragg-Gresham J.L., Chang H.Y., Demirkan A., Den Hertog H.M., Do R., Donnelly L.A., Ehret G.B., Esko T., Feitosa M.F., Ferreira T., Fischer K., Fontanillas P., Fraser R.M., Freitag D.F., Gurdasani D., Heikkilä K., Hyppönen E., Isaacs A., Jackson A.U., Johansson Å., Johnson T., Kaakinen M., Kettunen J., Kleber M.E., Li X., Luan J., Lyytikäinen L.P., Magnusson P.K., Mangino M., Mihailov E., Montasser M.E., Müller-Nurasyid M., Nolte I.M., O’Connell J.R., Palmer C.D., Perola M., Petersen A.K., Sanna S., Saxena R., Service S.K., Shah S., Shungin D., Sidore C., Song C., Strawbridge R.J., Surakka I., Tanaka T., Teslovich T.M., Thorleifsson G., Van den Herik E.G., Voight B.F., Volcik K.A., Waite L.L., Wong A., Wu Y., Zhang W., Absher D., Asiki G., Barroso I., Been L.F., Bolton J.L., Bonnycastle L.L., Brambilla P., Burnett M.S., Cesana G., Dimitriou M., Doney A.S., Döring A., Elliott P., Epstein S.E., Ingi Eyjolfsson G., Gigante B., Goodarzi M.O., Grallert H., Gravito M.L., Groves C.J., Hallmans G., Hartikainen A.L., Hayward C., Hernandez D., Hicks A.A., Holm H., Hung Y.J., Illig T., Jones M.R., Kaleebu P., Kastelein J.J., Khaw K.T., Kim E., Klopp N., Komulainen P., Kumari M., Langenberg C., Lehtimäki T., Lin S.Y., Lindström J., Loos R.J., Mach F., McArdle W.L., Meisinger C., Mitchell B.D., Müller G., Nagaraja R., Narisu N., Nieminen T.V., Nsubuga R.N., Olafsson I., Ong K.K., Palotie A., Papamarkou T., Pomilla C., Pouta A., Rader D.J., Reilly M.P., Ridker P.M., Rivadeneira F., Rudan I., Ruokonen A., Samani N., Scharnagl H., Seeley J., Silander K., Stančáková A., Stirrups K., Swift A.J., Tiret L., Uitterlinden A.G., van Pelt L.J., Vedantam S., Wainwright N., Wijmenga C., Wild S.H., Willemsen G., Wilsgaard T., Wilson J.F., Young E.H., Zhao J.H., Adair L.S., Arveiler D., Assimes T.L., Bandinelli S., Bennett F., Bochud M., Boehm B.O., Boomsma D.I., Borecki I.B., Bornstein S.R., Bovet P., Burnier M., Campbell H., Chakravarti A., Chambers J.C., Chen Y.D., Collins F.S., Cooper R.S., Danesh J., Dedoussis G., de Faire U., Feranil A.B., Ferrières J., Ferrucci L., Freimer N.B., Gieger C., Groop L.C., Gudnason V., Gyllensten U., Hamsten A., Harris T.B., Hingorani A., Hirschhorn J.N., Hofman A., Hovingh G.K., Hsiung C.A., Humphries S.E., Hunt S.C., Hveem K., Iribarren C., Järvelin M.R., Jula A., Kähönen M., Kaprio J., Kesäniemi A., Kivimaki M., Kooner J.S., Koudstaal P.J., Krauss R.M., Kuh D., Kuusisto J., Kyvik K.O., Laakso M., Lakka T.A., Lind L., Lindgren C.M., Martin N.G., März W., McCarthy M.I., McKenzie C.A., Meneton P., Metspalu A., Moilanen L., Morris A.D., Munroe P.B., Njølstad I., Pedersen N.L., Power C., Pramstaller P.P., Price J.F., Psaty B.M., Quertermous T., Rauramaa R., Saleheen D., Salomaa V., Sanghera D.K., Saramies J., Schwarz P.E., Sheu W.H., Shuldiner A.R., Siegbahn A., Spector T.D., Stefansson K., Strachan D.P., Tayo B.O., Tremoli E., Tuomilehto J., Uusitupa M., van Duijn C.M., Vollenweider P., Wallentin L., Wareham N.J., Whitfield J.B., Wolffenbuttel B.H., Ordovas J.M., Boerwinkle E., Palmer C.N., Thorsteinsdottir U., Chasman D.I., Rotter J.I., Franks P.W., Ripatti S., Cupples L.A., Sandhu M.S., Rich S.S., Boehnke M., Deloukas P., Kathiresan S., Mohlke K.L., Ingelsson E, & Abecasis G.R. (2013). Discovery and Refinement of Loci Associated with Lipid Levels. Nature genetics, 45(11), 1274-1283.
Publication 2013
BLOOD Cardiovascular System Cholesterol Cholesterol, beta-Lipoprotein Genetic Diversity Genome-Wide Association Study High Density Lipoprotein Cholesterol Lipids Phenotype Single Nucleotide Polymorphism Triglycerides
3
Genome-Wide Analysis of Lipid Traits
Cited 316 times
The full Methods are in Supplementary Information and provide information about: (1) study samples and phenotypes; (2) genotyping and imputation; (3) genome-wide association analyses; (4) meta-analyses of directly typed and imputed SNPs; (5) estimation of effect sizes; (6) conditional analyses of top signals; (7) sex-specific analyses; (8) cis-expression quantitative trait locus analyses; (9) analyses of lipid-associated SNPs in European and non-European samples; (10) analyses of lipid-associated SNPs in individuals with and without CAD; (11) analyses of associated SNPs in patients with extreme LDL-C, HDL-C, or TG levels; (12) simulation studies to assess overlap between GWAS signals and Mendelian disease loci; and (13) details of mouse studies.
Teslovich T.M., Musunuru K., Smith A.V., Edmondson A.C., Stylianou I.M., Koseki M., Pirruccello J.P., Ripatti S., Chasman D.I., Willer C.J., Johansen C.T., Fouchier S.W., Isaacs A., Peloso G.M., Barbalic M., Ricketts S.L., Bis J.C., Aulchenko Y.S., Thorleifsson G., Feitosa M.F., Chambers J., Orho-Melander M., Melander O., Johnson T., Li X., Guo X., Li M., Cho Y.S., Go M.J., Kim Y.J., Lee J.Y., Park T., Kim K., Sim X., Ong R.T., Croteau-Chonka D.C., Lange L.A., Smith J.D., Song K., Zhao J.H., Yuan X., Luan J., Lamina C., Ziegler A., Zhang W., Zee R.Y., Wright A.F., Witteman J.C., Wilson J.F., Willemsen G., Wichmann H.E., Whitfield J.B., Waterworth D.M., Wareham N.J., Waeber G., Vollenweider P., Voight B.F., Vitart V., Uitterlinden A.G., Uda M., Tuomilehto J., Thompson J.R., Tanaka T., Surakka I., Stringham H.M., Spector T.D., Soranzo N., Smit J.H., Sinisalo J., Silander K., Sijbrands E.J., Scuteri A., Scott J., Schlessinger D., Sanna S., Salomaa V., Saharinen J., Sabatti C., Ruokonen A., Rudan I., Rose L.M., Roberts R., Rieder M., Psaty B.M., Pramstaller P.P., Pichler I., Perola M., Penninx B.W., Pedersen N.L., Pattaro C., Parker A.N., Pare G., Oostra B.A., O'Donnell C.J., Nieminen M.S., Nickerson D.A., Montgomery G.W., Meitinger T., McPherson R., McCarthy M.I., McArdle W., Masson D., Martin N.G., Marroni F., Mangino M., Magnusson P.K., Lucas G., Luben R., Loos R.J., Lokki M., Lettre G., Langenberg C., Launer L.J., Lakatta E.G., Laaksonen R., Kyvik K.O., Kronenberg F., König I.R., Khaw K.T., Kaprio J., Kaplan L.M., Johansson Å., Jarvelin M.R., Janssens A.C., Ingelsson E., Igl W., Hovingh G.K., Hottenga J.J., Hofman A., Hicks A.A., Hengstenberg C., Heid I.M., Hayward C., Havulinna A.S., Hastie N.D., Harris T.B., Haritunians T., Hall A.S., Gyllensten U., Guiducci C., Groop L.C., Gonzalez E., Gieger C., Freimer N.B., Ferrucci L., Erdmann J., Elliott P., Ejebe K.G., Döring A., Dominiczak A.F., Demissie S., Deloukas P., de Geus E.J., de Faire U., Crawford G., Collins F.S., Chen Y.D., Caulfield M.J., Campbell H., Burtt N.P., Bonnycastle L.L., Boomsma D.I., Boekholdt S.M., Bergman R.N., Barroso I., Bandinelli S., Ballantyne C.M., Assimes T.L., Quertermous T., Altshuler D., Seielstad M., Wong T.Y., Tai E.S., Feranil A.B., Kuzawa C.W., Adair L.S., Taylor HA J.r., Borecki I.B., Gabriel S.B., Wilson J.G., Stefansson K., Thorsteinsdottir U., Gudnason V., Krauss R.M., Mohlke K.L., Ordovas J.M., Munroe P.B., Kooner J.S., Tall A.R., Hegele R.A., Kastelein J.J., Schadt E.E., Rotter J.I., Boerwinkle E., Strachan D.P., Mooser V., Holm H., Reilly M.P., Samani N.J., Schunkert H., Cupples L.A., Sandhu M.S., Ridker P.M., Rader D.J., van Duijn C.M., Peltonen L., Abecasis G.R., Boehnke M, & Kathiresan S. (2010). Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids. Nature, 466(7307), 707-713.
Publication 2010
Europeans Genome-Wide Association Study Lipids Mice, Laboratory Patients Phenotype Quantitative Trait Loci Single Nucleotide Polymorphism
4
Genome-Wide Analysis of Lipid Traits
Cited 311 times
The full Methods are in Supplementary Information and provide information about: (1) study samples and phenotypes; (2) genotyping and imputation; (3) genome-wide association analyses; (4) meta-analyses of directly typed and imputed SNPs; (5) estimation of effect sizes; (6) conditional analyses of top signals; (7) sex-specific analyses; (8) cis-expression quantitative trait locus analyses; (9) analyses of lipid-associated SNPs in European and non-European samples; (10) analyses of lipid-associated SNPs in individuals with and without CAD; (11) analyses of associated SNPs in patients with extreme LDL-C, HDL-C, or TG levels; (12) simulation studies to assess overlap between GWAS signals and Mendelian disease loci; and (13) details of mouse studies.
Teslovich T.M., Musunuru K., Smith A.V., Edmondson A.C., Stylianou I.M., Koseki M., Pirruccello J.P., Ripatti S., Chasman D.I., Willer C.J., Johansen C.T., Fouchier S.W., Isaacs A., Peloso G.M., Barbalic M., Ricketts S.L., Bis J.C., Aulchenko Y.S., Thorleifsson G., Feitosa M.F., Chambers J., Orho-Melander M., Melander O., Johnson T., Li X., Guo X., Li M., Cho Y.S., Go M.J., Kim Y.J., Lee J.Y., Park T., Kim K., Sim X., Ong R.T., Croteau-Chonka D.C., Lange L.A., Smith J.D., Song K., Zhao J.H., Yuan X., Luan J., Lamina C., Ziegler A., Zhang W., Zee R.Y., Wright A.F., Witteman J.C., Wilson J.F., Willemsen G., Wichmann H.E., Whitfield J.B., Waterworth D.M., Wareham N.J., Waeber G., Vollenweider P., Voight B.F., Vitart V., Uitterlinden A.G., Uda M., Tuomilehto J., Thompson J.R., Tanaka T., Surakka I., Stringham H.M., Spector T.D., Soranzo N., Smit J.H., Sinisalo J., Silander K., Sijbrands E.J., Scuteri A., Scott J., Schlessinger D., Sanna S., Salomaa V., Saharinen J., Sabatti C., Ruokonen A., Rudan I., Rose L.M., Roberts R., Rieder M., Psaty B.M., Pramstaller P.P., Pichler I., Perola M., Penninx B.W., Pedersen N.L., Pattaro C., Parker A.N., Pare G., Oostra B.A., O'Donnell C.J., Nieminen M.S., Nickerson D.A., Montgomery G.W., Meitinger T., McPherson R., McCarthy M.I., McArdle W., Masson D., Martin N.G., Marroni F., Mangino M., Magnusson P.K., Lucas G., Luben R., Loos R.J., Lokki M., Lettre G., Langenberg C., Launer L.J., Lakatta E.G., Laaksonen R., Kyvik K.O., Kronenberg F., König I.R., Khaw K.T., Kaprio J., Kaplan L.M., Johansson Å., Jarvelin M.R., Janssens A.C., Ingelsson E., Igl W., Hovingh G.K., Hottenga J.J., Hofman A., Hicks A.A., Hengstenberg C., Heid I.M., Hayward C., Havulinna A.S., Hastie N.D., Harris T.B., Haritunians T., Hall A.S., Gyllensten U., Guiducci C., Groop L.C., Gonzalez E., Gieger C., Freimer N.B., Ferrucci L., Erdmann J., Elliott P., Ejebe K.G., Döring A., Dominiczak A.F., Demissie S., Deloukas P., de Geus E.J., de Faire U., Crawford G., Collins F.S., Chen Y.D., Caulfield M.J., Campbell H., Burtt N.P., Bonnycastle L.L., Boomsma D.I., Boekholdt S.M., Bergman R.N., Barroso I., Bandinelli S., Ballantyne C.M., Assimes T.L., Quertermous T., Altshuler D., Seielstad M., Wong T.Y., Tai E.S., Feranil A.B., Kuzawa C.W., Adair L.S., Taylor HA J.r., Borecki I.B., Gabriel S.B., Wilson J.G., Stefansson K., Thorsteinsdottir U., Gudnason V., Krauss R.M., Mohlke K.L., Ordovas J.M., Munroe P.B., Kooner J.S., Tall A.R., Hegele R.A., Kastelein J.J., Schadt E.E., Rotter J.I., Boerwinkle E., Strachan D.P., Mooser V., Holm H., Reilly M.P., Samani N.J., Schunkert H., Cupples L.A., Sandhu M.S., Ridker P.M., Rader D.J., van Duijn C.M., Peltonen L., Abecasis G.R., Boehnke M, & Kathiresan S. (2010). Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids. Nature, 466(7307), 707-713.
Publication 2010
Europeans Genome-Wide Association Study Lipids Mice, Laboratory Patients Phenotype Quantitative Trait Loci Single Nucleotide Polymorphism
5
Optimizing Lipid Force Field Parameters
Cited 290 times
The Methods section is divided into three subsections. In the first, lipid molecule and atom names are briefly reviewed to avoid confusion among several commonly used conventions. Section 2.2 describes the QM methods used in justifying modifications to the partial charges for certain atoms in the head group of the lipid molecules along with the optimization procedure used to produce optimized non-bond parameters for the ester moieties in the glycerol linker region. This subsection also describes the QM methods used to obtain highly accurate torsional profiles for certain head group and alkene dihedrals. The Section 2.3 describes the methods used in the molecular dynamics (MD) simulations. The C36 additive all-atom lipid force field may be downloaded from the MacKerell group web page at http://mackerell.umaryland.edu/CHARMM_ff_params.html.
Klauda J.B., Venable R.M., Freites J.A., O’Connor J.W., Tobias D.J., Mondragon-Ramirez C., Vorobyov I., MacKerell AD J.r, & Pastor R.W. (2010). Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types. The journal of physical chemistry. B, 114(23), 7830-7843.
Publication 2010
Alkenes Conferences Esters Glycerin Head Lipids

Most recents protocols related to «Lipids»

1
Adipocyte Lipid Accumulation Inhibition by Compound A
Not available on PMC !

Example 8

Human subcutaneous pre-adipocytes (Zenbio (RTP, NC, U.S.A.)) were received pre-plated in white-walled 96-well plates. A schematic description of the protocol used for examining the effects of Compound A on lipid accumulation in differentiating human adipocytes is shown in FIG. 9. Upon arrival of cells (Day 1) 150 μL media in the wells was replaced with adipocyte differentiation media (Zenbio (RTP, NC, U.S.A.)). The following day media was replaced as described for Day 1. Media was subsequently replaced as described every two to three days. On Day 6, 150 μL of the adipocyte differentiation media was replaced with vehicle (0.1% DMSO), or the SCD1 inhibitors at the concentrations indicated. After two days (Day 8) 150 μL media was replaced with 150 μL adipocyte maintenance media containing vehicle (0.1% DMSO) or the SCD1 inhibitors at the concentrations indicated as described above. Following a further four days of incubation (Day 12) cells were stained with AdipoRed™ (Lonza Bioscience (Walkersville, MD, U.S.A.)) according to the manufacturer's instructions. Cytotoxicity following incubation of adipocytes with Compound A was determined in separate wells, not used for Adipored™ staining, and was measured using CellTiter-Glo® (Promega (Madison, WI)) according to the manufacturer's instructions. Following a 10 min room temperature incubation the luminescence measured as relative light units (RLU) was determined in a luminescent plate reader. For adipocytes treated with concentrations of 1.2-100 nM Compound A for 6 days, cell viability as determined by RLU following CellTiter-Glo® remained greater than 75% of the value obtained with vehicle-treated adipocytes. The RLU dropped to 72% of vehicle in adipocytes treated with 1 μM Compound A (data not shown). These findings indicate that the decrease in lipid accumulation in the differentiating primary human adipocytes following Compound A treatment is not associated with cytotoxicity at least up to 100 nM Compound A.

Calculation of the IC50 for inhibition of triglyceride accumulation in human adipocytes was determined by non-linear regression analysis of the RFU, using a variable slope, 4-parameter fit (GraphPad PRISM®). FIG. 10 Shows the reduction in lipid accumulation following treatment of differentiating primary human adipocytes with 100 nM Compound A and analogs Compounds B, D, E, G and H for six days. FIG. 11 shows a representative study comparing the concentration-dependent reduction in lipid accumulation with Compounds A, G and H. Compound D was tested at 5 μM only. The relative IC50 values for Compound A, G and H in this study were 9.3 nM, 24.2 nM and 56 nM respectively.

US11865113B2. Methods of treating a patient afflicted with non-alcoholic steatohepatitis (NASH) (2024-01-09). Lipidio Pharmaceuticals Inc. [US]. Inventors: Nigel R. A. Beeley [US], J. Gordon Foulkes [US], Kieran George Mooney [US], Charles Rodney Greenaway Evans [GB], Keith Arthur Johnson [US], Howard G. Welgus [US], Celia P. Jenkinson [US].
Full text: Click here
Patent 2024
Adipocytes Cells Cell Survival Cytotoxin Homo sapiens inhibitors Light Lipids Luminescence prisma Promega Psychological Inhibition Sulfoxide, Dimethyl Triglycerides
2
Immunogenicity of Betacoronavirus mRNA Vaccines
Not available on PMC !

Example 20

The instant study is designed to test the immunogenicity in rabbits of candidate betacoronavirus (e.g., MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1 or a combination thereof) vaccines comprising a mRNA polynucleotide encoding the spike (S) protein, the S1 subunit (S1) of the spike protein, or the S2 subunit (S2) of the spike protein obtained from a betacoronavirus (e.g., MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1).

Rabbits are vaccinated on week 0 and 3 via intravenous (IV), intramuscular (IM), or intradermal (ID) routes. One group remains unvaccinated and one is administered inactivated betacoronavirus. Serum is collected from each rabbit on weeks 1, 3 (pre-dose) and 5. Individual bleeds are tested for anti-S, anti-S1 or anti-S2 activity via a virus neutralization assay from all three time points, and pooled samples from week 5 only are tested by Western blot using inactivated betacoronavirus (e.g., inactivated MERS-CoV, SARS-CoV, HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-NL, HCoV-NH or HCoV-HKU1).

In experiments where a lipid nanoparticle (LNP) formulation is used, the formulation may include a cationic lipid, non-cationic lipid, PEG lipid and structural lipid in the ratios 50:10:1.5:38.5. The cationic lipid is DLin-KC2-DMA (50 mol %) or DLin-MC3-DMA (50 mol %), the non-cationic lipid is DSPC (10 mol %), the PEG lipid is PEG-DOMG (1.5 mol %) and the structural lipid is cholesterol (38.5 mol %), for example.

US11872278B2. Combination HMPV/RSV RNA vaccines (2024-01-16). ModernaTX, Inc. [US]. Inventors: Giuseppe Ciaramella [US], Sunny Himansu [US].
Full text: Click here
Patent 2024
Antigens Betacoronavirus Biological Assay Cations Cholesterol Coronavirus 229E, Human Coronavirus OC43, Human Hemorrhage Human coronavirus HKU1 Lipid Nanoparticles Lipids Middle East Respiratory Syndrome Coronavirus M protein, multiple myeloma NL63, Human Coronavirus Oryctolagus cuniculus Polynucleotides Protein Subunits Rabbits RNA, Messenger Serum Severe acute respiratory syndrome-related coronavirus spike protein, SARS-CoV-2 Vaccines Virus Physiological Phenomena
3
CRISPR-Cas9 RNA Editing in iPSCs
Not available on PMC !

Example 2

iPS cells were prepared according to protocols known in the art and seeded in a Geltrex®-Matrix coated 12-well culture dish. Transfection was performed in iPSCs with 3 ul of Lipofectamine® 2000 or 3 ul of Lipofectamine® 3000 as indicated and according to manufacturer's instructions, to deliver a GeneArt® CRISPR Nuclease vector targeting the HPRT locus. Transfection was also performed with GeneArt® CRISPR Nuclease RNA editing system targeting the HPRT locus and 3 ul of Formulation 21 lipid aggregate complex. RNA editing system utilizes a Cas9 mRNA, which was prepared via in vitro transcription with the Ambion® mMESSAGE mMACHINE® Kit, and a gRNA which was transcribed using the Ambion® MEGAshortscript™ Kit. Cells were harvested 72-hours post-transfection and cleavage efficiency was determined using the GeneArt® Genomic Cleavage Detection Kit.

Results are shown in FIG. 2A and FIG. 2B, which clearly demonstrate that using an mRNA based form of Cas9 with a guide-RNA for gene editing with the lipid aggregates described herein for transfection results in at least 4-fold more targeted cleavage of the host cell genome when compared to standard DNA based editing approaches.

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, the descriptions and examples should not be construed as limiting the scope of the invention. The disclosures of all patent and scientific literature cited herein are expressly incorporated in their entirety by reference.

US11872285B2. Compositions and methods for efficient delivery of molecules to cells (2024-01-16). LIFE TECHNOLOGIES CORPORATION [US]. Inventors: Xavier de Mollerat du Jeu [US], Nektaria Andronikou [US].
Full text: Click here
Patent 2024
Cells Cloning Vectors Clustered Regularly Interspaced Short Palindromic Repeats Cytokinesis Genes Genome Hyperostosis, Diffuse Idiopathic Skeletal Induced Pluripotent Stem Cells Lipids Lipofectamine lipofectamine 2000 RNA, Messenger Transcription, Genetic Transfection
4
Optimizing Dendrimer Nanoparticles for siRNA Delivery
Not available on PMC !

Example 8

To evaluate which lipid composition within the dendrimer nanoparticles lead to improved siRNA delivery, the identity and concentration of different phospholipids and PEG-lipids were varied. Three different cell lines (HeLa-Luc, A549-Luc, and MDA-MB231-Luc) were used. The cells were present at 10K cells per well and a 24 hour incubation. The readout was determined 24 hours post transfection. In the nanoparticles, DSPC and DOPE were used as phospholipids and PEG-DSPE, PEG-DMG, and PEG-DHD were used as PEG-lipids. The compositions contain a lipid or dendrimer:cholesterol:phospholipid:PEG-lipid mole ratio of 50:38:10:2. The mole ratio of lipid/dendrimer to siRNA was 100:1 with 100 ng dose being used. The RiboGreen, Cell-titer Fluor, and OneGlo assays were used to determine the effectiveness of these compositions. Results show the relative luciferase activity in HeLa-Luc cells (FIG. 17A), A549-Luc (FIG. 17B), and MDA-MB231-Luc (FIG. 17C). The six formulations used in the studies include: dendrimer (lipid)+cholesterol+DSPC+PEG-DSPE (formulation 1), dendrimer (lipid)+cholesterol+DOPE+PEG-DSPE (formulation 2), dendrimer (lipid)+cholesterol+DSPC+PEG-DMG (formulation 3), dendrimer (lipid)+cholesterol+DOPE+PEG-DMG (formulation 4), dendrimer (lipid)+cholesterol+DSPC+PEG-DSPE (formulation 5), and dendrimer (lipid)+cholesterol+DOPE+PEG-DHD (formulation 6).

Further experiments were run to determine which phospholipids showed the increased delivery of siRNA molecules. A HeLa-Luc cell line was used with 10K cells per well, 24 hour incubation, and readout 24 hours post transfections. The compositions contained either DOPE or DOPC as the phospholipid with PEG-DHD as the PEG-lipid. The ratio of lipid (or dendrimer):cholesterol:phospholipid:PEG-lipid was 50:38:10:2 in a mole ratio with the mole ratio of dendrimer (or lipid) to siRNA of 200:1. These compositions was tested at a 50 ng dose using the Cell-titer Fluor and OneGlo assays. These results are shown in FIGS. 18A & 18B.

US11858884B2. Compositions and methods for modulating gene or gene product in cells (2024-01-02). The Board of Regents of The University of Texas System [US]. Inventors: Daniel J. Siegwart [US], Kejin Zhou [US].
Full text: Click here
Patent 2024
1,2-oleoylphosphatidylcholine Biological Assay Cell Lines Cells Cholesterol Dendrimers Figs HeLa Cells Lipid Nanoparticles Lipids Luciferases Nevus Obstetric Delivery Phospholipids polyethylene glycol-distearoylphosphatidylethanolamine RNA, Small Interfering Transfection
5
Efficient Antibody Generation via mRNA Immunization
Not available on PMC !

Example 4

An overview of the immunization strategies for lectin-binding proteins, such as galectin-3, is shown in Table 18.

BALB/c mice were immunized with 2 mg/kg mRNA, complexed with LNPs, or 20 μg recombinant protein as indicated in Table 18. Plasma anti-galectin-3 IgG titers were assayed 7 days after the final boost, which was delivered at day 55.

FIG. 3 shows that the use of galectin-3 mRNA as a final boosting agent resulted in a significantly higher target-specific IgG titer than when purified recombinant protein (a traditional immunogen) was used. This effect was observed regardless of whether the antigens were delivered subcutaneously or intravenously.

Hybridomas producing galectin-3-specific antibodies were generated, and high affinity monoclonal anti-galectin-3 antibodies were obtained from further screens.

TABLE 18
Priming ImmunizationBoostFinal Boost
(Day 0)(Day 7)(Day 55)
mRNA (I.V.)mRNA (I.V.)mRNA (I.V.)
mRNA (I.V.)mRNA (I.V.)Recombinant protein
(I.V.)
mRNA (S.C.)mRNA (S.C.)mRNA (S.C.)
mRNA (S.C.)mRNA (S.C.)Recombinant protein
(S.C.)
Summary of the Hit Rates Attainable by mRNA-Mediated Immunization

Table 19 provides a target protein-specific summary of the total number of hybridoma wells (generally about one third (⅓) of these wells contain hybridomas) screened and the number of confirmed target-specific antibodies obtained from those hybridomas wells following the use of lipid-encapsulated mRNA as an immunogen.

Table 20 provides a comparison of mRNA-LNP immunization methods with other conventional methods of immunization by number of hybridomas producing target-specific antibodies. In general, these data suggest that mRNA-LNP immunization is an effective method for inducing an immune response to a target protein antigen and for obtaining a higher number/rate of target protein-specific antibodies. In particular, these results confirm that mRNA-LNP immunization is surprisingly more effective than conventional immunization methods for obtaining antibodies specific for transmembrane proteins, e.g., multi-pass transmembrane proteins, such as GPCRs, which are difficult to raise antibodies against, and for poorly immunogenic proteins (e.g., proteins which produce low or no detectable target-specific IgGs in plasma of animals immunized with traditional antigen).

TABLE 19
Number of
Number ofhybridomas
hybridomaproducing
Proteinwellstarget-specific
targetType of proteinscreenedantibodies
RXFP1Multi-pass Transmembrane20240207
protein/GPCR
SLC52A2Multi-pass Transmembrane12880228
protein
ANGPTL8Soluble protein22816542
TSHRTransmembraneTBD130
protein/GPCR
APJTransmembrane22080230
protein/GPCR
GP130Single-pass Transmembrane23920614
protein

TABLE 20
Method of immunization and number of hybridomas producing
target-specific antibodies
Whole Virus-likeProtein/
ProteinType ofmRNA-cellsparticlesCDNApeptide
targetproteinLNP1onlyonlyonlyonly
RXFP1GPCR/20766NDNDND
multi-pass
SLC52A2multi-228NSTNSTNDNST
pass
TSHRGPCR/130NDND 42413
multi-pass
APJGPCR/230 94621 ND
multi-pass
1Immunization with mRNA-LNP alone or in combination with another antigen format (e.g., protein/peptide).
2Sanders et al. 2002 Thyroid stimulating monoclonal antibodies Thyroid 12(12): 1043-1050.
3Oda et al. 2000. Epitope analysis of the human thyrotropin (TSH) receptor using monoclonal antibodies. Thyroid 10(12): 1051-1059.
ND—Not determined; antigen format not tested
NST—No specific titers detected. Because no target-specific IgG titers were detectable in plasma, hybridoma generation was not initiated on these groups.

In general, successful generation of hybridomas producing antigen-specific antibodies have been achieved for at least 15 different targets utilizing mRNA-LNP immunization methods as exemplified herein. These results show that the mRNA immunization methods described herein are capable of eliciting an immune response against a wide range of antigens (e.g., transmembrane proteins, for example multi-pass transmembrane proteins, such as GPCRs) in host animals, and are effective methods for producing high affinity monoclonal antibodies, which can serve as parentals for generation of chimeric variants, humanized variants, and affinity matured variants.

US11878060B2. mRNA-mediated immunization methods (2024-01-23). NOVARTIS AG [CH]. Inventors: John Dominy [US], Robert Dunn [US], Scott Glaser [US], Mark Keating [US], Carla Klattenhoff [US], Igor Splawski [US].
Full text: Click here
Patent 2024
Animals anti-IgG Antibodies Antigens Binding Proteins Cells Chimera DNA, Complementary Epitopes Galectin 3 Histocompatibility Antigens Class II Homo sapiens Hybridomas Integral Membrane Proteins Lectin Lipids Mice, Inbred BALB C Monoclonal Antibodies Parent Peptides Plasma Proteins Protein Targeting, Cellular Recombinant Proteins Response, Immune RNA, Messenger Soluble Glycoprotein 130 Thyroid Gland Thyrotropin Thyrotropin Receptor Vaccination Viral Proteins

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More about "Lipids"

Lipids are a diverse class of organic molecules that play vital roles in various biological processes.
These fat-soluble compounds encompass a wide range of substances, including triglycerides (fats and oils), phospholipids, sterols (such as cholesterol), and waxes.
Lipids are essential for energy storage, cell membrane structure, and signaling pathways, making them crucial for understanding and maintaining human health.
Researching lipids is essential, as imbalances in lipid metabolism can contribute to the development of various conditions, including heart disease, diabetes, and neurological disorders.
The RNeasy Lipid Tissue Mini Kit and RNeasy Lipid Tissue Kit are valuable tools for extracting and purifying RNA from lipid-rich tissues, enabling researchers to study the molecular aspects of lipid-related processes.
The Oil Red O staining technique is commonly used to visualize and quantify lipid droplets, which are important for energy storage and cellular signaling.
Cholesterol, a type of sterol lipid, plays a vital role in maintaining cell membrane structure and function, and is also a precursor for the synthesis of steroid hormones.
The Mini-extruder is a device used in lipid research to create uniform liposomes, which are spherical vesicles composed of phospholipids.
Chloroform is a commonly used solvent in lipid extraction and purification processes, while Fetal Bovine Serum (FBS) is often used as a growth supplement in cell culture media, providing lipids and other essential nutrients.
Lipofectamine 2000 is a transfection reagent that can be used to deliver genetic material, such as DNA or RNA, into cells, enabling the study of lipid-related gene expression and signaling pathways.
The phospholipid 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) is a commonly used component in liposome formation and lipid bilayer studies.
The fluorescent dye BODIPY 493/503 is a valuable tool for visualizing and quantifying lipid droplets in cells, allowing researchers to study lipid metabolism and storage dynamics.
By leveraging these tools and techniques, researchers can advance the field of lipid research and uncover new insights into the role of lipids in human health and disease.