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Plasma protein Z

Plasma Protein Z is a vitamin K-dependent plasma glycoprotein that plays a crucial role in the regulation of blood coagulation.
It acts as an anticoagulant by inhibiting the activated form of factor X, a key enzyme in the clotting cascade.
Plasma Protein Z is synthesized in the liver and circulates in the bloodstream, where it helps maintain the delicate balance between pro- and anti-coagulant factors.
Alterations in Plasma Protein Z levels have been associated with an increased risk of thrombotic disorders, such as deep vein thrombosis and ischemic stroke.
Understanding the complex interplay of Plasma Protein Z and its influence on hemostasis is essential for developing targeted therapies and improving patient outcomes.
Leveraging the powerful tools of PubCompare.ai can help researchers optimize their Plasma Protein Z studies, enhance reproducibility, and uncover valuable insights to advance this critical area of hemostasis research.

Most cited protocols related to «Plasma protein Z»

1
DIA Mass Spectrometry Protocol for Peptide Analysis
Cited 100 times
The digested peptides were analysed on a nanoAcquity (Waters) (running as 5µl/min microflow LC) coupled to a TripleTOF 6600 (Sciex). 2 µg of the protein digest was injected and the peptides were separated with a 23-minute (yeast), 21-minute (plasma) or 19-minute (K562) non-linear gradient starting with 4% acetonitrile/0.1 % formic acid and increasing to 36% acetonitrile/0.1% formic acid. A Waters HSS T3 column (150mm x 300µm, 1.8µm particles) was used. The DIA method consisted of an MS1 scan from m/z 400 to m/z 1250 (50ms accumulation time) and 40 MS2 scans (35ms accumulation time) with variable precursor isolation width covering the mass range from m/z 400 to m/z 1250.
The library generation with “gas-phase fractionation” was performed using the same LC-MS/MS setup as mentioned above. The peptides were separated with a 120 minute (plasma samples) and 45 minute (yeast samples) linear gradient (3% acetonitrile/0.1% formic acid to 60% acetonitrile/0.1 formic acid). Repetitive injections were performed to cover the following scan ranges: m/z 400 – 500, m/z 495 – 600, m/z 595 – 700, m/z 695 – 800, m/z 795 – 900, m/z 895 – 1000, m/z 995 – 1100, m/z 1095 – 1250 (yeast) and m/z 400 – 500, m/z 500 – 600, m/z 600 – 700, m/z 700 – 800, m/z 800– 900, m/z 900 – 1000, m/z 1000 – 1250 (plasma). The precursor selection windows were m/z 4 (m/z 1 overlap) for all acquisitions except the yeast m/z 1095 – 1250, for which m/z 5 (m/z 1 overlap) windows were used. For the plasma acquisitions, each acquisition cycle was split into two subcycles with the second subcycle having the isolation windows shifted by m/z 1.5.
Demichev V., Messner C.B., Vernardis S.I., Lilley K.S, & Ralser M. (2019). DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput. Nature methods, 17(1), 41-44.
Publication 2019
acetonitrile cDNA Library formic acid Fractionation, Chemical isolation Peptides Plasma Proteins Radionuclide Imaging Tandem Mass Spectrometry Yeast, Dried
2
Leveraging PubCompare Database for Metabolite Profiling
Cited 38 times
The apLCMS package utilizes the database in R data frame format. Key components of the database include feature ID, possible chemical identity, m/z (theoretical value for known metabolites or mean observed value for unidentified historically observed features, standard deviation, range), retention time (mean, standard deviation, range), log intensity (mean, standard deviation, range), and the frequency the feature is detected in historical data (Supporting Figure 1). Features derived from known metabolites may not have retention time and intensity information available, while features from historical data may not have their chemical identities available.
Various methods can be used to store the database. Simple ones include tab-delimited text files and directly saving the data table in R binary format, both of which can be done in a single line of command. We illustrate them on the apLCMS website. More complex methods include using the R interface to relational databases, such as the RMySQL package available from CRAN. The database itself may store more information than apLCMS utilizes, and only necessary information needs to be extracted for use in apLCMS.
In the current study, we merged metabolites from HMDB 29 (link) and features found from the example dataset of the apLCMS package 9 (link), 20 (link). The data was generated using anion exchange column with formic acid gradient combined electrospray ionization (ESI) 30 (link). Briefly, human plasma samples were collected, and protein precipitation was performed by adding acetonitrile (2:1, v/v). Analyte was separated using the Hamilton PRPX-110S (2.1×10 cm) anion exchange column together with a short, end-capped C18 pre-column (Higgins Analytical Targa guard) for desalting and optimal separation. A formic acid gradient was used. Solution A was made of 0.1% (v/v) formic acid in a 1:1 water:acetonitrile mix. Solution B was made of 1% (v/v) formic acid in a 1:1 water:acetonitrile mix. Mass spectrometry data was collected using either a Thermo LTQ-FT mass spectrometer or a Thermo Orbitrap-Velos mass spectrometer (Thermo Fisher, San Diego, CA) using and m/z range of 85 to 850. For more details please refer to Johnson et al30 (link).
Some metabolites have the same chemical composition, hence identical m/z values. For a group of metabolites sharing m/z, a single entry was created, which contains several possible chemical identities. When new information becomes available to separate those metabolites, the single entry can be split into multiple entries. To unravel such groups needs techniques such as LC-MS/MS, which is out of the scope of this study.
At the 10 ppm tolerance level, we merged known metabolites with features found in the example dataset. For HMDB metabolites, we only considered one ion form [M + H]+, because ESI mostly generate protonated molecular ions. The choice of ion form is highly dependent on the experimental platform. Users can incorporate other ion forms, as well as isotopic forms of the known metabolites by generating entries based on the neutral mass of the metabolites and creating their own database. Detailed format of the database is explained in the manual on the apLCMS website. Although other ion forms are ignored, they can be incorporated into the database if they are found in real data. From features found in the example dataset, we only incorporated reliable ones into the database by requiring a feature to be present in at least 50% of the profiles.
Yu T., Park Y., Li S, & Jones D.P. (2013). Hybrid feature detection and information accumulation using high-resolution LC- MS metabolomics data. Journal of proteome research, 12(3), 1419-1427.
Publication 2013
acetonitrile Anions chemical composition formic acid Homo sapiens Immune Tolerance Isotopes Mass Spectrometry Plasma Proteins Reading Frames Retention (Psychology) Tandem Mass Spectrometry
3
DIA Mass Spectrometry Protocol for Peptide Analysis
Cited 30 times
The digested peptides were analysed on a nanoAcquity (Waters) (running as 5µl/min microflow LC) coupled to a TripleTOF 6600 (Sciex). 2 µg of the protein digest was injected and the peptides were separated with a 23-minute (yeast), 21-minute (plasma) or 19-minute (K562) non-linear gradient starting with 4% acetonitrile/0.1 % formic acid and increasing to 36% acetonitrile/0.1% formic acid. A Waters HSS T3 column (150mm x 300µm, 1.8µm particles) was used. The DIA method consisted of an MS1 scan from m/z 400 to m/z 1250 (50ms accumulation time) and 40 MS2 scans (35ms accumulation time) with variable precursor isolation width covering the mass range from m/z 400 to m/z 1250.
The library generation with “gas-phase fractionation” was performed using the same LC-MS/MS setup as mentioned above. The peptides were separated with a 120 minute (plasma samples) and 45 minute (yeast samples) linear gradient (3% acetonitrile/0.1% formic acid to 60% acetonitrile/0.1 formic acid). Repetitive injections were performed to cover the following scan ranges: m/z 400 – 500, m/z 495 – 600, m/z 595 – 700, m/z 695 – 800, m/z 795 – 900, m/z 895 – 1000, m/z 995 – 1100, m/z 1095 – 1250 (yeast) and m/z 400 – 500, m/z 500 – 600, m/z 600 – 700, m/z 700 – 800, m/z 800– 900, m/z 900 – 1000, m/z 1000 – 1250 (plasma). The precursor selection windows were m/z 4 (m/z 1 overlap) for all acquisitions except the yeast m/z 1095 – 1250, for which m/z 5 (m/z 1 overlap) windows were used. For the plasma acquisitions, each acquisition cycle was split into two subcycles with the second subcycle having the isolation windows shifted by m/z 1.5.
Demichev V., Messner C.B., Vernardis S.I., Lilley K.S, & Ralser M. (2019). DIA-NN: Neural networks and interference correction enable deep proteome coverage in high throughput. Nature methods, 17(1), 41-44.
Publication 2019
acetonitrile cDNA Library formic acid Fractionation, Chemical isolation Peptides Plasma Proteins Radionuclide Imaging Tandem Mass Spectrometry Yeast, Dried
4
Plasma Metabolomics via LC-MS Profiling
Cited 30 times
Plasma samples were collected using EDTA tubes in the WHI and PREDIMED cohorts and processed immediately. All WHI specimens were stored in a −70° freezer within 2 hours of collection or stored at −20° for up to 2 days and shipped on dry ice and stored at −70° until processing. The majority of the WHI samples had been thawed once prior to our study, with 7 samples (6 cases and 1 control) having been thawed twice. PREDIMED samples were shipped at −4°, and then stored at −70° until analysis. Metabolomic measurements were made using four complimentary LC-MS methods resulting in 371 metabolites for the WHI samples. Only the HILIC-positive and C8-positive methods were available in the PREDIMED samples. For each method, pooled plasma reference samples were included every 20 samples and results were standardized using the ratio of the value of the sample to the value of the nearest pooled reference multiplied by the median of all reference values for the metabolite.
HILIC analyses of water soluble metabolites in the positive ionization mode (HILIC-pos) were conducted using an LC-MS system comprised of a Shimadzu Nexera X2 U-HPLC (Shimadzu Corp.; Marlborough, MA) coupled to a Q Exactive hybrid quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific; Waltham, MA). Plasma samples (10 µL) were prepared via protein precipitation with the addition of nine volumes of 74.9:24.9:0.2 v/v/v acetonitrile/methanol/formic acid containing stable isotope-labeled internal standards (valine-d8, Sigma-Aldrich; St. Louis, MO; and phenylalanine-d8, Cambridge Isotope Laboratories; Andover, MA). The samples were centrifuged (10 min, 9,000 × g, 4°C), and the supernatants were injected directly onto a 150 × 2 mm, 3 µm Atlantis HILIC column (Waters; Milford, MA). The column was eluted isocratically at a flow rate of 250 µL/min with 5% mobile phase A (10 mM ammonium formate and 0.1% formic acid in water) for 0.5 minute followed by a linear gradient to 40% mobile phase B (acetonitrile with 0.1% formic acid) over 10 minutes. MS analyses were carried out using electrospray ionization in the positive ion mode using full scan analysis over 70–800 m/z at 70,000 resolution and 3 Hz data acquisition rate. Other MS settings were: sheath gas 40, sweep gas 2, spray voltage 3.5 kV, capillary temperature 350°C, S-lens RF 40, heater temperature 300°C, microscans 1, automatic gain control target 1e6, and maximum ion time 250 ms.
HILIC analyses of water soluble metabolites in the negative ionization mode (HILIC-neg) were conducted using an LC-MS system comprised of an AQUITY UPLC system (Waters; Milford, MA and a 5500 QTRAP mass spectrometer (SCIEX; Framingham, MA). Plasma samples (30 µL) were prepared via protein precipitation with the addition of four volumes of 80% methanol containing inosine-15N4, thymine-d4 and glycocholate-d4 internal standards (Cambridge Isotope Laboratories; Andover, MA). The samples were centrifuged (10 min, 9,000 × g, 4°C), and the supernatants were injected directly onto a 150 × 2.0 mm Luna NH2 column (Phenomenex; Torrance, CA). The column was eluted at a flow rate of 400 µL/min with initial conditions of 10% mobile phase A (20 mM ammonium acetate and 20 mM ammonium hydroxide in water) and 90% mobile phase B (10 mM ammonium hydroxide in 75:25 v/v acetonitrile/methanol) followed by a 10 min linear gradient to 100% mobile phase A. MS analyses were carried out using electrospray ionization and selective multiple reaction monitoring scans in the negative ion mode. To create the method, declustering potentials and collision energies were optimized for each metabolite by infusion of reference standards. The ion spray voltage was −4.5 kV and the source temperature was 500°C.
Positive ion mode analyses of polar and non-polar plasma lipids (C8-pos) were conducted using an LC-MS system comprised of a Shimadzu Nexera X2 U-HPLC (Shimadzu Corp.; Marlborough, MA) coupled to a Exactive Plus orbitrap mass spectrometer (Thermo Fisher Scientific; Waltham, MA). Plasma samples (10 µL) were extracted for lipid analyses using 190 µL of isopropanol containing 1,2-didodecanoyl-sn-glycero-3-phosphocholine (Avanti Polar Lipids; Alabaster, AL). After centrifugation, supernatants were injected directly onto a 100 × 2.1 mm, 1.7 µm ACQUITY BEH C8 column (Waters; Milford, MA). The column was eluted isocratically with 80% mobile phase A (95:5:0.1 vol/vol/vol 10mM ammonium acetate/methanol/formic acid) for 1 minute followed by a linear gradient to 80% mobile-phase B (99.9:0.1 vol/vol methanol/formic acid) over 2 minutes, a linear gradient to 100% mobile phase B over 7 minutes, then 3 minutes at 100% mobile-phase B. MS analyses were carried out using electrospray ionization in the positive ion mode using full scan analysis over 200–1000 m/z at 70,000 resolution and 3 Hz data acquisition rate. Other MS settings were: sheath gas 50, in source CID 5 eV, sweep gas 5, spray voltage 3 kV, capillary temperature 300°C, S-lens RF 60, heater temperature 300°C, microscans 1, automatic gain control target 1e6, and maximum ion time 100 ms. Lipid identities were determined based on comparison to reference plasma extracts and were denoted by total number of carbons in the lipid acyl chain(s) and total number of double bonds in the lipid acyl chain(s).
Negative ion mode analyses of free fatty acids and bile acids (C18-neg) were conducted using an LC-MS system comprised of a Shimadzu Nexera X2 U-HPLC (Shimadzu Corp.; Marlborough, MA) coupled to a Q Exactive hybrid quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific; Waltham, MA). Plasma samples (30 µL) were extracted using 90 uL of methanol containing PGE2-d4 (Cayman Chemical Co.; Ann Arbor, MI) and centrifuged (10 min, 9,000 × g, 4°C). The samples were injected onto a 150 × 2 mm ACQUITY T3 column (Waters; Milford, MA). The column was eluted isocratically at a flow rate of 400 µL/min with 60% mobile phase A (0.1% formic acid in water) for 4 minutes followed by a linear gradient to 100% mobile phase B (acetonitrile with 0.1% formic acid) over 8 minutes. MS analyses were carried out in the negative ion mode using electrospray ionization, full scan MS acquisition over 200–550 m/z, and a resolution setting of 70,000. Metabolite identities were confirmed using authentic reference standards. Other MS settings were: sheath gas 45, sweep gas 5, spray voltage −3.5 kV, capillary temperature 320°C, S-lens RF 60, heater temperature 300°C, microscans 1, automatic gain control target 1e6, and maximum ion time 250 ms.
Raw data from Q Exactive/Exactive Plus instruments were processed using TraceFinder software (Thermo Fisher Scientific; Waltham, MA) and Progenesis QI (Nonlinear Dynamics; Newcastle upon Tyne, UK) while MultiQuant (SCIEX; Framingham, MA) was used to process 5500 QTRAP data. For each method, metabolite identities were confirmed using authentic reference standards or reference samples. CVs were calculated using pooled plasma samples from the first 800 WHI-OS participants.
Paynter N.P., Balasubramanian R., Giulianini F., Wang D.D., Tinker L.F., Gopal S., Deik A.A., Bullock K., Pierce K.A., Scott J., Martínez-González M.A., Estruch R., Manson J.E., Cook N.R., Albert C.M., Clish C.B, & Rexrode K.M. (2018). Metabolic Predictors of Incident Coronary Heart Disease in Women. Circulation, 137(8), 841-853.
Publication 2018
5
Affinity Capture of Protein-SOMAmer Complexes
Cited 23 times
Not all complexes are able to be captured in the “Catch-3” step of the affinity capture assay (Figure 5), and therefore analyzed by PAGE, because the 3′ end of the SOMAmer is sometimes involved in its structure or interaction with the target. Additional affinity capture examples for the subset of the CKD-related targets whose complexes can be captured on “Catch-3” beads are shown in Figure 5.
50% plasma samples were prepared by diluting ethylene diamine tetraacetic acid (EDTA)-plasma 2X in SB18T with 2 µM Z-Block_2 (the modified nucleotide sequence (AC-BnBn)7-AC). The plasma spike samples were prepared by diluting 500 ng protein with the 50% plasma in SB17T (SB18T with 1 mM EDTA) with 4-(2-Aminoethyl) benzenesulfonyl fluoride hydrochloride (AEBSF) and ethylene glycol tetraacetic acid (EGTA). The plasma samples were prepared by diluting the 50% plasma in SB17T with AEBSF and EGTA. The buffer spike samples were prepared by diluting 500 ng protein in SB17T with AEBSF and EGTA. These samples were combined with 10 pmoles of SOMAmer to give final concentrations of 10% plasma, 2 mM AEBSF, 0.5 mM EGTA, and 100 nM SOMAmer. Complexes were formed by incubating at 37°C for 45 minutes. 50 µL of a 20% slurry of Streptavidin agarose beads (ThermoFisher Scientific) was added to each sample and shaken for 10 minutes at room temperature. The samples were added to a MultiScreen HV Plate to perform washes under vacuum filtration. Each sample was washed one time quickly with 200 µL of SB17T, one time for one minute with 200 µL of 100 µM biotin in SB17T with shaking, one time with 200 µL of SB17T for one minute with shaking, and one time with 200 µL of SB17T for nine minutes with shaking. Proteins in the sample were labeled with both biotin and a fluorophore by incubating each sample in 100 µL of 1 mM EZ Link NHS-PEO4-biotin (Pierce), 0.25 mM NHS-Alexa-647 (Invitrogen) in SB17T for five minutes with shaking. Each sample was washed one time with 200 µL of 20 mM glycine in SB17T and five times with 200 µL of SB17T, shaking each wash for one minute. The final wash was removed using centrifugation at 1000 relative centrifugal force (RCF) for 30 seconds. The beads were resuspended with 100 µL of SB17T. SOMAmers (complexed and free) were released from the beads by exposure under a BlackRay light source (UVP XX-Series Bench Lamps, 365 nm) for ten minutes with shaking. The samples were spun out of the plate by centrifugation at 1000 RCF for 30 seconds. 10 µL of each sample was removed and reserved as “Catch-1 eluate” for SDS-PAGE analysis. The remainder of the samples was captured through the biotinylated proteins by adding 20 µL of a 20% slurry of monomeric Avidin beads and shaking for ten minutes. The beads were transferred to a MultiScreen HV Plate and washed four times with 100 µL of SB17T for one minute with shaking. The final wash was removed using centrifugation at 1000 RCF for 30 seconds. Proteins were eluted from the beads by incubating each sample with 100 µL of 2 mM biotin in SB17T for five minutes with shaking. Each eluate was transferred to 0.4 mg MyOne Streptavidin beads with a bound biotinylated-primer complementary to the 3′ fixed region of the SOMAmer. The samples were incubated for five minutes with shaking to anneal the bead-bound fixed region to the SOMAmer complexes. Each sample was washed two times with 100 µL of 1XSB17T for one minute each with shaking and one time with 100 µL of 1XSB19T (5 mM HEPES, 100 mM NaCl, 5 mM KCl, 5 mM MgCl2, 1 mM EDTA, 0.05% Tween-20, pH 7.5) for one minute with shaking, all by magnetic separation. The complexes were eluted by incubating with 45 µL of 20 mM NaOH for two minutes with shaking. 40 µL of each eluate was added to 10 µL of 80 mM HCl with 0.05% Tween-20 in a new plate. 10 µL of each sample was removed and reserved as “Catch-2 aptamer-bound eluate” for SDS-PAGE analysis. Gel samples were run on NuPAGE 4–12% Bis Tris Glycine gels (Invitrogen) under reducing and denaturing conditions according to the manufacturer's directions. Gels were imaged on an Alpha Innotech FluorChem Q scanner in the Cy5 channel to image the proteins.
Gold L., Ayers D., Bertino J., Bock C., Bock A., Brody E.N., Carter J., Dalby A.B., Eaton B.E., Fitzwater T., Flather D., Forbes A., Foreman T., Fowler C., Gawande B., Goss M., Gunn M., Gupta S., Halladay D., Heil J., Heilig J., Hicke B., Husar G., Janjic N., Jarvis T., Jennings S., Katilius E., Keeney T.R., Kim N., Koch T.H., Kraemer S., Kroiss L., Le N., Levine D., Lindsey W., Lollo B., Mayfield W., Mehan M., Mehler R., Nelson S.K., Nelson M., Nieuwlandt D., Nikrad M., Ochsner U., Ostroff R.M., Otis M., Parker T., Pietrasiewicz S., Resnicow D.I., Rohloff J., Sanders G., Sattin S., Schneider D., Singer B., Stanton M., Sterkel A., Stewart A., Stratford S., Vaught J.D., Vrkljan M., Walker J.J., Watrobka M., Waugh S., Weiss A., Wilcox S.K., Wolfson A., Wolk S.K., Zhang C, & Zichi D. (2010). Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery. PLoS ONE, 5(12), e15004.
Publication 2010

Most recents protocols related to «Plasma protein Z»

1
Genetic Prediction Models for Alzheimer's Disease
The genome and plasma proteome data of European descendants included in the INTERVAL study (subcohort 1 and subcohort 2) was used to establish and validate protein genetic prediction models. Detailed information about the INTERVAL study dataset has been described elsewhere [16 (link)]. In brief, participants were aged 18–80 and were generally in good health. The SOMAscan assay was used to measure the relative concentrations of 3620 plasma proteins or protein complexes. Quality control (QC) was performed at the sample and SOMAmer level. After excluding eight non-human protein targets, a total of 3283 SOMAmers remained for further study. DNA was used to assay ~ 830,000 variants on the Affymetrix Axiom UK Biobank genotyping array. Standard sample and variant QC was conducted, as described in the original publication [16 (link)]. SNPs were further phased using SHAPEIT3 and imputed using a combined 1000 Genomes Phase 3-UK10K reference panel via the Sanger Imputation Server, resulting in over 87 million imputed variants. Such SNPs were filtered using criteria of (1) imputation quality of at least 0.7, (2) minor allele frequency (MAF) of at least 5%, (3) Hardy–Weinberg equilibrium (HWE) p ≥ 5 × 10−6, (4) missing rates < 5%, and (5) presenting in the 1000 Genome Project data for European populations. In total, there were 4,662,360 variants passing these criteria.
In subcohort 1 (N = 2481), protein levels were log transformed and adjusted for age, sex, duration between blood draw and processing, and the first three principal components of ancestry. For the rank-inverse normalized residuals of each protein of interest, we followed the TWAS/FUSION framework [17 (link)] to develop genetic prediction models, using nearby SNPs (within 100 kb) of potentially associated SNPs as potential predictors. A false discovery rate (FDR) < 0.05 and P-value ≤ 5 × 10−8 were used to determine potentially associated SNPs in cis- and trans- regions, respectively. We defined cis-region as a region within 1 Mb of the transcriptional start site (TSS) of the gene encoding the target protein of interest. Subsequently, we extracted all SNPs located within 100 kb of the aforementioned potentially associated SNPs to serve as potential predictors for establishing protein prediction models, excluding any ambiguous SNPs. In order to include potential predictors from both cis and trans regions, we converted all the chromosome numbers to Z and combined them as a single pseudo chromosome. Four methods, namely, best linear unbiased predictor, elastic net, LASSO, and top1, were used for establishing the models. For developed protein prediction models with prediction performance (R2) of at least 0.01 [15 (link), 18 (link)–23 (link)], which is a common threshold used in relevant studies, we further conducted external validation using subcohort 2 (N = 820) data. In brief, we generated predicted expression levels by applying the established protein prediction models to the genetic data, and then compared the predicted v.s. measured levels of each protein of interest. We selected proteins with a model prediction R2 of ≥ 0.01 in subcohort 1 and a correlation coefficient of ≥ 0.1 in subcohort 2 for the downstream association analysis.
To assess the associations between genetically predicted circulating protein levels and AD risk, we applied the validated protein prediction models to the summary statistics from a large GWAS meta-analysis of AD risk [24 (link)]. Instead of using the conventional approach of including clinically diagnosed AD alone, this GWAS combined clinically confirmed and parental diagnoses based by-proxy phenotypes, which has been demonstrated to confer great value in substantially increasing statistical power [25 (link)]. In brief, this study included a total of 85,934 cases (39,106 clinically diagnosed AD and 46,828 proxy AD) and 401,577 controls of European ancestry, which were obtained from various sources including The European Alzheimer & Dementia Biobank dataset (EADB), GR@ACE/DEGESCO study, The Rotterdam Study (RS1 and RS2), European Alzheimer’s Disease Initiative (EADI) Consortium, Genetic and Environmental Risk in AD (GERAD) Consortium/Defining Genetic, Polygenic, and Environmental Risk for Alzheimer’s Disease (PERADES) Consortium, The Norwegian DemGene Network, The Neocodex–Murcia study (NxC), The Copenhagen City Heart Study (CCHS), Bonn studies, and UK Biobank. Detailed information on study participants as well as genotyping and imputation methods for the samples from each of the included study can be found in the supplementary files of the original GWAS paper [24 (link)]. Risk estimates for the single marker association analyses were adjusted for sex, batch (if applicable), age (if applicable), and top principal components (PCs).
The TWAS/FUSION framework was used to determine the protein-AD associations, by leveraging correlation information between SNPs included in the prediction models from the phase 3, 1000 Genomes Project data of European ancestry [17 (link)]. We calculated the PWAS test statistic Z-score = w'Z/(w'Σs,sw)1/2, where the Z is a vector of standardized effect sizes of SNPs for a given protein (Wald z-scores), w is a vector of prediction weights for the abundance feature of the protein being tested, and the Σs,s is the LD matrix of the SNPs estimated from the 1000 Genomes Project as the LD reference panel. The Bonferroni correction P-value < 0.05 was used to determine significant associations between genetically predicted protein concentrations and AD risk.
Ingenuity Pathway Analysis (IPA, Ingenuity System Inc, USA)) and Protein–Protein Interaction analysis via STRING database (version 12.0) with 0.400 confidence level [26 (link)] was implemented to cluster and classify enriched pathways for the identified proteins using default interaction resources, including Textmining, Experiments, Databases, Co-expression, Neighborhood, Gene Fusion, and Co-occurrence. We also investigated potentially repositionable drugs targeting the genes encoding associated proteins, by using the GREP (Genome for REPositioning drugs) tool [27 (link)]. We further conducted molecular docking analysis considering ATP1A1 protein as the drug target protein and almitrine and ciclopirox as the drug agents [28 ].
Zhu J., Liu S., Walker K.A., Zhong H., Ghoneim D.H., Zhang Z., Surendran P., Fahle S., Butterworth A., Alam M.A., Deng H.W., Wu C, & Wu L. (2024). Associations between genetically predicted plasma protein levels and Alzheimer’s disease risk: a study using genetic prediction models. Alzheimer's Research & Therapy, 16, 8.
Publication 2024
2
Quantifying TUDCA in Diaphragm and Plasma
Diaphragms and plasma were collected from vehicle and TUDCA treated mice (WT and SEPN1 KO), and muscle tissue were homogenized in RIPA lysis solution (1:10, w/v). After the addition of the naproxen (internal standard, IS) to the homogenate or plasma, the samples were mixed with 1% acetic acid in cold methanol (1:10, v/v) and centrifuged for protein precipitation. Then, supernatants were dried under nitrogen and the residues were re-suspended and injected into the HPLC-MS/MS system (HPLC Alliance 2695 - Micromass Quattro micro API triple quadrupole, Waters). Separation was done following a gradient elution (mobile phase A, 0.1% CH3COOH in ammonium acetate 25mM; mobile phase B, 0.1% CH3COOH in MeOH) on a Gemini C18 column (Phenomenex Inc) with and mass spectrometric analysis was done with a triple quadrupole mass spectrometer in negative ion mode and multiple reaction monitoring (MRM) mode, measuring the fragmentation products of the deprotonated pseudo-molecular ions (quantitation ion transitions: TUDCA, m/z 498.4 → m/z 124.1; IS, m/z 229.3 → m/z 170.0). Diaphragm and plasma samples of treated mice were run and analyzed in parallel with calibration curves linear in the range 0.1–100 μg/g and 0.02–20 μg/mL respectively.
Germani S., Van Ho A.T., Cherubini A., Varone E., Chernorudskiy A., Renna G.M., Fumagalli S., Gobbi M., Lucchetti J., Bolis M., Guarrera L., Craparotta I., Rastelli G., Piccoli G., de Napoli C., Nogara L., Poggio E., Brini M., Cattaneo A., Bachi A., Simmen T., Calì T., Quijano-Roy S., Boncompagni S., Blaauw B., Ferreiro A, & Zito E. (2024). SEPN1-related myopathy depends on the oxidoreductase ERO1A and is druggable with the chemical chaperone TUDCA. Cell Reports Medicine, 5(3), 101439.
Publication 2024
3
Mendelian Randomization Analysis of Protein and Gene Expression
For the exposure–outcome pairs that showed evidence of an MR association at pFDR < 0.05, we conducted follow-up MR analysis using genetic instruments of circulating plasma protein levels (pQTLs) and brain gene expression levels (eQTLs) where available. The genetic associations with plasma protein levels were obtained from genome-wide association studies conducted in a cohort of 35,559 Icelandic individuals [9 (link)]. Genetic associations with gene expression levels in the cortex, hippocampus, and spinal cord were obtained from a meta-analysis of 14 cohorts, consisting of up to 2683, 168, and 108 European ancestry individuals, respectively [21 (link)].
Instrument selection was done as described above; i.e., variants associated with protein abundance or expression at p < 5 × 10−8 within ±1 Mb of the coding gene, clumped at r2 < 0.01, were selected as instruments. To account for multiple testing, the pFDR values were calculated separately for the plasma protein and tissue-specific analyses. MR analyses were performed in the same manner as described for the primary analysis. MR estimates are reported per 1-SD increase for both circulating plasma protein-binding aptamer RFUs (effect sizes were calculated after inverse-normal rank transformations) and brain gene expression (effect sizes were calculated from z-scores assuming that var(y) = 1) and were considered statistically significant at pFDR < 0.05.
de La Harpe R., Zagkos L., Gill D., Cronjé H.T, & Karhunen V. (2024). Cerebrospinal and Brain Proteins Implicated in Neuropsychiatric and Risk Factor Traits: Evidence from Mendelian Randomization. Biomedicines, 12(2), 327.
Publication 2024
4
Aging Proteome Trajectory Analysis
Statistical analyses of differences in replicative lifespan and molecular and cellular markers of senescence were via two-tailed unpaired t-test. To analyze the changes in protein levels throughout the aging process, the plasma protein levels were standardized using z-scores. Locally estimated scatterplot smoothing (LOESS) regression was then applied to each plasma factor to estimate their trajectories. To identify groups of proteins with similar trajectory patterns, pairwise differences between LOESS estimates were computed using Euclidean distance. Hierarchical clustering was performed using the complete method to cluster the proteins based on their trajectory patterns. To detect the effect of age on protein abundance a linear model was fitted in R, using the function “lm(age ~ protein expression)”. Statistical significance was subjected to multiple testing correction by using “p.adjust(p-value, method=”BH”)” function in R with an FDR cutoff of p<0.05.
Wang X., Tazearslan C., Kim S., Guo Q., Contreras D., Yang J., Hudgins A.D, & Suh Y. (2024). In vitro heterochronic parabiosis identifies pigment epithelium-derived factor as a systemic mediator of rejuvenation by young blood. bioRxiv.
Publication Preprint 2024
5
Plasma Protein Associations with Alzheimer's
Prior to analysis, we transformed the plasma protein levels and AD‐related endophenotype values by z‐score normalization using the “normalize()” function in the R som package (v0.3‐5.1). We then determined the associations between the normalized protein levels and clinical phenotypes (ie, AD or MCI vs CN), adjusting for age, sex, history of cardiovascular disease (ie, heart disease, hypertension, diabetes mellitus, and hyperlipidemia), and BMI using the following linear regression model:
Normalizedproteinlevel∼β1AD+β2MCI+β3Age+β4Sex+β5CVD+β6BMI+ε, where β is the weighted coefficient for the corresponding factors and ε is the intercept of the linear equation. Similarly, the associations between normalized protein levels and AD‐related endophenotypes were determined using the following linear regression model:
Normalizedproteinlevel∼β1ADrelatedendophenotype+β2Age+β3Sex+β4CVD+β5BMI+ε.
We considered plasma proteins with a false discovery rate (FDR)‐adjusted p‐value of <0.05 as being significantly associated with AD or AD‐related endophenotypes.
Jiang Y., Uhm H., Ip F.C., Ouyang L., Lo R.M., Cheng E.Y., Cao X., Tan C.M., Law B.C., Ortiz‐Romero P., Puig‐Pijoan A., Fernández‐Lebrero A., Contador J., Mok K.Y., Hardy J., Kwok T.C., Mok V.C., Suárez‐Calvet M., Zetterberg H., Fu A.K, & Ip N.Y. (2024). A blood‐based multi‐pathway biomarker assay for early detection and staging of Alzheimer's disease across ethnic groups. Alzheimer's & Dementia, 20(3), 2000-2015.
Publication 2024

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More about "Plasma protein Z"

Plasma Protein Z (PPZ) is a key player in the regulation of blood coagulation.
This vitamin K-dependent plasma glycoprotein acts as an anticoagulant by inhibiting the activated form of factor X, a crucial enzyme in the clotting cascade.
Synthesized in the liver, PPZ circulates in the bloodstream, helping to maintain the delicate balance between pro- and anti-coagulant factors.
Alterations in PPZ levels have been linked to an increased risk of thrombotic disorders, such as deep vein thrombosis and ischemic stroke.
Understanding the complex interplay of PPZ and its influence on hemostasis is essential for developing targeted therapies and improving patient outcomes.
Researchers can leverage the powerful tools of PubCompare.ai to optimize their PPZ studies.
This AI-driven platform helps locate the best protocols and products from literature, pre-prints, and patents, allowing researchers to enhance reproducibility and accuracy in their studies.
For example, techniques like Acquity UPLC, UV-2600 spectrophotometry, and confocal microscopy (e.g., LSM 510 META) can be used to analyze PPZ levels and interactions.
Key subtopics in PPZ research include the role of PPZ in blood coagulation, its association with thrombotic disorders, and the development of PPZ-targeted therapies.
Researchers may also explore the use of various reagents and instruments, such as FBS, Z-Gly-Gly-Arg-AMC, Prism 6, V-PLEX Neuroinflammation Panel 1 Human Kit, Formic acid, and Q Exactive HF mass spectrometer, to further understand the complexities of PPZ and its impact on hemostasis.
By leveraging the insights and tools provided by PubCompare.ai, researchers can elevate their PPZ studies, uncover valuable insights, and advance this critical area of hemostasis research, ultimately leading to improved patient outcomes.