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Membrane Transport Proteins

Membrane Transport Proteins are a class of integral membrane proteins responsible for the selective movement of substances across biological membranes.
They play crucial roles in cellular processes, such as the transport of ions, nutrients, signaling molecules, and waste products.
These proteins are involved in a wide range of physiological functions, including ion homeostasis, neurotransmission, and energy metabolism.
Undertsanding the structure, function, and regulation of Membrane Transport Proteins is essential for developing therapeutic interventions for various diseases, such as neurological disorders, metabolic conditions, and cancer.
This MeSH term provides a comprehensive overview of this important family of proteins and their significance in biomedical research.

Most cited protocols related to «Membrane Transport Proteins»

Factors Limiting Bacterial Division Rate

Cited 88 times

To show the power and usefulness of BioNumbers we address a specific thought experiment: What limits the maximal rate at which a bacterium can divide? That is, why does E. coli under ideal conditions of LB medium and 37°C divide every ∼20 min (BNID 100260) and not every ∼2 min? Clearly the ability to divide at faster rates would provide an overwhelming selective advantage, at least in laboratory conditions. There are many cellular processes that could potentially limit E. coli to a ∼20 min doubling time. But for most such processes, it seems possible for the bacterium to overcome the limitation by increasing the amount of the limiting factor, for instance by increasing the number of nutrient transporters, the number of DNA replication circles, or the number of RNA polymerase complexes. But ribosomes are an interesting partial exception to this rule. Ribosomes translate all the proteins in the cell including those that are assembled into new ribosomes. Doubling ribosome content would necessitate translating twice the number of ribosomal proteins. Here then is a potentially limiting rate: the time that it takes a ribosome to translate enough amino acids to copy itself (4 ). We demonstrate the use of the BioNumbers database with a brief analysis of these considerations. An E. coli ribosome contains in total ∼7500 amino acids (7459, Search term: ‘ribosome’, BNID101175) and the translation rate is as high as ∼21 aa/sec (Search term: ‘translation ribosome’, BNID100059). Translating a single copy of all of the ribosomal proteins thus minimally requires ∼7500/21 ≈ 400 sec ≈ 7 min. In order to make a new cell of the same size, each ribosome must make a copy of itself. Taking into account essential translational cofactors like the elongation factors EF-Tu and EF-G would increase the required time to ∼9 min. It therefore seems impossible to obtain a cellular doubling time faster than ∼9 min. Perhaps when further requirements for ribosome duplication are taken into account, it will be evident why E. coli double in ∼20 min. We thus see that with simple calculations and with several useful biological numbers on hand, we can generate an intriguing hypothesis for what sets a lower bound on the proliferation rate of E. coli.

Milo R., Jorgensen P., Moran U., Weber G, & Springer M. (2009). BioNumbers—the database of key numbers in molecular and cell biology. Nucleic Acids Research, 38(Database issue), D750-D753.

Publication 2009

Amino Acids Bacteria Biopharmaceuticals Cells Culture Media, Conditioned DNA-Directed RNA Polymerase DNA Replication EEF1A1 protein, human Escherichia coli Membrane Transport Proteins Nutrients Physiology, Cell Protein Biosynthesis Proteins ribosomal A-protein Ribosomal Proteins Ribosomes

Comprehensive Drug-Target Interaction Dataset

Cited 54 times

Dataset for drugs and targets with known pharmacological interactions were extracted from DrugBank database (http://drugbank.ca/, accessed on June 1st 2011), which so far contains 6707 drug entries including 1436 FDA-approved small molecule drugs, 134 FDA-approved biotech (protein/peptide) drugs, 83 nutraceuticals and 5086 experimental drugs. Additionally, 4228 non-redundant protein (i.e. drug target/enzyme/transporter/carrier) sequences are also potentially linked to these entries. To confirm the quality of this data set, we have carefully compared this database with other databases such as STITCH, SuperTarget and KEGG database, as well as the literature [22] (link), [23] (link). In the process of building dataset, some drugs and targets (such as nitric oxide and ribosomal protein Thx) were omitted since their chemical descriptors cannot be calculated (details are provided in Supporting Information S1). As a result, a dataset including 6511 drugs and 3987 targets was applied in this work as the benchmark dataset (detailed information of these drugs and targets was given in Supporting Information S2 and S3).

Yu H., Chen J., Xu X., Li Y., Zhao H., Fang Y., Li X., Zhou W., Wang W, & Wang Y. (2012). A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data. PLoS ONE, 7(5), e37608.

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Publication 2012

Drug Delivery Systems Enzymes Investigational New Drugs Membrane Transport Proteins Nutraceuticals Oxide, Nitric Peptides Pharmaceutical Preparations Proteins Ribosomal Proteins Synapsin I

Identifying Protein Domains in Fungal SM Clusters

Cited 23 times

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Publication 2010

Anabolism Enzymes Genes Genome Gliotoxin Melanins Membrane Transport Proteins Polyketides Protein Domain pseurotin Siderophores Transcription, Genetic Vertebral Column

Standardizing Pharmacogenomic Terminology via Delphi Survey

Cited 21 times

The Delphi survey technique is an established approach for seeking expert consensus on a given topic.^{8 -10 (link)} The method uses a series of repeated structured questionnaires, or “rounds.” The rounds are usually anonymous and provide written, systematic refinement of expert opinion, where feedback of group opinion is provided after each round.¹¹ Delphi survey technique guidelines proposed by Hasson et al. were consulted in the design of the project.^{12 (link)} The St. Jude Children’s Research Hospital’s Institutional Review Board (IRB) determined that this project does not meet the definition of research and was exempt for IRB purview.
For the Delphi method used (Figure 1), CPIC solicited pharmacogenetic experts by email invitation to members of CPIC, PGRN, pharmacogenetic-related working groups for the Clinical Genome Resource (ClinGen; https://www.clinicalgenome.org), Institute of Medicine (IOM) DIGITizE Action Collaborative (http://iom.nationalacademies.org/Activities/Research/GenomicBasedResearch/Innovation-Collaboratives/EHR.aspx), Centers for Disease Control and Prevention PGx nomenclature workgroup,¹³ Global Alliance for Genomics and Health (GA4GH; http://ga4gh.org), ACMG (https://www.acmg.net), Electronic Medical Records and Genomics (eMERGE; https://emerge.mc.vanderbilt.edu/), the CHAMP online resource for AMP members (http://champ.amp.org), and the College of American Pathologists (CAP). In addition, experts not included in the above groups were solicited by posting a description of the project on the PharmGKB website. All individuals who volunteered were included in survey 1
Individuals were invited to participate in a series of surveys using an internet-based survey tool (SurveyMonkey Inc, Palo Alto, CA; http://www.surveymonkey.com), supplemented with multiple live webinars that were used to explain the survey and solicit feedback. The webinars were designed to facilitate understanding of the survey to encourage completion; however, towards the end of the process an additional webinar was used to assist in developing consensus. Each survey also included questions regarding the expert’s workplace setting and degree of pharmacogenetic expertise (i.e., role in clinical pharmacogenetics, time devoted to pharmacogenetics). Responses were included in the analysis if the respondent provided their name and contact information, which were necessary to enable follow up with the respondent for the subsequent round (trainees were not excluded). Responses were tabulated as numeric counts and frequencies for each phase to determine whether consensus was reached. Analyses were also performed to determine if there were differences in responses based on the expert’s role in clinical pharmacogenetics. These analyses tested clinician versus non-clinician responses using Chi squared tests with an alpha of 0.05 to ensure the final set of terms would be likely to be adopted by clinicians as well as laboratory based researchers. All analyses were conducted in R version 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org).
The goal of this project was to standardize terms used to characterize: 1) allele functional status (i.e. allele descriptive terms), and 2) inferred phenotypes based on the combined impact of both alleles (i.e., diplotypes). The terms used in the initial survey were identified by querying genetic testing laboratories and reviewing literature for currently used terms for CPIC Level A genes (https://cpicpgx.org/genes-drugs/). This was informed by a literature review of references in the CPIC guidelines’ evidence tables and the terms used in these papers to describe allele function and clinical phenotypes for genes with current CPIC guidelines (i.e., CYP2D6, CYP2C19, CYP3A5, CYP2C9, TPMT, DPYD, HLA-B, UGT1A1, SLCO1B1, and VKORC1) (Supplemental Figures S1-S4). We also queried genetic testing laboratories listed at https://www.genetests.org/laboratories/ and translational software companies and created a list of terms currently being used in laboratory reports.
For the first two survey rounds (survey 1 and survey 2), terms that were found acceptable by at least 70% of the experts were retained for use in the next round. To improve semantic consistency, terms that were retained after survey 1 were assembled into value sets, which together described the range of possible descriptors of alleles or phenotypes. These value sets were evaluated in surveys 2 through 4 and the top value sets were retained until 70% consensus was reached. For survey 1 and 2, genes that encode enzymes with similar metabolic function were combined where appropriate (e.g., DPYD and TPMT were combined as were all the CYP enzymes excluding CYP3A5) and experts were given the opportunity to suggest alternative terms. In survey 1, experts were also asked the number of categories of function/phenotype they felt were needed (e.g., three major categories for TPMT - high/normal, medium/some, no activity - versus five major categories for CYP enzymes). To promote consensus, a summary of comments from previous surveys was provided and experts were asked to read the comments prior to answering the questions (https://cpicpgx.org/resources/term-standardization/). These comments were emphasized during the webinars to promote thoughtful discussion. Experts also had access to the full survey results. Of note, experts from surveys 1 and 2 commented in the survey and during webinar discussions that the standardized terms should be consistent across all pharmacogenes if possible. Based on this feedback and feedback from CPIC members, three categories of value sets were proposed and grouped together in survey 3: 1) drug metabolizing enzymes (all CYP enzymes, UGT1A1, DPYD, and TPMT), 2) drug transporters (e.g., SLCO1B1) and non-drug metabolizing enzymes (e.g., VKORC1), and 3) high-risk genotypes (e.g., HLA-B). These groupings were used for the remainder of the surveys. Because consensus was not reached after survey 4, experts were invited to a conference call to discuss and recommend final terms, which included weighing the potential disruptive impact of adopting a new term for clinical laboratories versus any anticipated benefit of adopting a new term. These recommended terms were included in survey 5.
While there is not a universal definition of consensus for the Delphi method, 70% has been recommended and it was considered a reasonable threshold given our diverse group of experts.^{14 ,15} Several new terms were added to survey 3 based on the feedback from rounds 1 and 2; these terms were built from existing terms and were included to improve semantic uniformity within a value set (Supplemental Figures S1-S4). The final survey (survey 5) measured the level of acceptance of the final sets of terms. Results from each round were posted on PharmGKB (https://cpicpgx.org/resources/term-standardization/) and were available to respondents throughout the process.

Caudle K.E., Dunnenberger H.M., Freimuth R.R., Peterson J.F., Burlison J.D., Whirl-Carrillo M., Scott S.A., Rehm H.L., Williams M.S., Klein T.E., Relling M.V, & Hoffman J.M. (2016). Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genetics in Medicine, 19(2), 215-223.

Publication 2016

Alleles Child Clinical Laboratory Services Conferences CYP2C19 protein, human CYP3A5 protein, human Cytochrome P-450 CYP2D6 Enzymes Ethics Committees, Research Feelings Genes Genome Genotype HLA-B Antigens Membrane Transport Proteins Pathologists Pharmaceutical Preparations Phenotype Protein Biosynthesis Thinking TPMT protein, human UGT1A1 protein, human

Comprehensive Drug Target Database

Cited 19 times

This tier incorporated the targets of approved drugs and drugs in clinical development. Proteins that are targets of approved small molecule and biotherapeutic drugs were identified using manually curated efficacy target information from release 17 of the ChEMBL database (61 (link)). An efficacy target was defined as the target for the intended drug indication as opposed to any other potential targets for which the drug shows high affinity binding. Where binding site information was available in ChEMBL, a non-drug-binding subunit of a protein complex was assigned to Tier 3, whereas the drug-binding subunit was included in Tier 1. Drugs in clinical development were identified from a number of sources: investor pipeline information from a number of large pharmaceutical companies [including Pfizer, Roche, GlaxoSmithKline, Novartis (oncology only), AstraZeneca, Sanofi, Lilly, Merck, Bayer, and Johnson & Johnson – accessed June-August 2013] monoclonal antibody candidates and USAN applications from the ChEMBL database (release 17), and drugs in active clinical trials from clinicaltrials.gov (62 ). Targets for these drug candidates were assigned from company pipeline information and scientific literature, where available. Where no reported target information could be found, a potential target was assigned through analysis of bioactivity data in ChEMBL, with the target having the highest dose-response measurement ≤ 100 nM for the compound being assigned. All other human targets having an IC50/EC50/GI50/XC50/AC50/Kd/Ki/potency ≤100 nM for an approved drug or USAN compound were also included in Tier 1. Genes involved in ADME/drug disposition (phase I and II metabolic enzymes, transporters, and modifiers) were identified from the PharmaADME.org extended set (63 ).

Finan C., Gaulton A., Kruger F.A., Lumbers R.T., Shah T., Engmann J., Galver L., Kelley R., Karlsson A., Santos R., Overington J.P., Hingorani A.D, & Casas J.P. (2017). The druggable genome and support for target identification and validation in drug development. Science translational medicine, 9(383), eaag1166.

Publication 2017

Binding Proteins Binding Sites Drug Delivery Systems Enzymes Genes Homo sapiens Membrane Transport Proteins Monoclonal Antibodies Neoplasms Pharmaceutical Preparations Proteins Protein Subunits

Most recents protocols related to «Membrane Transport Proteins»

Validation of Differentially Expressed Genes in Chaetoceros socialis

Six DEGs were validated through a real-time qPCR analysis (Table S5). Three DEGs were randomly chosen, in addition to the most downregulated high-affinity nitrate transporter (NTR2:6) and one NADH-nitrate reductase, which are related to nitrate uptake, and a silicon efflux transporter (LSI3) related to the deposition of silicon in spore valves. Two genotyped strains of C. socialis, namely APC12 and MCA6 were used for this purpose: the former strain is the one used for the transcriptome experiment, while MCA6 is a freshly established strain isolated at station LTER-MC in the Gulf of Naples and for which the D1–D3 region of the nuclear-encoded large subunit ribosomal DNA (partial 28S rDNA) has been sequenced as in [70 ] to confirm its identity.
Triplicate cultures of both strains were maintained in control and low N media, with the same nutrient concentrations used for the RNA-seq experiment. Cells were harvested on day 2 in the control, when the percentage of spores was zero, and on day 3 in the treatments, when the percentage of spores was ~ 33 and ~ 38% for APC12 and MCA6, respectively, corresponding to the ones recorded at T3 of the transcriptome experiment. RNA extraction and purification were performed as illustrated above. Total RNA was reverse-transcribed using the QuantiTect® Reverse Transcription Kit (Qiagen, Venlo, Limburgo, Nederlands).
RTqPCR amplification was performed with cDNA diluted 1:10, in a 10 µl reaction containing each primer at a final concentration of 1 µM and Fast SYBR Green Master mix with ROX (Applied Biosystems) using a ViiA™ 7 Real-Time PCR System (Applied Biosystems by Life Technologies, Carlsbad, CA, USA) and the following cycling parameters: 95 °C for 20 s, 40 cycles at 95 °C for 1 s, 60 °C for 20 s, 95 °C for 15 s, 60 °C 1 min, and a gradient from 60 °C to 95 °C for 15 min. Raw results were processed using the ViiA™ 7 Software and exported into Microsoft Excel for further analyses. The reference gene used was the tubulin gamma chain (TUB G) designed using sequence information from the transcriptome and the software Primer3Plus v.2.4.2 ([71 (link)]). The sequences for the forward and reverse primers are 5’- TGCAGAGTTTGGTCGATGAG -3’and 5’-GGAAGCCAAAGAGTCTGCTG-3’, respectively, yielding a PCR product of 197 bp (Table S5). Primers for all other tested DEGs were designed using the same approach. log2(FC)s were obtained with the Relative Expression Software Tool-Multiple Condition Solver (REST-MCS) ([72 (link)]). A pairwise fixed reallocation randomisation test has been used to identify statistically significant results (P ≤ 0.05).

Pelusi A., Ambrosino L., Miralto M., Chiusano M.L., Rogato A., Ferrante M.I, & Montresor M. (2023). Gene expression during the formation of resting spores induced by nitrogen starvation in the marine diatom Chaetoceros socialis. BMC Genomics, 24, 106.

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Publication 2023

Cells DNA, Complementary DNA, Ribosomal Fast Green Gamma Rays Genes Membrane Transport Proteins NADH-Nitrate Reductase Nitrates Nitrate Transporter Nutrients Oligonucleotide Primers Reverse Transcription Ribosome Subunits, Large RNA-Seq Silicon Spores Strains Transcriptome Tubulin

Phylogenetic Validation of Mitochondrial Proteins

To confirm the taxonomic identity of the putative mitochondrial-related protein identified in P. canceri and eliminate the possibility of residual contamination, maximum-likelihood phylogenetic trees were constructed (supplementary fig. S5, Supplementary Material online). Except for the mitochondrial ABC transporter gene (atm1), the phylogenetic analysis workflow was performed as follows. All mitochondrial-related proteins identified in P. canceri were queried against the NCBI nr database (August, 2020) with BLAST v.2.1.9 (Altschul et al. 1990 (link)) using the BLASTP algorithm. The top 5,000 hits with an e-value less than 1e-10 (or 1e-5 if few hits were identified) were retrieved and clustered at 90% identity with CD-HIT v.4.8.1 (Edgar 2010 (link)). The predicted proteomes of M. mackini and C. pagurus were searched with BLASTP to retrieve homologous proteins. Lastly, a reciprocal BLASTP in all P. canceri predicted proteoms was performed. The sequences were aligned (Mafft v.7.407 (Katoh and Standley 2013 (link)), mafft-auto). The alignments were trimmed of ambiguous sites with (trimAL v.1.4.1 (Capella-Gutierrez et al. 2009 (link)), -automated1). The amino acid substitution model was determined with IQ-TREE2.1.6.5 using the default settings (Kalyaanamoorthy et al. 2017 (link)). Phylogenies and 1,000 ultrafast bootstrap trees with 1,000 SH-aLRT replicates were constructed with IQ-TREE2 v.1.6.5 (Minh et al. 2013 (link)). These initial phylogenies were visualized in FigTree v.1.4.4 and manually pruned to reduce the number of taxa. The reduced data set was aligned (Mafft v.7.407 (Katoh and Standley 2013 (link)), mafft-linsi). Removal of ambiguous sites, evaluation of amino acid substitution models, and phylogenetic reconstruction proceeded as above. For the putative atm1 transporter, a Hidden Markov Model profile for orthologous group KOG0057 (retrieved from EggNOG 5.0.0 (Huerta-Cepas et al. 2019 (link)) database) was used to retrieve the protein models of P. canceri and M. mackini using the default settings of with hmmsearch. The resulting hits were used as queries against the NCBI nr database (August 2020) as described above. This dataset was supplemented with atm1 sequences reported previously (Freibert et al. 2017 (link)). The proteins were aligned with hmmalign from HMMER v.3.2.1 (http://hmmer.org/) and the Atm1 phylogeny was performed as described above.

Onuț-Brännström I., Stairs C.W., Campos K.I., Thorén M.H., Ettema T.J., Keeling P.J., Bass D, & Burki F. (2023). A Mitosome With Distinct Metabolism in the Uncultured Protist Parasite Paramikrocytos canceri (Rhizaria, Ascetosporea). Genome Biology and Evolution, 15(3), evad022.

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Publication 2023

Amino Acid Substitution ATP-Binding Cassette Transporters FCER2 protein, human Genes, Mitochondrial Membrane Transport Proteins Mitochondrial Proteins Pagurus Proteins Proteome Trees

Comprehensive Transcriptome Analysis Workflow

To convert the transcript sequences to the orthologues, BLASTX with E-value ≤ 10⁻⁵ was applied against the Arabidopsis Information Resource (TAIR). The functional enrichment analysis of modules was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID) [40 (link)] for categories of Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was also carried out in the web-based DAVID [40 (link)]. P-value < 0.01 was considered to be significant; moreover, the identification and classification of TFs, TRs, and PKs were carried out through applying the transcript sequences to BLASTX search against the iTAK database [41 (link)]. To identify transporters, BLASTX was carried out on transcript sequences against the transporter classification database (TCDB) with E-value ≤ 10⁻²⁰ [42 (link)]. Wu et al. (2012) identified 2660 mlncRNAs candidates, which were considered as an emerging class of regulators, using a computational mlncRNA identification pipeline in D. purpurea [43 (link)]. After the creation of the mlncRNAs-derived local database through CLC Genomics Workbench 11.0, the searching procedure was carried out for all of the transcripts in the significant major modules using BLASTN with a cut-off E-value ≤ 10⁻⁵ to uncover important mlncRNAs.

Amiri F., Moghadam A., Tahmasebi A, & Niazi A. (2023). Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea. PLOS ONE, 18(3), e0277293.

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Publication 2023

Arabidopsis Biological Processes Cellular Structures Genes Genome Membrane Transport Proteins

Engineered Antibody Constructs for Targeting Protein Aggregates

The four bivalent monoclonal IgG
antibodies and proteins used in the experiments were designed, expressed,
and purified according to earlier published work.^{6 (link),21 (link)} The
RmAb158 monoclonal antibodies selectively bind to Aβ protofibrils,^{22 (link)} whereas the RmAb2G7 monoclonal antibodies selectively
bind to high-mobility group box 1 (HGMB1) proteins.^{23 (link)} In short, the heavy and light chain scFv8D3 transferrin
receptor transporter variable region sequence^{20 (link)} was connected to the C-terminus of the RmAb2G7 or RmAb158 light
chain with in-house designed linkers (APGSYTGSAPG or APGSGTGSAPG,
respectively). Figure 2 shows the cartoon representations of the antibody design, showing
the location of conjugated scFv8D3 in the modified antibodies.
The four recombinant antibodies were expressed
using Expi293 cells
(Thermo Fisher) transiently transfected with pcDNA3.4 vectors using
polyethyleneimine (PEI) as the transfection reagent. All antibodies
were purified on a protein G column (Cytiva) and eluted with an increasing
gradient of 0.7% acetic acid. The buffer was exchanged for phosphate-buffered
saline (PBS) (Gibco) immediately after elution, and the protein concentration
was determined at A280.

Morrison J.I., Petrovic A., Metzendorf N.G., Rofo F., Yilmaz C.U., Stenler S., Laudon H, & Hultqvist G. (2023). Standardized Preclinical In Vitro Blood–Brain Barrier Mouse Assay Validates Endocytosis-Dependent Antibody Transcytosis Using Transferrin-Receptor-Mediated Pathways. Molecular Pharmaceutics, 20(3), 1564-1576.

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Publication 2023

Acetic Acid Antibodies Buffers Cells Cloning Vectors G-substrate HMGB1 Protein Immunoglobulins Membrane Transport Proteins Monoclonal Antibodies Phosphates Proteins Staphylococcal Protein A TNFSF14 protein, human Transfection

Yeast-based Mg2+ Transporter Assay

The Mg²⁺ translocation ability of each candidate gene protein was examined by a yeast complementation assay. The yeast mutant CM66, which lacks plasma membrane Mg²⁺ transporters ALR1 and ALR2, was used (Li et al., 2001 (link)). The open reading frames of all candidate genes were amplified from the full-length cDNA of rice cv. Nipponbare, and the primer sequences were shown in Supplementary Table S3. Each candidate gene was ligated into a pYES2 vector with correct direction.
Empty vector pYES2 and candidate genes vectors were introduced into CM66 yeast cells, respectively, according to the manufacturer’s protocol (Yeast Transformation Kit; Beijing Kulaibo Technology Co. Ltd, China), and transformants were selected on synthetic dextrose medium without uracil (SD-U). Positive clones were cultured in SD-U liquid medium until the early logarithmic phase, concentrated and washed three times with sterile distilled water. After sequential 10-fold dilution, 8 μL of the cell suspension were spotted on SD-U plates containing 1, 4, 64 mmol/L MgCl₂, respectively. The plates were incubated at 30°C for 3 d before the growth phenotypes were evaluated.
The growth of CM66 yeast strain transformed with various plasmids in liquid SD-U media containing Mg²⁺ was determined. Overnight yeast cells were prepared and the optical density (OD) at 600 nm was adjusted to 0.5 with sterile distilled water. Then, 20 μL of cell suspensions was added to 20 mL liquid SD-U media containing 4, 64, 128 mmol/L MgCl₂ in each bottle. The OD values at 600 nm were determined at indicated time.

Zhi S., Zou W., Li J., Meng L., Liu J., Chen J, & Ye G. (2023). Mapping QTLs and gene validation studies for Mg2+ uptake and translocation using a MAGIC population in rice. Frontiers in Plant Science, 14, 1131064.

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Publication 2023

Biological Assay Cells Clone Cells Cloning Vectors Culture Media DNA, Complementary Gene Products, Protein Genes Genetic Vectors Glucose Magnesium Chloride Membrane Transport Proteins Oligonucleotide Primers Open Reading Frames Oryza sativa Phenotype Plasma Plasma Membrane Plasmids Protein Translocation Saccharomyces cerevisiae Sterility, Reproductive Strains Technique, Dilution Translocation, Chromosomal Uracil Vision

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More about "Membrane Transport Proteins"

Membrane transport proteins, also known as integral membrane proteins or transmembrane proteins, are a crucial class of biomolecules responsible for the selective movement of substances across biological membranes.
These proteins play pivotal roles in a wide range of cellular processes, including ion homeostasis, neurotransmission, energy metabolism, and the transport of nutrients, signaling molecules, and waste products.
Understanding the structure, function, and regulation of membrane transport proteins is essential for developing effective therapeutic interventions for various diseases, such as neurological disorders, metabolic conditions, and cancer.
Researchers studying these proteins often utilize techniques like gene expression analysis, protein purification, and in vitro transport assays to elucidate their mechanisms and interactions.
Common experimental tools employed in membrane transport protein research include TRIzol reagent for RNA extraction, RNeasy Mini Kit for purification, FBS for cell culture, High-Capacity cDNA Reverse Transcription Kit for cDNA synthesis, Transporter 5 Transfection Reagent or Lipofectamine 2000 for gene delivery, and Verapamil as a model calcium channel blocker.
Bioinformatic tools like Prism 6 and PrimeScript RT reagent kit can also aid in data analysis and interpretation.
Expanding our knowledge of membrane transport proteins is crucial for advancing our understanding of cellular physiology and paving the way for novel therapeutic strategies targeting these crucial biomolecules.
Whether you're investigating ion channels, nutrient transporters, or drug efflux pumps, a comprehensive understanding of membrane transport proteins can unlock new insights and drive progress in the field of biomedical research.