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Classifying potassium ion channels from sequence

Classifying potassium ion channels from sequence
Classifying potassium ion channels from sequence

CLASSIFYING POTASSIUM CHANNELS FROM SEQUENCE

ASHOK PALANIAPPAN a, b AND ERIC G. JAKOBSSON a, b, c, d, e*

a Center for Biophysics and Computational Biology,

b Beckman Institute,

c Departments of Biochemistry an

d d Molecular and Integrativ

e Physiology, e NCSA Senior Research Scientist, University o

f Illinois, Urbana, IL 61820, USA

ABSTRACT:

Potassium channels are an ancient, diverse protein family. Individual K channels are assigned to various subfamilies based on biochemical and electrophysiological characterization. Computational methods utilizing sequence, structure and function data and schematizing a classification that provides for annotation of new channel sequences are useful. A difficulty in classifying K channels arises from the diversity in their attributes, namely membrane topology, gating, conduction, and other electrophysiological properties. We suggest a hypothesis that the permeation pathway, the one structural domain common to all K channels, contains enough information about the entire channel that a phylogenetic analysis of this domain functions as a channel subfamily classifier. The hypothesis is validated by: 1) showing that it replicates the classification for human potassium channels existing in the Human Genome

*Corresponding author(jake@https://www.doczj.com/doc/dc17889053.html,)

Organization (HUGO) database, 2) providing a classification for two-pore K channels that also points to their origin, and 3) showing the existence of a new K channel subfamily that had not previously been identified in the human genome. We provide classification for many human potassium channel genes that had not been previously classified. A related investigation into the evolutionary conservation of structural features of the K channel permeation pathway in human and prokaryotes is also detailed.

INTRODUCTION:

Potassium channels are the most ancient family of the voltage-gated ion channel superfamily. All fully sequenced eukaryotic genomes code for at least one potassium channel protein, and most code for many, which also agrees with the theory that the “pump-leak” model for ion and water cellular homeostasis, which depends on potassium channels in the plasma membrane, is ubiquitous in eukaryotic cells (Jakobsson, 1980). An ability to selectively pass potassium ions must be important to most prokaryotes too (supplement to Koonin (2000)). Potassium channels have evolved to play critical roles in many diverse cellular processes. This functional diversity has come about in large part by their acquisition of various regulatory modules to modulate their gating and conductance properties and in some eukaryotes, by their association with auxiliary components as well (Yi et al., 2001). Some key roles played by potassium channels include electrical excitability, electrolyte balance, volume regulation, sensory signal transduction and immune response regulation. Regulatory factors for potassium channels include membrane voltage, pH, cyclic nucleotides, intracellular calcium, ATP, polyamines, G-proteins, and temperature. The classification of potassium channels into distinct subfamilies has classically depended on their biochemical characterization (i.e., the nature of the particular physiological regulation) as well as electrophysiological characteristics (large vs. small conductance, fast vs.

slow inactivation, depolarization-activated or hyperpolarization-activated etc.). Automated sequence annotation pipelines, including those using

‘profile-based’ approaches, do not succeed in discriminating among even the primary voltage-gated ion channel families, i.e., the K+-, Na+-, and Ca2+- channel families. Since electrophysiological studies are time-consuming in the least, it would be useful to devise a robust computationally-based classification method that proceeds from and utilizes the already present large repository of information about potassium channels. An analytical approach to classification would ideally take stock of the biological definition of potassium channels, and account for their diversity.

Potassium channels, structurally, are membrane proteins that have a signature selectivity filter (almost always G[YF]G) (Heginbotham et al., 1994) in a “pore region” that is flanked by transmembrane helices on either side. Together, this structural motif constitutes the “permeation pathway” of the channel and subfamily designations commonly indicate the ‘attachments’ to this basic design. The pore-forming monomers are usually referred to as alpha-subunits and most often form homotetramers in the membrane in order to function. It is reasonable to suppose that the diverse functional properties of potassium channels necessarily stem from the nature of their particular sequences (Anfinsen, 1973; Danchin, 1999; Orengo et al., 1999). Hydropathy plots, which can be used to infer

transmembrane (TM) topology, are a measure of potassium channel diversity. Analysis of TM topology was earlier used by Jan and Jan (1997) to classify potassium channels in the animal kingdom into voltage-gated channels, which possess a 6TM topology, and inward-rectifying channels, which have a 2TM topology. Information on potassium channels has so increased to present a need to further update this classification. Coetzee et al.(1999) expanded the scheme by including the 4TM topology class of two-pore “leak” potassium channels, first described by Lesage et al. (1996), and refining the 6TM class using sequence similarity measures. Potassium channels with a 8 TM-2 pore topology have also been discovered in yeast (Ketchum et al., 1995). Joiner and Quinn (2001) provide a description of the expansion of K channel subfamilies in higher animals based on topology and sequence similarity of many sequences.

The physiological diversity of potassium channels places a significant bottleneck to formulating a well-delineated classification scheme for this protein family. The work described in this paper addresses the problem of developing a comprehensive classification scheme, and validating it with available experimental information. We restrict our current effort to classifying potassium channels in the human genome. The diverse complement of potassium channels found in this genome, and the substantial classification and annotation effort that have already been expended on this genome, provide a benchmark to measure our effort.

The tasks laying the foundation for a systematic study were in identifying the genome-wide set of subfamilies, and the genome-wide content of potassium channels. The next sections describe our dataset, methodology and results in detail.

MATERIALS AND METHODS:

Catalog of human pore-forming potassium channel subfamilies based on prior identification:

We chose to adopt the nomenclature of K channel subfamilies developed in the HUGO Gene Nomenclature Committee (HGNC) repository

(https://www.doczj.com/doc/dc17889053.html,/cgi-bin/nomenclature/searchgenes.pl). The HGNC database was searched for potassium channel subfamilies using the query “symbol begins with KCN” (KCN being the designated prefix for K ChaNnel). From these results, KCNE and KCNIP subfamilies were eliminated since they do not form pores and this left us with 13 subfamilies. We enhanced this set with the following observations:

1.we cross-checked the list with an alternative HGNC search

query: “Full name contains potassium”. In addition to the

subfamilies obtained above, we identified another class that

would be of interest to our analysis: the hyperpolarization-

activated cyclic nucleotide-gated channels (HCN). We added

these pacemaker ion channels to our list, since they are K+-

selective, and possess a transmembrane topology identical to

voltage-gated K channels (Kaupp and Seifert, 2001).

2.the cyclic nucleotide-gated channels (CNG channels) were also

added to the list. The CNG channels are presumably homologous with potassium channels, since their transmembrane topology is identical to that of the potassium channel as well as from strong experimental evidence of close functional connection

(Heginbotham et al., 1992). CNG channels are cation-selective and they are important in sensory processes of olfaction,

auditory transduction and visual reception. The CNG channel family includes designated alpha and beta subunits and since

both subunits are necessary to form a pore(Zhong et al., 2002), each subunit was considered a separate subfamily (CNGA and CNGB subfamilies respectively).

3.it is probable that the two-pore channels originated from either a

gene duplication or a gene fusion of ancestral single-pore

potassium channels. This is supported by the observation that prokaryotic potassium channels are single-pore (unpublished

data). There is also experimental evidence that each of the two pores in these channels can form functional channels on their own(Saldana et al., 2002). In light of this, we surmise that the pores of the two-pore potassium channel are distinct, and assign them separate subfamily definitions, 1/2-KCNK denoting the

first pore-forming region (N-terminal) in the sequence, and 2/2-

KCNK, the second one.

Table 1 includes these subfamilies along with their functional descriptors.

Identification of the genome-complement of human potassium channels:

An Entrez (https://www.doczj.com/doc/dc17889053.html,/Entrez/) text query was carried out for each major subfamily as defined by the above process to retrieve one representative member from the Entrez Genbank database. The sequence of this member was then used to seed PSI-BLAST(Altschul et al., 1997) searches that were iterated to convergence with relatively relaxed E-values chosen to make sure all possible relatives of the sequence in the human genome were picked up. The prokaryotic potassium channel sequence, KcsA, was also used as a Psi-Blast probe and cycled to convergence to pick up distant human potassium channels. (All initial E-values were set at a value of 10; inclusion thresholds were 0.01). In parallel, we also carried out an Entrez text search with the following ternary query: “Homo sapiens [Organism] AND ((KCN* OR Kcn* OR

kcn*) OR (Potassium* OR potassium*))”. KCN is also the K Channel designation adopted by GenBank for annotating its potassium channels. This combination of text searching and sequence searching using multiple probes was done to ensure complete retrieval of potassium channel sequences. The human sequences mined from the above searches were

consolidated into a single dataset. Each sequence in the dataset was then examined for satisfaction of the following constraints sequentially:

1. presence of the canonical selectivity filter triplet G[YF]G.

Exceptions to take into account are the EYG, GLG and GLE

(Salinas et al., 1999) filters of two-pore channels and the cation-

selective filters of CNG channels (detected from BLAST results,

and verified with published alignments).

2. presence of hydropathic segments (putative transmembrane

helices) on either side of the selectivity filter, as identified by

computational algorithms (TMAP (Persson and Argos, 1997) and

TMHMM (Sonnhammer et al., 1998)), followed by manual

inspection.

This ensured that positives are true positives and negatives are true negatives, i.e. the procedure is both specific and sensitive. We obtained a total of 478 such sequences in the human genome. Of these, 91 had two selectivity filters, and are presumably two-pore channels. We then eliminated redundant sequences in the dataset showing >=99% pair-wise sequence identity. (The program of Li et al. (2001) was used in this regard.) This ensured that essentially identical sequences were represented exactly once in the dataset, while channels with quite minor sequence differences were retained;this increases the effective ‘signal-to-noise’ ratio of the dataset. In this process, proteins that were >=99%

identical to some part of a larger protein were considered ‘redundant’. We obtained a final count of 123 non-redundant potassium channel proteins. 23 had two selectivity filters each.

RESULTS:

In principle, a classification of protein function should agree with the clustering pattern arising from with a corresponding phylogenetic analysis of protein sequences. Attempts to classify potassium channels by phylogeny of aligned complete protein sequences are problematic, since full potassium channel sequences of different subfamilies are resistant to alignment by standard techniques such as Clustal (Jeanmougin et al., 1998). We find that use of advanced multiple alignment techniques such as secondary structure-guided alignment (using the KcsA structure (Doyle et al., 1998)), gap penalty masks, and matrices based upon transmembrane amino acid frequencies (for example, PHAT(Ng et al., 2000)) to address the issue, do not improve the situation much. The reason for this is apparent in large part from Figure 1, which plots the position of the selectivity filter in the sequence against its total length for each sequence. There are two features of the figure:

1.the range in the sequence lengths of potassium channels. They can

be as short as 120 amino acids to as long as 1349 amino acids

2.the variance in the location of the selectivity filter along the

sequence. It can be close to the N-terminus or close to the C-

terminus, or almost any position between.

The only common element of all potassium channels is the permeation pathway. There is evidence that the structure of the permeation pathway of the prokaryotic potassium channel, KcsA, is conserved in eukaryotes (LeMasurier et al., 2001; Lu et al., 2001; MacKinnon et al., 1998). There is also experimental evidence that a subset of properties that are individual to a subfamily may be intrinsic to the permeation pathway(for example, see Chen et al., 2002; Lippiat et al., 2000; Rich et al., 2002; Vergani et al., 1998). These observations when taken together, suggested that the permeation pathway, besides being the homologous segment to all potassium channels, may support a basis for a biologically sound classification scheme.

To test this hypothesis, we initially culled out the permeation pathways of all the channels in the non-redundant set obtained above. The permeation pathway is composed of the following structural parts (from the N-terminal part of the protein): outer transmembrane helix, turret, pore partial helix, selectivity filter, extended selectivity filter, and the inner transmembrane helix. Of these, the turret region is the most variable, believed to evolve rapidly under the pressure of peptide toxins that are

potentially lethal to channel function (Olivera, 1997). To accommodate potential errors in the identification of transmembrane helix ends, we considered sequences that extended four residues N-term to the start of the predicted outer helix, and extended four residues C-term to the end of the predicted inner helix. Two permeation pathways were obtained for those channels with two selectivity filters. Thus we had a total of

100+23*2= 146 permeation pathway sequences. The average length of the permeation pathways was 113 residues, which is about 20% the average length of a full channel.

The 146 permeation pathways so obtained were aligned in a two-tier process, which helped to minimize errors in the alignment of the turret region. In the first step, sequences of approximately the same lengths were aligned together. In the second step, these alignments were treated as profiles and put together using techniques of profile-profile alignment (Jeanmougin et al., 1998). The overall alignment obtained was cross-validated for accuracy with sections of published alignments. For eg., the alignment of cyclic nucleotide-gated channels (which are the only sequences to have an alignment gap in the selectivity filter region) was verified with other alignments, like those in Leng et al. (2002), and Warmke and Ganetzky (1994). In some cases, hand editing was necessary to reconcile the alignment with channel crystallographic data (Doyle et al., 1998;Jiang et al., 2002). The inner helix alignment was adjusted with

the consideration that the glycine hinges should line up (Jiang et al., 2002). The final alignment thus obtained showed clear regions of outer helix, pore helix, selectivity filter, and inner helix. This pattern agrees very well with previous alignments we have seen (Shealy et al., 2003).

This alignment, included in the supporting information, was used to generate the dendrogram shown in Figure 2.

If the dendrogram functions as a classification of potassium channels, there must exist points on the dendrogram that radiate each subfamily. there must be distinct clusters for each subfamily. We readily noted the clusters formed by 1/2-KCNK and 2/2-KCNK subfamilies. It is observed that the 2/2-KCNK subfamily formed two clusters, due to the high degree of divergence within the subfamily. The critical validation step was to replace the labels on the dendrogram with the annotation deposited in Genbank (as found in the CDS /gene entry under ‘Features’ field of a Genbank record) and observing that in cases where annotation was available, the sequences were found to lie strictly in clusters containing like-subfamily sequences. The subfamily clusters were correspondingly inferred and are given on the right hand side of Figure 2. It then remained to be verified that the sequences that were missing annotation were also members of the subfamily as determined by the dendrogram. We validated this by doing directed full-sequence searches for these

sequences. For example, if the permeation pathway of a given sequence placed itself in a cluster of known calcium-activated channels, we verified that the full sequence of the unannotated sequence had calcium-binding domains in it.

In the course of our validation, we observed a cluster comprising three sequences that was missing clear annotation. Sequence analysis revealed the sequences of this cluster to be most closely related to the rat Slack potassium channel subfamily (Joiner et al., 1998). We infer that these three sequences do in fact code for human Slack channels.

To summarize our main results, there is a one-to-one correspondence between the clusters of the dendrogram as noted on the right hand side of Figure 2 and the potassium channel subfamilies defined in Table 1. This verifies our hypothesis that the phylogeny of the permeation pathway can be used to reliably classify potassium channels. The functional annotation and classification of the potassium channels in the human genome as derived from the dendrogram is provided in Table 2. The corresponding state of the Genbank annotation for these sequences is also provided. The Slack subfamily is represented as a new subfamily. It must be noted that the number of members of each subfamily found here may be lower than the true numbers due to removal of redundant sequences in our analysis (refer Materials & Methods); noteworthy comparisons can still be drawn

with other published subfamily membership numbers. For example, there are 7 CNG channels from our analysis, compared with 6 CNG channels noted in Kaupp and Seifert (2002); 35 Kv channels are identified in this work,comparable numbers being 22 given in Yellen (2002) and 26 in Ottschytsch et al.(2002).

DISCUSSION:

It is interesting to observe in Fig.2 the preservation of evolutionary patterns we would predict for full channels. Notably, all voltage-gated subfamilies involved in electrical excitability (namely the KCN A, B, C, D, F, G and S subfamilies) cluster with each other. A similar co-clustering of clusters is observed for subfamilies that possess cyclic-nucleotide

subfamilies.

The dendrogram also provided a method to examine the hypothesis regarding the two-pore channels. The following observations were made:

1.each permeation pathway of the 1/2-KCNK subfamily is closest to

another member of the 1/2-KCNK subfamily than to any member of the 2/2-KCNK subfamily and vice versa. Thus, the 1/2-KCNK and

2/2-KCNK subfamilies were represented in the dendrogram as non-

overlapping clusters.

2.the composition of the selectivity filter of the two proposed

subfamilies is distinctive: middle residue in the selectivity filter of the 1/2-KCNK subfamily is mostly a tyrosine (19/23 times) and

never a leucine, whereas in the case of the 2/2-KCNK subfamily it

is mostly a phenylalanine (18/23 times) and never a tyrosine.

3.the turret region of the 1/2-KCNK subfamily is consistently long

(average length: 57 residues; range: 49-63), whereas the 2/2-KCNK subfamily has a consistently short turret region (average length: 5.3 residues; range: 5-6). This pattern dates the possibility of a gene

fusion as fairly recent.

The above observations suggest collectively that the two-pore channels arose from the splicing together of two distinct potassium channel genes.

Our classification was based in part on experimental observations about the structural conservation of the permeation pathway from prokaryotes to higher eukaryotes. With a reliable alignment of the permeation pathways in place, it is possible and useful to revisit these observations from sequence. To do this, we aligned the KcsA potassium channel onto the existing alignment of the human channels, and noted the conservation of amino acid character for each residue position in the KcsA structure. For comparison, a similar alignment and calculation of conservation in the permeation pathway of prokaryotic potassium channels was done using a

Psi-Blast search of the KcsA prokaryotic channel. The two sets of conservation scores computed for human and prokaryotic potassium permeation pathways respectively were projected onto the KcsA structure and this is shown in Figure 3.

An overall impression from Figure 3 is that conservation patterns in the prokaryotic and human channel permeation pathways are remarkably similar. The overall conservation in the human sequences is a bit stronger, reflecting the relatively shorter period of evolutionary time in which divergence has been taking place. In both human and prokaryotic sequences there is strong conservation in the pore helix and selectivity filter and in those regions of the TM helices involved in intrasubunit packing; i.e., packing between the inner helix and the pore helix and packing between the inner and outer helix

These conservation patterns suggest that essentially the same packing pattern has been preserved for the majority of potassium channels throughout all of evolutionary history since the first potassium channels

in prokaryotes. There is also strong conservation along the lumenal face of the inner helix, suggesting that the detailed structure of the intracellular end of the permeation pathway is conserved to perhaps nearly the same degree as the pore helix and selectivity filter that define the extracellular end of the pathway. The conserved glycine in the middle of the inner helix postulated as the gating-hinge (Jiang et al., 2002), can be

readily observed. The apparent conservation of the interface between the inner and outer helices suggests to its role in channel gating among a majority of K channel subfamilies.

Another notable aspect of Figure 3 is that the regions not strongly conserved are the faces looking out of the figure for both the inner and outer helices. These are regions of intersubunit packing, where the different monomers that make up the tetrameric potassium channel pack up with each other. It appears that these surfaces are quite variable. To postulate a significance for this, we note that the vast majority of potassium channels are homotetramers. The variable surfaces at the point of intersubunit packing would mitigate against the assembly of heterotetramers. In early prokaryotic evolution, where lateral transfer is common (see for example, Gogarten et al., 2002), variable surfaces would prevent a well-adapted potassium channel from being structurally contaminated with another channel monomer. In eukaryotes, which invariably have multiple potassium channels, these variable interfaces would similarly prevent the formation of heterotetramers (except for a few special cases where heterotetramers are known to form in vivo (Joiner et al., 1998; Ottschytsch et al., 2002)).

CONCLUSION:

It is perhaps remarkable that the clustering derived from the dendrogram in Fig. 2, derived using only the permeation pathway, provides an accurate and complete picture of potassium channel classification. The result is counterintuitive, in the sense that the permeation and regulatory functions are technically ascribed to different regions of the gene, such as the voltage sensor, calcium-binding region, cyclic nucleotide binding region, etc. Thus, the biological implication of our finding is that the permeation pathway encodes distinctive descriptors of gating, conduction, inactivation, and character of tetramerization, for each subfamily. The demonstrated scheme appears to be highly extendable to the analysis of other genomes. The full complement of human potassium channels identified in this work could go towards a foundation for systems biology approaches to studying neuroscience and behavior [46].A last consequence of our finding is the similar possibility raised for the functional annotation of other protein families, particularly ion channel families, through an analogous analysis of targeted segments of the protein. Routine genome annotation protocols are based on automated sequence searching, making them prone to tradeoffs between sensitivity and specificity with an attendant compromise in reliability (see [47-49]), and it will be of interest to pursue alternative approaches, such as ours.

ACKNOWLEDGEMENTS:

We would like to thank Dr. R. MacKinnon for valuable discussions and Dr. J.H. Thomas for also valuable discussions.We acknowledge funding from NSF grant 0235792 and NIH grant RO1GM054651 and computing time via a grant from the National Computational Science Alliance on machines at the National Center for Supercomputing Applications.

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氟化钾安全技术说明书

氟化钾安全技术说明书 第一部分:化学品名称 化学品中文名称:氟化钾 化学品英文名称:potassium fluoride 技术说明书编码:761 CAS No.:7789-23-3 分子式:KF 分子量:58.10 第二部分:成分/组成信息 有害物成分含量 CAS No. 氟化钾≥95.0% 7789-23-3 第三部分:危险性概述 危险性类别: 侵入途径: 健康危害:本品对粘膜、上呼吸道、眼睛、皮肤组织有极强的破坏作用。吸入后可因喉、支气管的炎症、水肿、痉挛,化学性肺炎、肺水肿而致死。中毒表现有烧灼感、咳嗽、喘息、喉炎、气短、头痛、恶心和呕吐。 环境危害: 燃爆危险:本品不燃,有毒,具刺激性,严重损害粘膜、上呼吸道、眼睛和皮肤。 第四部分:急救措施 皮肤接触:立即脱去污染的衣着,用大量流动清水冲洗至少15分钟。就医。 眼睛接触:立即提起眼睑,用大量流动清水或生理盐水彻底冲洗至少15分钟。就医。 吸入:迅速脱离现场至空气新鲜处。保持呼吸道通畅。如呼吸困难,给输氧。如呼吸停止,立即进行人工呼吸。就医。 食入:用水漱口,给饮牛奶或蛋清。就医。

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复方甘草酸苷片说明书

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门冬氨酸钾镁

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氯化钠安全技术说明书

安全技术说明 书 MSDS)

氯化钠

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1、(B )以下测定项目不属于煤样的半工业组成的是。 A 、水分 B 、总硫 C 、固定碳 D 、挥发分 2. ( B )不属于钢铁中五元素的是。 A 、硫 B 、铁 C 、锰 D 、磷 3. (A )试样的采取和制备必须保证所取试样具有充分的 A 、代表性 B 、唯一性 C 、针对性 D 、准确性 4、(C )测定水样化学需氧量,取水样100mL ,空白滴定和反滴定时耗用的硫酸亚铁铵标 准溶液分别为15.0mL 和5.0mL 其浓度为0.1mol/L ,该水样的化学需氧量为 A 40mg/L ; B 160mg/L ; C 80mg/L ; D 8mg/L 【COD Cr =)/(801000100 1.08)515(10008)(210L mg V C V V =???-=???-】 5、(B )对工业气体进行分析时,一般测量气体的。 A 、重量 B 、体积 C 、物理性质 D 、化学性质 6、(C )不能用于分析气体的的仪器是 。 A 、折光仪 B 、奥氏仪 C 、电导仪 D 、色谱仪 7、(A )在国家、行业标准的代号与编号GB 18883-2002中GB 是指。 A 、强制性国家标准 B 、推荐性国家标准 C 、推荐性化工部标准 D 、强制性化工部标准 8、(C )从下列标准中选出必须制定为强制性标准的是 A 、国家标准 B 、分析方法标准 C 、食品卫生标准 D 、产品标准 9、( D )分解硅酸盐最好的溶剂是 A 、盐酸 B 、硫酸 C 、磷酸 D 、氢氟酸 10、(A )下列吸收剂能用于吸收CO 2的是 A 、KOH 溶液 B 、H 2SO 4溶液 C 、KMnO 4 溶液 D 、饱和溴水 11、(B )动物胶凝聚硅酸时,温度一般控制在---- A .40~50℃ B.60~70℃ C.80~90℃ D、90~100℃ 12、(B )煤中灰分测定灼烧温度为 A .550℃ B.810℃ C.950℃ D、1100℃ 13、(c )煤中挥发分测定时加热温度为

药历模板

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浅谈门冬氨酸钾镁在心血管疾病上的临床应用

浅谈门冬氨酸钾镁在心血管疾病上的临 床应用 (作者:___________单位: ___________邮编: ___________) 【论文关键词】门冬氨酸钾镁;心血管疾病;临床应用 【论文摘要】门冬氨酸钾镁(DL-potassiummagnesiumaspartate,DL)做为良好的钾镁制剂,在临床治疗中应用日渐广泛。钾离子、镁离子是细胞内含量最多的阳离子,门冬氨酸对细胞亲和力较强,与K+、Mg2+结合后,形成稳固的钾盐、镁盐。这样门冬氨酸便成为钾镁离子的载体,使这两种离子进入机体细胞内。较氯化钾,硫酸镁等无机盐能更有效地提高细胞内钾、镁的含量,可用来防治多种疾病。 现将门冬氨酸钾镁在心血管疾病上的应用综述如下。 1心力衰竭

1.1心衰病人通常有食欲不振,恶心,呕吐等,有心源性肝硬化者,肝功能失代偿期消化系统症状者常见。这样钾、镁的摄入必然减少。 1.2心衰病人常规用利尿剂治疗,排钠的同时排钾、排镁也增多,有时医生注意到用保钾利尿剂减少了心衰病人缺钾的发生率,但目前尚无保镁利尿剂。 1.3心衰病人体内的RAA系统激活,这本身是一种代偿机制,但明显心衰病人常处于过度代偿状态,即发生所谓继发性醛固酮增加症。肾远曲小管重吸收钠增加,而排钾排镁,致使病人低钾低镁。 据研究慢性心衰病人体内钾总量比正常人低34%,尽管许多患者血清钾仍在正常范围内,但心肌内钾含量却是明显减少的。可以说心脏病患者心脏体积越大,心衰越重,心肌内钾、镁含量的减少越明显。 机体的能量代谢必须镁离子参与,故Mg2+有“能量离子”之美誉。机体内大约有300个以上的酶系统,其中许多存在于心血管系统中,这些酶系统的激活需要Mg2+。尤其重要的是,Mg2+能激活Na+-K+-ATP酶;钠泵和心肌腺苷酸环化酶对维持心肌线粒体的完整性和促进其氧化磷酸化过程起重要作用。严重缺K+、Mg2+可使心肌细胞线粒体肿胀和破裂,以致影响心肌代谢;加上ATP酶活性降低,影响对心肌的能量工作供应,导致心肌收缩力减弱,SV下降,CO上升,致心衰加重。K+为高能磷酸化合物合成与分解的重要辅酶,促进心肌细胞除极化,维持心肌收缩,降低心肌耗氧量,

疏血通胶囊说明书

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10、房颤强心心衰:西地兰胺碘酮 11、平喘药:氨茶碱 12、利尿心衰药:速尿 13、保护胃粘膜:奥美拉唑 14、肾病:肾康注射液 15、高血压急症:乌拉地尔 16、高血糖:普通胰岛素注射液 17、常用皮下胰岛素:门冬30 来得时诺和锐 18、退烧药:氨基比林 19、过敏:苯海拉明肾上腺素地米 20、癫痫:地西泮 21、急救药:可拉明阿拉明洛贝林正肾副肾阿托品 口服: 1、高血压:倍他乐克 卡托普利依那普利贝那普利福辛普利 厄贝沙坦替米沙坦氯沙坦缬沙坦

拜新同尼福达玄宁波依定 2、高血糖:格列齐特格列吡嗪(缓释片瑞易宁)格列美脲 诺和龙 二甲双胍 罗格列酮 拜糖平倍欣 3、高血脂:辛伐他汀阿托伐他汀 4、眩晕:西比灵 5、营养脑细胞:尼麦角林奥拉西坦胶囊 6、抗血小板:阿士匹林氯吡格雷 7、扩冠:欣康(丽珠欣乐)复方丹参滴丸万爽力(盐酸曲美他嗪片) 8、利尿:双克螺内酯 9、化痰:复方鲜竹沥羧甲司坦 10、平喘:阿斯美 11、抗生素:莫西沙星阿莫西林罗红霉素

(完整版)化学品安全技术说明书大全MSDS

化学品安全技术说明书大全(MSDS)

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