Mechanism behind negative conductance of ion channels

I am struggling to understand negative conductance shown on I-V curves on ion channels. Mechanistically, negative conductance means that inward (or outward) current increases when voltage across membrane decreases. I-V curves of such a channel have both positive and negative slopes. How it is achieved by ion-channels?

For example, this is in addition to the example posted in the answer by aandreev.

This is the figure OP talks about:

Negative Conductance Caused by Entry of Sodium and Cesium Ions into the Potassium Channels of Squid Axons, Francisco Bezanill, Clay M. Armstrong (1972).

Short answer is that in non-linear mode of ion channel operation other ions start passing through it (channel loses specificity). Cumulative effect (because of different concentrations of different ions) is negative total conductance, if you don't measure currents of specific ions.

The experiment described in the linked article Bazanilla & Armstrong (1972) is about a voltage clamp experiment in squid axon. Voltage clamping basically means that the potential difference across the axonal membrane can be set at will by an external artificial electronic power source.

NMDA receptors are channels that conduct positively charged ions (mainly Na+). If the voltage is clamped at negative potentials under physiological conditions, positive current will be drawn inward. This is generally plotted as a negative current, as shown in your picture in your post. If, however, the voltage is made more positive, current will reverse at a certain point, and positive currents will be measured by the electrodes. This positive current is characterized by an outward flow of positive ions through the NMDA receptor.

The sign of the current is arbitrary, but the most important point to make is that most channels allow current to pass both ways. There are rectifier channels, however, that will only allow one-way traffic. Rectifiers are more exceptions than the rule.

Linked reference
- Bazanilla & Armstrong, J Gen Physiol (1972); 60: 588-608

Labelled by Charles Darwin as a “most wonderful” plant, the Venus flytrap is more than a carnivorous curiosity. The rapid closing of its leaves when brushed by prey offers researchers a way to investigate how plants sense their environment through &hellip Continue reading &rarr

Teams are pursuing a dizzying array of therapeutic strategies to stymie COVID-19. It’s not yet clear which approach, or combination of approaches, will work best. *Editor’s Note: We’re providing a preview of this content due to the urgent and rapidly &hellip Continue reading &rarr

The burgeoning epidemic of obesity and type 2 diabetes mellitus presents a major health and therapeutic challenge.  Transcriptional regulation is the fundamental control mechanism for metabolism, but a gap remains in our knowledge of gene regulatory pathways that control lipid and glucose homeostasis.  Thus, we seek to identify modulable pathways that may be leveraged to counteract diabetes mellitus and its comorbidities, particularly cardiovascular disease.  In this effort, we use a variety of genetic, molecular, next-generation sequencing, biochemical methods and physiological models.  Our recent work has helped to reveal the genomic architecture for transcriptional regulation in innate immunity, which plays a key role in both diabetes mellitus and atherosclerosis.  Surprisingly, although macrophage regulatory elements are often at significant linear distance from their associated genes, we identified interplay between transcriptional activators and repressors that is highly proximate, occurring at shared nucleosomal domains (Genes & Development, 2010).  Moreover, we discovered a powerful role for the BCL6 transcriptional repressor to maintain macrophage quiescence and prevent atherosclerosis (Cell Metabolism, 2012). 

Currently, we are exploring the impact of activator–repressor interactions on enhancer function and transcription, the signal-dependent control of repression and the functional impact of transcriptional activators and repressors on inflammatory and metabolic disease. In particular, we strive to further understand the role for B cell lymphoma 6 (BCL6), a C2H2-type zinc finger repressor, in innate immunity and metabolism. 

In related work, we are developing new methods for cell-specific isolation of RNA and chromatin from tissues composed of mixed cell populations. These genetic tools will allow us to explore transcriptional regulation in living animals with unprecedented precision and global scope using transcriptome sequencing and ChIP-sequencing. We anticipate that these approaches will identify new candidate regulators and mechanisms underlying cardiovascular and metabolic disease. 

For more information, please see Dr. Barish's faculty profile.

Cystic Fibrosis Conductance Regulator (CFTR) Protein & Mutations

As previously mentioned, the CFTR protein serves as a gate at the cell surface, which opens to allow chloride ions to cross the cell membrane. The chloride channel is an ATP-binding cassette (ABC) transporter and is comprised of three distinct domains or parts, which include two nucleotide-binding domains (NBD 1 and 2), two membrane-spanning domains (MSD 1 and 2), and a regulatory domain (R domain). The NBDs bind ATP, which provide the energy necessary to open and close the channel. The MSDs then help to anchor the channel securely in the cell membrane so that it stays at the cell surface. Additionally, the R domain allows for phosphorylation which generally regulates the opening and closing of the channel.

The structure of the CFTR, although seemingly abstract and uninteresting, is important because mutations in this gene, responsible for CF, can occur in any of these three regions resulting in two primary defects: a chloride channel, which is not in the proper shape and therefore cannot insert into the apical membrane, or a channel that does not open and close properly on the membrane. Either situation may result in reduced water flow and as previously described will create thick mucous. The review by Dr. Cutting points out that the these two defects create a disturbance in the cell leading to CF by affecting “the quantity and/or function of CFTR at the cell membrane.”

Since the discovery of the CFTR gene twenty-five years ago, nearly 2,000 mutations have been identified within the gene. A record of the known mutations can be found in the Cystic Fibrosis Mutation Database . Among the known mutations, Dr. Cutting cites, “40% are predicted to cause substitution of a single amino acid, 36% are expected to alter RNA processing (including nonsense, frameshift and mis-splicing variants),

3% involve large rearrangements of CFTR, and 1% affects promoter regions 14% seem to be neutral variants and the effect of the remaining 6% is unclear.”

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Despite intense experimental scrutiny for almost 25 years, the molecular and atomic origin(s) of the ability of Kv channels to enter a non-conducting conformation in the presence of a sustained (voltage) stimulus has remained enigmatic. A major challenge with addressing the contribution of individual side chains in the selectivity filter and pore helix to slow inactivation is that many amino acids lack naturally occurring analogs that allow subtle manipulation without dramatic disruption of the overall structure of this critical protein region. Furthermore, since slow inactivation is tightly coupled to ion occupancy in the selectivity filter, it has been difficult to distinguish direct effects on the mechanistic underpinnings of slow inactivation from indirect effects arising from changes in the structural integrity of the selectivity filter. Here, we overcome this hurdle by employing subtle synthetic analogs of naturally occurring amino acids and by introducing isolated mutations in single subunits.

When using concatenated Shaker constructs to introduce single mutations in a fourfold symmetric channel, it is crucial to confirm that the concatenated subunits do not form functional channels that vary in their stoichiometry from that predicted from the cloning strategy. Albeit possible (McCormack et al., 1992 Hurst et al., 1995), we believe the constructs used here assemble correctly for three reasons. First, the Thr439Val concatemers and the Tyr445Ala concatemers showed almost complete inactivation over a period of only 200 ms (when corrected for the amount of gating charge). Second, we observed very low ratios of Imax to Qmax for Thr439Val concatemers and Tyr445Ala concatemers. Both scenarios are not compatible with the idea of a significant WT-only channel subpopulation Lastly, Sigworth and co-workers have used the same concatemers and successfully demonstrated that channels containing only a single mutated subunit generally assemble in the correct stoichiometry (Yang et al., 1997). We conclude that the (vast majority of) concatemers assemble correctly, although we cannot ultimately rule out a small subpopulation of channels with WT-like slow inactivation.

Further, we employed fluorinated Trp derivatives, which have been used extensively to probe electrostatic (cation-pi) interactions (Dougherty, 1996) between Trp side chains and organic cations as fluorination allows a step-wise dispersion of the electronegative surface potential of aromatic side chains (Pless and Ahern, 2013). As such, our finding that F4-Trp in position 434 significantly slows channel inactivation could be interpreted as a result of a cation-pi interaction at Trp434 that is being diminished by fluorination. However, if this were true, Ind, a synthetic amino acid which lacks H-bonding ability, should have no effect on channel inactivation as it is isosteric and isoelectric to the native Trp side chain. By contrast, we observe a substantial increase in the rate of slow inactivation with Ind in position 434, a result not compatible with the notion of an energetically significant cation-pi interaction at Trp434. We thus conclude that it is the ability of the indole nitrogen to participate in a H-bond that regulates the strength of the intra-subunit interaction between Trp434 and Asp447.

Together, our experimental approaches provide strong evidence for two H-bonds that are critical for slow inactivation of Kv channels: one that confers stability within an individual subunit (the Trp434–Asp447 interaction), and a second that stabilizes the relative orientation of two adjacent subunits (the Tyr445–Thr439 interaction) (Figure 7). Although disruption of the Trp434–Asp447 interaction has profound effects on slow inactivation, breaking of the Tyr445–Thr439 interaction elicits more functionally significant phenotypes. We surmise that these comparatively more severe phenotypes seen when disrupting the Tyr445–Thr439 pair arise from its location at the inter-subunit interface, but cannot exclude the possibility that this difference arises from the fact that the Tyr445 backbone carbonyl is also directly involved in coordinating permeant ions. Furthermore, the Tyr445–Thr439 interaction is, to our knowledge, the first evidence for an inter-subunit interaction contributing to slow inactivation, possibly providing an explanation for the observed subunit cooperativity during slow inactivation. However, although previous studies have suggested evidence for both constriction (Baukrowitz and Yellen, 1996 Liu et al., 1996, 1997) and dilation (Hoshi and Armstrong, 2013) of the selectivity filter, the data here is not definitive in distinguishing these models of slow inactivation.

A network of inter- and intra-subunit H-bonds regulates slow inactivation.

(A) The left panel shows a top view of pore helix and selectivity filter (based on the Kv1.2/2.1 chimera structure (2R9R) individual subunits are colored in gray, cyan, green and yellow, respectively). Note the backbone carbonyls are shown for Tyr445 to highlight their role in the coordination of potassium ions (gray circle). The center panel highlights the two proposed H-bonds: Thr439–Tyr445 (inter-subunit, red oval,) and Asp447–Trp434 (intra-subunit, blue oval), all by Shaker numbering. The panel on the right compares the averaged inactivation time constants over a range of voltages for different concatemers (data reproduced from Figure 3 and Figure 5 note that for Tyr445Ala and Thr439Val only the fast components are displayed). Note the arrows pointing to the respective interactions in the model in the center.

The notion that side chains critical to slow inactivation cluster around the ‘aromatic cuff’ (formed between the extracellular end of the selectivity filter and the pore helix) is further supported by the marked differences between side chains at the outer vs the inner end of the selectivity filter and pore helix: only those located around the ‘aromatic cuff’ result in notable effects on slow inactivation that are propagated to the entire channel (Figures 2–5), while those residing in the middle or lower section of the selectivity filter do not affect slow inactivation (see Figure 6 for positions 441 and 442 see (Heginbotham et al., 1994) for position 443).

Overall, the results point towards an intriguing molecular explanation for the mechanism of slow inactivation: upon depolarization and channel opening, the stability of the channel open state is proportional to the strength of two H-bonds that regulate entry into slow inactivation, thus endowing Kv channels with an intrinsic timing mechanism that tightly regulates their biological activity. During a sustained voltage stimulus, channels experience a sequential breaking of the Trp434–Asp447 and Tyr445–Thr439 H-bonds and given the relative arrangement of their hydroxyl moieties this would likely result in an anti-clockwise swivel movement of the Tyr445 backbone carbonyl away from the permeation pathway, ultimately disrupting the coordination and occupancy of potassium ions at the outer end of the selectivity filter. Such a scenario would lead to mutual repulsion between the Tyr445 backbone carbonyls of the remaining three subunits (Almers and Armstrong, 1980 Hoshi and Armstrong, 2013), further lowering filter-occupancy at its outer mouth. The resulting strain could trigger a cascade of disrupted H-bonds critical to inactivation near the extracellular end of the selectivity filter in all subunits, ultimately resulting in a fully inactivated channel.

Cellular mechanisms

A number of important questions arise at the level of the cellular and molecular mechanisms that underlie the phenomena described thus far, but an extensive description and analysis of these mechanisms would merit a separate review. Briefly, it is crucial that the following questions be experimentally examined: What mechanisms may generate variability of ionic current expression? What mechanisms may determine the correlated expression of ion channels in a cell or across a population of cells? Are there distinct rules that govern how synaptic or intrinsic variability is generated and handled by an organism?

Mechanisms that generate ionic current variability at the individual level may include well-known mechanisms of transcription, translation, or posttranslational regulation and their interactions. For example, the alternate activation of activity-dependent regulation of channel density and the action of some hormone or neuromodulator on the level of those same channels may result in varying degrees of channel expression, depending on the relative timing of those interactions. At the population level, variations among individuals will be the consequence of the same mechanisms acting on individuals but recorded at different times in their lives after undergoing different regulatory experiences over that time.

A surprisingly simple mechanism that explains how variable levels of different ionic currents may be correlated across a population was recently reported by O’Leary and colleagues ( 2013) and was described earlier. Starting with randomly different initial values of different channel populations, correlated expression results very simply if the levels of those channels are regulated by activity. This is not restricted to one pair but can affect multiple channel types, as long as they are all regulated by activity. Another recently discovered mechanism is the transcriptional coupling of ion channels (Bergquist et al. 2010). This mechanism was shown to homeostatically limit the total amount of transient K + current in Drosophila neurons.

About whether distinct rules govern synaptic or intrinsic variability, there is no a priori reason for which that should necessarily be the case. Both channel types can, for example, be regulated by activity. A good example comes from work from Turrigiano and colleagues (1998, Desai et al. 1999), who showed that not only synaptic currents but also intrinsic currents in rat cortical pyramidal neurons are homeostatically regulated by the same activity modifications. The exact molecular mechanisms involved in the regulation of each class of channels, however, may not be exactly the same, but that still needs to be fully worked out in that and many other systems.

The Burridge lab studies the role of the genome in influencing drug responses, known as pharmacogenomics or personalized medicine. Our major model is human induced pluripotent stem cells (hiPSC), generated from patient's blood or skin. We use a combination of next generation sequencing, automation and robotics, high-throughput drug screening, high-content imaging, tissue engineering, electrophysiological and physiological testing to better understand the mechanisms of drug response and action.

Our major effort has been related to patient-specific responses to chemotherapy agents. We ask the question: what is the genetic reason why some patients have a minimal side effects to their cancer treatment, whilst others have encounter highly detrimental side-effects? These side-effects  can include cardiomyopathy (heart failure or arrhythmias), peripheral neuropathy,  or hepatotoxicity (liver failure). It is our aim to add to risk-based screening by functionally validating genetic changes that predispose a patient to a specific drug response.

Recent Findings

  • Human induced pluripotent stem cells predict breast cancer patients’ predilection to doxorubicin-induced cardiotoxicity
  • Chemically defined generation of human cardiomyocytes

Current Projects

  • Modeling the role of the genome in doxorubicin-induced cardiotoxicity using hiPSC
  • Investigating the pharmacogenomics of tyrosine kinase inhibitor cardiotoxicity
  • hiPSC reprogramming, culture and differentiation techniques
  • High-throughput and high-content methodologies in hiPSC-based screening

Neuromorphic Computing: Modeling The Brain

Competing models vie to show how the brain works, but none is perfect.

Can you tell the difference between a pedestrian and a bicycle? How about between a skunk and a black and white cat? Or between your neighbor’s dog and a colt or fawn? Of course you can, and you probably can do that without much conscious thought. Humans are very good at interpreting the world around them, both visually and through other sensory input.

Computers are not. Though their sheer calculation speed surpassed that of human “calculators” long ago, large data centers equipped with terabyte-scale databases are only beginning to match the image recognition capabilities of an average human child.

Meanwhile, humans are creating larger and more complex digital archives and asking more complex questions about them. How do you find the photo you want in a collection of thousands? How does a music service answer a customer request for “more like this?” How can computers support technical decision making when the source data is often noisy and ambiguous?

Neuromorphic computing seeks to build systems informed by the architecture of biological brains. Such systems have the potential to analyze data sets more rapidly, more accurately, and with fewer computing resources than conventional analysis.

In the current state of the art, people who discuss neuromorphic computing and big data analysis are usually talking about neural networks. While current-generation neural networks are important for practical problem solving and will be discussed in a future article, they don’t really have much resemblance to biological brains.

Fig. 1: Neuron cell diagram. Source: Wikimedia Commons.

How neurons work
The first important difference is the sheer scale of connectivity in biological brains. The nucleus of a nerve cell is at the center of a web of fibers, or axons, each of which branches into potentially thousands of dendrites. Each dendrite can connect to a neighboring neuron across a junction known as a synapse. Though electronic analogues often define this web of connections as fixed, it is not. Synaptic connections are made and broken constantly. As Jeff Hawkins, co-founder of Numenta, explained in a talk at the 2015 IEEE Electron Device Meeting, “[Biological] memory is a wiring problem, not a storage problem,” and a large wiring problem at that.

In the human neocortex—responsible for functions like sensory perception, spatial reasoning, and language—there are millions of neurons, each of which may communicate with thousands of neighbors. The neocortex alone has billions of connections. The brain as a whole has trillions. For comparison, the largest server-based neural networks have about 11 billion connections.

Furthermore, the brain is an analog system. Transistors in electronic circuits are either on or off. Memory elements store either 1 or 0. Synaptic connections are not directly equivalent to memory capacitors, but they can be strong or weak, and can be reinforced or depressed in response to stimuli.

More precisely, neurons communicate through electrical currents resulting from the flow of sodium and potassium ions. There are differences in ion concentrations between intracellular and extracellular fluids. When a pre-synaptic neuron releases a neurotransmitter compound, the ion channels in the post-synaptic neuron are either excited or depressed, increasing or decreasing the flow of ions between the cell and the extracellular fluid. Doo Seok Jeong, senior scientist at the Korea Institute of Science and Technology, explained that the cell membrane of the post-synaptic neuron acts as a capacitor. Ions accumulate until a critical threshold is reached, at which point a “synaptic current” spike propagates along the neural fibers to other synapses and other neurons.

The capacitor will charge and discharge repeatedly until the neurotransmitter concentration dissipates, so the synaptic current actually consists of a chain of related spikes. The length of the chain and the frequency of individual spikes depends on the original stimulus. The response of a particular neuron to a particular synaptic current chain is generally not linear. The relationship between the input and output signals is the “gain” of the neuron.

It must be emphasized, though, that the relationship between external stimuli and synaptic current is not clear. Biological brains produce chains of synaptic current spikes that appear to encode information. But it is not possible to draw a line between the image of “cat” received by the photoreceptors in the retina and a specific pattern of synaptic spikes generated by the visual cortex, much less the positive and negative associations with “catness” that the image might produce elsewhere in the brain. A number of factors, such as the non-uniform cell membrane potential, introduce “noise” into the signal and cause the loss of some information. However, the brain clearly has mechanisms for extracting critical information from noisy data, for discarding irrelevant stimuli, and for accommodating noise-induced data loss. The biological basis for these mechanisms is not known at this time.

Firing synaptic current spikes
In modeling the brain, at least two levels need to be considered. The first is the biological mechanism by which chains of synaptic current are generated and propagated. The second is the role of these spikes in memory and learning. Both levels face a tradeoff between biological accuracy and computational efficiency. For example, many commercial neural networks use a “leaky integrate and fire” (LIF) model to describe the propagation of synaptic spikes. Each neuron has a pre-determined threshold, and will “fire” a synaptic signal to its neighbors when that threshold is exceeded. In electronic networks, similarly, each neuron applies pre-determined weights to input signals to determine the output signal. Rapid determination of the appropriate weights for a particular problem is one of the central challenges of neural network design, but once the weights are known, the output signal is simply the dot product of the input signal with the weight matrix.

This approach is computationally efficient, but not biologically realistic. Among other things, the LIF model ignores the timing of synaptic spikes, and therefore the causal relationship between them. That is, signal “A” may precede or follow signal “B” and the response of biological neurons will depend on both the relative strength and the relative timing of the two signals. A strict LIF model will only recognize whether the combination of the two exceeded the node’s threshold. The biological behavior is analog in nature, while the electronic behavior of conventional neural networks is not.

Two alternatives to the LIF model incorporate additional biophysical pathways, increasing biological realism at the expense of computational efficiency. The spiking neuron model takes into account the cell membrane’s recovery rate—how quickly the membrane potential returns to its nominal value. This model can describe different kinds of neurons, but it preserves computational efficiency by only considering variations in the membrane potential.

A much more sophisticated alternative, the Hodgkin-Huxley model, considers several different biophysical contributions, including membrane potential and the sodium and potassium ion currents. It establishes the dependence between the conductance of ion channels and the membrane potential. Further extensions of the original Hodgkin-Huxley model recognize several different potassium and sodium currents and incorporate neurotransmitters and their receptors. The HH model is substantially more realistic, but also much more computationally complex.

These three models describe the fundamental mechanisms of synaptic current generation and propagation in increasing levels of detail. For the processes we call “thinking,” — memory, learning, analysis — an additional step is required. In biological brains, this is synaptic plasticity, which is the ability of the brain to strengthen and weaken, break and remake synaptic connections. The chains of synaptic spikes provide the input for learning rules, the next level in brain modeling.

From current to data: synaptic plasticity
One of the most basic learning rules—proposed in 1982 by Brown University researchers Elie Bienenstock, Leon Cooper, and Paul Munro (BCM)—expresses synaptic change as a product of the pre-synaptic activity and a nonlinear function of post-synaptic activity. It is expressed in terms of firing rates, and cannot predict timing dependent modification of synapses.

A somewhat more sophisticated model, spike timing-dependent plasticity, recognizes that the relative timing of two signals also matters. Is a positive or negative experience associated with a particular stimulus? How closely? These details affect the relative strengths of synaptic connections. The most basic STDP models compare the timing of pairs of spikes. If the pre-synaptic spike comes before the post-synaptic spike, the connection is enhanced. Otherwise, it is weakened. However, the basic STDP model does not reproduce experimental data as well as the BCM model does.

One proposed modification, a triplet-based STDP learning rule, compares groups of three spikes, rather than pairs. It behaves as a generalized BCM rule in that post-synaptic neurons respond to both input spiking patterns and correlations between input and output spikes. These higher order correlations are ubiquitous in natural stimuli, so it’s not surprising that the triplet rule reproduces experimental data more accurately.

Which of these models and learning rules is the “best” choice largely depends on the situation. Neuroscientists seek to develop models that can accurately reproduce the behavior of biological brains, hoping to gain insight into the biological mechanisms behind human psychology. Neuromorphic computing seeks to use biological mechanisms to inform the architecture of electronic systems, ultimately deriving improved solutions to practical data analysis problems. Reproduction of a specific chain of synaptic spikes or a specific learning behavior is secondary to accuracy and computational efficiency.

Part two of this series will show why the “best” neural networks are not necessarily the ones with the most “brain-like” behavior.

Related Stories
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New FETs, qubits, neuromorphic approaches, and advanced packaging.
Neural Net Computing Explodes
Deep-pocket companies begin customizing this approach for specific applications—and spend huge amounts of money to acquire startups.
Inside Neuromorphic Computing (Jan 2016)
General Vision’s chief executive talks about why there is such renewed interest in this technology and how it will be used in the future.
Neuromorphic Chip Biz Heats Up (Jan 2016)
Old concept gets new attention as device scaling becomes more difficult.

Vascular Biology

Hypoxic culture and in vivo inflammatory environments affect the assumption of pericyte characteristics by human adipose and bone marrow progenitor cells

  • Peter J. Amos,
  • Carolyn L. Mulvey,
  • Scott A. Seaman,
  • Joseph Walpole,
  • Katherine E. Degen,
  • Hulan Shang,
  • Adam J. Katz, and
  • Shayn M. Peirce
  • 2011 Dec 01
  • : C1378-C1388

Previous studies have shown that exposure to a hypoxic in vitro environment increases the secretion of pro-angiogenic growth factors by human adipose-derived stromal cells (hASCs) [Cao Y, et al., Biochem Biophys Res Commun 332: 370–379, 2005 Kokai LE, et al., Plast Reconstr Surg 116: 1453–1460, 2005 Park BS, et al., Biomed Res (Tokyo) 31: 27–34, 2010 Rasmussen JG, et al., Cytotherapy 13: 318–328, 2010 Rehman J, et al., Circulation 109: 1292–1298, 2004]. Previously, it has been demonstrated that hASCs can differentiate into pericytes and promote microvascular stability and maintenance during angiogenesis in vivo (Amos PJ, et al., Stem Cells 26: 2682–2690, 2008 Traktuev DO, et al., Circ Res 102: 77–85, 2008). In this study, we tested the hypotheses that angiogenic induction can be increased and pericyte differentiation decreased by pretreatment of hASCs with hypoxic culture and that hASCs are similar to human bone marrow-derived stromal cells (hBMSCs) in these regards. Our data confirms previous studies showing that hASCs: 1) secrete pro-angiogenic proteins, which are upregulated following culture in hypoxia, and 2) migrate up gradients of PDGF-BB in vitro, while showing for the first time that a rat mesenteric model of angiogenesis induced by 48/80 increases the propensity of both hASCs and hBMSCs to assume perivascular phenotypes following injection. Moreover, culture of both cell types in hypoxia before injection results in a biphasic vascular length density response in this model of inflammation-induced angiogenesis. The effects of hypoxia and inflammation on the phenotype of adult progenitor cells impacts both the therapeutic and the basic science applications of the cell types, as hypoxia and inflammation are common features of natural and pathological vascular compartments in vivo.

Role of caveolin-1 in endothelial BKCa channel regulation of vasoreactivity

  • Melissa A. Riddle,
  • Jennifer M. Hughes, and
  • Benjimen R. Walker
  • 2011 Dec 01
  • : C1404-C1414

A novel vasodilatory influence of endothelial cell (EC) large-conductance Ca 2+ -activated K + (BKCa) channels is present following in vivo exposure to chronic hypoxia (CH) and may exist in other pathological states. However, the mechanism of channel activation that results in altered vasoreactivity is unknown. We tested the hypothesis that CH removes an inhibitory effect of the scaffolding domain of caveolin-1 (Cav-1) on EC BKCa channels to permit activation, thereby affecting vasoreactivity. Experiments were performed on gracilis resistance arteries and ECs from control and CH-exposed (380 mmHg barometric pressure for 48 h) rats. EC membrane potential was hyperpolarized in arteries from CH-exposed rats and arteries treated with the cholesterol-depleting agent methyl-β-cyclodextrin (MBCD) compared with controls. Hyperpolarization was reversed by the BKCa channel antagonist iberiotoxin (IBTX) or by a scaffolding domain peptide of Cav-1 (AP-CAV). Patch-clamp experiments documented an IBTX-sensitive current in ECs from CH-exposed rats and in MBCD-treated cells that was not present in controls. This current was enhanced by the BKCa channel activator NS-1619 and blocked by AP-CAV or cholesterol supplementation. EC BKCa channels displayed similar unitary conductance but greater Ca 2+ sensitivity than BKCa channels from vascular smooth muscle. Immunofluorescence imaging demonstrated greater association of BKCa α-subunits with Cav-1 in control arteries than in arteries from CH-exposed rats, although fluorescence intensity for each protein did not differ between groups. Finally, AP-CAV restored myogenic and phenylephrine-induced constriction in arteries from CH-exposed rats without affecting controls. AP-CAV similarly restored diminished reactivity to phenylephrine in control arteries pretreated with MBCD. We conclude that CH unmasks EC BKCa channel activity by removing an inhibitory action of the Cav-1 scaffolding domain that may depend on cellular cholesterol levels.

A novel mechanism in maggot debridement therapy: protease in excretion/secretion promotes hepatocyte growth factor production

  • Kenjiro Honda,
  • Koji Okamoto,
  • Yasuhiro Mochida,
  • Kunihiro Ishioka,
  • Machiko Oka,
  • Kyoko Maesato,
  • Ryota Ikee,
  • Hidekazu Moriya,
  • Sumi Hidaka,
  • Takayasu Ohtake,
  • Kent Doi,
  • Toshiro Fujita,
  • Shuzo Kobayashi, and
  • Eisei Noiri
  • 2011 Dec 01
  • : C1423-C1430

Maggot debridement therapy (MDT) is effective for treating intractable wounds, but its precise molecular mechanism, including the association between MDT and growth factors, remains unknown. We administered MDT to nine patients (66.3 ± 11.8 yr, 5 male and 4 female) with intractable wounds of lower extremities because they did not respond to conventional therapies. Significant increases of hepatocyte growth factor (HGF) levels were observed in femoral vein blood during 48 h of MDT (P < 0.05), but no significant change was found for vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF), transforming growth factor-β1 (TGF-β1), or tumor necrosis factor-α (TNF-α). We conducted NIH-3T3 cell stimulation assay to evaluate the relation between HGF and protease activity in excretion/secretion (ES) derived from maggots. Compared with the control group, HGF was significantly higher in the 0.05 μg/ml ES group (P < 0.01). Furthermore, protease inhibitors suppressed the increase of HGF (P < 0.05). The HGF expression was increased in proportion to the ES protein concentration of 0.025 to 0.5 μg/ml. In fact, ES showed stronger capability of promoting HGF production and less cytotoxicity than chymotrypsin or bromelain. HGF is an important factor involved in cutaneous wound healing. Therefore, these results suggest that formation of healthy granulation tissue observed during MDT results from the increased HGF. Further investigation to identify molecules enhancing HGF expression by MDT will contribute greatly to drug target discovery for intractable wound healing therapy.

Watch the video: Ion Specificy and Structure of Ion Channels (January 2022).