A 12R-lipoxygenase in human skin: Mechanistic evidence, molecular cloning, and expression

A recognized feature of psoriasis and other proliferative dermatoses is accumulation in the skin of the unusual arachidonic acid metabolite, 12R-hydroxyeicosatetraenoic acid (12R-HETE). This hydroxy fatty acid is opposite in chirality to the product of the well-known 12S-lipoxygenase and heretofore in mammals is known only as a product of cytochrome P450s. Here we provide mechanistic evidence for a lipoxygenase route to 12R-HETE in human psoriatic tissue and describe a 12R-lipoxygenase that can account for the biosynthesis. Initially we demonstrated retention of the C-12 deuterium of octadeuterated arachidonic acid in its conversion to 12R-HETE in incubations of psoriatic scales, indicating the end product is not formed by isomerization from 12S-H(P)ETE via the 12-keto derivative. Secondly, analysis of product formed from [10R-3H] and [10S-3H]-labeled arachidonic acids revealed that 12R-HETE synthesis is associated with stereospecific removal of the pro-R hydrogen from the 10-carbon of arachidonate. This result is compatible with 12R-lipoxygenase-catalyzed formation of 12R-HETE and not with a P450-catalyzed route to 12R-HETE in psoriatic scales. We cloned a lipoxygenase from human keratinocytes; the cDNA and deduced amino acid sequences share ≤50% identity to other human lipoxygenases. This enzyme, when expressed in Hela cells, oxygenates arachidonic acid to 12-HPETE, >98% 12R in configuration. The 12R-lipoxygenase cDNA is detectable by PCR in psoriatic scales and as a 2.5-kilobase mRNA by Northern analysis of keratinocytes. Identification of this enzyme extends the known distribution of R-lipoxygenases to humans and presents an additional target for potential therapeutic interventions in psoriasis.

Synchronization between prefrontal and posterior association cortex during human working memory

We measured coherence between the electroencephalogram at different scalp sites while human subjects performed delayed response tasks. The tasks required the retention of either verbalizable strings of characters or abstract line drawings. In both types of tasks, a significant enhancement in coherence in the θ range (4–7 Hz) was found between prefrontal and posterior electrodes during 4-s retention intervals. During 6-s perception intervals, far fewer increases in θ coherence were found. Also in other frequency bands, coherence increased; however, the patterns of enhancement made a relevance for working memory processes seem unlikely. Our results suggest that working memory involves synchronization between prefrontal and posterior association cortex by phase-locked, low frequency (4–7 Hz) brain activity.

Blind separation of auditory event-related brain responses into independent components

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected—and undetected—target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Modeling the dynamics of human hair cycles by a follicular automaton

The hair follicle cycle successively goes through the anagen, catagen, telogen, and latency phases, which correspond, respectively, to hair growth, arrest, shedding, and absence before a new anagen phase is initiated. Experimental observations collected over a period of 14 years in a group of 10 male volunteers, alopecic and nonalopecic, allowed us to determine the characteristics of scalp hair follicle cycles. On the basis of these observations, we propose a follicular automaton model to simulate the dynamics of human hair cycles. The automaton model is defined by a set of rules that govern the stochastic transitions of each follicle between the successive states anagen, telogen, and latency, and the subsequent return to anagen. The transitions occur independently for each follicle, after time intervals given stochastically by a distribution characterized by a mean and a variance. The follicular automaton model accounts both for the dynamical transitions observed in a single follicle and for the behavior of an ensemble of independently cycling follicles. Thus, the model successfully reproduces the evolution of the fractions of follicle populations in each of the three phases, which fluctuate around steady-state or slowly drifting values. We apply the follicular automaton model to the study of spatial patterns of follicular growth that result from a spatially heterogeneous distribution of parameters such as the mean duration of anagen phase. When considering that follicles die or miniaturize after going through a critical number of successive cycles, the model can reproduce the evolution to hair patterns similar to well known types of diffuse or androgenetic alopecia.

Information processing in the human brain: magnetoencephalographic approach.

Rapid progress in effective methods to image brain functions has revolutionized neuroscience. It is now possible to study noninvasively in humans neural processes that were previously only accessible in experimental animals and in brain-injured patients. In this endeavor, positron emission tomography has been the leader, but the superconducting quantum interference device-based magnetoencephalography (MEG) is gaining a firm role, too. With the advent of instruments covering the whole scalp, MEG, typically with 5-mm spatial and 1-ms temporal resolution, allows neuroscientists to track cortical functions accurately in time and space. We present five representative examples of recent MEG studies in our laboratory that demonstrate the usefulness of whole-head magnetoencephalography in investigations of spatiotemporal dynamics of cortical signal processing.

Selective attention to stimulus location modulates the steady-state visual evoked potential.

Steady-state visual evoked potentials (SSVEPs) were recorded from the scalp of human subjects who were cued to attend to a rapid sequence of alphanumeric characters presented to one visual half-field while ignoring a concurrent sequence of characters in the opposite half-field. These two-character sequences were each superimposed upon a small square background that was flickered at a rate of 8.6 Hz in one half-field and 12 Hz in the other half-field. The amplitude of the frequency-coded SSVEP elicited by either of the task-irrelevant flickering backgrounds was significantly enlarged when attention was focused upon the character sequence at the same location. This amplitude enhancement with attention was most prominent over occipital-temporal scalp areas of the right cerebral hemisphere regardless of the visual field of stimulation. These findings indicate that the SSVEP reflects an enhancement of neural responses to all stimuli that fall within the "spotlight" of spatial attention, whether or not the stimuli are task-relevant. Recordings of the SSVEP provide a new approach for studying the neural mechanisms and functional properties of selective attention to multi-element visual displays.

Temporal fluctuations in coherence of brain waves.

As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ.