Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
Developing Deep Learning Models (DL) for Classifying Emotions through Brainwaves
Reporter: Abhisar Anand, Research Assistant I Research Team: Abhisar Anand, Srinivas Sriram
2021 LPBI Summer Internship in Data Science and Website construction. This article reports on a research study conducted till December 2020. Research completed before the 2021 LPBI Summer Internship began in 6/15/2021.
As the field of Artificial Intelligence progresses, various algorithms have been implemented by researchers to classify emotions from EEG signals. Few researchers from China and Singapore released a paper (“An Investigation of Deep Learning Models from EEG-Based Emotion Recognition”) analyzing different types of DL model architectures such as deep neural networks (DNN), convolutional neural networks (CNN), long short-term memory (LSTM), and a hybrid of CNN and LSTM (CNN-LSTM). The dataset used in this investigation was the DEAP Dataset which consisted of EEG signals of patients that watched 40 one-minute long music videos and then rated them in terms of the levels of arousal, valence, like/dislike, dominance and familiarity. The result of the investigation presented that CNN (90.12%) and CNN-LSTM (94.7%) models had the highest performance out of the batch of DL models. On the other hand, the DNN model had a very fast training speed but was not able to perform as accurately as other other models. The LSTM model was also not able to perform accurately and the training speed was much slower as it was difficult to achieve convergence.
This research in the various model architectures provides a sense of what the future of Emotion Classification with AI holds. These Deep Learning models can be implemented in a variety of different scenarios across the world, all to help with detecting emotions in scenarios where it may be difficult to do so. However, there needs to be more research implemented in the model training aspect to ensure the accuracy of the classification is top-notch. Along with that, newer and more reliable hardware can be implemented in society to provide an easy-to-access and portable EEG collection device that can be used in any different scenario across the world. Overall, although future improvements need to be implemented, the future of making sure that emotions are accurately detected in all people is starting to look a lot brighter thanks to the innovation of AI in the neuroscience field.
Emotions are a key factor in any person’s day to day life. Most of the time, we as humans can detect these emotions through physical cues such as movements, facial expressions, and tone of voice. However, in certain individuals, it can be hard to identify their emotions through their visible physical cues. Recent studies in the Machine Learning and AI field provide a particular development in the ability to detect emotions through brainwaves, more specifically EEG brainwaves. These researchers from across the world utilize the same concept of EEG implemented in AI to help predict the state an individual is in at any given moment.
Emotion classification based on brain wave: a survey (Figure 4)
EEGs can detect and compile normal and abnormal brain wave activity and indicate brain activity or inactivity that correlates with physical, emotional, and intellectual activities. EEG signals are classified mainly by brain wave frequencies. The most commonly studied are delta, theta, alpha, sigma, and beta waves. Alpha waves, 8 to 12 hertz, are the key wave that occurs in normal awake people. They are the defining factor for the everyday function of the adult brain. Beta waves, 13 to 30 hertz, are the most common type of wave in both children and adults. They are found in the frontal and central areas of the brain and occur at a certain frequency which, if slowed, is likely to cause dysfunction. Theta waves, 4 to 7 hertz, are also found in the front of the brain, but they slowly move backward as drowsiness increases and the brain enters the early stages of sleep. Theta waves are known as active during focal seizures. Delta waves, 0.5 to 4 hertz, are found in the frontal areas of the brain during deep sleep. Sigma waves, 12-16 hertz, are very slow frequency waves that occur during sleep. These EEG signals can help for the detection of emotions based on the frequencies that the signals happen in and the activity of the signals (whether they are active or relatively calm).
Sources:
Zhang, Yaqing, et al. “An Investigation of Deep Learning Models for EEG-Based Emotion Recognition.” Frontiers in Neuroscience, vol. 14, 2020. Crossref, doi:10.3389/fnins.2020.622759.
Nayak, Anilkumar, Chetan, Arayamparambil. “EEG Normal Waveforms.” National Center for Biotechnology Information, StatPearls Publishing LLC., 4 May 2021, http://www.ncbi.nlm.nih.gov/books/NBK539805.
Other related articles published in this Open Access Online Scientific Journal include the Following:
We evaluated the effect of cognitive stimulation (CS) on platelet total phospholipases A2activity (tPLA2A) in patients with mild cognitive impairment (MCI_P). At baseline, tPLA2A negatively correlated with Mini-Mental State Examination score (MMSE_s): patients with MMSE_s <26 (Subgroup 1) had significantly higher activity than those with MMSE_s ≥26 (Subgroup 2), who had values similar to the healthy elderly. Regarding CS effect, Subgroup 1 had a significant tPLA2A reduction, whereas Subgroup 2 did not significantly changes after training. Our results showed for the first time that tPLA2A correlates with the cognitive conditions of MCI_P, and that CS acts selectively on subjects with a dysregulated tPLA2A.
Phospholipases A2 (PLA2) form a superfamily of enzymes that catalyze production of lyso-phospholipids and free fatty acids by the hydrolysis of phospholipids sn-2 ester bond. They play a pivotal role in many physiological processes, including membrane remodelingand cell signaling [1, 2], and are involved in neurodegenerative disorders [3, 4].
PLA2 modulation is a potential therapeutic target [5, 6]; in this context, cognitive stimulation (CS) is particularly promising, not only because in animal models it has effective regulating properties [7], but also because it is non-invasive, has no side effects, and presents no contraindications.
To date, only one study has been performed in humans: in a little cohort of healthy elderly subjects, a memory training intervention was proved to modulate platelet PLA2 activity [8]. The use of platelet PLA2 as peripheral biomarker of the neuronal enzyme is convincing in light of the recent finding that total PLA2 (tPLA2) activity in thrombocytes may mirror thetotal activity in the brain [9]. Moreover, platelets are widely considered “circulating neurons” because of the similarities existing between the two cells in terms of enzymes, receptors, and metabolic products [10, 11].
On these grounds, we evaluated the effects of CS on platelet tPLA2 activity in a cohort of subjects with mild cognitive impairment (MCI).
The present study showed that in subjects with MCI, platelet tPLA2 activity correlates with patients’ cognitive conditions, and that CS acts selectively on the enzyme, i.e., it modulates the parameter only in individuals with deregulated values in comparison to the healthy elderly.
Based on the MMSE score, it was possible to subdivide at baseline the MCI cohort into two subgroups: patients with more evident cognitive impairment (MMSE score <26) and significantly higher tPLA2 activity, and individuals cognitively more preserved (MMSE score ≥26), who had tPLA2 activity similar to the healthy elderly. The finding that the increase of tPLA2 activity and the severity of the global cognitive status impairment are significantly linked suggests a possible role of tPLA2 in MCI progression. PLA2 activity alterations may lead to the synthesis of excessive proinflammatory mediators and peroxidative products [19], and inflammation and oxidative stress may contribute to the pathogenesis of Alzheimer’s disease (AD) [20, 21], of which MCI could be a prodromal condition. It is therefore conceivable that the more deregulated tPLA2 is, the more harmful molecules might be released, and the more severe the pathological consequences might become. The finding that in patients affected by AD tPLA2 activity is significantly higher than in healthy controls [22, 23] as well as in MCI subjects [23] is in line with this hypothesis.
As far as the therapeutic potentialities of CS are concerned, the protocol not only exerted positive effects on several cognitive outcomes, but also counteracted the peripheral enzymatic deregulation. Indeed, CS improved parameters linked to memory, attention, and verbal, confirming the results of others [24]. It is worth noting that CS acts on tPLA2 activity in a “dysfunction-dependent” mode: in subjects with an initial enzymatic activity higher than in the healthy elderly (Subgroup 1), CS reduced the value; in subjects with an initial enzymatic activity similar to the healthy elderly (Subgroup 2) CS did not induce any significant change. Thus, CS seems to have homeostatic properties on tPLA2 activity. This result may seem in contradictionwith the observation that in the healthy elderly CS induces platelet tPLA2 increase [8]. Actually, it is conceivable that, in absence of pathology, increased activity produced by the training improves cell functioning while in MCI, where the increased values might be linked to inflammation and oxidative stress, the protocol acts in the opposite way. Indeed, recent evidence supports the use of specific PLA2 inhibitors as preventive/therapeutic agents for inflammatory disorders [25], and several studies showed that environmental enrichment exert anti-inflammatory and neuromodulatory effects [26]. Thus, in MCI and AD, where the involvement of neuroinflammation is well established [27, 28], CS may produce a down regulation effect in the central nervous system, which might influence also circulation blood components.
In conclusion, this study suggests that platelet tPLA2 activity may be useful as peripheral biomarker to differentiate MCI patients at different pathological stages, and sustains the use of CS as non-pharmacological therapeutic strategy.
JNK: A Putative Link Between Insulin Signaling and VGLUT1 in Alzheimer’s Disease
In the present work, the involvement of JNK in insulin signaling alterations and its role in glutamatergic deficits in Alzheimer’s disease (AD) has been studied. In postmortem cortical tissues, pJNK levels were increased, while insulin signaling and the expression of VGLUT1 were decreased. A significant correlation was found between reduced expression of insulin receptor and VGLUT1. The administration of a JNK inhibitor reversed the decrease in VGLUT1 expression found in a mice model of insulin resistance. It is suggested that activation of JNK in AD inhibits insulin signaling which could lead to a decreased expression of VGLUT1, therefore contributing to the glutamatergic deficit in AD.
Normal Amplitude of Electroretinography and Visual Evoked Potential Responses in AβPP/PS1 Mice
Alzheimer’s disease has been shown to affect vision in human patients and animal models. This may pose the risk of bias in behavior studies and therefore requires comprehensive investigation. We recorded electroretinography (ERG) under isoflurane anesthesia and visual evoked potentials (VEP) in awake amyloid expressing AβPPswe/PS1dE9 (AβPP/PS1) and wild-type littermate mice at a symptomatic age. The VEPs in response to patterned stimuli were normal in AβPP/PS1 mice. They also showed normal ERG amplitude but slightly shortened ERG latency in dark-adapted conditions. Our results indicate subtle changes in visual processing in aged male AβPP/PS1 mice specifically at a retinal level.
Brain Metabolism Correlates of The Free and Cued Selective Reminding Test in Mild Cognitive Impairment
Free and Cued Selective Reminding Test (FCSRT) measures immediate and delayed episodic memory and cueing sensitivity and is suitable to detect prodromal Alzheimer’s disease (AD). The present study aimed at investigating the segregation effect of FCSRT scores on brain metabolism of memory-related structures, usually affected by AD pathology, in the Mild Cognitive Impairment (MCI) stage. A cohort of forty-eight MCI patients underwent FCSRT and 18F-FDG-PET. Multiple regression analysis showed that Immediate Free Recall correlated with brain metabolism in the bilateral anterior cingulate and delayed free recall with the left anterior cingulate and medial frontal gyrus, whereas semantic cueing sensitivity with the left posterior cingulate. FCSRT in MCI is associated with neuro-functional activity of specific regions of memory-related structures connected to hippocampal formation, such as the cingulate cortex, usually damaged in AD.
The Presence of Select Tau Species in Human Peripheral Tissues and Their Relation to Alzheimer’s Disease
Tau becomes excessively phosphorylated in Alzheimer’s disease (AD) and is widely studied within the brain. Further examination of the extent and types of tau present in peripheral tissues and their relation to AD is warranted given recent publications on pathologic spreading. Cases were selected based on the presence of pathological tau spinal cord deposits (n = 18). Tissue samples from sigmoid colon, scalp, abdominal skin, liver, and submandibular gland were analyzed by western blot and enzyme-linked immunosorbent assays (ELISAs) for certain tau species; frontal cortex gray matter was used for comparison. ELISAs revealed brain to have the highest total tau levels, followed by submandibular gland, sigmoid colon, liver, scalp, and abdominal skin. Western blots with antibodies recognizing tau phosphorylated at threonine 231(pT231), serine 396 and 404 (PHF-1), and an unmodified total human tau between residues 159 and 163 (HT7) revealed multiple banding patterns, some of which predominated in peripheral tissues. As submandibular gland had the highest levels of peripheral tau, a second set of submandibular gland samples were analyzed (n = 36; 19 AD, 17 non-demented controls). ELISAs revealed significantly lower levels of pS396 (p = 0.009) and pT231 (p = 0.005) in AD cases but not total tau (p = 0.18). Furthermore, pT231 levels in submandibular gland inversely correlated with Braak neurofibrillary tangle stage (p = 0.04), after adjusting for age at death, gender, and postmortem interval. These results provide evidence that certain tau species are present in peripheral tissues. Of potential importance, submandibular gland pT231 is progressively less abundant with increasing Braak neurofibrillary tangle stage.
Non-Verbal Episodic Memory Deficits in Primary Progressive Aphasias are Highly Predictive of Underlying Amyloid Pathology
Diagnostic distinction of primary progressive aphasias (PPA) remains challenging, in particular for the logopenic (lvPPA) and nonfluent/agrammatic (naPPA) variants. Recent findings highlight that episodic memory deficits appear to discriminate these PPA variants from each other, as only lvPPA perform poorly on these tasks while having underlying amyloid pathology similar to that seen in amnestic dementias like Alzheimer’s disease (AD). Most memory tests are, however, language based and thus potentially confounded by the prevalent language deficits in PPA. The current study investigated this issue across PPA variants by contrasting verbal and non-verbal episodic memory measures while controlling for their performance on a language subtest of a general cognitive screen. A total of 203 participants were included (25 lvPPA; 29 naPPA; 59 AD; 90 controls) and underwent extensive verbal and non-verbal episodic memory testing, with a subset of patients (n = 45) with confirmed amyloid profiles as assessed by Pittsburgh Compound B and PET. The most powerful discriminator between naPPA and lvPPA patients was a non-verbal recall measure (Rey Complex Figure delayed recall), with 81% of PPA patients classified correctly at presentation. Importantly, AD and lvPPA patients performed comparably on this measure, further highlighting the importance of underlying amyloid pathology in episodic memory profiles. The findings demonstrate that non-verbal recall emerges as the best discriminator of lvPPA and naPPA when controlling for language deficits in high load amyloid PPA cases.
Prion and other amyloid-forming diseases represent a group of neurodegenerative disorders that affect both animals and humans. The role of metal ions, especially copper and zinc is studied intensively in connection with these diseases. Their involvement in protein misfolding and aggregation and their role in creation of reactive oxygen species have been shown. Recent data also show that metal ions not only bind the proteins with high affinity, but also modify their biochemical properties, making them important players in prion-related diseases. In particular, the level of zinc ions is tightly regulated by several mechanisms, including transporter proteins and the low molecular mass thiol-rich metallothioneins. From four metallothionein isoforms, metallothionein-3, a unique brain-specific metalloprotein, plays a crucial role only in this regulation. This review critically evaluates the involvement of metallothioneins in prion- and amyloid-related diseases in connection with the relationship between metallothionein isoforms and metal ion regulation of their homeostasis.
Do Microglia Default on Network Maintenance in Alzheimer’s Disease?
Although the cause of Alzheimer’s disease (AD) remains unknown, a number of new findings suggest that the immune system may play a critical role in the early stages of the disease. Genome-wide association studies have identified a wide array of risk-associated genes for AD, many of which are associated with abnormal functioning of immune cells. Microglia are the brain’s immune cells. They play an important role in maintaining the brain’s extracellular environment, including clearance of aggregated proteins such as amyloid-β (Aβ). Recent studies suggest that microglia play a more active role in the brain than initially considered. Specifically, microglia provide trophic support to neurons and also regulate synapses. Microglial regulation of neuronal activity may have important consequences for AD. In this article we review the function of microglia in AD and examine the possible relationship between microglial dysfunction and network abnormalities, which occur very early in disease pathogenesis.
Alzheimer’s disease (AD) is a progressive, neurodegenerative disease that primarily affects the regions of the brain that are associated with high functioning. AD is characterized by progressive dementia that begins with mood changes, memory loss, and reduced cognition [1]. The primary pathogenic process in AD is the accumulation of amyloid-β protein (Aβ) [1–3]. Aβ aggregates into extracellular amyloid plaques that are a hallmark pathological feature of the disease. Aβ is cleaved from the larger amyloid-β protein precursor (AβPP) [4–6]. However, it remains unclear why Aβ, a protein fragment normally only present in small amounts within the brain, is able to accumulate in the AD brain and cause toxicity. In a small percentage (5%) of AD sufferers, the cause of the disease is genetic. Inherited mutations within the AβPP gene itself appear to predispose the protein to Aβ production [7]. Mutations within the presenilin 1 and 2 genes, encoding proteins that form part of the secretase complex that cleaves the Aβ peptide from AβPP, also result in inherited AD due to accumulations of Aβ [8–11].
The cause of AD is largely unknown for the remaining 95% of cases of sporadic AD, which typically develops a decade or two later than familial AD [12]. However, the degenerative processes are nearly identical between the two forms of the disease. Therefore, it is reasonable to assume that the underlying disease process is the same between the two forms of the disease. Genetic studies have identified a number of genetic risk factors for AD. An early discovery was that allelic variants of apolipoprotein E (ApoE) carry inherently different risks of AD [13–15]. In particular, the ɛ4 allele carries a high risk of AD, with risk of disease occurring in a dose-dependent manner based onzygosity. The ApoE ɛ4 allele is associated with increased Aβ aggregation, reduced lipid transport, and reduced receptor-mediated Aβ clearance [16]. Interestingly, ApoE is predominantly expressed by non-neuronal cells— astrocytes and microglia, rather than by neurons [17]. These findings suggested that although clinical AD manifests from neuronal degeneration, other cells of the central nervous system (CNS) may be intimately involved in pathogenesis or disease progression.
More recently, genome-wide association studies (GWAS) have been used to identify a large number of risk genes for AD. In 2013, a mutation in the triggering receptor expressed on myeloid cells 2 (TREM2) was identified [18, 19]. TREM2 is almost exclusively expressed by immune cells within the brain, and mutations to TREM2 are associated with decreased phagocytosis and an increased pro-inflammatory reactive phenotype. Individuals heterozygous for TREM2 mutations have a high risk of developing AD, however the mutation is rare [18, 19]. Additional AD risk factor genes that have been identified include genes associated with lipid processing, endocytosis, and the immune response, which have recently been covered in excellent reviews [20, 21]. The common unifying feature of these immune-associated mutations is that they are proposed to interfere with microglial function, in particular, the efficiency of phagocytosis [20]. Specifically, mutations to complement receptor 1 (CR1) and cluster of differentiation 33 (CD33) can result in reduced activity of the complement system and reduced phagocytosis [22, 23]. Phosphatidylinositol binding clathrin assembly protein (PICALM) and bridging integrator 1 (BIN1) mutations affect clathrin-mediated endocytosis [24, 25] and SORL1 mutations reduce intracellular trafficking of AβPP [26]. The function of some of these proteins in relation to phagocytosis is discussed later in this review. The identification of such a wide array of risk genes associated with reduced immune cell function now leads us to believe that abnormal functioning of immune cells may play a more important role in the early stages of disease than previously considered.
MICROGLIA
Microglia are the immune cells of the CNS and account for approximately 10% of the CNS cellpopulation, with regional variation in density [27, 28]. During embryonic development, microglia originate from yolk sac progenitor cells that migrate into the developing CNS during early embryogenesis [29,30].Following construction of the blood-brain barrier (BBB), microglia are renewed by local turnover [31]. In the healthy brain, microglia actively support neurons through the release of insulin-like growth factor 1, nerve growth factor, ciliary neurotrophic factor, and brain-derived neurotrophic factor (BDNF) [32–34]. Microglia also provide indirect support to neurons by clearance of debris to maintain the extracellular environment, and phagocytosis of apoptotic cells to facilitate neurogenesis [35, 36]. In the adult brain, microglia coordinate much of their activity with astrocytes and activate in response to similar stimuli [37, 38]. Dysfunctional signaling between microglia and astrocytes often results in chronic inflammation, a characteristic of many neurodegenerative diseases [39, 40].
Historically, it has been thought that microglia ‘rest’ when not responding to inflammatory stimuli or damage [41, 42]. However, this notion is being increasingly recognized as inaccurate [43]. When not involved in active inflammatory signaling, microglia constantly patrol the neuropil by extension and retraction of their finely branched processes [44]. Microglial activation is often broadly classified into two states; pro-inflammatory (M1) or anti-inflammatory (M2) [36, 45], based on similar phenotypes in peripheral macrophages [46]. M1 activated microglia are characterized by increased expression of pro-inflammatory mediators and cytokines, including inducible nitric oxide synthase, tumor necrosis factor-α, and interleukin-1β, often under the control of the transcription factor nuclear factor-κB [45]. Pro-inflammatory microglia rapidly retract their processes and adopt an amoeboid morphology and often migrate closer to the site of injury [47]. Anti-inflammatory M2 activation of microglia, often referred to as alternative activation, represents the other side of microglial behavior. Anti-inflammatory activation is characterized by increased expression of cytokines including arginase 1 and interleukin-10, and is associated with increased ramification of processes [45]. The polarization of microglia into M1 or M2 throughout the brain is well characterized, especially in neurodegenerative diseases [48]. In the AD brain, microglia expressing markers of M1 activation are typically localized to brain regions such as the hippocampus that are most heavily affected in the disease [49]. However, it is important to note that M1 and M2 classifications of microglia may over-simplify microglial phenotypes and may only represent the extremes of microglial activation [50]. It has been more recently proposed that microglia likely occupy a continuum between these phenotypes [39, 51].
Do microglia have multiple roles in AD?
Classical pro-inflammatory activation of microglia has long been associated with AD [39, 49]. Samples taken from late-stage AD brains contain characteristic signs of inflammation, including amoeboid morphology of microglia, high levels of pro-inflammatory cytokines in the cerebrospinal fluid, and evidence of neuronal damage due to chronic exposure to pro-inflammatory cytokines and oxidative stress [52, 53]. The cause of this inflammation may be in response to direct toxicity of Aβ to neurons resulting in activation of nearby microglia and astrocytes [53, 54]. However, Aβ may also induce inflammatory activation of microglia and astrocytes. Activated immune cells are typically present surrounding amyloid plaques [55–57], with such peri-plaque cells exhibiting strong evidence of pro-inflammatory activation [56, 58–60]. The presence of undigested Aβ particles within these activated microglia may suggest that the Aβ peptide itself is a pro-inflammatory signal for microglia [61–64]. In vitro experiments provide supporting evidence for the in vivo studies, with Aβ promoting pro-inflammatory microglial activation [65, 66], and also acting as a potent chemotactic signal [67].
However, it is important to note that although widespread inflammation is characteristic of late-stage AD, it remains unclear what role inflammation could play in early stages of the disease. Some evidence suggests that reducing inflammation through the long-term use of some non-steroidal anti-inflammatory drugs (NSAIDs) can reduce the risk of AD [68]. However, these findings have not yet been verified in clinical trials [69, 70]. Little is understood about how NSAIDs and related compounds affect the delicate balance of pro- versus anti-inflammatory microglial activity within the brain. Although there is considerable evidence to suggest that chronic inflammation may contribute to pathology in the later stages of AD, it is important to note that inflammation normally only represents a small aspect of microglial function. The non-inflammatory functions of microglia may play a more important role in early disease; specifically, microglial functions relating to maintenance of the CNS.
Phagocytosis: A vital role of microglia that may be lost in AD
SYNAPTIC PRUNING: MICROGLIA CAN REGULATE NETWORK ACTIVITY
Recently, a new function has been proposed for microglia. A number of studies have provided evidence that microglia prune synapses throughout life. Microglia are known to remove extraneous synapses during development to ensure that only meaningful connections remain [43]. It was, however, thought that differentiated astrocytes performed the majority of synaptic pruning in the adult brain [91]. The discovery that microglial processes are constantly active within the brain and are often positioned near synapses raised the question of whether microglial synaptic pruning continued throughout life [44, 47, 92–94]. This question was answered in 2014 in a study that demonstrated that microglia do prune synapses into adulthood, and that this activity is important for normal brain function [95]. These findings supported those found a year earlier in a study reporting that ablation of microglia from brain slices increases synapse density and results in abnormal firing of hippocampalneurons [96].
Altered microglial behavior may underlie altered neuronal firing in AD
Altered neuronal activity is an early phenomenon in AD
The cause of DMN hypoactivity in AD is not yet clear; however studies performed in cohorts that are genetically predisposed to AD suggest that DMN hypoactivity is preceded by a period of hyperactivity and increased functional connectivity [123, 136], often manifesting as an absence of normal DMN deactivation during external tasks [137–140]. DMN hyperactivity may interfere with hippocampal memory encoding, leading to the memory deficits that are present in mild cognitive impairment [141, 142]. It has been proposed that hippocampal hyperexcitability in AD may develop as a protective mechanism against increased input from the DMN [142–144]. As AD progresses, the initial hyperexcitability of the DMN and hippocampus may result in hypoactivity due to exhaustion of compensatory mechanisms [123, 136]. Evidence from both transgenic AD mice and longitudinal human studies supports an exhaustion model of hyperactivation leading to later hypoactivation [143, 145–147]. Interestingly, a number of studies report a lower incidence of AD among those who regularly practice meditation which specifically ‘calms’ the DMN [148].
Our understanding of AD as a disease is changing. Historically considered to be primarily a disease of neuronal degeneration, this neurocentric view has widened to encompass non-neuronal cells such as astrocytes into our understanding of the disease process and pathogenesis. A proposed model for microglia in AD is shown in Fig. 2. Microglia perform a wide range of functions in the CNS and although this includes induction of an inflammatory reaction in response to damage, they also have critical roles for maintaining normal function in the brain. Recent evidence shows that microglia regulate neuronal activity through synaptic pruning throughout life as an extension on their normal phagocytosis behavior. The discovery of a large number of AD risk genes associated with reduced immune cell function suggests that perturbed microglial phagocytosis could lead to AD. In our model, altered microglial phagocytosis of synapses results in network dysfunction and onset of AD, occurring downstream of Aβ.
The immune system and microglia represent a novel target for intervention in AD. Importantly, a large number of anti-inflammatory drugs are already in use for other conditions. What is important to know at this stage is exactly how to best target immune cell function. The studies outlined here provide evidence that an indiscriminate dampening down of all microglial activity may result in a worse outcome for individuals by suppressing normal microglial regulatory functions. We currently do not know whether future microglial-based therapies should focus on reducing chronic inflammation or conversely, whether they should be aimed at boosting microglial phagocytosis. It is also likely that future treatment strategies may use a combination of approaches to target Aβ, immune cell phagocytosis and network activity. An increasing view in the AD field is that any drug or therapy needs to be provided very early in the disease process to maximize its beneficial effects. Although we are currently unable to effectively target those at risk of AD at such an early stage, advances in neuroimaging for subtle changes in network activity, or in assays for immune cell function, may provide new avenues for identification of early damage and risk of disease.
REFERENCES
[1]
Selkoe DJ ((2011) ) Alzheimer’s disease. Cold Spring Harb Perspect Biol 3: , pii: a004457.
[2]
Masters CL , Simms G , Weinman NA , Multhaup G , McDonald BL , Beyreuther K ((1985) ) Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad Sci U S A 82: , 4245–4249.
[3]
Glenner GG , Wong CW ((1984) ) Alzheimer’s disease: Initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res Commun 120: , 885–890.
[4]
Goldgaber D , Lerman MI , McBride OW , Saffiotti U , Gajdusek DC ((1987) ) Characterization and chromosomal localization of a cDNA encoding brain amyloid of Alzheimer’s disease. Science 235: , 877–880.
[5]
Kang J , Lemaire HG , Unterbeck A , Salbaum JM , Masters CL , Grzeschik KH , Multhaup G , Beyreuther K , Muller-Hill B ((1987) ) The precursor of Alzheimer’s disease amyloid A4 protein resembles a cell-surface receptor. Nature 325: , 733–736.
[6]
Robakis NK , Ramakrishna N , Wolfe G , Wisniewski HM ((1987) ) Molecular cloning and characterization of a cDNA encoding the cerebrovascular and the neuritic plaque amyloid peptides. Proc Natl Acad Sci U S A 84: , 4190–4194.
[7]
Levy E , Carman MD , Fernandez-Madrid IJ , Power MD , Lieberburg I , van Duinen SG , Bots GT , Luyendijk W , Frangione B ((1990) ) Mutation of the Alzheimer’s disease amyloid gene in hereditary cerebral hemorrhage, Dutch type. Science 248: , 1124–1126.
[8]
Levy-Lahad E , Wasco W , Poorkaj P , Romano DM , Oshima J , Pettingell WH , Yu CE , Jondro PD , Schmidt SD , Wang K , et al ((1995) ) Candidate gene for the chromosome 1 familial Alzheimer’s disease locus. Science 269: , 973–977.
[9]
Rogaev EI , Sherrington R , Rogaeva EA , Levesque G , Ikeda M , Liang Y , Chi H , Lin C , Holman K , Tsuda T , et al ((1995) ) Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene. Nature 376: , 775–778.
[10]
Sherrington R , Rogaev EI , Liang Y , Rogaeva EA , Levesque G , Ikeda M , Chi H , Lin C , Li G , Holman K , Tsuda T , Mar L , Foncin JF , Bruni AC , Montesi MP , Sorbi S , Rainero I , Pinessi L , Nee L , Chumakov I , Pollen D , Brookes A , Sanseau P , Polinsky RJ , Wasco W , Da Silva HA , Haines JL , Perkicak-Vance MA , Tanzi RE , Roses AD , Fraser PE , Rommens JM , St George-Hyslop PH ((1995) ) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375: , 754–760.
Late-Onset Metachromatic Leukodystrophy with Early Onset Dementia Associated with a Novel Missense Mutation in the Arylsulfatase A Gene
A 48-year-old male patient presented with personality changes and progressive memory loss over 2 years with initially suspected Hashimoto’s encephalopathy. Strategy of diagnostic workup of early onset dementia included dementia from neurodegenerative, neuroinflammatory, metabolic/toxic, and psychiatric origin. The patient’s neurological exam was normal. MRI revealed a leukencephalopathy, predominantly in the frontal periventricular white matter, without notable changes over 2 years. On neurophysiological examination, prolonged central conduction times and a sensorimotor polyneuropathy were noted. Neuropsychological impairment included disorientation in place and a reduced short time memory. Behavioral alterations were predominated by sudden mood changes and disinhibition. Cerebrospinal fluid was normal. Despite presence of thyroid autoantibodies, glucocorticosteroid treatment did not improve the dementia. A metachromatic leukodystrophy was diagnosed by decreased arylsulfatase-A activity in leucocytes/fibroblasts and identification of a compound heterozygous mutation in the ARSA gene: c.542T>G (exon 3) and the novel mutation c.1013T>C (exon 6). Pathogenic function was suggested by bioinformatic mutation search. In a patient with early onset dementia, strategic diagnostic workup including genetic assessment revealed an adult-onset metachromatic leukodystrophy with a novel mutation in the arylsulfatase A gene.
Most-Read JAD Articles in March 2016
Microbes and Alzheimer’s Disease – Openly Available Itzhaki, Ruth F. | Lathe, Richard | Balin, Brian J. | Ball, Melvyn J. | Bearer, Elaine L. | Braak, Heiko | Bullido, Maria J. | Carter, Chris | Clerici, Mario | Cosby, S. Louise | Del Tredici, Kelly | Field, Hugh | Fulop, Tamas | Grassi, Claudio | Griffin, W. Sue T. | Haas, Jürgen | Hudson, Alan P. | Kamer, Angela R. | Kell, Douglas B. | Licastro, Federico | Letenneur, Luc | Lövheim, Hugo | Mancuso, Roberta | Miklossy, Judith | Otth, Carola | Palamara, Anna Teresa | Perry, George | Preston, Christopher | Pretorius, Etheresia | Strandberg, Timo | Tabet, Naji | Taylor-Robinson, Simon D. | Whittum-Hudson, Judith A.
Perineuronal nets, shown in green, in three regions of the mouse brain. Credit: S.F. Palida et al.
Cognition and behavior rely on communication between individual neurons and extensive interactions between neural networks. But when synaptic dysfunction occurs, the results can be dire, leading to neurodegenerative symptoms in Alzheimer’s disease.
“The brain is the seed of our personal identity,” said Valina Dawson, Ph.D., director of neurogeneration and stem cell programs at Johns Hopkins University in Baltimore, Maryland. “It allows us to interact with our world but when things go wrong in the brain, it’s disastrous for the individual as well as the family.
“Our ability to treat these diseases is limited at the moment. We need new insight into what goes wrong.”
A lesser-known protein
Researchers, for years, have targeted amyloid beta (Aβ) in attempts to halt the progression of Alzheimer’s disease, and have recently, shown increased interest in the protein, tau.
But Paula Pousinha, Ph.D., at the French National Centre for Scientific Research, has focused her research on a lesser-known protein fragment: amyloid precursor protein intracellular domain (AICD). AICD is a fragment of amyloid precursor protein (APP), which is formed at the same time as Aβ in the brain. New evidence suggests that in addition to Aβ, AICD also disrupts communication between neurons during the progression of Alzheimer’s disease. Pousinha presented thesepublished findings at this year’s Society for Neuroscience (SfN) conference, which took place from October 17 to 21 in Chicago.
“Although AICD has been known for more than 10 years, it has been poorly studied,” said Pousinha.
Pousinha’s research team demonstrated that overexpressing AICD levels with AAV vector in rats’ brains “perturbs neuronal communication in the hippocampus,” a key structure necessary in forming memories and an area earliest affected in Alzheimer’s disease.
“In normal animals, if we apply to these neurons a high-frequency stimulation, afterward the neurons are stronger,” said Pousinha. “Neurons where we overexpressed AICD failed to have this potentization.”
Pousinha doesn’t negate the importance of Aβ in the development of neurodegenerative diseases. “Our study doesn’t exclude the pathological effects of Aβ,” she said. “We believe that Alzheimer’s disease is much more complex and has more than one candidate that has implications.
“It’s very important for the scientific community to understand the role of all these APP fragments of neuroinflammation — different pieces of the puzzle of how we can stop the disease progression.”
How do memories persist in the brain long term?
New research, also presented at this year’s SfN, has implications for understanding memory to develop treatments for Alzheimer’s disease and dementias. Sakina Palida, a graduate student at the University of California, San Diego found that localized modifications in the perineuronal net (PNN) at synapses could be a mechanism by which information is stably encoded and preserved in the brain over time.
“We still don’t understand how we stably encode and store memories in our brains for up to our entire lifetimes,” said Palida. The prevailing idea on how memories are maintained over time generally focus on postsynaptic proteins, said Palida. “But the problem with looking at intracellular synaptic proteins is that the majority turn over rapidly, of hours to at most a few days. So they’re very unstable.”
So, Palida and her team identified PNN as an ideal substrate for long-term memory. “Kind of like how you carve into stone — stone is a stable substrate — you retain the information regardless of what comes and goes over it.” They demonstrated that individual PNN proteins are highly stable, and that the PNN is locally degraded when synapses are strengthened.
And the team also demonstrated that mice lacking enzymes that degrade the PNN have deficient long-term, but not short-term, memory. “Which is a really exciting new result,” said Palida.
To track the PNN in live animals, Palida and her team fused a fluorescent protein to a small link protein in the PNN to allow tracking of PNN dynamics in real time. They also monitored PNN degradation in live cells after stimulating neurons with brain-derived neurotrophic factor (BDNF), a chemical secreted in the nervous system to enhance signaling — and observed localized degradation of the PNN at some newly formed synapses.
Crtl 1-Venus. Fusion of a fluorescent protein to small link proteins in the PNN allows tracking of PNN dynamics over time. Credit: S.F. Palida et al. Crtl1-Venus Neurons. Tracking PNN dynamics in live cells, in mouse brain tissue. Credit: S.F. Palida et al.
What’s next? “We’re currently making transgenic animals to express this protein, which would allow us to monitor PNN dynamics simultaneously with synaptic dynamics in a live animal brain, and really investigate this hypothesis further,” said Palida.
Increased APP intracellular domain (AICD) production perturbs synaptic signal integration via increased NMDAR function
Alzheimer’s disease (AD) is a neurodegenerative disease that begins as mild short-term memory deficits and culminates in total loss of cognition and executive functions. The main culprit of the disease, resulting from Amyloid-Precursor Protein (APP) processing, has been thought to be amyloid-b peptide (Ab). However, despite the genetic and cell biological evidence that supports the amyloid cascade hypothesis, it is becoming clear that AD etiology is complex and that Ab alone is unable to account for all aspects of AD [Pimplikar et al. J Neurosci.30: 14946. 2010]. Gamma-secretase not only liberates Ab, but also its C-terminal intracellular counterpart called APP intracellular domain (AICD) [Passer. et al. JAlzheimers Dis.2: 289-301. 2000], which is known to also accumulate in AD patient’s brain [Ghosal et al. PNAS.106:18367. 2009], but surprisingly little is known about its functions in the hippocampus. To address this crucial issue, we increased AICD production in vivo in adult CA1 pyramidal neurons, mimicking the human pathological condition. Different ex-vivo electrophysiological and pharmacological approaches, including double- patch of neighbor neurons were used. We clearly demonstrate that in vivo AICD production increases synaptic NMDA receptor currents. This causes a frequency-dependent disruption of synaptic signal integration, leading to impaired long-term potentiation, which we were able to rescue by different pharmacological approaches. Our results provide convincing and entirely novel evidence that increased in vivo production of AICD is enough, per se, to cause synaptic dysfunction in CA1 hippocampal neurons.
Society for Neuroscience Annual Meeting Showcases Strides in Brain Research
10/23/2015 – Stephanie Guzowski, Editor
CHICAGO – Nearly 30,000 researchers from more than 80 countries gathered this week at the annual Society for Neuroscience (SfN) meeting, the world’s largest conference focused on scientific discovery related to the brain and nervous system.
The 45th annual SfN meeting at McCormick Place convention center showcased more than 15,000 scientific presentations on advances in technologies and new research about brain structure, disease and treatments, and 517 exhibitors, according to event organizers.
Presentations covered a wide variety of topics including new technologies to study the brain, the science behind addiction, potential treatments for spinal cord injuries, and the role of synapses in neurological conditions.
Of particular focus was the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, the large collaborative quest to develop technologies for a dynamic view of the brain. In early October, the National Institutes of Health announced its second round of funding to support goals, bringing the NIH investment to $85 million in fiscal year 2015.
“But now, we know that tau is not simply a bystander but also a player,” Li said. “Both proteins work together to damage cell functions as the disease unfolds.”
Targeting tau
In the healthy brain, tau protein helps with the building and functioning of neurons. But when tau malfunctions, it creates abnormal clumps of protein fibers—neurofibrillary tangles—which spread rapidly throughout the brain. This highly toxic and altered form of the brain protein tau is called “tau oligomer.”
“There’s growing evidence that tau oligomers, not tau protein in general, are responsible for the development of neurodegenerative diseases, like Alzheimer’s,” said Julia Gerson, a graduate student in neuroscience at the University of Texas Medical Branch.
In Gerson’s research, which she presented at this year’s Society for Neuroscience meeting in Washington, D.C., Gerson and her team injected tau oligomers from people with Alzheimer’s into the brains of healthy mice. Subsequent testing revealed that the mice had developed memory loss.
“When we inject mice with tau oligomers, we see that they spend the same amount of time exploring a familiar object as an unfamiliar object,” said Gerson. “So they’re incapable of remembering that they’ve already seen this familiar object.”
What’s more, the molecules had multiplied throughout the animals’ brains. “This suggests that tau oligomers may spread from the injection site to other unaffected regions,” said Gerson.
Future treatments
Understanding tau’s connection to Alzheimer’s could have implications for potential therapies. “If we can stop the spread of these toxic tau oligomers, we may be capable of either preventing, or reversing, symptoms,” said Gerson. Gerson’s lab is currently investigating antibodies, which specifically fight tau oligomers.
Click to Enlarge. Normal brain vs. Alzheimer’s brain (Credit: Garrondo)
Erik Roberson, M.D., Ph.D., at the University of Alabama at Birmingham, and colleagues looked at how boosting the function of a specific type of neurotransmitter receptor, the NMDA receptor, provided benefit to people with the second most common type of dementia: frontotemporal dementia (FTD), a disease in which people experience rapid and dramatic changes in behavior, personality and social skills. People often quickly deteriorate and usually die about three years after diagnosis; there is also no effective treatment for FTD.
Since mutated tau impairs synapses—the connections between neurons—by reducing the size of NMDA receptors, “boosting the function of remaining NMDA receptors may help restore synaptic firing, and reverse behavioral abnormalities,” said Roberson.
Roberson’s, along with others’ work presented at the Society of Neuroscience meeting, focused on using animal models that mimic developing tau pathology. “These new mouse models, which contain both tau tangles and amyloid plaques” said Dr. Li, “offer the possibility of more accurately testing therapies directed at delaying the onset of amyloid beta plaques, tau accumulation and neuronal loss, all characteristic features of Alzheimer’s.”
Are clinical trials next?
Potentially, yes. “This arena of academic research has been ongoing for several years—it’s a younger area in terms of involvement of drug discovery,” said Sangram Sisodia, Ph.D., director of the Center for Molecular Neurobiology at the University of Chicago. “But I believe there is growing interest in pharma companies about targeting tau.
“The tau protein plays an incredibly complex role in the development of Alzheimer’s and other neurodegenerative diseases,” said Sisodia. “We are in the early stages of understanding that role, which will be crucial for developing effective preventions or treatments.”
CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics – Part IIB
Curator: Larry H Bernstein, MD, FCAP
Part I: The Initiation and Growth of Molecular Biology and Genomics – Part I From Molecular Biology to Translational Medicine: How Far Have We Come, and Where Does It Lead Us?
Part IIB. “CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” lays the manifold multivariate systems analytical tools that has moved the science forward to a groung that ensures clinical application.
Part IIC. “CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease “ will extend the discussion to advances in the management of patients as well as providing a roadmap for pharmaceutical drug targeting.
Part IIB. “CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics” is a continuation of a previous discussion on the role of genomics in discovery of therapeutic targets titled, Directions for Genomics in Personalized Medicine, which focused on:
key drivers of cellular proliferation,
stepwise mutational changes coinciding with cancer progression, and
potential therapeutic targets for reversal of the process.
It is a direct extension of The Initiation and Growth of Molecular Biology and Genomics – Part I
These articles review a web-like connectivity between inter-connected scientific discoveries, as significant findings have led to novel hypotheses and many expectations over the last 75 years. This largely post WWII revolution has driven our understanding of biological and medical processes at an exponential pace owing to successive discoveries of
Genome sequencing projects have provided rich troves of information about
stretches of DNA that regulate gene expression, as well as
how different genetic sequences contribute to health and disease.
But these studies miss a key element of the genome—its spatial organization—which has long been recognized as an important regulator of gene expression.
Regulatory elements often lie thousands of base pairs away from their target genes, and recent technological advances are allowing scientists to begin examining
how distant chromosome locations interact inside a nucleus.
The creation and function of 3-D genome organization, some say, is the next frontier of genetics.
Mapping and sequencing may be completely separate processes. For example, it’s possible to determine the location of a gene—to “map” the gene—without sequencing it. Thus, a map may tell you nothing about the sequence of the genome, and a sequence may tell you nothing about the map. But the landmarks on a map are DNA sequences, and mapping is the cousin of sequencing. A map of a sequence might look like this:
On this map, GCC is one landmark; CCCC is another. Here we find, the sequence is a landmark on a map. In general, particularly for humans and other species with large genomes,
creating a reasonably comprehensive genome map is quicker and cheaper than sequencing the entire genome.
mapping involves less information to collect and organize than sequencing does.
Completed in 2003, the Human Genome Project (HGP) was a 13-year project. The goals were:
identify all the approximately 20,000-25,000 genes in human DNA,
determine the sequences of the 3 billion chemical base pairs that make up human DNA,
store this information in databases,
improve tools for data analysis,
transfer related technologies to the private sector, and
address the ethical, legal, and social issues (ELSI) that may arise from the project.
Though the HGP is finished, analyses of the data will continue for many years. By licensing technologies to private companies and awarding grants for innovative research, the project catalyzed the multibillion-dollar U.S. biotechnology industry and fostered the development of new medical applications. When genes are expressed, their sequences are first converted into messenger RNA transcripts, which can be isolated in the form of complementary DNAs (cDNAs). A small portion of each cDNA sequence is all that is needed to develop unique gene markers, known as sequence tagged sites or STSs, which can be detected using the polymerase chain reaction (PCR). To construct a transcript map, cDNA sequences from a master catalog of human genes were distributed to mapping laboratories in North America, Europe, and Japan. These cDNAs were converted to STSs and their physical locations on chromosomes determined on one of two radiation hybrid (RH) panels or a yeast artificial chromosome (YAC) library containing human genomic DNA. This mapping data was integrated relative to the human genetic map and then cross-referenced to cytogenetic band maps of the chromosomes. (Further details are available in the accompanying article in the 25 October issue of SCIENCE).
Tremendous progress has been made in the mapping of human genes, a major milestone in the Human Genome Project. Apart from its utility in advancing our understanding of the genetic basis of disease, it provides a framework and focus for accelerated sequencing efforts by highlighting key landmarks (gene-rich regions) of the chromosomes. The construction of this map has been possible through the cooperative efforts of an international consortium of scientists who provide equal, full and unrestricted access to the data for the advancement of biology and human health.
There are two types of maps: genetic linkage map and physical map. The genetic linkage map shows the arrangement of genes and genetic markers along the chromosomes as calculated by the frequency with which they are inherited together. The physical map is representation of the chromosomes, providing the physical distance between landmarks on the chromosome, ideally measured in nucleotide bases. Physical maps can be divided into three general types: chromosomal or cytogenetic maps, radiation hybrid (RH) maps, and sequence maps.
Kind J, van Steensel B. Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands.
The nuclear lamina, a filamentous protein network that coats the inner nuclear membrane, has long been thought to interact with specific genomic loci and regulate their expression. Molecular mapping studies have now identified
large genomic domains that are in contact with the lamina.
Genes in these domains are typically repressed, and artificial tethering experiments indicate that
the lamina can actively contribute to this repression.
Furthermore, the lamina indirectly controls gene expression in the nuclear interior by sequestration of certain transcription factors.
Peric-Hupkes D, Meuleman W, Pagie L, Bruggeman SW, Solovei I, …., van Steensel B. Division of Gene Regulation, Netherlands Cancer Institute, Amsterdam, The Netherlands.
To visualize three-dimensional organization of chromosomes within the nucleus, we generated high-resolution maps of genome-nuclear lamina interactions during subsequent differentiation of mouse embryonic stem cells via lineage-committed neural precursor cells into terminally differentiated astrocytes. A basal chromosome architecture present in embryonic stem cells is cumulatively altered at hundreds of sites during lineage commitment and subsequent terminal differentiation. This remodeling involves both
individual transcription units and multigene regions and
affects many genes that determine cellular identity.
genes that move away from the lamina are concomitantly activated;
others, remain inactive yet become unlocked for activation in a next differentiation step.
lamina-genome interactions are widely involved in the control of gene expression programs during lineage commitment and terminal differentiation.
Various cell types share a core architecture of genome-nuclear lamina interactions
During differentiation, hundreds of genes change their lamina interactions
Changes in lamina interactions reflect cell identity
Release from the lamina may unlock some genes for activation
Fractal “globule”
About 10 years ago—just as the human genome project was completing its first draft sequence—Dekker pioneered a new technique, called chromosome conformation capture (C3) that allowed researchers to get a glimpse of how chromosomes are arranged relative to each other in the nucleus. The technique relies on the physical cross-linking of chromosomal regions that lie in close proximity to one another. The regions are then sequenced to identify which regions have been cross-linked. In 2009, using a high throughput version of this basic method, called Hi-C, Dekker and his collaborators discovered that the human genome appears to adopt a “fractal globule” conformation—
a manner of crumpling without knotting.
In the last 3 years, Jobe Dekker and others have advanced technology even further, allowing them to paint a more refined picture of how the genome folds—and how this influences gene expression and disease states. Dekker’s 2009 findings were a breakthrough in modeling genome folding, but the resolution—about 1 million base pairs— was too crude to allow scientists to really understand how genes interacted with specific regulatory elements. The researchers report two striking findings.
First, the human genome is organized into two separate compartments, keeping
active genes separate and accessible
while sequestering unused DNA in a denser storage compartment.
Chromosomes snake in and out of the two compartments repeatedly
as their DNA alternates between active, gene-rich and inactive, gene-poor stretches.
Second, at a finer scale, the genome adopts an unusual organization known in mathematics as a “fractal.” The specific architecture the scientists found, called
a “fractal globule,” enables the cell to pack DNA incredibly tightly —
the information density in the nucleus is trillions of times higher than on a computer chip — while avoiding the knots and tangles that might interfere with the cell’s ability to read its own genome. Moreover, the DNA can easily Unfold and Refold during
gene activation,
gene repression, and
cell replication.
Dekker and his colleagues discovered, for example, that chromosomes can be divided into folding domains—megabase-long segments within which
genes and regulatory elements associate more often with one another than with other chromosome sections.
The DNA forms loops within the domains that bring a gene into close proximity with a specific regulatory element at a distant location along the chromosome. Another group, that of molecular biologist Bing Ren at the University of California, San Diego, published a similar finding in the same issue of Nature. Dekker thinks the discovery of [folding] domains will be one of the most fundamental [genetics] discoveries of the last 10 years. The big questions now are
how these domains are formed, and
what determines which elements are looped into proximity.
“By breaking the genome into millions of pieces, we created a spatial map showing how close different parts are to one another,” says co-first author Nynke van Berkum, a postdoctoral researcher at UMass Medical School in Dekker‘s laboratory. “We made a fantastic three-dimensional jigsaw puzzle and then, with a computer, solved the puzzle.”
Lieberman-Aiden, van Berkum, Lander, and Dekker’s co-authors are Bryan R. Lajoie of UMMS; Louise Williams, Ido Amit, and Andreas Gnirke of the Broad Institute; Maxim Imakaev and Leonid A. Mirny of MIT; Tobias Ragoczy, Agnes Telling, and Mark Groudine of the Fred Hutchison, Cancer Research Center and the University of Washington; Peter J. Sabo, Michael O. Dorschner, Richard Sandstrom, M.A. Bender, and John Stamatoyannopoulos of the University of Washington; and Bradley Bernstein of the Broad Institute and Harvard Medical School.
2C. three-dimensional structure of the human genome
Using a new technology called Hi-C and applying it to answer the thorny question of how each of our cells stows some three billion base pairs of DNA while maintaining access to functionally crucial segments. The paper comes from a team led by scientists at Harvard University, the Broad Institute of Harvard and MIT, University of Massachusetts Medical School, and the Massachusetts Institute of Technology. “We’ve long known that on a small scale, DNA is a double helix,” says co-first author Erez Lieberman-Aiden, a graduate student in the Harvard-MIT Division of Health Science and Technology and a researcher at Harvard’s School of Engineering and Applied Sciences and in the laboratory of Eric Lander at the Broad Institute. “But if the double helix didn’t fold further, the genome in each cell would be two meters long. Scientists have not really understood how the double helix folds to fit into the nucleus of a human cell, which is only about a hundredth of a millimeter in diameter. This new approach enabled us to probe exactly that question.”
The mapping technique that Aiden and his colleagues have come up with bridges a crucial gap in knowledge—between what goes on at the smallest levels of genetics (the double helix of DNA and the base pairs) and the largest levels (the way DNA is gathered up into the 23 chromosomes that contain much of the human genome). The intermediate level, on the order of thousands or millions of base pairs, has remained murky. As the genome is so closely wound, base pairs in one end can be close to others at another end in ways that are not obvious merely by knowing the sequence of base pairs. Borrowing from work that was started in the 1990s, Aiden and others have been able to figure out which base pairs have wound up next to one another. From there, they can begin to reconstruct the genome—in three dimensions.
Even as the multi-dimensional mapping techniques remain in their early stages, their importance in basic biological research is becoming ever more apparent. “The three-dimensional genome is a powerful thing to know,” Aiden says. “A central mystery of biology is the question of how different cells perform different functions—despite the fact that they share the same genome.” How does a liver cell, for example, “know” to perform its liver duties when it contains the same genome as a cell in the eye? As Aiden and others reconstruct the trail of letters into a three-dimensional entity, they have begun to see that “the way the genome is folded determines which genes were
2D. “Mr. President; The Genome is Fractal !”
Eric Lander (Science Adviser to the President and Director of Broad Institute) et al. delivered the message on Science Magazine cover (Oct. 9, 2009) and generated interest in this by the International HoloGenomics Society at a Sept meeting.
First, it may seem to be trivial to rectify the statement in “About cover” of Science Magazine by AAAS.
The statement “the Hilbert curve is a one-dimensional fractal trajectory” needs mathematical clarification.
The mathematical concept of a Hilbert space, named after David Hilbert, generalizes the notion of Euclidean space. It extends the methods of vector algebra and calculus from the two-dimensional Euclidean plane and three-dimensional space to spaces with any finite or infinite number of dimensions. A Hilbert space is an abstract vector space possessing the structure of an inner product that allows length and angle to be measured. Furthermore, Hilbert spaces must be complete, a property that stipulates the existence of enough limits in the space to allow the techniques of calculus to be used. A Hilbert curve (also known as a Hilbert space-filling curve) is a continuous fractal space-filling curve first described by the German mathematician David Hilbert in 1891,[1] as a variant of the space-filling curves discovered by Giuseppe Peano in 1890.[2] For multidimensional databases, Hilbert order has been proposed to be used instead of Z order because it has better locality-preserving behavior.
Representation as Lindenmayer system
The Hilbert Curve can be expressed by a rewrite system (L-system).
Alphabet : A, B
Constants : F + –
Axiom : A
Production rules:
A → – B F + A F A + F B –
B → + A F – B F B – F A +
Here, F means “draw forward”, – means “turn left 90°”, and + means “turn right 90°” (see turtle graphics).
While the paper itself does not make this statement, the new Editorship of the AAAS Magazine might be even more advanced if the previous Editorship did not reject (without review) a Manuscript by 20+ Founders of (formerly) International PostGenetics Society in December, 2006.
Second, it may not be sufficiently clear for the reader that the reasonable requirement for the DNA polymerase to crawl along a “knot-free” (or “low knot”) structure does not need fractals. A “knot-free” structure could be spooled by an ordinary “knitting globule” (such that the DNA polymerase does not bump into a “knot” when duplicating the strand; just like someone knitting can go through the entire thread without encountering an annoying knot): Just to be “knot-free” you don’t need fractals. Note, however, that
the “strand” can be accessed only at its beginning – it is impossible to e.g. to pluck a segment from deep inside the “globulus”.
This is where certain fractals provide a major advantage – that could be the “Eureka” moment for many readers. For instance,
the mentioned Hilbert-curve is not only “knot free” –
but provides an easy access to “linearly remote” segments of the strand.
If the Hilbert curve starts from the lower right corner and ends at the lower left corner, for instance
the path shows the very easy access of what would be the mid-point
if the Hilbert-curve is measured by the Euclidean distance along the zig-zagged path.
Likewise, even the path from the beginning of the Hilbert-curve is about equally easy to access – easier than to reach from the origin a point that is about 2/3 down the path. The Hilbert-curve provides an easy access between two points within the “spooled thread”; from a point that is about 1/5 of the overall length to about 3/5 is also in a “close neighborhood”.
This may be the “Eureka-moment” for some readers, to realize that
the strand of “the Double Helix” requires quite a finess to fold into the densest possible globuli (the chromosomes) in a clever way
that various segments can be easily accessed. Moreover, in a way that distances between various segments are minimized.
This marvellous fractal structure is illustrated by the 3D rendering of the Hilbert-curve. Once you observe such fractal structure, you’ll never again think of a chromosome as a “brillo mess”, would you? It will dawn on you that the genome is orders of magnitudes more finessed than we ever thought so.
Those embarking at a somewhat complex review of some historical aspects of the power of fractals may wish to consult the ouvre of Mandelbrot (also, to celebrate his 85th birthday). For the more sophisticated readers, even the fairly simple Hilbert-curve (a representative of the Peano-class) becomes even more stunningly brilliant than just some “see through density”. Those who are familiar with the classic “Traveling Salesman Problem” know that “the shortest path along which every given n locations can be visited once, and only once” requires fairly sophisticated algorithms (and tremendous amount of computation if n>10 (or much more). Some readers will be amazed, therefore, that for n=9 the underlying Hilbert-curve helps to provide an empirical solution.
refer to pellionisz@junkdna.com
Briefly, the significance of the above realization, that the (recursive) Fractal Hilbert Curve is intimately connected to the (recursive) solution of TravelingSalesman Problem, a core-concept of Artificial Neural Networks can be summarized as below.
Accomplished physicist John Hopfield (already a member of the National Academy of Science) aroused great excitement in 1982 with his (recursive) design of artificial neural networks and learning algorithms which were able to find reasonable solutions to combinatorial problems such as the Traveling SalesmanProblem. (Book review Clark Jeffries, 1991, see also 2. J. Anderson, R. Rosenfeld, and A. Pellionisz (eds.), Neurocomputing 2: Directions for research, MIT Press, Cambridge, MA, 1990):
“Perceptions were modeled chiefly with neural connections in a “forward” direction: A -> B -* C — D. The analysis of networks with strong backward coupling proved intractable. All our interesting results arise as consequences of the strong back-coupling” (Hopfield, 1982).
The Principle of Recursive Genome Function surpassed obsolete axioms that blocked, for half a Century, entry of recursive algorithms to interpretation of the structure-and function of (Holo)Genome. This breakthrough, by uniting the two largely separate fields of Neural Networks and Genome Informatics, is particularly important for
those who focused on Biological (actually occurring) Neural Networks (rather than abstract algorithms that may not, or because of their core-axioms, simply could not
represent neural networks under the governance of DNA information).
3A. The FractoGene Decade
from Inception in 2002 to Proofs of Concept and Impending Clinical Applications by 2012
Junk DNA Revisited (SF Gate, 2002)
The Future of Life, 50th Anniversary of DNA (Monterey, 2003)
Mandelbrot and Pellionisz (Stanford, 2004)
Morphogenesis, Physiology and Biophysics (Simons, Pellionisz 2005)
PostGenetics; Genetics beyond Genes (Budapest, 2006)
ENCODE-conclusion (Collins, 2007)
The Principle of Recursive Genome Function (paper, YouTube, 2008)
Cold Spring Harbor presentation of FractoGene (Cold Spring Harbor, 2009)
Mr. President, the Genome is Fractal! (2009)
HolGenTech, Inc. Founded (2010)
Pellionisz on the Board of Advisers in the USA and India (2011)
ENCODE – final admission (2012)
Recursive Genome Function is Clogged by Fractal Defects in Hilbert-Curve (2012)
Geometric Unification of Neuroscience and Genomics (2012)
US Patent Office issues FractoGene 8,280,641 to Pellionisz (2012)
When the human genome was first sequenced in June 2000, there were two pretty big surprises. The first was thathumans have only about 30,000-40,000 identifiable genes, not the 100,000 or more many researchers were expecting. The lower –and more humbling — number
means humans have just one-third more genes than a common species of worm.
The second stunner was
how much human genetic material — more than 90 percent — is made up of what scientists were calling “junk DNA.”
The term was coined to describe similar but not completely identical repetitive sequences of amino acids (the same substances that make genes), which appeared to have no function or purpose. The main theory at the time was that these apparently non-working sections of DNA were just evolutionary leftovers, much like our earlobes.
If biophysicist Andras Pellionisz is correct, genetic science may be on the verge of yielding its third — and by far biggest — surprise.
With a doctorate in physics, Pellionisz is the holder of Ph.D.’s in computer sciences and experimental biology from the prestigious Budapest Technical University and the Hungarian National Academy of Sciences. A biophysicist by training, the 59-year-old is a former research associate professor of physiology and biophysics at New York University, author of numerous papers in respected scientific journals and textbooks, a past winner of the prestigious Humboldt Prize for scientific research, a former consultant to NASA and holder of a patent on the world’s first artificial cerebellum, a technology that has already been integrated into research on advanced avionics systems. Because of his background, the Hungarian-born brain researcher might also become one of the first people to successfully launch a new company by using the Internet to gather momentum for a novel scientific idea.
The genes we know about today, Pellionisz says, can be thought of as something similar to machines that make bricks (proteins, in the case of genes), with certain junk-DNA sections providing a blueprint for the different ways those proteins are assembled. The notion that at least certain parts of junk DNA might have a purpose for example, many researchers now refer to with a far less derogatory term: introns.
In a provisional patent application filed July 31, Pellionisz claims to have unlocked a key to the hidden role junk DNA plays in growth — and in life itself. His patent application covers all attempts to count, measure and compare the fractal properties of introns for diagnostic and therapeutic purposes.
3B. The Hidden Fractal Language of Intron DNA
To fully understand Pellionisz’ idea, one must first know what a fractal is.
Fractals are a way that nature organizes matter. Fractal patterns can be found in anything that has a nonsmooth surface (unlike a billiard ball), such as coastal seashores, the branches of a tree or the contours of a neuron (a nerve cell in the brain). Some, but not all, fractals are self-similar and stop repeating their patterns at some stage; the branches of a tree, for example, can get only so small. Because they are geometric, meaning they have a shape, fractals can be described in mathematical terms. It’s similar to the way a circle can be described by using a number to represent its radius (the distance from its center to its outer edge). When that number is known, it’s possible to draw the circle it represents without ever having seen it before.
Although the math is much more complicated, the same is true of fractals. If one has the formula for a given fractal, it’s possible to use that formula
to construct, or reconstruct,
an image of whatever structure it represents,
no matter how complicated.
The mysteriously repetitive but not identical strands of genetic material are in reality building instructions organized in a special type
of pattern known as a fractal. It’s this pattern of fractal instructions, he says, that
tells genes what they must do in order to form living tissue,
everything from the wings of a fly to the entire body of a full-grown human.
In a move sure to alienate some scientists, Pellionisz has chosen the unorthodox route of making his initial disclosures online on his own Web site. He picked that strategy, he says, because it is the fastest way he can document his claims and find scientific collaborators and investors. Most mainstream scientists usually blanch at such approaches, preferring more traditionally credible methods, such as publishing articles in peer-reviewed journals.
Basically, Pellionisz’ idea is that a fractal set of building instructions in the DNA plays a similar role in organizing life itself. Decode the way that language works, he says, and in theory it could be reverse engineered. Just as knowing the radius of a circle lets one create that circle, the more complicated fractal-based formula would allow us to understand how nature creates a heart or simpler structures, such as disease-fighting antibodies. At a minimum, we’d get a far better understanding of how nature gets that job done.
The complicated quality of the idea is helping encourage new collaborations across the boundaries that sometimes separate the increasingly intertwined disciplines of biology, mathematics and computer sciences.
Background: Several studies have shown that genomes can be studied via a multifractal formalism. Recently, we used a multifractal approach to study the genetic information content of the Caenorhabditis elegans genome. Here we investigate the possibility that the human genome shows a similar behavior to that observed in the nematode.
Results: We report here multifractality in the human genome sequence. This behavior correlates strongly on the
presence of Alu elements and
to a lesser extent on CpG islands and (G+C) content.
In contrast, no or low relationship was found for LINE, MIR, MER, LTRs elements and DNA regions poor in genetic information.
Gene function,
cluster of orthologous genes,
metabolic pathways, and
exons tended to increase their frequencies with ranges of multifractality and
large gene families were located in genomic regions with varied multifractality.
Additionally, a multifractal map and classification for human chromosomes are proposed.
Conclusions
we propose a descriptive non-linear model for the structure of the human genome,
This model reveals
a multifractal regionalization where many regions coexist that are far from equilibrium and
this non-linear organization has significant molecular and medical genetic implications for understanding the role of
Alu elements in genome stability and structure of the human genome.
Given the role of Alu sequences in
gene regulation,
genetic diseases,
human genetic diversity,
adaptation
and phylogenetic analyses,
these quantifications are especially useful.
MiIP: The Monomer Identification and Isolation Program
Repetitive elements within genomic DNA are both functionally and evolutionarilly informative. Discovering these sequences ab initio is
computationally challenging, compounded by the fact that
sequence identity between repetitive elements can vary significantly.
Here we present a new application, the Monomer Identification and Isolation Program (MiIP), which provides functionality to both
search for a particular repeat as well as
discover repetitive elements within a larger genomic sequence.
To compare MiIP’s performance with other repeat detection tools, analysis was conducted for
synthetic sequences as well as
several a21-II clones and
HC21 BAC sequences.
The primary benefit of MiIP is the fact that it is a single tool capable of searching for both
known monomeric sequences as well as
discovering the occurrence of repeats ab initio, per the user’s required sensitivity of the search.
Methods for Examining Genomic and Proteomic Interactions
1. An Integrated Statistical Approach to Compare Transcriptomics Data Across Experiments: A Case Study on the Identification of Candidate Target Genes of the Transcription Factor PPARα
An effective strategy to elucidate the signal transduction cascades activated by a transcription factor is to compare the transcriptional profiles of wild type and transcription factor knockout models. Many statistical tests have been proposed for analyzing gene expression data, but most
tests are based on pair-wise comparisons. Since the analysis of microarrays involves the testing of multiple hypotheses within one study, it is
generally accepted that one should control for false positives by the false discovery rate (FDR). However, it has been reported that
this may be an inappropriate metric for comparing data across different experiments.
Here we propose an approach that addresses the above mentioned problem by the simultaneous testing and integration of the three hypotheses (contrasts) using the cell means ANOVA model.
These three contrasts test for the effect of
a treatment in wild type,
gene knockout, and
globally over all experimental groups.
We illustrate our approach on microarray experiments that focused on the identification of candidate target genes and biological processes governed by the fatty acid sensing transcription factor PPARα in liver. Compared to the often applied FDR based across experiment comparison, our approach identified a conservative but less noisy set of candidate genes with same sensitivity and specificity. However, our method had the advantage of
properly adjusting for multiple testing while
integrating data from two experiments, and
was driven by biological inference.
We present a simple, yet efficient strategy to compare
differential expression of genes across experiments
while controlling for multiple hypothesis testing.
The complexity of biomolecular interactions and influences is a major obstacle to their comprehension and elucidation. Visualizing knowledge of biomolecular interactions increases comprehension and facilitates the development of new hypotheses. The rapidly changing landscape of high-content experimental results also presents a challenge for the maintenance of comprehensive knowledgebases. Distributing the responsibility for maintenance of a knowledgebase to a community of subject matter experts is an effective strategy for large, complex and rapidly changing knowledgebases. Cognoscente serves these needs by
building visualizations for queries of biomolecular interactions on demand,
by managing the complexity of those visualizations, and
by crowdsourcing to promote the incorporation of current knowledge from the literature.
Imputing functional associations between biomolecules and imputing directionality of regulation for those predictions each
require a corpus of existing knowledge as a framework to build upon. Comprehension of the complexity of this corpus of knowledge
will be facilitated by effective visualizations of the corresponding biomolecular interaction networks.
as an effective visualization tool for systems biology research and education.
Cognoscentecurrently contains over 413,000 documented interactions, with coverage across multiple species. Perl, HTML, GraphViz1, and a MySQL database were used in the development of Cognoscente. Cognoscente was motivated by the need to
update the knowledgebase of biomolecular interactions at the user level, and
flexibly visualize multi-molecule query results for heterogeneous interaction types across different orthologs.
Satisfying these needs provides a strong foundation for developing new hypotheses about regulatory and metabolic pathway topologies. Several existing tools provide functions that are similar to Cognoscente, so we selected several popular alternatives to
assess how their feature sets compare with Cognoscente( Table 1 ). All databases assessed had
easily traceable documentation for each interaction, and
included protein-protein interactions in the database.
Most databases, with the exception of BIND,
provide an open-access database that can be downloaded as a whole.
Most databases, with the exceptions of EcoCyc and HPRD, provide
support for multiple organisms.
Most databases support web services for interacting with the database contents programatically, whereas this is a planned feature for Cognoscente.
INT, STRING, IntAct, EcoCyc, DIP and Cognoscente provide built-in visualizations of query results,
which we consider among the most important features for facilitating comprehension of query results.
BIND supports visualizations via Cytoscape. Cognoscente is among a few other tools that support multiple organisms in the same query,
protein->DNA interactions, and
multi-molecule queries.
Cognoscente has planned support for small molecule interactants (i.e. pharmacological agents). MINT, STRING, and IntAct provide a prediction (i.e. score) of functional associations, whereas Cognoscente does not currently support this. Cognoscente provides support for multiple edge encodings to visualize different types of interactions in the same display,
a crowdsourcing web portal that allows users to submit interactions
that are then automatically incorporated in the knowledgebase, and displays orthologs as compound nodes to provide clues about potential
orthologous interactions.
The main strengths of Cognoscente are that
it provides a combined feature set that is superior to any existing database,
it provides a unique visualization feature for orthologous molecules, and relatively unique support for
multiple edge encodings,
crowdsourcing, and
connectivity parameterization.
The current weaknesses of Cognoscente relative to these other tools are
that it does not fully support web service interactions with the database,
it does not fully support small molecule interactants, and
it does not score interactions to predict functional associations.
Web services and support for small molecule interactants are currently under development.
Other related articles on thie Open Access Online Sceintific Journal, include the following:
English: DNA replication or DNA synthesis is the process of copying a double-stranded DNA molecule. This process is paramount to all life as we know it. (Photo credit: Wikipedia)
Français : Deletion chromosomique (Photo credit: Wikipedia)
A slight mutation in the matched nucleotides can lead to chromosomal aberrations and unintentional genetic rearrangement. (Photo credit: Wikipedia)
2012pharmaceutical
Amandeep Kaur
Aashir Awan, Phd
Abhisar Anand
Adina Hazan
Alan F. Kaul, PharmD., MS, MBA, FCCP
alexcrystal6
anamikasarkar
apreconasia
aviralvatsa
David Orchard-Webb, PhD
danutdaagmailcom
Demet Sag, Ph.D., CRA, GCP
Daniel Menzin
Dror Nir
dsmolyar
Ethan Coomber
evelinacohn
Gail S Thornton
Irina Robu
jayzmit48
jdpmd
jshertok
kellyperlman
Ed Kislauskis
komal23ingle
larryhbern
Madison Davis
marzankhan
megbaker58
ofermar2020
Dr. Pati
pkandala
Rosalind Codrington, PhD
ritusaxena
Rick Mandahl
satwik Sunnam
sjwilliamspa
Srinivas Sriram
stuartlpbi
Dr. Sudipta Saha
tildabarliya
vaishnavee24
zraviv06
zs22
