Epigenomic and transcriptomic analysis of developing, adult and aging brain: mechanisms of brain folding, neuronal function and finding novel therapy for dementia

Histone modifications and gene expression are tightly regulated processes in the brain that has been shown to play crucial role from the beginning of brain development, learning-memory formation and aging. While brain comprises of numerous types of neurons and non-neuronal cells, this regulation is highly cell type specific. To gain more mechanistic insights on cell type specific epigenetic and transcriptomic processes, in this thesis, I demonstrated brain nuclei isolation, cell nuclei specific antibody staining and FACS sorting can be successfully utilized to perform cell type specific genome wide histone mark characterization, gene expression and single nuclei RNA sequencing. I have applied these tools to gain valuable mechanistic insights of the causal epigenetic mechanism for cortical folding, functional role of a histone methyltransferase in memory impairment, and multi omics-based characterization of aged induced cognitive decline model. In the first manuscript, we found that embryonic mice treated with histone deacetylase inhibitors (therefore, increasing histone acetylation) led to higher amounts of basal progenitor (BP) cells in their cortex. This resulted into higher number of mature neurons, thereby producing cortical gyration phenotypes in lissencephalic rodent brains. To understand causal mechanisms, I established and performed for the first time, BP nuclei specific gene expression and histone 3 lysine 9(H3K9) acetylation dataset from embryonic mice cortex. This cell type specific analysis led to discovering distinct increased H3K9ac induced gene expression signature, that contained key regulatory transcription factor, resulting into higher amount of BP proliferation. Further validation experiments via epigenome editing confirmed the epigenetic basis of cortical gyrification in a lissencephalic brain via increasing histone acetylation. For the second manuscript, I investigated the molecular role of a histone methyltransferase (HMT), Setd1b in mature neurons. Forebrain excitatory neuron specific Setd1B conditional knockout (cKO) resulted into severe memory impairment which required further characterization of neuron specific epigenetic and transcriptomic perturbation due to this cKO. To understand molecular function of Setd1b cKO in neurons, I isolated neuron specific nuclei from WT vs cKO mice hippocampal CA region and performed 4 different histone modification ChIPseq (H3K4me3, H3K4me1, H3K9ac, H3K27ac) and neuron specific nuclear RNA seq. Bioinformatic data analysis revealed promoter specific alteration of all 4 marks and significant down regulation of memory forming genes. Comparison with other two previously studied HMT revealed Setd1b to be having broadest H3K4me3 peaks and regulating distinct sets of genes, which manifested to the severe most behavioral deficit. To understand expression pattern of those three HMTs, I performed single nuclei RNA sequencing of sorted neurons from wild type mice and found, even though Setd1b is expressed in a small subset of neurons, those neurons had the highest level of neuronal function and memory forming gene expression, compared to other two HMT expressing neurons studied previously by our group. Overall, our work shows neuron specific role of Setd1b and its contribution towards hippocampal memory formation. In the third manuscript, I applied neuronal and non-neuronal epigenome and transcriptome data generation and analysis of 3 vs 16 months old mice. As it is well known that memory impairment starts during the middle of life, and previous gene expression studies in mice showed very little to no changes while having cognitive deficit, I utilized nuclei based cell sorting method to study two promoter epigenetic marks(H3K4me3, H3K27me3) and RNA expression (including coding and non-coding) in neuronal and non-neuronal cells separately. Due to the novelty of the data, I first characterized the basal activatory H3K4me3, inhibitory H3K27me3, bivalent regions and gene expression in neuronal and non-neuronal nuclei. These epigenomic and transcriptomic datasets would be a valuable resource to the community to compare cell type specific gene expression and epigenomes with their datasets. Moreover, profiling epigenetic marks in old hippocampal CA1 neurons and non-neurons revealed massive decrease of epigenetic marks mostly in the non-neurons, while neurons only had decreased inhibitory H3K27me3 mark. Mechanistically, these epigenome changes correspond to probable non-neuronal dysfunction and neuronal upregulation of aberrant developmental pathways. Surprisingly, nuclear RNAseq revealed significant number of genes deregulated in non-neuronal cells, compared to neurons. By integrating transcriptome and epigenome, I found decreased H3K4me3 leading to decreased gene expression in non-neuronal cells, that resulted into probably downregulated neuronal support function and downregulated important glial metabolic pathways related to extra cellular matrix. Therefore, in this thesis, I have described cell type specific neurodevelopmental, neuronal and cognitive decline related epigenetic and transcriptional pathways that would add valuable knowledge and resources to the neuroscientific community.2021-12-3

An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator

The traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm

KMT2A and KMT2B Mediate Memory Function by Affecting Distinct Genomic Regions

Kmt2a and Kmt2b are H3K4 methyltransferases of the Set1/Trithorax class. We have recently shown the importance of Kmt2b for learning and memory. Here, we report that Kmt2a is also important in memory formation. We compare the decrease in H3K4 methylation and de-regulation of gene expression in hippocampal neurons of mice with knockdown of either Kmt2a or Kmt2b. Kmt2a and Kmt2b control largely distinct genomic regions and different molecular pathways linked to neuronal plasticity. Finally, we show that the decrease in H3K4 methylation resulting from Kmt2a knockdown partially recapitulates the pattern previously reported in CK-p25 mice, a model for neurodegeneration and memory impairment. Our findings point to the distinct functions of even closely related histone-modifying enzymes and provide essential insight for the development of more efficient and specific epigenetic therapies against brain diseases.Beca Ramón y CajalGAIN- Agencia Gallega de Innovació

KMT2A and KMT2B Mediate Memory Function by Affecting Distinct Genomic Regions

Kmt2a and Kmt2b are H3K4 methyltransferases of the Set1/Trithorax class. We have recently shown the importance of Kmt2b for learning and memory. Here, we report that Kmt2a is also important in memory formation. We compare the decrease in H3K4 methylation and de-regulation of gene expression in hippocampal neurons of mice with knockdown of either Kmt2a or Kmt2b. Kmt2a and Kmt2b control largely distinct genomic regions and different molecular pathways linked to neuronal plasticity. Finally, we show that the decrease in H3K4 methylation resulting from Kmt2a knockdown partially recapitulates the pattern previously reported in CK-p25 mice, a model for neurodegeneration and memory impairment. Our findings point to the distinct functions of even closely related histone-modifying enzymes and provide essential insight for the development of more efficient and specific epigenetic therapies against brain diseases

Rainwater Harvesting Potentials in Commercial Buildings in Dhaka: Reliability and Economic Analysis

Despite numerous studies on residential rainwater tank, studies on commercial rainwater tank are scarce. Corporate authorities pay little heed on this sustainable feature. With the aim of encouraging corporate authorities, this study presents the feasibility and economic benefits of rainwater harvesting (RWH) in commercial buildings in the capital city of Bangladesh, where water authority struggles to maintain town water supply. The analysis was conducted using a daily water balance model under three climate scenarios (wet, dry and normal year) for five commercial buildings having catchment areas varying from 315 to 776 m2 and the storage tank capacity varying from 100 to 600 m3. It was found that for a water demand of 30 L per capita per day (lpcd), about 11% to 19% and 16% to 26.80% of the annual water demand can be supplemented by rainwater harvesting under the normal year and wet year climate conditions, respectively. The payback periods are found to be very short, only 2.25 to 3.75 years and benefit–cost (B/C) ratios are more than 1.0, even for building having the smallest catchment area (i.e., 315 m2) and no significant overflow would occur during monsoon, which leads to both economic and environmental benefits. Though the findings cannot be translated to other cities as those are dependent on factors like water price, interest rate, rainfall amount and pattern, however other cities having significant rainfall amounts should conduct similar studies to expedite implementations of widescale rainwater harvesting

DL-CRC: Deep Learning-Based Chest Radiograph Classification for COVID-19 Detection: A Novel Approach

With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities

Asynchronous Federated Learning-based ECG Analysis for Arrhythmia Detection

With the rapid elevation of technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI), the traditional cloud analytics-based approach is not suitable for a long time and secure health monitoring and lacks online learning capability. The privacy issues of the acquired health data of the subjects have also arisen much concern in the cloud analytics approach. To establish a proof-of-concept, we have considered a critical use-case of cardiac activity monitoring by detecting arrhythmia from analyzing Electrocardiogram (ECG). We have investigated two Federated Learning (FL) architectures for arrhythmia classification utilizing the private ECG data acquired within each smart logic-in-sensor, deployed at the Ultra-Edge Nodes (UENs). The envisioned paradigm allows privacy-preservation as well as the ability to accomplish online knowledge sharing by performing localized and distributed learning in a lightweight manner. Our proposed federated learning architecture for ECG analysis is further customized by asynchronously updating the shallow and deep model parameters of a custom Convolutional Neural Network (CNN)-based lightweight AI model to minimize valuable communication bandwidth consumption. The performance and generalization abilities of the proposed system are assessed by considering multiple heartbeats classes, employing four different publicly available datasets. The experimental results demonstrate that the proposed asynchronous federated learning (Async-FL) approach can achieve encouraging classification efficiency while also ensuring privacy, adaptability to different subjects, and minimizing the network bandwidth consumption.This work was made possible by the NPRP award [NPRP13S-0205-200270] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. Also, the work was supported by Mitacs

Non-Invasive Detection Method for Recycled Flash Memory Using Timing Characteristics †

Counterfeiting electronic components is a serious problem for the security and reliability of any electronic systems. Unfortunately, the number of counterfeit components has increased considerably after the introduction of horizontal semiconductor supply chain. In this paper, we propose and experimentally demonstrate an approach for detecting recycled Flash memory. The proposed method is based on measurement of change in Flash array characteristics (such as erase time, program time, fail bit count, etc.) with its usage. We find that erase time is the best metric to distinguish a used Flash chip from a fresh one for the following reasons: (1) erase time shows minimal variation among different fresh memory blocks/chip and (2) erase time increases significantly with usage. We verify our method for a wide range of commercial off the shelf Flash chips from several vendors, technology nodes, storage density and storage type (single-bit per cell and multi-bit per cell). The minimum detectable chip usage varies from 0.05% to 3.0% of its total lifetime depending on the exact details of the chip

Impact of Industrially Affected Soil on Humans: A Soil-Human and Soil-Plant-Human Exposure Assessment

Heavy metal (HM) contaminated soil can affect human health via ingestion of foodstuffs, inhalation of soil dust, and skin contact of soil. This study estimates the level of some heavy metals in soils of industrial areas, and their exposures to human body via dietary intake of vegetables and other pathways. Mean concentrations of Cr, Fe, Cu, Zn, As and Pb in the studied soil were found to be 61.27, 27,274, 42.36, 9.77, 28.08 and 13.69 mg/kg, respectively, while in vegetables the respective values were 0.53, 119.59, 9.76, 7.14, 1.34 and 2.69 mg/kg. Multivariate statistical analysis revealed that Fe, Cu, Zn, and Pb originated from lithogenic sources, while Cr and As are derived from anthropogenic sources. A moderate enrichment was noted by Cr, As, and Pb in the entire sampling site, indicating a progressive depletion of soil quality. The bioaccumulation factor (BCF) value for all the vegetables was recorded as BCF &lt; 1; however, the metal pollution index (MPI) stipulates moderately high value of heavy metal accumulation in the vegetable samples. Hazard Index (HI) of &gt;0.1 was estimated for adults but &gt;1 for children by direct soil exposure, whereas HI &lt; 1 for both children and adults via dietary intake of vegetables. Estimated Total carcinogenic risk (TCR) value due to soil exposure showed safe for adults but unsafe for children, while both the population groups were found to be safe via food consumption. Children are found more vulnerable receptors than adults, and health risks (carcinogenic and non-carcinogenic) via direct soil exposure proved unsafe. Overall, this study can be used as a reference for similar types of studies to evaluate heavy metal contaminated soil impact on the population of Bangladesh and other countries as well.</jats:p

Epigenetic gene expression links heart failure to memory impairment

Abstract In current clinical practice, care of diseased patients is often restricted to separated disciplines. However, such an organ‐centered approach is not always suitable. For example, cognitive dysfunction is a severe burden in heart failure patients. Moreover, these patients have an increased risk for age‐associated dementias. The underlying molecular mechanisms are presently unknown, and thus, corresponding therapeutic strategies to improve cognition in heart failure patients are missing. Using mice as model organisms, we show that heart failure leads to specific changes in hippocampal gene expression, a brain region intimately linked to cognition. These changes reflect increased cellular stress pathways which eventually lead to loss of neuronal euchromatin and reduced expression of a hippocampal gene cluster essential for cognition. Consequently, mice suffering from heart failure exhibit impaired memory function. These pathological changes are ameliorated via the administration of a drug that promotes neuronal euchromatin formation. Our study provides first insight to the molecular processes by which heart failure contributes to neuronal dysfunction and point to novel therapeutic avenues to treat cognitive defects in heart failure patients