Raising the Connectome: the Emergence of Neuronal Activity and Behavior in C
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Conservation and Divergence of Related Neuronal Lineages in The
RESEARCH ARTICLE Conservation and divergence of related neuronal lineages in the Drosophila central brain Ying-Jou Lee, Ching-Po Yang, Rosa L Miyares, Yu-Fen Huang, Yisheng He, Qingzhong Ren, Hui-Min Chen, Takashi Kawase, Masayoshi Ito, Hideo Otsuna, Ken Sugino, Yoshi Aso, Kei Ito, Tzumin Lee* Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States Abstract Wiring a complex brain requires many neurons with intricate cell specificity, generated by a limited number of neural stem cells. Drosophila central brain lineages are a predetermined series of neurons, born in a specific order. To understand how lineage identity translates to neuron morphology, we mapped 18 Drosophila central brain lineages. While we found large aggregate differences between lineages, we also discovered shared patterns of morphological diversification. Lineage identity plus Notch-mediated sister fate govern primary neuron trajectories, whereas temporal fate diversifies terminal elaborations. Further, morphological neuron types may arise repeatedly, interspersed with other types. Despite the complexity, related lineages produce similar neuron types in comparable temporal patterns. Different stem cells even yield two identical series of dopaminergic neuron types, but with unrelated sister neurons. Together, these phenomena suggest that straightforward rules drive incredible neuronal complexity, and that large changes in morphology can result from relatively simple fating mechanisms. *For correspondence: Introduction [email protected] In order to understand how the genome encodes behavior, we need to study the developmental mechanisms that build and wire complex centers in the brain. The fruit fly is an ideal model system Competing interests: The to research these mechanisms. The Drosophila field has extensive genetic tools. -
A Transparent Window Into Biology: a Primer on Caenorhabditis Elegans* Ann K
A Transparent window into biology: A primer on Caenorhabditis elegans* Ann K. Corsi1§, Bruce Wightman2§, and Martin Chalfie3§ 1Biology Department, The Catholic University of America, Washington, DC 20064 2Biology Department, Muhlenberg College, Allentown, PA 18104 3Department of Biological Sciences, Columbia University, New York, NY 10027 Table of Contents 1. Introduction ............................................................................................................................2 2. C. elegans basics .....................................................................................................................4 2.1. Growth and maintenance ................................................................................................ 4 2.2. Sexual forms and their importance .................................................................................... 5 2.3. Life cycle ....................................................................................................................7 3. C. elegans genetics ...................................................................................................................8 4. Why choose C. elegans? ......................................................................................................... 10 5. C. elegans tissues ................................................................................................................... 11 5.1. Epidermis: a model for extracellular matrix production, wound healing, and cell fusion ............ 12 5.2. Muscles—controlling -
Data Mining in Genomics, Metagenomics and Connectomics
Csaba Kerepesi DATA MINING IN GENOMICS, METAGENOMICS AND CONNECTOMICS PhD Thesis Supervisor: Dr. Vince Grolmusz Department of Computer Science E¨otv¨osLor´andUniversity, Hungary PhD School of Computer Science E¨otv¨osLor´andUniversity, Hungary Dr. Erzs´ebet Csuhaj-Varj´u PhD Program of Information Systems Dr. Andr´as Bencz´ur Budapest, 2017 Acknowledgements I would like to thank my supervisor Dr. Vince Grolmusz for his tireless support with which he started my scientific career. I am very grateful to the PhD School of Computer Science and the Faculty of Informatics, ELTE for their inexhaustible support. I would like to thank my co-authors for their precious work and I also would like to thank all my colleagues, who helped me anything in my re- searches. Finally, I would like to thank my family for their everlasting support. 2 Contents 1 Introduction 6 2 Data Mining in Genomics and Metagenomics 14 2.1 AmphoraNet: The Webserver Implementation of the AM- PHORA2 Metagenomic Workflow Suite . 14 2.1.1 Introduction . 14 2.1.2 Results and Discussion . 15 2.2 Visual Analysis of the Quantitative Composition of Metage- nomic Communities: the AmphoraVizu Webserver . 17 2.2.1 Introduction . 17 2.2.2 Results and Discussion . 19 2.3 Evaluating the Quantitative Capabilities of Metagenomic Analysis Software . 21 2.3.1 Introduction . 21 2.3.2 Results and discussion . 22 2.3.3 Methods . 25 2.3.4 Availability . 29 2.4 The \Giant Virus Finder" Discovers an Abundance of Giant Viruses in the Antarctic Dry Valleys . 30 2.4.1 Introduction . 30 3 2.4.2 Results and discussion . -
Polygenic Evidence and Overlapped Brain Functional Connectivities For
Sun et al. Translational Psychiatry (2020) 10:252 https://doi.org/10.1038/s41398-020-00941-z Translational Psychiatry ARTICLE Open Access Polygenic evidence and overlapped brain functional connectivities for the association between chronic pain and sleep disturbance Jie Sun 1,2,3,WeiYan2,Xing-NanZhang2, Xiao Lin2,HuiLi2,Yi-MiaoGong2,Xi-MeiZhu2, Yong-Bo Zheng2, Xiang-Yang Guo3,Yun-DongMa2,Zeng-YiLiu2,LinLiu2,Jia-HongGao4, Michael V. Vitiello 5, Su-Hua Chang 2,6, Xiao-Guang Liu 1,7 and Lin Lu2,6 Abstract Chronic pain and sleep disturbance are highly comorbid disorders, which leads to barriers to treatment and significant healthcare costs. Understanding the underlying genetic and neural mechanisms of the interplay between sleep disturbance and chronic pain is likely to lead to better treatment. In this study, we combined 1206 participants with phenotype data, resting-state functional magnetic resonance imaging (rfMRI) data and genotype data from the Human Connectome Project and two large sample size genome-wide association studies (GWASs) summary data from published studies to identify the genetic and neural bases for the association between pain and sleep disturbance. Pittsburgh sleep quality index (PSQI) score was used for sleep disturbance, pain intensity was measured by Pain Intensity Survey. The result showed chronic pain was significantly correlated with sleep disturbance (r = 0.171, p-value < 0.001). Their genetic correlation was rg = 0.598 using linkage disequilibrium (LD) score regression analysis. Polygenic score (PGS) association analysis showed PGS of chronic pain was significantly associated with sleep and vice versa. 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Nine shared functional connectivity (FCs) were identified involving prefrontal cortex, temporal cortex, precentral/ postcentral cortex, anterior cingulate cortex, fusiform gyrus and hippocampus. -
A Critical Look at Connectomics
EDITORIAL A critical look at connectomics There is a public perception that connectomics will translate directly into insights for disease. It is essential that scientists and funding institutions avoid misrepresentation and accurately communicate the scope of their work. onnectomes are generating interest and excitement, both among nervous system, dubbed the classic connectome because it is currently neuroscientists and the public. This September, the first grants the only wiring diagram for an animal’s entire nervous system at the level Cunder the Human Connectome Project, totaling $40 million of the synapse. Although major neurobiological insights have been made over 5 years, were awarded by the US National Institutes of Health using C. elegans and its known connectivity and genome, there are still (NIH). In the public arena, striking, colorful pictures of human brains many questions remaining that can only be answered by hypothesis-driven have accompanied claims that imply that understanding the complete experiments. For example, we still don’t completely understand the process connectivity of the human brain’s billions of neurons by a trillion synapses of axon regeneration, relevant to spinal cord injury in humans, in this is not only possible, but that this will also directly translate into insights comparatively simple system. Thus, a connectome, at any resolution, is for neurological and psychiatric disorders. Even a press release from only one of several complementary tools necessary to understand nervous the NIH touted that the Human Connectome Project would map the system disease and injury. wiring diagram of the entire living human brain and would link these There are substantial efforts aimed at generating connectomes for circuits to the full spectrum of brain function in health and disease. -
Connectome Imaging for Mapping Human Brain Pathways
OPEN Molecular Psychiatry (2017) 22, 1230–1240 www.nature.com/mp EXPERT REVIEW Connectome imaging for mapping human brain pathways Y Shi and AW Toga With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. Molecular Psychiatry (2017) 22, 1230–1240; doi:10.1038/mp.2017.92; published online 2 May 2017 INTRODUCTION These rich resources in connectome imaging present great Mapping the connectivity of brain circuits is fundamentally opportunities to map human brain pathways at unprecedented 22,23 important for our understanding of the functions of human brain. accuracy. On the other hand, this avalanche of BIG DATA in 24,25 Various neurological and mental disorders have been linked to the connectomics also poses challenges for existing image disruption of brain connectivity in circuits such as the cortico– analysis algorithms. -
Ome Sweet Ome: What Can the Genome Tell Us About the Connectome? Jeff W Lichtman and Joshua R Sanes
Available online at www.sciencedirect.com Ome sweet ome: what can the genome tell us about the connectome? Jeff W Lichtman and Joshua R Sanes Some neuroscientists argue that detailed maps of synaptic however, the idea of applying an ‘omics-scale’ approach to connectivity – wiring diagrams – will be needed if we are to neural circuit tracing will require considerable vetting. understand how the brain underlies behavior and how brain Here, in contrast to common practice in this journal, we malfunctions underlie behavioral disorders. Such large-scale take the charge of providing our current opinion seriously. circuit reconstruction, which has been called connectomics, We compare and contrast connectomics with genomics to may soon be possible, owing to numerous advances in ask whether mapping neural connections could ulti- technologies for image acquisition and processing. Yet, the mately have the same kind of value as sequencing genes. community is divided on the feasibility and value of the enterprise. Remarkably similar objections were voiced when A (very) short history of connectomics the Human Genome Project, now widely viewed as a success, As with almost everything in neuroscience, the idea of was first proposed. We revisit that controversy to ask if it holds mapping circuits can be traced back to Cajal. He enun- any lessons for proposals to map the connectome. ciated both the neuron doctrine and the law of dynamic polarization [1]. These two ideas, along with Sherrington’s Address electrophysiological analysis of reflexes, provided the Department of Molecular and Cellular Biology and Center for Brain rationale for thinking about brain mechanisms in terms Science, Harvard University, Cambridge, MA 02138, USA of circuits: Neurons were nodes that were electrically Corresponding author: Lichtman, Jeff W ([email protected]) and connected to each other via two types of wires, axons Sanes, Joshua R ([email protected]) and dendrites. -
Scarcity of Scale-Free Topology Is Universal Across Biochemical
www.nature.com/scientificreports OPEN Scarcity of scale‑free topology is universal across biochemical networks Harrison B. Smith1,5, Hyunju Kim1,2,3 & Sara I. Walker1,2,3,4* Biochemical reactions underlie the functioning of all life. Like many examples of biology or technology, the complex set of interactions among molecules within cells and ecosystems poses a challenge for quantifcation within simple mathematical objects. A large body of research has indicated many real‑world biological and technological systems, including biochemistry, can be described by power‑law relationships between the numbers of nodes and edges, often described as “scale‑free”. Recently, new statistical analyses have revealed true scale‑free networks are rare. We provide a frst application of these methods to data sampled from across two distinct levels of biological organization: individuals and ecosystems. We analyze a large ensemble of biochemical networks including networks generated from data of 785 metagenomes and 1082 genomes (sampled from the three domains of life). The results confrm no more than a few biochemical networks are any more than super‑weakly scale‑free. Additionally, we test the distinguishability of individual and ecosystem‑level biochemical networks and show there is no sharp transition in the structure of biochemical networks across these levels of organization moving from individuals to ecosystems. This result holds across diferent network projections. Our results indicate that while biochemical networks are not scale‑free, they nonetheless exhibit common structure across diferent levels of organization, independent of the projection chosen, suggestive of shared organizing principles across all biochemical networks. Statistical mechanics was developed in the nineteenth century for studying and predicting the behavior of systems with many components. -
Pioneers of Connectome Gradients Ralph Kimmlingen Siemens Healthineers, HC DI MR TR R&D-PL, Erlangen, Germany
122 Technology MAGNETOM Flash (68) 2/2017 www.siemens.com/magnetom-world Pioneers of Connectome Gradients Ralph Kimmlingen Siemens Healthineers, HC DI MR TR R&D-PL, Erlangen, Germany anatomical and neural-tracing techniques, mammalian Abstract brains like that of mice or primates are still under investigation. These methods are capable of an in-plane A typical human brain contains 100 billion neurons resolution of 40 µm [6, 7]. which have about 10,000 individual connections with their neighbors. Being able to map structural and New non-invasive imaging methods which enable the functional connectivity of an individual brain could be study of brain connectivity of living humans have been a first step on a new way of understanding and developed since the beginning of this century. They are diagnosing mental illnesses. Continuous known as MR Imaging of anisotropic diffusion of water improvements on noninvasive magnetic resonance in the brain, and resting state fMRI [8–10]. The related imaging (MRI) methods like functional MRI, resting- advances in imaging technologies and data evaluation state MRI, and diffusion MRI enable this information are empowering us today to study the human brain as an (connectomics) to be obtained for the first time on a entire organ. large human databasis. A key parameter is the A group known as ‘Blueprint for Neuroscience Research’, available gradient field strength for diffusion-sensitive a collaboration among 15 National Institutes of Health MRI sequences [1–3]. Siemens MR has developed two (NIH) in Bethesda, Maryland, USA, decided in 2009 to fund powerful prototype gradient systems for this purpose, a five-year initiative for mapping the brain’s long-distance which have been employed at five different locations communications network. -
Biotechnology Explorer™ C
Biotechnology Explorer™ C. elegans Behavior Kit Instruction Manual explorer.bio-rad.com Catalog #166-5120EDU This kit contains temperature-sensitive reagents. Open immediately and see individual components for storage temperature. Please see redemption instructions on how to receive your C. elegans. Duplication of any part of this document is permitted for classroom use only. Please visit explorer.bio-rad.com to access our selection of language translations for Biotechnology Explorer kit curricula. Dear Educator, One of the greatest challenges in studying the biology of how human cells and organ systems function is that experimentation on humans is expensive, complex, and often unethical. It is because of this that scientists worldwide have adopted the use of model organisms that share much of the cellular machinery present in human cells, and study these organisms to help improve our understanding of the nature of our own cellular functions. In this kit, students will be introduced to one of the most widely used model organisms, the microscopic nematode Caenorhabditis elegans. Students will utilize C. elegans to understand the function of a conserved protein phosphatase, DAF-18. As with most C. elegans genes, daf-18 has a human homolog, PTEN, which is involved in a number of human pathologies such as cancer, autism, and learning disorders. While cancer is not a phenotype that is easily studied in C. elegans, because their short life span does not allow for the cellular progression often seen in humans, this kit does explore the role of daf-18 in learning and behavioral disorders. Students will be provided with two strains of C. -
Past and Recent Endeavours to Simulate Caenorhabditis Elegans
Past and Recent Endeavours to Simulate Caenorhabditis elegans Alexey Petrushin, Lorenzo Ferrara and Axel Blau Dept. of Neuroscience and Brain Technologies (NBT), Italian Institute of Technology (IIT), 16163 Genoa, Italy Keywords: Brain-Inspired Computation, Nervous System Emulation, Soft Body Simulation, Virtual Embodiment, Neurocomputational Response Models on Field-Programmable Gate Arrays (Fpgas). Abstract: Biological nervous systems are robust and highly adaptive information processing entities that excel current computer architectures in almost all aspects of sensory-motor integration. While they are slow and inefficient in the serial processing of stimuli or data chains, they outperform artificial computational systems in seemingly ordinary pattern recognition, orientation or navigation tasks. Even one of the simplest nervous systems in nature, that of the hermaphroditic nematode Caenorhabditis elegans with just 302 neurons and less than 8,000 synaptic connections, gives rise to a rich behavioural repertoire that – among controlling vital functions - encodes different locomotion modalities (crawling, swimming and jumping). It becomes evident that both robotics and information and computation technology (ICT) would strongly benefit if the working principles of nervous systems could be extracted and applied to the engineering of brain-mimetic computational architectures. C. elegans, being one of the five best-characterized animal model systems, promises to serve as the most manageable organism to elucidate the information coding and control mechanisms that give rise to complex behaviour. This short paper reviews past and present endeavours to reveal and harvest the potential of nervous system function in C. elegans. 1 INTRODUCTION 2 PAST PROJECTS ON SIMULATING C. elegans Caenorhabditis elegans, a tiny roundworm (L: 1 mm, Ø 80 µm) with a life span of a few weeks, is With the advent of sufficiently powerful among the five best characterized organisms in computational resources in the 80’s of the last nature (Epstein and Shakes, 1995). -
This Is Your Brain: Mapping the Connectome
Life Science Technologies The Connectome Produced by the Science/AAAS Custom Publishing Office This is Your Brain: Mapping the Connectome It’s been 20 years since Francis Crick and Edward Jones, in the midst of the so-called Decade of the Brain, lamented science’s lack of even a basic understanding of human neuroanatomy. “Clearly what is needed for a modern human brain anatomy is the introduction of some radically new techniques,” the pair wrote in 1993. Clearly, researchers were listening. Today, they are using novel technologies and automation to map neural circuitry with unparalleled resolution and completeness. The NIH has dedicated nearly $40 million to chart the wiring of the human brain, and the Allen Brain Institute has poured in millions more to map the mouse brain. The data will take years to compile, and even longer to understand. But the results may reveal nothing less than the nature of human individuality. As MIT neuroscientist Sebastian Seung writes, “You are more than your genes. You are your connectome.” By Jeffrey M. Perkel hen Seung says in Connectome: “Genomes are child’s play compared with connectomes.” How the Brain’s Wiring Makes Nevertheless, researchers are making a stab at the problem. From the WUs Who We Are, “You are your so-called macroscale of magnetic resonance imaging, to the microscale connectome,” what he means is that neu- of electron microscopy, the connectome is slowly coming into focus, ral connectivity is like a fingerprint. Each one synapse at a time. person has their own unique blend of ge- netics, environmental influences, and life The Human Connectome Project experience.