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The Development of a Novel Fusion Protein to Facilitate Connectomic Analysis of Brain Networks Gurion Marks Bronx High School of Science, Bronx, NY

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The Development of a Novel Fusion Protein to Facilitate Connectomic

Analysis of Brain NetworksGurion Marks

Bronx High School of Science, Bronx, NY

The Problem: Neurodegenerative Disease• According to the World Health Organization, Alzheimer’s

disease and other forms of dementia represent the fourth highest disease burden in high-income countries .– The US has the 10th highest GDP per capita and ranks 34th in the world in life

expectancy, showing its increased risk of population suffering from Alzheimer’s or memory related disorders.

• 35% of Europe’s health problems stem from brain disorders.• Economically, the effects are huge.

– The 2010 World Alzheimer Report by Alzheimer’s Disease International reported that the cost of caring for the disease exceeded US$600 billion, globally – consuming approximately 1% of the entire world’s GDP [3].

– Worldwide, Alzheimer’s is projected to cost US$1.1 trillion and affect 114.5 million people by 2050.

Curing Neurodegenerative Diseases

• Many neurodegenerative disorders are circuit based

• To understand and cure these ailments, one must discern how neurons are affected at cellular and intercellular levels – how neurons work in circuits– Understand how the change or decrease in neural

“wiring” creates diseases

Circuitry of the Brain – the “Connectome”

• The ‘Connectome’ is a “comprehensive structural description of the network of elements and connections forming the brain.”– In short, it’s a “wiring diagram” of the brain– They show which neurons connect to which other

neurons

A Simple Neural Circuit

A Simple Model of a Neural Circuit - A simple neural system in which there is a connection from neuron 1 to neuron 2, and neuron 2 to neuron 1. Neuron 3 connects to both neurons 1 and 2; neither neuron 1 nor neuron 2 connects to neuron 3. This may only be a tiny fraction of a real neural circuit which encompasses a vastly greater number of neurons.

 

 

 

Neuron 1

Neuron 2

Neuron 3

Photo Credit: Lichtman et al. 2008

Finding Connectomes

• Many types of data must be utilized. – Light or fluorescence microscopy may be used to

functionally identify neurons within a circuit. – Electron microscopy is used to make a high

resolution stack of images. • These images are then analyzed by tracing identified

neurons to form three-dimensional reconstructions.

The Scale of Connectomics

The Scale of Connectomics – the isolation of one neuron in the larval zebrafish hindbrain. Massive sets of data are needed to utilize the resolution necessary for connectomics research. The scope of data is so large that humans will never analyze an entire brain by hand, expressing the need for machine learning strategies to reconstruct neural matrices.

Endoplasmic Reticulum Mitochondrion

Soma

Problems in Finding Connectomes

• Massive amounts of data and an inability to use computer software to trace neurons effectively–Massive amounts of data have to be analyzed by

hand, as machine learning algorithms do not have the pattern recognizing abilities for interpolating stacks of electron microscopy images

• Neural constructs look similar near the soma– Algorithms cannot differentiate between axons and

dendrites

Issue with Electron Microscopy

Branching Dendrite

The Uncertainty of Electron Microscopy – errors in electron microscopy leading to the ineffectiveness of machine learning techniques. (a) Shows issues regarding too little contrast. Without distinct boundaries between Soma 1 and Soma 2, the branch of Soma 1 may be incorrectly linked to Soma 2. There are great numbers of instances like these in any set of data. Computer algorithms cannot distinguish these errors as humans can, thus connectomes created by machine learning strategies with current imaging technology are largely erroneous. (b) Shows another instance of error – overexposure of a region or group of cells makes it nearly impossible for computer vision to trace branches of neurons.

Soma 2Soma 1

Lack of Contrast between Somae

Overexposure creates uncertainty in distinguishing neural constructs

(a) (b)

Axon and Dendrite Near the Soma(a)

(b)

Branching Axon

Branching Dendrite

Differentiating Axons and Dendrites – (a) shows an axon branching off the soma, while (b) shows a branching dendrite. Both constructs appear very similar and are not easily differentiated close to the soma. This spurs a need to create a tag to mark only one of the two structures.

Both constructs look essentially the same, showing why computers are not able to tell the difference

How Can We Fix Those Issues?

• Using genetically encoded tags to increase EM contrast at only the axon, close to the soma. – This region is called the axon initial segment– Engineered Ascorbate Peroxidase (APEX) is a

recently created tag that increases EM contrast– The protein AnkyrinG is localized to the axon

initial segment

Components of the Plasmid• APEX is a 28 kDa monomeric peroxidase. The

peroxidase, upon the addition of H2O2, catalyzes the oxidation of diaminobenzidine to create a local precipitate, which when treated with OsO4 gives local electron microscopy contrast.

• Fluorescent proteins – ‘X’FP for checking expression• The protein AnkyrinG is localized to the axon initial

segment• The Tol1 transposon system for incorporating the

plasmid DNA into the zebrafish genome

Methods

pDon122 Vector BackbonepEGFP-N1 Vector Backbone

AnkG-XFP

XbaIHindIII

AnkG-XFP

XbaIHindIII

pDon122-mCherry-GCaMP6f

Connexin43-GFP-APEX2

XbaI

Connexin43-EGFPAPEX2AscI

Starting Plasmids, AnkG-XFP, pDon122-mCherry-GCaMP6f, Connexin43-GFP-APEX2 – plasmids were amplified in DH5 and extracted via Qiagen Maxi-prep, then digested at appropriate restriction sites for subsequent gel extraction

pcDNA3 Vector Backbone

Starting Plasmids

Methods Continued

HindIII

AnkG-XFP

XbaIHindIII XbaI

pDon122 Vector Backbone

AscIAPEX2

XbaI

Cut and Purified DNA Fragments – AnkG-XFP and pDon122-mCherry-GCaMP6f were cut at HindIII and XbaI. Connexin43-GFP-APEX2 was cut at AscI and XbaI

 10 kb

  

8.5 kb 

3.024 kb  

2 kb       

.4 kb

AnkG-mCherry AnkG-GFP pDon122 2-Log DNA Ladder (NEB)

 10 kb

7.2 kb 

3 kb    

1 kb.793 kb

       

1 kb DNA Ladder (NEB) APEX2 APEX2

Gel Purification of (a) AnkG-XFP and pDon122 Vector Backbone; (b) APEX2 – The 8.5 kb AnkG-XFP constructs were extracted via a QiaQUICK gel extraction kit after running a 0.8% agarose gel at 100 V for 1 hr. The 0.793 kb APEX2 was extracted via the same methods, after a 1.0% agarose gel at 100 V for 45 min.

(a) (b)

Methods Continued

APEX2

XbaI

AnkG-XFPAnkG-XFP

XbaI

AscIAPEX2

HindIII

HindIII

pDon122 Vector Backbone

AscIAPEX2

XbaI

Ligation Steps to Obtain pDon122-AnkG-XFP-APEX2 – APEX2 was ligated into the pDon122 vector backbone. Then AnkG-XFP ws ligated into pDon122-APEX2. Finally, the plasmid pDon122-AnkG-XFP-APEX2 was created by blunt ligating the construct to fuse the site formerly XbaI on AnkG-XFP with the site formerly AscI on APEX2.

pDon122 Vector Backbone

XbaI

pDon122 Vector Backbone

HindIII

Ligation of Plasmid

Results

  

>10 kb ≈ 12.5 kb   

10 kb    

5 kb            

1 kb Supercoiled DNA Ladder (NEB) pDon122-AnkG-GFP-APEX2 pDon122-AnkG-mCherry-APEX2

Ligation Product pDon122-AnkG-XFP-APEX2 – Creation of pDon122-AnkG-XFP-APEX2 was confirmed after running the ligation product on a 0.8% agarose gel for 1 hr.

Confirmation of Ligation

Significance• This construct will allow for greater contrast to be

created in specific areas of neurons, allowing for machine learning algorithms to be used in connectomics, greatly expediting the process of finding connectomes.

• Determining axons, rather than dentrites, will allow for macroscale connectomes that show the linkage of brain regions

• Faster creation of connectomes will allow for a more comprehensive picture of neural circuitry, and a more informed view into neurodegenerative disease

Significance Continued

• Significance of AnkyrinG– In abnormal animals, AnkG has been seen to

migrate out of the axon initial segment, and accumulate in beta-amyloid plaque• A main cause of Alzheimer’s Disease

– This construct will allow for pinpointing of AnkG and the tracing of AnkG beta-amyloid plaque

– Opens the possibility of AnkG mediated therapeutics

Further Research

• Testing in zebrafish for expression• Testing of algorithms on tagged EM data• Functional Connectome – Tag synaptic

vesicles to determine the strength of synapses between neurons (step after finding the structural “wiring diagram” connectome)

• Studies testing link of AnkG and beta-amyloid plaque

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