Periodic Reporting for period 2 - BrainBreaks (DNA Breaks Shape Neural Genome Heterogeneity)
Okres sprawozdawczy: 2022-07-01 do 2023-12-31
DNA breakage is an inherent event within living cells. While DNA breaks were considered to have negative consequences for cell fate, they actually play an important physiological role. What exactly do DNA breaks do to cells and their behavior? Imagine these DNA breaks as puzzle pieces that, when connected, yield diverse patterns crucial for specific bodily functions. Interestingly, the cells responsible for generating building blocks for the brain, known as neural progenitor cells, exhibit a unique trait involving DNA breaks. These cells, during replication under stressful conditions, display a tendency to generate concentrated DNA breaks within genes. Notably, these DNA breaks predominantly manifest in genes essential for intercellular communication within the brain. Intriguingly, these same genes have established connections to neuropsychiatric disorders and specific cancers in humans.
These discoveries unfurl a captivating array of questions: What drives this phenomenon in neural progenitor cells? Could these DNA breaks signify a programmed facet of brain development, amplified under stress? How do the frequencies of these breaks differ between normal and stressed conditions? Are the amplified DNA breaks associated with brain disease phenotypes? And perhaps most significantly, what implications arise for the brain when neural progenitor cells experience an alteration in the quantity of DNA breaks? In other words: do these DNA breaks play a vital role in the functioning of the brain?
The BrainBreaks project endeavors to decipher the intricate puzzle of DNA breaks occurring within neural progenitor cells. The three pivotal objectives of BrainBreaks are:
1. Delve into the mechanics underlying the formation and repair of clusters of recurrent DNA breaks.
2. Map the precise locations and frequencies of these breaks as the brain undergoes its developmental journey.
3. Investigate the impact of DNA replication stress on a specific gene associated with these breaks within the brain.
In essence, BrainBreaks embarks on a transformative journey to unravel the enigma of DNA breaks and their role in sculpting the brain's genetic diversity. Our ultimate objective is to illuminate the effects of DNA breaks on brain function and enhance our comprehension of the intricate web of brain-related conditions.
These discoveries unfurl a captivating array of questions: What drives this phenomenon in neural progenitor cells? Could these DNA breaks signify a programmed facet of brain development, amplified under stress? How do the frequencies of these breaks differ between normal and stressed conditions? Are the amplified DNA breaks associated with brain disease phenotypes? And perhaps most significantly, what implications arise for the brain when neural progenitor cells experience an alteration in the quantity of DNA breaks? In other words: do these DNA breaks play a vital role in the functioning of the brain?
The BrainBreaks project endeavors to decipher the intricate puzzle of DNA breaks occurring within neural progenitor cells. The three pivotal objectives of BrainBreaks are:
1. Delve into the mechanics underlying the formation and repair of clusters of recurrent DNA breaks.
2. Map the precise locations and frequencies of these breaks as the brain undergoes its developmental journey.
3. Investigate the impact of DNA replication stress on a specific gene associated with these breaks within the brain.
In essence, BrainBreaks embarks on a transformative journey to unravel the enigma of DNA breaks and their role in sculpting the brain's genetic diversity. Our ultimate objective is to illuminate the effects of DNA breaks on brain function and enhance our comprehension of the intricate web of brain-related conditions.
During the first half phase of BrainBreaks' funding period, we successfully deciphered the fundamental physiological processes that give rise to recurrent DNA breaks. Our findings unveiled that DNA breaks occurring in neural progenitor cells stem from the collision between transcription and DNA replication. To illustrate, envision transcription and replication as two trains on the same track. When moving in the same direction at comparable speeds, no collision occurs. However, when DNA replication slows down, transcription naturally clashes with it on the shared genomic DNA track. Remarkably, in many genes of neural progenitor cells, DNA replication and transcription occur in opposing directions. In these regions, the collision between transcription and replication becomes particularly pronounced, resulting in a 30% increase in the frequency of DNA breaks compared to when both processes run in the same direction. Crucially, we observed that eliminating the transcription train led to the disappearance of DNA breaks beneath these genes in the genome. Given that the configuration of transcription and DNA replication is unique to neural progenitor cells, we are inclined to categorize recurrent DNA breaks as "programmed" events in the developmental process of the brain. The research detail is available now on an open access online portal bioRxiv.
Our endeavors to investigate the influence of replication stress on the formation of recurrent DNA breaks have yielded significant progress. Our second research objective is to comprehend the impact of stressful DNA replication conditions, whether accelerated or decelerated, on genome diversity within the brain. We proposed an approach that avoids the necessity for genes or mutations known to play a role in cell growth or survival. This approach ensures that we do not selectively focus on specific gene-dependent effects during our investigation. We have successfully established a mouse model wherein neural progenitor cells have their cell cycle duration shortened from 10 hours to 5 hours. This implies that each cell must efficiently replicate the 2.5 billion nucleic acid pairs in half of the initially allotted time. We predict that cells might become less precise and generate more errors. These errors manifest as DNA breaks or mutations, which become evident in the genomic DNA of neural progenitor cells. While employing an indirect method to count DNA breaks, opposite from expected, we found that these rapidly cycling cells did not exhibit an increase in DNA breaks. However, a fascinating observation came to light in these embryonic brains: We found recurrent genomic DNA loss occurred within genes identified to have issues with transcription and DNA replication clash—where recurrent DNA breaks occur. This intriguing finding indirectly suggests that DNA breaks within neuronal genes are orchestrated events within neural progenitor cells. Furthermore, the diversity of these embryonic brains, also referred to as the mutation burden of the animal, was found to be profoundly increased fivefold compared to that of normal embryonic brains. We then anticipate that, if genome mosaicism has a function in neurogenesis, it would reflect on brain architecture. When analyzing brain cell composition, brain shape, and the size of these embryonic mice, we found the brain size was marginally smaller than that of normal embryos. Collectively, our work establishes altered DNA replication as a risk factor in brain development. Further in-depth analyses are slated for the subsequent phase of BrainBreaks' funding period.
Our endeavors to investigate the influence of replication stress on the formation of recurrent DNA breaks have yielded significant progress. Our second research objective is to comprehend the impact of stressful DNA replication conditions, whether accelerated or decelerated, on genome diversity within the brain. We proposed an approach that avoids the necessity for genes or mutations known to play a role in cell growth or survival. This approach ensures that we do not selectively focus on specific gene-dependent effects during our investigation. We have successfully established a mouse model wherein neural progenitor cells have their cell cycle duration shortened from 10 hours to 5 hours. This implies that each cell must efficiently replicate the 2.5 billion nucleic acid pairs in half of the initially allotted time. We predict that cells might become less precise and generate more errors. These errors manifest as DNA breaks or mutations, which become evident in the genomic DNA of neural progenitor cells. While employing an indirect method to count DNA breaks, opposite from expected, we found that these rapidly cycling cells did not exhibit an increase in DNA breaks. However, a fascinating observation came to light in these embryonic brains: We found recurrent genomic DNA loss occurred within genes identified to have issues with transcription and DNA replication clash—where recurrent DNA breaks occur. This intriguing finding indirectly suggests that DNA breaks within neuronal genes are orchestrated events within neural progenitor cells. Furthermore, the diversity of these embryonic brains, also referred to as the mutation burden of the animal, was found to be profoundly increased fivefold compared to that of normal embryonic brains. We then anticipate that, if genome mosaicism has a function in neurogenesis, it would reflect on brain architecture. When analyzing brain cell composition, brain shape, and the size of these embryonic mice, we found the brain size was marginally smaller than that of normal embryos. Collectively, our work establishes altered DNA replication as a risk factor in brain development. Further in-depth analyses are slated for the subsequent phase of BrainBreaks' funding period.
In the middle of the BrainBreaks project, we invented computational tools and mathematical models to solve problems developed during our investigations. These are developments that are beyond the state of the art in the field of genomics and neurogenesis.
1. Utilizing Artificial Intelligence to Decipher DNA Replication Direction. The extensive application of artificial intelligence has primarily centered around images. Particularly within the realm of pathology, numerous programs have been developed to learn cell composition, shape, and pattern counts within diseased tissues. They aid in determining the grade and type of diseases based on histology or radiology images. In a similar vein, genomics data share resemblances to image data to a certain extent. Analogous to pixels that define the resolution of a DNA-associated marker, genomics data encapsulate intensity, distribution, and density information just as imaging data does. Although this approach is common in the field of pathology, its implementation within genomics remains relatively unexplored. Recognizing this, we opted to leverage the capabilities of artificial intelligence to predict DNA replication direction. The dynamic nature of DNA replication patterns presents unique challenges. While a similar approach has been devised in the past for specific datatype linked to DNA replication, its technical intricacy has confined its adoption to only a few select laboratories worldwide. In our pursuit, we have chosen to establish an artificial intelligence program for an alternative approach- one that demands significantly lower technical prerequisites. Our aim is to make this approach accessible and widely utilized by researchers within the genomics domain.
Grounded in a convolutional neural network, our artificial intelligence is structured in three distinct phases. The first phase entails pinpointing genomic regions housing specific start and end features associated with DNA replication. In the subsequent phase, training data is generated, outlining regions containing one to several start and end features. Finally, the third phase involves training the model using the designated training data. With a resounding success, this network exhibits the capacity to ascertain DNA replication direction with an impressive confidence level exceeding 80%, alongside high reproducibility. To enhance accessibility, this network has been made available on an open-access GitHub repository.
2. Exploring a Theoretical Mathematical Model
Our journey in Aim 2 revealed a rather unexpected fact: very little is known about the clonal dynamics, neural progenitor cell pool size, and the factors- both intrinsic and extrinsic-that govern embryonic neurogenesis. Upon thorough literature research, we uncovered only a handful of papers that had experimentally investigated mice neurogenesis. Interestingly, these studies primarily focused on observations within the frontal cortex. These efforts led to the development of models based on the relative speed of DNA incorporation and the proportion of neural progenitor cells. However, none of these models managed to explain the pool size or account for the occurrence of clonal expansion at different branch points.
To address this intriguing puzzle, my laboratory is currently collaborating with a systems biology lab at the German Cancer Research Center, which is my host institute. Our goal is to craft a mathematical model for embryonic neurogenesis. Unlike existing approaches, our model hinges on the mutation frequency within DNA sequences. It seeks to unveil the molecular lineage of each mutation, pinpoint the earliest common ancestor, and identify the driving force that emerges at the branching point where clonal expansion takes place.
While this kind of approach is well-established in the realm of cancer genomics, it's quite novel when applied to normal tissues or embryos. Through this innovative approach, we aim to shed light on the intricate process of embryonic neurogenesis and unveil the underlying factors that contribute to its complexity. We anticipate this model will reveal the ground-breaking dynamic of embryonic neurogenesis.
3. Unveiling the Frequency of DNA Breaks in Neural Progenitor Cells
At the heart of BrainBreaks lies a pivotal endeavor: deciphering the frequency of DNA breaks within neural progenitor cells. This is the very core of our exploration. Currently, we're in the process of assembling the definitive materials required to directly measure DNA breaks within the developing brain, specifically focusing on neural progenitor cells. Our aim is to investigate these cells both in their natural, unstressed state and under conditions of stress. Our ambitious goal is to compile a comprehensive map showcasing the locations of DNA breaks that occur in vivo. We're excited to share that we're on track to complete this mapping endeavor by the conclusion of the BrainBreaks period. This accomplishment promises to provide us with profound insights into the world of neural progenitor cells and their intricate DNA dynamics.
1. Utilizing Artificial Intelligence to Decipher DNA Replication Direction. The extensive application of artificial intelligence has primarily centered around images. Particularly within the realm of pathology, numerous programs have been developed to learn cell composition, shape, and pattern counts within diseased tissues. They aid in determining the grade and type of diseases based on histology or radiology images. In a similar vein, genomics data share resemblances to image data to a certain extent. Analogous to pixels that define the resolution of a DNA-associated marker, genomics data encapsulate intensity, distribution, and density information just as imaging data does. Although this approach is common in the field of pathology, its implementation within genomics remains relatively unexplored. Recognizing this, we opted to leverage the capabilities of artificial intelligence to predict DNA replication direction. The dynamic nature of DNA replication patterns presents unique challenges. While a similar approach has been devised in the past for specific datatype linked to DNA replication, its technical intricacy has confined its adoption to only a few select laboratories worldwide. In our pursuit, we have chosen to establish an artificial intelligence program for an alternative approach- one that demands significantly lower technical prerequisites. Our aim is to make this approach accessible and widely utilized by researchers within the genomics domain.
Grounded in a convolutional neural network, our artificial intelligence is structured in three distinct phases. The first phase entails pinpointing genomic regions housing specific start and end features associated with DNA replication. In the subsequent phase, training data is generated, outlining regions containing one to several start and end features. Finally, the third phase involves training the model using the designated training data. With a resounding success, this network exhibits the capacity to ascertain DNA replication direction with an impressive confidence level exceeding 80%, alongside high reproducibility. To enhance accessibility, this network has been made available on an open-access GitHub repository.
2. Exploring a Theoretical Mathematical Model
Our journey in Aim 2 revealed a rather unexpected fact: very little is known about the clonal dynamics, neural progenitor cell pool size, and the factors- both intrinsic and extrinsic-that govern embryonic neurogenesis. Upon thorough literature research, we uncovered only a handful of papers that had experimentally investigated mice neurogenesis. Interestingly, these studies primarily focused on observations within the frontal cortex. These efforts led to the development of models based on the relative speed of DNA incorporation and the proportion of neural progenitor cells. However, none of these models managed to explain the pool size or account for the occurrence of clonal expansion at different branch points.
To address this intriguing puzzle, my laboratory is currently collaborating with a systems biology lab at the German Cancer Research Center, which is my host institute. Our goal is to craft a mathematical model for embryonic neurogenesis. Unlike existing approaches, our model hinges on the mutation frequency within DNA sequences. It seeks to unveil the molecular lineage of each mutation, pinpoint the earliest common ancestor, and identify the driving force that emerges at the branching point where clonal expansion takes place.
While this kind of approach is well-established in the realm of cancer genomics, it's quite novel when applied to normal tissues or embryos. Through this innovative approach, we aim to shed light on the intricate process of embryonic neurogenesis and unveil the underlying factors that contribute to its complexity. We anticipate this model will reveal the ground-breaking dynamic of embryonic neurogenesis.
3. Unveiling the Frequency of DNA Breaks in Neural Progenitor Cells
At the heart of BrainBreaks lies a pivotal endeavor: deciphering the frequency of DNA breaks within neural progenitor cells. This is the very core of our exploration. Currently, we're in the process of assembling the definitive materials required to directly measure DNA breaks within the developing brain, specifically focusing on neural progenitor cells. Our aim is to investigate these cells both in their natural, unstressed state and under conditions of stress. Our ambitious goal is to compile a comprehensive map showcasing the locations of DNA breaks that occur in vivo. We're excited to share that we're on track to complete this mapping endeavor by the conclusion of the BrainBreaks period. This accomplishment promises to provide us with profound insights into the world of neural progenitor cells and their intricate DNA dynamics.