RORγt-Expressing Pathogenic CD4+ T Cells Cause Brain Inflammation during Chronic Colitis

Mickael, Michel‐Edwar; Bhaumik, Suniti; Chakraborti, Ayanabha; Umfress, Alan; Groen, Thomas van; Macaluso, Matthew; Totenhagen, John; Sorace, Anna G.; Bibb, James A.; Standaert, David G.; Basu, Rajatava

doi:10.4049/jimmunol.2100869

Cited by 23 publications

(22 citation statements)

References 69 publications

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“…The objective of the case study is to repurpose a compound that could target the inflammatory process regulated by Th17 infiltration of the brain during depression [ 32 ]. The constraints on the characteristics of the repurposed drug include its ability to inhibit Th17 differentiation, enhance depression prognosis and pass through the blood–brain barrier as well as being biologically safe [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…We investigated repurposing anti-oxidant compounds that could be used to regulate Th17 cells in depression. Previously, we found that Th17 infiltrates the blood–brain barrier through a paracellular route causing depression-like behavior [ 32 , 33 ]. The main objective of the in silico validation step was to find anti-oxidant drugs that could be used to reduce the effect of Th17 in depression and score at least four out of five on Lipinski’s score.…”

Section: Introductionmentioning

confidence: 99%

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Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks

et al. 2022

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Drug repurposing in the context of neuroimmunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and focuses on discovering new usages for known medications. Neuroimmunological diseases, such as Alzheimer’s, Parkinson’s, multiple sclerosis, and depression, include various pathologies that result from the interaction between the central nervous system and the immune system. However, the repurposing of NI medications is hindered by the vast amount of information that needs mining. We previously presented Adera1.0, which was capable of text mining PubMed for answering query-based questions. However, Adera1.0 was not able to automatically identify chemical compounds within relevant sentences. To challenge the need for repurposing known medications for neuroimmunological diseases, we built a deep neural network named Adera2.0 to perform drug repurposing. The workflow uses three deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network uses a mean squared error (MSE) loss function to predict answers in the form of embedded matrices. The third network, which constitutes the main novelty in our updated workflow, also uses a MSE loss function. Its main usage is to extract compound names from relevant sentences resulting from the previous network. To optimize the network function, we compared eight different designs. We found that a deep neural network consisting of an RNN neural network and a leaky ReLU could achieve 0.0001 loss and 67% sensitivity. Additionally, we validated Adera2.0’s ability to predict NI drug usage against the DRUG Repurposing Hub database. These results establish the ability of Adera2.0 to repurpose drug candidates that can shorten the development of the drug cycle. The workflow could be download online.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks

et al. 2022

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show abstract

“…The objective of the case study is to repurpose a compound that could target the inflammatory process regulated by Th17 infiltration of the brain during the depression. The constraints on the characteristic of the repurposed drug include the ability to inhibit Th17 differentiation, enhance depression prognosis and pass through the blood-brain barrier as well as being biologically safe 8,11 . The question used to query the system is "What are anti-oxidant drugs".…”

Section: Case Studymentioning

confidence: 99%

“…We investigated repurposing anti-oxidants compounds that could be used to regulate Th17 cells in depression. Previously, we have found that Th17 infiltrates the blood-brain barrier through a paracellular route causing depression-like behavior 8,9 . The main objective of the in-silico validation step was to find anti-oxidant drugs that could be used to reduce the effect of Th17 in depression.…”

Section: Introductionmentioning

confidence: 99%

Adera: A drug repurposing workflow for neuro-immunological investigations using neural networks

Łazarczyk

Duda

Mickael

et al. 2022

Preprint

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Drug repurposing in the context of neuro-immunological (NI) investigations is still in its primary stages. Drug repurposing is an important method that bypasses lengthy drug discovery procedures and rather focuses on discovering new usage for known medications. Neuro-immunological diseases such as Alzheimer’s, Parkinson, multiple sclerosis and depression include various pathologies that resulted from the interaction between the central nervous system and the immune system. However, repurposing of medications is hindered by the vast amount of information that needs mining. To challenge the need for repurposing known medications for neuro-immunological diseases, we built a deep neural network named Adera to perform drug repurposing. The model uses two deep learning networks. The first network is an encoder and its main task is to embed text into matrices. The second network we explored the usage of two different loss function, binary cross entropy and means square error (MSE). Furthermore, we investigated the effect of ten different network architecture with each loss function. Our results show that for the binary cross entropy loss function, the best architecture consists of a two layers of convolution neural network and it achieves a loss of less than 0.001. In the case of MSE loss function a shallow network using aRelu activation achieved an accuracy of over 98 % and loss of 0.001. Additionally, Adera was able to predict various drug repurposing targets in agreement with DRUG Repurposing Hub. These results establish the ability of Adera to repurpose with high accuracy drug candidates that can shorten the development of the drug cycle. The software could be downloaded from https://github.com/michel-phylo/ADERA1.

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“…However, they diverged in their functions. Th17 can be proinflammatory by producing several pro-inflammatory cytokines such as IL17A, IL17F, IL1, and IL6 [3]. Treg is an anti-inflammatory cell [4].…”

Section: Introductionmentioning

confidence: 99%

Investigation of mutated in colorectal cancer evolution history indicate a putative role in Th17/Treg differentiation

Mickael

2022

Preprint

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The MCC family of genes plays a role in colorectal cancer development through various immunological pathways, including the Th17/Treg axis. We have previously shown that MCC1 and not MCC2 play a role in Treg differentiation. Our understanding of the genetic divergence patterns and evolutionary history of the MCC family in relation to its function, in general, and the Th17/Treg axis, in particular, remains incomplete. In this investigation, we explored 12 species' genomes to study the phylogenetic origin, structure, and functional specificity of this family. In vertebrates, both MCC1 and MCC2 homologs have been discovered, while invertebrates have a single MCC homolog. We found MCC homologs as early as Cnidarians and Trichoplax, suggesting that the MCC family first appeared 741 million years ago (Ma), whereas MCC divergence into MCC1 and MCC2 families occurred at 540 Ma. In general, we did not detect significant positive selection regulating MCC evolution. Our investigation, based on MCC1 structural similarity, suggests that they may play a role in the evolutionary changes in Tregs' emergence towards complexity, including the ability to utilize calcium for differentiation through the use of the EFH calcium-binding domain. We also found that the motif NPSTGE was highly conserved in MCC1 but not in MCC2. The NPSTGE motif binds KEAP1 with high affinity, suggesting an Nrf2-mediated function for Nrf2. In the case of MCC2, we found that the "Modifier of rudimentary" motif is highly conserved. This motif contributes to the regulation of alternative splicing. Overall, our study sheds light on how the evolution of the MCC family is connected to its function in regulating the Th17/Treg axis.

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RORγt-Expressing Pathogenic CD4+ T Cells Cause Brain Inflammation during Chronic Colitis

Cited by 23 publications

References 69 publications

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks

Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks

Adera: A drug repurposing workflow for neuro-immunological investigations using neural networks

Investigation of mutated in colorectal cancer evolution history indicate a putative role in Th17/Treg differentiation

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