Evolution of nanobodies specific for BCL11A

Yin, Maolu; Izadi, Manizheh; Tenglin, Karin; Viennet, Thibault; Zhai, Lu; Ge, Zheng; Arthanari, Haribabu; Dassama, Laura M. K.; Orkin, Stuart H.

doi:10.1073/pnas.2218959120

Cited by 22 publications

(16 citation statements)

References 58 publications

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“…We found that predicted brain ages generated by a densely connected neural network using 3 distinct sets of neuroimaging features (FW-corrected dMRI, T1-weighted MRI, combined FW+T1) all showed high correlation with actual age in baseline CU participants, which confirms findings from previous literature that have accurately predicted chronological age of healthy adults using neuroimaging-derived measures with machine learning approaches including deep learning 11,12,17,26–29 . Importantly, the top-contributing neuroimaging features identified for each model ( Figure 2 ) provide biological interpretability as they include brain regions that have been associated with both normal aging and AD neuropathology.…”

Section: Discussionsupporting

confidence: 86%

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

Yang,

Sathe,

Schilling

et al. 2023

Preprint

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The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66,p=1.62x10-32; T1: r=0.61,p=1.45x10-26, FW+T1: r=0.77,p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW:p=0.006; T1:p=0.048; FW+T1:p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094,p=6.32x10-7; T1: β=-1.331,p=6.52x10-7; FW+T1: β=-1.476,p=2.53x10-10; executive function, FW: β=-1.276,p=1.46x10-9; T1: β=-1.337,p=2.52x10-7; FW+T1: β=-1.850,p=3.85x10-17) and longitudinal cognition (memory, FW: β=-0.091,p=4.62x10-11; T1: β=-0.097,p=1.40x10-8; FW+T1: β=-0.101,p=1.35x10-11; executive function, FW: β=-0.125,p=1.20x10-10; T1: β=-0.163,p=4.25x10-12; FW+T1: β=-0.158,p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

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Section: Discussionsupporting

confidence: 86%

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

Yang,

Sathe,

Schilling

et al. 2023

Preprint

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“…We compared the performance of our model with two different models from the literature - Peng et al [24] and Yin et al [25]. The Peng et al model was originally trained on data from UK Biobank T1ws, whereas the Yin et al model was originally trained on ADNI T1ws.…”

Section: Resultsmentioning

confidence: 99%

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Chattopadhyay,

Joshy,

Ozarkar

et al. 2024

Preprint

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Deep learning models based on convolutional neural networks (CNNs) have been used to classify Alzheimer’s disease or infer dementia severity from T1-weighted brain MRI scans. Here, we examine the value of adding diffusion-weighted MRI (dMRI) as an input to these models. Much research in this area focuses on specific datasets such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), which assesses people of North American, largely European ancestry, so we examine how models trained on ADNI, generalize to a new population dataset from India (the NIMHANS cohort). We first benchmark our models by predicting “brain age” - the task of predicting a person’s chronological age from their MRI scan and proceed to AD classification. We also evaluate the benefit of using a 3D CycleGAN approach to harmonize the imaging datasets before training the CNN models. Our experiments show that classification performance improves after harmonization in most cases, as well as better performance for dMRI as input.

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“…Preventing BCL11A from binding to the DNA would be a preferred therapeutic route, however directly competing with DNA using a small molecule seems overly challenging. One plausible route is to find antibodies or nanobodies 28 binding BCL11A and inducing protein degradation (PROTAC approach). Alternatively, small molecules could achieve the same results or stabilize the apo conformation of BCL11A preventing it from binding to DNA.…”

Section: Discussionmentioning

confidence: 99%

Structural Insights into the DNA-Binding Mechanism of BCL11A: The Integral Role of ZnF6

Viennet,

Yin,

Jayaraj

et al. 2024

Preprint

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The transcription factor BCL11A is a critical regulator of the switch from fetal hemoglobin (HbF: α2γ2) to adult hemoglobin (HbA: α2β2) during development. BCL11A binds at a cognate recognition site (TGACCA) in the γ-globin gene promoter and represses its expression. DNA-binding is mediated by a triple zinc finger domain, designated ZnF456. Here, we report comprehensive investigation of ZnF456, leveraging X-ray crystallography and NMR to determine the structures in both the presence and absence of DNA. We delve into the dynamics and mode of interaction with DNA. Moreover, we discovered that the last zinc finger of BCL11A (ZnF6) plays a special role in DNA binding and γ-globin gene repression. Our findings help account for some rare γ-globin gene promoter mutations that perturb BCL11A binding and lead to increased HbF in adults (hereditary persistence of fetal hemoglobin). Comprehending the DNA binding mechanism of BCL11A opens avenues for the strategic, structure-based design of novel therapeutics targeting sickle cell disease and β-thalassemia.

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Evolution of nanobodies specific for BCL11A

Cited by 22 publications

References 58 publications

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

Brain Age Analysis and Dementia Classification using Convolutional Neural Networks trained on Diffusion MRI: Tests in Indian and North American Cohorts

Structural Insights into the DNA-Binding Mechanism of BCL11A: The Integral Role of ZnF6

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