Multi-omics analysis of naïve B cells of patients harboring the C104R mutation in TACI.

Common variable immunodeficiency (CVID) is the most prevalent form of symptomatic primary immunodeficiency in humans. The genetic cause of CVID is still unknown in about 70% of cases. Ten percent of CVID patients carry heterozygous mutations in the tumor necrosis factor receptor superfamily member 13B gene (TNFRSF13B), encoding TACI. Mutations in TNFRSF13B alone may not be sufficient for the development of CVID, as 1% of the healthy population carry these mutations. The common hypothesis is that TACI mutations are not fully penetrant and additional factors contribute to the development of CVID. To determine these additional factors, we investigated the perturbations of transcription factor (TF) binding and the transcriptome profiles in unstimulated and CD40L/IL21-stimulated naïve B cells from CVID patients harboring the C104R mutation in TNFRSF13B and compared them to their healthy relatives with the same mutation. In addition, the proteome of stimulated naïve B cells was investigated. For functional validation, intracellular protein concentrations were measured by flow cytometry. Our analysis revealed 8% less accessible chromatin in unstimulated naïve B cells and 25% less accessible chromatin in class-switched memory B cells from affected and unaffected TACI mutation carriers compared to healthy donors. The most enriched TF binding motifs in TACI mutation carriers involved members from the ETS, IRF, and NF-κB TF families. Validation experiments supported dysregulation of the NF-κB and MAPK pathways. In steady state, naïve B cells had increased cell death pathways and reduced cell metabolism pathways, while after stimulation, enhanced immune responses and decreased cell survival were detected. Using a multi-omics approach, our findings provide valuable insights into the impaired biology of naïve B cells from TACI mutation carriers.

A New Method of Image Classification Based on Domain Adaptation.

Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich sample data and a target domain with only a few or zero labeled samples and, thus, complete the transfer of knowledge by aligning the distribution between domains through methods, such as domain adaptation. Previous domain adaptation methods mostly align the features in the feature space of all categories on a global scale. Recently, the method of locally aligning the sub-categories by introducing label information achieved better results. Based on this, we present a deep fuzzy domain adaptation (DFDA) that assigns different weights to samples of the same category in the source and target domains, which enhances the domain adaptive capabilities. Our experiments demonstrate that DFDA can achieve remarkable results on standard domain adaptation datasets.

Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL.

The Bruton tyrosine kinase (BTK) inhibitor ibrutinib provides effective treatment for patients with chronic lymphocytic leukemia (CLL), despite extensive heterogeneity in this disease. To define the underlining regulatory dynamics, we analyze high-resolution time courses of ibrutinib treatment in patients with CLL, combining immune-phenotyping, single-cell transcriptome profiling, and chromatin mapping. We identify a consistent regulatory program starting with a sharp decrease of NF-κB binding in CLL cells, which is followed by reduced activity of lineage-defining transcription factors, erosion of CLL cell identity, and acquisition of a quiescence-like gene signature. We observe patient-to-patient variation in the speed of execution of this program, which we exploit to predict patient-specific dynamics in the response to ibrutinib based on the pre-treatment patient samples. In aggregate, our study describes time-dependent cellular, molecular, and regulatory effects for therapeutic inhibition of B cell receptor signaling in CLL, and it establishes a broadly applicable method for epigenome/transcriptome-based treatment monitoring.