CRISPRi-mediated knock-down of PRDM1/BLIMP1 programs central memory differentiation in ex vivo-expanded human T cells.

Introduction: B lymphocyte-induced maturation protein 1 (BLIMP1) encoded by the positive regulatory domain 1 gene (PRDM1), is a key regulator in T cell differentiation in mouse models. BLIMP1-deficiency results in a lower effector phenotype and a higher memory phenotype. Methods: In this study, we aimed to determine the role of transcription factor BLIMP1 in human T cell differentiation. Specifically, we investigated the role of BLIMP1 in memory differentiation and exhaustion of human T cells. We used CRISPR interference (CRISPRi) to knock-down BLIMP1 and investigated the differential expressions of T cell memory and exhaustion markers in BLIMP1-deficient T cells in comparison with BLIMP1-sufficient ex vivo expanded human T cells. Results: BLIMP1-deficiency caused an increase in central memory (CM) T cells and a decrease in effector memory (EM) T cells. There was a decrease in the amount of TIM3 exhaustion marker expression in BLIMP1-deficient T cells; however, there was an increase in PD1 exhaustion marker expression in BLIMP1-deficient T cells compared with BLIMP1-sufficient T cells. Conclusion: Our study provides the first functional evidence of the impact of BLIMP1 on the regulation of human T cell memory and exhaustion phenotype. These findings suggest that BLIMP1 may be a promising target to improve the immune response in adoptive T cell therapy settings.

Enhanced Waddington landscape model with cell-cell communication can explain molecular mechanisms of self-organization.

MOTIVATION: The molecular mechanisms of self-organization that orchestrate embryonic cells to create astonishing patterns have been among major questions of developmental biology. It is recently shown that embryonic stem cells (ESCs), when cultured in particular micropatterns, can self-organize and mimic the early steps of pre-implantation embryogenesis. A systems-biology model to address this observation from a dynamical systems perspective is essential and can enhance understanding of the phenomenon. RESULTS: Here, we propose a multicellular mathematical model for pattern formation during in vitro gastrulation of human ESCs. This model enhances the basic principles of Waddington epigenetic landscape with cell-cell communication, in order to enable pattern and tissue formation. We have shown the sufficiency of a simple mechanism by using a minimal number of parameters in the model, in order to address a variety of experimental observations such as the formation of three germ layers and trophectoderm, responses to altered culture conditions and micropattern diameters and unexpected spotted forms of the germ layers under certain conditions. Moreover, we have tested different boundary conditions as well as various shapes, observing that the pattern is initiated from the boundary and gradually spreads towards the center. This model provides a basis for in-silico modeling of self-organization. AVAILABILITY AND IMPLEMENTATION: https://github.com/HFooladi/Self_Organization. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Meta-aligner: long-read alignment based on genome statistics.

BACKGROUND: Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. RESULTS: We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates statistics from reads and the reference genome automatically. Meta-aligner is implemented in C++ and runs in popular POSIX-like operating systems such as Linux. CONCLUSIONS: Meta-aligner achieves high recall rates and precisions especially for long reads and high error rates. Also, it improves performance of alignment in the case of PacBio long-reads in comparison with traditional schemes.