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PNAS Papers on Mycobacterial Disease Susceptibility, Axolotl Genome Assembly, Single-Cell Algorithm

Editor's Note: Some of the articles described below are not yet available at the PNAS site, but they are scheduled to be posted this week.

An international team led by investigators in France describes homozygous ZNFX1 variants identified in individuals with inborn interferon-gamma errors or other immune deficiencies that render them particularly susceptible to mycobacterial disease. The researchers used exome sequencing to assess an individual with severe tuberculosis, a sibling with intermittent monocytosis, and an unrelated individual with a condition called Mendelian susceptibility to mycobacterial disease (MSMD), narrowing in on rare, non-synonymous coding changes in ZNFX1 that was suspected of contributing to the death of child from the first family who could not be tested. From these and other findings, they suggest that ZNFX1 "is essential for monocyte homeostasis and protective immunity to mycobacteria, via mechanisms potentially involving myeloid cells, as the patients described here displayed monocytosis, and ZNFX1 levels are highest in monocytes."

Researchers at Austria's Institute of Molecular Pathology and the University of Kentucky report on findings from a genome analysis of the giant axolotl (Ambystoma mexicanum), a model organism used for studies of limb regeneration and other biological processes. Using a Hi-C chromosome conformation capture method, the team put together a chromosome scale assembly of the 32-gigabase axolotl genome, which was compared with other available axolotl sequences and with the human genome to find key genome organization features at different stages of the cell cycle. "We use Hi-C contact information and the annotated genome to uncover the topological organization of long-distance transcriptional regulatory units and of mitotic chromosomes in this giant genome," the authors write, noting that the assembly offered gene regulatory clues as well as insights into the evolution of the major histocompatibility complex and other syntenic multigene clusters.

A Stanford University team outlines a computational tool for predicting expression profiles in individual cells profiled using single-cell chromatin accessibility (scATAC-seq) profiles or, conversely, estimating chromatin patterns in cells assessed with single-cell RNA sequencing. The approach, known as BABEL, relies on deep learning to model architectural features such as expression, chromatin, methylation, and related proteomic patterns in individual cells, the researchers explain, noting that the algorithm "makes it possible to computationally synthesize matched multi-omic measurements when only one modality is experimentally available." When they applied the approach to scATAC-seq data on a patient-derived basal cell carcinoma (BCC) sample, for example, the algorithm came up with single-cell expression profiles that tracked with those described in BCC samples in the past. "These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL's training data," the authors write, adding that the algorithm "can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein."