Independent validation of induced overexpression efficiency across 242 experiments shows a success rate of 39

A Bayesian Noisy Logic Model for Inference of Transcription Factor Activity from Single Cell and Bulk Transcriptomic Data

The advent of high-throughput sequencing has made it possible to measure the expression of genes at relatively low cost. However, direct measurement of regulatory mechanisms, such as Transcription Factor (TF) activity is still not readily feasible in a high-throughput manner. Consequently, there is a need for computational approaches that can reliably estimate regulator activity from observable gene expression data. In this work, we present a noisy Boolean logic Bayesian model for TF activity inference from differential gene expression data and causal graphs. Our approach provides a flexible framework to incorporate biologically motivated TF-gene regulation logic models. Using simulations and controlled over-expression experiments in cell cultures, we demonstrate that our method can accurately identify TF activity. Moreover, we apply our method to bulk and single cell transcriptomics measurements to investigate transcriptional regulation of fibroblast phenotypic plasticity. Finally, to facilitate usage, we provide user-friendly software packages and a web-interface to query TF activity from user input differential gene expression data: https://umbibio.math.umb.edu/nlbayes/.

Human METTL7B is an alkyl thiol methyltransferase that metabolizes hydrogen sulfide and captopril

Methylation of alkyl thiols is a biotransformation pathway designed to reduce thiol reactivity and potential toxicity, yet the gene and protein responsible for human alkyl thiol methyltransferase (TMT) activity remain unknown. Here we demonstrate with a range of experimental approaches using cell lines, in vitro systems, and recombinantly expressed enzyme, that human methyltransferase-like protein 7B (METTL7B) catalyzes the transfer of a methyl group from S-adenosyl-L-methionine (AdoMet) to hydrogen sulfide (H2S) and other exogenous thiol small molecules. METTL7B gene modulation experiments, including knockdown in HepG2 cells and overexpression in HeLa cells, directly alter the methylation of the drug captopril, a historic probe substrate for TMT activity. Furthermore, recombinantly expressed and purified wild-type METTL7B methylates several thiol compounds, including H2S, 7α-thiospironolactone, L-penicillamine, and captopril, in a time- and concentration-dependent manner. Typical for AdoMet-dependent small molecule methyltransferases, S-adenosyl-L-homocysteine (AdoHcy) inhibited METTL7B activity in a competitive fashion. Similarly, mutating a conserved aspartate residue, proposed to anchor AdoMet into the active site, to an alanine (D98A) abolished methylation activity. Endogenous thiols such as glutathione and cysteine, or classic substrates for other known small molecule S-, N-, and O-methyltransferases, were not substrates for METTL7B. Our results confirm, for the first time, that METTL7B, a gene implicated in multiple disease states including rheumatoid arthritis and breast cancer, encodes a protein that methylates small molecule alkyl thiols. Identifying the catalytic function of METTL7B will enable future pharmacological research in disease pathophysiology where altered METTL7B expression and, potentially H2S levels, can disrupt cell growth and redox state.

Arrayed CRISPR reveals genetic regulators of tau aggregation, autophagy and mitochondria in Alzheimer's disease model

Alzheimer's disease (AD) is a common neurodegenerative disease with poor prognosis. New options for drug discovery targets are needed. We developed an imaging based arrayed CRISPR method to interrogate the human genome for modulation of in vitro correlates of AD features, and used this to assess 1525 human genes related to tau aggregation, autophagy and mitochondria. This work revealed (I) a network of tau aggregation modulators including the NF-κB pathway and inflammatory signaling, (II) a correlation between mitochondrial morphology, respiratory function and transcriptomics, (III) machine learning predicted novel roles of genes and pathways in autophagic processes and (IV) individual gene function inferences and interactions among biological processes via multi-feature clustering. These studies provide a platform to interrogate underexplored aspects of AD biology and offer several specific hypotheses for future drug discovery efforts.