Biology subjects

Kooshkbaghi, M.

Publications and source records attributed to Kooshkbaghi, M..

3 recordsLinked to original sources

bioRxiv2022

Specificity, synergy, and mechanisms of splice-modifying drugs

Drugs that target pre-mRNA splicing hold great therapeutic potential, but the quantitative understanding of how these drugs work is limited. Here we introduce mechanistically interpretable quantitative models for the sequence-specific and concentration-dependent behavior of splice-modifying drugs. Using massively parallel splicing assays, RNA-seq experiments, and precision dose-response curves, we obtain quantitative models for two small-molecule drugs, risdiplam and branaplam, developed for treating spinal muscular atrophy. The results quantitatively characterize the specificities of risdiplam and branaplam for 5 splice site sequences, suggest that branaplam recognizes 5 splice sites via two distinct interaction modes, and disprove the prevailing two-site hypothesis for risdiplam activity at SMN2 exon 7. The results also show that anomalous single-drug cooperativity, as well as multi-drug synergy, are widespread among small-molecule drugs and antisense-oligonucleotide drugs that promote exon inclusion. Our quantitative models thus clarify the mechanisms of existing treatments and provide a basis for the rational development of new therapies.

biophysics↗

bioRxiv2022

Structural and mechanistic basis of σ-dependent transcriptional pausing

In {sigma}-dependent transcriptional pausing, the transcription initiation factor {sigma}, translocating with RNA polymerase (RNAP), makes sequence-specific protein-DNA interactions with a promoter-like sequence element in the transcribed region, inducing pausing. It has been proposed that, in {sigma}-dependent pausing, the RNAP active center can access off-pathway "backtracked" states that are substrates for the transcript-cleavage factors of the Gre family, and on-pathway "scrunched" states that mediate pause escape. Here, using site-specific protein-DNA photocrosslinking to define positions of the RNAP trailing and leading edges and of {sigma} relative to DNA at the{lambda} PR promoter, we show directly that {sigma}-dependent pausing in the absence of GreB in vitro predominantly involves a state backtracked by 2-4 bp, and that {sigma}-dependent pausing in the presence of GreB in vitro and in vivo predominantly involves a state scrunched by 2-3 bp. Analogous experiments with a library of 47 ([~]16,000) transcribed-region sequences show that the state scrunched by 2-3 bp--and only that state--is associated with the consensus sequence, T-3N-2Y-1G+1, (where -1 corresponds to the position of the RNA 3 end), which is identical to the consensus for pausing in initial transcription, and which is related to the consensus for pausing in transcription elongation. Experiments with heteroduplex templates show that sequence information at position T-3 resides in the DNA nontemplate strand. A cryo-EM structure of a complex engaged in {sigma}-dependent pausing reveals positions of DNA scrunching on the DNA nontemplate and template strands and suggests that position T-3 of the consensus sequence exerts its effects by facilitating scrunching.

biochemistry↗

bioRxiv2020

Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53

In this paper, we analyze the dynamical behavior of the tumor suppressor protein p53, an essential player in the cellular stress response, which prevents a cell from dividing if severe DNA damage is present. When this response system is malfunctioning, e.g. due to mutations in p53, uncontrolled cell proliferation may lead to the development of cancer. Understanding the behavior of p53 is thus crucial to prevent its failing. It has been shown in various experiments that periodicity of the p53 signal is one of the main descriptors of its dynamics, and that its pulsing behavior (regular vs. spontaneous) indicates the level and type of cellular stress. In the present work, we introduce an algorithm to score the local periodicity of a given time series (such as the p53 signal), which we call Detrended Autocorrelation Periodicity Scoring (DAPS). It applies pitch detection (via autocorrelation) on sliding windows of the entire time series to describe the overall periodicity by a distribution of localized pitch scores. We apply DAPS to the p53 time series obtained from single cell experiments and establish a correlation between the periodicity scoring of a cells p53 signal and the number of cell division events. In particular, we show that high periodicity scoring of p53 is correlated to a low number of cell divisions and vice versa. We show similar results with a more computationally intensive state-of-the-art periodicity scoring algorithm based on topology known as Sw1PerS. This correlation has two major implications: It demonstrates that periodicity scoring of the p53 signal is a good descriptor for cellular stress, and it connects the high variability of p53 periodicity observed in cell populations to the variability in the number of cell division events. AMS subject classification92C42, 92C37, 62M10

bioinformatics↗