Temperature sensitive (TS) missense mutants have been foundational for characterization of essentialgene function. However, an unbiased approach for analysis of biochemical and biophysical changes in TSmissense mutants within the context of their functional proteomes is lacking. We applied massspectrometry (MS) based thermal proteome profiling (TPP) to investigate the proteome-wide effects ofmissense mutations in an application that we refer to as mutant Thermal Proteome Profiling (mTPP).This study characterized global impacts of temperature sensitivity-inducing missense mutations in twodifferent subunits of the 26S proteasome. The majority of alterations identified by RNA-Seq and globalproteomics were similar between the mutants, which could suggest that a similar functional disruption isoccurring in both missense variants. Results from mTPP, however, provide unique insights into themechanisms that contribute to the TS phenotype in each mutant, revealing distinct changes that were notobtained using only steady-state transcriptome and proteome analyses. Computationally, multisite λ-dynamics simulations add clear support for mTPP experimental findings. This work shows that mTPP is aprecise approach to measure changes in missense mutant containing proteomes without the requirementfor large amounts of starting material, specific antibodies against proteins of interest, and/or geneticmanipulation of the biological system. Although experiments were performed under permissiveconditions, mTPP provided insights into the underlying protein stability changes that cause dramaticcellular phenotypes observed at non-permissive temperatures. Overall, mTPP provides uniquemechanistic insights into missense mutation dysfunction and connection of genotype to phenotype in arapid, non-biased fashion.

RNA Polymerase II (RNAPII) transcription termination is regulated by the phosphorylation status of the C-terminal domain (CTD). The phosphatase Rtr1 has been shown to regulate serine 5 phosphorylation on the CTD; however, its role in the regulation of RNAPII termination has not been explored. As a consequence of RTR1 deletion, interactions within the termination machinery and between the termination machinery and RNAPII were altered as quantified by Disruption-Compensation (DisCo) network analysis. Of note, interactions between RNAPII and the cleavage factor IA (CF1A) subunit Pcf11 were reduced in rtr1Δ, whereas interactions with the CTD and RNA-binding termination factor Nrd1 were increased. Globally, rtr1Δ leads to decreases in numerous noncoding RNAs that are linked to the Nrd1, Nab3 and Sen1 (NNS)-dependent RNAPII termination pathway. Genome-wide analysis of RNAPII and Nrd1 occupancy suggests that loss of RTR1 leads to increased termination at noncoding genes. Additionally, premature RNAPII termination increases globally at protein-coding genes with a decrease in RNAPII occupancy occurring just after the peak of Nrd1 recruitment during early elongation. The effects of rtr1Δ on RNA expression levels were lost following deletion of the exosome subunit Rrp6, which works with the NNS complex to rapidly degrade a number of noncoding RNAs following termination. Overall, these data suggest that Rtr1 restricts the NNS-dependent termination pathway in WT cells to prevent premature termination of mRNAs and ncRNAs. Rtr1 facilitates low-level elongation of noncoding transcripts that impact RNAPII interference thereby shaping the transcriptome.

The study of low-abundance proteins is a challenge to discovery-based proteomics. Mass spectrometry (MS) applications, such as thermal proteome profiling (TPP), face specific challenges in the detection of the whole proteome as a consequence of the use of nondenaturing extraction buffers. TPP is a powerful method for the study of protein thermal stability, but quantitative accuracy is highly dependent on consistent detection. Therefore, TPP can be limited in its amenability to study low-abundance proteins that tend to have stochastic or poor detection by MS. To address this challenge, we incorporated an affinity-purified protein complex sample at submolar concentrations as an isobaric trigger channel into a mutant TPP (mTPP) workflow to provide reproducible detection and quantitation of the low-abundance subunits of the cleavage and polyadenylation factor (CPF) complex. The inclusion of an isobaric protein complex trigger channel increased detection an average of 40× for previously detected subunits and facilitated detection of CPF subunits that were previously below the limit of detection. Importantly, these gains in CPF detection did not cause large changes in melt temperature ( T m ) calculations for other unrelated proteins in the samples, with a high positive correlation between T m estimates in samples with and without isobaric trigger channel addition. Overall, the incorporation of an affinity-purified protein complex as an isobaric trigger channel within a tandem mass tag (TMT) multiplex for mTPP experiments is an effective and reproducible way to gather thermal profiling data on proteins that are not readily detected using the original TPP or mTPP protocols.

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures ( T m ) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3 , 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

The use of CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein-ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impacts the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and will make the R based program Inflect available for research community use. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared towards specific applications.