Functional consequences of a p53-MDM2-p21 incoherent feedforward loop.

Genetically identical cells can respond heterogeneously to cancer therapy, with a subpopulation of cells often entering a temporarily arrested treatment-tolerant state before repopulating the tumor. To investigate how heterogeneity in the cell cycle arrest protein p21 arises, we imaged the dynamics of p21 transcription and protein expression along with those of p53, its transcriptional regulator, in single cells using live cell fluorescence microscopy. Surprisingly, we found that the rate of p21 transcription depends on the change in p53 rather than its absolute level. Through combined theoretical and experimental modeling, we determined that p21 transcription is governed by an incoherent feedforward loop mediated by MDM2. This network architecture facilitates rapid induction of p21 expression and variability in p21 transcription. Abrogating the feedforward loop overcomes rapid S-phase p21 degradation, with cells transitioning into a quiescent state that transcriptionally resembles a treatment-tolerant persister state. Our findings have important implications for therapeutic strategies based on activating p53.

MSModDetector: a tool for detecting mass shifts and post-translational modifications in individual ion mass spectrometry data.

MOTIVATION: Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs on the same protein molecule, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even PTMs at distant sites to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. RESULTS: We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I2MS) data. I2MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I2MS data and experimental I2MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/marjanfaizi/MSModDetector.

Reciprocal antagonism of PIN1-APC/CCDH1 governs mitotic protein stability and cell cycle entry.

Induced oncoproteins degradation provides an attractive anti-cancer modality. Activation of anaphase-promoting complex (APC/CCDH1) prevents cell-cycle entry by targeting crucial mitotic proteins for degradation. Phosphorylation of its co-activator CDH1 modulates the E3 ligase activity, but little is known about its regulation after phosphorylation and how to effectively harness APC/CCDH1 activity to treat cancer. Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1)-catalyzed phosphorylation-dependent cis-trans prolyl isomerization drives tumor malignancy. However, the mechanisms controlling its protein turnover remain elusive. Through proteomic screens and structural characterizations, we identify a reciprocal antagonism of PIN1-APC/CCDH1 mediated by domain-oriented phosphorylation-dependent dual interactions as a fundamental mechanism governing mitotic protein stability and cell-cycle entry. Remarkably, combined PIN1 and cyclin-dependent protein kinases (CDKs) inhibition creates a positive feedback loop of PIN1 inhibition and APC/CCDH1 activation to irreversibly degrade PIN1 and other crucial mitotic proteins, which force permanent cell-cycle exit and trigger anti-tumor immunity, translating into synergistic efficacy against triple-negative breast cancer.

Comparison of TP53 Mutations in Myelodysplasia and Acute Leukemia Suggests Divergent Roles in Initiation and Progression.

TP53 mutation predicts adverse prognosis in many cancers, including myeloid neoplasms, but the mechanisms by which specific mutations impact disease biology, and whether they differ between disease categories, remain unknown. We analyzed TP53 mutations in four myeloid neoplasm subtypes (MDS, AML, AML with myelodysplasia-related changes (AML-MRC), and therapy-related acute myeloid leukemia (tAML)), and identified differences in mutation types, spectrum, and hotspots between disease categories and compared to solid tumors. Missense mutations in the DNA-binding domain were most common across all categories, whereas inactivating mutations and mutations outside the DNA binding domain were more common in AML-MRC compared to MDS. TP53 mutations in MDS were more likely to retain transcriptional activity, and co-mutation profiles were distinct between disease categories and mutation types. Our findings suggest that mutated TP53 contributes to initiation and progression of neoplasia via distinct mechanisms, and support the utility of specific identification of TP53 mutations in myeloid malignancies.

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data.

Motivation: Post-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. Results: We developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a protein's PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. Availability: The source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

Single-cell biology: what does the future hold?

In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss recent breakthroughs, current challenges, and emerging opportunities in single-cell biology and share their vision of "where the field is headed."

Multi-range ERK responses shape the proliferative trajectory of single cells following oncogene induction.

Oncogene-induced senescence is a phenomenon in which aberrant oncogene expression causes non-transformed cells to enter a non-proliferative state. Cells undergoing oncogenic induction display phenotypic heterogeneity, with some cells senescing and others remaining proliferative. The causes of heterogeneity remain unclear. We studied the sources of heterogeneity in the responses of human epithelial cells to oncogenic BRAFV600E expression. We found that a narrow expression range of BRAFV600E generated a wide range of activities of its downstream effector ERK. In population-level and single-cell assays, ERK activity displayed a non-monotonic relationship to proliferation, with intermediate ERK activities leading to maximal proliferation. We profiled gene expression across a range of ERK activities over time and characterized four distinct ERK response classes, which we propose act in concert to generate the ERK-proliferation response. Altogether, our studies map the input-output relationships between ERK activity and proliferation, elucidating how heterogeneity can be generated during oncogene induction.

Reciprocal inhibition of PIN1 and APC/CCDH1 controls timely G1/S transition and creates therapeutic vulnerability.

Cyclin-dependent kinases (CDKs) mediated phosphorylation inactivates the anaphase-promoting complex (APC/CCDH1), an E3 ubiquitin ligase that contains the co-activator CDH1, to promote G1/S transition. PIN1 is a phosphorylation-directed proline isomerase and a master cancer signaling regulator. However, little are known about APC/CCDH1 regulation after phosphorylation and about PIN1 ubiquitin ligases. Here we uncover a domain-oriented reciprocal inhibition that controls the timely G1/S transition: The non-phosphorylated APC/CCDH1 E3 ligase targets PIN1 for degradation in G1 phase, restraining G1/S transition; APC/CCDH1 itself, after phosphorylation by CDKs, is inactivated by PIN1-catalyzed isomerization, promoting G1/S transition. In cancer, PIN1 overexpression and APC/CCDH1 inactivation reinforce each other to promote uncontrolled proliferation and tumorigenesis. Importantly, combined PIN1- and CDK4/6-inhibition reactivates APC/CCDH1 resulting in PIN1 degradation and an insurmountable G1 arrest that translates into synergistic anti-tumor activity against triple-negative breast cancer in vivo. Reciprocal inhibition of PIN1 and APC/CCDH1 is a novel mechanism to control timely G1/S transition that can be harnessed for synergistic anti-cancer therapy.

Principles, mechanisms and functions of entrainment in biological oscillators.

Entrainment is a phenomenon in which two oscillators interact with each other, typically through physical or chemical means, to synchronize their oscillations. This phenomenon occurs in biology to coordinate processes from the molecular to organismal scale. Biological oscillators can be entrained within a single cell, between cells or to an external input. Using six illustrative examples of entrainable biological oscillators, we discuss the distinctions between entrainment and synchrony and explore features that contribute to a system's propensity to entrain. Entrainment can either enhance or reduce the heterogeneity of oscillations within a cell population, and we provide examples and mechanisms of each case. Finally, we discuss the known functions of entrainment and discuss potential functions from an evolutionary perspective.

Louder for longer: Myc amplifies gene expression by extended transcriptional bursting.

Transcription is a complex, dynamic process. Using live single-cell measurements, Patange et al. show, in a recent issue of Cell Reports, that elevated levels of the transcription factor MYC enhance target gene RNA production by increasing the duration but not frequency of transcriptional bursts.

Time-series transcriptomics and proteomics reveal alternative modes to decode p53 oscillations.

The cell stress-responsive transcription factor p53 influences the expression of its target genes and subsequent cellular responses based in part on its dynamics (changes in level over time). The mechanisms decoding p53 dynamics into subsequent target mRNA and protein dynamics remain unclear. We systematically quantified p53 target mRNA and protein expression over time under two p53 dynamical regimes, oscillatory and rising, using RNA-sequencing and TMT mass spectrometry. Oscillatory dynamics allowed for a greater variety of dynamical patterns for both mRNAs and proteins. Mathematical modeling of empirical data revealed three distinct mechanisms that decode p53 dynamics. Specific combinations of these mechanisms at the transcriptional and post-transcriptional levels enabled exclusive induction of proteins under particular dynamics. In addition, rising induction of p53 led to higher induction of proteins regardless of their functional class, including proteins promoting arrest of proliferation, the primary cellular outcome under rising p53. Our results highlight the diverse mechanisms cells employ to distinguish complex transcription factor dynamics to regulate gene expression.

Cycling cancer persister cells arise from lineages with distinct programs.

Non-genetic mechanisms have recently emerged as important drivers of cancer therapy failure1, where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment2. Although most cancer persisters remain arrested in the presence of the drug, a rare subset can re-enter the cell cycle under constitutive drug treatment. Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drugs. To study this rare, transiently resistant, proliferative persister population, we developed Watermelon, a high-complexity expressed barcode lentiviral library for simultaneous tracing of each cell's clonal origin and proliferative and transcriptional states. Here we show that cycling and non-cycling persisters arise from different cell lineages with distinct transcriptional and metabolic programs. Upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation are associated with persister proliferative capacity across multiple cancer types. Impeding oxidative stress or metabolic reprogramming alters the fraction of cycling persisters. In human tumours, programs associated with cycling persisters are induced in minimal residual disease in response to multiple targeted therapies. The Watermelon system enabled the identification of rare persister lineages that are preferentially poised to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.

p53 dynamics vary between tissues and are linked with radiation sensitivity.

Radiation sensitivity varies greatly between tissues. The transcription factor p53 mediates the response to radiation; however, the abundance of p53 protein does not correlate well with the extent of radiosensitivity across tissues. Given recent studies showing that the temporal dynamics of p53 influence the fate of cultured cells in response to irradiation, we set out to determine the dynamic behavior of p53 and its impact on radiation sensitivity in vivo. We find that radiosensitive tissues show prolonged p53 signaling after radiation, while more resistant tissues show transient p53 activation. Sustaining p53 using a small molecule (NMI801) that inhibits Mdm2, a negative regulator of p53, reduced viability in cell culture and suppressed tumor growth. Our work proposes a mechanism for the control of radiation sensitivity and suggests tools to alter the dynamics of p53 to enhance tumor clearance. Similar approaches can be used to enhance killing of cancer cells or reduce toxicity in normal tissues following genotoxic therapies.

Connecting Timescales in Biology: Can Early Dynamical Measurements Predict Long-Term Outcomes?

Prediction of long-term outcomes from short-term measurements remains a fundamental challenge. Quantitative assessment of signaling dynamics, and the resulting transcriptomic and proteomic responses, has yielded fundamental insights into cellular outcomes. However, the utility of these measurements is limited by their short timescale (hours to days), while the consequences of these events frequently unfold over longer timescales. Here, we discuss the predictive power of static and dynamic measurements, drawing examples from fields that have harnessed the predictive capabilities of such measurements. We then explore potential approaches to close this timescale gap using complementary measurements and computational approaches, focusing on the example of dynamic measurements of signaling factors and their impacts on cellular outcomes.

A Switch in p53 Dynamics Marks Cells That Escape from DSB-Induced Cell Cycle Arrest.

Cellular responses to stimuli can evolve over time, resulting in distinct early and late phases in response to a single signal. DNA damage induces a complex response that is largely orchestrated by the transcription factor p53, whose dynamics influence whether a damaged cell will arrest and repair the damage or will initiate cell death. How p53 responses and cellular outcomes evolve in the presence of continuous DNA damage remains unknown. Here, we have found that a subset of cells switches from oscillating to sustained p53 dynamics several days after undergoing damage. The switch results from cell cycle progression in the presence of damaged DNA, which activates the caspase-2-PIDDosome, a complex that stabilizes p53 by inactivating its negative regulator MDM2. This work defines a molecular pathway that is activated if the canonical checkpoints fail to halt mitosis in the presence of damaged DNA.

Quantifying the Central Dogma in the p53 Pathway in Live Single Cells.

Transcription factors (TFs) integrate signals to regulate target gene expression, but we generally lack a quantitative understanding of how changes in TF levels regulate mRNA and protein production. Here, we established a system to simultaneously monitor the levels of p53, a TF that shows oscillations following DNA damage, and the transcription and protein levels of its target p21 in individual cells. p21 transcription tracked p53 dynamics, while p21 protein steadily accumulated. p21 transcriptional activation showed bursts of mRNA production, with p53 levels regulating the probability but not magnitude of activation. Variations in p53 levels between cells contributed to heterogeneous p21 transcription while independent p21 alleles exhibited highly correlated behaviors. Pharmacologically elevating p53 increased the probability of p21 transcription with minor effects on its magnitude, leading to a strong increase in p21 protein levels. Our results reveal quantitative mechanisms by which TF dynamics can regulate protein levels of its targets. A record of this paper's transparent peer review process is included in the Supplemental Information.

The effects of proliferation status and cell cycle phase on the responses of single cells to chemotherapy.

DNA-damaging chemotherapeutics are widely used in cancer treatments, but for solid tumors they often leave a residual tumor-cell population. Here we investigated how cellular states might affect the response of individual cells in a clonal population to cisplatin, a DNA-damaging chemotherapeutic agent. Using a live-cell reporter of cell cycle phase and long-term imaging, we monitored single-cell proliferation before, at the time of, and after treatment. We found that in response to cisplatin, cells either arrested or died, and the ratio of these outcomes depended on the dose. While we found that the cell cycle phase at the time of cisplatin addition was not predictive of outcome, the proliferative history of the cell was: highly proliferative cells were more likely to arrest than to die, whereas slowly proliferating cells showed a higher probability of death. Information theory analysis revealed that the dose of cisplatin had the greatest influence on the cells' decisions to arrest or die, and that the proliferation status interacted with the cisplatin dose to further guide this decision. These results show an unexpected effect of proliferation status in regulating responses to cisplatin and suggest that slowly proliferating cells within tumors may be acutely vulnerable to chemotherapy.