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Article ; Online: Ten simple rules for effective presentation slides.

Naegle, Kristen M

2021 Volume 17, Issue 12, Page(s) e1009554

MeSH term(s)	Audiovisual Aids ; Communication ; Data Display ; Educational Technology ; Humans ; Science/education ; Teaching Materials
Language	English
Publishing date	2021-12-02
Publishing country	United States
Document type	Editorial
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1009554
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Article: A systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms.

Crowl, Sam / Coleman, Maeve Bella / Chaphiv, Andrew / Naegle, Kristen M

bioRxiv : the preprint server for biology

2024

Abstract: Post-translational modifications (PTMs) and splicing are known to be important regulatory processes for controlling protein function and activity. However, there have been limitations in analyzing the interplay of alternative splicing and PTMs, both from ...

Abstract	Post-translational modifications (PTMs) and splicing are known to be important regulatory processes for controlling protein function and activity. However, there have been limitations in analyzing the interplay of alternative splicing and PTMs, both from the standpoint of PTM presence and in the possible diversification of the regulatory windows of PTMs, which define the connection to regulatory enzymes and possible binding partners. Limitations stem from the deep differences in genomic and proteomic databases, where PTMs are predominantly identified by mass spectrometry and subsequently assigned to the canonical isoform of the protein in databases. In this work, we bridge the protein- and genome-centric world views to map PTMs to genomic locations for subsequent projection of PTMs onto alternative isoforms. We then perform a systematic analysis of the diversification of PTMs within all defined protein isoforms, focusing on the PTM-specific profiles that may differ across the various major modifications found in humans, including exploration of how often alternative splicing leads to diversification of the regulatory sequences directly flanking a PTM. We found the interplay between splicing and PTMs is PTM-specific across a range of behaviors, such as PTM inclusion rates across isoforms and tissues. Additionally we found that ≈ 2% of prospective PTM sites exhibited altered regulatory sequences surrounding the modification site, suggesting that regulatory or binding interactions might be diversified in these proteoforms. In addition to exploring isoforms as defined by Ensembl, we applied this PTM-to-isoform mapping approach to explore the impacts of disease-related splicing in prostate cancer, identifying possible new hypotheses as to the variable mechanisms of ESRP1 expression in different cancers.
Language	English
Publishing date	2024-01-12
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.01.10.575062
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Article ; Online: Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.

Nelson, Anders R / Christiansen, Steven L / Naegle, Kristen M / Saucerman, Jeffrey J

Proceedings of the National Academy of Sciences of the United States of America

2024 Volume 121, Issue 5, Page(s) e2303513121

Abstract: Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti- ...

Abstract	Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high-content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high-content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models. We apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.
MeSH term(s)	Humans ; Actins ; Fibroblasts ; Machine Learning ; Fibrosis ; Myosins ; Phosphatidylinositol 3-Kinases
Chemical Substances	Actins ; Myosins (EC 3.6.4.1) ; Phosphatidylinositol 3-Kinases (EC 2.7.1.-)
Language	English
Publishing date	2024-01-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2303513121
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Article: Logic-based mechanistic machine learning on high-content images reveals how drugs differentially regulate cardiac fibroblasts.

Nelson, Anders R / Christiansen, Steven L / Naegle, Kristen M / Saucerman, Jeffrey J

bioRxiv : the preprint server for biology

2023

Abstract	Fibroblasts are essential regulators of extracellular matrix deposition following cardiac injury. These cells exhibit highly plastic responses in phenotype during fibrosis in response to environmental stimuli. Here, we test whether and how candidate anti-fibrotic drugs differentially regulate measures of cardiac fibroblast phenotype, which may help identify treatments for cardiac fibrosis. We conducted a high content microscopy screen of human cardiac fibroblasts treated with 13 clinically relevant drugs in the context of TGFβ and/or IL-1β, measuring phenotype across 137 single-cell features. We used the phenotypic data from our high content imaging to train a logic-based mechanistic machine learning model (LogiMML) for fibroblast signaling. The model predicted how pirfenidone and Src inhibitor WH-4-023 reduce actin filament assembly and actin-myosin stress fiber formation, respectively. Validating the LogiMML model prediction that PI3K partially mediates the effects of Src inhibition, we found that PI3K inhibition reduces actin-myosin stress fiber formation and procollagen I production in human cardiac fibroblasts. In this study, we establish a modeling approach combining the strengths of logic-based network models and regularized regression models, apply this approach to predict mechanisms that mediate the differential effects of drugs on fibroblasts, revealing Src inhibition acting via PI3K as a potential therapy for cardiac fibrosis.
Language	English
Publishing date	2023-10-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.01.530599
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Article ; Online: ASPEN, a methodology for reconstructing protein evolution with improved accuracy using ensemble models.

Sloutsky, Roman / Naegle, Kristen M

eLife

2019 Volume 8

Abstract: Evolutionary reconstruction algorithms produce models of the evolutionary history of proteins or species. Such algorithms are highly sensitive to their inputs: the sequences used and their alignments. Here, we asked whether the variance introduced by ... ...

Abstract	Evolutionary reconstruction algorithms produce models of the evolutionary history of proteins or species. Such algorithms are highly sensitive to their inputs: the sequences used and their alignments. Here, we asked whether the variance introduced by selecting different input sequences could be used to better identify accurate evolutionary models. We subsampled from available ortholog sequences and measured the distribution of observed relationships between paralogs produced across hundreds of models inferred from the subsamples. We observed two important phenomena. First, the reproducibility of an all-sequence, single-alignment reconstruction, measured by comparing topologies inferred from 90% subsamples, directly correlates with the accuracy of that single-alignment reconstruction, producing a measurable value for something that has been traditionally unknowable. Second, topologies that are most consistent with the observations made in the ensemble are more accurate and we present a meta algorithm that exploits this property to improve model accuracy.
MeSH term(s)	Computational Biology/methods ; Evolution, Molecular ; Plant Proteins/genetics ; Plants/genetics
Chemical Substances	Plant Proteins
Language	English
Publishing date	2019-10-17
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.47676
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Article ; Online: New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data.

Ronan, Tom / Garnett, Roman / Naegle, Kristen M

The Journal of biological chemistry

2020 Volume 295, Issue 32, Page(s) 11346–11363

Abstract: Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and ... ...

Abstract	Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2-pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding
MeSH term(s)	High-Throughput Screening Assays/methods ; Humans ; Peptides/chemistry ; Protein Binding ; Reproducibility of Results ; src Homology Domains
Chemical Substances	Peptides
Language	English
Publishing date	2020-06-15
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2997-x
ISSN	1083-351X ; 0021-9258
ISSN (online)	1083-351X
ISSN	0021-9258
DOI	10.1074/jbc.RA120.012503
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Article ; Online: KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions.

Xue, Bingjie / Jordan, Benjamin / Rizvi, Saqib / Naegle, Kristen M

PLoS computational biology

2021 Volume 17, Issue 2, Page(s) e1008681

Abstract: Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from ... ...

Abstract	Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown-predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.
MeSH term(s)	Algorithms ; Binding Sites ; Catalytic Domain ; Databases, Protein ; Humans ; Likelihood Functions ; Phosphoproteins/metabolism ; Phosphorylation ; Protein Interaction Mapping ; Protein Kinases/metabolism ; Protein-Serine-Threonine Kinases/metabolism ; Proteome ; Proteomics/methods ; Substrate Specificity
Chemical Substances	Phosphoproteins ; Proteome ; Protein Kinases (EC 2.7.-) ; Protein-Serine-Threonine Kinases (EC 2.7.11.1)
Language	English
Publishing date	2021-02-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008681
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Article ; Online: KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data.

Crowl, Sam / Jordan, Ben T / Ahmed, Hamza / Ma, Cynthia X / Naegle, Kristen M

Nature communications

2022 Volume 13, Issue 1, Page(s) 4283

Abstract: Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination ... ...

Abstract	Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients.
MeSH term(s)	Algorithms ; Breast Neoplasms/pathology ; Female ; Humans ; Phosphoproteins/metabolism ; Phosphorylation ; Phosphotransferases ; Protein Kinase Inhibitors/pharmacology ; Protein Kinase Inhibitors/therapeutic use ; Proteomics
Chemical Substances	Phosphoproteins ; Protein Kinase Inhibitors ; Phosphotransferases (EC 2.7.-)
Language	English
Publishing date	2022-07-25
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-32017-5
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Article ; Online: Proteome-Level Analysis Indicates Global Mechanisms for Post-Translational Regulation of RRM Domains.

Sloutsky, Roman / Naegle, Kristen M

Journal of molecular biology

2017 Volume 430, Issue 1, Page(s) 41–44

Abstract: RRM, or RNA-recognition motif, domains are the largest class of single-stranded RNA binding domains in the human proteome and play important roles in RNA processing, splicing, export, stability, packaging, and degradation. Using a current database of ... ...

Abstract	RRM, or RNA-recognition motif, domains are the largest class of single-stranded RNA binding domains in the human proteome and play important roles in RNA processing, splicing, export, stability, packaging, and degradation. Using a current database of post-translational modifications (PTMs), ProteomeScout, we found that RRM domains are also one of the most heavily modified domains in the human proteome. Here, we present two interesting findings about RRM domain modifications, found by mapping known PTMs onto RRM domain alignments and structures. First, we find significant overlap of ubiquitination and acetylation within RRM domains, suggesting the possibility for ubiquitination-acetylation crosstalk. Additionally, an analysis of quantitative study of ubiquitination changes in response to proteasome inhibition highlights the uniqueness of RRM domain ubiquitination - RRM domain ubiquitination decreases in response to proteasome inhibition, whereas the majority of sites increase. Second, we found conservation of tyrosine phosphorylation within the RNP1 and RNP2 consensus sequences, which coordinate RNA binding - suggesting a possible role for regulation of RNA binding by tyrosine kinase signaling. These observations suggest there are unique regulatory mechanisms of RRM function that have yet to be uncovered and that the RRM domain represents a model system for further studies on understanding PTM crosstalk.
MeSH term(s)	Acetylation ; Amino Acid Sequence ; Humans ; Models, Molecular ; Phosphorylation/genetics ; Protein Binding/genetics ; Protein Domains/genetics ; Protein Processing, Post-Translational/genetics ; Protein-Tyrosine Kinases/genetics ; Proteome/genetics ; RNA Recognition Motif/genetics ; Sequence Alignment/methods ; Tyrosine/genetics ; Ubiquitination/genetics
Chemical Substances	Proteome ; Tyrosine (42HK56048U) ; Protein-Tyrosine Kinases (EC 2.7.10.1)
Language	English
Publishing date	2017-11-14
Publishing country	England
Document type	Journal Article
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2017.11.001
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Article ; Online: High-Resolution Identification of Specificity Determining Positions in the LacI Protein Family Using Ensembles of Sub-Sampled Alignments.

Sloutsky, Roman / Naegle, Kristen M

PloS one

2016 Volume 11, Issue 9, Page(s) e0162579

Abstract: Since the advent of large-scale genomic sequencing, and the consequent availability of large numbers of homologous protein sequences, there has been burgeoning development of methods for extracting functional information from multiple sequence alignments ...

Abstract	Since the advent of large-scale genomic sequencing, and the consequent availability of large numbers of homologous protein sequences, there has been burgeoning development of methods for extracting functional information from multiple sequence alignments (MSAs). One type of analysis seeks to identify specificity determining positions (SDPs) based on the assumption that such positions are highly conserved within groups of sequences sharing functional specificity, but conserved to different amino acids in different specificity groups. This unsupervised approach to utilizing evolutionary information may elucidate mechanisms of specificity in protein-protein interactions, catalytic activity of enzymes, sensitivity to allosteric regulation, and other types of protein functionality. We present an analysis of SDPs in the LacI family of transcriptional regulators in which we 1) relax the constraint that all specificity groups must contribute to SDP signal, and 2) use a novel approach to robust treatment of sequence alignment uncertainty based on sub-sampling. We find that the vast majority of SDP signal occurs at positions with a conservation pattern that significantly complicates detection by previously described methods. This pattern, which we term "partial SDP", consists of the commonly accepted SDP conservation pattern among a subset of specificity groups and strong degeneracy among the rest. An upshot of this fact is that the SDP complement of every specificity group appears to be unique. Additionally, sub-sampling gives us the ability to assign a confidence interval to the SDP score, as well as increase fidelity, as compared to analysis of a single, comprehensive alignment-the current standard in multiple sequence alignment methodologies.
Language	English
Publishing date	2016-09-28
Publishing country	United States
Document type	Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0162579
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