Journal: Cell Death and Differentiation

Article Title: Deletion of heat shock protein 60 in adult mouse cardiomyocytes perturbs mitochondrial protein homeostasis and causes heart failure

doi: 10.1038/s41418-019-0374-x

Figure Lengend Snippet: Proteomic analysis and validation of mitochondrial proteins in control and HSP60 CKO hearts 6 weeks post tamoxifen injection. a Pie chart showing functional classification of mitochondrial-localized proteins characterized from proteomic analysis and distribution of each functional category. b Numbers of differentially expression proteins (DEPs) including downregulated (Down) and upregulated (Up) proteins in each functional category. c Expression changes of individual mitochondrial proteins in HSP60 CKO hearts compared with control hearts revealed by the proteomic analysis. d Western blot analysis was used to validate expression changes of individual mitochondrial proteins in HSP60 CKO hearts. Mitochondria were isolated from control and HSP60 CKO hearts at 6 weeks (6w) and 11 weeks (11w) post tamoxifen injection, and total 10 proteins including HSP60, SUCLG2, LDHD, CPT2, ALDH2, BCKDHB, SIRT3, RTN4IP1, OXCT1, and VDAC1 were analyzed by western blot

Article Snippet: To minimize the variation of protein expression between animals, mitochondrial fractions isolated from three independent biological replicates were pooled for both control and HSP60 CKO samples, and submitted for proteomic analysis (PTM Biolabs, Hangzhou, China).

Techniques: Biomarker Discovery, Control, Injection, Functional Assay, Expressing, Western Blot, Isolation

Journal: Briefings in Bioinformatics

Article Title: Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models

doi: 10.1093/bib/bbae574

Figure Lengend Snippet: Integration of EHR and proteomics data of pregnant patients using electronic medical record–trained FMs. (a) To train a model capable of efficiently generating proteomics profiles from existing EHR records of pregnant patients, we collected paired EHR–proteomics samples. Proteomics data were collected for each patient from a minimum of one and a maximum of three plasma samples collected per patient. One thousand three hundred five proteins were measured per patient. Patient EHR records were obtained from the earliest EHR entry at Stanford to the sample collection date. Our final cohort had n = 171 samples from N = 61 unique individuals. G1, G2, and G3 represent various gestation time periods where plasma was sampled for proteomics (run on SomaLogic’s platform). (b) EHR records of samples encompassed a wide range of duration, spanning a minimum of 1 month to a maximum of 14.3 years with a median of 1.5 years. (c) Two state-of-the-art EHR foundation models were used to generate low-dimensional latent representations of EHR data for the generation of proteomics expression. EHR records encompassed five categories: demographics, drugs, conditions, procedures, and measurements. Preprocessed EHR data were fed into FMs MOTOR and CLMBR to generate a 768-dimensional vector representation of a sample’s EHR data up to and including the sample collection date. Representations and paired protein expression from proteomics data were used to train 1305 single-task neural networks consisting of two fully connected layers to generate protein expression values for 1305 proteins. Generative performance was assessed by calculating the Spearman correlation between actual and generated values of each protein with a P -value corrected for multiple hypothesis testing using the Benjamini–Hochberg method.

Article Snippet: Plasma was sent for proteomics analysis (SomaLogic Inc., Boulder, CO).

Techniques: Clinical Proteomics, Expressing, Plasmid Preparation, Generated

Journal: Briefings in Bioinformatics

Article Title: Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models

doi: 10.1093/bib/bbae574

Figure Lengend Snippet: FM representations of EHR data generate proteomics expression values. (a) Scatterplot demonstrates that both MOTOR and CLMBR representations of EHR data are useful in generating protein expression values from EHR data. Axes plot Spearman coefficients between actual and generated values for each protein when generated using MOTOR ( x -axis) and CLMBR ( y -axis) representations. Select top proteins are labeled. Gray dots indicate proteins with adjusted P -value >.05 for both models. Dotted red line indicates theoretically equal performance by both models. Pearson correlation of the Spearman coefficients for proteins across MOTOR and CLMBR was calculated to assess the correlation of performance. P -value of the Pearson coefficient was 3.01e-147. (b) To determine if the choice of FM matters for proteomics generation, we directly compared the generative performance of MOTOR versus CLMBR. Line graph shows the change in Spearman correlation for each protein when generated using MOTOR versus CLMBR representations of EHR data. Gray lines are proteins with adjusted P -value >.05 for either model representation. Red lines indicate an increase in Spearman correlation for a given protein from CLMBR to MOTOR while blue lines indicate a decrease in Spearman correlation. * denotes significant adjusted P -value ( P = 4.94e-10) using paired Wilcoxon test. (c): Venn diagram of the number of proteins with significant adjusted P -value (<.05) for each model shows MOTOR had approximately four times as many significant proteins compared to CLMBR. (d) Scatterplot showing actual ( x -axis) and generated ( y -axis) values for the top six proteins generated using MOTOR and CLMBR representations. Generated protein expression values for each patient sample are the average generated value of 10 bootstrap iterations. Line shows the line of best fit with a 95% confidence interval shaded. n = 171.

Article Snippet: Plasma was sent for proteomics analysis (SomaLogic Inc., Boulder, CO).

Techniques: Expressing, Generated, Labeling

Journal: Briefings in Bioinformatics

Article Title: Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models

doi: 10.1093/bib/bbae574

Figure Lengend Snippet: Dropout feature importance analysis reveals proteomic signature of gestational diabetes. (a) In addition to simple linear associations, machine learning models can capture complex nonlinear relationships between features and output. To identify such complex biological relationships useful in proteomics generation, dropout feature importance was performed to identify EHR features most helpful in generating proteomics expressions. A total of 1799 unique EHR records that were recorded for at least one sample are grouped into the five EHR categories as shown. (b) Each category of EHR information in was dropped one at a time before creating FM-derived representations of EHR data for a dropout feature importance analysis. These five dropout representations were used as input to models for each protein trained on the full EHR representation created in . Dropout model performance was compared to the full model’s performance by comparing normalized Spearman correlations to the full model (Spearman correlation of dropout EHR representation/Spearman correlation of full EHR representation) for each protein. X -axis labels are formatted as follows: − X where X is the EHR category removed when creating FM representations. * denotes adjusted P -value statistical significance up to four decimal places using paired Wilcoxon test with multiple hypothesis correction using the Benjamini–Hochberg method. Condition codes were most important for MOTOR representations, while drugs were least important. (c) To identify which specific condition codes were most important in generative performance, a dropout experiment for individual condition codes was conducted similar to using MOTOR representations. Only MOTOR-significant proteins (177 proteins) were used for analysis. * All conditions shown have normalized Spearman correlation significantly different from that of the full model (paired Wilcoxon test with Benjamini–Hochberg correction for multiple hypothesis testing). See for a full list. (d) Out of the top conditions shown in , gestational diabetes was particularly interesting due to its specificity. To determine a proteomic signature for gestational diabetes, we identified all proteins with a decrease in generative performance when the gestational diabetes code was removed from their EHR. One hundred fifteen proteins had decreased Spearman coefficients when compared to Spearman coefficients generated with the full model, indicating a link between them and gestational diabetes. The top 10 are highlighted here. For a full list, see . Proteins with established and novel links to gestational diabetes were identified.

Article Snippet: Plasma was sent for proteomics analysis (SomaLogic Inc., Boulder, CO).

Techniques: Derivative Assay, Generated