A time-resolved multi-omic atlas of the developing mouse liver.

Liver organogenesis and development are composed of a series of complex, well-orchestrated events. Identifying key factors and pathways governing liver development will help elucidate the physiological and pathological processes including those of cancer. We conducted multidimensional omics measurements including protein, mRNA, and transcription factor (TF) DNA-binding activity for mouse liver tissues collected from embryonic day 12.5 (E12.5) to postnatal week 8 (W8), encompassing major developmental stages. These data sets reveal dynamic changes of core liver functions and canonical signaling pathways governing development at both mRNA and protein levels. The TF DNA-binding activity data set highlights the importance of TF activity in early embryonic development. A comparison between mouse liver development and human hepatocellular carcinoma (HCC) proteomic profiles reveal that more aggressive tumors are characterized with the activation of early embryonic development pathways, whereas less aggressive ones maintain liver function-related pathways that are elevated in the mature liver. This work offers a panoramic view of mouse liver development and provides a rich resource to explore in-depth functional characterization.

Urinary protein biomarker panel predicts esophageal squamous carcinoma from control cases and other tumors.

PURPOSE: Discovery of noninvasive urinary biomarkers for the early diagnosis of esophageal squamous carcinoma (ESCC). METHODS: We conducted proteomic analyses of 499 human urine samples obtained from healthy individuals (n = 321) and ESCC (n = 83), bladder cancer (n = 17), breast cancer (n = 12), colorectal cancer (n = 16), lung cancer (n = 33) and thyroid cancer (n = 17) patients from multiple medical centers. Those samples were divided into a discovery set (n = 247) and an independent validation set (n = 157). RESULTS: Among urinary proteins identified in the comprehensive quantitative proteomics analysis, we selected a panel of three urinary biomarkers (ANXA1, S100A8, TMEM256), and established a logistic regression model in the discovery set that can correctly classify the majority of ESCC cases in the validation sets with the area under the curve (AUC) values of 0.825. This urinary biomarker panel not only discriminates ESCC patients from healthy individuals but also differentiates ESCC from other common tumors. Notably, the panel distinguishes stage I ESCC patients from healthy individuals with AUC values of 0.886. On the analysis of stage-specific biomarkers, another combination panel of protein (ANXA1, S100A8, SOD3, TMEM256) demonstrated a good AUC value of 0.792 for stage I ESCC. CONCLUSIONS: Urinary biomarker panel represents a promising auxiliary diagnostic tool for ESCC, including early-stage ESCC.

Urine proteomics identifies biomarkers for diabetic kidney disease at different stages.

BACKGROUND: Type 2 diabetic kidney disease is the most common cause of chronic kidney diseases (CKD) and end-stage renal diseases (ESRD). Although kidney biopsy is considered as the 'gold standard' for diabetic kidney disease (DKD) diagnosis, it is an invasive procedure, and the diagnosis can be influenced by sampling bias and personal judgement. It is desirable to establish a non-invasive procedure that can complement kidney biopsy in diagnosis and tracking the DKD progress. METHODS: In this cross-sectional study, we collected 252 urine samples, including 134 uncomplicated diabetes, 65 DKD, 40 CKD without diabetes and 13 follow-up diabetic samples, and analyzed the urine proteomes with liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We built logistic regression models to distinguish uncomplicated diabetes, DKD and other CKDs. RESULTS: We quantified 559 ± 202 gene products (GPs) (Mean ± SD) on a single sample and 2946 GPs in total. Based on logistic regression models, DKD patients could be differentiated from the uncomplicated diabetic patients with 2 urinary proteins (AUC = 0.928), and the stage 3 (DKD3) and stage 4 (DKD4) DKD patients with 3 urinary proteins (AUC = 0.949). These results were validated in an independent dataset. Finally, a 4-protein classifier identified putative pre-DKD3 patients, who showed DKD3 proteomic features but were not diagnosed by clinical standards. Follow-up studies on 11 patients indicated that 2 putative pre-DKD patients have progressed to DKD3. CONCLUSIONS: Our study demonstrated the potential for urinary proteomics as a noninvasive method for DKD diagnosis and identifying high-risk patients for progression monitoring.

Proteome-Wide Profiling of Readers for DNA Modification.

DNA modifications, represented by 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC), play important roles in epigenetic regulation of biological processes. The specific recognition of DNA modifications by the transcriptional protein machinery is thought to be a potential mechanism for epigenetic-driven gene regulation, and many modified DNA-specific binding proteins have been uncovered. However, the panoramic view of the roles of DNA modification readers at the proteome level remains largely unclear. Here, a recently developed concatenated tandem array of consensus transcription factor (TF) response elements (catTFREs) approach is employed to profile the binding activity of TFs at DNA modifications. Modified DNA-binding activity is quantified for 1039 TFs, representing 70% of the TFs in the human genome. Additionally, the modified DNA-binding activity of 600 TFs is monitored during the mouse brain development from the embryo to the adult stages. Readers of these DNA modifications are predicted, and the hierarchical networks between the transcriptional protein machinery and modified DNA are described. It is further demonstrated that ZNF24 and ZSCAN21 are potential readers of 5fC-modified DNA. This study provides a landscape of TF-DNA modification interactions that can be used to elucidate the epigenetic-related transcriptional regulation mechanisms under physiological conditions.

A region-resolved mucosa proteome of the human stomach.

The human gastric mucosa is the most active layer of the stomach wall, involved in food digestion, metabolic processes and gastric carcinogenesis. Anatomically, the human stomach is divided into seven regions, but the protein basis for cellular specialization is not well understood. Here we present a global analysis of protein profiles of 82 apparently normal mucosa samples obtained from living individuals by endoscopic stomach biopsy. We identify 6,258 high-confidence proteins and estimate the ranges of protein expression in the seven stomach regions, presenting a region-resolved proteome reference map of the near normal, human stomach. Furthermore, we measure mucosa protein profiles of tumor and tumor nearby tissues (TNT) from 58 gastric cancer patients, enabling comparisons between tumor, TNT, and normal tissue. These datasets provide a rich resource for the gastrointestinal tract research community to investigate the molecular basis for region-specific functions in mucosa physiology and pathology including gastric cancer.

A time-resolved multi-omic atlas of the developing mouse stomach.

The mammalian stomach is structurally highly diverse and its organ functionality critically depends on a normal embryonic development. Although there have been several studies on the morphological changes during stomach development, a system-wide analysis of the underlying molecular changes is lacking. Here, we present a comprehensive, temporal proteome and transcriptome atlas of the mouse stomach at multiple developmental stages. Quantitative analysis of 12,108 gene products allows identifying three distinct phases based on changes in proteins and RNAs and the gain of stomach functions on a longitudinal time scale. The transcriptome indicates functionally important isoforms relevant to development and identifies several functionally unannotated novel splicing junction transcripts that we validate at the peptide level. Importantly, many proteins differentially expressed in stomach development are also significantly overexpressed in diffuse-type gastric cancer. Overall, our study provides a resource to understand stomach development and its connection to gastric cancer tumorigenesis.

Author Correction: A proteomic landscape of diffuse-type gastric cancer.

The original version of this Article contained an error in the email address of the corresponding author Jun Qin. The correct email is jqin1965@126.com. The error has been corrected in the HTML and PDF versions of the Article.

A proteomic landscape of diffuse-type gastric cancer.

The diffuse-type gastric cancer (DGC) is a subtype of gastric cancer with the worst prognosis and few treatment options. Here we present a dataset from 84 DGC patients, composed of a proteome of 11,340 gene products and mutation information of 274 cancer driver genes covering paired tumor and nearby tissue. DGC can be classified into three subtypes (PX1-3) based on the altered proteome alone. PX1 and PX2 exhibit dysregulation in the cell cycle and PX2 features an additional EMT process; PX3 is enriched in immune response proteins, has the worst survival, and is insensitive to chemotherapy. Data analysis revealed four major vulnerabilities in DGC that may be targeted for treatment, and allowed the nomination of potential immunotherapy targets for DGC patients, particularly for those in PX3. This dataset provides a rich resource for information and knowledge mining toward altered signaling pathways in DGC and demonstrates the benefit of proteomic analysis in cancer molecular subtyping.

A mouse tissue transcription factor atlas.

Transcription factors (TFs) drive various biological processes ranging from embryonic development to carcinogenesis. Here, we employ a recently developed concatenated tandem array of consensus TF response elements (catTFRE) approach to profile the activated TFs in 24 adult and 8 fetal mouse tissues on proteome scale. A total of 941 TFs are quantitatively identified, representing over 60% of the TFs in the mouse genome. Using an integrated omics approach, we present a TF network in the major organs of the mouse, allowing data mining and generating knowledge to elucidate the roles of TFs in various biological processes, including tissue type maintenance and determining the general features of a physiological system. This study provides a landscape of TFs in mouse tissues that can be used to elucidate transcriptional regulatory specificity and programming and as a baseline that may facilitate understanding diseases that are regulated by TFs.