Sai-Ming Ngai

The human neuroblastoma cell lines SH-SY5Y and IMR-32 can be differentiated into neuron-like phenotypes through treatment with all-trans-retinoic acid (ATRA). After differentiation, these cell lines are extensively utilized as in vitro models to study various aspects of neuronal cell biology. However, temporal and quantitative profiling of the proteome and phosphoproteome of SH-SY5Y and IMR-32 cells throughout ATRA-induced differentiation has been limited. Here, we performed relative quantification of the proteomes and phosphoproteomes of SH-SY5Y and IMR-32 cells at multiple time points during ATRA-induced differentiation. Relative quantification of proteins and phosphopeptides with subsequent gene ontology analysis revealed that several biological processes, including cytoskeleton organization, cell division, chaperone function and protein folding, and one-carbon metabolism, were associated with ATRA-induced differentiation in both cell lines. Furthermore, kinase-substrate enrichment analysis predicted altered activities of several kinases during differentiation. Among these, CDK5 exhibited increased activity, while CDK2 displayed reduced activity. The data presented serve as a valuable resource for investigating temporal protein and phosphoprotein abundance changes in SH-SY5Y and IMR-32 cells during ATRA-induced differentiation.

Increasing evidence has revealed that plant secretory peptides are involved in the long-distance signaling pathways that help to regulate plant development and signal stress responses. In this study, we purified small peptides from soybean (Glycine max) xylem sap via o-chlorophenol extraction and conducted an in-depth peptidomic analysis using a mass spectrometry (MS) and bioinformatics approach. We successfully identified 14 post-translationally modified peptide groups belonging to the peptide families CEP (C-terminally encoded peptides), CLE (CLAVATA3/embryo surrounding region-related), PSY (plant peptides containing tyrosine sulfation), and XAP (xylem sap-associated peptides). Quantitative PCR (qPCR) analysis showed unique tissue expression patterns among the peptide-encoding genes. Further qPCR analysis of some of the peptide-encoding genes showed differential stress-response profiles toward various abiotic stress factors. Targeted MS-based quantification of the nitrogen deficiency-responsive peptides, GmXAP6a and GmCEP-XSP1, demonstrated upregulation of peptide translocation in xylem sap under nitrogen-deficiency stress. Quantitative proteomic analysis of GmCEP-XSP1 overexpression in hairy soybean roots revealed that GmCEP-XSP1 significantly impacts stress response-related proteins. This study provides new insights that root-to-shoot peptide signaling plays important roles in regulating plant stress-response mechanisms.

Lymph node metastasis is the most reliable indicator of a poor prognosis for patients with oral tongue cancers. Currently, there are no biomarkers to predict whether a cancer will spread in the future if it has not already spread at the time of diagnosis. The aim of this study was to quantitatively profile the proteomes of extracellular vesicles (EVs) isolated from blood samples taken from patients with oral tongue squamous cell carcinoma with and without lymph node involvement and non-cancer controls. EVs were enriched using size exclusion chromatography (SEC) from pooled plasma samples of patients with non-nodal and nodal oral tongue squamous cell carcinoma (OTSCC) and non-cancer controls. Protein cargo was quantitatively profiled using isobaric labelling (iTRAQ) and two-dimensional high-performance liquid chromatography followed by tandem mass spectrometry. We identified 208 EV associated proteins and, after filtering, generated a short list of 136 proteins. Over 85% of the EV-associated proteins were associated with the GO cellular compartment term “extracellular exosome”. Comparisons between non-cancer controls and oral tongue squamous cell carcinoma with and without lymph node involvement revealed 43 unique candidate EV-associated proteins with deregulated expression patterns. The shortlisted EV associated proteins described here may be useful discriminatory biomarkers for differentiating OTSCC with and without nodal disease or non-cancer controls.

Energy metabolism in plant cells requires a balance between the activities of chloroplasts and mitochondria, as they are the producers and consumers of carbohydrates and reducing equivalents, respectively. Recently, we showed that the overexpression of Arabidopsis thaliana purple acid phosphatase 2 (AtPAP2), a phosphatase dually anchored on the outer membranes of chloroplasts and mitochondria, can boost the plant growth and seed yield of Arabidopsis thaliana by coordinating the activities of both organelles. However, when AtPAP2 is solely overexpressed in chloroplasts, the growth-promoting effects are less optimal, indicating that active mitochondria are required for dissipating excess reducing equivalents from chloroplasts to maintain the optimal growth of plants. It is even more detrimental to plant productivity when AtPAP2 is solely overexpressed in mitochondria. Although these lines contain high level of adenosine triphosphate (ATP), they exhibit low leaf sucrose, low seed yield, and early senescence. These transgenic lines can be useful tools for studying how hyperactive chloroplasts or mitochondria affect the physiology of their counterparts and how they modify cellular metabolism and plant physiology.

Venomics, the study of biological venoms, could potentially provide a new source of therapeutic compounds, yet information on the venoms from marine organisms, including cnidarians (sea anemones, corals, and jellyfish), is limited. This study identified the putative toxins of two species of jellyfish—edible jellyfish Rhopilema esculentum Kishinouye, 1891, also known as flame jellyfish, and Amuska jellyfish Sanderia malayensis Goette, 1886. Utilizing nano-flow liquid chromatography tandem mass spectrometry (nLC–MS/MS), 3000 proteins were identified from the nematocysts in each of the above two jellyfish species. Forty and fifty-one putative toxins were identified in R. esculentum and S. malayensis, respectively, which were further classified into eight toxin families according to their predicted functions. Amongst the identified putative toxins, hemostasis-impairing toxins and proteases were found to be the most dominant members (>60%). The present study demonstrates the first proteomes of nematocysts from two jellyfish species with economic and environmental importance, and expands the foundation and understanding of cnidarian toxins.

A subset of soybean long non-coding RNA candidates encode small peptides of fewer than 100 amino acids, enabling further investigations into their functional ro

This study reported a comprehensive approach (comparing the extraction yields, chemical profiles, antioxidant properties and CYP450-inhibitory effects) to evaluated the effectiveness of various extraction methods [microwave-assisted extraction using water (MAE-W), heat reflux extraction using water (HRE-W), ultrasonic extraction using 70 % ethanol and ultrasonic extraction using ethanol (UE-E)] for Huang-Qin (HQ), the dried root of Scutellaria baicalensis Georgi. The HQ extraction efficiency by MAE-W was the best. The chemical profiles of extracts obtained using HRE-W and MAE-W were similar; whereas more flavones but less flavone glycosides were detected in the UE-E extract. There was no difference in the antioxidant properties among different extracts. In vitro human liver microsome assays illustrated that all extracts possessed herb–drug interaction potentials but the UE-E extract are shown with a potent interaction with CYP3A4-metabolized drugs. MAE-W is a favorable method for the preparation of HQ extracts based on extraction yield, pharmacological properties and safety.

Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), squamous cell lung cancer (SQCLC) as well as large cell lung cancer. Many previous studies demonstrated that DNA methylation has emerged as potential lung cancer-specific biomarkers. However, whether there exists a set of DNA methylation markers simultaneously distinguishing such three types of lung cancers remains elusive. In the present study, ROC (Receiving Operating Curve), RFs (Random Forests) and mRMR (Maximum Relevancy and Minimum Redundancy) were proposed to capture the unbiased, informative as well as compact molecular signatures followed by machine learning methods to classify LADC, SQCLC and SCLC. As a result, a panel of 16 DNA methylation markers exhibits an ideal classification power with an accuracy of 86.54%, 84.6% and a recall 84.37%, 85.5% in the leave-one-out cross-validation (LOOCV) and independent data set test experiments, respectively. Besides, comparison results indicate that ensemble-based feature selection methods outperform individual ones when combined with the incremental feature selection (IFS) strategy in terms of the informative and compact property of features. Taken together, results obtained suggest the effectiveness of the ensemble-based feature selection approach and the possible existence of a common panel of DNA methylation markers among such three types of lung cancer tissue, which would facilitate clinical diagnosis and treatment.