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Liu, Xiaowen

Publications and source records attributed to Liu, Xiaowen.

9 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Top-down proteomics

Proteoforms arising from posttranslational modifications, genetic polymorphisms, and RNA splice variants, play a pivotal role as the key drivers in biology. Thus, a comprehensive understanding of proteoforms is essential for unraveling the intricacies of biological systems and bridging the gap between genotype and phenotype. By analyzing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and presents a next-generation approach for deciphering protein function, uncovering disease mechanisms, and advancing precision medicine. This Primer embarks on a journey into the world of TDP by encapsulating its historical context, underlying principles, recent advances, and an outlook on the future of TDP. The experimental section navigates instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques, and data collection. Results decipher raw data, visualize intact protein spectra, unravel data analysis, and explain proteoform identification, characterization, and quantitation, as well as statistical analysis. Various applications of TDP spanning the human proteoform project, biomedical, biopharmaceutical, and clinical applications are described. These are complemented by discussions on measurement reproducibility, limitations, and a forward-looking perspective outlining uncharted waters where the field can advance, and potential exciting future applications of TDP.

Roberts, David S.↗
DOE OSTI2023

Meeting Report on the 3rd Chinese American Society for Mass Spectrometry Conference—Advancing Biological and Pharmaceutical Mass Spectrometry

Following the highly successful Chinese American Society for Mass Spectrometry (CASMS) conferences in the previous 2 years, the 3rd CASMS Conference was held virtually on August 28–31, 2023, using the Gather. Town platform to bring together scientists in the MS field. The conference offered a 4-day agenda with a scientific program consisting of two plenary lectures, and 14 parallel symposia in which a total of 70 speakers presented technological innovations and their applications in proteomics and biological MS and metabo-lipidomics and pharmaceutical MS. In addition, 16 invited speakers/panelists presented at two research-focused and three career development workshops. Moreover, 86 posters, 12 lightning talks, 3 sponsored workshops, and 11 exhibitions were presented, from which 9 poster awards and 2 lightning talk awards were selected. Furthermore, the conference featured four young investigator awardees to highlight early-career achievements in MS from our society. In conclusion, the conference provided a unique scientific platform for young scientists (i.e. graduate students, postdocs, and junior faculty/investigators) to present their research, meet with prominent scientists, learn about career development, and job opportunities (http://casms.org).

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Cholesterol promotes the migration and invasion of renal carcinoma cells by regulating the KLF5/miR-27a/FBXW7 pathway

Highlights: • Cholesterol treatment promoted the expression of KLF5 and miR-27a. • KLF5 positively regulated the transcription of miR-27a. • FBXW7 was identified as a direct target of miR-27a. • Inhibition of FBXW7 accelerated 786-O cells migration and invasion. Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of renal cell carcinoma. Accumulation of cholesterol and cholesterol ester is a remarkable feature of ccRCC. However, the effect of cholesterol on ccRCC remains unknown. Out results showed that cholesterol treatment significantly promoted cells migration and invasion in ccRCC. Mechanism analysis indicated that cholesterol induced KLF5 expression. KLF5 positively regulated the transcription of miR-27a, increasing miR-27a expression. MiR-27a directly targeted FBXW7 by binding to its 3′UTR, reducing FBXW7 expression. FBXW7 silencing further increased the expression of KLF5 and miR-27a, and promoted cells migration and invasion. These results suggested that cholesterol accelerated ccRCC cells migration and invasion by regulating KLF5/miR-27a/FBXW7 axis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Assessing the capability of continuum and discrete particle methods to simulate gas-solids flow using DNS predictions as a benchmark

For this study, gas–solids flow in a three-dimension periodic domain was numerically investigated by direct numerical simulation (DNS), computational fluid dynamic-discrete element method (CFD-DEM) and two-fluid model (TFM). DNS data obtained by finely resolving the flow around every particle are used as a benchmark to assess the validity of coarser DEM and TFM approaches. The CFD-DEM predicts the correct cluster size distribution and under-predicts the macro-scale slip velocity even with a grid size as small as twice the particle diameter. The TFM approach predicts larger cluster size and lower slip velocity with a homogeneous drag correlation. Although the slip velocity can be matched by a simple modification to the drag model, the predicted voidage distribution is still different from DNS: Both CFD-DEM and TFM over-predict the fraction of particles in dense regions and under-predict the fraction of particles in regions of intermediate void fractions. Also, the cluster aspect ratio of DNS is smaller than CFD-DEM and TFM. Since a simple correction to the drag model can predict a correct slip velocity, it is hopeful that drag corrections based on more elaborate theories that consider voidage gradient and particle fluctuations may be able to improve the current predictions of cluster distribution.

42 ENGINEERING↗
DOE OSTI2016

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets showed that TopMG outperformed existing methods in identifying complex proteoforms.

Dynamic Programming↗
DOE OSTI2016

Characterization of Proteoforms with Unknown Post-translational Modifications Using the MIScore

Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform-spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗