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A Computational Framework for Protein Identification and Quantification in Metaproteomics Using Data-Independent Acquisition

Award Number: R15LM013460
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Summary Extensive efforts to characterize the human microbiome have tremendously increased the knowledge about the diversity of the microbiome and about its composition in health and in disease. Dysbiosis in human microbiota underlies the development of many diseases, such as obesity, diabetes, and inflammatory bowel disease. Metaproteomics based on mass spectrometry (MS) has become widely used in microbiome research for gaining insights into the functional states of microbial communities. Mass spectrometry with data-dependent acquisition (DDA) is the most common method of choice for identifying and quantifying microbial proteins in metaproteomics, but this technique is fundamentally limited in terms of reproducibility and comprehensiveness. Proteomics using data-independent acquisition (DIA) can, in theory, resolve the fundamental problems associated with the DDA method. However, the lack of bioinformatics tools still presents unresolved challenges in the context of DIA, and only few DIA applications on microbiome or host-microbe interactions have been reported. MS-based metaproteomics is a challenging measurement due to the high complexity with thousands of species at vastly different abundances. To obtain a comprehensive characterization of the functional state of microbial communities requires considering proteins not just from dominant microorganisms but also low-abundance microorganisms. This proposal addresses the need for identifying and quantifying proteins through the availability of a set of computational tools that use DIA data to identify and quantify peptides and their variants at the microbial strain level. The false peptide identifications are controlled by newly proposed methods for false discovery rate assessment at multiple granularities. The protein inference and quantification are optimized by linear programming models that contain information from genome/transcriptome sequencing data and metaproteome sample replicas. The improvement will increase the number of identified protein variants, especially those from the low-abundance microorganisms, which can help accurately characterize the functional composition in microbial communities and reveal the functional redundancy.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $0 )
2024	2020	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Medical Library Assistance	000	1	12/11/2023	NEW	$0
														Subtotal = $0

Issue Date FY: 2020 ( Subtotal = $361,302 )
2020	2020	UNIVERSITY OF NORTH TEXAS	1155 UNION CIR #305250	DENTON	TX	76203	DENTON	USA	Medical Library Assistance	000	1	6/15/2020	NEW	$361,302
														Subtotal = $361,302

Grand Total All Awards = $361,302

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A Computational Framework for Protein Identification and Quantification in Metaproteomics Using Data-Independent Acquisition

Award Number: R15LM013460

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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