Page 51 - Read Online
P. 51

Page 16 of 17                 Wang et al. Microbiome Res Rep 2024;3:39  https://dx.doi.org/10.20517/mrr.2024.21

               Copyright
               © The Author(s) 2024.


               REFERENCES
               1.       Hou K, Wu ZX, Chen XY, et al. Microbiota in health and diseases. Signal Transduct Target Ther 2022;7:135.  DOI  PubMed  PMC
               2.       Guzzo GL, Andrews JM, Weyrich LS. The neglected gut microbiome: fungi, protozoa, and bacteriophages in inflammatory bowel
                   disease. Inflamm Bowel Dis 2022;28:1112-22.  DOI  PubMed  PMC
               3.       Hrncir T. Gut microbiota dysbiosis: triggers, consequences, diagnostic and therapeutic options. Microorganisms 2022;10:578.  DOI
                   PubMed  PMC
               4.       Zhang Z, Tang H, Chen P, Xie H, Tao Y. Demystifying the manipulation of host immunity, metabolism, and extraintestinal tumors by
                   the gut microbiome. Signal Transduct Target Ther 2019;4:41.  DOI  PubMed  PMC
               5.       Ogunrinola GA, Oyewale JO, Oshamika OO, Olasehinde GI. The human microbiome and its impacts on health. Int J Microbiol
                   2020;2020:8045646.  DOI  PubMed  PMC
               6.       Dinakaran V, Rathinavel A, Pushpanathan M, Sivakumar R, Gunasekaran P, Rajendhran J. Elevated levels of circulating DNA in
                   cardiovascular disease patients: metagenomic profiling of microbiome in the circulation. PLoS One 2014;9:e105221.  DOI  PubMed
                   PMC
               7.       Kinross JM, Darzi AW, Nicholson JK. Gut microbiome-host interactions in health and disease. Genome Med 2011;3:14.  DOI
                   PubMed  PMC
               8.       Fekete EE, Figeys D, Zhang X. Microbiota-directed biotherapeutics: considerations for quality and functional assessment. Gut
                   Microbes 2023;15:2186671.  DOI  PubMed  PMC
               9.       Ferrocino I, Rantsiou K, McClure R, et al; MicrobiomeSupport Consortium. The need for an integrated multi-OMICs approach in
                   microbiome science in the food system. Compr Rev Food Sci Food Saf 2023;22:1082-103.  DOI  PubMed
               10.      Berg G, Rybakova D, Fischer D, et al. Microbiome definition re-visited: old concepts and new challenges. Microbiome 2020;8:103.
                   DOI  PubMed  PMC
               11.      Zhang X, Li L, Butcher J, Stintzi A, Figeys D. Advancing functional and translational microbiome research using meta-omics
                   approaches. Microbiome 2019;7:154.  DOI  PubMed  PMC
               12.      Creskey M, Li L, Ning Z, et al. An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic
                   analysis. Proteomics 2023;23:e2200116.  DOI  PubMed
               13.      Pietilä S, Suomi T, Elo LL. Introducing untargeted data-independent acquisition for metaproteomics of complex microbial samples.
                   ISME Commun 2022;2:51.  DOI  PubMed  PMC
               14.      Fernández-Costa C, Martínez-Bartolomé S, McClatchy DB, Saviola AJ, Yu NK, Yates JR 3rd. Impact of the identification strategy on
                   the reproducibility of the DDA and DIA results. J Proteome Res 2020;19:3153-61.  DOI  PubMed  PMC
               15.      Zhang F, Ge W, Ruan G, Cai X, Guo T. Data-independent acquisition mass spectrometry-based proteomics and software tools: a
                   glimpse in 2020. Proteomics 2020;20:e1900276.  DOI  PubMed
               16.      Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep
                   proteome coverage in high throughput. Nat Methods 2020;17:41-4.  DOI  PubMed  PMC
               17.      Meier F, Brunner AD, Frank M, et al. diaPASEF: parallel accumulation-serial fragmentation combined with data-independent
                   acquisition. Nat Methods 2020;17:1229-36.  DOI  PubMed
               18.      Guzman UH, Martinez-Val A, Ye Z, et al. Ultra-fast label-free quantification and comprehensive proteome coverage with narrow-
                   window data-independent acquisition. Nat Biotechnol 2024.  DOI  PubMed
               19.      Zhao J, Yang Y, Xu H, et al. Data-independent acquisition boosts quantitative metaproteomics for deep characterization of gut
                   microbiota. NPJ Biofilms Microbiomes 2023;9:4.  DOI  PubMed  PMC
               20.      Dumas T, Martinez Pinna R, Lozano C, et al. The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex
                   samples is a quantum leap in the functional analysis of microbiomes. Microbiome 2024;12:46.  DOI  PubMed  PMC
               21.      Gómez-Varela D, Xian F, Grundtner S, Sondermann JR, Carta G, Schmidt M. Increasing taxonomic and functional characterization of
                   host-microbiome interactions by DIA-PASEF metaproteomics. Front Microbiol 2023;14:1258703.  DOI  PubMed  PMC
               22.      Zhang X, Deeke SA, Ning Z, et al. Metaproteomics reveals associations between microbiome and intestinal extracellular vesicle
                   proteins in pediatric inflammatory bowel disease. Nat Commun 2018;9:2873.  DOI  PubMed  PMC
               23.      Karaduta O, Dvanajscak Z, Zybailov B. Metaproteomics-an advantageous option in studies of host-microbiota interaction.
                   Microorganisms 2021;9:980.  DOI  PubMed  PMC
               24.      Starr AE, Deeke SA, Li L, et al. Proteomic and metaproteomic approaches to understand host-microbe interactions. Anal Chem
                   2018;90:86-109.  DOI  PubMed
               25.      Sberro H, Fremin BJ, Zlitni S, et al. Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell
                   2019;178:1245-59.e14.  DOI  PubMed  PMC
               26.      Petruschke H, Schori C, Canzler S, et al. Discovery of novel community-relevant small proteins in a simplified human intestinal
                   microbiome. Microbiome 2021;9:55.  DOI  PubMed  PMC
               27.      Ma Y, Guo Z, Xia B, et al. Identification of antimicrobial peptides from the human gut microbiome using deep learning. Nat
                   Biotechnol 2022;40:921-31.  DOI  PubMed
   46   47   48   49   50   51   52   53   54   55   56