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INTRODUCTION: Investigating the taxonomic and functional composition of human microbiomes can aid in the understanding of disease etiologies, diagnosis, and therapy monitoring for several diseases, including inflammatory bowel disease or obesity. One method for microbiome monitoring is metaproteomics, which assesses human and microbial proteins and thus enables the study of host-microbiome interactions. This advantage led to increased interest in metaproteome analyses and significant developments to introduce this method into a clinical context.; AREAS COVERED: This review summarizes the recent progress from a technical side and an application-related point of view.; EXPERT OPINION: Numerous publications imply the massive potential of metaproteomics to impact human health care. However, the key challenges of standardization and validation of experimental and bioinformatic workflows and accurate quantification methods must be overcome.

Additional file 3: Figure S3.1. Sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS–PAGE) using 25 µg protein extract of the sample of the corresponding H2-feeding scheme. (A) Protein profile of four samples of BM-24/0. (B) Protein profile of five samples of BM-12/12. (C) Protein profiles of three samples of BM-18/6 and BM-6/18. (D) Protein profiles of five samples of BM-12/12–20%.

Additional file 12: Figure S12.1. The Kyoto encyclopaedia of genes and genomes (KEGG) map of the central carbon metabolism (map01200) of Methanothrix in the biological methanation reactor based on metagenomic data.

Additional file 7: Table S7.1. Hydrogenase enzymes of Methanothrix during discontinuous H2-feeding experiments. The numbers represent the spectral count of the annotated metaproteins F420-dependent hydrogenase (Frh) and viologen-reducing hydrogenase (Vht); the number in brackets corresponds to the different proteins identified.

Additional file 5. It includes variation in identified proteins of each MAG for BM-24/0 and BM-12/12 using the Kyoto encyclopaedia of genes and genomes (KEGG) map of the central carbon metabolism (map01200). The colours correspond to logarithmic expression ratio [X = Log2 (KO BM-12/12/KO BM-24/0)]. p > 0.05 considered significant, p ≤ 0.05 see colour legend at the right side of each map (t test). KO (KEGG Orthology) values are the mean normalised values of the abundance of defined orthologs in the metaproteins of the BM pattern. Each of the KEGG map reported protein variations in the one MAG as presented in these figures: Figure S5.1. Methanobacterium (MAG5), Figure S5.2. Methanobacterium (MAG3), Figure S5.3. Methanobacteriaceae (MAG14), Figure S5.4. Methanobacteriaceae (MAG8), Figure S5.5. Petrimonas mucosa (MAG16), Figure S5.6. Sporomusa sphaeroides (MAG13), Figure S5.7. Defluviitoga tunisiensis (MAG6), Figure S5.8. Limnochordia (MAG7), Fig. S5.9. Lutispora (MAG12), Figure S5.10. Firmicutes (MAG9), Figure S5.11. Firmicutes (MAG15), Figure S5.12. Bacteriodales (MAG10), Figur S5.13. Bacteriodales(MAG11).

Additional file 2: Table S2.1. Metagenome assembly and binning results. Table S2.2. Taxonomic affiliation of metagenome-assembled genomes (MAGs) based on GDTB results. Table S2.3. Number of metagenome sequences mapped onto the MAG genome, relative abundance, metagenome completeness and contamination of the 12 identified MAGs.

Empirical studies in software engineering are often conducted with open-source developers or in industrial collaborations. Seemingly, this resulted in few experiments using financial incentives (e.g., money, vouchers) as a strategy to motivate the participants' behavior; which is typically done in other research communities, such as economics or psychology. Even the current version of the SIGSOFT Empirical Standards does mention payouts for completing surveys only, but not for mimicking the real-world or motivating realistic behavior during experiments. So, there is a lack of understanding regarding whether financial incentives can or cannot be useful for software-engineering experimentation. To tackle this problem, we plan a survey based on which we will conduct a controlled laboratory experiment. Precisely, we will use the survey to elicit incentivization schemes we will employ as (up to) four payoff functions (i.e., mappings of choices or performance in an experiment to a monetary payment) during a code-review task in the experiment: (1) a scheme that employees prefer, (2) a scheme that is actually employed, (3) a scheme that is performance-independent, and (4) a scheme that mimics an open-source scenario. Using a between-subject design, we aim to explore how the different schemes impact the participants' performance. Our contributions help understand the impact of financial incentives on developers in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers., Comment: Registered Report under PCI RR review