A highly pure biomethane stream (≈97% CH₄) was produced continuously under halo-alkaline conditions (pH > 9, 0.6 M Na⁺) from complex alkaline organic waste residue originating from biopolymer extraction from sewage sludge. During the proof-of-concept operation, the substrate was degraded with similar efficiency (40% of the volatile solids, VS) compared to neutral conditions (36% of the VS). Operational data was utilised in a technical evaluation to identify bottlenecks for full-scale implementation at an early stage of process development and for comparison to conventional biogas upgrading using pressure swing and membranes. Initially identified bottlenecks for alkaline fermentation were related to overcautious assumptions, while others could be technically solved. Alkaline fermentation offers an attractive method for supplying increasingly needed high-purity biomethane using various recalcitrant substrates that have undergone alkaline pre-treatment. This is more feasible than the conventional ex-situ biogas upgrading. Next, upscaling steps for alkaline fermentation should be pursued. Strategies for integrated CO₂ sequestration and nutrient recovery are outlined, which will offer additional benefits in the future.

Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a de novo metaproteomics pipeline that performs sequence alignment of de novo sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of in silico and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a de novo metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases.

The tremendous progress in sequencing technologies has made DNA sequencing routine for microbiome studies. Additionally, advances in mass spectrometric techniques have extended conventional proteomics into the field of microbial ecology. However, systematic studies that provide a better understanding of the complementary nature of these 'omics' approaches, particularly for complex environments such as wastewater treatment sludge, are urgently needed. Here, we describe a comparative metaomics study on aerobic granular sludge from three different wastewater treatment plants. For this, we employed metaproteomics, whole metagenome, and 16S rRNA amplicon sequencing to study the same granule material with uniform size. We furthermore compare the taxonomic profiles using the Genome Taxonomy Database (GTDB) to enhance the comparability between the different approaches. Though the major taxonomies were consistently identified in the different aerobic granular sludge samples, the taxonomic composition obtained by the different omics techniques varied significantly at the lower taxonomic levels, which impacts the interpretation of the nutrient removal processes. Nevertheless, as demonstrated by metaproteomics, the genera that were consistently identified in all techniques cover the majority of the protein biomass. The established metaomics data and the contig classification pipeline are publicly available, which provides a valuable resource for further studies on metabolic processes in aerobic granular sludge.