Cheap and renewable feedstocks such as the one-carbon substrate formate are emerging for sustainable production in a growing chemical industry. We investigated the acetogen Acetobacterium woodii as a potential host for bioproduction from formate alone and together with autotrophic and heterotrophic co-substrates by quantitatively analyzing physiology, transcriptome, and proteome in chemostat cultivations in combination with computational analyses. Continuous cultivations with a specific growth rate of 0.05 h^-1 on formate showed high specific substrate uptake rates (47 mmol g^-1 h^-1). Co-utilization of formate with H₂, CO, CO₂ or fructose was achieved without catabolite repression and with acetate as the sole metabolic product. A transcriptomic comparison of all growth conditions revealed a distinct adaptation of A. woodii to growth on formate as 570 genes were changed in their transcript level. Transcriptome and proteome showed higher expression of the Wood-Ljungdahl pathway during growth on formate and gaseous substrates, underlining its function during utilization of one-carbon substrates. Flux balance analysis showed varying flux levels for the WLP (0.7-16.4 mmol g^-1 h^-1) and major differences in redox and energy metabolism. Growth on formate, H₂/CO₂, and formate + H₂/CO₂ resulted in low energy availability (0.20-0.22 ATP/acetate) which was increased during co-utilization with CO or fructose (0.31 ATP/acetate for formate + H₂/CO/CO₂, 0.75 ATP/acetate for formate + fructose). Unitrophic and mixotrophic conversion of all substrates was further characterized by high energetic efficiencies. In silico analysis of bioproduction of ethanol and lactate from formate and autotrophic and heterotrophic co-substrates showed promising energetic efficiencies (70-92%). Collectively, our findings reveal A. woodii as a promising host for flexible and simultaneous bioconversion of multiple substrates, underline the potential of substrate co-utilization to improve the energy availability of acetogens and encourage metabolic engineering of acetogenic bacteria for the efficient synthesis of bulk chemicals and fuels from sustainable one carbon substrates.

Background: Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a highly diverse group of secondary metabolites (SM) of bacterial and fungal origin. While RiPPs have been intensively studied in bacteria, little is known about fungal RiPPs. In Fungi only six classes of RiPPs are described. Current strategies for genome mining are based on these six known classes. However, the genes involved in the biosynthesis of theses RiPPs are normally organized in biosynthetic gene clusters (BGC) in fungi. Results: Here we describe a comprehensive strategy to mine fungal genomes for RiPPs by combining and adapting existing tools (e.g. antiSMASH and RiPPMiner) followed by extensive manual curation based on conserved domain identification, (comparative) phylogenetic analysis, and RNASeq data. Deploying this strategy, we could successfully rediscover already known fungal RiPPs. Further, we analysed four fungal genomes from the Trichoderma genus. We were able to find novel potential RiPP BGCs in Trichoderma using our unconventional mining approach. Conclusion: We demonstrate that the unusual mining approach using tools developed for bacteria can be used in fungi, when carefully curated. Our study is the first report of the potential of Trichoderma to produce RiPPs, the detected clusters encode novel uncharacterized RiPPs. The method described in our study will lead to further mining efforts in all subdivisions of the fungal kingdom.

Secondary metabolites (SMs) are a vast group of compounds with different structures and properties that have been utilized as drugs, food additives, dyes, and as monomers for novel plastics. In many cases, the biosynthesis of SMs is catalysed by enzymes whose corresponding genes are co-localized in the genome in biosynthetic gene clusters (BGCs). Notably, BGCs may contain so-called gap genes, that are not involved in the biosynthesis of the SM. Current genome mining tools can identify BGCs, but they have problems with distinguishing essential genes from gap genes. This can and must be done by expensive, laborious, and time-consuming comparative genomic approaches or transcriptome analyses. In this study, we developed a method that allows semi-automated identification of essential genes in a BGC based on co-evolution analysis. To this end, the protein sequences of a BGC are blasted against a suitable proteome database. For each protein, a phylogenetic tree is created. The trees are compared by treeKO to detect co-evolution. The results of this comparison are visualized in different output formats, which are compared visually. Our results suggest that co-evolution is commonly occurring within BGCs, albeit not all, and that especially those genes that encode for enzymes of the biosynthetic pathway are co-evolutionary linked and can be identified with FunOrder. In light of the growing number of genomic data available, this will contribute to the studies of BGCs in native hosts and facilitate heterologous expression in other organisms with the aim of the discovery of novel SMs.

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques—including statistical, regularization, deep learning, decision trees, and Bayesian methods—were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation.

Discrimination-related chronic psychosocial stress is associated with cardiovascular disease (CVD) risk through multisystem dysregulation. Stress-associated differences in gut microbial ecology, lipid metabolism, and intrinsic brain connectivity may contribute to cardiometabolic vulnerability, but integrated multiomic evidence remains limited. We studied 117 premenopausal women recruited from Los Angeles and stratified by Everyday Discrimination Scale (EDS) scores into higher (> 6, n = 64) and lower (≤ 6, n = 53) exposure groups. Participants completed clinical and psychological assessments, fasting blood and stool collection, and neuroimaging via MRI. Microbiome profiling targeted the V4 region of 16S rDNA; lipidomics was performed using LC–MS. Analyses included covariate-adjusted group models, exploratory cross-domain correlations, single-mediator mediation models, and moderation models. Participants with higher EDS scores exhibited higher anxiety, depression, perceived stress, and gastrointestinal-specific anxiety, together with lower mental and physical health scores. Lipidomic profiling identified lower Phosphatidylethanolamine and altered Phosphatidylcholine, fatty acids, Lysophosphatidylcholine, and Plasmenyl‑phosphatidylethanolamine species; total Phosphatidylserine was directionally lower but not significant in the group-level general linear model. Microbiome analyses identified higher Mollicutes RF39 and Ruminococcaceae UCG.009, whereas Prevotella 2 and Candidatus Stoquefichus showed directional but non-significant differences in this subset. Resting-state fMRI identified higher connectivity primarily among parieto-occipital/occipital region of interest (ROI) pairs and one supramarginal-insular connection. Mediation, moderation, and cross-domain correlation analyses yielded hypothesis-generating indirect-effect, interaction, and association signals involving selected microbial, lipidomic, and connectivity features. Because these analyses were exploratory and cross-sectional, they do not establish temporal ordering, biological pathways, or mechanistic mediation. Higher discrimination-related psychosocial stress was associated with psychological symptoms and selected microbial, lipidomic, and resting-state connectivity differences in premenopausal women. Larger longitudinal studies are needed to establish the reproducibility and temporal relationships of these findings.

Coevolution is an important biological process that shapes interacting species or even proteins – may it be physically interacting proteins or consecutive enzymes in a metabolic pathway. The detection of co-evolved proteins will contribute to a better understanding of biological systems. Previously, we developed a semi-automated method, termed FunOrder, for the detection of co-evolved genes from an input gene or protein set. We demonstrated the usability and applicability of FunOrder by identifying essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. To fully automatize the method, we used several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output. Further, we replaced the BLAST with the DIAMOND tool, which enhanced speed and allows the future integration of larger proteome databases. The introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity. Additionally, the changes lay the foundation for future high-throughput applications of FunOrder 2.0 in different phyla to solve different biological problems. AUTHOR SUMMARY Coevolution is a process which arises between different species or even different proteins that interact with each other. Any change occurring in one partner must be met by a corresponding change in the other partner to maintain the interaction throughout evolution. These interactions may occur in symbiotic relationships or between rivaling species. Within an organism, consecutive enzymes of metabolic pathways are also subjected to coevolution. We developed a fully automated method, FunOrder 2.0, for the detection of co-evolved proteins, which will contribute to a better understanding of protein interactions within an organism. We demonstrate that this method can be used to identify essential genes of the secondary metabolism of fungi, but FunOrder 2.0 may also be used to detect pathogenicity factors or remains of horizontal gene transfer next to many other biological systems that were shaped by coevolution.