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Abstract Lignin is a universal waste product of the agricultural industry and is currently seen as a potential feedstock for more sustainable manufacturing. While it is the second most abundant biopolymer in the world, most of it is currently burned as it is a very recalcitrant material. Many recent studies, however, have demonstrated the viability of biocatalysis to improve the value of this feedstock and convert it into more useful chemicals, such as polyhydroxybutyrate, and clean fuels like hydrogen and n-butanol.Rhodopseudomonas palustrisis a gram-negative bacterium which demonstrates a plethora of desirable metabolic capabilities, including aromatic catabolism useful for lignin degradation. This study uses a multi-omics approach, including the first usage of CRISPRi inR. palustris, to investigate the lignin consumption mechanisms ofR. palustris, the essentiality of redox homeostasis to lignin consumption, elucidate a potential lignin catabolic superpathway, and enable more economically viable sustainable lignin valorization processes. 

Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C. 

Abstract Opaque kernels in maize may result from mutations in many genes, such as OPAQUE-2. In this study, a maize null mutant of RNA-DIRECTED DNA METHYLATION 4 (RDM4) showed an opaque kernel phenotype, as well as plant developmental delay, male sterility, and altered response to cold stress. We found that in opaque kernels, all zein proteins were reduced and amino acid content was changed, including increased lysine. Transcriptomic and proteomic analysis confirmed the zein reduction and proteomic rebalancing of non-zein proteins, which was quantitatively and qualitatively different from opaque-2. Global transcriptional changes were found in endosperm and leaf, including many transcription factors and tissue-specific expressed genes. Furthermore, of the more than 8000 significantly differentially expressed genes in wild type in response to cold, a significant proportion (25.9% in moderate cold stress and 40.8% in near freezing stress) were not differentially expressed in response to cold in rdm4, suggesting RDM4 may participate in regulation of abiotic stress tolerance. This initial characterization of maize RDM4 provides a basis for further investigating its function in endosperm and leaf, and as a regulator of normal and stress-responsive development. 

Abstract Switchgrass ( Panicum virgatum L.) is an important crop for biofuel production but it also serves as host for greenbugs ( Schizaphis graminum Rondani; GB). Although transcriptomic studies have been done to infer the molecular mechanisms of plant defense against GB, little is known about the effect of GB infestation on the switchgrass protein expression and phosphorylation regulation. The global response of the switchgrass cultivar Summer proteome and phosphoproteome was monitored by label-free proteomics shotgun in GB-infested and uninfested control plants at 10 days post infestation. Peptides matching a total of 3,594 proteins were identified and 429 were differentially expressed proteins in GB-infested plants relative to uninfested control plants. Among these, 291 and 138 were up and downregulated by GB infestation, respectively. Phosphoproteome analysis identified 310 differentially phosphorylated proteins (DP) from 350 phosphopeptides with a total of 399 phosphorylated sites. These phosphopeptides had more serine phosphorylated residues (79%), compared to threonine phosphorylated sites (21%). Overall, KEGG pathway analysis revealed that GB feeding led to the enriched accumulation of proteins important for biosynthesis of plant defense secondary metabolites and repressed the accumulation of proteins involved in photosynthesis. Interestingly, defense modulators such as terpene synthase, papain-like cysteine protease, serine carboxypeptidase, and lipoxygenase2 were upregulated at the proteome level, corroborating previously published transcriptomic data.