Identification of Biomolecules Involved in the Adaptation to the Environment of Cold-Loving Microorganisms and Metabolic Pathways for Their Production

Cold-loving microorganisms of all three domains of life have unique and special abilities that allow them to live in harsh environments. They have acquired structural and molecular mechanisms of adaptation to the cold that include the production of anti-freeze proteins, carbohydrate-based extracellular polymeric substances and lipids which serve as cryo- and osmoprotectants by maintaining the fluidity of their membranes. They also produce a wide diversity of pigmented molecules to obtain energy, carry out photosynthesis, increase their resistance to stress and provide them with ultraviolet light protection. Recently developed analytical techniques have been applied as high-throughoutput technologies for function discovery and for reconstructing functional networks in psychrophiles. Among them, omics deserve special mention, such as genomics, transcriptomics, proteomics, glycomics, lipidomics and metabolomics. These techniques have allowed the identification of microorganisms and the study of their biogeochemical activities. They have also made it possible to infer their metabolic capacities and identify the biomolecules that are parts of their structures or that they secrete into the environment, which can be useful in various fields of biotechnology. This Review summarizes current knowledge on psychrophiles as sources of biomolecules and the metabolic pathways for their production. New strategies and next-generation approaches are needed to increase the chances of discovering new biomolecules.

Hyperglycemia Regulated Circulating MicroRNAs and Their Effects on Renal Function Decline in Type 2 Diabetes

Background: It has been reported that microRNAs (miRNAs) play an important role in the pathogenesis of diabetic complications. We aimed to search for circulating miRNA that were associated with hyperglycemia in type 2 diabetes and examine their effects on renal function decline. Methods: Using the next-generation sequencing-based HTG EdgeSeq miRNA platform, a total of 2,083 miRNAswere measured in baseline plasma specimens obtained from73 subjects with type 2 diabetes (T2D) and normal renal function (discovery panel) and 136 subjects with T2D and impaired renal function (replication panel). Subjects in both panels were followed for 6–12 years to determine eGFR decline. Results: We identified 11 candidate miRNAsthat were strongly associated with elevated levels of glycated hemoglobin (HbA1c) in both screening and replication panels. Using bioinformatics analyses, we found that the candidate miRNAs targeted proteins of 6 pathways (the Ras signaling pathway, Signaling pathways regulating pluripotency of stem cells, the MAPK pathway, Glutamatergic synapse, the Rap 1 signaling pathway, and the AMPK signaling pathway). Importantly, 4 of these 11 miRNAs were significantly associated with risk of renal function decline. Conclusion: There were few previous reports about the association between circulating miRNAs, hyperglycemia, and diabetic kidney disease in T2D. The present study comprehensively examined and identified hyperglycemia-regulated miRNAs in human samples. Our finding are novel in that circulatingmiRNAsregulated by hyperglycemia are associated with risk of eGFR decline in T2D.

Metabo-Endotypes of Asthma Reveal Clinically Important Differences in Lung Function: Discovery and validation in two TOPMed Cohorts

Current guidelines do not sufficiently capture the heterogeneous nature of asthma; a detailed molecular classification is needed. Metabolomics represents a novel and compelling approach to derive asthma endotypes, i.e., subtypes defined by functional/pathobiological mechanisms. In two cohorts of asthmatics, untargeted metabolomic profiling and Similarity Network Fusion was used to derive and validate five “metabo-endotypes” of asthma, which displayed significant differences in asthma-relevant phenotypes including pre-bronchodilator and post-bronchodilator forced expiratory volume/forced vital capacity (FEV1/FVC). The “most-severe” asthma metabo-endotype was defined by the lowest FEV1/FVC and characterized by altered levels of phospholipids and polyunsaturated fatty acids, suggesting dysregulation of pulmonary surfactant homeostasis. This was supported by genetic analyses as members of this endotype were more likely to carry variants in key pulmonary surfactant regulation genes including BMPR1B (meta-analyzed p=2.8x10-4) and BMP3 (meta-analyzed p=5.23x10-4). These findings suggest clinically meaningful endotypes can be derived and validated using metabolomic data. Interrogating the drivers of these metabo-endotypes can help understand their pathophysiology.

Co-Expression Networks in Chlamydomonas Reveal Significant Rhythmicity in Batch Cultures and Empower Gene Function Discovery

The unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis and chloroplast metabolism, cilium assembly and function, lipid and starch metabolism, and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes. We extracted several hundred high-confidence genes at the intersection of multiple curated lists involved in cilia, cell division, and photosynthesis, illustrating the power of our method. Surprisingly, Chlamydomonas experiments retained a significant rhythmic component across the transcriptome, suggesting an underappreciated variable during sample collection, even in samples collected in constant light. Our results therefore document substantial residual synchronization in batch cultures, contrary to assumptions of asynchrony. We provide step-by-step protocols for the analysis of co-expression across transcriptome data sets from Chlamydomonas and other species to help foster gene function discovery.

Co-Expression Networks in the Green Alga Chlamydomonas reinhardtii Empower Gene Discovery and Functional Exploration

ABSTRACTThe unicellular green alga Chlamydomonas reinhardtii is a choice reference system for the study of photosynthesis, cilium assembly and function, lipid and starch metabolism and metal homeostasis. Despite decades of research, the functions of thousands of genes remain largely unknown, and new approaches are needed to categorically assign genes to cellular pathways. Growing collections of transcriptome and proteome data now allow a systematic approach based on integrative co-expression analysis. We used a dataset comprising 518 deep transcriptome samples derived from 58 independent experiments to identify potential co-expression relationships between genes. We visualized co-expression potential with the R package corrplot, to easily assess co-expression and anti-correlation between genes from manually-curated and community-generated gene lists. We extracted 400 high-confidence cilia-related genes at the intersection of multiple co-expressed lists, illustrating the power of our simple method. Surprisingly, Chlamydomonas experiments did not cluster according to an obvious pattern, suggesting an underappreciated variable during sample collection. One possible source of variation may stem from the strong clustering of nuclear genes as a function of their diurnal phase, even in samples collected in constant conditions, indicating substantial residual synchronization in batch cultures. We provide a step-by-step guide into the analysis of co-expression across Chlamydomonas transcriptome datasets to help foster gene function discovery.One-sentence summarywe reveal co-expression potential between Chlamydomonas genes and describe strong synchronization of cells grown in batch cultures as a possible source of underappreciated variation.

A High-Efficacy CRISPR Interference System for Gene Function Discovery in Zymomonas mobilis

ABSTRACT Zymomonas mobilis is a promising biofuel producer due to its high alcohol tolerance and streamlined metabolism that efficiently converts sugar to ethanol. Z. mobilis genes are poorly characterized relative to those of model bacteria, hampering our ability to rationally engineer the genome with pathways capable of converting sugars from plant hydrolysates into valuable biofuels and bioproducts. Many of the unique properties that make Z. mobilis an attractive biofuel producer are controlled by essential genes; however, these genes cannot be manipulated using traditional genetic approaches (e.g., deletion or transposon insertion) because they are required for viability. CRISPR interference (CRISPRi) is a programmable gene knockdown system that can precisely control the timing and extent of gene repression, thus enabling targeting of essential genes. Here, we establish a stable, high-efficacy CRISPRi system in Z. mobilis that is capable of perturbing all genes—including essential genes. We show that Z. mobilis CRISPRi causes either strong knockdowns (>100-fold) using single guide RNA (sgRNA) spacers that perfectly match target genes or partial knockdowns using spacers with mismatches. We demonstrate the efficacy of Z. mobilis CRISPRi by targeting essential genes that are universally conserved in bacteria, are key to the efficient metabolism of Z. mobilis, or underlie alcohol tolerance. Our Z. mobilis CRISPRi system will enable comprehensive gene function discovery, opening a path to rational design of biofuel production strains with improved yields. IMPORTANCE Biofuels produced by microbial fermentation of plant feedstocks provide renewable and sustainable energy sources that have the potential to mitigate climate change and improve energy security. Engineered strains of the bacterium Z. mobilis can convert sugars extracted from plant feedstocks into next-generation biofuels like isobutanol; however, conversion by these strains remains inefficient due to key gaps in our knowledge about genes involved in metabolism and stress responses such as alcohol tolerance. Here, we develop CRISPRi as a tool to explore gene function in Z. mobilis. We characterize genes that are essential for growth, required to ferment sugar to ethanol, and involved in resistance to isobutanol. Our Z. mobilis CRISPRi system makes it straightforward to define gene function and can be applied to improve strain engineering and increase biofuel yields.

Colocality to Cofunctionality: Eukaryotic Gene Neighborhoods as a Resource for Function Discovery

Diverging from the classic paradigm of random gene order in eukaryotes, gene proximity can be leveraged to systematically identify functionally related gene neighborhoods in eukaryotes, utilizing techniques pioneered in bacteria. Current methods of identifying gene neighborhoods typically rely on sequence similarity to characterized gene products. However, this approach is not robust for nonmodel organisms like algae, which are evolutionarily distant from well-characterized model organisms. Here, we utilize a comparative genomic approach to identify evolutionarily conserved proximal orthologous gene pairs conserved across at least two taxonomic classes of green algae. A total of 317 gene neighborhoods were identified. In some cases, gene proximity appears to have been conserved since before the streptophyte–chlorophyte split, 1,000 Ma. Using functional inferences derived from reconstructed evolutionary relationships, we identified several novel functional clusters. A putative mycosporine-like amino acid, “sunscreen,” neighborhood contains genes similar to either vertebrate or cyanobacterial pathways, suggesting a novel mosaic biosynthetic pathway in green algae. One of two putative arsenic-detoxification neighborhoods includes an organoarsenical transporter (ArsJ), a glyceraldehyde 3-phosphate dehydrogenase-like gene, homologs of which are involved in arsenic detoxification in bacteria, and a novel algal-specific phosphoglycerate kinase-like gene. Mutants of the ArsJ-like transporter and phosphoglycerate kinase-like genes in Chlamydomonas reinhardtii were found to be sensitive to arsenate, providing experimental support for the role of these identified neighbors in resistance to arsenate. Potential evolutionary origins of neighborhoods are discussed, and updated annotations for formerly poorly annotated genes are presented, highlighting the potential of this strategy for functional annotation.