Metabolic response to a heterologous poly-3-hydroxybutyrate (PHB) pathway in Phaeodactylum tricornutum

The marine diatom Phaeodactylum tricornutum is an emerging host for metabolic engineering, but little is known about how introduced pathways are integrated into the existing metabolic framework of the host or influence transgene expression. In this study, we expressed the heterologous poly-3-hydroxybutyrate (PHB) pathway using episomal expression, which draws on the precursor acetyl coenzyme-A (AcCoA). By experimentally perturbing cultivation conditions, we gained insight into the regulation of the endogenous metabolism in transgenic lines under various environmental scenarios, as well as on alterations in AcCoA flux within the host cell. Biosynthesis of PHB led to distinct shifts in the metabolome of the host, and further analysis revealed a condition-dependent relationship between endogenous and transgenic metabolic pathways. Under N limitation, which induced a significant increase in neutral lipid content, both metabolic and transcriptomic data suggest that AcCoA was preferably shunted into the endogenous pathway for lipid biosynthesis over the transgenic PHB pathway. In contrast, supply of organic carbon in the form of glycerol supported both fatty acid and PHB biosynthesis, suggesting cross-talk between cytosolic and plastidial AcCoA precursors. This is the first study to investigate the transcriptomic and metabolomic response of diatom cell lines expressing a heterologous multi-gene pathway under different environmental conditions, providing useful insights for future engineering attempts for pathways based on the precursor AcCoA. KEY POINTS: • PHB expression had minimal effects on transcription of adjacent pathways. • N limitation favoured native lipid rather than transgenic PHB synthesis. • Glycerol addition allowed simultaneous lipid and PHB accumulation.

Cellular specificity is key to deciphering epigenetic changes underlying Alzheimer's disease

Different cell types in the brain play distinct roles in Alzheimer's disease (AD) progression. Late onset AD (LOAD) is a complex disease, with a large genetic component, but many risk loci fall in non-coding genome regions. Epigenetics implicates the non-coding genome with control of gene expression. The epigenome is highly cell-type specific and dynamically responds to the environment. Therefore, epigenetic mechanisms are well placed to explain genetic and environmental factors that are associated with AD. However, given this cellular specificity, purified cell populations or single cells need to be profiled to avoid effect masking. Here we review the current state of cell-type specific genome-wide profiling in LOAD, covering DNA methylation (CpG, CpH, and hydroxymethylation), histone modifications, and chromatin changes. To date, these data reveal that distinct cell types contribute and react differently to AD progression through epigenetic alterations. This review addresses the current gap in prior bulk-tissue derived work by spotlighting cell-specific changes that govern the complex interplay of cells throughout disease progression and are critical in understanding and developing effective treatments for AD.

Chemotaxis shapes the microscale organization of the ocean's microbiome

The capacity of planktonic marine microorganisms to actively seek out and exploit microscale chemical hotspots has been widely theorized to affect ocean-basin scale biogeochemistry^1-3, but has never been examined comprehensively in situ among natural microbial communities. Here, using a field-based microfluidic platform to quantify the behavioural responses of marine bacteria and archaea, we observed significant levels of chemotaxis towards microscale hotspots of phytoplankton-derived dissolved organic matter (DOM) at a coastal field site across multiple deployments, spanning several months. Microscale metagenomics revealed that a wide diversity of marine prokaryotes, spanning 27 bacterial and 2 archaeal phyla, displayed chemotaxis towards microscale patches of DOM derived from ten globally distributed phytoplankton species. The distinct DOM composition of each phytoplankton species attracted phylogenetically and functionally discrete populations of bacteria and archaea, with 54% of chemotactic prokaryotes displaying highly specific responses to the DOM derived from only one or two phytoplankton species. Prokaryotes exhibiting chemotaxis towards phytoplankton-derived compounds were significantly enriched in the capacity to transport and metabolize specific phytoplankton-derived chemicals, and displayed enrichment in functions conducive to symbiotic relationships, including genes involved in the production of siderophores, B vitamins and growth-promoting hormones. Our findings demonstrate that the swimming behaviour of natural prokaryotic assemblages is governed by specific chemical cues, which dictate important biogeochemical transformation processes and the establishment of ecological interactions that structure the base of the marine food web.

how_are_we_stranded_here: quick determination of RNA-Seq strandedness

Background

Quality control checks are the first step in RNA-Sequencing analysis, which enable the identification of common issues that occur in the sequenced reads. Checks for sequence quality, contamination, and complexity are commonplace, and allow users to implement steps downstream which can account for these issues. Strand-specificity of reads is frequently overlooked and is often unavailable even in published data, yet when unknown or incorrectly specified can have detrimental effects on the reproducibility and accuracy of downstream analyses.

Results

To address these issues, we developed how_are_we_stranded_here, a Python library that helps to quickly infer strandedness of paired-end RNA-Sequencing data. Testing on both simulated and real RNA-Sequencing reads showed that it correctly measures strandedness, and measures outside the normal range may indicate sample contamination.

Conclusions

how_are_we_stranded_here is fast and user friendly, making it easy to implement in quality control pipelines prior to analysing RNA-Sequencing data. how_are_we_stranded_here is freely available at https://github.com/betsig/how_are_we_stranded_here.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04572-7.