Environmental metabolomics characterization of modern stromatolites and annotation of ibhayipeptolides.

Lithified layers of complex microbial mats known as microbialites are ubiquitous in the fossil record, and modern forms are increasingly identified globally. A key challenge to developing an understanding of microbialite formation and environmental role is how to investigate complex and diverse communities in situ. We selected living, layered microbialites (stromatolites) in a peritidal environment near Schoenmakerskop, Eastern Cape, South Africa to conduct a spatial survey mapping the composition and small molecule production of the microbial communities from environmental samples. Substrate core samples were collected from nine sampling stations ranging from the upper point of the freshwater inflow to the lower marine interface where tidal overtopping takes place. Substrate cores provided material for parallel analyses of microbial community diversity by 16S rRNA gene amplicon sequencing and metabolomics using LC-MS2. Species and metabolite diversities were correlated, and prominent specialized metabolites were targeted for preliminary characterization. A new series of cyclic hexadepsipeptides, named ibhayipeptolides, was most abundant in substrate cores of submerged microbialites. These results demonstrate the detection and identification of metabolites from mass-limited environmental samples and contribute knowledge about microbialite chemistry and biology, which facilitates future targeted studies of specialized metabolite function and biosynthesis.

DELVE: feature selection for preserving biological trajectories in single-cell data.

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Integration of Untargeted Metabolomics and Microbial Community Analyses to Characterize Distinct Deep-Sea Methane Seeps.

Deep-sea methane seeps host highly diverse microbial communities whose biological diversity is distinct from other marine habitats. Coupled with microbial community analysis, untargeted metabolomics of environmental samples using high resolution tandem mass spectrometry provides unprecedented access to the unique specialized metabolisms of these chemosynthetic microorganisms. In addition, the diverse microbial natural products are of broad interest due to their potential applications for human and environmental health and well-being. In this exploratory study, sediment cores were collected from two methane seeps (-1000 m water depth) with very different gross geomorphologies, as well as a non-seep control site. Cores were subjected to parallel metabolomic and microbial community analyses to assess the feasibility of representative metabolite detection and identify congruent patterns between metabolites and microbes. Metabolomes generated using high resolution liquid chromatography tandem mass spectrometry were annotated with predicted structure classifications of the majority of mass features using SIRIUS and CANOPUS. The microbiome was characterized by analysis of 16S rRNA genes and analyzed both at the whole community level, as well as the small subgroup of Actinobacteria, which are known to produce societally useful compounds. Overall, the younger Dagorlad seep possessed a greater abundance of metabolites while there was more variation in abundance, number, and distribution of metabolites between samples at the older Emyn Muil seep. Lipid and lipid-like molecules displayed the greatest variation between sites and accounted for a larger proportion of metabolites found at the older seep. Overall, significant differences in composition of the microbial community mirrored the patterns of metabolite diversity within the samples; both varied greatly as a function of distance from methane seep, indicating a deterministic role of seepage. Interdisciplinary research to understand microbial and metabolic diversity is essential for understanding the processes and role of ubiquitous methane seeps in global systems and here we increase understanding of these systems by visualizing some of the chemical diversity that seeps add to marine systems.

Advances in Magnetic Microbead Affinity Selection Screening: Discovery of Natural Ligands to the SARS-CoV-2 Spike Protein.

Affinity selection-mass spectrometry, which includes magnetic microbead affinity selection-screening (MagMASS), is ideal for the discovery of ligands in complex mixtures that bind to pharmacological targets. Therapeutic agents are needed to prevent or treat COVID-19, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infection of human cells by SARS-CoV-2 involves binding of the virus spike protein subunit 1 (S1) to the human cell receptor angiotensin converting enzyme-2 (ACE2). Like antibodies, small molecules have the potential to block the interaction of the viral S1 protein with human ACE2 and prevent SARS-CoV-2 infection. Therefore, a MagMASS assay was developed for the discovery of ligands to the S1 protein. Unlike previous MagMASS approaches, this new assay used robotics for 5-fold enhancement of throughput and sensitivity. The assay was validated using the SBP-1 peptide, which is identical to the ACE2 amino acid sequence recognized by the S1 protein, and then applied to the discovery of natural ligands from botanical extracts. Small molecule ligands to the S1 protein were discovered in extracts of the licorice species, Glycyrrhiza inflata. In particular, the licorice ligand licochalcone A was identified through dereplication and comparison with standards using HPLC with high-resolution tandem mass spectrometry.

Xanthohumol Pyrazole Derivative Improves Diet-Induced Obesity and Induces Energy Expenditure in High-Fat Diet-Fed Mice.

The energy intake exceeding energy expenditure (EE) results in a positive energy balance, leading to storage of excess energy and weight gain. Here, we investigate the potential of a newly synthesized compound as an inducer of EE for the management of diet-induced obesity and insulin resistance. Xanthohumol (XN), a prenylated flavonoid from hops, was used as a precursor for the synthesis of a pyrazole derivative tested for its properties on high-fat diet (HFD)-induced metabolic impairments. In a comparative study with XN, we report that 4-(5-(4-hydroxyphenyl)-1-methyl-1H-pyrazol-3-yl)-5-methoxy-2-(3-methylbut-2-en-1-yl)benzene-1,3-diol (XP) uncouples oxidative phosphorylation in C2C12 cells. In HFD-fed mice, XP improved glucose tolerance and decreased weight gain by increasing EE and locomotor activity. Using an untargeted metabolomics approach, we assessed the effects of treatment on metabolites and their corresponding biochemical pathways. We found that XP and XN reduced purine metabolites and other energy metabolites in the plasma of HFD-fed mice. The induction of locomotor activity was associated with an increase in inosine monophosphate in the cortex of XP-treated mice. Together, these results suggest that XP, better than XN, affects mitochondrial respiration and cellular energy metabolism to prevent obesity in HFD-fed mice.

Autophagy regulates the localization and degradation of p16INK4a.

The tumor suppressor protein p16INK4a (p16) is a well-established hallmark of aging that induces cellular senescence in response to stress. Previous studies have focused primarily on p16 regulation at the transcriptional level; comparatively little is known about the protein's intracellular localization and degradation. The autophagy-lysosomal pathway has been implicated in the subcellular trafficking and turnover of various stress-response proteins and has also been shown to attenuate age-related pathologies, but it is unclear whether p16 is involved in this pathway. Here, we investigate the role of autophagy, vesicular trafficking, and lysosomal degradation on p16 expression and localization in human epithelial cells. Time-lapse fluorescence microscopy using an endogenous p16-mCherry reporter revealed that serum starvation, etoposide, and hydrogen peroxide stimulate autophagy and drive p16 recruitment to acidic cytoplasmic vesicles within 4 hr. Blocking lysosomal proteases with leupeptin and ammonium chloride resulted in the accumulation of p16 within lysosomes and increased total p16 levels suggesting that p16 is degraded by this pathway. Furthermore, autophagy blockers chloroquine and bafilomycin A1 caused p16 aggregation within stalled vesicles containing autophagosome marker LC3. Increase of p16 within these vesicles coincided with the accumulation of LC3-II. Knockdown of autophagosome chaperone p62 attenuated the formation of p16 aggregates in lysosomes, suggesting that p16 is targeted to these vesicles by p62. Taken together, these results implicate the autophagy pathway as a novel regulator of p16 degradation and localization, which could play a role in the etiology of cancer and age-related diseases.

Inheritance of OCT4 predetermines fate choice in human embryonic stem cells.

It is well known that clonal cells can make different fate decisions, but it is unclear whether these decisions are determined during, or before, a cell's own lifetime. Here, we engineered an endogenous fluorescent reporter for the pluripotency factor OCT4 to study the timing of differentiation decisions in human embryonic stem cells. By tracking single-cell OCT4 levels over multiple cell cycle generations, we found that the decision to differentiate is largely determined before the differentiation stimulus is presented and can be predicted by a cell's preexisting OCT4 signaling patterns. We further quantified how maternal OCT4 levels were transmitted to, and distributed between, daughter cells. As mother cells underwent division, newly established OCT4 levels in daughter cells rapidly became more predictive of final OCT4 expression status. These results imply that the choice between developmental cell fates can be largely predetermined at the time of cell birth through inheritance of a pluripotency factor.