ABSTRACT Glycosylation, the enzymatic addition of carbohydrate moieties to proteins, is essential for immune recognition, protein folding, and disease progression. The structural complexity of glycans and the heterogeneity of glycosylation sites present significant challenges towards accurate identification and quantification, necessitating advanced methodologies for comprehensive characterization. Tandem MS (MS/MS) has emerged as the primary analytical platform for glycomics and glycoproteomics. This review highlights the recent developments in fragmentation techniques, ranging from well-established techniques such as CID/HCD and ETD, to newer and more advanced techniques such as electron-based methods (EThcD), photodissociation strategies (UVPD, IRMPD), and hybrid approaches (sceHCD, EThcD-sceHCD, HCD-pd-ETD), each providing distinct advantages towards glycan structure elucidation and glycosite mapping. This review also discusses emerging computational strategies, especially deep learning for automated interpretation of complex glycomics and glycoproteomics data.

Nature - The primatologist challenged what it meant to be a scientist.

Most bacteria exist in dense aggregates, yet this lifestyle is relatively poorly understood compared with planktonic cultures. This Review explores biophysical models of aggregate development, and how...

Lipidomic profiling generates vast datasets, making manual annotation and trend interpretation complex and time-intensive. The structural and chemical diversity of the lipidome further complicates the analysis. While existing tools support targeted lipid identification, they often lack automated workflows and seamless integration with statistical and bioinformatics tools. Here, we introduce the comprehensive lipidomics automated workflow for multiple reaction monitoring (CLAW-MRM), a platform designed to automate lipid annotation, statistical analysis, and data parsing using custom multiple reaction monitoring (MRM) precursor product ion transitions. CLAW-MRM employs trimmed mean of m-value (TMM) normalization to account for lipid load differences, enabling robust cross-sample comparisons. To evaluate CLAW-MRM’s performance, we analyzed lipid profiles in liver tissues of Alzheimer’s disease (AD) mice and age-matched wild-type controls under conditions of constant and variable tissue mass, assessing the impact of normalization strategies on TMM-normalized lipidomic outcomes. Additionally, we isolated and profiled lipid droplets from individual brain regions of 18- to 24-month-old AD male mice and controls, leveraging nearly 1,500 MRM transitions across 11 lipid classes. Enhancing biological relevance, CLAW-MRM integrates LIGER (lipidome gene enrichment reactions), linking lipid expression with gene activation and suppression patterns. Through CLAW-MRM-based LIGER, we identified metabolic pathways enriched in differentially expressed lipids, offering insights into altered lipid metabolism in AD. To improve usability, CLAW-MRM incorporates a natural language interface powered by large language models, enabling artificial intelligence (AI)-driven user interaction for statistical and bioinformatics analyses. By automating lipid structural identification and integrating AI-assisted bioinformatics, CLAW-MRM provides an end-to-end workflow from data acquisition to interpretation, streamlining high-throughput lipidomics.

Genomic and biochemical analyses of prokaryotic sulfur metabolism identify diverse microorganisms with the capacity to oxidize sulfide using iron(iii).

Abstract. Soil microorganisms often thrive as microcolonies or biofilms within pores of soil aggregates exposed to the soil atmosphere. However, previous s

Experimental perturbation of soil pH leads to a generalizable model of the soil microcosm comprising three functional regimes with distinct mechanisms linking environmental change to metabolite dynamics.

In Escherichia coli, lateral cell-wall expansion during growth occurs by crosslinking of new glycan chains to the existing peptidoglycan network. Here, Liang et al. show a different mechanism in the G...

Human activities are driving rapid environmental change on a global scale, with pollutants playing a critical role in pushing planetary systems beyond safe limits. These changes have profound conseque...

Soil ecosystems form the foundation of planetary health and play a critical role in climate feedback mechanisms. However, they are also vulnerable to the impacts of global change.

Anaerobic microorganisms inhabit diverse anoxic environments and play a fundamental role in global biogeochemical cycles. Expanding our knowledge of these organisms offers the potential to transform our understanding of ecological and evolutionary processes and to uncover new biotechnological applications. Recent advances in DNA sequencing have revealed a striking gap between the vast microbial diversity found in anoxic environments and the small number of anaerobes – fewer than 0.1% of the estimated millions of anaerobic species in nature – that have been cultivated in the laboratory. This challenge reflects the 'great plate count anomaly', in which most microorganisms observed in nature fail to grow in laboratory conditions, and aligns with the 'scout' model, which suggests that only a few opportunistic species thrive under standard cultivation approaches. Overcoming these limitations requires a shift in cultivation strategies. Here, we review recent advances in the cultivation and isolation of novel anaerobes. Specifically, we introduce a growth-curve-guided strategy that uses real-time monitoring of microbial growth (primarily at the species level) to inform approaches that leverage relative growth advantages and improve the recovery of target microorganisms. By prioritizing growth performance over abundance and selectively removing non-target microbes, this approach offers a more predictable and adaptable framework for isolating anaerobes from complex communities.

Rising temperatures change the structure and function of plant microbial communities

Abstract. Plant roots form associations with beneficial and pathogenic soil microorganisms. Although members of the rhizosphere microbiome can protect agai