Evidence for an RNAi-independent role of Arabidopsis DICER-LIKE2 in growth inhibition and basal antiviral resistance.

Flowering plant genomes encode four or five DICER-LIKE (DCL) enzymes that produce small interfering RNAs (siRNAs) and microRNAs, which function in RNA interference (RNAi). Different RNAi pathways in plants effect transposon silencing, antiviral defense, and endogenous gene regulation. DCL2 acts genetically redundantly with DCL4 to confer basal antiviral defense. However, DCL2 may also counteract DCL4 since knockout of DCL4 causes growth defects that are suppressed by DCL2 inactivation. Current models maintain that RNAi via DCL2-dependent siRNAs is the biochemical basis of both effects. Here, we report that DCL2-mediated antiviral resistance and growth defects cannot be explained by the silencing effects of DCL2-dependent siRNAs. Both functions are defective in genetic backgrounds that maintain high levels of DCL2-dependent siRNAs, either with specific point mutations in DCL2 or with reduced DCL2 dosage because of heterozygosity for dcl2 knockout alleles. Intriguingly, all DCL2 functions require its catalytic activity, and the penetrance of DCL2-dependent growth phenotypes in dcl4 mutants correlates with DCL2 protein levels but not with levels of major DCL2-dependent siRNAs. We discuss this requirement and correlation with catalytic activity but not with resulting siRNAs, in light of other findings that reveal a DCL2 function in innate immunity activation triggered by cytoplasmic double-stranded RNA.

Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application.

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.

Addressing the Protease Bias in Quantitative Proteomics.

Protein quantification strategies using multiple proteases have been shown to deliver poor interprotease accuracy in label-free mass spectrometry experiments. By utilizing six different proteases with different cleavage sites, this study explores the protease bias and its effect on accuracy and precision by using recombinant protein standards. We established 557 SRM assays, using a recombinant protein standard resource, toward 10 proteins in human plasma and determined their concentration with multiple proteases. The quantified peptides of these plasma proteins spanned 3 orders of magnitude (0.02-70 µM). In total, 60 peptides were used for absolute quantification and the majority of the peptides showed high robustness. The retained reproducibility was achieved by quantifying plasma proteins using spiked stable isotope standard recombinant proteins in a targeted proteomics workflow.

Lipid levels correlate with neuronal and dopaminergic markers during the differentiation of SH-SY5Y cells.

Parkinson's Disease (PD) is characterised by the loss of dopaminergic neurons and the deposition of protein inclusions called Lewy Bodies (LBs). LBs are heterogeneous structures composed of protein and lipid molecules and their main constituent is the presynaptic protein α-synuclein. SH-SY5Y cells are neuroblastoma cells commonly used to model PD because they express dopaminergic markers and α-synuclein and they can be differentiated into neuronal cells using established protocols. Despite increasing evidence pointing towards a role of lipids in PD, limited knowledge is available on the lipidome of undifferentiated and differentiated SH-SY5Y cells. Using a combination of lipidomics, proteomics, morphological and electrophysiological measurements, we identified specific lipids, including sphingolipids, whose levels are affected by the differentiation of SH-SY5Y neuroblastoma cells and found that the levels of these lipids correlate with those of neuronal and dopaminergic markers. These results provide a quantitative characterisation of the changes in lipidome associated with the differentiation of SH-SY5Y cells into more neuronal and dopaminergic-like phenotype and serve as a basis for further characterisation of lipid disruptions in association with PD and its risk factors in this dopaminergic-like neuronal cell model.

Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma.

Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.

TET2 lesions enhance the aggressiveness of CEBPA-mutant acute myeloid leukemia by rebalancing GATA2 expression.

The myeloid transcription factor CEBPA is recurrently biallelically mutated (i.e., double mutated; CEBPADM) in acute myeloid leukemia (AML) with a combination of hypermorphic N-terminal mutations (CEBPANT), promoting expression of the leukemia-associated p30 isoform, and amorphic C-terminal mutations. The most frequently co-mutated genes in CEBPADM AML are GATA2 and TET2, however the molecular mechanisms underlying this co-mutational spectrum are incomplete. By combining transcriptomic and epigenomic analyses of CEBPA-TET2 co-mutated patients with models thereof, we identify GATA2 as a conserved target of the CEBPA-TET2 mutational axis, providing a rationale for the mutational spectra in CEBPADM AML. Elevated CEBPA levels, driven by CEBPANT, mediate recruitment of TET2 to the Gata2 distal hematopoietic enhancer thereby increasing Gata2 expression. Concurrent loss of TET2 in CEBPADM AML induces a competitive advantage by increasing Gata2 promoter methylation, thereby rebalancing GATA2 levels. Of clinical relevance, demethylating treatment of Cebpa-Tet2 co-mutated AML restores Gata2 levels and prolongs disease latency.