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The chemistry of boron compounds has been profoundly investigated in the last decades, leading to ubiquity in many synthetic applications as well as biologically active molecules. Since the advent of photoredox catalysis, an upsurge in the discovery of suitable radical precursors has been witnessed, and boron is becoming a protagonist in this field. Despite many studies focused on trifluoroborates, the use of boronic acids and esters have received much less attention in this regard. Because of their high oxidation potential, the development of methods to enable their involvement in photocatalyzed reactions is only recent. Nonetheless, this review summarizes novel strategies developed to unlock their role as radical precursors in photochemical reactions and discloses the potential that boronic acids and esters own, when their intrinsic chemical properties are exploited.

Cyanobactins are linear and cyclic post-translationally modified peptides. Here we show that the prenyl-D-Arg-containing autum-nalamide A is a member of the cyanobactin family. Biochemical assays demonstrate that the AutF prenyltransferase targets the guanidinium moiety in arginine and homoarginine and is a useful tool for biotechnological applications.

Background Patient-derived bulk expression profiles of cancers can provide insight into the transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Hence, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. However, consensus independent component analyses (c-ICA) can capture statistically independent transcriptional footprints of both subtle and more pronounced metabolic processes. Methods We performed c-ICA with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. Gene set enrichment analysis with 608 gene sets that describe metabolic processes was performed to identify the transcriptional components enriched for metabolic processes (mTCs). The activity of these mTCs was determined in all samples to create a metabolic transcriptional landscape. Results A set of 555 mTCs was identified of which many were robust across different datasets, platforms, and patient-derived tissues and cell lines. We demonstrate how the metabolic transcriptional landscape defined by the activity of these mTCs in samples can be used to explore the associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. Conclusions To facilitate the use of our transcriptional metabolic landscape, we have provided access to all data via a web portal (www.themetaboliclandscapeofcancer.com). We believe this resource will contribute to the formulation of new hypotheses on how to metabolically engage the tumor or its (immune) microenvironment.

Next-generation organic photovoltaic technology is currently geared towards non-fullerene organic solar cells. Among the non-fullerene small molecule acceptors, ITIC derivatives play a central role with power conversion efficiency above 15% in single junction cells. However, knowledge about the stability of these new types of devices is lagging behind, creating an efficiency-lifetime gap for commercial viability. Here, we study the photostability of three ITIC derivatives, namely ITIC together with IT-M and IT-F, representative of methylated and halogenated ITIC, the usual modification to this small molecule acceptor. While the best performing solar cell yields a PCE of 8.6%, we find that the photostability of the devices greatly depends on the structure of the acceptor and the configuration of the devices. The methylation of ITIC into IT-M improves Voc but does not greatly affect the photostability of the devices. While fluorination generally decreases the efficiency of the cells, the stability of the fluorinated ITIC-based cells appears to depend on the device structure. Thus, the change from ITIC to IT-F may not in all cases necessarily be beneficial to the advancement of the technology. Subtle changes in molecular structure coupled with imbalance charge mobilities is at the origin of the observed differences in degradation.

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS2PIP and Prosit were combined with the canonical scores from MaxQuant in the Percolator postprocessing tool for protein sequence databases constructed out of ribosome profiling and nanopore RNA-Seq analyses. The presented results provide evidence that this approach enhances both the identification rate as well as the validation stringency in a proteogenomic setting., Graphical abstract, Highlights • First proteogenomics with PSM rescoring using machine learning–predicted spectra • Demonstrated on both ribosome profiling and nanopore RNA-Seq–derived databases • Rescoring leads to elevated stringency and increased identification rates • Rescoring compensates for the search space size issues in proteogenomics, In Brief Proteogenomics suffers from statistical issues as the sequencing information inflates the database size. To compensate for this, rescoring with the machine learning–based spectrum predictors MS2PIP and Prosit was implemented in a proteogenomics approach. This was demonstrated for both ribosome profiling and nanopore RNA-Seq derived databases. Postprocessing with Percolator showed that these techniques result in recovered and often even elevated stringency levels and identification rates. In this way, it allows to validate novel proteoforms through proteogenomics with unsurpassed confidence levels.

The cell types affected by biological perturbations in complex tissues are uncovered by single-cell analysis., We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur outperforms existing methods based on differential gene expression. Augur identified the neural circuits restoring locomotion in mice following spinal cord neurostimulation.