Defining mitochondrial protein functions through deep multiomic profiling.

Mitochondria are epicentres of eukaryotic metabolism and bioenergetics. Pioneering efforts in recent decades have established the core protein componentry of these organelles1 and have linked their dysfunction to more than 150 distinct disorders2,3. Still, hundreds of mitochondrial proteins lack clear functions4, and the underlying genetic basis for approximately 40% of mitochondrial disorders remains unresolved5. Here, to establish a more complete functional compendium of human mitochondrial proteins, we profiled more than 200 CRISPR-mediated HAP1 cell knockout lines using mass spectrometry-based multiomics analyses. This effort generated approximately 8.3 million distinct biomolecule measurements, providing a deep survey of the cellular responses to mitochondrial perturbations and laying a foundation for mechanistic investigations into protein function. Guided by these data, we discovered that PIGY upstream open reading frame (PYURF) is an S-adenosylmethionine-dependent methyltransferase chaperone that supports both complex I assembly and coenzyme Q biosynthesis and is disrupted in a previously unresolved multisystemic mitochondrial disorder. We further linked the putative zinc transporter SLC30A9 to mitochondrial ribosomes and OxPhos integrity and established RAB5IF as the second gene harbouring pathogenic variants that cause cerebrofaciothoracic dysplasia. Our data, which can be explored through the interactive online MITOMICS.app resource, suggest biological roles for many other orphan mitochondrial proteins that still lack robust functional characterization and define a rich cell signature of mitochondrial dysfunction that can support the genetic diagnosis of mitochondrial diseases.

Improved identification and quantification of peptides in mass spectrometry data via chemical and random additive noise elimination (CRANE).

MOTIVATION: The output of electrospray ionization-liquid chromatography mass spectrometry (ESI-LC-MS) is influenced by multiple sources of noise and major contributors can be broadly categorized as baseline, random and chemical noise. Noise has a negative impact on the identification and quantification of peptides, which influences the reliability and reproducibility of MS-based proteomics data. Most attempts at denoising have been made on either spectra or chromatograms independently, thus, important 2D information is lost because the mass-to-charge ratio and retention time dimensions are not considered jointly. RESULTS: This article presents a novel technique for denoising raw ESI-LC-MS data via 2D undecimated wavelet transform, which is applied to proteomics data acquired by data-independent acquisition MS (DIA-MS). We demonstrate that denoising DIA-MS data results in the improvement of peptide identification and quantification in complex biological samples. AVAILABILITY AND IMPLEMENTATION: The software is available on Github (https://github.com/CMRI-ProCan/CRANE). The datasets were obtained from ProteomeXchange (Identifiers-PXD002952 and PXD008651). Preliminary data and intermediate files are available via ProteomeXchange (Identifiers-PXD020529 and PXD025103). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Real-time health monitoring through urine metabolomics.

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

A Case Study and Methodology for OpenSWATH Parameter Optimization Using the ProCan90 Data Set and 45 810 Computational Analysis Runs.

In the current study, we show how ProCan90, a curated data set of HEK293 technical replicates, can be used to optimize the configuration options for algorithms in the OpenSWATH pipeline. Furthermore, we use this case study as a proof of concept for horizontal scaling of such a pipeline to allow 45 810 computational analysis runs of OpenSWATH to be completed within four and a half days on a budget of US $10 000. Through the use of Amazon Web Services (AWS), we have successfully processed each of the ProCan 90 files with 506 combinations of input parameters. In total, the project consumed more than 340 000 core hours of compute and generated in excess of 26 TB of data. Using the resulting data and a set of quantitative metrics, we show an analysis pathway that allows the calculation of two optimal parameter sets, one for a compute rich environment (where run time is not a constraint), and another for a compute poor environment (where run time is optimized). For the same input files and the compute rich parameter set, we show a 29.8% improvement in the number of quality protein (>2 peptide) identifications found compared to the current OpenSWATH defaults, with negligible adverse effects on quantification reproducibility or drop in identification confidence, and a median run time of 75 min (103% increase). For the compute poor parameter set, we find a 55% improvement in the run time from the default parameter set, at the expense of a 3.4% decrease in the number of quality protein identifications, and an intensity CV decrease from 14.0% to 13.7%.

Oxidative Cross-Coupling of sp(3)- and sp(2)-Hybridized C-H Bonds: Vanadium-Catalyzed Aminomethylation of Imidazo[1,2-a]pyridines.

The vanadium-catalyzed oxidative coupling of substituted 2-arylimidiazo[1,2-a]pyridines to N-methylmorpholine oxide, which acts as both a coupling partner and an oxidant, has been achieved. This reaction was applied to various substituted imidiazo[1,2-a]pyridine and indole substrates, resulting in yields as high as 90%. Mechanistic investigations indicate that the reaction may proceed via a Mannich-type process. This work demonstrates how oxidative aminomethylation can be used as a useful method to introduce tertiary amines into heterocycles, thus providing an alternative method for conventional Mannich-type reactions.

Detection of Medium-Sized Polycyclic Aromatic Hydrocarbons via Fluorescence Energy Transfer.

Reported herein is the use of proximity-induced non-covalent energy transfer for the detection of medium-sized polycyclic aromatic hydrocarbons (PAHs). This energy transfer occurs within the cavity of γ-cyclodextrin in various aqueous environments, including human plasma and coconut water. Highly efficient energy transfer was observed, and the efficiency of the energy transfer is independent of the concentration of γ-cyclodextrin used, demonstrating the importance of hydrophobic binding in facilitating such energy transfer. Low limits of detection were also observed for many of the PAHs investigated, which is promising for the development of fluorescence-based detection schemes.