We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for highthroughput applications, as it is fast and enables deep and confident proteome coverage when employed in combination with fast chromatographic methods. Proteomics provides the functional links between the genome and metabolome of a cell, and is rapidly gaining importance within both personalised medicine and the emerging field of data-driven biology 1-4. The generation of data is hampered, however, by the inherent complexity of the proteome. In mass spectrometry-based (bottom-up) proteomics, this complexity leads to stochasticity in peptide detection, reducing the proteomic sampling depth 5,6. A popular solution to these issues is to decrease sample complexity by proteome or peptidome pre-fractionation. Extensive fractionation promotes excellent proteome coverage, Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Highlights d A standardized, ultra-high-throughput clinical platform for serum and plasma proteomics d Platform enables high precision quantification of 180 human proteomes per day at low cost d 27 biomarkers are differentially expressed between WHO severity grades for COVID-19 d Biomarkers include proteins not previously associated with COVID-19 infection

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

cWe show here that oxidative stress is involved in both sclerotial differentiation (SD) and aflatoxin B1 biosynthesis in Aspergillus flavus. Specifically, we observed that (i) oxidative stress regulates SD, as implied by its inhibition by antioxidant modulators of reactive oxygen species and thiol redox state, and that (ii) aflatoxin B1 biosynthesis and SD are comodulated by oxidative stress. However, aflatoxin B1 biosynthesis is inhibited by lower stress levels compared to SD, as shown by comparison to undifferentiated A. flavus. These same oxidative stress levels also characterize a mutant A. flavus strain, lacking the global regulatory gene veA. This mutant is unable to produce sclerotia and aflatoxin B1. (iii) Further, we show that hydrogen peroxide is the main modulator of A. flavus SD, as shown by its inhibition by both an irreversible inhibitor of catalase activity and a mimetic of superoxide dismutase activity. On the other hand, aflatoxin B1 biosynthesis is controlled by a wider array of oxidative stress factors, such as lipid hydroperoxide, superoxide, and hydroxyl and thiyl radicals. Humans and animals are exposed to carcinogenic aflatoxins through contaminated food and feed, air, and drinking water (1, 2). Aspergillus flavus is the primary cause of aflatoxin-contaminated crops. A. flavus is a heterothallic fungus, and laboratory crosses produce ascospore-bearing ascocarps embedded within sclerotia. In the field, sclerotia are dispersed during crop harvest and require an additional incubation period on the soil for sexual reproduction (3). Despite the significant contribution of A. flavus to crop aflatoxin contamination, it is not yet known what the role of oxidative stress is for its sclerotial differentiation (SD) and aflatoxin B1 biosynthesis. Deciphering this relationship could contribute to the development of nontoxic antifungal means via the coinhibition of A. flavus SD and aflatoxin B1 biosynthesis.Several toxigenic and phytopathogenic fungi spread and survive in nature through the formation of conidiophores and resistant sclerotia. It has been known that oxidative stress regulates the sclerotial differentiation of filamentous phytopathogenic fungi such as Rhizoctonia solani, Sclerotium rolfsii, Sclerotinia sclerotiorum, and Sclerotinia minor (4, 5). Moreover, it has been established that the regulation of morphogenesis in aspergilli and other fungi is genetically linked to secondary metabolism (6-9). In A. flavus, both SD and aflatoxin biosynthesis are governed by the regulatory protein VeA (10). Deletion of the veA gene in this fungus results in the inhibition of sclerotia formation and aflatoxin biosynthesis (10). However, it is not known whether SD in A. flavus is regulated by oxidative stress and whether the deletion of veA could alter its oxidative stress levels.Previous reports have linked aflatoxin biosynthesis with oxidative stress in A. flavus and A. parasiticus both at the metabolic and transcriptional levels. Specifically, aflatoxin biosynthesis in both species is activated by high ...

The COVID-19 pandemic is an unprecedented global challenge. Highly variable in its presentation, spread and clinical outcome, novel point-of-care diagnostic classifiers are urgently required. Here, we describe a set of COVID-19 clinical classifiers discovered using a newly designed low-cost high-throughput mass spectrometry-based platform. Introducing a new sample preparation pipeline coupled with short-gradient high-flow liquid chromatography and mass spectrometry, our methodology facilitates clinical implementation and increases sample throughput and quantification precision. Providing a rapid assessment of serum or plasma samples at scale, we report 27 biomarkers that distinguish mild and severe forms of COVID-19, of which some may have potential as therapeutic targets. These proteins highlight the role of complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling upstream and downstream of Interleukin 6. Application of novel methodologies hence transforms proteomics from a research tool into a rapid-response, clinically actionable technology adaptable to infectious outbreaks. Highlights-A completely redesigned clinical proteomics platform increases throughput and precision while reducing costs.-27 biomarkers are differentially expressed between WHO severity grades for COVID-19.-The study highlights potential therapeutic targets that include complement factors, the coagulation system, inflammation modulators as well as pro-inflammatory signalling both upstream and downstream of interleukin 6.