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Chromogranin A (CgA), a major protein of chromaffin granules, has been described as a potential marker for neuroendocrine tumours. Because of an extensive proteolysis which leads to a large heterogeneity of circulating fragments, its presence in blood has been assessed in most cases either by competitive immunoassays or with polyclonal antibodies. In the present study, 24 monoclonal antibodies were raised against native or recombinant human CgA. Their mapping with proteolytic peptides showed that they defined eight distinct epitopic groups which spanned two-thirds of the C-terminal part of human CgA. All monoclonal antibodies were tested by pair and compared with a reference radioimmunoassay (RIA) involving CGS06, one of the monoclonal antibodies against the 198-245 sequence. It appears that CgA C-terminal end seems to be highly affected by proteolysis and the association of C-terminal and median-part monoclonal antibodies is inadequate for total CgA assessment. Our new immunoradiometric assay involves two monoclonal antibodies, whose contiguous epitopes lie within the median 145-245 sequence. This assay allows a sensitive detection of total human CgA and correlates well with RIA because dibasic cleavage sites present in the central domain do not seem to be affected by degradation. It has been proved to be efficient in measuring CgA levels in patients with neuroendocrine tumours. [ABSTRACT FROM AUTHOR]

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities., Competing Interests: Conflict of interest The authors declare no conflicts of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Coffea arabica, an allotetraploid hybrid of Coffea eugenioides and Coffea canephora, is the source of approximately 60% of coffee products worldwide, and its cultivated accessions have undergone several population bottlenecks. We present chromosome-level assemblies of a di-haploid C. arabica accession and modern representatives of its diploid progenitors, C. eugenioides and C. canephora. The three species exhibit largely conserved genome structures between diploid parents and descendant subgenomes, with no obvious global subgenome dominance. We find evidence for a founding polyploidy event 350,000-610,000 years ago, followed by several pre-domestication bottlenecks, resulting in narrow genetic variation. A split between wild accessions and cultivar progenitors occurred ~30.5 thousand years ago, followed by a period of migration between the two populations. Analysis of modern varieties, including lines historically introgressed with C. canephora, highlights their breeding histories and loci that may contribute to pathogen resistance, laying the groundwork for future genomics-based breeding of C. arabica., (© 2024. The Author(s).)

Smoothing orientation data is a fundamental task in different fields of research. Different methods of smoothing time series in quaternion algebras have been described in the literature, but their application is still an open point. This paper develops a smoothing approach for smoothing quaternion time series to obtain good performance in classification problems. Starting from an existing method which involves an angular velocity transformation of unit quaternion time series, a new method which employ the logarithm function to transform the quaternion time series to a real three-dimensional time series is proposed. Empirical evidences achieved on real data set and artificially noisy data sets confirm the effectiveness of the proposed method compared with the classical approach based on angular velocity transformation. The R functions developed for this paper will be provided in a Github repository., (© 2023. The Author(s).)

Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio-temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi-supervised clustering. We generalize a compromise-based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise-based clustering approach with the method mergeTrees that falls into a sub-class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise-based clustering is a more appropriate approach in this context., (© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)

The recent and sudden outbreak of monkeypox in numerous non-endemic countries requires expanding its surveillance immediately and understanding its origin and spread. As learned from the COVID-19 pandemic, appropriate detection techniques are crucial to achieving such a goal. Mass spectrometry has the advantages of a rapid response, low analytical interferences, better precision, and easier multiplexing to detect various pathogens and their variants. In this proteomic dataset, we report experimental data on the proteome of the monkeypox virus (MPXV) recorded by state-of-the-art shotgun proteomics, including data-dependent and data-independent acquisition for comprehensive coverage. We highlighted 152 viral proteins, corresponding to an overall proteome coverage of 79.5 %. Among the 1371 viral peptides detected, 35 peptides with the most intense signals in mass spectrometry were selected, representing a subset of 13 viral proteins. Their relevance as potential candidate markers for virus detection by targeted mass spectrometry is discussed. This report should assist the rapid development of mass spectrometry-based tests to detect a pathogen of increasing concern., (© 2022 Wiley-VCH GmbH.)

Since the beginning of the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the gastrointestinal (GI) tract has emerged as an important organ influencing the propensity to and potentially the severity of the related COVID-19 disease. However, the contribution of the SARS-CoV-2 intestinal infection on COVID-19 pathogenesis remains to be clarified. In this exploratory study, we highlighted a possible link between alterations in the composition of the gut microbiota and the levels of SARS-CoV-2 RNA in the gastrointestinal tract, which could be more important than the presence of SARS-CoV-2 in the respiratory tract, COVID-19 severity and GI symptoms. As established by metaproteomics, altered molecular functions in the microbiota profiles of high SARS-CoV-2 RNA level faeces highlight mechanisms such as inflammation-induced enterocyte damage, increased intestinal permeability and activation of immune response that may contribute to vicious cycles. Uncovering the role of this gut microbiota dysbiosis could drive the investigation of alternative therapeutic strategies to favour the clearance of the virus and potentially mitigate the effect of the SARS-CoV-2 infection., (© 2022 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.)

Solutions to assess walking deficiencies are widespread and largely used in healthcare. Wearable sensors are particularly appealing, as they offer the possibility to monitor gait in everyday life, outside a facility in which the context of evaluation biases the measure. While some wearable sensors are powerful enough to integrate complex walking activity recognition models, non-invasive lightweight sensors do not always have the computing or memory capacity to run them. In this paper, we propose a walking activity recognition model that offers a viable solution to this problem for any wearable sensors that measure rotational motion of body parts. Specifically, the model was trained and tuned using data collected by a motion sensor in the form of a unit quaternion time series recording the hip rotation over time. This time series was then transformed into a real-valued time series of geodesic distances between consecutive quaternions. Moving average and moving standard deviation versions of this time series were fed to standard machine learning classification algorithms. To compare the different models, we used metrics to assess classification performance (precision and accuracy) while maintaining the detection prevalence at the level of the prevalence of walking activities in the data, as well as metrics to assess change point detection capability and computation time. Our results suggest that the walking activity recognition model with a decision tree classifier yields the best compromise in terms of precision and computation time. The sensor that was used had purposely low computing and memory capacity so that reported performances can be thought of as the lower bounds of what can be achieved. Walking activity recognition is performed online, i.e., on-the-fly, which further extends the range of applicability of our model to sensors with very low memory capacity.

Here, we report the molecular engineering of nanobodies that bind with picomolar affinity to both SARS-CoV-1 and SARS-CoV-2 receptor-binding domains (RBD) and are highly neutralizing. We applied deep mutational engineering to VHH72, a nanobody initially specific for SARS-CoV-1 RBD with little cross-reactivity to SARS-CoV-2 antigen. We first identified all the individual VHH substitutions that increase binding to SARS-CoV-2 RBD and then screened highly focused combinatorial libraries to isolate engineered nanobodies with improved properties. The corresponding VHH-Fc molecules show high affinities for SARS-CoV-2 antigens from various emerging variants and SARS-CoV-1, block the interaction between ACE2 and RBD, and neutralize the virus with high efficiency. Its rare specificity across sarbecovirus relies on its peculiar epitope outside the immunodominant regions. The engineered nanobodies share a common motif of three amino acids, which contribute to the broad specificity of recognition. Our results show that deep mutational engineering is a very powerful method, especially to rapidly adapt existing antibodies to new variants of pathogens.

COVID-19 is the most disturbing pandemic of the past hundred years. Its causative agent, the SARS-CoV-2 virus, has been the subject of an unprecedented investigation to characterize its molecular structure and intimate functioning. While markers for its detection have been proposed and several diagnostic methodologies developed, its propensity to evolve and evade diagnostic tools and the immune response is of great concern. The recent spread of new variants with increased infectivity requires even more attention. Here, we document how shotgun proteomics can be useful for rapidly monitoring the evolution of the SARS-CoV-2 virus. We evaluated the heterogeneity of purified SARS-CoV-2 virus obtained after culturing in the Vero E6 cell line. We found that cell culture induces significant changes that are translated at the protein level, such changes being detectable by tandem mass spectrometry. Production of viral particles requires careful quality control which can be easily performed by shotgun proteomics. Although considered relatively stable so far, the SARS-CoV-2 genome turns out to be prone to frequent variations. Therefore, the sequencing of SARS-CoV-2 variants from patients reporting only the consensus genome after its amplification would deserve more attention and could benefit from more in-depth analysis of low level but crystal-clear signals, as well as complementary and rapid analysis by shotgun proteomics., (© 2021. The Author(s).)

Caffeine is the most consumed alkaloid stimulant in the world. It is synthesized through the activity of three known N-methyltransferase proteins. Here we are reporting on the 422-Mb chromosome-level assembly of the Coffea humblotiana genome, a wild and endangered, naturally caffeine-free, species from the Comoro archipelago. We predicted 32,874 genes and anchored 88.7% of the sequence onto the 11 chromosomes. Comparative analyses with the African Robusta coffee genome (C. canephora) revealed an extensive genome conservation, despite an estimated 11 million years of divergence and a broad diversity of genome sizes within the Coffea genus. In this genome, the absence of caffeine is likely due to the absence of the caffeine synthase gene which converts theobromine into caffeine through an illegitimate recombination mechanism. These findings pave the way for further characterization of caffeine-free species in the Coffea genus and will guide research towards naturally-decaffeinated coffee drinks for consumers.

Wild genetic resources and their ability to adapt to environmental change are critically important in light of the projected climate change, while constituting the foundation of agricultural sustainability. To address the expected negative effects of climate change on Robusta coffee trees (Coffea canephora), collecting missions were conducted to explore its current native distribution in Uganda over a broad climatic range. Wild material from seven forests could thus be collected. We used 19 microsatellite (SSR) markers to assess genetic diversity and structure of this material as well as material from two ex-situ collections and a feral population. The Ugandan C. canephora diversity was then positioned relative to the species' global diversity structure. Twenty-two climatic variables were used to explore variations in climatic zones across the sampled forests. Overall, Uganda's native C. canephora diversity differs from other known genetic groups of this species. In northwestern (NW) Uganda, four distinct genetic clusters were distinguished being from Zoka, Budongo, Itwara and Kibale forests A large southern-central (SC) cluster included Malabigambo, Mabira, and Kalangala forest accessions, as well as feral and cultivated accessions, suggesting similarity in genetic origin and strong gene flow between wild and cultivated compartments. We also confirmed the introduction of Congolese varieties into the SC region where most Robusta coffee production takes place. Identified populations occurred in divergent environmental conditions and 12 environmental variables significantly explained 16.3% of the total allelic variation across populations. The substantial genetic variation within and between Ugandan populations with different climatic envelopes might contain adaptive diversity to cope with climate change. The accessions that we collected have substantially enriched the diversity hosted in the Ugandan collections and thus contribute to ex situ conservation of this vital genetic resource. However, there is an urgent need to develop strategies to enhance complementary in-situ conservation of Coffea canephora in native forests in northwestern Uganda., Competing Interests: DC and LB are employed by Nestlé Centre Tours. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

Alternative methods to RT-PCR for SARS-CoV-2 detection are investigated to provide complementary data on viral proteins, increase the number of tests performed, or identify false positive/negative results. Here, we have developed a simple mass spectrometry assay for SARS-CoV-2 in nasopharyngeal swab samples using common laboratory reagents. The method employs high sensitivity and selectivity targeted mass spectrometry detection, monitoring nine constitutive peptides representative of the three main viral proteins and a straightforward pellet digestion protocol for convenient routine applications. Absolute quantification of N, M, and S proteins was achieved by addition of isotope-labeled versions of best peptides. Limit of detection, recovery, precision, and linearity were thoroughly evaluated in four representative viral transport media (VTM) containing distinct total protein content. The protocol was sensitive in all swab media with limit of detection determined at 2 × 10 3 pfu/mL, corresponding to as low as 30 pfu injected into the LC-MS/MS system. When tested on VTM-stored nasopharyngeal swab samples from positive and control patients, sensitivity was similar to or better than rapid immunoassay dipsticks, revealing a corresponding RT-PCR detection threshold at Ct ∼ 24. The study represents the first thorough evaluation of sensitivity and robustness of targeted mass spectrometry in nasal swabs, constituting a promising SARS-CoV-2 antigen assay for the first-line diagnosis of COVID-19 and compatible with the constraints of clinical settings. The raw files generated in this study can be found on PASSEL (Peptide Atlas) under data set identifier PASS01646.

Background and Purpose: The ever-growing availability of imaging led to increasing incidentally discovered unruptured intracranial aneurysms (UIAs). We leveraged machine-learning techniques and advanced statistical methods to provide new insights into rupture intracranial aneurysm (RIA) risks., Methods: We analysed the characteristics of 2505 patients with intracranial aneurysms (IA) discovered between 2016 and 2019. Baseline characteristics, familial history of IA, tobacco and alcohol consumption, pharmacological treatments before the IA diagnosis, cardiovascular risk factors and comorbidities, headaches, allergy and atopy, IA location, absolute IA size and adjusted size ratio (aSR) were analysed with a multivariable logistic regression (MLR) model. A random forest (RF) method globally assessed the risk factors and evaluated the predictive capacity of a multivariate model., Results: Among 994 patients with RIA (39.7%) and 1511 patients with UIA (60.3 %), the MLR showed that IA location appeared to be the most significant factor associated with RIA (OR, 95% CI: internal carotid artery, reference; middle cerebral artery, 2.72, 2.02-3.58; anterior cerebral artery, 4.99, 3.61-6.92; posterior circulation arteries, 6.05, 4.41-8.33). Size and aSR were not significant factors associated with RIA in the MLR model and antiplatelet-treatment intake patients were less likely to have RIA (OR: 0.74; 95% CI: 0.55-0.98). IA location, age, following by aSR were the best predictors of RIA using the RF model., Conclusions: The location of IA is the most consistent parameter associated with RIA. The use of 'artificial intelligence' RF helps to re-evaluate the contribution and selection of each risk factor in the multivariate model., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) has resulted in a pandemic and is continuing to spread rapidly around the globe. No effective vaccine is currently available to prevent COVID-19, and intense efforts are being invested worldwide into vaccine development. In this context, all technology platforms must overcome several challenges resulting from the use of an incompletely characterized new virus. These include finding the right conditions for virus amplification for the development of vaccines based on inactivated or attenuated whole viral particles. Here, we describe a shotgun tandem mass spectrometry workflow, the data produced can be used to guide optimization of the conditions for viral amplification. In parallel, we analysed the changes occurring in the host cell proteome following SARS-CoV-2 infection to glean information on the biological processes modulated by the virus that could be further explored as potential drug targets to deal with the pandemic.

Rapid but yet sensitive, specific, and high-throughput detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical samples is key to diagnose infected people and to better control the spread of the virus. Alternative methodologies to PCR and immunodiagnostics that would not require specific reagents are worthy to investigate not only for fighting the COVID-19 pandemic but also to detect other emergent pathogenic threats. Here, we propose the use of tandem mass spectrometry to detect SARS-CoV-2 marker peptides in nasopharyngeal swabs. We documented that the signal from the microbiota present in such samples is low and can be overlooked when interpreting shotgun proteomic data acquired on a restricted window of the peptidome landscape. In this proof-of-concept study, simili nasopharyngeal swabs spiked with different quantities of purified SARS-CoV-2 viral material were used to develop a nanoLC-MS/MS acquisition method, which was then successfully applied on COVID-19 clinical samples. We argue that peptides ADETQALPQR and GFYAQGSR from the nucleocapsid protein are of utmost interest as their signal is intense and their elution can be obtained within a 3 min window in the tested conditions. These results pave the way for the development of time-efficient viral diagnostic tests based on mass spectrometry.

The humoral responses of Ebola virus (EBOV) survivors mainly target the surface glycoprotein GP, and anti-GP neutralizing antibodies have been associated with protection against EBOV infection. In order to elicit protective neutralizing antibodies through vaccination a native-like conformation of the antigen is required. We therefore engineered and expressed in CHO cells several GP variants from EBOV (species Zaire ebolavirus , Mayinga variant), including a soluble GP ΔTM, a mucin-like domain-deleted GP ΔTM-ΔMUC, as well as two GP ΔTM-ΔMUC variants with C-terminal trimerization motifs in order to favor their native trimeric conformation. Inclusion of the trimerization motifs resulted in proteins mimicking GP metastable trimer and showing increased stability. The mucin-like domain appeared not to be critical for the retention of the native conformation of the GP protein, and its removal unmasked several neutralizing epitopes, especially in the trimers. The soluble GP variants inhibited mAbs neutralizing activity in a pseudotype transduction assay, further confirming the proteins' structural integrity. Interestingly, the trimeric GPs, a native-like GP complex, showed stronger affinity for antibodies raised by natural infection in EBOV disease survivors rather than for antibodies raised in volunteers that received the ChAd3-EBOZ vaccine. These results support our hypothesis that neutralizing antibodies are preferentially induced when using a native-like conformation of the GP antigen. The soluble trimeric recombinant GP proteins we developed represent a novel and promising strategy to develop prophylactic vaccines against EBOV and other filoviruses., (Copyright © 2020 Agnolon, Kiseljak, Wurm, Wurm, Foissard, Gallais, Wehrle, Muñoz-Fontela, Bellanger, Correia, Corradin and Spertini.)

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a crucial tool for fighting the COVID-19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS-CoV-2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data-dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass-spectrometry method development and diagnostic of the new SARS-CoV-2 is proposed and the best candidates are commented., (© 2020 The Authors. Proteomics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Cephalosporins are of particular importance in human medicine and should be reserved for second-line curative treatment in the veterinary field to avoid any emerging antimicrobial resistance. Due to misuse of ceftiofur in the poultry sector in France, it is now recommended to completely stop using cephalosporins in this sector. Methods currently used for the control of veterinary practices are mostly based on liquid chromatography coupled to mass spectrometry in a targeted mode, including parent compounds and any major metabolites. The aim of the present study was to evaluate the relevance of untargeted metabolomic approaches to highlight a possible exposure of laying hens to cephalosporins using a predictive model including selected treatment biomarkers. An experimentation carried out on living animals involved the administration of cefquinome and ceftiofur. Three biological matrices-droppings, eggs and liver-were investigated. Metabolites were extracted and analysed by liquid chromatography coupled to high resolution mass spectrometry in a full scan mode. Metabolites impacted by the treatment were selected by using univariate and multivariate statistical analyses. Predictive models built from the potential biomarkers selected in the "droppings" matrix were validated and able to classify "treated" and "control" hens. PLS-DA and logistic regression models were compared and both models gave satisfactory results in terms of prediction. Results were of less interest for other matrices in which only biomarkers of exposure to cefquinome were detected., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Coffee species such as Coffea canephora P. (Robusta) and C. arabica L. (Arabica) are important cash crops in tropical regions around the world. C. arabica is an allotetraploid (2n = 4x = 44) originating from a hybridization event of the two diploid species C. canephora and C. eugenioides (2n = 2x = 22). Interestingly, these progenitor species harbour a greater level of genetic variability and are an important source of genes to broaden the narrow Arabica genetic base. Here, we describe the development, evaluation and use of a single-nucleotide polymorphism (SNP) array for coffee trees. A total of 8580 unique and informative SNPs were selected from C. canephora and C. arabica sequencing data, with 40% of the SNP located in annotated genes. In particular, this array contains 227 markers associated to 149 genes and traits of agronomic importance. Among these, 7065 SNPs (~82.3%) were scorable and evenly distributed over the genome with a mean distance of 54.4 Kb between markers. With this array, we improved the Robusta high-density genetic map by adding 1307 SNP markers, whereas 945 SNPs were found segregating in the Arabica mapping progeny. A panel of C. canephora accessions was successfully discriminated and over 70% of the SNP markers were transferable across the three species. Furthermore, the canephora-derived subgenome of C. arabica was shown to be more closely related to C. canephora accessions from northern Uganda than to other current populations. These validated SNP markers and high-density genetic maps will be useful to molecular genetics and for innovative approaches in coffee breeding., (© 2018 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)