Single Amino Acid Variants Research Articles

Identification of Novel Genes and Proteoforms in Angiostrongylus costaricensis through a Proteogenomic Approach.

RNA sequencing (RNA-Seq) and mass-spectrometry-based proteomics data are often integrated in proteogenomic studies to assist in the prediction of eukaryote genome features, such as genes, splicing, single-nucleotide (SNVs), and single-amino-acid variants (SAAVs). Most genomes of parasite nematodes are draft versions that lack transcript- and protein-level information and whose gene annotations rely only on computational predictions. Angiostrongylus costaricensis is a roundworm species that causes an intestinal inflammatory disease, known as abdominal angiostrongyliasis (AA). Currently, there is no drug available that acts directly on this parasite, mostly due to the sparse understanding of its molecular characteristics. The available genome of A. costaricensis, specific to the Costa Rica strain, is a draft version that is not supported by transcript- or protein-level evidence. This study used RNA-Seq and MS/MS data to perform an in-depth annotation of the A. costaricensis genome. Our prediction improved the reference annotation with (a) novel coding and non-coding genes; (b) pieces of evidence of alternative splicing generating new proteoforms; and (c) a list of SNVs between the Brazilian (Crissiumal) and the Costa Rica strain. To the best of our knowledge, this is the first time that a multi-omics approach has been used to improve the genome annotation of A. costaricensis. We hope this improved genome annotation can assist in the future development of drugs, kits, and vaccines to treat, diagnose, and prevent AA caused by either the Brazil strain (Crissiumal) or the Costa Rica strain.

Improvement of mutated peptide identification through MS/MS signals searching against the protein libraries generated from transcriptome and translatome

Single-nucleotide variants (SNVs) in the coding region of human genome, termed as nonsynonymous SNVs (nsSNVs), are regarded a causal factor of cancer. The cancer related SNVs have not been widely confirmed at protein level. Translatome is referred to the active mRNA population associated with ribosomes and is measurable through ribosome-nascent chain complex sequencing (RNC-seq). Herein, an approach was proposed, in which the protein library derived from transcriptome or translatome was generated and served for MS/MS signals searching. A cell line of human colorectal cancer, HCT116, was cultured and its mRNAs, ribosome-bound mRNAs and proteins were extracted and measured in parallel. The nsSNVs calling from transcriptome or translatome were taken to construct the protein libraries with single amino acid variants (SAVs). Meanwhile, another protein library with SAVs was made by COSMIC dataset. The MS/MS data were acquired from HCT116 proteins and were searched for the corresponding peptides against the protein libraries derived from transcriptome, translatome and COSMIC, resulting in 258, 146 and 62 SAV peptides identified, respectively. Of these peptides, 96% of peptides come from the translatome library were overlapped with that from transcriptome, whereas only three peptides from the COSMIC library were co-identified by the other two libraries. The corresponding MS/MS spectra of these identified SAV peptides were further assessed by posterior error probability (PEP). The SAV peptides from the transcriptome or translatome library possessed significantly lower PEP values as compared with that from the COSMIC library. Importantly, further evaluation with different PEP thresholds appeared the percentiles of the remaining SAV peptides from translatome consistently higher than that from transcriptome. MS/MS signal searching against the translatome library, therefore, has been proven as an efficient approach to identify SAV peptides.

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.

The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants. E-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101146 human protein single amino acid variants in 13661 proteins, derived from public resources. When tested on a blind set comprising 10266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets. Supplementary data are available at Bioinformatics online.

Disabling Uncompetitive Inhibition of Oncogenic IDH Mutations Drives Acquired Resistance.

Comprehensive scanning of IDH single amino acid variants in base-edited leukemia cells uncovers recurrent mutations conferring resistance to IDH inhibition through disabling NADPH-dependent uncompetitive inhibition. Together with targeted sequencing, structural, and functional studies, we identify a new class of pathogenic mutations and mechanisms for acquired resistance to IDH-targeting cancer therapies. This article is highlighted in the In This Issue feature, p. 1.

Struct2Graph: a graph attention network for structure based predictions of protein–protein interactions

BackgroundDevelopment of new methods for analysis of protein–protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains.ResultsIn this study, we address this problem and describe a PPI analysis based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a fivefold cross validation average accuracy of 99.42%. Moreover, Struct2Graph can potentially identify residues that likely contribute to the formation of the protein–protein complex. The identification of important residues is tested for two different interaction types: (a) Proteins with multiple ligands competing for the same binding area, (b) Dynamic protein–protein adhesion interaction. Struct2Graph identifies interacting residues with 30% sensitivity, 89% specificity, and 87% accuracy.ConclusionsIn this manuscript, we address the problem of prediction of PPIs using a first of its kind, 3D-structure-based graph attention network (code available at https://github.com/baranwa2/Struct2Graph). Furthermore, the novel mutual attention mechanism provides insights into likely interaction sites through its unsupervised knowledge selection process. This study demonstrates that a relatively low-dimensional feature embedding learned from graph structures of individual proteins outperforms other modern machine learning classifiers based on global protein features. In addition, through the analysis of single amino acid variations, the attention mechanism shows preference for disease-causing residue variations over benign polymorphisms, demonstrating that it is not limited to interface residues.

Proteogenomics and Differential Ion Mobility Enable the Exploration of the Mutational Landscape in Colon Cancer Cells.

The sensitivity and depth of proteomic analyses are limited by isobaric ions and interferences that preclude the identification of low abundance peptides. Extensive sample fractionation is often required to extend proteome coverage when sample amount is not a limitation. Ion mobility devices provide a viable alternate approach to resolve confounding ions and improve peak capacity and mass spectrometry (MS) sensitivity. Here, we report the integration of differential ion mobility with segmented ion fractionation (SIFT) to enhance the comprehensiveness of proteomic analyses. The combination of differential ion mobility and SIFT, where narrow windows of ∼m/z 100 are acquired in turn, is found particularly advantageous in the analysis of protein digests and typically provided more than 60% gain in identification compared to conventional single-shot LC-MS/MS. The application of this approach is further demonstrated for the analysis of tryptic digests from different colorectal cancer cell lines where the enhanced sensitivity enabled the identification of single amino acid variants that were correlated with the corresponding transcriptomic data sets.

Single amino acid variations drive functional divergence of cytochrome P450s in Helicoverpa species

Divergence of gene function is a hallmark of evolution, but assessing such divergence in one species or between species requires information on functional alterations of the alleles and homologs. Here, we explore the functional divergence of two paralogs, CYP6AE19 and CYP6AE20, from Helicoverpa armigera, and two close orthologs, CYP6B8 and CYP6B7, from two related species (Helicoverpa zea and H. armigera); although there is high sequence identity within each pair of enzymes, the latter P450 of each pair has lost metabolic competence towards the plant allelochemical xanthotoxin. Multiple chimeric and single/double site mutants were created by exchanging the diverse substrate recognition sites (SRSs) and amino acids within each pair of P450s. Heterologous expression in Sf9 cells and in vitro metabolism studies showed that the exchange of SRS4 swapped the activity of CYP6AE19 and CYP6AE20, and subsequent site-directed mutagenesis demonstrated that the CYP6AE20 V318M substitution causes a gain-of-function towards xanthotoxin. Meanwhile, a single amino acid substitution (L489P) in SRS6 was found to swap activity between the CYP6B orthologs. Sequence alignments of CYP6AE paralogs and all reported insect xanthotoxin-metabolizing P450s suggest M318 and P489 are essential for the catalytic activities of CYP6AE paralogs and CYP6B orthologs, respectively, but P450s in different subfamilies may have different mechanisms towards the same substrate. Our findings demonstrate that a single amino acid substitution can suffice to alter substrate metabolism and this functional divergence resulting from natural mutations will help to further our understanding of the process of natural selection of P450 genes and their role in insect-host plant interactions.

Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads.

Mass spectrometry-based proteome analysis implies matching the mass spectra of proteolytic peptides to amino acid sequences predicted from genomic sequences. Reliability of peptide variant identification in proteogenomic studies is often lacking. We propose a way to interpret shotgun proteomics results, specifically in the data-dependent acquisition mode, as protein sequence coverage by multiple reads as it is done in nucleic acid sequencing for calling of single nucleotide variants. Multiple reads for each sequence position could be provided by overlapping distinct peptides, thus confirming the presence of certain amino acid residues in the overlapping stretch with a lower false discovery rate. Overlapping distinct peptides originate from miscleaved tryptic peptides in combination with their properly cleaved counterparts and from peptides generated by multiple proteases after the same specimen is subject to parallel digestion and analyzed separately. We illustrate this approach using publicly available multiprotease data sets and our own data generated for the HEK-293 cell line digests obtained using trypsin, LysC, and GluC proteases. Totally, up to 30% of the whole proteome was covered by tryptic peptides with up to 7% covered twofold and more. The proteogenomic analysis of the HEK-293 cell line revealed 36 single amino acid variants, seven of which were supported by multiple reads.

DDGun: an untrained predictor of protein stability changes upon amino acid variants.

Estimating the functional effect of single amino acid variants in proteins is fundamental for predicting the change in the thermodynamic stability, measured as the difference in the Gibbs free energy of unfolding, between the wild-type and the variant protein (ΔΔG). Here, we present the web-server of the DDGun method, which was previously developed for the ΔΔG prediction upon amino acid variants. DDGun is an untrained method based on basic features derived from evolutionary information. It is antisymmetric, as it predicts opposite ΔΔG values for direct (A → B) and reverse (B → A) single and multiple site variants. DDGun is available in two versions, one based on only sequence information and the other one based on sequence and structure information. Despite being untrained, DDGun reaches prediction performances comparable to those of trained methods. Here we make DDGun available as a web server. For the web server version, we updated the protein sequence database used for the computation of the evolutionary features, and we compiled two new data sets of protein variants to do a blind test of its performances. On these blind data sets of single and multiple site variants, DDGun confirms its prediction performance, reaching an average correlation coefficient between experimental and predicted ΔΔG of 0.45 and 0.49 for the sequence-based and structure-based versions, respectively. Besides being used for the prediction of ΔΔG, we suggest that DDGun should be adopted as a benchmark method to assess the predictive capabilities of newly developed methods. Releasing DDGun as a web-server, stand-alone program and docker image will facilitate the necessary process of method comparison to improve ΔΔG prediction.

Multiaspect Examinations of Possible Alternative Mappings of Identified Variant Peptides: A Case Study on the HEK293 Cell Line.

Adopting proteogenomics approach to validate single nucleotide variation events by identifying corresponding single amino acid variant peptides from mass spectrometry (MS)-based proteomics data facilitates translational and clinical research. Although variant peptides are usually identified from MS data with a stringent false discovery rate (FDR), FDR control could fail to eliminate dubious results caused by several issues; thus, postexamination to eliminate dubious results is required. However, comprehensive postexaminations of identification results are still lacking. Therefore, we propose a framework of three bottom-up levels, peptide–spectrum match, peptide, and variant event levels, that consists of rigorous 11-aspect examinations from the MS perspective to further confirm the reliability of variant events. As a proof of concept and showing feasibility, we demonstrate 11 examinations on the identified variant peptides from an HEK293 cell line data set, where various database search strategies were applied to maximize the number of identified variant PSMs with an FDR <1% for postexaminations. The results showed that only FDR criterion is insufficient to validate identified variant peptides and the 11 postexaminations can reveal low-confidence variant events detected by shotgun proteomics experiments. Therefore, we suggest that postexaminations of identified variant events based on the proposed framework are necessary for proteogenomics studies.

Analysis of Integrin αIIb Subunit Dynamics Reveals Long-Range Effects of Missense Mutations on Calf Domains.

Integrin αIIbβ3, a glycoprotein complex expressed at the platelet surface, is involved in platelet aggregation and contributes to primary haemostasis. Several integrin αIIbβ3 polymorphisms prevent the aggregation that causes haemorrhagic syndromes, such as Glanzmann thrombasthenia (GT). Access to 3D structure allows understanding the structural effects of polymorphisms related to GT. In a previous analysis using Molecular Dynamics (MD) simulations of αIIb Calf-1 domain structure, it was observed that GT associated with single amino acid variation affects distant loops, but not the mutated position. In this study, experiments are extended to Calf-1, Thigh, and Calf-2 domains. Two loops in Calf-2 are unstructured and therefore are modelled expertly using biophysical restraints. Surprisingly, MD revealed the presence of rigid zones in these loops. Detailed analysis with structural alphabet, the Proteins Blocks (PBs), allowed observing local changes in highly flexible regions. The variant P741R located at C-terminal of Calf-1 revealed that the Calf-2 presence did not affect the results obtained with isolated Calf-1 domain. Simulations for Calf-1 + Calf-2, and Thigh + Calf-1 variant systems are designed to comprehend the impact of five single amino acid variations in these domains. Distant conformational changes are observed, thus highlighting the potential role of allostery in the structural basis of GT.

HPMPdb: A machine learning-ready database of protein molecular phenotypes associated to human missense variants.

Current human Single Amino acid Variants (SAVs) databases provide a link between a SAVs and their effect on the carrier individual phenotype, often dividing them into Deleterious/Neutral variants. This is a very coarse-grained description of the genotype-to-phenotype relationship because it relies on un-realistic assumptions such as the perfect Mendelian behavior of each SAV and considers only dichotomic phenotypes. Moreover, the link between the effect of a SAV on a protein (its molecular phenotype) and the individual phenotype is often very complex, because multiple level of biological abstraction connect the protein and individual level phenotypes. Here we present HPMPdb, a manually curated database containing human SAVs associated with the detailed description of the molecular phenotype they cause on the affected proteins. With particular regards to machine learning (ML), this database can be used to let researchers go beyond the existing Deleterious/Neutral prediction paradigm, allowing them to build molecular phenotype predictors instead. Our class labels describe in a succinct way the effects that each SAV has on 15 protein molecular phenotypes, such as protein-protein interaction, small molecules binding, function, post-translational modifications (PTMs), sub-cellular localization, mimetic PTM, folding and protein expression. Moreover, we provide researchers with all necessary means to re-producibly train and test their models on our database. The webserver and the data described in this paper are available at hpmp.esat.kuleuven.be.

Molecular diagnosis of three outbreaks during three successive years (2018, 2019, and 2020) of Lumpy skin disease virus in cattle in Sharkia Governorate, Egypt.

Background:Lumpy skin disease (LSD) is endemic in Egypt despite the Egyptian authorities’ annual mass vaccination of cattle with sheeppox vaccine (Veterinary Serum and Vaccine Research Institute, Egypt), and the LSD virus (LSDV) continues to thrive practically every summer. The disease has a huge economic impact on the trade of the animal and its by-product.Aim:This paper study the molecular characterization of LSDV strains that have been circulating in Sharkia Governorate, Egypt, for three successive years (2018, 2019, and 2020).Methods:A total of 61 specimens (26 skin nodules and 35 oculonasal swabs) were collected from a clinic in the hospital of veterinary medicine, Zagazig University, during the summer months (July, August, and September) of three outbreaks in 2018, 2019, and 2020. These were examined by polymerase chain reaction (PCR) based on the open reading frame 103 (ORF103) gene to confirm the suspected cases and determine the degree of homology between the three different outbreaks during three successive years between each other and between the derived sequences of GenBank.Results:Cattle is thought to be infected with LSDV due to the presence of scattered local or diffuse circumscribed skin nodules along with fever and lymph node enlargement and sometimes leg edema. The PCR approach proved rapid, sensitive, and specific for the detection of LSDV and confirmative diagnosis of the disease. Forty-six were detected to be positive by PCR (75.4%). The seven sequenced samples were translated to amino acid and registered in GenBank under accession number MW357655-MW357661. A single nucleotide mutation and amino acid variation were observed at positions 161 C (2020)/T (2018&2019) and consequently, a change in the amino acid at position 54 P (2020)/L (2018&2019) between the outbreak in 2020 and those in 2018 and 2019, respectively. The field LSDV isolates from Egypt cattle were more closely related to other LSDV sequences from Africa (Kenya), Asia, Europe, and the United States.These findings highlight the necessity of ongoing surveillance and characterization of circulating strains and the need to improve procedures for distinguishing vaccine strains from field viruses.

Embeddings from protein language models predict conservation and variant effects

The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient—MCC—for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA, and PredictProtein.

Insight on molecular pathogenesis and pharmacochaperoning potential in phosphomannomutase 2 deficiency, provided by novel human phosphomannomutase 2 structures.

Phosphomannomutase 2 (PMM2) deficiency, the most frequent congenital disorder of glycosylation (PMM2-CDG), is a severe condition, which has no cure. Due to the identification of destabilizing mutations, our group aims at increasing residual activity in PMM2-CDG patients, searching for pharmacochaperones. Detailed structural knowledge of hPMM2 might help identify variants amenable to pharmacochaperoning. hPMM2 structural information is limited to one incomplete structure deposited in the Protein Databank without associated publication, which lacked ligands and residues from a crucial loop. Here we report five complete crystal structures of hPMM2, three for wild-type and two for the p.Thr237Met variant frequently found among Spanish PMM2-CDG patients, free and bound to the essential activator glucose-1,6-bisphosphate (Glc-1,6-P2 ). In the hPMM2 homodimer, each subunit has a different conformation, reflecting movement of the distal core domain relative to the dimerization cap domain, supporting an opening/closing process during catalysis. Two Mg2+ ions bind to the core domain, one catalytic and one structural. In the cap domain, the site for Glc-1,6-P2 is well delineated, while a Cl- ion binding at the intersubunit interface is predicted to strengthen dimerization. Patient-found amino acid substitutions are nonhomogeneously distributed throughout hPMM2, reflecting differential functional or structural importance for various parts of the protein. We classify 93 of 101 patient-reported single amino acid variants according to five potential pathogenetic mechanism affecting folding of the core and cap domains, linker 2 flexibility, dimerization, activator binding, and catalysis. We propose that ~80% and ~50% of the respective core and cap domains substitutions are potential candidates for pharmacochaperoning treatment.

Improving the Genome Annotation of Rhizoctonia solani Using Proteogenomics

Background Rhizoctonia solani is a pathogenic fungus that causes serious diseases in many crops, including rice, wheat, and soybeans. In crop production, it is very important to understand the pathogenicity of this fungus, which is still elusive. It might be helpful to comprehensively understand its genomic information using different genome annotation strategies.MethodsAiming to improve the genome annotation of R. solani, we performed a proteogenomic study based on the existing data. Based on our study, a total of 1060 newly identified genes, 36 revised genes, 139 single amino acid variants (SAAVs), 8 alternative splicing genes, and diverse post-translational modifications (PTMs) events were identified in R. solani AG3. Further functional annotation on these 1060 newly identified genes was performed through homology analysis with its 5 closest relative fungi.ResultsBased on this, 2 novel candidate pathogenic genes, which might be associated with pathogen-host interaction, were discovered. In addition, in order to increase the reliability and novelty of the newly identified genes in R. solani AG3, 1060 newly identified genes were compared with the newly published available R. solani genome sequences of AG1, AG2, AG4, AG5, AG6, and AG8. There are 490 homologous sequences. We combined the proteogenomic results with the genome alignment results and finally identified 570 novel genes in R. solani.ConclusionThese findings extended R. solani genome annotation and provided a wealth of resources for research on R. solani.

The Impact of Rare Human Variants on Barrier-To-Auto-Integration Factor 1 (Banf1) Structure and Function.

Barrier-to-Autointegration Factor 1 (Banf1/BAF) is a critical component of the nuclear envelope and is involved in the maintenance of chromatin structure and genome stability. Banf1 is a small DNA binding protein that is conserved amongst multicellular eukaryotes. Banf1 functions as a dimer, and binds non-specifically to the phosphate backbone of DNA, compacting the DNA in a looping process. The loss of Banf1 results in loss of nuclear envelope integrity and aberrant chromatin organisation. Significantly, mutations in Banf1 are associated with the severe premature ageing syndrome, Néstor–Guillermo Progeria Syndrome. Previously, rare human variants of Banf1 have been identified, however the impact of these variants on Banf1 function has not been explored. Here, using in silico modelling, biophysical and cell-based approaches, we investigate the effect of rare human variants on Banf1 structure and function. We show that these variants do not significantly alter the secondary structure of Banf1, but several single amino acid variants in the N- and C-terminus of Banf1 impact upon the DNA binding ability of Banf1, without altering Banf1 localisation or nuclear integrity. The functional characterisation of these variants provides further insight into Banf1 structure and function and may aid future studies examining the potential impact of Banf1 function on nuclear structure and human health.

Mass spectrometry-based targeted proteomics for analysis of protein mutations.

Cancers are caused by accumulated DNA mutations. This recognition of the central role of mutations in cancer and recent advances in next-generation sequencing, has initiated the massive screening of clinical samples and the identification of 1000s of cancer-associated gene mutations. However, proteomic analysis of the expressed mutation products lags far behind genomic (transcriptomic) analysis. With comprehensive global proteomics analysis, only a small percentage of single nucleotide variants detected by DNA and RNA sequencing have been observed as single amino acid variants due to current technical limitations. Proteomic analysis of mutations is important with the potential to advance cancer biomarker development and the discovery of new therapeutic targets for more effective disease treatment. Targeted proteomics using selected reaction monitoring (also known as multiple reaction monitoring) and parallel reaction monitoring, has emerged as a powerful tool with significant advantages over global proteomics for analysis of protein mutations in terms of detection sensitivity, quantitation accuracy and overall practicality (e.g., reliable identification and the scale of quantification). Herein we review recent advances in the targeted proteomics technology for enhancing detection sensitivity and multiplexing capability and highlight its broad biomedical applications for analysis of protein mutations in human bodily fluids, tissues, and cell lines. Furthermore, we review recent applications of top-down proteomics for analysis of protein mutations. Unlike the commonly used bottom-up proteomics which requires digestion of proteins into peptides, top-down proteomics directly analyzes intact proteins for more precise characterization of mutation isoforms. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale targeted detection and quantification of important protein mutations are discussed.

Repatriation of a patient with COVID-19 contributed to the importation of an emerging carbapenemase producer

ObjectivesPatients hospitalised abroad can become colonised with multidrug-resistant bacteria and import them to their home countries. In this study, we characterised an OXA-484 carbapenemase-producing Escherichia coli strain from a Swiss patient infected by SARS-CoV-2 and repatriated from India.MethodsAt admission to Switzerland (April 2021), the patient undertook a nasopharyngeal swab to search for SARS-CoV-2 and a rectal swab to detect multidrug-resistant bacteria. Both SARS-CoV-2 and E. coli isolates were whole-genome sequenced and analysed for phylogenetic relatedness.ResultsThe patient was infected with the SARS-CoV-2 B.1.617.2 lineage (VOC Delta), a lineage that began to be reported across Switzerland at that time. He was also colonised with a sequence type 410 (ST410) E. coli strain (L3452210II) producing OXA-484, a single amino acid variant of OXA-181. The blaOXA-484 gene was carried by a 51.5 kb IncX3 plasmid identical to those described in blaOXA-181-harbouring ST410 E. coli strains. Core genome analysis showed that L3452210II was identical (ΔSNV ≤23) to two ST410 OXA-484 producers recently reported in Qatar and Germany, but differed from other ST410 OXA-181 producers reported worldwide.ConclusionThe patient was infected by an emerging SARS-CoV-2 variant and also imported an E. coli producing OXA-484, an OXA-48-like carbapenemase not yet reported in Switzerland. The genetic background of L3452210II indicated that blaOXA-484 shared the same plasmid as blaOXA-181, but its bacterial host differed from most of the pandemic OXA-181-producing ST410 strains reported previously. This case description underlines that the COVID-19 crisis can contribute to the worldwide spread of emerging carbapenemase producers.

Plant death caused by inefficient induction of antiviral R-gene-mediated resistance may function as a suicidal population resistance mechanism

Land plant genomes carry tens to hundreds of Resistance (R) genes to combat pathogens. The induction of antiviral R-gene-mediated resistance often results in a hypersensitive response (HR), which is characterized by virus containment in the initially infected tissues and programmed cell death (PCD) of the infected cells. Alternatively, systemic HR (SHR) is sometimes observed in certain R gene–virus combinations, such that the virus systemically infects the plant and PCD induction follows the spread of infection, resulting in systemic plant death. SHR has been suggested to be the result of inefficient resistance induction; however, no quantitative comparison has been performed to support this hypothesis. In this study, we report that the average number of viral genomes that establish cell infection decreased by 28.7% and 12.7% upon HR induction by wild-type cucumber mosaic virus and SHR induction by a single-amino acid variant, respectively. These results suggest that a small decrease in the level of resistance induction can change an HR to an SHR. Although SHR appears to be a failure of resistance at the individual level, our simulations imply that suicidal individual death in SHR may function as an antiviral mechanism at the population level, by protecting neighboring uninfected kin plants.