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Background: Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. Results: We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database—but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. Conclusions: By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself. Author summary: MetaNovo is an open-source software pipeline that integrates existing tools with a custom algorithm to produce targeted protein sequence databases for mass spectrometry metaproteomic analysis as an intermediate filtering step prior to standard sequence database search approaches. MetaNovo uses open-source tools to match peptide mass spectrometry spectra to sequence database entries in a parallelised and scalable manner and can be installed in a cluster or run standalone on a Linux machine. The software is scalable to the number of input files and search sequence database size. As inputs the software requires raw mass spectrometry data in MGF format, and a UniProt FASTA sequence database to search. The pipeline is relevant to users analysing protein data from multiple organisms, where the exact species composition is unknown, such as microbiome or environmental samples, and provides an avenue for analysis when matched metagenomics data or accurate taxonomic characterisation is not available as it infers the organisms and proteins present directly from the raw data and the parent sequence database. The targeted sequence database produced can be used with standard downstream peptide identification software that relies on a targeted input sequence database to search the raw data against and allows greater sensitivity in peptide spectral matching in metaproteomic datasets. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic continues to affect individuals across the globe, with some individuals experiencing more severe disease than others. The relatively high frequency of re-infections and breakthrough infections observed with SARS-CoV-2 highlights the importance of extending our understanding of immunity to COVID-19. Here, we aim to shed light on the importance of antibody titres and epitope utilization in protection from re-infection. Health care workers are highly exposed to SARS-CoV-2 and are therefore also more likely to become re-infected. We utilized quantitative, multi-antigen, multi-epitope SARS-CoV-2 protein microarrays to measure IgG and IgA titres against various domains of the nucleocapsid and spike proteins. Potential re-infections in a large, diverse health care worker cohort (N = 300) during the second wave of the pandemic were identified by assessing the IgG anti-N titres before and after the second wave. We assessed epitope coverage and antibody titres between the 'single infection' and 're-infection' groups. Clear differences were observed in the breadth of the anti-N response before the second wave, with the epitope coverage for both IgG (p = 0.019) and IgA (p = 0.015) being significantly increased in those who did not become re-infected compared to those who did. Additionally, the IgG anti-N (p = 0.004) and anti-S titres (p = 0.018) were significantly higher in those not re-infected. These results highlight the importance of the breadth of elicited antibody epitope coverage following natural infection in protection from re-infection and disease in the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Vaccines against SARS-CoV-2 have been pivotal in overcoming the COVID-19 pandemic yet understanding the subsequent outcomes and immunological effects remain crucial, especially for at-risk groups e.g., people living with human immunodeficiency virus (HIV) (PLWH). In this study we report the longitudinal IgA and IgG antibody titers, as well as antibody-mediated angiotensin converting enzyme 2 (ACE2) binding blockade, against the SARS-CoV-2 spike (S) proteins after 1 and 2 doses of the ChAdOx1 nCoV-19 vaccine in a population of Black PLWH. Here, we report that PLWH (N = 103) did not produce an anti-S IgA response after infection or vaccination, however, anti-S IgG was detected in response to vaccination and infection, with the highest level detected for infected vaccinated participants. The anti-IgG and ACE2 blockade assays revealed that both vaccination and infection resulted in IgG production, however, only vaccination resulted in a moderate increase in ACE2 binding blockade to the ancestral S protein. Vaccination with a previous infection results in the greatest anti-S IgG and ACE2 blockade for the ancestral S protein. In conclusion, PLWH produce an anti-S IgG response to the ChAdOx1 nCoV-19 vaccine and/or infection, and ChAdOx1 nCoV-19 vaccination with a previous infection produced more neutralizing antibodies than vaccination alone. [ABSTRACT FROM AUTHOR]

Karyopherin beta 1 (Kpnβ1) is the principal nuclear importer of cargo proteins and plays a role in many cellular processes. Its expression is upregulated in cancer and essential for cancer cell viability, thus the identification of its binding partners might help in the discovery of anti-cancer therapeutic targets and cancer biomarkers. Herein, we applied immunoprecipitation coupled to mass spectrometry (IP-MS) to identify Kpnβ1 binding partners in normal and cancer cells. IP-MS identified 100 potential Kpnβ1 binding partners in non-cancer hTERT-RPE1, 179 in HeLa cervical cancer, 147 in WHCO5 oesophageal cancer and 176 in KYSE30 oesophageal cancer cells, including expected and novel interaction partners. 38 binding proteins were identified in all cell lines, with the majority involved in RNA metabolism. 18 binding proteins were unique to the cancer cells, with many involved in protein translation. Western blot analysis validated the interaction of known and novel binding partners with Kpnβ1 and revealed enriched interactions between Kpnβ1 and select proteins in cancer cells, including proteins involved in cancer development, such as Kpnα2, Ran, CRM1, CCAR1 and FUBP1. Together, this study shows that Kpnβ1 interacts with numerous proteins, and its enhanced interaction with certain proteins in cancer cells likely contributes to the cancer state. [ABSTRACT FROM AUTHOR]

In late December 2019, pneumonia cases of unknown origin were reported in Wuhan, China. This virus was named SARS-CoV2 and the clinical syndrome was named coronavirus disease 19 (COVID-19). South Africa, despite strict and early lockdown has the highest infection rate in Africa. A key component of South Africa's response to SARSCoV2 was the rapid scale-up of diagnostic testing. The Abbott SARS-CoV2 assay detects IgG antibodies against the Nucleocapsid (N) protein of the SARS-CoV2 virus. This study undertook to validate and evaluate performance criteria of the Abbott assay and to establish whether this assay would show clinical utility in our population. Positive patients (n = 391) and negative controls (n = 139) were included. The Architect-i and Alinity-i systems were analyzers that were used to perform the SARS-CoV-2 IgG assay. In-house ELISA was incorporated into the study as a confirmatory serology test. A total of number of 530 participants was tested, 87% were symptomatic with infection and 13% were asymptomatic. When compared to RT-qPCR, the sensitivity of Architect and Alinity SARS-CoV2 assays was 69.5% and 64.8%, respectively. Specificity for Architect and Alinity assays was 95% and 90.3%, respectively. The Abbott assay was also compared to in house ELISA assay, with sensitivity for the Architect and Alinity assays of 94.7% and 92.5%, respectively. Specificity for Abbott Alinity assays was 91.7% higher than Abbott Architect 88.1%. Based on the current findings testing of IgG after 14 days is recommended in South Africa and supports other studies performed around the world. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is the leading cause of cancer-related death worldwide. Surgical resection remains the definitive curative treatment for early-stage disease offering an overall 5-year survival rate of 62%. Despite careful case selection, a significant proportion of early-stage cancers relapse aggressively within the first year post-operatively. Identification of these patients is key to accurate prognostication and understanding the biology that drives early relapse might open up potential novel adjuvant therapies.Methods: We performed an unsupervised interrogation of >1600 serum-based autoantibody biomarkers using an iterative machine-learning algorithm.Results: We identified a 13 biomarker signature that was highly predictive for survivorship in post-operative early-stage lung cancer; this outperforms currently used autoantibody biomarkers in solid cancers. Our results demonstrate significantly poor survivorship in high expressers of this biomarker signature with an overall 5-year survival rate of 7.6%.Conclusions: We anticipate that the data will lead to the development of an off-the-shelf prognostic panel and further that the oncogenic relevance of the proteins recognised in the panel may be a starting point for a new adjuvant therapy. [ABSTRACT FROM AUTHOR]

Rheumatic heart disease (RHD) is the major cause of cardiovascular morbidity and mortality in children and young adults in low- and middle-income countries. Acute rheumatic fever (ARF) is characterised by multiorgan infl ammatory symptoms initiated through cross reaction of immune responses (IRs) to group A streptococcus (GAS) proteins to host proteins. Recurrence of these IRs targeting the heart valves may lead to permanent damage, a sequela which is termed RHD. Preliminary studies suggested genetic associations in RF reactions, but that other host factors are also involved, leaving the determinants of RHD progression incompletely understood. Previous clinical and recent epidemiological studies support differential clinical phenotypes, with varying history from different settings. This review summarises the protein-centric biomolecular changes in RHD and highlights outstanding molecular gaps where urgent focus is required to improve our understanding RHD pathophysiology. Numerous studies have confi rmed alterations in the expression of structural and immune response proteins, but the modifi cations giving rise to neo-epitopes and their involvement in RHD have not been established. As RHD is associated with poor living conditions, identifi cation of other factors driving infl ammation to enhance RHD progression is necessary to advance our knowledge and improve patient management. Furthermore, biomarkers for early identifi cation, disease stratifi cation, and alternative therapeutic strategies are necessary to improve treatment and prevention strategies in order to reduce the burden of RHD. Relevance: Despite the explosion of scientifi c innovation over the last few decades, fundamental scientifi c studies to understand the pathophysiological mechanisms of RHD remain in their infancy and the determinants of RHD progression thus remain uncertain. Moreover, inconsistency in natural history and phenotypic presentations are seen between Africans and other cohorts in which preliminary studies were conducted, implying that differences in genetic complexity and environmental factors may be responsible for the differential disease progression rates. [ABSTRACT FROM AUTHOR]

The presence or absence of autoantibodies against citrullinated proteins (ACPAs) distinguishes two main groups of rheumatoid arthritis (RA) patients with different etiologies, prognoses, disease severities, and, presumably, disease pathogenesis. The heterogeneous responses of RA patients to various biologics, even among ACPA-positive patients, emphasize the need for further stratification of the patients. We used high-density protein array technology for fingerprinting of ACPA reactivity. Identification of the proteome recognized by ACPAs may be a step to stratify RA patients according to immune reactivity. Pooled plasma samples from 10 anti-CCP-negative and 15 anti-CCP-positive RA patients were assessed for ACPA content using a modified protein microarray containing 1631 different natively folded proteins citrullinated in situ by protein arginine deiminases (PADs) 2 and PAD4. IgG antibodies from anti-CCP-positive RA plasma showed high-intensity binding to 87 proteins citrullinated by PAD2 and 99 proteins citrullinated by PAD4 without binding significantly to the corresponding native proteins. Curiously, the binding of IgG antibodies in anti-CCP-negative plasma was also enhanced by PAD2- and PAD4-mediated citrullination of 29 and 26 proteins, respectively. For only four proteins, significantly more ACPA binding occurred after citrullination with PAD2 compared to citrullination with PAD4, while the opposite was true for one protein. We demonstrate that PAD2 and PAD4 are equally efficient in generating citrullinated autoantigens recognized by ACPAs. Patterns of proteins recognized by ACPAs may serve as a future diagnostic tool for further subtyping of RA patients. [ABSTRACT FROM AUTHOR]