Tuberculosis: current challenges and beyond.

Despite being a preventable and curable disease, tuberculosis (TB) is still a major global health threat and the second leading cause of death due to an infectious agent worldwide. All the efforts invested to end TB have resulted overall in rather slow decreases in TB incidence and mortality rates, which have been further negatively affected by the ongoing coronavirus disease 2019 (COVID-19) pandemic. While the majority of targets of the End TB Strategy remain off track, and we have not yet overcome the disruptions caused by the COVID-19 pandemic, recent conflicts such as the ongoing war in Ukraine are threatening the decrease of the burden of TB even further. To get back on track and get closer to ending TB, we need urgent, global, well-structured and committed multi-sectoral actions that go beyond national and global TB programmes with the support of deep investments in research and facilitation of equitable and rapid implementation of innovation worldwide.

Implementing molecular tuberculosis diagnostic methods in limited-resource and high-burden countries.

Tuberculosis (TB) is one of the deadliest infectious diseases in the world with more than a million people dying of TB each year. Accurate and timely TB diagnosis has the potential to alleviate the global TB burden; therefore, one of the pillars of the End TB Strategy developed by the World Health Organization (WHO) is the early diagnosis of TB, including universal drug-susceptibility testing (DST). The WHO emphasises the importance of DST before treatment initiation, using molecular WHO-recommended rapid diagnostic tests (mWRDs). Currently available mWRDs are nucleic acid amplification tests, line probe assays, whole genome sequencing, and targeted next-generation sequencing. However, implementing the sequencing mWRDs in routine laboratories in low-income countries is constrained by the existing infrastructure, high cost, the specialised skills needed, data storage, and the current delay in results compared with other routine methods. These limitations are pronounced in resource-limited settings, which often have a high TB burden and need for innovative TB diagnostic technologies. In this article we propose several possible solutions, like adapting infrastructure capacity to needs, advocating for lowering costs, building bioinformatics and laboratory capacity, and increasing the use of open-access resources for software and publications.

Unsupervised outlier detection applied to SARS-CoV-2 nucleotide sequences can identify sequences of common variants and other variants of interest.

As of June 2022, the GISAID database contains more than 11 million SARS-CoV-2 genomes, including several thousand nucleotide sequences for the most common variants such as delta or omicron. These SARS-CoV-2 strains have been collected from patients around the world since the beginning of the pandemic. We start by assessing the similarity of all pairs of nucleotide sequences using the Jaccard index and principal component analysis. As shown previously in the literature, an unsupervised cluster analysis applied to the SARS-CoV-2 genomes results in clusters of sequences according to certain characteristics such as their strain or their clade. Importantly, we observe that nucleotide sequences of common variants are often outliers in clusters of sequences stemming from variants identified earlier on during the pandemic. Motivated by this finding, we are interested in applying outlier detection to nucleotide sequences. We demonstrate that nucleotide sequences of common variants (such as alpha, delta, or omicron) can be identified solely based on a statistical outlier criterion. We argue that outlier detection might be a useful surveillance tool to identify emerging variants in real time as the pandemic progresses.

Increased protease-activated receptor 1 autoantibodies are associated with severe COVID-19.

In patients with severe #COVID19, increased levels of autoantibodies against PAR1 were found. These might serve as allosteric agonists of PAR1 on endothelial cells and platelets, and thus might contribute to the pathogenesis of microthrombosis in COVID-19. https://bit.ly/3pqM9Vv.

Increased protease-activated receptor 1 autoantibodies are associated with severe COVID-19

Immune perturbation is a hallmark of Coronavirus Disease 2019 (COVID-19) with ambiguous roles of various immune cell compartments. Plasma cells, responsible for antibody production, have a two-pronged response while mounting an immune defence with 1) physiological immune response producing neutralizing antibodies against protein structures of SARS-CoV-2 and 2) potentially deleterious autoantibody generation. Growing evidence hints towards broad activation of plasma cells and the presence of pathologic autoantibodies (abs) that mediate immune perturbation in acute COVID-19 [1]. Recently, a systematic screening for abs confirmed induction of diverse functional abs in SARS-CoV-2 infection, targeting several immunomodulatory proteins, including cytokines/chemokines and their respective G-protein coupled receptors (GPCR) [1]. Abs against GPCR act as agonistic and allosteric receptor modulators and are linked to chronic inflammatory diseases [2] and, as we recently demonstrated, disease severity in acute COVID-19 [3].

Cross-reactive immunity against the SARS-CoV-2 Omicron variant is low in pediatric patients with prior COVID-19 or MIS-C.

Neutralization capacity of antibodies against Omicron after a prior SARS-CoV-2 infection in children and adolescents is not well studied. Therefore, we evaluated virus-neutralizing capacity against SARS-CoV-2 Alpha, Beta, Gamma, Delta and Omicron variants by age-stratified analyses (<5, 5-11, 12-21 years) in 177 pediatric patients hospitalized with severe acute COVID-19, acute MIS-C, and in convalescent samples of outpatients with mild COVID-19 during 2020 and early 2021. Across all patients, less than 10% show neutralizing antibody titers against Omicron. Children <5 years of age hospitalized with severe acute COVID-19 have lower neutralizing antibodies to SARS-CoV-2 variants compared with patients >5 years of age. As expected, convalescent pediatric COVID-19 and MIS-C cohorts demonstrate higher neutralization titers than hospitalized acute COVID-19 patients. Overall, children and adolescents show some loss of cross-neutralization against all variants, with the most pronounced loss against Omicron. In contrast to SARS-CoV-2 infection, children vaccinated twice demonstrated higher titers against Alpha, Beta, Gamma, Delta and Omicron. These findings can influence transmission, re-infection and the clinical disease outcome from emerging SARS-CoV-2 variants and supports the need for vaccination in children.

Unsupervised cluster analysis of SARS-CoV-2 genomes indicates that recent (June 2020) cases in Beijing are from a genetic subgroup that consists of mostly European and South(east) Asian samples, of which the latter are the most recent

Research efforts of the ongoing SARS-CoV-2 pandemic have focused on viral genome sequence analysis to understand how the virus spread across the globe. Here, we assess three recently identified SARS-CoV-2 genomes in Beijing from June 2020 and attempt to determine the origin of these genomes, made available in the GISAID database. The database contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Including the three new samples and excluding samples with missing annotations, we analyzed 7, 643 SARS-CoV-2 genomes. Using principal component analysis computed on a similarity matrix that compares all pairs of the SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index, we find that the newly discovered virus genomes from Beijing are in a genetic cluster that consists mostly of cases from Europe and South(east) Asia. The sequences of the new cases are most related to virus genomes from a small number of cases from China (March 2020), cases from Europe (February to early May 2020), and cases from South(east) Asia (May to June 2020). These findings could suggest that the original cases of this genetic cluster originated from China in March 2020 and were re-introduced to China by transmissions from samples from South(east) Asia between April and June 2020.

Unsupervised cluster analysis of SARS-CoV-2 genomes reflects its geographic progression and identifies distinct genetic subgroups of SARS-CoV-2 virus

Over 10,000 viral genome sequences of the SARS-CoV-2 virus have been made readily available during the ongoing coronavirus pandemic since the initial genome sequence of the virus was released on the open access Virological website ( http://virological.org/ ) early on January 11. We utilize the published data on the single stranded RNAs of 11, 132 SARS-CoV-2 patients in the GISAID (Elbe and Buckland-Merrett, 2017;Shu and McCauley, 2017) database, which contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Among many important research questions which are currently being investigated, one aspect pertains to the genetic characterization/classification of the virus. We analyze data on the nucleotide sequencing of the virus and geographic information of a subset of 7, 640 SARS-CoV-2 patients without missing entries that are available in the GISAID database. Instead of modelling the mutation rate, applying phylogenetic tree approaches, etc., we here utilize a model-free clustering approach that compares the viruses at a genome-wide level. We apply principal component analysis to a similarity matrix that compares all pairs of these SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index (Jaccard, 1901;Tan et al., 2005;Prokopenko et al., 2016;Schlauch et al., 2017). Our analysis results of the SARS-CoV-2 genome data illustrates the geographic and chronological progression of the virus, starting from the first cases that were observed in China to the current wave of cases in Europe and North America. We also observe that, based on their sequence data, the SARS-CoV-2 viruses cluster in distinct genetic subgroups. It is the subject of ongoing research to examine whether the genetic subgroup could be related to diseases outcome and its potential implications for vaccine development.

Genome-wide association analysis of COVID-19 mortality risk in SARS-CoV-2 genomes identifies mutation in the SARS-CoV-2 spike protein that colocalizes with P.1 of the Brazilian strain.

SARS-CoV-2 mortality has been extensively studied in relation to host susceptibility. How sequence variations in the SARS-CoV-2 genome affect pathogenicity is poorly understood. Starting in October 2020, using the methodology of genome-wide association studies (GWAS), we looked at the association between whole-genome sequencing (WGS) data of the virus and COVID-19 mortality as a potential method of early identification of highly pathogenic strains to target for containment. Although continuously updating our analysis, in December 2020, we analyzed 7548 single-stranded SARS-CoV-2 genomes of COVID-19 patients in the GISAID database and associated variants with mortality using a logistic regression. In total, evaluating 29,891 sequenced loci of the viral genome for association with patient/host mortality, two loci, at 12,053 and 25,088 bp, achieved genome-wide significance (p values of 4.09e-09 and 4.41e-23, respectively), though only 25,088 bp remained significant in follow-up analyses. Our association findings were exclusively driven by the samples that were submitted from Brazil (p value of 4.90e-13 for 25,088 bp). The mutation frequency of 25,088 bp in the Brazilian samples on GISAID has rapidly increased from about 0.4 in October/December 2020 to 0.77 in March 2021. Although GWAS methodology is suitable for samples in which mutation frequencies varies between geographical regions, it cannot account for mutation frequencies that change rapidly overtime, rendering a GWAS follow-up analysis of the GISAID samples that have been submitted after December 2020 as invalid. The locus at 25,088 bp is located in the P.1 strain, which later (April 2021) became one of the distinguishing loci (precisely, substitution V1176F) of the Brazilian strain as defined by the Centers for Disease Control. Specifically, the mutations at 25,088 bp occur in the S2 subunit of the SARS-CoV-2 spike protein, which plays a key role in viral entry of target host cells. Since the mutations alter amino acid coding sequences, they potentially imposing structural changes that could enhance viral infectivity and symptom severity. Our analysis suggests that GWAS methodology can provide suitable analysis tools for the real-time detection of new more transmissible and pathogenic viral strains in databases such as GISAID, though new approaches are needed to accommodate rapidly changing mutation frequencies over time, in the presence of simultaneously changing case/control ratios. Improvements of the associated metadata/patient information in terms of quality and availability will also be important to fully utilize the potential of GWAS methodology in this field.

Unsupervised cluster analysis of SARS-CoV-2 genomes reflects its geographic progression and identifies distinct genetic subgroups of SARS-CoV-2 virus.

Over 10,000 viral genome sequences of the SARS-CoV-2virus have been made readily available during the ongoing coronavirus pandemic since the initial genome sequence of the virus was released on the open access Virological website (http://virological.org/) early on January 11. We utilize the published data on the single stranded RNAs of 11,132 SARS-CoV-2 patients in the GISAID database, which contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Among many important research questions which are currently being investigated, one aspect pertains to the genetic characterization/classification of the virus. We analyze data on the nucleotide sequencing of the virus and geographic information of a subset of 7640 SARS-CoV-2 patients without missing entries that are available in the GISAID database. Instead of modeling the mutation rate, applying phylogenetic tree approaches, and so forth, we here utilize a model-free clustering approach that compares the viruses at a genome-wide level. We apply principal component analysis to a similarity matrix that compares all pairs of these SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index. Our analysis results of the SARS-CoV-2 genome data illustrates the geographic and chronological progression of the virus, starting from the first cases that were observed in China to the current wave of cases in Europe and North America. This is in line with a phylogenetic analysis which we use to contrast our results. We also observe that, based on their sequence data, the SARS-CoV-2 viruses cluster in distinct genetic subgroups. It is the subject of ongoing research to examine whether the genetic subgroup could be related to diseases outcome and its potential implications for vaccine development.

COVID-19 -Tuberculosis interactions: When dark forces collide.

The SARS-2 pandemic which has moved with frightening speed over the last 5 months has several synergies with another older, and far more neglected airborne disease, tuberculosis. Patients with tuberculosis are not only more likely to be infected by SARS-CoV-2 but also likely to have adverse outcomes once infected. The sequelae of more severe forms of COVID-19 in patients who have recovered from TB but have residual compromised lung function, are also likely to be devastating. These diseases share almost identical bio-social determinants like poverty, overcrowding, diabetes and pollution and some clinical similarities. The consequences of the COVID-19 pandemic, and our global response to it with lockdowns, are likely to leave a profound and long-lasting impact on TB diagnosis and control, potentially leading to an additional 6.3 million cases of TB between 2020 and 2025, and an additional 1.4 million TB deaths during this time. Novel solutions will need to be urgently devised or else TB control targets will never be met and indeed may be set back by 5-8 years.

Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19.

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

Respirators evaluated by fit testing.

FFP2/KN95 respirators can vary greatly in their ability to protect the wearer. Fit testing of respirators should be performed by all healthcare workers at risk for airborne transmitted diseases like #COVID19, #influenza or #tuberculosis https://bit.ly/3lFuwy5.

Unsupervised cluster analysis of SARS-CoV-2 genomes indicates that recent (June 2020) cases in Beijing are from a genetic subgroup that consists of mostly European and South(east) Asian samples, of which the latter are the most recent.

Research efforts of the ongoing SARS-CoV-2 pandemic have focused on viral genome sequence analysis to understand how the virus spread across the globe. Here, we assess three recently identified SARS-CoV-2 genomes in Beijing from June 2020 and attempt to determine the origin of these genomes, made available in the GISAID database. The database contains fully or partially sequenced SARS-CoV-2 samples from laboratories around the world. Including the three new samples and excluding samples with missing annotations, we analyzed 7, 643 SARS-CoV-2 genomes. Using principal component analysis computed on a similarity matrix that compares all pairs of the SARS-CoV-2 nucleotide sequences at all loci simultaneously, using the Jaccard index, we find that the newly discovered virus genomes from Beijing are in a genetic cluster that consists mostly of cases from Europe and South(east) Asia. The sequences of the new cases are most related to virus genomes from a small number of cases from China (March 2020), cases from Europe (February to early May 2020), and cases from South(east) Asia (May to June 2020). These findings could suggest that the original cases of this genetic cluster originated from China in March 2020 and were re-introduced to China by transmissions from samples from South(east) Asia between April and June 2020.