UVAE: Integration of Heterogeneous Unpaired Data with Imbalanced Classes (preprint)

We introduce the Unbiasing Variational Autoencoder (UVAE), a novel computational framework developed for the integration of unpaired biomedical data streams, with a particular focus on clinical flow cytometry. UVAE effectively addresses the challenges of batch effect correction and data alignment by training a semi-supervised model on partially labeled datasets. This approach enables the simultaneous normalisation and integration of diverse data within a shared latent space. The framework is implemented in Python with a descriptive interface for the specification and incorporation of multiple, partially overlapping data series. UVAE employs a probabilistic model for batch effect normalisation, with a generative capacity for unbiased data reconstruction and inference from heterogeneous samples. Its training process strategically balances class contents during various stages, ensuring accurate representation in statistical analyses. The model's convergence is achieved through a stable, non-adversarial training mechanism, complemented by an automated selection of hyper-parameters via Bayesian optimization. We quantitatively validate the performance of UVAE's constituent components and consequently apply it to the real problem of integrating heterogeneous clinical flow cytometry data collected from COVID-19 patients. We show that the alignment process enhances the statistical signal of cell types associated with severity and enables clustering of subpopulations without the impediment of batch effects. Finally, we demonstrate that homogeneous data generated by UVAE can be used to improve the performance of longitudinal regression for predicting peak disease severity from temporal patient samples.

Reviewing the rates of diagnosing Gonorrhoea in an era of COVID

P119 Table 1Drug sensitivities in 71 Neisseria gonorrhoeae infections for May 2019 using direct plating at the bedside Tested antimicrobial Drug resistance (as%) to the tested antimicrobial Azithromycin 3 Tetracycline 49 Ciprofloxacin 31 Ceftriaxone 0 Cefixime 0 Drug sensitivities in 78 Neisseria gonorrhoeae infections for May 2020 using the Copan ESwab Tested antimicrobial Drug resistance (as%) to the tested antimicrobial Azithromycin 0 Tetracycline 55 Ciprofloxacin 18 Ceftriaxone 0 Cefixime 5 ReferencesUnemo, M., Seifert, H., Hook III, E., Hawkes, S., Ndowa, F. and Dillon, J., 2019. Gonorrhoea. Nature Reviews, [online] 5(79). Available at: <https://www.nature.com/articles/s41572-019-0128-6.pdf> [Accessed 5 May 2021].Public Health England, 2019. Sexually transmitted infections and screening for Chlamydia in England, 2019. [online] Public Health England. Available at: <https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/914184/STI_NCSP_report_2019.pdf> [Accessed 5 May 2021].Fifer, H., Saunders, J., Soni, S., Sadiq, S. and FitzGerald, M., 2019. 2018 UK national guideline for the management of infection with Neisseria gonorrhoeae. International Journal of STD & AIDS, 31(1), pp.4-15.Vickerman, P., Peeling, R., Watts, C. and Mabey, D., 2005. Detection of Gonococcal Infection. Molecular Diagnosis, 9(4), pp.175-179.Ndowa, F., Lusti-Narasimhan, M. and Unemo, M., 2012. The serious threat of multidrug-resistant and untreatable gonorrhoea: the pressing need for global action to control the spread of antimicrobial resistance, and mitigate the impact on sexual and reproductive health. Sexually Transmitted Infections, 88(5), pp.317-318.Olsen, C., Schwebke, J., Benjamin, W., Beverly, A. and Waites, K., 1999. Comparison of Direct Inoculation and Copan Transport Systems for Isolation of Neisseria gonorrhoeae from Endocervical Specimens. Journal of Clinical Microbiology, 37(11), pp.3583-3585.Copan Diagnostics, 2019. Copan Liquid Based Microbiology. [online] Copanusa.com. Available at: Madjunkov, M., Dviri, M. and Librach, C., 2020. A comprehensive review of the impact of COVID-19 on human reproductive biology, assisted reproduction care and pregnancy: a Canadian perspective. Journal of Ovarian Research, 13(1).World Health Organisation, 2021. WHO Coronavirus (COVID-19) Dashboard. [online] Covid19.who.int. Available at: <https://covid19.who.int/> [Accessed 5 May 2021].Han, ., Tan, M., Turk, E., Sridhar, D., Leung, G., Shibuya, K., Asgari, N., Oh, J., García-Basteiro, A., Hanefeld, J., Cook, A., Hsu, L., Teo, Y., Heymann, D., Clark, H., McKee, M. and Legido-Quigley, H., 2020. Lessons learnt from easing COVID-19 restrictions: an analysis of countries and regions in Asia Pacific and Europe. The Lancet, 396(10261), pp.1525-1534.Kuitunen, I. and Ponkilainen, V., 2021. COVID-19-related nationwide lockdown did not reduce the reported diagnoses of Chlamydia trachomatis and Neisseria gonorrhoeae in Finland. Sexually Transmitted Infections, pp.sextrans-2020-054881.Cusini, M., Benardon, S., Vidoni, G., Brignolo, L., Veraldi, S. and Mandolini, P., 2020. Trend of main STIs during COVID-19 pandemic in Milan, Italy. Sexually Transmitted Infections, 97(2), pp.99-99.Steffen, R., Lautenschlager, S. and Fehr, J., 2020. Travel restrictions and lockdown during the COVID-19 pandemic—impact on notified infectious diseases in Switzerland. Journal of Travel Medicine, 27(8).Rodríguez, I. and Hernández, Y., 2020. Sexually Transmitted Diseases during the COVID-19 pandemic: a focus on syphilis and gonorrhoea in Cuba. Public Health in Practice, p.100072.Li, W., Li, G., Xin, C., Wang, Y. and Yang, S., 2020. Challenges in the Practice of Sexual Medicine in the Time of COVID-19 in China. The Journal of Sexual Medicine, 17(7), pp.1225-1228.Crane, M., Popovic, A., Stolbach, A. and Ghanem, K., 2020. Reporting of sexually transmitted infections during the COVID-19 pandemic. Sexually Transmitted Infections, 97(2), pp.101-102.British Association for Sexual Health and HIV, 2020. BASHH Guidance on Sex, Social Distancing and COVID-19. [online] Available at: <https://members.bashh.org/Covid-19> [Accessed 11 May 2021].Unemo, M. and Shafer, W., 2014. Antimicrobial Resistance in Neisseria gonorrhoeae in the 21st Century: Past, Evolution, and Future. Clinical Microbiology Reviews, 27(3), pp.587-613.Chernesky, M., Castriciano, S., Jang, D. and Smieja, M., 2006. Use of Flocked Swabs and a Universal Transport Medium To Enhance Molecular Detection of Chlamydia trachomatis and Neisseria gonorrhoeae. Journal of Clinical Microbiology, 44(3), pp.1084-1086.Graveling, E., Venkatesh, H. and Banerjee, N., 2021. Evaluation of Sigma Transwab® in Liquid Amies Transport Medium for Neisseria gonorrhoeae Culture. [online] Available at: <http://file:///C:/Users/evtw/Downloads/EV0537%20(2).pdf> [Accessed 5 May 2021].Astill, N., Wallace, H., Phyu, K., Coates, M., Inns, H., Wilson, J. and Page, E., 2021. Sensitivity of COPAN E-swab versus direct inoculation for culture of Neisseria gonorrhoeae (NG). [online] Available at: <https://www.bhiva.org/file/NpJyojfInOrtq/P48.pdf> [Accessed 5 May 2021].Gumede, L., Radebe, F., Nhlapo, D., Maseko, V. and Kufa-Chakezha, T., 2017. Evaluation of the Copan eSwab®, a liquid-based microbiology transport system, for the preservation of Neisseria gonorrhoeae at different temperatures. Southern African Journal of Infectious Diseases, 32(3), pp.96-99.van der Veer, B., Hoebe, C., Dukers-Muijrers, N., van Alphen, L. and Wolffs, P., 2020. Men and Women Have Similar Neisseria gonorrhoeae Bacterial Loads: a Comparison of Three Anatomical Sites. Journal of Clinical Microbiology, 58(11).Buchan, B., Olson, W., Mackey, T. and Ledeboer, N., 2014. Clinical Evaluation of the Walk-Away Specimen Processor and ESwab for Recovery of Streptococcus agalactiae Isolates in Prenatal Screening Specimens. Journal of Clinical Microbiology, 52(6), pp.2166-2168.Hyndman, I., Nugent, D., Whitlock, G., McOwan, A. and Girometti, N., 2021. COVID-19 restrictions and changing sexual behaviours in HIV-negative MSM at high risk of HIV infection in London, UK. Sexually Transmitted Infections, pp.sextrans-2020-054768.

Non-O ABO blood group genotypes differ in their associations with Plasmodium falciparum rosetting and severe malaria (preprint)

Blood group O is associated with protection against severe malaria and reduced size and stability of P. falciparum- host red blood cell (RBC) rosettes compared to non-O blood groups. Whether the non-O blood groups encoded by the specific ABO genotypes AO, BO, AA, BB and AB differ in their associations with severe malaria and rosetting is unknown. The A and B antigens are host RBC receptors for rosetting, hence we hypothesized that the higher levels of A and/or B antigen on RBCs from AA, BB and AB genotypes compared to AO/BO genotypes could lead to larger rosettes, increased microvascular obstruction and higher risk of malaria pathology. We used a case-control study of Kenyan children and in vitro adhesion assays to test the hypothesis that “double dose” non- O genotypes ( AA, BB, AB ) are associated with increased risk of severe malaria and larger rosettes than “single dose” heterozygotes ( AO, BO ). In the case-control study, compared to OO , the double dose genotypes consistently had higher odds ratios (OR) for severe malaria than single dose genotypes, with AB (OR 1.93) and AO (OR 1.27) showing most marked difference (P=0.02, Wald test). In vitro experiments with blood group A-preferring P. falciparum parasites showed that significantly larger rosettes were formed with AA and AB host RBCs compared to OO , whereas AO genotype rosettes were indistinguishable from OO . Overall, the data show that ABO genotype influences P. falciparum rosetting and support the hypothesis that double dose non- O genotypes confer a greater risk of severe malaria than AO/BO heterozygosity.

The SARS-CoV-2 Alpha variant is associated with increased clinical severity of disease (preprint)

BackgroundThe B.1.1.7 (Alpha) SARS-CoV-2 variant of concern was associated with increased transmission relative to other variants present at the time of its emergence and several studies have shown an association between the B.1.1.7 lineage infection and increased 28-day mortality. However, to date none have addressed the impact of infection on severity of illness or the need for oxygen or ventilation. MethodsIn this prospective clinical cohort sub-study of the COG-UK consortium, 1475 samples from hospitalised and community cases collected between the 1st November 2020 and 30th January 2021 were collected. These samples were sequenced in local laboratories and analysed for the presence of B.1.1.7-defining mutations. We prospectively matched sequence data to clinical outcomes as the lineage became dominant in Scotland and modelled the association between B.1.1.7 infection and severe disease using a 4-point scale of maximum severity by 28 days: 1. no support, 2. oxygen, 3. ventilation and 4. death. Additionally, we calculated an estimate of the growth rate of B.1.1.7-associated infections following introduction into Scotland using phylogenetic data. ResultsB.1.1.7 was responsible for a third wave of SARS-CoV-2 in Scotland, and rapidly replaced the previously dominant second wave lineage B.1.177) due to a significantly higher transmission rate ([~]5 fold). Of 1475 patients, 364 were infected with B.1.1.7, 1030 with B.1.177 and 81 with other lineages. Our cumulative generalised linear mixed model analyses found evidence (cumulative odds ratio: 1.40, 95% CI: 1.02, 1.93) of a positive association between increased clinical severity and lineage (B.1.1.7 versus non-B.1.1.7). Viral load was higher in B.1.1.7 samples than in non-B.1.1.7 samples as measured by cycle threshold (Ct) value (mean Ct change: -2.46, 95% CI: -4.22, -0.70). ConclusionsThe B.1.1.7 lineage was associated with more severe clinical disease in Scottish patients than co-circulating lineages. FundingCOG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute. Funding was also provided by UKRI through the JUNIPER consortium (grant number MR/V038613/1). Sequencing and bioinformatics support was funded by the Medical Research Council (MRC) core award (MC UU 1201412).

Genomic epidemiology of SARS-CoV-2 spread in Scotland highlights the role of European travel in COVID-19 emergence (preprint)

SARS-CoV-2, the causative agent of COVID-19, emerged in Wuhan, China in December 2019 and spread rapidly throughout the world. Understanding the introductions of this new coronavirus in different settings may assist control efforts and the establishment of frameworks to support rapid response in future infectious disease outbreaks. We investigated the first four weeks of emergence of the SARS-CoV-2 virus in Scotland after the first case reported on the 1st March 2020. We obtained full genome sequences from 452 individuals with a laboratory-confirmed diagnosis of COVID-19, representing 20% of all cases until 1st April 2020 (n=2310). This permitted a genomic epidemiology approach to study the introductions and spread of the SARS-2 virus in Scotland. From combined phylogenetic and epidemiological analysis, we estimated at least 113 introductions of SARS-CoV-2 into Scotland during this period. Clusters containing multiple sequences suggestive of onward transmission occurred in 48/86 (56%). 42/86 (51%) clusters had no known international travel history indicating undetected introductions. The majority of viral sequences were most closely related to those circulating in other European countries, including Italy, Austria and Spain. Travel-associated introductions of SARS-CoV-2 into Scotland predated travel restrictions in the UK and other European countries. The first local transmission occurred three days after the first case. A shift from travel-associated to sustained community transmission was apparent after only 11 days. Undetected introductions occurred prior to the first known case of COVID-19. Earlier travel restrictions and quarantine measures might have resulted in fewer introductions into Scotland, thereby reducing the number of cases and the subsequent burden on health services. The high number of introductions and transmission rates were likely to have impacted on national contact tracing efforts. Our results also demonstrate that local real-time genomic epidemiology can be used to monitor transmission clusters and facilitate control efforts to restrict the spread of COVID-19. FundingMRC (MC UU 1201412), UKRI/Wellcome (COG-UK), Wellcome Trust Collaborator Award (206298/Z/17/Z - ARTIC Network; TCW Wellcome Trust Award 204802/Z/16/Z Research in contextO_ST_ABSEvidence before this studyC_ST_ABSCoronavirus disease-2019 (COVID-19) was first diagnosed in Scotland on the 1st of March 2020 following the emergence of the causative severe acute respiratory system coronavirus 2 (SARS-CoV-2) virus in China in December 2019. During the first month of the outbreak in Scotland, 2310 positive cases of COVID-19 were detected, associated with 1832 hospital admissions, 207 intensive care admissions and 126 deaths. The number of introductions into Scotland and the source of those introductions was not known prior to this study. Added value of this studyUsing a combined phylogenetic and epidemiological approach following real-time next generation sequencing of 452 SARS-CoV-2 samples, it was estimated that the virus was introduced to Scotland on at least 113 occasions, mostly from other European countries, including Italy, Austria and Spain. Localised outbreaks occurred in the community across multiple Scottish health boards, within healthcare facilities and an international conference and community transmission was established rapidly, before local and international lockdown measures were introduced.