Trainee Presentation Session 2021

Venue: Pao Yue Kong Auditorium, Hong Kong Academy of Medicine Jockey Club Building

Time: 27 Nov 2021 9:00 - 12:30

Detail schedule in pdf

Introduction

Dr. YAU Tsz Wai Derek

9:00 - 9:10

Platform Oral Presentation

9:10 - 11:30

2021 TPS Abstract Book in pdf

Dr. CHIU Hei-Yung Kelvin

Emergence of Staplyococcus Lugdunensis as a Cause of Urinary Tract Infection: Results of the Routine Use of MALDI-TOF MS

Dr. SIN Ching-Tai Eugene

A Risk Stratification Model for Vancomycin-Resistant Enterococcus Colonization Amongst Traced Hospital Contacts

Dr. LAM Wing-Kit

MYD88 Mutation in Diffuse Large B-cell Lymphoma: Single Center Study in Hong Kong

Dr. LAM Ki

Single sIgE for Dermatophagoides pteronyssinus (Dp) is a Good Screening Tool for Atopic Sensitization

Dr. HUNG Ling-Yin

X-Chromosome Inactivation and PCDH19- associated Epileptic Encephalopathy: A Novel PCDH19 variant in a Chinese family

Dr. TSANG Yat-Ming

Evaluation of Nanopore-based 16S rDNA Amplicons Sequencing for Detection of Bacteria on Culture-Negative Specimens with Clinical Evidence of Infection

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Dr. LI Ting-Hon Stanford

A Retrospective Observational Study On The Red Cell Morphological Changes And Changes In Haematological Parameters Associated With Alectinib

Dr. FUNG Ching-Ki

Uterine Sarcoma with MEIS1-NCOA2 fusion - A Case Report and Review of the Literature

Dr. LI Xin

Asymptomatic Shedding of SARS-CoV-2 in Conjunctival Secretions

Poster Presentation

11:30 - 12:30

Dr. GAO Yang

Splenic Littoral Cell Haemangioendothelioma: Case Report

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Dr. LO Chun-Hai Haison

A Retrospective Study on Cytogenetic Features and Prognosis of Chinese Myeloma Patients in a Tertiary Referral Centre in Hong Kong

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Dr. HAU Man-Nga

Intestinal Ganglioneuromatosis: An Unusual Presenting Feature in MEN2B Syndrome As Acute Toxic Megacolon

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Dr. WONG Yuen-Sze Sivia

Microscopic Colitis: Report of Two Cases of Collagenous Colitis

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Dr. LI Wai-Yan Jamilla

Langerhans Cell Sarcoma: A Case Report

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Dr. LEUNG Hoi Shan

Biotin Interference and Biotin-Depletion Protocol

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Dr. LI Ting-Hon Stanford

A Retrospective Study on Cytogenetic Features and Prognosis of Chinese Myeloma Patients in a Tertiary Referral Centre in Hong Kong

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Judges Meeting and Words from Judges

12:05 - 12:15

Prize and Certificates Presentation

12:15 - 12:30

Results of College Exam 2021 (Council Meeting 29 Septmeber 2021)

 

Fellowship Assessment:

 

EXAM NO.	RESULT
E21201	PASS
E21202	FAIL
E21203	FAIL
E21204	PASS
E21205	PASS
E21206	PASS
E21207	PASS
E21208	PASS
E21209	PASS
E21210	FAIL
E21211	PASS
E21212	PASS
E21213	PASS
E21214	PASS
E21215	PASS
E21216	PASS
E21217	PASS
E21218	FAIL
E21219	PASS
E21220	PASS
E21221	PASS
E21222	PASS

 

Membership Examination:

 

EXAM NO.	RESULT
E21101	FAIL
E21103	PASS
E21104	PASS
E21105	PASS
E21106	PASS
E21107	FAIL
E21108	PASS
E21109	PASS
E21110	FAIL
E21111	FAIL
E21113	PASS
E21114	PASS

 

 

 

Information related to Covid19-Vaccination for Public

2019冠狀病毒病疫苗資訊

Poster　海報

Download Poster 下載海報

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Video 視頻

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Results of College Written Exam 2021 in Chemical Pathology, Clinical Microbiology & Infection and Haematology

CANDIDATE NO.	RESULT
E21111	PASS
E21114	PASS
E21213	PASS
E21214	PASS
E21215	PASS
E21216	PASS
E21219	PASS
E21220	PASS

Passed candidates can proceed further in Fellowship Assessment / Membership Examination in 2021.

Combined metabolomics and genomics approach for the diagnosis of inherited metabolic disorders (IMD)

Volume 16, Issue 2, July 2021  (download full article in pdf)

Editorial note:

In this topical update, Dr Eric Law reviews and updates on the current development in metabolomics and genomics and their integrated approach in the study of inherited metabolic disorders (IMD). We welcome any feedback or suggestions. Please direct them to Dr. Sammy Chen of Education Committee, the Hong Kong College of Pathologists. Opinions expressed are those of the authors or named individuals, and are not necessarily those of the Hong Kong College of Pathologists.

Dr Chun-yiu LAW

Consultant, Division of Chemical Pathology, Department of Pathology, Queen Mary Hospital

Introduction

Inherited metabolic disorders (IMD) refer to a group of heterogeneous biochemical disorders involved in different pathways of metabolism in humans. Metabolism refers to biochemical processes that occur in cells. These are the fundamental chemical reactions related to cell viability, growth, division, etc. Metabolism can be broadly classified into two major processes. One is catabolism, the production of energy from various nutrients, such as glucose, fat, and amino acids. The other process is anabolism, the biosynthesis of new cellular components, such as protein synthesis. A very early description of IMD was detailed by Sir Archibald E. Garrod about alkaptonuria back in 1902, who proposed the conditions to be inheritable and was caused by a specific enzymatic defect [1]. Indeed, IMD is more than just an enzymopathy and the knowledge of IMD and the human metabolome is still expanding in the next 100 years after the discovery of alkaptonuria. A computational analysis of the complete human genome has assigned 2,709 human enzymes to 896 bioreactions [2]. A more recent annotation includes 3,044 human molecular pathways covering 9,022 gene products [3]. According to the latest human metabolome database (HMDB version 4.0), there are 115,398 metabolites that linked with 5,702 different proteins [4].

The human metabolomes

Knowledge of the human metabolomes and their metabolic interactions is important for the understanding of human diseases. For example, it is now recognized that mitochondria are not only a factory for oxidative phosphorylation and energy metabolism, mitochondria also orchestrated with over 1,000 proteins and linked with multiple biochemical processes [5]. Indeed, a disrupted mitochondrial homeostasis had also been observed in some organic acidurias [6, 7]. The interactions of human metabolomes are far more complex than once perceived and involve different cell types, diets, drugs, disease status, microorganisms, and many others (Figure 1). The collective ‘big picture’ can be better studied through exometabolomics [8-10]. For example, acetic acid and trimethylamine (TMA) have been identified as biomarkers of bacterial urinary tract infection (UTI) and Escherichia coli associated UTI, respectively [11, 12]. TMA is one of the examples of mammalian-microbial co-metabolism which the host metabolizes TMA into trimethylamine N-oxide (TMAO) via flavin monooxygenase 3 (FMO3), and in E-coli associated UTI, the endogenous TMAOs are converted back to TMA possibly through the action from bacterial TMAO reductase. Similar mammalian-microbial co-metabolism has been described in aspects of human health that include cardiovascular disease, immunity, gastrointestinal disorders, and cancer [13-16]. The whole metabolomics network is more complex when toxico-metabolomics from drugs, chemicals, and environmental pollutants are taken into account. To-date, 2,280 drug and drug metabolites have been reported in the DrugBank database, and 3,670 toxins and pollutants have been reported in the Toxin and Toxin Target Database [17, 18], and the databank is still expanding.

Figure 1: Simplified diagram to illustrate the structure of a human metabolome which is a complex interplays between host (human) and various factors, e.g. diet, microorganisms, drugs, etc.

Metabolism is the heart of many disease processes. Insights gained from the knowledge of metabolism will inform diagnoses and lead to new treatments. For example, 116 treatable intellectual disability caused by IMD has been described in a 2021 review [19]. In this newsletter, more emphasis will be put on IMD, a heterogeneous condition involving disorders of synthesis, catabolism/anabolism, transport, and storage of metabolites.

Classifications of IMD

The definition for IMD is further refined as described in [20]. Indeed, the presence of an abnormal metabolite is no longer essential for the classification of IMD, but instead includes any condition resulting in the dysfunction of the specific enzymes or biochemical pathways that is intrinsic to the pathomechanism. In individuals, IMD is rare. However, in a population they are collectively “common”. The estimated incidence of IMD is 1 per 4,122 to 4,355 live births [21, 22]. The true prevalence of IMD is difficult to measure due to various factors. It was estimated as 1 in 800 to 2,500 newborns in one study in 2020 [23]. According to the Society for the Study of Inborn Errors of Metabolism (SSIEM), there are over 600 different IMDs, and they are grouped into 15 hierarchical classifications based on the biochemical pathway involved. They are (1) disorders of amino acids and peptide metabolism, (2) disorders of carbohydrate metabolism, (3) disorders of fatty acid and ketone body metabolism, (4) disorders of energy metabolism, (5) disorders of the metabolism of purines, pyrimidines and nucleotides, (6) disorders of the metabolism of sterols, (7) disorders of porphyrin and haem metabolism, (8) disorders of lipid and lipoprotein metabolism, (9) congenital disorders of glycosylation and other disorders of protein modification, (10) lysosomal disorders, (11) peroxisomal disorders, (12) disorders of neurotransmitter metabolism, (13) disorders of the metabolism of vitamins and (non-protein) cofactors, (14) disorders of the metabolism of trace elements and metals, and (15) disorders of and variants in the metabolism of xenobiotics (For more details, please refer to https://www.ssiem.org/resources/resources/inborn-errors-classification). This is a 2012 classification from SSIEM. Knowledge of IMDs is still expanding with the advancements in next-generation sequencing (NGS) which has led to the discovery of more disease-causing genes and disease classes in IMD. For example, new class, such as congenital disorders of autophagy, which cause multiple system involvement have been described in patients with inborn errors of neuro-metabolism [24].

Examples to enhance diagnostic workflow in IMD

Recently, the International Classification of Inborn Metabolic Disorders assigned 1,450 monogenic conditions related to metabolism to 24 categories [25]. These conditions can have overlapping signs and symptoms. Some are rapidly fatal, mainly due to the accumulation of toxic metabolites and/or deprivation of energy; the diagnosis of IMD is clinically vital in this regard since it permits interventions to prevent further metabolic insults and irreversible damages. Unfortunately, there is no simple and single biochemical analysis that encompasses all pathognomonic markers in each IMD. Various methods had been described to decipher human metabolomes [26, 27]. Urine organic acid (UOA) via gas chromatography mass spectroscopy (GC-MS) was introduced in the 1960s and has since been adopted by most clinical laboratories. GC-MS is robust because it generates highly reproducible mass spectra, which allows positive identification using libraries, such as that of the National Institute of Standards and Technology (NIST). Nevertheless, there are several pitfalls of GC-MS. These include low-level metabolites, co-elution, age-dependent variation of metabolite levels, etc. Data interpretation by pathologists is a labour-intensive process. For this reason, an in-house automatic solution was established to address the above pitfalls and assist the UOA reporting process. A checklist composed of almost 100 key metabolites was constructed using over 1,600 sets of UOA GC-MS data and partitioned according to different age groups. Positive identification of metabolites was defined according to their retention times and electron ionisation spectra. The 95th percentile for each compound and in each age group was used as a cut-off to define abnormally high OAs, which would be flagged for in-depth review by chemical pathologists. This algorithm allows: (1) a graphical display of individual UOA levels and comparison with controls according to different age groups, (2) calculation of ratios of metabolites useful in interpreting low-level, but clinically significant, metabolites, (3) pathway analysis by a holistic correlation analysis of all studied OAs, and (4) continual database enrichment. An example of a case of aromatic L-amino acid decarboxylase (AADC) deficiency, a neurotransmitter disorder is shown in figure 2 where a significant increase of vanillactic acid (VLA) is identified. Despite this refinement, the analytical process is time consuming and remains a bottleneck for rapid diagnosis. To further streamline the analytical process for IMDs, we further explored the use of nuclear magnetic resonance (NMR) spectroscopy for IMD diagnosis as an alternative or complementary for GC-MS analysis. This approach for IMD was proposed decades ago in 1999 [28]. To-date, at least 100 IMD conditions can be diagnosed by NMR spectroscopy, as reviewed by Engelke [29] and Moolenaar [30]. In addition, this technique allows for the identification of novel IMDs. Examples include aminoacylase 1 deficiency in patients with metabolic brain diseases [31], beta-ureidopropionase deficiency in patients with movement disorders [32], defective polyol metabolism in patients with leukoencephalopathy [33], and dimethylglycine dehydrogenase in patients with muscle disorders [34].

Figure 2: UOA spectrum by GC-MS from a case of aromatic L-amino acid decarboxylase (AADC) deficiency. The blue arrow points to the diagnostic marker, vanillactic acid (VLA) (Upper). Distribution of VLA levels in different age groups generated from in-house UOA algorithm. The AADC patient shows a marked increase of VLA (red arrow) comparing with age-matched subjects.

A national screening program in Turkey had applied NMR clinically to screen for IEM in newborns [35]. A total of 989 urine samples were collected from neonates and analysed by two laboratories. The results were used to establish a database and routine clinical screening. This NMR-based newborn urine screening has been further extended, covering up to 75 different IEM conditions [36]. The increasing awareness of clinical NMR applications has been further elaborated elsewhere in a 2021 review [37]. In our experience, the diagnostic utility of NMR has been substantiated in various clinical cases, for example, beta-ketothiolase deficiency, beta-ureidopropionase deficiency, citrin deficiency, fructose 1,6 bisphosphatase deficiency, holocarboxylase synthase deficiency, 3-hydroxyisobutyric aciduria, hyperornithinaemia-hyperammonaemia-homocitrullinuria (HHH) syndrome, methylmalonic aciduria (MMA), propionic acid, and succinic semialdehyde dehydrogenase deficiency (SSADHD). The merits of NMR-based urinalysis over GC-MS techniques are the simple sample preparation workflow and a relatively fast analytical time. Sample preparation is a two-step procedure that could be handled in

The choice of metabolic analysis depends on the nature of the pathognomonic metabolites. No single biochemical test that can detect them all. Pathologists have provided input concerning the choice of tests, advice on patient preparation and sample requirements, and clinical interpretations. The many examples include plasma acylcarnitine analysis, plasma/urine/CSF amino acids, urine acylglycines, bile acids, biotinidase activity, chitotriosidase activity, dried blood spot metabolic screening, homocysteine, transferrin isoelectric focusing, glycosaminoglycans analysis, CSF neurotransmitters, urine organic acids, urine sugars, urine guanidinoacetate and creatine analysis, urine oligosaccharides, orotic acids, red blood cells plasmalogens, porphyrins, phytosterols, pristanic and phytanic acids, purine and pyrimidines, very long chain fatty acids, and many more tests. Unfortunately, not all IMD-related tests are available or can be performed in a single centre.

The use of NGS will be a solution which provides additional insight from a genetic dimension, in particular if a biochemical assay is not available or the diagnosis cannot be easily demystified by biochemical tests. Primary coenzyme Q10 deficiency is one of the examples [38]. Affected individuals usually presented with non-specific symptoms and biochemical findings. Indeed, we have reported three cases of COQ4-related mitochondriopathy and identified a hotspot pathogenic variant in this locality using a genomics approach [39]. A plasma COQ10 assay was later developed for this potentially under-recognized condition.

Some IMD conditions are caused by multiple genes, for example, glutaric aciduria type II, methylmalonic aciduria (MMA), maple syrup urine disease (MSUD), propionic academia, phytosterolemia, congenital lactic acidosis, etc. Instead of a conventional gene-after-gene analysis, advances in NGS could allow the detection of the underlying genetic defect through a gene panel approach which effectively saves the time and manpower from managing a huge number of PCR primer bank and gene-specific protocols, not to mention the time spent in their revisions, updates and accreditations.

The diagnostic yield of NGS-based diagnosis for IMD is variable. In one report, NGS diagnosed 59% of the cases with clear clinical and biochemical features and a diagnostic yield of 8% for patients with an unclear phenotype [40]. Another group achieved an overall diagnostic yield of 50% and up to 78% in cases with a clear phenotype [41]. It is technically difficult to compare diagnostic yields of different studies, for reasons that include the scope of the IMD panel used, clinical and analytical aspects that can differ between centres.

Conclusions

With the expanding knowledge of metabolome and genome, more novel metabolites and genes have been discovered. These discoveries are enriching the understanding of IMD. Genomic and metabolomic analyses should be complementary to each other in the study of IMD, particularly in cases with atypical genetic findings or when a particular biochemical assay is not yet available. With the advancement of pharmacological chaperoning, small molecules and gene therapies, etc., more treatment options with improved care will be available in near future. At the same time, there will be increasing role from Pathologists for clinical use of cross-omics approach for disease diagnosis, monitoring and prognostication, with a more accurate and individualized characterization of disease progress.

Acknowledgement

The author would like to thank the supervision from Prof. Ching-wan Lam, Department of Pathology, The University of Hong Kong for his supervision on the NMR-related works. The author thanks Dr Gary Wong and Dr Jacky Ling, Division of Chemical Pathology, Department of Pathology, Queen Mary Hospital for their works on the in-house automatic solution in UOA analysis.

Reference

1. Mussap, M., M. Zaffanello, and V. Fanos, Metabolomics: a challenge for detecting and monitoring inborn errors of metabolism. Ann Transl Med, 2018. 6(17): p. 338.
2. Romero, P., et al., Computational prediction of human metabolic pathways from the complete human genome. Genome Biol, 2005. 6(1): p. R2.
3. Sorokin, M., et al., Algorithmic Annotation of Functional Roles for Components of 3,044 Human Molecular Pathways. Front Genet, 2021. 12: p. 617059.
4. Wishart, D.S., et al., HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res, 2018. 46(D1): p. D608-D617.
5. Marchi, S., et al., MitopatHs: a new logically-framed tool for visualizing multiple mitochondrial pathways. iScience, 2021. 24(4): p. 102324.
6. Chen, Z., M. Berquez, and A. Luciani, Mitochondria, mitophagy, and metabolic disease: towards assembling the puzzle. Cell Stress, 2020. 4(6): p. 147-150.
7. Dimitrov, B., et al., Organic acidurias: Major gaps, new challenges, and a yet unfulfilled promise. J Inherit Metab Dis, 2021. 44(1): p. 9-21.
8. Knott, M.E., et al., Metabolic Footprinting of a Clear Cell Renal Cell Carcinoma in Vitro Model for Human Kidney Cancer Detection. J Proteome Res, 2018. 17(11): p. 3877-3888.
9. Lima, A.R., et al., Discrimination between the human prostate normal and cancer cell exometabolome by GC-MS. Sci Rep, 2018. 8(1): p. 5539.
10. Palama, T.L., et al., Identification of bacterial species by untargeted NMR spectroscopy of the exo-metabolome. Analyst, 2016. 141(15): p. 4558-61.
11. Lam, C.W., et al., NMR-based metabolomic urinalysis: a rapid screening test for urinary tract infection. Clin Chim Acta, 2014. 436: p. 217-23.
12. Lam, C.W., et al., Quantitative metabolomics of urine for rapid etiological diagnosis of urinary tract infection: evaluation of a microbial-mammalian co-metabolite as a diagnostic biomarker. Clin Chim Acta, 2015. 438: p. 24-8.
13. Claus, S.P., Mammalian-microbial cometabolism of L-carnitine in the context of atherosclerosis. Cell Metab, 2014. 20(5): p. 699-700.
14. Brown, J.M. and S.L. Hazen, Microbial modulation of cardiovascular disease. Nat Rev Microbiol, 2018. 16(3): p. 171-181.
15. Lin, L. and J. Zhang, Role of intestinal microbiota and metabolites on gut homeostasis and human diseases. BMC Immunol, 2017. 18(1): p. 2.
16. Roager, H.M. and T.R. Licht, Microbial tryptophan catabolites in health and disease. Nat Commun, 2018. 9(1): p. 3294.
17. Wishart, D.S., et al., DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res, 2006. 34(Database issue): p. D668-72.
18. Wishart, D., et al., T3DB: the toxic exposome database. Nucleic Acids Res, 2015. 43(Database issue): p. D928-34.
19. Hoytema van Konijnenburg, E.M.M., et al., Treatable inherited metabolic disorders causing intellectual disability: 2021 review and digital app. Orphanet J Rare Dis, 2021. 16(1): p. 170.
20. Morava, E., et al., Quo vadis: the re-definition of "inborn metabolic diseases". J Inherit Metab Dis, 2015. 38(6): p. 1003-6.
21. Chong, S.C., et al., Expanded newborn metabolic screening programme in Hong Kong: a three-year journey. Hong Kong Med J, 2017. 23(5): p. 489-96.
22. Lee, H.C., et al., Analysis of inborn errors of metabolism: disease spectrum for expanded newborn screening in Hong Kong. Chin Med J (Engl), 2011. 124(7): p. 983-9.
23. Schuck, P.F., G.C. Ferreira, and M.C. McKenna, Recent advances in the pathophysiology of inherited metabolic diseases. Int J Dev Neurosci, 2020. 80(1): p. 50-51.
24. Ebrahimi-Fakhari, D., et al., Congenital disorders of autophagy: an emerging novel class of inborn errors of neuro-metabolism. Brain, 2016. 139(Pt 2): p. 317-37.
25. Ferreira, C.R., et al., An international classification of inherited metabolic disorders (ICIMD). J Inherit Metab Dis, 2021. 44(1): p. 164-177.
26. Roca, M., et al., Reviewing the metabolome coverage provided by LC-MS: Focus on sample preparation and chromatography-A tutorial. Anal Chim Acta, 2021. 1147: p. 38-55.
27. Tebani, A., C. Afonso, and S. Bekri, Advances in metabolome information retrieval: turning chemistry into biology. Part I: analytical chemistry of the metabolome. J Inherit Metab Dis, 2018. 41(3): p. 379-391.
28. Bamforth, F.J., et al., Diagnosis of inborn errors of metabolism using 1H NMR spectroscopic analysis of urine. J Inherit Metab Dis, 1999. 22(3): p. 297-301.
29. Engelke, U., et al., Handbook of 1H-NMR spectroscopy in inborn errors of metabolism: body fluid NMR spectroscopy and in vivo MR spectroscopy: SPS Publications, 2007. 2007.
30. Moolenaar, S.H., U.F. Engelke, and R.A. Wevers, Proton nuclear magnetic resonance spectroscopy of body fluids in the field of inborn errors of metabolism. Ann Clin Biochem, 2003. 40(Pt 1): p. 16-24.
31. Engelke, U.F., et al., NMR spectroscopy of aminoacylase 1 deficiency, a novel inborn error of metabolism. NMR Biomed, 2008. 21(2): p. 138-47.
32. Moolenaar, S.H., et al., beta-Ureidopropionase deficiency: a novel inborn error of metabolism discovered using NMR spectroscopy on urine. Magn Reson Med, 2001. 46(5): p. 1014-7.
33. Moolenaar, S.H., et al., In vivo and in vitro NMR spectroscopy reveal a putative novel inborn error involving polyol metabolism. NMR Biomed, 2001. 14(3): p. 167-76.
34. Moolenaar, S.H., et al., Defect in dimethylglycine dehydrogenase, a new inborn error of metabolism: NMR spectroscopy study. Clin Chem, 1999. 45(4): p. 459-64.
35. Aygen, S., et al., NMR-Based Screening for Inborn Errors of Metabolism: Initial Results from a Study on Turkish Neonates. JIMD Rep, 2014. 16: p. 101-11.
36. > Embade, N., et al., NMR-based newborn urine screening for optimized detection of inherited errors of metabolism. Sci Rep, 2019. 9(1): p. 13067.
37. Speyer, C.B. and J.D. Baleja, Use of nuclear magnetic resonance spectroscopy in diagnosis of inborn errors of metabolism. Emerg Top Life Sci, 2021.
38. Yubero, D. and R. Artuch, NGS for Metabolic Disease Diagnosis. EJIFCC, 2018. 29(3): p. 227-229.
39. Ling, T.K., et al., Clinical whole-exome sequencing reveals a common pathogenic variant in patients with CoQ10 deficiency: An underdiagnosed cause of mitochondriopathy. Clin Chim Acta, 2019. 497: p. 88-94.
40. Ghosh, A., et al., Diagnosing childhood-onset inborn errors of metabolism by next-generation sequencing. Arch Dis Child, 2017. 102(11): p. 1019-1029.
41. Yubero, D., et al., Targeted Next Generation Sequencing in Patients with Inborn Errors of Metabolism. PLoS One, 2016. 11(5): p. e0156359.

2020 College Annual Report and Year Book 2020-21

The 2020 College Annual Report and Year Book are available for download

Annual Report 2020

Year Book 2020/21

 

Diagnosis of COVID-19

Volume 16, Issue 1, January 2021  (download full article in pdf)

Editorial note

Coronavirus disease 2019 (COVID-19) is undoubtedly the most topical subject not only in the medical field, but also for humanity globally. In this issue of the Topical Update, Dr. Derek Hung and Prof. Kwok Yung Yuen present an overview on the diagnosis of COVID-19, which underpins effective disease control. We welcome any feedback or suggestion. Please direct them to Dr. Janice Lo, Education Committee, The Hong Kong College of Pathologists. Opinions expressed are those of the authors or named individuals, and are not necessarily those of the Hong Kong College of Pathologists.

Dr. Derek HUNG and Prof. Kwok Yung YUEN

Resident, Department of Microbiology, Queen Mary Hospital, Hospital Authority and

Professor, Department of Microbiology, Faculty of Medicine, The University of Hong Kong

Overview

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since December 2019 has infected 54 million population in all six major continents, resulting in over 1.3 million deaths by mid-November 2020. One of the most important aspects in curbing the spread of the virus is rapid yet accurate diagnosis of infection followed by timely isolation and contact tracing. Molecular testing is now the mainstay of diagnosis, supplemented by viral antigen testing. Antibody detection aids in assessment of immunity and disease prevalence in the population. As the disease progresses, there are worldwide efforts in developing a multitude of diagnostic platforms, both in-house and commercial. Studies also endeavour to assess optimal types and timing of specimen collection to enhance diagnostic yield. In this review, we would look at some of the knowledge and practices in making a diagnosis of COVID-19.

Specimen collection

Obtaining the best specimen optimizes the possibility of getting the correct diagnosis based on clinical suspicion. Being a predominantly respiratory pathogen, obtaining respiratory specimens for viral detection remains the primary modality for making a diagnosis of acute infection by SARS-CoV-2. The viral load is highest at or soon after symptom onset, with the viral load in the upper respiratory tract peaking earlier than the lower respiratory tract. The viral load decreases in the respiratory tract at a rate of 1 log10 per week. The World Health Organization (WHO) suggests that testing upper respiratory tract specimens is adequate for early stage infection, especially asymptomatic or mild cases. The Centers for Disease Control and Prevention (CDC) recognizes nasopharyngeal swab, nasopharyngeal wash, nasal wash obtained by health care professionals; nasal mid-turbinate swab, nasal swab obtained by either health care professionals or supervised self-collection on site; and posterior oropharyngeal saliva (POS) by supervised self-collection as valid specimens. Patients with lower respiratory tract symptoms such as productive cough, shortness of breath, or suspicious radiological findings should send sputum to enhance sensitivity. Induced sputum is not recommended due to increased risk of aerosol transmission,. Among different respiratory specimens, broncho-alveolar lavage (BAL) showed the highest positive rate.

For the upper respiratory tract specimen, comparing combined nasal swab/throat swab with nasopharyngeal swab, Vlek et al showed high concordance between these two methods (kappa coefficient 0.95) despite the cycle threshold value (Ct value) obtained from nasopharyngeal swab being lower. Another study suggested nasal swab alone also has good concordance with nasopharyngeal sampling. In contrast, oropharyngeal swab alone has inferior performance. Wang et al showed the sensitivity of oropharyngeal swab was 21.1% and meta-analysis by Bwire et al suggested the positive rate is as low as 7.6% in suspected cases, comparing with 69.6% and 71.3% for nasopharyngeal swab and lower respiratory tract specimen respectively. POS is increasingly studied as an alternative respiratory tract specimen for diagnosis. Theoretically well produced POS can concentrate secretions dripping down from nasopharynx and lower respiratory secretion moved up by ciliary activity of respiratory epithelium. It can be saved by patients themselves with instructions, thus reducing discomfort in specimen collection and minimizing aerosol exposure for health care professionals. The cost of collecting POS could be 2.59-fold lower than nasopharyngeal specimen, which could be significant in resource limited setting. The concordance between POS and nasopharyngeal swab is high, especially in the first 7 days of infection, up to 96.6% positive percent agreement. The sensitivity is comparable with nasopharyngeal swab in properly collected specimen. The sensitivity does not vary much between early morning and at least 2 hours after meal, which provides a convenient option for specimen collection. CDC and Hospital Authority of Hong Kong have adopted POS as an alternative option for upper respiratory specimen collection.

Viral shedding is also found in other specimens with stool being more studied. Meta-analysis showed viral shedding was found in faecal material in 40.5% of patients. The viral shedding in stool is more prevalent in those with gastrointestinal symptoms and may last longer than the shedding in respiratory tract. Viral RNA detected in blood and urine is relatively uncommon, respectively only 1% and 0% in one study with more than 200 patients10. Even without ocular symptoms, the conjunctival secretion may contain a small amount of SARS-CoV-2 RNA in around 8% of patients, warranting appropriate infection control measure in ophthalmological assessment.

Molecular testing

Detection of nucleic acid remains the backbone of diagnosing COVID-19 for treatment and public health purposes. Reverse-transcriptase polymerase chain reaction (RT-PCR) is the most widely used technique. After transcribing the viral RNA into complementary DNA (cDNA) with reverse transcriptase, the cDNA would be amplified and detected by real-time PCR. Potential molecular targets for SARS-CoV-2 include genes encoding structural proteins, e.g. spike (S), envelop (E), helicase (hel), nucleocapsid (N-N1 and N2), transmembrane (M); and non-structural regions, e.g. RNA-dependent RNA polymerase region (RdRp), haemagglutinin-esterase (HE), and open reading frame 1a (ORF1a) and ORF1b. Most scientific institutes and commercial platforms would design primers to target more than one gene, or to target multiple loci of the same gene to enhance diagnostic sensitivity and specificity. Though N gene RNA is shown by nanopore direct RNA sequencing study to be the most abundantly expressed transcript in SARS-CoV-2 infected cells, there is no consensus on which gene confers the best diagnostic performance. Presently, one conserved and one specific target region are recommended to mitigate effect of random mutation or genetic drift while maintaining specificity25. Various regimens for testing are proposed in the literature. Corman et al recommended the Charité protocol, which was to use E gene for screening and RdRp gene for confirmation. CDC used N1 and N2 genes as their diagnostic panel. Chu et al used N gene as screening test and ORF1b as confirmatory assay because the screening N gene assay is 10 times more sensitive than ORF1b. As an alternative confirmatory assay, Chan et al developed a real-time RT-PCR assay locally, targeting RdRp/Hel. This COVID-19-RdRp/Hel assay demonstrated significantly higher sensitivity and specificity for the detection of SARS-CoV-2 RNA than the RdRp-P2 assay in clinical evaluation.

Multiple commercial platforms were developed for molecular SARS-CoV-2 diagnosis for their high throughput, rapid turnaround time and ease of use with automation. Examples are Roche Cobas 6800/8800 system (targets ORF1a and E genes) and Abbott Alinity m SARS-CoV-2 assay (targets RdRp and N genes), where sample preparation, genetic material extraction, target amplification and result reporting are automated inside the system. Molecular point-of-care testing (POCT) refers to diagnostic platform that is portable (often desktop-size), requires minimal sample preparation steps and can provide reliable molecular results within 2 hours. POCT like Cepheid GeneXpert (Xpert Xpress SARS-CoV-2 assay, targets E and N2 genes) enables rapid testing near the site of collection in areas with little laboratory support. Fewer steps in manipulation reduce risk of cross contamination and laboratory error in processing. Many evaluation studies have been published to compare the performance of these commercial platforms against in-house diagnostic tests and for head-to-head comparison between platforms. For example, Cobas system is shown to have high diagnostic agreement with in-house molecular assays,, as well as with other commercial platforms such as Hologic Panther Fusion system and Cepheid GeneXpert. Cepheid GeneXpert reaches an agreement of 100 % compared to three in-house RT-PCRs in a multicentre evaluation in the Netherlands. Among commercial platforms there might be minor discordance between assays at very high Ct values and the viral load of clinical samples used in evaluative studies should be noted in particular.

Another molecular technique is reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) test. Using multiple primers for the genetic target, RT-LAMP amplified nucleic acid by strand displacement in an isothermal condition of around 60- 65oC. It allows synthesis of large amount of genetic material up to 106 to 109 copies of target DNA within 30-60 minutes2. Without the need of thermal cycler as in RT-PCR, RT-LAMP facilitates development of rapid molecular POCT and has an expanding market in commercial diagnostic platform. On the down side, since multiple primers over a relatively small genetic region are needed for amplification, there are constraints in properly designing the primers. Abbott ID NOW is a commercial POCT platform using RT-LAMP, allowing real time detection of SARS-CoV-2 within 15 minutes targeting RdRp gene. Evaluation of ID NOW against other RT-PCR based platforms appears suboptimal in terms of diagnostic sensitivity. Compared to Cobas, ID NOW achieved only 73.9% positive agreement while GeneXpert achieved 98.9% positive agreement. In samples with Ct values greater than 30, positive agreement was 34.3% for ID Now and 97.1% for GeneXpert. A lower sensitivity of ID NOW over GeneXpert was also reported in another evaluation by Basu et al. In contrary, good diagnostic utility has been demonstrated in many other centres including Hong Kong that have designed their own RT-LAMP for COVID-19. Chow et al reported sensitivity of 95% at 60 minutes using RT-LAMP targeting a region across ORF3a/E gene as compared to RT-PCR. Lu et al achieved concordance rate of 93% against RT-PCR using in-house E gene RT-LAMP assay.

In order to improve the diagnostic sensitivity of molecular assays, clustered regularly interspaced short palindromic repeat (CRISPR)-based technology has been employed by coupling with Cas enzyme. The enzyme would be directed to the target DNA/RNA by a guide RNA complementary to the target sequence. Once bound, the collateral nuclease activity of the Cas enzyme would cleave surrounding reporter fluorophore and lead to signal amplification. DETECTR technology uses Cas12a enzyme to bind target DNA; while SHERLOCK technology uses Cas13a enzymes to bind target RNA. This technology can be incorporated in molecular techniques especially RT-LAMP to enhance the sensitivity and to lower the detection limit.

Next generation sequencing (NGS) enables sequencing of the entire genome in a relatively short period of time. Sharing of genetic data facilitates tracking of disease spread, understanding of disease transmission route, monitoring viral genome evolution and detecting emergence of mutation that may escape detection or enhance virulence. The cost and infrastructure required of NGS and the need of bioinformatics expertise limit its use to larger hospital and research centres.

Antigen detection

Like other respiratory viruses such as influenza and respiratory syncytial virus (RSV), direct antigen detection from respiratory specimen especially nasopharyngeal sample is another way of making a diagnosis of COVID-19. N protein was found previously to be the predominant structural protein released in large amount in nasopharyngeal aspirate during infection of SARS-CoV, and the same phenomenon is also shown in SARS-CoV-2 where the abundantly expressed N protein is widely used as an antigen detection target in COVID-19. Detection is achieved by capturing viral antigen in clinical specimens by monoclonal antibodies or monospecific polyclonal antibody fixed on a membrane, usually indicated by colour change of the strip in colorimetric lateral flow immunoassay. The assay can be delivered as POCT in an office setting since no complex laboratory support is required and the result can be available within a short period of time, usually <30 minutes. The major setback is the suboptimal sensitivity as compared to molecular diagnosis especially in samples with high Ct values. Evaluation by Lambert-Niclot et al using COVID-19 Ag Respi-Strip CORIS, a nitrocellulose membrane technology with colloidal gold nanoparticles sensitized with monoclonal antibodies directed against SARS-CoV-2 nucleoprotein (NP) antigens, showed sensitivity of only 50% when compared against multiple RT-PCR platforms. For samples with Ct value <25, the sensitivity is higher at 82.2%. In a local evaluation using Biocredit COVID-19 Ag test, the antigen test is 105 fold less sensitive than RT-PCR and it yielded a positive result in 45.7% RT-PCR positive combined nasopharyngeal swab/throat swab specimens only. There are attempts to improve sensitivity of rapid antigen assay. Porte et al evaluated an immune-chromatographic antigen assay using fluorescence signal showing sensitivity of 93% but the Ct value of the sample included in this study is relatively low with mean of 20. Other approaches by concentrating the antigen in specimens before testing with monoclonal antibodies targeting multiple different epitopes of the antigen were also reported. Based on a meta-analysis by Dinnes et al, the average sensitivity is around 56.2% for antigen assay with a high average specificity of 99.5%. Further refinement in antigen detection employs the detection of the change in bioelectric property by antigen binding to the antibody coated membrane. In Seo et al, anti-S antibody binds to SARS-CoV-2 particles to fabricate graphene-based field-effect-transistors (FET) biosensors and can respond down to 16 pfu/mL of virus. One challenge to this advance is the high background noise which can reduce sensitivity of detection. Overall, rapid antigen detection serves only an adjunctive role to molecular assay in making a diagnosis especially in outbreak situation where prevalence is high and molecular assay is not available. WHO has issued interim guidance of use of rapid antigen immunoassays.

Antibody detection

While antibody testing may not be useful in acute setting for COVID-19, it helps establish retrospective diagnosis, predict immunity and understand seroprevalence in a defined community. Commonly employed techniques are lateral flow immunoassay, chemiluminescent immunoassay, immunofluorescent assay, and enzyme-linked immunosorbent assay (ELISA). Median seroconversion times following symptom onset are 11 days for total antibodies, 12 days and 14 days for IgM and IgG respectively. Detection rate for IgM ranges from 11-71% in the first 7 days of infection, 36-87% between 8-14 days, and 56-97% after 14 days. For IgG, it ranges from 4-57% in first 7 days, 54-88% between 8-14 days, and 91-100% after 14 days. For SARS-CoV-2, there does not seem to have significant time difference between IgM and IgG response. IgM peaked at around 3 weeks after symptom onset and fell to baseline level at around day 36. The duration of IgG seropositivity remains unknown and longer longitudinal studies are required. Study from Iceland involving over 1200 confirmed patients showed no evidence of antiviral antibody decline by 4 months after diagnosis; and most other studies showed persistently detectable antibodies by 2-3 months after infection60. On the other hand, there are some evidences that the IgG level may decline faster in mild and asymptomatic61 COVID-19 cases.

S protein is an important antigen for neutralizing antibody production. The S1 domain is responsible for receptor binding while the S2 domain is responsible for fusion. The receptor binding domain (RBD) is located at S1. NP, which is a structural component of the helical nucleocapsid, also appears to be an important antigen for the development of serological assays to detect COVID-19. Earlier in the pandemic, using sera collected more than 14 days after symptom onset from 16 patients, To et al showed rates of seropositivity were 94% for anti-NP IgG, 88% for anti-NP IgM, 100% for anti-RBD IgG, and 94% for anti-RBD IgM. Another study compares sensitivity and specificity in testing anti-S and anti-NP IgG for evidence of immunity across multiple platforms, which shows they are comparable by day 37 after infection though seroconversion of anti-NP IgG may precede anti-S IgG by around 2 days (day 9-10 v day 11-12). Caruana et al observed that the decline of anti-NP antibody may be faster than anti-S and thus could be less sensitive longer after infection. Also titre of anti-S antibody may better reflect protection against reinfection67. Multiple commercial platforms were developed for high-throughput antibody testing in clinical laboratory. Automatic platforms such as Abbott SARS-CoV-2 IgG, which is a chemiluminescent micro-particle immunoassay, are also used in public hospital of Hong Kong for a shorter turnaround time.

Neutralization antibody test is important in assessing in vitro the functional capacity of the humoral response of COVID-19 patients to prevent reinfection by the virus. Traditional neutralization assay such as microneutralization and plaque reduction assay require manipulation of live virus and necessitate biosafety level 3 laboratories. As a result, pseudovirus neutralization assay has been developed. Vesicular stomatitis virus (VSV) expressing S protein of SARS-CoV-2, containing the RBD, is used so that the assay can be performed in biosafety level 2 facilities. SARS-CoV-2 neutralizing antibody starts to rise at around 7-10 days after symptom onset and the median peak time is 33 days after symptom onset. The neutralization titres then decline in 93% of the patients and by a median level of 35% over 3 months. Patients with more severe disease requiring ICU admission have accelerated and augmented neutralizing antibody response compared with non-ICU cases. In non-severe cases who have low peak neutralizing antibody titre, neutralizing antibody level might return to baseline within 2 months. Another clinical use of neutralization assay would be to confirm potentially false positive SARS-CoV-2 serology result. Three children with Kawasaki disease without symptoms or epidemiological linkage to COVID-19 were tested positive to anti-RBD and anti-NP antibodies by a microparticle-based immunoassay but were confirmed negative by microneutralization test.

Studies have shown there are serological cross-reactivity between SARS-CoV-2 and SARS-CoV. Testing sera taken from COVID-19 patients by ELISA, cross-reactivity is seen against S protein and RBD of SARS-CoV, though the intensity of cross-reaction against RBD is weaker than S protein. For the full length S protein, the amino acid sequence homology between SARS-CoV-2 and SARS-CoV is around 75%. The homology between them for RBD which is located in S1 domain is around 74%. For the receptor binding motif (RBM) of the RBD where the virus directly binds to angiotensin-converting enzyme 2 (ACE2), the homology is only 50%. The degree of amino acid homology explains the difference in the level of cross-reaction between them on ELISA. Chia et al showed even more significant cross-reactivity between SARS-CoV-2 and SARS-CoV antibody against NP by Luminex assay than antibody against S1 or RBD as the homology between the NP of these 2 viruses is around 90%. Despite some cross-reaction between antibodies against RBD on ELISA, there does not seem to have significant cross neutralization effect73. Only 1 out of 15 COVID-19 sera showed cross neutralization with SARS-CoV at very low titre. Overall the effect of cross-protection in vaccination and whether antibody-dependent enhancement effect would be seen between these 2 closely related viruses remains unknown.

Cross-reactivity against other human coronaviruses in SARS-CoV-2 infection has been investigated in a few trials. In a study by Wölfel et al, using immunofluorescence assay against recombinant S protein, cross-reactivity of SARS-CoV-2 sera is found against human coronaviruses OC43, NL63, HKU1 and 229E on comparing the titres between admission and convalescence samples, especially HKU1 and OC43 which are both betacoronavirus. In Shrock et al, deep serological profiling of sera from SARS-CoV-2 patients and pre-COVID sera are performed. Antibodies against S and NP are the most specific assay to differentiate SARS-CoV-2 and pre-COVID sera. Those with dramatic increase in anti-S antibody after COVID-19 infection also have increase in the intensity of cross-reactivity against other human coronaviruses, especially over more homologous regions of the S protein e.g. at residue 811-830 and 1144-1163. It could be novel antibodies of SARS-CoV-2 that cross-react or boost the anamnestic response against SARS-CoV-2 infection due to existing memory towards other human coronaviruses from past exposure. Moreover, pre-COVID sera also show some cross-reaction towards the homologous region of SARS-CoV-2 S protein and ORF1 in the same study.

Viral culture

Demonstration of live SARS-CoV-2 in cell culture requires biosafety level 3 facilities and are not routinely performed in most of the clinical laboratories. However, live virus isolation is still important for some diagnostic and research purposes so as to determine whether the amount of virus present is infectious to others, to evaluate therapeutic efficacy of potential antiviral compound, to develop viral neutralization assay for testing convalescent sera, to provide positive control for molecular assay development, and to develop vaccine strains. The host cell receptor for SARS-CoV-2 is ACE2. Non-human cell lines such as Vero E6 and Vero CCL-61 which have abundant ACE2 expression are commonly used for isolation. Cytopathic effect is seen by 3 days after inoculation. SARS-CoV-2 also grows in human continuous cell lines such as Calu3 (pulmonary cell line), Caco2 (intestinal cell line), Huh7 (hepatic cell line), and 293T (renal cell line). It grows modestly on U251 (neuronal cell line) which is not seen in SARS-CoV81. Confirmation of SARS-CoV-2 replication in the cell line can be done by molecular testing or immunostaining techniques. Cell lines can be engineered to express a transmembrane serine protease TMPRSS2 for priming of S protein and to facilitate the entry of SARS-CoV-2 into host cell. Organoid systems such as bat and human intestinal organoids are susceptible to SARS-CoV-2 and are developed to better study tissue tropism, the dynamics of infection and testing of therapeutic targets.

Radiological diagnosis and artificial intelligence

There are no pathognomonic radiological features on chest imaging for COVID-19 and the disease should not be ruled in or ruled out based on imaging alone. However, presence of suggestive imaging features can prompt further investigations in suspicious cases, such as lower respiratory tract viral testing for confirmation. Reports in literature have suggested that in some patients, radiological findings may precede the detection of SARS-CoV-2 in clinical specimen,. Chest X-ray (CXR) is a less sensitive modality than computed tomography of the thorax (CT thorax) with a reported CXR sensitivity of 69%85. As in other viral pneumonia, COVID-19 typically presents with multifocal air-space disease, especially with a bilateral lower lung distribution. More specific to COVID-19, it tends to have peripheral lung involvement, seen in 58% of CXR in one study. CT thorax has a higher sensitivity than CXR, quoted at around 60-98%. CT thorax often demonstrates the typical findings of peripheral bilateral ground glass opacities (GGO) with or without consolidation or ‘crazy-paving pattern’. Sometimes the GGO would arrange in a rounded pattern. Isolated lobar or segmental consolidation without GGO, centrilobular shadows, cavitory changes, lymphadenopathy and pleural effusions are rare86. As the disease advances, the opacities might coalesce, affecting central and bilateral upper lobes and may manifest as ‘white lung’ with diffuse infiltrate. The abnormalities usually peak by 2 weeks after symptom onset, replaced by scar tissue with recovery. In the COVID-19 pandemic, artificial intelligence (AI) programme is increasingly studied for screening abnormal radiological result which would be particularly useful for mass screening strategy in outbreak situation. The performance of AI is dependent on the radiological imaging algorithm being fed into the system for deep learning process. So far the result of this research has been promising with reported area under receiver operating characteristic curves greater than 0.9,. However, there are still lots of technical and ethical issue to resolve which include dataset bias, data privacy, and the distribution of ultimate accountability of result.

Detection of host inflammatory reaction

In COVID-19, there are studies to diagnose and predict severe diseases by the host inflammatory response. Apart from direct viral damage, uncontrolled cytokine storm triggered by the virus leads to tissue damage and multiorgan failure. Mean interleukin-6 (IL-6) concentration in serum was found to be 2.9 fold higher in patients with complicated COVID-19 disease than non-complicated disease. It became one of the markers clinicians could use to predict progression into severe disease. Roche Elecsys IL-6 immunoassay received FDA Emergency Use Authorization to help identify patients at high risk of requiring intubation with mechanical ventilation. Molecules targeting IL-6 such as tocilizumab are also studied as therapeutic to prevent disease progress by blocking the inflammatory pathway. It does not show efficacy in preventing intubation or death in moderately ill hospitalized patients in the BACC Bay trial. Elevated CRP is associated with worse outcome, as well as elevated IL-10 which may be related to compensatory anti-inflammatory response and secondary infections. Haematologically, severe disease is associated with higher absolute neutrophil count, D-dimer and LDH but lower absolute lymphocyte101 and platelet count.

Conclusion

Global COVID-19 pandemic stimulates global effort in development of rapid yet accurate diagnostic techniques. Diagnosis is often limited by the low level of viral particles in the specimen and the subtle clinical features in early infection. Though traditional methods like RT-PCR are still the mainstay, we see expanding endeavours to strive for higher speed and lower limit of detection at an earlier time. Molecular techniques such as RT-LAMP, CRISPR/Cas, biosensor technology in antigen detection, AI operating system for image interpretation are pushing the diagnostic ability to the limit. Despite these scientific advances, there are still a lot of gaps to fill especially in understanding the nature and duration of humoral immunity response and its protection against re-infection. All these require continuous global cooperation and information exchange to make them possible.