Integrative genomics important to understand host-pathogen interactions

Infectious diseases are the leading cause of morbidity and mortality worldwide. Causative pathogenic microbes readily mutate their genome and lead to outbreaks, challenging the healthcare and the medical support. Understanding how certain symptoms manifest clinically is integral for therapeutic decisions and vaccination efficacy/protection. Notably, the interaction between infecting pathogens, host response and co-presence of microbes influence the trajectories of disease progression and clinical outcome. The spectrum of observed symptomatic patients (mild, moderate and severe) and the asymptomatic infections highlight the challenges and the potential for understanding the factors driving protection/susceptibility. With the increasing repertoire of high-throughput tools, such as cutting-edge multi-omics profiling and next-generation sequencing, genetic drivers of factors linked to heterogeneous disease presentations can be investigated in tandem. However, such strategies are not without limits in terms of effectively integrating host-pathogen interactions. Nonetheless, an integrative genomics method (for example, RNA sequencing data) for exploring multiple layers of complexity in host-pathogen interactions could be another way to incorporate findings from high-throughput data. We further propose that a Holo-transcriptome-based technique to capture transcriptionally active microbial units can be used to elucidate functional microbiomes. Thus, we provide holistic perspective on investigative methodologies that can harness the same genomic data to investigate multiple seemingly independent but deeply interconnected functional domains of host-pathogen interaction that modulate disease severity and clinical outcomes.

Cancer treatment comes to age: from one-size-fits-all to next-generation sequencing (NGS) technologies

Cancer is one of the leading causes of death worldwide and one of the greatest challenges in extending life expectancy. The paradigm of one-size-fits-all medicine has already given way to the stratification of patients by disease subtypes, clinical characteristics, and biomarkers (stratified medicine). The introduction of next-generation sequencing (NGS) in clinical oncology has made it possible to tailor cancer patient therapy to their molecular profiles. NGS is expected to lead the transition to precision medicine (PM), where the right therapeutic approach is chosen for each patient based on their characteristics and mutations. Here, we highlight how the NGS technology facilitates cancer treatment. In this regard, first, precision medicine and NGS technology are reviewed, and then, the NGS revolution in precision medicine is described. In the sequel, the role of NGS in oncology and the existing limitations are discussed. The available databases and bioinformatics tools and online servers used in NGS data analysis are also reviewed. The review ends with concluding remarks.

Diversity and independent evolutionary profiling of rodent-borne viruses in Hainan, a tropical island of China

The risk of emerging infectious diseases (EID) is increasing globally. More than 60% of EIDs worldwide are caused by animal-borne pathogens. This study aimed to characterize the virome, analyze the phylogenetic evolution, and determine the diversity of rodent-borne viruses in Hainan Province, China. We collected 682 anal and throat samples from rodents, combined them into 28 pools according to their species and location, and processed them for next-generation sequencing and bioinformatics analysis. The diverse viral contigs closely related to mammals were assigned to 22 viral families. Molecular clues of the important rodent-borne viruses were further identified by polymerase chain reaction for phylogenetic analysis and annotation of genetic characteristics such as arenavirus, coronavirus, astrovirus, pestivirus, parvovirus, and papillomavirus. We identified pestivirus and bocavirus in Leopoldoms edwardsi from Huangjinjiaoling, and bocavirus in Rattus andamanensis from the national nature reserves of Bangxi with low amino acid identity to known pathogens are proposed as the novel species, and their rodent hosts have not been previously reported to carry these viruses. These results expand our knowledge of viral classification and host range and suggest that there are highly diverse, undiscovered viruses that have evolved independently in their unique wildlife hosts in inaccessible areas.

Identification of a Novel Astrovirus in Pinnipeds

Astroviruses infect human and animals and cause diarrhea, fever, and vomiting. In severe cases, these infections may be fatal in infants and juvenile animals. Previous evidence showed that humans in contact with infected animals can develop serological responses to astroviruses. Mamastrovirus 11 is a species of Mamastrovirus and was first reported in 2018. It was detected in the fecal samples of a California sea lion. The genome sequence of its capsid protein (CP) was submitted to GenBank. However, the genome sequence of its non-structural protein region was not elucidated. In the present study, we characterized the genome sequences of the novel astroviruses AstroV-HMU-1 and AstroV-like-HMU-2. These were obtained from California sea lions (Zalophus californianus) and walruses (Odobenus rosmarus) presenting with loose stools. A phylogenetic analysis revealed that the CP of AstroV-HMU-1 closely clustered with Mamastrovirus 11 while its RNA-dependent RNA polymerase (RdRp) and serine protease (SP) were closely related to the mink astrovirus in the genus Mamastrovirus. The genome of AstroV-HMU-1 provided basic information regarding the NS protein regions of Mamastrovirus 11. Recombination analyses showed that the genomes of Z. californianus AstroV-HMU-1, VA2/human and the mink astrovirus may have recombined long ago. The NS of AstroV-like-HMU-2 segregated from the Astroviridae in the deep root of the phylogenetic tree and exhibited 36% amino acid identity with other mamastroviruses. Thus, AstroV-like-HMU-2 was proposed as a member of a new genus in the unclassified Astroviridae. The present study suggested that that the loose stools of pinnipeds may be the result of occasional infection by this novel astrovirus. This discovery provides a scientific basis for future investigations into other animal-borne infectious diseases.

Identification of microbial agents in tissue specimens of ocular and periocular sarcoidosis using a metagenomics approach

Background: Metagenomic sequencing has the potential to identify a wide range of pathogens in human tissue samples. Sarcoidosis is a complex disorder whose etiology remains unknown and for which a variety of infectious causes have been hypothesized. We sought to conduct metagenomic sequencing on cases of ocular and periocular sarcoidosis, none of them with previously identified infectious causes. Methods: Archival tissue specimens of 16 subjects with biopsies of ocular and periocular tissues that were positive for non-caseating granulomas were used as cases. Four archival tissue specimens that did not demonstrate non-caseating granulomas were also included as controls. Genomic DNA was extracted from tissue sections. DNA libraries were generated from the extracted genomic DNA and the libraries underwent next-generation sequencing. Results: We generated between 4.8 and 20.7 million reads for each of the 16 cases plus four control samples. For eight of the cases, we identified microbial pathogens that were present well above the background, with one potential pathogen identified for seven of the cases and two possible pathogens for one of the cases. Five of the eight cases were associated with bacteria ( Campylobacter concisus, Neisseria elongata, Streptococcus salivarius, Pseudopropionibacterium propionicum, and Paracoccus yeei), two cases with fungi ( Exophiala oligosperma, Lomentospora prolificans and Aspergillus versicolor) and one case with a virus (Mupapillomavirus 1). Interestingly, four of the five bacterial species are also part of the human oral microbiome. Conclusions: Using a metagenomic sequencing we identified possible infectious causes in half of the ocular and periocular sarcoidosis cases analyzed. Our findings support the proposition that sarcoidosis could be an etiologically heterogenous disease. Because these are previously banked samples, direct follow-up in the respective patients is impossible, but these results suggest that sequencing may be a valuable tool in better understanding the etiopathogenesis of sarcoidosis and in diagnosing and treating this disease.

Deep learning-based real-time detection of novel pathogens during sequencing

Novel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state of the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens but require relatively long input sequences and processed data from a finished sequencing run. Incomplete sequences contain less information, leading to a trade-off between sequencing time and detection accuracy. Using a workflow for real-time pathogenic potential prediction, we investigate which subsequences already allow accurate inference. We train deep neural networks to classify Illumina and Nanopore reads and integrate the models with HiLive2, a real-time Illumina mapper. This approach outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we observe an 80-fold sensitivity increase compared to real-time mapping. The first 250 bp of Nanopore reads, corresponding to 0.5 s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. The approach could also be used for screening synthetic sequences against biosecurity threats.

Interpretable detection of novel human viruses from genome sequencing data

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks

MOTIVATION

We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, which limits their performance on unknown, unrecognized and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads, even though the biological context is unavailable.

RESULTS

We present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a flexible framework allowing easy evaluation of neural architectures with reverse-complement parameter sharing. We show that convolutional neural networks and LSTMs outperform the state-of-the-art based on both sequence homology and machine learning. Combining a deep learning approach with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art.

AVAILABILITY AND IMPLEMENTATION

The code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A 9-month retrospective evaluation of the aetiology and management of patients presenting with encephalitis/meningoencephalitis at a South London hospital

Encephalitis causes high morbidity and mortality. An incidence of 4.3 cases of encephalitis/100 000 population has been reported in the UK. We performed a retrospective evaluation of the diagnosis and management of adults admitted to hospital with a clinical diagnosis of encephalitis/meningoencephalitis. Clinical, laboratory and radiological data were collated from electronic records. Thirty-six patients, median age 55 years and 24 (67%) male were included. The aetiology was confirmed over nine months in 25 (69%) of whom 16 were infections (six viral, seven bacterial, two parasitic and one viral and parasitic co-infection); 7 autoimmune; 1 metabolic and 1 neoplastic. Of 24 patients with fever, 15 (63%) had an infection. The median time to computed topography, magnetic resonance imaging and electroencephalography (EEG) was 1, 8 and 3 days respectively. Neuroimaging was abnormal in 25 (69%) and 17 (89%) had abnormal EEGs. Only 19 (53%) received aciclovir treatment. Six (17%) made good recoveries, 16 (44%) had moderate disability, 8 (22%) severe disability and 6 (17%) died. Outcomes were worse for those with an infectious cause. In summary, a diagnosis was made in 69.4% of patients admitted with encephalitis/meningoencephalitis. Autoimmune causes are important to consider at an early stage due to a successful response to treatment. Only 53% of patients received aciclovir on admission. Neuroimaging and EEG studies were delayed. The results of this work resulted in further developing the clinical algorithm for managing these patients.

Amedofu G, A. Awandare G, Wonkam A. Enhancing Genetic Medicine: Rapid and Cost-Effective Molecular Diagnosis for a GJB2 Founder Mutation for Hearing Impairment in Ghana

In Ghana, gap-junction protein β 2 (GJB2) variants account for about 25.9% of familial hearing impairment (HI) cases. The GJB2-p.Arg143Trp (NM_004004.6:c.427C>T/OMIM: 121011.0009/rs80338948) variant remains the most frequent variant associated with congenital HI in Ghana, but has not yet been investigated in clinical practice. We therefore sought to design a rapid and cost-effective test to detect this variant. We sampled 20 hearing-impaired and 10 normal hearing family members from 8 families segregating autosomal recessive non syndromic HI. In addition, a total of 111 unrelated isolated individuals with HI were selected, as well as 50 normal hearing control participants. A restriction fragment length polymorphism (RFLP) test was designed, using the restriction enzyme NciI optimized and validated with Sanger sequencing, for rapid genotyping of the common GJB2-p.Arg143Trp variant. All hearing-impaired participants from 7/8 families were homozygous positive for the GJB2-p.Arg143Trp mutation using the NciI-RFLP test, which was confirmed with Sanger sequencing. The investigation of 111 individuals with isolated non-syndromic HI that were previously Sanger sequenced found that the sensitivity of the GJB2-p.Arg143Trp NciI-RFLP testing was 100%. All the 50 control subjects with normal hearing were found to be negative for the variant. Although the test is extremely valuable, it is not 100% specific because it cannot differentiate between other mutations at the recognition site of the restriction enzyme. The GJB2-p.Arg143Trp NciI-RFLP-based diagnostic test had a high sensitivity for genotyping the most common GJB2 pathogenic and founder variant (p.Arg143Trp) within the Ghanaian populations. We recommend the adoption and implementation of this test for hearing impairment genetic clinical investigations to complement the newborn hearing screening program in Ghana. The present study is a practical case scenario of enhancing genetic medicine in Africa.

Microbiological Diagnosis of Respiratory Illness

A wide variety of microorganisms are potential respiratory pathogens, and the spectrum of known pathogens for each respiratory infection syndrome has not changed markers over recent years. Detection of likely etiologic agents of respiratory infections can help direct management and can also play an important role in disease surveillance. For this purpose, we are still reliant on many traditional diagnostic tools that have been used for decades in order to determine the microbial etiology of respiratory infections. However, these tools have been increasingly supplemented by newer methods, particular molecular diagnostic techniques, which have enabled the more rapid detection of many pathogens that were previously difficult to detect. These advances have particularly lead to improvements in the ability to detect respiratory viruses and also other microorganisms that do not normally colonize the respiratory tract. Recognition of the existence of the lung microbiome has challenged the traditional views of pneumonia pathogenesis and may provide the opportunity for new diagnostic tools that are focused on more than just detection of specific known pathogens. Continued liaison between clinicians and laboratory staff is vital in order to facilitate the most cost-effective use of laboratory diagnostics.

Nonsuppurative (Aseptic) Meningoencephalomyelitis Associated with Neurovirulent Astrovirus Infections in Humans and Animals

Astroviruses are thought to be enteric pathogens. Since 2010, a certain group of astroviruses has increasingly been recognized, using up-to-date random amplification and high-throughput next-generation sequencing (NGS) methods, as potential neurovirulent (Ni) pathogens of severe central nervous system (CNS) infections, causing encephalitis, meningoencephalitis, and meningoencephalomyelitis. To date, neurovirulent astrovirus cases or epidemics have been reported for humans and domesticated mammals, including mink, bovines, ovines, and swine. This comprehensive review summarizes the virology, epidemiology, pathology, diagnosis, therapy, and future perspective related to neurovirulent astroviruses in humans and mammals, based on a total of 30 relevant articles available in PubMed (searched by use of the terms "astrovirus/encephalitis" and "astrovirus/meningitis" on 2 March 2018). A paradigm shift should be considered based on the increasing knowledge of the causality-effect association between neurotropic astroviruses and CNS infection, and attention should be drawn to the role of astroviruses in unknown CNS diseases.

Investigation of next-generation sequencing data of Klebsiella pneumoniae using web-based tools

PURPOSE

Rapid identification and characterization of multidrug-resistant Klebsiella pneumoniae strains is necessary due to the increasing frequency of severe infections in patients. The decreasing cost of next-generation sequencing enables us to obtain a comprehensive overview of genetic information in one step. The aim of this study is to demonstrate and evaluate the utility and scope of the application of web-based databases to next-generation sequenced (NGS) data.

METHODOLOGY

The whole genomes of 11 clinical Klebsiella pneumoniae isolates were sequenced using Illumina MiSeq. Selected web-based tools were used to identify a variety of genetic characteristics, such as acquired antimicrobial resistance genes, multilocus sequence types, plasmid replicons, and identify virulence factors, such as virulence genes, cps clusters, urease-nickel clusters and efflux systems.

RESULTS

Using web-based tools hosted by the Center for Genomic Epidemiology, we detected resistance to 8 main antimicrobial groups with at least 11 acquired resistance genes. The isolates were divided into eight sequence types (ST11, 23, 37, 323, 433, 495 and 562, and a new one, ST1646). All of the isolates carried replicons of large plasmids. Capsular types, virulence factors and genes coding AcrAB and OqxAB efflux pumps were detected using BIGSdb-Kp, whereas the selected virulence genes, identified in almost all of the isolates, were detected using CLC Genomic Workbench software.

CONCLUSION

Applying appropriate web-based online tools to NGS data enables the rapid extraction of comprehensive information that can be used for more efficient diagnosis and treatment of patients, while data processing is free of charge, easy and time-efficient.

Deep sequencing approach for investigating infectious agents causing fever

Acute undifferentiated fever (AUF) poses a diagnostic challenge due to the variety of possible aetiologies. While the majority of AUFs resolve spontaneously, some cases become prolonged and cause significant morbidity and mortality, necessitating improved diagnostic methods. This study evaluated the utility of deep sequencing in fever investigation. DNA and RNA were isolated from plasma/sera of AUF cases being investigated at Cairns Hospital in northern Australia, including eight control samples from patients with a confirmed diagnosis. Following isolation, DNA and RNA were bulk amplified and RNA was reverse transcribed to cDNA. The resulting DNA and cDNA amplicons were subjected to deep sequencing on an Illumina HiSeq 2000 platform. Bioinformatics analysis was performed using the program Kraken and the CLC assembly-alignment pipeline. The results were compared with the outcomes of clinical tests. We generated between 4 and 20 million reads per sample. The results of Kraken and CLC analyses concurred with diagnoses obtained by other means in 87.5 % (7/8) and 25 % (2/8) of control samples, respectively. Some plausible causes of fever were identified in ten patients who remained undiagnosed following routine hospital investigations, including Escherichia coli bacteraemia and scrub typhus that eluded conventional tests. Achromobacter xylosoxidans, Alteromonas macleodii and Enterobacteria phage were prevalent in all samples. A deep sequencing approach of patient plasma/serum samples led to the identification of aetiological agents putatively implicated in AUFs and enabled the study of microbial diversity in human blood. The application of this approach in hospital practice is currently limited by sequencing input requirements and complicated data analysis.

State-of-the-Art Microbiologic Testing for Community-Acquired Meningitis and Encephalitis

Meningitis and encephalitis are potentially life-threatening diseases with a wide array of infectious, postinfectious, and noninfectious causes. Diagnostic testing is central to determining the underlying etiology, treatment, and prognosis, but many patients remain undiagnosed due to suboptimal testing and lack of tests for all pathogens. In this article, we summarize the epidemiology, barriers to diagnosis, and current best tests for meningitis and encephalitis in developed countries. We end with a brief discussion of new test methods, such as multiplex panel-based tests and metagenomic sequencing, which are likely to alter diagnostic strategies for these conditions in the near future.

The utility of diversity profiling using Illumina 18S rRNA gene amplicon deep sequencing to detect and discriminate Toxoplasma gondii among the cyst-forming coccidia

Next-generation sequencing (NGS) has the capacity to screen a single DNA sample and detect pathogen DNA from thousands of host DNA sequence reads, making it a versatile and informative tool for investigation of pathogens in diseased animals. The technique is effective and labor saving in the initial identification of pathogens, and will complement conventional diagnostic tests to associate the candidate pathogen with a disease process. In this report, we investigated the utility of the diversity profiling NGS approach using Illumina small subunit ribosomal RNA (18S rRNA) gene amplicon deep sequencing to detect Toxoplasma gondii in previously confirmed cases of toxoplasmosis. We then tested the diagnostic approach with species-specific PCR genotyping, histopathology and immunohistochemistry of toxoplasmosis in a Risso's dolphin (Grampus griseus) to systematically characterise the disease and associate causality. We show that the Euk7A/Euk570R primer set targeting the V1-V3 hypervariable region of the 18S rRNA gene can be used as a species-specific assay for cyst-forming coccidia and discriminate T. gondii. Overall, the approach is cost-effective and improves diagnostic decision support by narrowing the differential diagnosis list with more certainty than was previously possible. Furthermore, it supplements the limitations of cryptic protozoan morphology and surpasses the need for species-specific PCR primer combinations.