Boosting pathogen genomics and bioinformatics workforce in Africa

A framework towards implementation of sequencing for antimicrobial-resistant and other health-care-associated pathogens

Antimicrobial resistance continues to be a growing threat globally, specifically in health-care settings in which antimicrobial-resistant pathogens cause a substantial proportion of health-care-associated infections (HAIs). Next-generation sequencing (NGS) and the analysis of the data produced therein (ie, bioinformatics) represent an opportunity to enhance our capacity to address these threats. The 3rd Geneva Infection Prevention and Control Think Tank brought together experts to identify gaps, propose solutions, and set priorities for the use of NGS for HAIs and antimicrobial-resistant pathogens. The major deliverable recommendation from this meeting was a proposed framework for implementing the sequencing of HAI pathogens, specifically those harbouring antimicrobial-resistance mechanisms. The key components of the proposed framework relate to wet laboratory quality, sequence data quality, database and tool selection, bioinformatic analyses, data sharing, and NGS data integration, to support public health and actions for infection prevention and control. In this Personal View we detail and discuss the framework in the context of global implementation, specifically in low-income and middle-income countries.

Implementing a national programme of pathogen genomics for public health: the Australian Pathogen Genomics Program (AusPathoGen)

Delivering large-scale routine pathogen genomics surveillance for public health is of considerable interest, although translational research models that promote national-level implementation are not well defined. We describe the development and deployment of the Australian Pathogen Genomics Program (AusPathoGen), a comprehensive national partnership between academia, public health laboratories, and public health agencies that commenced in January, 2021. Successfully establishing and delivering a national programme requires inclusive and transparent collaboration between stakeholders, defined and clear focus on public health priorities, and support for strengthening national genomics capacity. Major enablers for delivering such a programme include technical solutions for data integration and analysis, such as the genomics surveillance platform AusTrakka, standard bioinformatic analysis methods, and national ethics and data sharing agreements that promote nationally integrated surveillance systems. Training of public health officials to interpret and act on genomic data is crucial, and evaluation and cost-effectiveness programmes will provide a benchmark and evidence for sustainable investment in genomics nationally and globally.

Expediting pathogen genomics adoption for enhanced foodborne disease surveillance in Africa

The role of genomics in public health surveillance has been accentuated by its crucial contributions during the COVID-19 pandemic, demonstrating its potential in addressing global disease outbreaks. While Africa has made strides in expanding multi-pathogen genomic surveillance, the integration into foodborne disease (FBD) surveillance remains nascent. Here we highlight the critical components to strengthen and scale-up the integration of whole genome sequencing (WGS) in foodborne disease surveillance across the continent. We discuss priority use-cases for FBD, and strategies for the implementation. We also highlight the major challenges such as data management, policy and regulatory frameworks, stakeholder engagement, the need for multidisciplinary collaborations and the importance of robust monitoring and evaluation, aiming to bolster Africa's preparedness and response to future health threats.

Engineering Stimuli-Responsive Materials for Precision Medicine

Over the past decade, precision medicine has garnered increasing attention, making significant strides in discovering new therapeutic drugs and mechanisms, resulting in notable achievements in symptom alleviation, pain reduction, and extended survival rates. However, the limited target specificity of primary drugs and inter-individual differences have often necessitated high-dosage strategies, leading to challenges such as restricted deep tissue penetration rates and systemic side effects. Material science advancements present a promising avenue for these issues. By leveraging the distinct internal features of diseased regions and the application of specific external stimuli, responsive materials can be tailored to achieve targeted delivery, controllable release, and specific biochemical reactions. This review aims to highlight the latest advancements in stimuli-responsive materials and their potential in precision medicine. Initially, we introduce disease-related internal stimuli and capable external stimuli, elucidating the reaction principles of responsive functional groups. Subsequently, we provide a detailed analysis of representative pre-clinical achievements of stimuli responsive materials across various clinical applications, including enhancements in the treatment of cancers, injury diseases, inflammatory diseases, infection diseases, and high-throughput microfluidic biosensors. Finally, we discuss some clinical challenges, such as off-target effects, long-term impacts of nano-materials, potential ethical concerns, and offer insights into future perspectives of stimuli-responsive materials.

Wastewater metagenomics in Africa: Opportunities and challenges

The advent of metagenomics has dramatically expanded our understanding of microbial communities, particularly through the study of wastewater, which serves as a rich source of microbial data. In Africa, wastewater metagenomics presents unparalleled opportunities for public health monitoring, antimicrobial resistance (AMR) tracking, and the discovery of new microbial species and functions. Utilizing high-throughput sequencing (HTS) technologies, this method allows for direct analysis of nucleic acids from wastewater samples, providing a cost-effective and comprehensive approach for pathogen surveillance. The potential of wastewater metagenomics in Africa is vast. It can revolutionize public health monitoring by acting as an early warning system for infectious disease outbreaks, offering near real-time data to shape effective responses. This is especially critical in densely populated urban areas with poor sanitation, where the risk of disease spread is high. Moreover, this approach enables the detection of emerging pathogens and insights into environmental health. However, the implementation of wastewater metagenomics in Africa faces several challenges. These include variability in wastewater composition due to differing local customs, limited infrastructure for sequencing and data analysis, and a shortage of bioinformatics expertise. Socio-political and ethical issues also complicate data sharing and the equitable distribution of benefits. To overcome these challenges, there is a need to enhance capacity through collaborative training, infrastructural development, and international partnerships. Investing and sustaining local genomics and bioinformatics infrastructure and expertise is crucial. Moreover, establishing robust data governance frameworks and engaging communities are essential for leveraging metagenomics to advance scientific knowledge and deliver tangible health and economic benefits. With strategic planning and collaboration, Africa can harness the transformative potential of wastewater metagenomics to improve disease surveillance, combat AMR, and foster scientific innovation, contributing significantly to sustainable development and improved quality of life.

Africa in the era of pathogen genomics: Unlocking data barriers

Rapid expansion of pathogen sequencing capacity in Africa has led to a paradigm shift from relying on others to locally generating genomic data and sharing it with the global community. However, several barriers remain to be unlocked for timely processing, analysis, dissemination, and effective use of pathogen sequence data for pandemic prevention, preparedness, and response.

Kidenya B, Kabahita JM, Namaganda MM, Nsubuga M, Nabisubi P, Ayitewala A, Kebirungi G, Nakafu E, Akwii NP. The rise of pathogen genomics in Africa

The routine genomic surveillance of pathogens in diverse geographical settings and equitable data sharing are critical to inform effective infection control and therapeutic development. The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of routine genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to detect emerging variants of concern. However, the majority of high-income countries sequenced >0.5% of their COVID-19 cases, unlike low- and middle-income countries. By the end of 2022, many countries around the world had managed to establish capacity for pathogen genomic surveillance. Notably, Beta and Omicron; 2 of the 5 current SARS-CoV-2 variants of concern were first discovered in Africa through an aggressive sequencing campaign led by African scientists. To sustain such infrastructure and expertise beyond this pandemic, other endemic pathogens should leverage this investment. Therefore, countries are establishing multi-pathogen genomic surveillance strategies. Here we provide a catalog of the current landscape of sequenced and publicly shared pathogens in different countries in Africa. Drawing upon our collective knowledge and expertise, we review the ever-evolving challenges and propose innovative recommendations.

Early mutational signatures and transmissibility of SARS-CoV-2 Gamma and Lambda variants in Chile

Genomic surveillance (GS) programmes were crucial in identifying and quantifying the mutating patterns of SARS-CoV-2 during the COVID-19 pandemic. In this work, we develop a Bayesian framework to quantify the relative transmissibility of different variants tailored for regions with limited GS. We use it to study the relative transmissibility of SARS-CoV-2 variants in Chile. Among the 3443 SARS-CoV-2 genomes collected between January and June 2021, where sampling was designed to be representative, the Gamma (P.1), Lambda (C.37), Alpha (B.1.1.7), B.1.1.348, and B.1.1 lineages were predominant. We found that Lambda and Gamma variants' reproduction numbers were 5% (95% CI: [1%, 14%]) and 16% (95% CI: [11%, 21%]) larger than Alpha's, respectively. Besides, we observed a systematic mutation enrichment in the Spike gene for all circulating variants, which strongly correlated with variants' transmissibility during the studied period (r = 0.93, p-value = 0.025). We also characterised the mutational signatures of local samples and their evolution over time and with the progress of vaccination, comparing them with those of samples collected in other regions worldwide. Altogether, our work provides a reliable method for quantifying variant transmissibility under subsampling and emphasises the importance of continuous genomic surveillance.

Overcoming colonialism in pathogen genomics

Historical legacies of colonialism affect the distribution and control of scientific knowledge today, including within the pathogen genomics field, which remains dominated by high-income countries (HICs). We discuss the imperatives for decolonising pathogen genomics, including the need for more equitable representation, collaboration, and capacity-strengthening, and the shared responsibilities that both low-income and middle-income countries (LMICs) and HICs have in this endeavour. By highlighting examples from LMICs, we illuminate the pathways and challenges that researchers in LMICs face in the bid to gain autonomy in this crucial domain. Recognising the inherent value of local expertise and resources, we argue for a more inclusive, globally collaborative approach to pathogen genomics. Such an approach not only fosters scientific growth and innovation, but also strengthens global health security by equipping all nations with the tools needed to respond to health crises.

Assessment of the pathogen genomics landscape highlights disparities and challenges for effective AMR Surveillance and outbreak response in the East African community

The East African Community (EAC) grapples with many challenges in tackling infectious disease threats and antimicrobial resistance (AMR), underscoring the importance of regional and robust pathogen genomics capacities. However, a significant disparity exists among EAC Partner States in harnessing bacterial pathogen sequencing and data analysis capabilities for effective AMR surveillance and outbreak response. This study assesses the current landscape and challenges associated with pathogen next-generation sequencing (NGS) within EAC, explicitly focusing on World Health Organization (WHO) AMR-priority pathogens. The assessment adopts a comprehensive approach, integrating a questionnaire-based survey amongst National Public Health Laboratories (NPHLs) with an analysis of publicly available metadata on bacterial pathogens isolated in the EAC countries. In addition to the heavy reliance on third-party organizations for bacterial NGS, the findings reveal a significant disparity among EAC member States in leveraging bacterial pathogen sequencing and data analysis. Approximately 97% (n = 4,462) of publicly available high-quality bacterial genome assemblies of samples collected in the EAC were processed and analyzed by external organizations, mainly in Europe and North America. Tanzania led in-country sequencing efforts, followed by Kenya and Uganda. The other EAC countries had no publicly available samples or had all their samples sequenced and analyzed outside the region. Insufficient local NGS sequencing facilities, limited bioinformatics expertise, lack of adequate computing resources, and inadequate data-sharing mechanisms are among the most pressing challenges that hinder the EAC's NPHLs from effectively leveraging pathogen genomics data. These insights emphasized the need to strengthen microbial pathogen sequencing and data analysis capabilities within the EAC to empower these laboratories to conduct pathogen sequencing and data analysis independently. Substantial investments in equipment, technology, and capacity-building initiatives are crucial for supporting regional preparedness against infectious disease outbreaks and mitigating the impact of AMR burden. In addition, collaborative efforts should be developed to narrow the gap, remedy regional imbalances, and harmonize NGS data standards. Supporting regional collaboration, strengthening in-country genomics capabilities, and investing in long-term training programs will ultimately improve pathogen data generation and foster a robust NGS-driven AMR surveillance and outbreak response in the EAC, thereby supporting global health initiatives.

Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa

BACKGROUND

Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E. coli, targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs.

RESULTS

Model performance varied across antibiotics. The Support Vector Machine excelled in predicting ciprofloxacin resistance (87% accuracy, F1 Score: 0.57), Light Gradient Boosting Machine for cefotaxime (92% accuracy, F1 Score: 0.42), and Gradient Boosting for ampicillin (58% accuracy, F1 Score: 0.66). In validation with data from Africa, Logistic Regression showed high accuracy for ampicillin (94%, F1 Score: 0.97), while Random Forest and Light Gradient Boosting Machine were effective for ciprofloxacin (50% accuracy, F1 Score: 0.56) and cefotaxime (45% accuracy, F1 Score:0.54), respectively. Key mutations associated with AMR were identified for these antibiotics.

CONCLUSION

As the threat of AMR continues to rise, the successful application of these models, particularly on genomic datasets from LMICs, signals a promising avenue for improving AMR prediction to support large AMR surveillance programs. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against AMR.