WYMM Tour: Montreal

Microbial genomics has been a considerable advancement in diagnostic, outbreak investigation and surveillance of infectious diseases. Genomic epidemiology helps understand the spread of infectious diseases by comparing the sequences of pathogens. The COVID-19 pandemic has had a major impact on society and the emergence of new variants is now part of our normal vocabulary. Outbreaks in long-term care facilities, hospitals, schools and workplaces were numerous and impactful. Genomic programs of SARS-CoV-2 have therefore been put in place in different jurisdictions. The utility and performance of these genomic programs need to be assessed. Sequencing during the early pandemic has allowed the discovery of the first key mutation, D614G, in the spike protein, and then surveillance of the emergence of numerous variants. Traditional epidemiological investigations do not always resolve the transmission chain in outbreaks. During an outbreak in a hospital, sequencing of SARS-CoV-2 with ONT allowed for rapid results. These analyses revealed that some patients contracted COVID-19 within the hospital while others did not. This precision changed our understanding of SARS-CoV-2 transmission and altered hospital interventions. The performance of such genomic programs needs to be assessed. We have conducted a COVID-19 surveillance programs in schools that included a genomics arm. We realised that our percentage of genome sequences meeting the quality standards was lower than anticipated. Therefore, we have conducted an analysis to identify the weak links of the genomic process. Our main hypotheses included the delays in between the day of symptom onset and sample collection, and sub-optimal PCR cycle threshold (Ct) cut-off for sequencing. Density graphs showed that Ct values from one of three PCR assays did not correlate with genome coverage and samples tested by this PCR could not reliably be sequenced. We argue that microbial genomic programs should establish quality indicators and monitor them regularly.

While many studies have described the transcriptomes of extracellular vesicles (EVs) in different cellular contexts, these efforts have typically relied on sequencing methods requiring RNA fragmentation, which limits interpretations on the integrity and isoform diversity of EV-targeted RNA populations. It has been assumed that mRNA signatures in EVs are likely to be fragmentation products of the cellular mRNA material, and the extent to which full-length mRNAs are present within EVs remains to be clarified. Using long-read nanopore RNA sequencing, we sought to characterize the full-length polyadenylated (poly-A) transcriptome of EVs released by human chronic myelogenous leukemia K562 cells. We detected 443 and 280 RNAs that were respectively enriched or depleted in EVs. EV-enriched poly-A transcripts consist of a variety of biotypes, including mRNAs, long non-coding RNAs, and pseudogenes. Our analysis revealed that 10.58% of all EV reads, and 18.67% of all cellular (WC) reads, corresponded to known full-length transcripts, with mRNAs representing the largest biotype for each group (EV=58.13%, WC=43.93%). We also observed that for many well-represented coding and non-coding genes, diverse full-length transcript isoforms were present in EV specimens, and these isoforms were reflective-of but often in different ratio compared to cellular samples. This work provides novel insights into the compositional diversity of poly-A transcript isoforms enriched within EVs, while also underscoring the potential usefulness of nanopore sequencing to interrogate secreted RNA transcriptomes.

The advance in research related to genomics are highly driven by technological innovations, and most recently the development of high-throughput sequencing approaches. Rare neurological disorders, due to the high clinical and genetic heterogeneity, have greatly benefit from these innovative technologies to increase diagnostic yield and improve our understanding of pathological mechanisms. However, there is still a significant number of patients that are without a molecular diagnosis or for whom the etiology of the disease is still unknown despite a diagnosis, representing important challenges for the research in neuro-genomics. In the case of neurological diseases, it is estimated that at least 30% are still without a diagnosis. For these patients and families, the application of novel technologies in a research setting is essential to identify potential genetic aberrations, elucidate pathological mechanisms and uncover potential therapeutic strategies. An in-depth understanding of the genetic etiology will benefit patients by providing knowledge that will in the future improve support in genetic counseling and clinical management. Furthermore, an increase knowledge of pathological mechanisms will also permit the development of novel or more personalized therapeutic approaches as well as contributing to the stratification of patients and thus, select those that would most benefit from existing treatments or participate in relevant clinical trials.

By 2050, the WHO predicts antimicrobial resistance (AMR) in critical pathogens will surpass cancer as the primary cause of mortality. As of 2024, 30% of newborns with sepsis die of an AMR infection. The scientific difficulties, financial and regulatory hurdles, lack of know-how, low success rates and, prohibitive costs of clinical trials have not given new antibiotic classes since 1987. Oxford Nanopore dynamic whole genome sequencing (ONT-WGS) from undiscovered isolated bacteria and fungi (iCHIP) and arthropods is critical to predict antimicrobial biosynthesis gene clusters (aBGCs) for non-traditional antibiotics and antifungals. However, antiSMASH 7.0 predicts aBGCs by sequence homology and pathway reconstruction with no a priori knowledge of expression and antimicrobial activity. We present a pipeline to identify novel aBGCs combining Oxford Nanopore high quality dynamic WGS, deep machine learning combined with AlphaFold2 structure prediction and, synthetic gene production. Batches of 192 genomic DNA libraries were sequenced using ONT-P2solo, assembled with Nanolite processing 180-200 genomes/hour. As proof of concept, phage depolymerases (PDPs) were predicted with PDP-Miner, combining gene annotation (Pharokka), SVM-based PDPs detection (DePP), conserved domain search (PfamScan) and AlphaFold2 neural network prediction of 3D critical structure domains. A similar workflow, AntiBFC-Miner, was developed to further predict and model antibacterial and antifungal BGCs.By leveraging genomics, deep learning, and dynamic ONT-WGS, we found promising antimicrobials using our unique iCHIP-Soil bacteria and fungi collection and the International Pseudomonas Consortium Database https://ipcd.ibis.ulaval.ca/. For PDPs, our discovery rates of 10-15 antimicrobials per 100 genomes are currently investigated in vitro and in animal models.

Well-regulated gene expression networks are responsible for establishing and maintaining cellular states during development. Of the early cell states present in development, pluripotency is the cellular state that has the potential to derive all cell lineages of the embryo. Mutations in genes associated with pluripotency often lead to abnormal development and embryo lethality, but much of the focus relating to these genes has been on protein-coding genes. However, the last decade has seen the rise of long non-coding RNAs (lncRNAs) as novel players in the control of pluripotency, development, and several diseases including cancer and neurological disorders. As such, our major goal is to understand the molecular mechanisms and functional interactions of lncRNAs in modulating cellular states. In this talk, I will focus on a novel lncRNA, that we named Tapir. It is expressed very early during development, in the 2-cell and 4-cell stages, in the inner cell mass, and in stem cells of the neuroepithelium and the myeloid lineages. I will discuss how this lncRNA interacts with mRNAs to influence splicing, gene expression, and ultimately cell fate by regulating stem cell maintenance.