Accelerating bioinformatics implementation in public health.

We have adopted an open bioinformatics ecosystem to address the challenges of bioinformatics implementation in public health laboratories (PHLs). Bioinformatics implementation for public health requires practitioners to undertake standardized bioinformatic analyses and generate reproducible, validated and auditable results. It is essential that data storage and analysis are scalable, portable and secure, and that implementation of bioinformatics fits within the operational constraints of the laboratory. We address these requirements using Terra, a web-based data analysis platform with a graphical user interface connecting users to bioinformatics analyses without the use of code. We have developed bioinformatics workflows for use with Terra that specifically meet the needs of public health practitioners. These Theiagen workflows perform genome assembly, quality control, and characterization, as well as construction of phylogeny for insights into genomic epidemiology. Additonally, these workflows use open-source containerized software and the WDL workflow language to ensure standardization and interoperability with other bioinformatics solutions, whilst being adaptable by the user. They are all open source and publicly available in Dockstore with the version-controlled code available in public GitHub repositories. They have been written to generate outputs in standardized file formats to allow for further downstream analysis and visualization with separate genomic epidemiology software. Testament to this solution meeting the requirements for bioinformatic implementation in public health, Theiagen workflows have collectively been used for over 5 million sample analyses in the last 2 years by over 90 public health laboratories in at least 40 different countries. Continued adoption of technological innovations and development of further workflows will ensure that this ecosystem continues to benefit PHLs.

The 2022 RSV surge was driven by multiple viral lineages.

The US experienced an early and severe respiratory syncytial virus (RSV) surge in autumn 2022. Despite the pressure this has put on hospitals and care centers, the factors promoting the surge in cases are unknown. To investigate whether viral characteristics contributed to the extent or severity of the surge, we sequenced 105 RSV-positive specimens from symptomatic patients diagnosed with RSV who presented to the Massachusetts General Hospital (MGH) and its outpatient practices in the Greater Boston Area. Genomic analysis of the resulting 77 genomes (54 with >80% coverage, and 23 with >5% coverage) demonstrated that the surge was driven by multiple lineages of RSV-A (91%; 70/77) and RSV-B (9%; 7/77). Phylogenetic analysis of all US RSV-A revealed 12 clades, 4 of which contained Massachusetts and Washington genomes. These clades individually had times to most recent common ancestor (tMRCA) between 2014 and 2017, and together had a tMRCA of 2009, suggesting that they emerged well before the COVID-19 pandemic. Similarly, the RSV-B genomes had a tMRCA between 2016 and 2019. We found that the RSV-A and RSV-B genomes in our sample did not differ statistically from the estimated clock rate of the larger phylogenetic tree (10.6 and 12.4 substitutions per year, respectively). In summary, the polyphyletic nature of viral genomes sequenced in the US during the autumn 2022 surge is inconsistent with the emergence of a single, highly transmissible causal RSV lineage.

Multimodal surveillance of SARS-CoV-2 at a university enables development of a robust outbreak response framework.

BACKGROUND: Universities are vulnerable to infectious disease outbreaks, making them ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures. Here, we analyze multimodal COVID-19-associated data collected during the 2020-2021 academic year at Colorado Mesa University and introduce a SARS-CoV-2 surveillance and response framework. METHODS: We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and WiFi-based co-location data) alongside pathogen surveillance data (wastewater and diagnostic testing, and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy. We applied relative risk, multiple linear regression, and social network assortativity to identify attributes or behaviors associated with contracting SARS-CoV-2. To characterize SARS-CoV-2 transmission, we used viral sequencing, phylogenomic tools, and functional assays. FINDINGS: Athletes, particularly those on high-contact teams, had the highest risk of testing positive. On average, individuals who tested positive had more contacts and longer interaction durations than individuals who never tested positive. The distribution of contacts per individual was overdispersed, although not as overdispersed as the distribution of phylogenomic descendants. Corroboration via technical replicates was essential for identification of wastewater mutations. CONCLUSIONS: Based on our findings, we formulate a framework that combines tools into an integrated disease surveillance program that can be implemented in other congregate settings with limited resources. FUNDING: This work was supported by the National Science Foundation, the Hertz Foundation, the National Institutes of Health, the Centers for Disease Control and Prevention, the Massachusetts Consortium on Pathogen Readiness, the Howard Hughes Medical Institute, the Flu Lab, and the Audacious Project.

Comparative transmissibility of SARS-CoV-2 variants Delta and Alpha in New England, USA.

The SARS-CoV-2 Delta variant rose to dominance in mid-2021, likely propelled by an estimated 40%-80% increased transmissibility over Alpha. To investigate if this ostensible difference in transmissibility is uniform across populations, we partner with public health programs from all six states in New England in the United States. We compare logistic growth rates during each variant's respective emergence period, finding that Delta emerged 1.37-2.63 times faster than Alpha (range across states). We compute variant-specific effective reproductive numbers, estimating that Delta is 63%-167% more transmissible than Alpha (range across states). Finally, we estimate that Delta infections generate on average 6.2 (95% CI 3.1-10.9) times more viral RNA copies per milliliter than Alpha infections during their respective emergence. Overall, our evidence suggests that Delta's enhanced transmissibility can be attributed to its innate ability to increase infectiousness, but its epidemiological dynamics may vary depending on underlying population attributes and sequencing data availability.

Transmission from vaccinated individuals in a large SARS-CoV-2 Delta variant outbreak.

An outbreak of over 1,000 COVID-19 cases in Provincetown, Massachusetts (MA), in July 2021-the first large outbreak mostly in vaccinated individuals in the US-prompted a comprehensive public health response, motivating changes to national masking recommendations and raising questions about infection and transmission among vaccinated individuals. To address these questions, we combined viral genomic and epidemiological data from 467 individuals, including 40% of outbreak-associated cases. The Delta variant accounted for 99% of cases in this dataset; it was introduced from at least 40 sources, but 83% of cases derived from a single source, likely through transmission across multiple settings over a short time rather than a single event. Genomic and epidemiological data supported multiple transmissions of Delta from and between fully vaccinated individuals. However, despite its magnitude, the outbreak had limited onward impact in MA and the US overall, likely due to high vaccination rates and a robust public health response.

Synthetic DNA spike-ins (SDSIs) enable sample tracking and detection of inter-sample contamination in SARS-CoV-2 sequencing workflows.

The global spread and continued evolution of SARS-CoV-2 has driven an unprecedented surge in viral genomic surveillance. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine laboratory processes and results. This challenge will increase with the expanding global production of sequences across a variety of laboratories for epidemiological and clinical interpretation, as well as for genomic surveillance of emerging diseases in future outbreaks. We present SDSI + AmpSeq, an approach that uses 96 synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination throughout the sequencing workflow. We apply SDSIs to the ARTIC Consortium's amplicon design, demonstrate their utility and efficiency in a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases and validate them across 6,676 diagnostic samples at multiple laboratories. We establish that SDSI + AmpSeq provides increased confidence in genomic data by detecting and correcting for relatively common, yet previously unobserved modes of error, including spillover and sample swaps, without impacting genome recovery.

Comparative transmissibility of SARS-CoV-2 variants Delta and Alpha in New England, USA.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant quickly rose to dominance in mid-2021, displacing other variants, including Alpha. Studies using data from the United Kingdom and India estimated that Delta was 40-80% more transmissible than Alpha, allowing Delta to become the globally dominant variant. However, it was unclear if the ostensible difference in relative transmissibility was due mostly to innate properties of Delta's infectiousness or differences in the study populations. To investigate, we formed a partnership with SARS-CoV-2 genomic surveillance programs from all six New England US states. By comparing logistic growth rates, we found that Delta emerged 37-163% faster than Alpha in early 2021 (37% Massachusetts, 75% New Hampshire, 95% Maine, 98% Rhode Island, 151% Connecticut, and 163% Vermont). We next computed variant-specific effective reproductive numbers and estimated that Delta was 58-120% more transmissible than Alpha across New England (58% New Hampshire, 68% Massachusetts, 76% Connecticut, 85% Rhode Island, 98% Maine, and 120% Vermont). Finally, using RT-PCR data, we estimated that Delta infections generate on average ∼6 times more viral RNA copies per mL than Alpha infections. Overall, our evidence indicates that Delta's enhanced transmissibility could be attributed to its innate ability to increase infectiousness, but its epidemiological dynamics may vary depending on the underlying immunity and behavior of distinct populations.

Evidence of transmission from fully vaccinated individuals in a large outbreak of the SARS-CoV-2 Delta variant in Provincetown, Massachusetts.

Multiple summer events, including large indoor gatherings, in Provincetown, Massachusetts (MA), in July 2021 contributed to an outbreak of over one thousand COVID-19 cases among residents and visitors. Most cases were fully vaccinated, many of whom were also symptomatic, prompting a comprehensive public health response, motivating changes to national masking recommendations, and raising questions about infection and transmission among vaccinated individuals. To characterize the outbreak and the viral population underlying it, we combined genomic and epidemiological data from 467 individuals, including 40% of known outbreak-associated cases. The Delta variant accounted for 99% of sequenced outbreak-associated cases. Phylogenetic analysis suggests over 40 sources of Delta in the dataset, with one responsible for a single cluster containing 83% of outbreak-associated genomes. This cluster was likely not the result of extensive spread at a single site, but rather transmission from a common source across multiple settings over a short time. Genomic and epidemiological data combined provide strong support for 25 transmission events from, including many between, fully vaccinated individuals; genomic data alone provides evidence for an additional 64. Together, genomic epidemiology provides a high-resolution picture of the Provincetown outbreak, revealing multiple cases of transmission of Delta from fully vaccinated individuals. However, despite its magnitude, the outbreak was restricted in its onward impact in MA and the US, likely due to high vaccination rates and a robust public health response.

DNA spike-ins enable confident interpretation of SARS-CoV-2 genomic data from amplicon-based sequencing.

The rapid global spread and continued evolution of SARS-CoV-2 has highlighted an unprecedented need for viral genomic surveillance and clinical viral sequencing. Amplicon-based sequencing methods provide a sensitive, low-cost and rapid approach but suffer a high potential for contamination, which can undermine lab processes and results. This challenge will only increase with expanding global production of sequences by diverse research groups for epidemiological and clinical interpretation. We present an approach which uses synthetic DNA spike-ins (SDSIs) to track samples and detect inter-sample contamination through a sequencing workflow. Applying this approach to the ARTIC Consortium's amplicon design, we define a series of best practices for Illumina-based sequencing and provide a detailed characterization of approaches to increase sensitivity for low-viral load samples incorporating the SDSIs. We demonstrate the utility and efficiency of the SDSI method amidst a real-time investigation of a suspected hospital cluster of SARS-CoV-2 cases.

Phylogenetic analysis of SARS-CoV-2 in Boston highlights the impact of superspreading events.

Analysis of 772 complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from early in the Boston-area epidemic revealed numerous introductions of the virus, a small number of which led to most cases. The data revealed two superspreading events. One, in a skilled nursing facility, led to rapid transmission and significant mortality in this vulnerable population but little broader spread, whereas other introductions into the facility had little effect. The second, at an international business conference, produced sustained community transmission and was exported, resulting in extensive regional, national, and international spread. The two events also differed substantially in the genetic variation they generated, suggesting varying transmission dynamics in superspreading events. Our results show how genomic epidemiology can help to understand the link between individual clusters and wider community spread.

Phylogenetic analysis of SARS-CoV-2 in the Boston area highlights the role of recurrent importation and superspreading events.

SARS-CoV-2 has caused a severe, ongoing outbreak of COVID-19 in Massachusetts with 111,070 confirmed cases and 8,433 deaths as of August 1, 2020. To investigate the introduction, spread, and epidemiology of COVID-19 in the Boston area, we sequenced and analyzed 772 complete SARS-CoV-2 genomes from the region, including nearly all confirmed cases within the first week of the epidemic and hundreds of cases from major outbreaks at a conference, a nursing facility, and among homeless shelter guests and staff. The data reveal over 80 introductions into the Boston area, predominantly from elsewhere in the United States and Europe. We studied two superspreading events covered by the data, events that led to very different outcomes because of the timing and populations involved. One produced rapid spread in a vulnerable population but little onward transmission, while the other was a major contributor to sustained community transmission, including outbreaks in homeless populations, and was exported to several other domestic and international sites. The same two events differed significantly in the number of new mutations seen, raising the possibility that SARS-CoV-2 superspreading might encompass disparate transmission dynamics. Our results highlight the failure of measures to prevent importation into MA early in the outbreak, underscore the role of superspreading in amplifying an outbreak in a major urban area, and lay a foundation for contact tracing informed by genetic data.

TLR Stimulation Produces IFN-ß as the Primary Driver of IFN Signaling in Nonlymphoid Primary Human Cells.

Several human autoimmune diseases are characterized by increased expression of type 1 IFN-stimulated genes in both the peripheral blood and tissue. The contributions of different type I IFNs to this gene signature are uncertain as the type I IFN family consists of 13 alphas and one each of ß, ε, κ, and ω subtypes. We sought to investigate the contribution of various IFNs to IFN signaling in primary human cell types. We stimulated primary skin, muscle, kidney, and PBMCs from normal healthy human donors with various TLR ligands and measured the expression of type I IFN subtypes and activation of downstream signaling by quantitative PCR. We show that IFNB1 is the dominant type I IFN expressed upon TLR3 and TLR4 stimulation, and its expression profile is associated with subsequent MX1 transcription. Furthermore, using an IFN-ß-specific neutralizing Ab, we show that MX1 expression is inhibited in a dose-dependent manner, suggesting that IFN-ß is the primary driver of IFN-stimulated genes following TLR3 and TLR4 engagement. Stimulation with TLR7/8 and TLR9 ligands induced IFNB1 and IFNA subtypes and MX1 expression only in PBMCs and not in tissue resident cell types. Concordantly, IFN-ß neutralization had no effect on MX1 expression in PBMCs potentially because of the combination of IFNB1 and IFNA expression. Combined, these data highlight the potential role for IFN-ß in driving local inflammatory responses in clinically relevant human tissue types and opportunities to treat local inflammation by targeting IFN-ß.

Novel biomarkers of a peripheral blood interferon signature associated with drug-naïve early arthritis patients distinguish persistent from self-limiting disease course.

We profiled gene expression signatures to distinguish rheumatoid arthritis (RA) from non-inflammatory arthralgia (NIA), self-limiting arthritis (SLA), and undifferentiated arthritis (UA) as compared to healthy controls as novel potential biomarkers for therapeutic responsiveness. Global gene expression profiles of PBMCs from 43 drug-naïve patients presenting with joint symptoms were evaluated and differentially expressed genes identified by comparative analysis with 24 healthy volunteers. Patients were assessed at presentation with follow up at 6 and 12 months. Gene ontology and network pathway analysis were performed using DAVID Bioinformatics Resources v6.7. Gene expression profiles were also determined after disease-modifying anti-rheumatic drug (DMARD) treatment in the inflammatory arthritis groups (i.e. RA and UA) and confirmed by qRT-PCR. Receiver operating characteristic (ROC) curves analysis and Area Under the Curve (AUC) estimation were performed to assess the diagnostic value of candidate gene expression signatures. A type I interferon (IFN) gene signature distinguished DMARD-naïve patients who will subsequently develop persistent inflammatory arthritis (i.e. RA and UA) from those with NIA. In patients with RA, the IFN signature is characterised by up-regulation of SIGLEC1 (p = 0.00597) and MS4A4A (p = 0.00000904). We also identified, EPHB2 (p = 0.000542) and PDZK1IP1 (p = 0.0206) with RA-specific gene expression profiles and elevated expression of the ST6GALNAC1 (p = 0.0023) gene in UA. ROC and AUC risk score analysis suggested that MSA4A (AUC: 0.894, 0.644, 0.720), PDZK1IP1 (AUC: 0.785, 0.806, 0.977), and EPHB2 (AUC: 0.794, 0.723, 0.620) at 0, 6, and 12 months follow-up can accurately discriminate patients with RA from healthy controls and may have practical value for RA diagnosis. In patients with early inflammatory arthritis, ST6GALNAC1 is a potential biomarker for UA as compared with healthy controls whereas EPHB2, MS4A4A, and particularly PDZK1IP1 may discriminate RA patients. SIGLEC1 may also be a useful marker of disease activity in UA.

Cancer-associated fibroblasts suppress SOX2-induced dysplasia in a lung squamous cancer coculture.

Tumorigenesis depends on intricate interactions between genetically altered tumor cells and their surrounding microenvironment. While oncogenic drivers in lung squamous carcinoma (LUSC) have been described, the role of stroma in modulating tissue architecture, particularly cell polarity, remains unclear. Here, we report the establishment of a 3D coculture system of LUSC epithelial cells with cancer-associated fibroblasts (CAFs) and extracellular matrix that together capture key components of the tumor microenvironment (TME). Single LUSC epithelial cells develop into acinar-like structures with 0.02% efficiency, and addition of CAFs provides proper tumor-stromal interactions within an appropriate 3D architectural context. Using this model, we recapitulate key pathological changes during tumorigenesis, from hyperplasia to dysplasia and eventually invasion, in malignant LUSC spheroids that undergo phenotypic switching in response to cell intrinsic and extrinsic changes. Overexpression of SOX2 is sufficient to mediate the transition from hyperplasia to dysplasia in LUSC spheroids, while the presence of CAFs makes them invasive. Unexpectedly, CAFs suppress the activity of high SOX2 levels, restore hyperplasia, and enhance the formation of acinar-like structures. Taken together, these observations suggest that stromal factors can override cell intrinsic oncogenic changes in determining the disease phenotype, thus providing fundamental evidence for the existence of dynamic reciprocity between the nucleus and the TME of LUSC.

Upregulation of RNA Processing Factors in Poorly Differentiated Lung Cancer Cells.

Intratumoral heterogeneity in non-small cell lung cancer (NSCLC) has been appreciated at the histological and cellular levels, but the association of less differentiated pathology with poor clinical outcome is not understood at the molecular level. Gene expression profiling of intact human tumors fails to reveal the molecular nature of functionally distinct epithelial cell subpopulations, in particular the tumor cells that fuel tumor growth, metastasis, and disease relapse. We generated primary serum-free cultures of NSCLC and then exposed them to conditions known to promote differentiation: the air-liquid interface (ALI) and serum. The transcriptional network of the primary cultures was associated with stem cells, indicating a poorly differentiated state, and worse overall survival of NSCLC patients. Strikingly, the overexpression of RNA splicing and processing factors was a prominent feature of the poorly differentiated cells and was also observed in clinical datasets. A genome-wide analysis of splice isoform expression revealed many alternative splicing events that were specific to the differentiation state of the cells, including an unexpectedly high frequency of events on chromosome 19. The poorly differentiated cells exhibited alternative splicing in many genes associated with tumor progression, as exemplified by the preferential expression of the short isoform of telomeric repeat-binding factor 1 (TERF1), also known as Pin2. Our findings demonstrate the utility of the ALI method for probing the molecular mechanisms that underlie NSCLC pathogenesis and provide novel insight into posttranscriptional mechanisms in poorly differentiated lung cancer cells.

Breast cancer cells respond differentially to modulation of TGFß2 signaling after exposure to chemotherapy or hypoxia.

Intratumoral heterogeneity helps drive the selection for diverse therapy-resistant cell populations. In this study, we demonstrate the coexistence of two therapy-resistant populations with distinct properties that are reproducibly enriched under conditions that characterize tumor pathophysiology. Breast cancer cells that survived chemotherapy or hypoxia were enriched for cells expressing the major hyaluronic acid receptor CD44. However, only CD44(hi) cells that survived chemotherapy exhibited cancer stem cell (CSC) phenotypes based on growth potential and gene expression signatures that represent oncogenic signaling and metastatic prowess. Strikingly, we identified TGFß2 as a key growth promoter of CD44(hi) cells that survived chemotherapy but also as a growth inhibitor of cells that survived hypoxia. Expression of the TGFß receptor TGFßR1 and its effector molecule SMAD4 was required for enrichment of CD44(hi) cells exposed to the chemotherapeutic drug epirubicin, which suggests a feed-forward loop to enrich for and enhance the function of surviving CSCs. Our results reveal context-dependent effects of TGFß2 signaling in the same tumor at the same time. The emergence of distinct resistant tumor cell populations as a consequence of prior therapeutic intervention or microenvironmental cues has significant implications for the responsiveness of recurring tumors to therapy.

Epigenetic reprogramming by tumor-derived EZH2 gain-of-function mutations promotes aggressive 3D cell morphologies and enhances melanoma tumor growth.

In addition to genetic alterations, cancer cells are characterized by myriad epigenetic changes. EZH2 is a histone methyltransferase that is over-expressed and mutated in cancer. The EZH2 gain-of-function (GOF) mutations first identified in lymphomas have recently been reported in melanoma (~2%) but remain uncharacterized. We expressed multiple EZH2 GOF mutations in the A375 metastatic skin melanoma cell line and observed both increased H3K27me3 and dramatic changes in 3D culture morphology. In these cells, prominent morphological changes were accompanied by a decrease in cell contractility and an increase in collective cell migration. At the molecular level, we observed significant alteration of the axonal guidance pathway, a pathway intricately involved in the regulation of cell shape and motility. Furthermore, the aggressive 3D morphology of EZH2 GOF-expressing melanoma cells (both endogenous and ectopic) was attenuated by EZH2 catalytic inhibition. Finally, A375 cells expressing exogenous EZH2 GOF mutants formed larger tumors than control cells in mouse xenograft studies. This study not only demonstrates the first functional characterization of EZH2 GOF mutants in non-hematopoietic cells, but also provides a rationale for EZH2 catalytic inhibition in melanoma.