Evolutionary and spatiotemporal analyses reveal multiple introductions and cryptic transmission of SARS-CoV-2 VOC/VOI in Malta

IMPORTANCE

Our study provides insights into the evolution of the coronavirus disease 2019 (COVID-19) pandemic in Malta, a highly connected and understudied country. We combined epidemiological and phylodynamic analyses to analyze trends in the number of new cases, deaths, tests, positivity rates, and evolutionary and dispersal patterns from August 2020 to January 2022. Our reconstructions inferred 173 independent severe acute respiratory syndrome coronavirus 2 introductions into Malta from various global regions. Our study demonstrates that characterizing epidemiological trends coupled with phylodynamic modeling can inform the implementation of public health interventions to help control COVID-19 transmission in the community.

Spatial habitat heterogeneity influences host-pathogen dynamics in a patchy population of Ranchman's tiger moth

Host-pathogen dynamics are influenced by many factors that vary locally, but models of disease rarely consider dynamics across spatially heterogeneous environments. In addition, theory predicts that dispersal will influence host-pathogen dynamics of populations that are linked, although this has not been examined empirically in natural systems. We examined the spatial dynamics of a patchy population of tiger moths and its baculovirus pathogen, in which habitat type and weather influence dynamics. Theoretical models of host-baculovirus dynamics predict that such variation in dynamics between habitat types could be driven by a range of factors, of which we predict two are likely to be operating in this system: (1) differences in the environmental persistence of pathogens or (2) differences in host intrinsic rates of increase. We used time series models and monitored infection rates of hosts to characterize population and disease dynamics and distinguish between these possibilities. We also examined the role of host dispersal (connectivity) and weather as important contributors to dynamics, using time series models and experiments. We found that the population growth rate was higher, delayed density dependence was weaker, and long-period oscillations had lower amplitudes in high-quality habitat patches. The infection rate was higher on average in high-quality habitat, and this was likely to have been driven by higher mean population densities and no differences in pathogen persistence in different habitats (delayed density dependence). Time series modeling and experiments also showed an interactive effect of temperature and precipitation on moth population growth rates (likely caused by variation in host plant quality and quantity), and an effect of connectivity. Our results showed that spatial heterogeneity, connectivity, climate, and their interactions were important in driving host-baculovirus dynamics. In particular, our study found that connected patches and spatial heterogeneity generated differences in dynamics that only partially aligned with theoretical predictions.

Enhancing Coordination Around Cancer Biomarker and Hereditary Genetic Testing Among Members of the Multidisciplinary Care Team

Introduction/Objective

Advances in precision medicine necessitate a closer integration across pathologists, genetic counselors, and other members of the multidisciplinary cancer care team (MDT). Recognizing that physical distancing and fragmented communication may hinder care delivery, the Association of Community Cancer Centers (ACCC) ran a multi-year initiative to explore ways to improve MDT care coordination.

Methods/Case Report

ACCC held a multistakeholder leadership summit to identify and discuss critical issues regarding biomarker and hereditary genetic testing and the ways in which pathology and genetic counseling professionals interface with the cancer MDT. Through a consensus-driven process, participants identified key opportunities for achieving optimal integration. ACCC also conducted a national survey to explore barriers around biomarker and hereditary genetic testing. Building off these insights, ACCC conducted virtual workshops at three community cancer programs to improve processes around biomarker testing and targeted treatment planning.

Results (if a Case Study enter NA)

The pathology leadership summit identified the following priorities: 1) streamline and standardize the biomarker test ordering process; 2) improve tissue handling to optimize timely biomarker testing; 3) strengthen communication between pathologists and genetic counselors; 4) empower pathologists with leadership opportunities. In the ACCC survey (n=659), 57% indicated that some or most of their pathologists were generalists. 21% only held one general tumor board. 6% indicated that pathologists often do not attend tumor boards or cancer committee meetings. 64% indicated that pathologists can directly access some or all medical oncology patient records. 67% sent some or most biopsy samples out for biomarker testing. In the ACCC workshops, cancer programs in KS, NC, and NV identified ways to improve the timeliness of genetic counseling referrals and coordinate biomarker testing. Following the workshops, 64% indicated they planned to make moderate or significant changes in their processes.

Conclusion

Improving collaboration across pathologists, genetic counselors, and the rest of the cancer MDT may lead to more timely and comprehensive biomarker and hereditary genetic testing. These findings may help cancer programs refine processes of care.

A Common Concern With a Rare Diagnosis: Pancreatic Neuroendocrine Neoplasms

Pancreatic neuroendocrine neoplasms (pNENs) are rare, representing only a small percentage of all pancreatic tumors. We report the clinical and radiological features of pNENs. Intraoperative pathology confirmed pNENs with clear margins and the patient did not require adjuvant chemoradiation. The patient is currently doing well and being closely monitored due to the high risk of relapse.

Genetic variation among 82 pharmacogenes: The PGRNseq data from the eMERGE network

Genetic variation can affect drug response in multiple ways, although it remains unclear how rare genetic variants affect drug response. The electronic Medical Records and Genomics (eMERGE) Network, collaborating with the Pharmacogenomics Research Network, began eMERGE‐PGx, a targeted sequencing study to assess genetic variation in 82 pharmacogenes critical for implementation of “precision medicine.” The February 2015 eMERGE‐PGx data release includes sequence‐derived data from ∼5,000 clinical subjects. We present the variant frequency spectrum categorized by variant type, ancestry, and predicted function. We found 95.12% of genes have variants with a scaled Combined Annotation‐Dependent Depletion score above 20, and 96.19% of all samples had one or more Clinical Pharmacogenetics Implementation Consortium Level A actionable variants. These data highlight the distribution and scope of genetic variation in relevant pharmacogenes, identifying challenges associated with implementing clinical sequencing for drug treatment at a broader level, underscoring the importance for multifaceted research in the execution of precision medicine.

Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems

We describe here the design and initial implementation of the eMERGE-PGx project. eMERGE-PGx, a partnership of the eMERGE and PGRN consortia, has three objectives : 1) Deploy PGRNseq, a next-generation sequencing platform assessing sequence variation in 84 proposed pharmacogenes, in nearly 9,000 patients likely to be prescribed drugs of interest in a 1–3 year timeframe across several clinical sites; 2) Integrate well-established clinically-validated pharmacogenetic genotypes into the electronic health record with associated clinical decision support and assess process and clinical outcomes of implementation; and 3) Develop a repository of pharmacogenetic variants of unknown significance linked to a repository of EHR-based clinical phenotype data for ongoing pharmacogenomics discovery. We describe site-specific project implementation and anticipated products, including genetic variant and phenotype data repositories, novel variant association studies, clinical decision support modules, clinical and process outcomes, approaches to manage incidental findings, and patient and clinician education methods.

Small town lead levels: a case study from the homes of pre-schoolers in Mt. Pleasant, Michigan

This study evaluates the relationship between household Pb levels and four variables (home age, distance to road, traffic volume adjacent to the home, and the amount of exposed soil) for 42 homes in a small city. As a whole, Pb levels for the Mt. Pleasant sample were very low compared to large cities. Home age appeared to have the greatest impact on Pb levels as determined by atomic absorption soil = 65 microg g(-1), vacuum dust = 620 microg g(-1), window sill = 291 microg m(-1), indoor play area = 22 microg m(-1), and home entrance = 291 microg m(-1). The correlation coefficient for increasing home age and soil Pb level was r=0.63 (p<0.000). An inverse relationship (r=-0.45, p=0.003) occurred between soil Pb levels and distance from the road. Household Pb levels generally increased both with higher traffic volumes and greater amounts of exposed soil although both trends were not statistically significant. Study participants kept their home in a good to excellent state of repair and resided on lightly trafficked streets; as such, Pb deposition through the weathering of Pb-based paint and the former combustion of leaded gasoline was minimized.

Analysis of the heat-induced denaturation of proteins using temperature gradient gel electrophoresis

A new technique, temperature gradient gel electrophoresis (TGGE), was applied to the study of heat-induced protein denaturation. The gels used contained 30 mM Borax + 75 mM boric acid, pH 8.4, and various concentrations of urea. When this technique was applied to bovine serum albumin, the protein showed discontinuous bands upon melting, indicating that the thermal transition is irreversible. The apparent melting temperature, calculated based on the relative intensity of two bands in the transition region, was 58 degrees C in 2 M urea. When the thermal denaturation of bovine serum albumin was analyzed spectroscopically, the transition was again irreversible, with an apparent transition temperature of 57 degrees C, consistent with the TGGE results. Recombinant stem cell factor, recombinant granulocyte macrophage-colony stimulating factor, and catalase were also analyzed by TGGE, indicating that the technique can be used to analyze denaturation of monomeric and multimeric proteins.