Erin L. Young

Background: Amplicon-based next-generation sequencing is a cornerstone of viral genomic surveillance, but it often produces datasets with highly uneven read coverage due to PCR amplification bias. This leads to excessive data generation and substantial computational overhead for downstream bioinformatic analysis. Results: We evaluated the efficacy of two data reduction strategies on 220 SARS-CoV-2 clinical specimens and 12 wastewater samples: random subsampling with SeqKit and read depth normalization with BBNorm. FASTQ files were reduced to target depths from 10X to 10,000X to compare genome coverage, lineage assignment concordance, and computational resource usage. Both strategies markedly reduced total processing time. For clinical isolates, read depth normalization corrected for amplification bias resulting in highly concordant lineage assignments at a target depth of just 200X. Random subsampling preserved inherent bias but produced reliable assignments at depths over 500X. Reducing target depths below 500X in complex wastewater mixtures severely degraded variant deconvolution accuracy and heavily skewed lineage abundance profiles for both methods. Conclusion: Both random subsampling and read depth normalization successfully reduce computational overhead while preserving key clinical sequencing metrics at appropriate reduction settings. The optimal method depends on the analytical goal, ranging from computationally lightweight random subsampling for rapid lineage assignment, to read depth normalization for generating high-quality consensus sequences. However, aggressive data reduction should be avoided in environmental surveillance, where maintaining a high baseline sequencing depth is critical for accurate mixed-variant deconvolution.

Rationale: Hypervirulent Klebsiella pneumoniae (hvKp) infections have principally been identified in Asia. Within a two-month period, two patients between the ages of 30 to 50 years old presented to a tertiary referral hospital in Texas with septic shock, hepatic abscess, and septic thrombophlebitis. Blood cultures were positive for Klebsiella pneumoniae (isolates 2020CK-00441 and 2021CK-00720 respectively). The first patient survived after a prolonged hospital course while the second patient expired. Objectives: Describe the clinical presentation of these two patients. Perform whole genome sequencing and bioinformatic analysis to evaluate potential outbreak of specific hvKp bacteria isolates. Methods: Whole genome sequencing was performed using both paired-end Illumina MiSeq and nanopore sequencing to obtain a Completed genome for both isolates. Main results: 2020CK-00441 belonged to ST23 type while 2021CK-00720 was a ST65 type isolate. Kleborate analyses predicted with high confidence both isolates were hvKp. Phylogenetic analyses showed the two strains are not closely related to each other nor to any known hvKp isolates reported. Both isolates had yersiniabactin, colibactin, aerobactin and salmochelin producing loci which likely confer these isolates hvKp phenotype. 2020CK-00441 and 2021CK-00720 had a unique pK2044 like plasmid. Conclusions: HvKp strains capable of causing devastating metastatic septic infections have emerged in Texas. These isolates are unique compared to other hvKp strains globally. Country-wide surveillance and whole genome sequencing of these strains is essential to prevent a major public health emergency in the USA.

From July–November 2020, mink (Neogale vison) on 12 Utah farms experienced an increase in mortality rates due to confirmed SARS-CoV-2 infection. We conducted epidemiologic investigations on six farms to identify the source of virus introduction, track cross-species transmission, and assess viral evolution. Interviews were conducted and specimens were collected from persons living or working on participating farms and from multiple animal species. Swabs and sera were tested by SARS-CoV-2 real-time reverse transcription polymerase chain reaction (rRT-PCR) and serological assays, respectively. Whole genome sequencing was attempted for specimens with cycle threshold values <30. Evidence of SARS-CoV-2 infection was detected by rRT-PCR or serology in ≥1 person, farmed mink, dog, and/or feral cat on each farm. Sequence analysis showed high similarity between mink and human sequences on corresponding farms. On farms sampled at multiple time points, mink tested rRT-PCR positive up to 16 weeks post-onset of increased mortality. Workers likely introduced SARS-CoV-2 to mink, and mink transmitted SARS-CoV-2 to other animal species; mink-to-human transmission was not identified. Our findings provide critical evidence to support interventions to prevent and manage SARS-CoV-2 in people and animals on mink farms and emphasizes the importance of a One Health approach to address emerging zoonoses.

Approximately 67% of U.S. households have pets. Limited data are available on SARS-CoV-2 in pets. We assessed SARS-CoV-2 infection in pets during a COVID-19 household transmission investigation. Pets from households with ≥1 person with laboratory-confirmed COVID-19 were eligible for inclusion from April–May 2020. We enrolled 37 dogs and 19 cats from 34 households. All oropharyngeal, nasal, and rectal swabs tested negative by rRT-PCR; one dog’s fur swabs (2%) tested positive by rRT-PCR at the first sampling. Among 47 pets with serological results, eight (17%) pets (four dogs, four cats) from 6/30 (20%) households had detectable SARS-CoV-2 neutralizing antibodies. In households with a seropositive pet, the proportion of people with laboratory-confirmed COVID-19 was greater (median 79%; range: 40–100%) compared to households with no seropositive pet (median 37%; range: 13–100%) (p = 0.01). Thirty-three pets with serologic results had frequent daily contact (≥1 h) with the index patient before the person’s COVID-19 diagnosis. Of these 33 pets, 14 (42%) had decreased contact with the index patient after diagnosis and none were seropositive; of the 19 (58%) pets with continued contact, four (21%) were seropositive. Seropositive pets likely acquired infection after contact with people with COVID-19. People with COVID-19 should restrict contact with pets and other animals.

PURPOSE: This is a SARS-CoV-2 specific protocol that covers the steps needed to establish a new NCBI submission environment for your laboratory, including the creation of new BioProject(s) and submission groups. Once these are step up, the protocol then walks through the process for submitting raw reads to SRA and sample metadata to BioSample through the Submission portal. For new submitters, there's quite a bit of groundwork that needs to be established before a laboratory can start its first data submission. We recommend that one person in the laboratory take a few days to get everything set up in advance of when you expect to do your first data submission. If you need a pipeline for frequent or large volume submissions, follow Step 1 in the SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject to get your NCBI submission environment established, then contact gb-admin@ncbi.nlm.nih.gov to set up an account for submitting through the API. These protocols cover submission using NCBI's Submission Portal web-interface. Complete in order (1 then 2): 1. SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject (included protocol) Step-by-step instructions for establishing a new NCBI laboratory submission account and for creating and linking a new BioProject to an existing umbrella effort. SARS-CoV-2 raw data submission to SRA (Sequence Read Archive) and metadata to BioSample. 2. SARS-CoV-2 NCBI consensus submission protocol: GenBank Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies to NCBI GenBank, linking to existing BioProject, BioSamples, and raw data. Metadata templates, explained! SOP for populating NCBI submission templates

PURPOSE: This is a SARS-CoV-2 specific protocol that covers the steps needed to establish a new NCBI submission environment for your laboratory, including the creation of new BioProject(s) and submission groups. Once these are step up, the protocol then walks through the process for submitting raw reads to SRA and sample metadata to BioSample through the Submission portal. For new submitters, there's quite a bit of groundwork that needs to be established before a laboratory can start its first data submission. We recommend that one person in the laboratory take a few days to get everything set up in advance of when you expect to do your first data submission. If you need a pipeline for frequent or large volume submissions, follow Step 1 in the SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject to get your NCBI submission environment established, then contact gb-admin@ncbi.nlm.nih.gov to set up an account for submitting through the API. These protocols cover submission using NCBI's Submission Portal web-interface. Complete in order (1 then 2): 1. SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject Step-by-step instructions for establishing a new NCBI laboratory submission account and for creating and linking a new BioProject to an existing umbrella effort. SARS-CoV-2 raw data submission to SRA (Sequence Read Archive) and metadata to BioSample. 2. SARS-CoV-2 NCBI consensus submission protocol: GenBank (included protocol) Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies to NCBI GenBank, linking to existing BioProject, BioSamples, and raw data. Metadata templates, explained! SOP for populating NCBI submission templates

PURPOSE: This protocol covers the steps for submitting a SARS-CoV-2 assembly to NCBI's GenBank For new submitters, there's quite a bit of groundwork that needs to be established before a laboratory can start its first data submission. We recommend that one person in the laboratory take a few days to get everything set up in advance of when you expect to do your first data submission. Two protocols cover the PHA4GE guidance for SARS-CoV-2 submission to NCBI (Raw sequence data, metadata, and assemblies). If you need a pipeline for frequent or large volume submissions, follow Step 1 in the SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject to get your NCBI submission environment established, then contact gb-admin@ncbi.nlm.nih.gov to set up an account for submitting through the API. These protocols cover submission using NCBI's Submission Portal web-interface. Complete in order (1 then 2): 1. SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject Step-by-step instructions for establishing a new NCBI laboratory submission account and for creating and linking a new BioProject to an existing umbrella effort. Submit SARS-CoV-2 raw data to SRA (Sequence Read Archive) and metadata to BioSample. 2. SARS-CoV-2 NCBI assembly submission protocol: GenBank (included protocol) Required: established BioProject and BioSamples Submit SARS-CoV-2 consensus sequences to NCBI GenBank, linking to existing BioProject, BioSamples, and raw data.

PURPOSE: This is a SARS-CoV-2 specific protocol that covers the steps needed to establish a new NCBI submission environment for your laboratory, including the creation of new BioProject(s) and submission groups. Once these are step up, the protocol then walks through the process for submitting raw reads to SRA and sample metadata to BioSample through the Submission portal. For new submitters, there's quite a bit of groundwork that needs to be established before a laboratory can start its first data submission. We recommend that one person in the laboratory take a few days to get everything set up in advance of when you expect to do your first data submission. If you need a pipeline for frequent or large volume submissions, follow Step 1 in the SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject to get your NCBI submission environment established, then contact gb-admin@ncbi.nlm.nih.gov to set up an account for submitting through the API. These protocols cover submission using NCBI's Submission Portal web-interface. Complete in order (1 then 2): 1. SARS-CoV-2 NCBI submission protocol: SRA, BioSample, and BioProject (included protocol) Step-by-step instructions for establishing a new NCBI laboratory submission account and for creating and linking a new BioProject to an existing umbrella effort. SARS-CoV-2 raw data submission to SRA (Sequence Read Archive) and metadata to BioSample. 2. SARS-CoV-2 NCBI assembly submission protocol: GenBank Required: established BioProject and BioSamples Submit SARS-CoV-2 assemblies to NCBI GenBank, linking to existing BioProject, BioSamples, and raw data.

The tumor suppressor protein p53 compiles information about cellular stressors to make decisions on whether the cell should survive or undergo apoptosis. However, the p53 response depends on the source of damage, displaying a ‘digital’ oscillatory response after ionizing radiation (IR) damage and a proportional non-oscillatory response following UV damage. We propose a mathematical model that qualitatively replicates this observed behavior. The difference in p53 dynamics in the model results from two mechanisms: IR damage is fully detected minutes after exposure while UV damage is detected over several hours; and the p53-controlled transcriptional response is dominated by inactive p53 following UV damage. In particular, we hypothesize that an unidentified positive feedback loop controlled by inactive p53 is required to maintain the qualitative high p53 response to UV damage. This work proposes an explanation for two distinct responses of p53 to DNA damage and how each response can lead to cell cycle arrest or apoptosis. Author summary: We propose a mathematical model hypothesizing how the tumor suppressor protein p53 produces two contrasting dynamical responses in response to different types of DNA damage. In particular, we predict the existence of a positive feedback loop controlled by the inactive form of p53, which allows the cell to respond to slowly detected damage. The existence of differing dynamic responses by p53 has implications for our understanding of tumor development and possibly p53-related therapeutic strategies.