Improved understanding of biorisk for research involving microbial modification using annotated sequences of concern.

Regulation of research on microbes that cause disease in humans has historically been focused on taxonomic lists of 'bad bugs'. However, given our increased knowledge of these pathogens through inexpensive genome sequencing, 5 decades of research in microbial pathogenesis, and the burgeoning capacity of synthetic biologists, the limitations of this approach are apparent. With heightened scientific and public attention focused on biosafety and biosecurity, and an ongoing review by US authorities of dual-use research oversight, this article proposes the incorporation of sequences of concern (SoCs) into the biorisk management regime governing genetic engineering of pathogens. SoCs enable pathogenesis in all microbes infecting hosts that are 'of concern' to human civilization. Here we review the functions of SoCs (FunSoCs) and discuss how they might bring clarity to potentially problematic research outcomes involving infectious agents. We believe that annotation of SoCs with FunSoCs has the potential to improve the likelihood that dual use research of concern is recognized by both scientists and regulators before it occurs.

SeqScreen: accurate and sensitive functional screening of pathogenic sequences via ensemble learning.

The COVID-19 pandemic has emphasized the importance of accurate detection of known and emerging pathogens. However, robust characterization of pathogenic sequences remains an open challenge. To address this need we developed SeqScreen, which accurately characterizes short nucleotide sequences using taxonomic and functional labels and a customized set of curated Functions of Sequences of Concern (FunSoCs) specific to microbial pathogenesis. We show our ensemble machine learning model can label protein-coding sequences with FunSoCs with high recall and precision. SeqScreen is a step towards a novel paradigm of functionally informed synthetic DNA screening and pathogen characterization, available for download at www.gitlab.com/treangenlab/seqscreen .

Categorizing Sequences of Concern by Function To Better Assess Mechanisms of Microbial Pathogenesis.

To identify sequences with a role in microbial pathogenesis, we assessed the adequacy of their annotation by existing controlled vocabularies and sequence databases. Our goal was to regularize descriptions of microbial pathogenesis for improved integration with bioinformatic applications. Here, we review the challenges of annotating sequences for pathogenic activity. We relate the categorization of more than 2,750 sequences of pathogenic microbes through a controlled vocabulary called Functions of Sequences of Concern (FunSoCs). These allow for an ease of description by both humans and machines. We provide a subset of 220 fully annotated sequences in the supplemental material as examples. The use of this compact (∼30 terms), controlled vocabulary has potential benefits for research in microbial genomics, public health, biosecurity, biosurveillance, and the characterization of new and emerging pathogens.

Simulating transmission of ESKAPE pathogens plus C. difficile in relevant clinical scenarios.

BACKGROUND: The prevalence of healthcare-acquired infections (HAI) and rising levels of antimicrobial resistance places significant economic and public health burdens on modern healthcare systems. A group of highly drug resistant pathogens known as the ESKAPE pathogens, along with C. difficile, are the leading causes of HAIs. Interactions between patients, healthcare workers, and environmental conditions impact disease transmission. Studying pathogen transfer under varying contact scenarios in a controlled manner is critical for understanding transmission and disinfectant strategies. In lieu of human subject research, this method has the potential to contribute to modeling the routes of pathogen transmission in healthcare settings. METHODS: To overcome these challenges, we have developed a method that utilizes a synthetic skin surrogate to model both direct (skin-to-skin) and indirect (skin-to fomite-to skin) pathogen transfer between infected patients and healthy healthcare workers. This surrogate material includes a background microbiome community simulating typical human skin flora to more accurately mimic the effects of natural flora during transmission events. RESULTS: We demonstrate the ability to modulate individual bacterial concentrations within this microbial community to mimic bacterial concentrations previously reported on the hands of human subjects. We also explore the effect of various decontamination approaches on pathogen transfer between human subjects, such as the use of handwashing or surface disinfectants. Using this method, we identify a potential outlier, S. aureus, that may persist and retain viability in specific transfer conditions better than the overall microbial community during decontamination events. CONCLUSIONS: Our work describes the development of an in vitro method that uses a synthetic skin surrogate with a defined background microbiota to simulate skin-to-skin and skin-to fomite-to skin contact scenarios. These results illustrate the value of simulating a holistic microbial community for transfer studies by elucidating differences in different pathogen transmission rates and resistance to common decontamination practices. We believe this method will contribute to improvements in pathogen transmission modeling in healthcare settings and increase our ability to assess the risk associated with HAIs, although additional research is required to establish the degree of correlation of pathogen transmission by skin or synthetic alternatives.

Short-read, high-throughput sequencing technology for STR genotyping.

DNA-based methods for human identification principally rely upon genotyping of short tandem repeat (STR) loci. Electrophoretic-based techniques for variable-length classification of STRs are universally utilized, but are limited in that they have relatively low throughput and do not yield nucleotide sequence information. High-throughput sequencing technology may provide a more powerful instrument for human identification, but is not currently validated for forensic casework. Here, we present a systematic method to perform high-throughput genotyping analysis of the Combined DNA Index System (CODIS) STR loci using short-read (150 bp) massively parallel sequencing technology. Open source reference alignment tools were optimized to evaluate PCR-amplified STR loci using a custom designed STR genome reference. Evaluation of this approach demonstrated that the 13 CODIS STR loci and amelogenin (AMEL) locus could be accurately called from individual and mixture samples. Sensitivity analysis showed that as few as 18,500 reads, aligned to an in silico referenced genome, were required to genotype an individual (>99% confidence) for the CODIS loci. The power of this technology was further demonstrated by identification of variant alleles containing single nucleotide polymorphisms (SNPs) and the development of quantitative measurements (reads) for resolving mixed samples.

A Rho-like small GTPase of Entamoeba histolytica contains an unusual amino acid residue in a conserved GDP-stabilization region and is not a substrate for C3 exoenzyme.

The Entamoeba histolytica small GTP-binding protein EhRho1 has an unusual amino acid residue at a conserved site found in all known Ras superfamily proteins. EhRho1 has an isoleucine at position 45, which corresponds to position 28 of human Ras and Rac and position 30 of human Rho and Cdc42. All other known small GTPases have an aromatic residue (typically phenylalanine) at this position, and mutation to a leucine renders other Ras proteins constitutively active by reason of diminished affinity for GDP. It was determined that the EhRho1 protein has a half-time of GDP dissociation similar to that of a human Rho protein, HsRhoA, and therefore an isoleucine at this site in EhRho1 is not likely to render EhRho1 constitutively active. It was also found that EhRho1 is not a substrate for the Rho-specific C3 exoenzyme. Thus EhRho1 appears to be an unusual member of the Ras family.