Function-based classification of hazardous biological sequences: Demonstration of a new paradigm for biohazard assessments

Abstract

Bioengineering applies analytical and engineering principles to identify functional biological building blocks for biotechnology applications. While these building blocks are leveraged to improve the human condition, the lack of simplistic, machine-readable definition of biohazards at the function level is creating a gap for biosafety practices. More specifically, traditional safety practices focus on the biohazards of known pathogens at the organism-level and may not accurately consider novel biodesigns with engineered functionalities at the genetic component-level. This gap is motivating the need for a paradigm shift from organism-centric procedures to function-centric biohazard identification and classification practices. To address this challenge, we present a novel methodology for classifying biohazards at the individual sequence level, which we then compiled to distinguish the biohazardous property of pathogenicity at the whole genome level. Our methodology is rooted in compilation of hazardous functions, defined as a set of sequences and associated metadata that describe coarse-level functions associated with pathogens (e.g., adherence, immune subversion). We demonstrate that the resulting database can be used to develop hazardous “fingerprints” based on the functional metadata categories. We verified that these hazardous functions are found at higher levels in pathogens compared to non-pathogens, and hierarchical clustering of the fingerprints can distinguish between these two groups. The methodology presented here defines the hazardous functions associated with bioengineering functional building blocks at the sequence level, which provide a foundational framework for classifying biological hazards at the organism level, thus leading to the improvement and standardization of current biosecurity and biosafety practices.

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

“…An accurate computational assessment of the infectiousness of a synthetic microbe is not currently possible nor is it likely to be in the next decade. We think our work and that of others can provide pointers for how such as assessment might be attempted ( Gemler et al, 2022 ; Godbold et al, 2022 ). Our criteria for functions of sequences of concern were described in our earlier publication and are available to the scientific community.…”

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond

“…Recent work in our laboratory analyzing novel SARS-CoV-2 demonstrated that UltraSEQ could identify SARS-CoV-2 as a SARS-CoV-1-related virus (data not shown). Furthermore, analytical models built from hazardous function signals (using the methodology reported in our recent publication [ 8 ]) demonstrated that SARS-CoV-2 clusters with other SARS-CoVs with similar host ranges and human receptor types. Future work will expand on these advances and improve UltraSEQ’s ability to characterize features of emerging pathogens of clinical and research significance.…”

“…UniRef100 provides coverage across the tree of life (pathogens and nonpathogens) and provides the foundation for taxonomic calls in UltraSEQ. In addition, we used Battelle’s Sequence of Concern (SoC) protein database, described previously ( 8 ). This database contains ~12,300 manually selected publicly available UniProt protein sequences that have been curated based on functions such as enabling antibiotic resistance, immune evasion, toxicity, and/or other threatening functions.…”

“…Predictions are backed by our curated database that provides end users with additional contextual metadata of predictions, such as whether the identified pathogen typically or rarely causes disease or is a potential normal flora contaminant as well as other user-defined characteristics. In our previous work, we described the development of this database ( 8 ), which uses a function-centric approach to distinguish between pathogenic and nonpathogenic organisms with increased confidence over the state of the art. Here, we expand on this approach to leverage this database by building the flexible UltraSEQ bioinformatic platform and demonstrate its potential utility for clinical metagenomic applications that go beyond simple taxonomic predictions.…”