Uncovering the magnitude of African pangolin poaching with extensive nanopore DNA genotyping of seized scales

Trade in pangolins is illegal, and yet tons of their scales and products are seized at various ports. These large seizures are challenging to process and comprehensively genotype for upstream provenance tracing and species identification for prosecution. We implemented a scalable DNA barcoding pipeline in which rapid DNA extraction and MinION sequencing were used to genotype a substantial proportion of pangolin scales subsampled from 2 record shipments seized in Singapore in 2019 (37.5 t). We used reference sequences to match the scales to phylogeographical regions of origin. In total, we identified 2346 cytochrome b (cytb) barcodes of white-bellied (Phataginus tricuspis) (from 1091 scales), black-bellied (Phataginus tetradactyla) (227 scales), and giant (Smutsia gigantea) (1028 scales) pangolins. Haplotype diversity was higher for P. tricuspis scales (121 haplotypes, 66 novel) than that for P. tetradactyla (22 haplotypes, 15 novel) and S. gigantea (25 haplotypes, 21 novel) scales. Of the novel haplotypes, 74.2% were likely from western and west-central Africa, suggesting potential resurgence of poaching and newly exploited populations in these regions. Our results illustrate the utility of extensively subsampling large seizures and outline an efficient molecular approach for rapid genetic screening that should be accessible to most forensic laboratories and enforcement agencies.

Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

BACKGROUND

Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction.

RESULTS

We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents.

CONCLUSIONS

We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.