In PLOS One, researchers from Sweden and Germany present a genome sequence-based analysis of American foulbrood, a honeybee disease caused by globally endemic Paenibacillus larvae bacteria. The team attempted to use core genome sequencing as a multilocus sequence typing tool, focusing on dozens of P. larvae isolates from mainland Sweden or from colonies in Gotland during a 2014 outbreak on the Baltic island. In addition to carrying out comparisons with "enterobacterial repetitive intergenic consensus" (EPIC)-based genotyping, the authors used genome sequence data to trace the Gotland outbreak to a single beekeeper. "Since [American foulbrood] is mainly spread through beekeeping activities such as moving contaminated hive material, [whole-genome sequencing] could be a useful tool to determine when and from where the pathogen was introduced in a geographical area, a beekeeping operation, or an apiary," they write.
A team from Colombia and the US explores microRNA profiles in blood samples from individuals with class IV systemic lupus erythematous for another PLOS One paper. Using miRNA sequencing, the researchers assessed peripheral blood samples from Colombian individuals with or without class IV lupus nephritis, a form of the disease that affects the kidneys. Based on miRNA profiles for four individuals with lupus nephritis, 10 nephritis-free systemic lupus erythematous patients, and seven autoimmune disease-free control individuals, they narrowed in on two dozen circulating miRNAs with a distinct expression pattern in the lupus nephritis cases compared to unaffected controls.
Finally, researchers in Turkey and the US consider cancer co-expression networks for five cancer types using proteomic data and bioinformatics. For this PLOS One study, the team turned to reverse phase protein array data generated for thousands of breast, lung, kidney, skin, or brain cancer samples profiled for the Cancer Proteome Atlas Project. "Performance of nine association estimators used in the network inference algorithms were examined on the proteomic data of five different cancer types in both pathway and gene levels," the authors write, noting that the use of several association estimators "can make a significant improvement in identifying the disease-associated co-expression modules when they are integrated with the [weighted gene co-expression network analysis] method."