Microevolution of Serial Clinical Isolates of Cryptococcus neoformans var. grubii and C. gattii.

The pathogenic species of Cryptococcus are a major cause of mortality owing to severe infections in immunocompromised as well as immunocompetent individuals. Although antifungal treatment is usually effective, many patients relapse after treatment, and in such cases, comparative analyses of the genomes of incident and relapse isolates may reveal evidence of determinative, microevolutionary changes within the host. Here, we analyzed serial isolates cultured from cerebrospinal fluid specimens of 18 South African patients with recurrent cryptococcal meningitis. The time between collection of the incident isolates and collection of the relapse isolates ranged from 124 days to 290 days, and the analyses revealed that, during this period within the patients, the isolates underwent several genetic and phenotypic changes. Considering the vast genetic diversity of cryptococcal isolates in sub-Saharan Africa, it was not surprising to find that the relapse isolates had acquired different genetic and correlative phenotypic changes. They exhibited various mechanisms for enhancing virulence, such as growth at 39°C, adaptation to stress, and capsule production; a remarkable amplification of ERG11 at the native and unlinked locus may provide stable resistance to fluconazole. Our data provide a deeper understanding of the microevolution of Cryptococcus species under pressure from antifungal chemotherapy and host immune responses. This investigation clearly suggests a promising strategy to identify novel targets for improved diagnosis, therapy, and prognosis.IMPORTANCE Opportunistic infections caused by species of the pathogenic yeast Cryptococcus lead to chronic meningoencephalitis and continue to ravage thousands of patients with HIV/AIDS. Despite receiving antifungal treatment, over 10% of patients develop recurrent disease. In this study, we collected isolates of Cryptococcus from cerebrospinal fluid specimens of 18 patients at the time of their diagnosis and when they relapsed several months later. We then sequenced and compared the genomic DNAs of each pair of initial and relapse isolates. We also tested the isolates for several key properties related to cryptococcal virulence as well as for their susceptibility to the antifungal drug fluconazole. These analyses revealed that the relapsing isolates manifested multiple genetic and chromosomal changes that affected a variety of genes implicated in the pathogenicity of Cryptococcus or resistance to fluconazole. This application of comparative genomics to serial clinical isolates provides a blueprint for identifying the mechanisms whereby pathogenic microbes adapt within patients to prolong disease.

Identifications of Putative PKA Substrates with Quantitative Phosphoproteomics and Primary-Sequence-Based Scoring.

Protein kinase A (PKA or cAMP-dependent protein kinase) is a serine/threonine kinase that plays essential roles in the regulation of proliferation, differentiation, and apoptosis. To better understand the functions of PKA, it is necessary to elucidate the direct interplay between PKA and their substrates in living human cells. To identify kinase target substrates in a high-throughput manner, we first quantified the change of phosphoproteome in the cells of which PKA activity was perturbed by drug stimulations. LC-MS/MS analyses identified 2755 and 3191 phosphopeptides from experiments with activator or inhibitor of PKA. To exclude potential indirect targets of PKA, we built a computational model to characterize the kinase sequence specificity toward the substrate target site based on known kinase-substrate relationships. Finally, by combining the sequence recognition model with the quantitative changes in phosphorylation measured in the two drug perturbation experiments, we identified 29 reliable candidates of PKA targeting residues in living cells including 8 previously known substrates. Moreover, 18 of these sites were confirmed to be site-specifically phosphorylated in vitro. Altogether this study proposed a confident list of PKA substrate candidates, expanding our knowledge of PKA signaling network.

The mutational landscape of phosphorylation signaling in cancer.

Somatic mutations in cancer genomes include drivers that provide selective advantages to tumor cells and passengers present due to genome instability. Discovery of pan-cancer drivers will help characterize biological systems important in multiple cancers and lead to development of better therapies. Driver genes are most often identified by their recurrent mutations across tumor samples. However, some mutations are more important for protein function than others. Thus considering the location of mutations with respect to functional protein sites can predict their mechanisms of action and improve the sensitivity of driver gene detection. Protein phosphorylation is a post-translational modification central to cancer biology and treatment, and frequently altered by driver mutations. Here we used our ActiveDriver method to analyze known phosphorylation sites mutated by single nucleotide variants (SNVs) in The Cancer Genome Atlas Research Network (TCGA) pan-cancer dataset of 3,185 genomes and 12 cancer types. Phosphorylation-related SNVs (pSNVs) occur in ~90% of tumors, show increased conservation and functional mutation impact compared to other protein-coding mutations, and are enriched in cancer genes and pathways. Gene-centric analysis found 150 known and candidate cancer genes with significant pSNV recurrence. Using a novel computational method, we predict that 29% of these mutations directly abolish phosphorylation or modify kinase target sites to rewire signaling pathways. This analysis shows that incorporation of information about protein signaling sites will improve computational pipelines for variant function prediction.