Plasmodium falciparum causes the most severe form of malaria and kills up to 2.7 million people annually. Despite the global importance of P. falciparum, the vast majority of its proteins have not been characterized experimentally. Here we identify P. falciparum protein-protein interactions using a high-throughput version of the yeast two-hybrid assay that circumvents the difficulties in expressing P. falciparum proteins in Saccharomyces cerevisiae. From more than 32,000 yeast two-hybrid screens with P. falciparum protein fragments, we identified 2,846 unique interactions, most of which include at least one previously uncharacterized protein. Informatic analyses of network connectivity, coexpression of the genes encoding interacting fragments, and enrichment of specific protein domains or Gene Ontology annotations were used to identify groups of interacting proteins, including one implicated in chromatin modification, transcription, messenger RNA stability and ubiquitination, and another implicated in the invasion of host cells. These data constitute the first extensive description of the protein interaction network for this important human pathogen.

Huntington's disease (HD) is a fatal neurodegenerative condition caused by expansion of the polyglutamine tract in the huntingtin (Htt) protein. Neuronal toxicity in HD is thought to be, at least in part, a consequence of protein interactions involving mutant Htt. We therefore hypothesized that genetic modifiers of HD neurodegeneration should be enriched among Htt protein interactors. To test this idea, we identified a comprehensive set of Htt interactors using two complementary approaches: high-throughput yeast two-hybrid screening and affinity pull down followed by mass spectrometry. This effort led to the identification of 234 high-confidence Htt-associated proteins, 104 of which were found with the yeast method and 130 with the pull downs. We then tested an arbitrary set of 60 genes encoding interacting proteins for their ability to behave as genetic modifiers of neurodegeneration in a Drosophila model of HD. This high-content validation assay showed that 27 of 60 orthologs tested were high-confidence genetic modifiers, as modification was observed with more than one allele. The 45% hit rate for genetic modifiers seen among the interactors is an order of magnitude higher than the 1%–4% typically observed in unbiased genetic screens. Genetic modifiers were similarly represented among proteins discovered using yeast two-hybrid and pull-down/mass spectrometry methods, supporting the notion that these complementary technologies are equally useful in identifying biologically relevant proteins. Interacting proteins confirmed as modifiers of the neurodegeneration phenotype represent a diverse array of biological functions, including synaptic transmission, cytoskeletal organization, signal transduction, and transcription. Among the modifiers were 17 loss-of-function suppressors of neurodegeneration, which can be considered potential targets for therapeutic intervention. Finally, we show that seven interacting proteins from among 11 tested were able to co-immunoprecipitate with full-length Htt from mouse brain. These studies demonstrate that high-throughput screening for protein interactions combined with genetic validation in a model organism is a powerful approach for identifying novel candidate modifiers of polyglutamine toxicity.

Endometriosis is a common gynecological condition with complex etiology defined by the presence of endometrial glands and stroma outside the womb. Endometriosis is a common cause of both cyclic and chronic pelvic pain, reduced fertility, and reduced quality-of-life. Diagnosis and treatment of endometriosis is, on average, delayed by 7–10 years from the onset of symptoms. Absence of a timely and non-invasive diagnostic tool is presently the greatest barrier to the identification and treatment of endometriosis. Twin and family studies have documented an increased relative risk in families. To identify genetic factors that contribute to endometriosis we conducted a two-stage genome-wide association study (GWAS) of a European cohort including 2,019 surgically confirmed endometriosis cases and 14,471 controls. Three of the SNPs we identify associated at P<5×10−8 in our combined analysis belong to two loci: LINC00339-WNT4 on 1p36.12 (rs2235529; P = 8.65×10−9, OR = 1.29, CI = 1.18–1.40) and RND3-RBM43 on 2q23.3 (rs1519761; P = 4.70×10−8, OR = 1.20, Cl = 1.13–1.29, and rs6757804; P = 4.05×10−8, OR = 1.20, Cl = 1.13–1.29). Using an adjusted Bonferoni significance threshold of 4.51×10−7 we identify two additional loci in our meta-analysis that associate with endometriosis:, RNF144B-ID4 on 6p22.3 (rs6907340; P = 2.19×10−7, OR = 1.20, Cl = 1.12–1.28), and HNRNPA3P1-LOC100130539 on 10q11.21 (rs10508881; P = 4.08×10−7, OR = 1.19, Cl = 1.11–1.27). Consistent with previously suggested associations to WNT4 our study implicate a 150 kb region around WNT4 that also include LINC00339 and CDC42. A univariate analysis of documented infertility, age at menarche, and family history did not show allelic association with these SNP markers. Clinical data from patients in our study reveal an average delay in diagnosis of 8.4 years and confirm a strong correlation between endometriosis severity and infertility (n = 1182, P<0.001, OR = 2.18). This GWAS of endometriosis was conducted with high diagnostic certainty in cases, and with stringent handling of population substructure. Our findings broaden the understanding of the genetic factors that play a role in endometriosis.

We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species. This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches. The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast, nematode, or fly, and 2,163 additional human proteins that interact with these homologs. Overall, the network consists of 3,271 binary interactions among 2,338 unique proteins. A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18.8 partners. Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance. To examine the relationship of this network to human aging phenotypes, we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle. In the case of both the longevity protein homologs and their interactors, we observed enrichments for differentially expressed genes in the network. To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates, homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi. Of 18 genes tested, 33% extended life span when knocked-down in Caenorhabditis elegans. These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging. They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins.

Prognostic testing for AIS has the potential to reduce psychological trauma, serial exposure to diagnostic radiation, unnecessary treatments, and direct and indirect costs-of-care related to scoliosis monitoring in low-risk patients. Further improvements in test performance are expected as the optimal markers for each locus are identified and the underlying biologic pathways are better understood. The validity of the test applies only to white AIS patients; versions of the test optimized for AIS patients of other races have yet to be developed.