Search

Background Forty-two percent of patients experience disease comorbidity, contributing substantially to mortality rates and increased healthcare costs. Yet, the possibility of underlying shared mechanisms for diseases remains not well established, and few studies have confirmed their molecular predictions with clinical datasets. Methods In this work, we integrated genome-wide association study (GWAS) associating diseases and single nucleotide polymorphisms (SNPs) with transcript regulatory activity from expression quantitative trait loci (eQTL). This allowed novel mechanistic insights for noncoding and intergenic regions. We then analyzed pairs of SNPs across diseases to identify shared molecular effectors robust to multiple test correction (False Discovery Rate FDReRNA 1.5, FDRcomorbidity

In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the "P N" high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions.In both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers' main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone.binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.

Continuously decreasing cost, speed and efficiency of DNA and RNA sequencing, coupled with advances in real-world sensing, storage of electronic health records, publicly available databases, and new data processing techniques enable precision medicine at unprecedented scale. Machine learning and artificial intelligence emerge naturally as tools for analyzing and summarizing data, supporting clinical decisions with data-driven insights and further unlocking genetically driven mechanisms underlying individualized risk. While these computational tools allow modeling of complex relations in large datasets, they pose new challenges especially because a patient's health is at stake. Due to an often black-box nature and high reliance on the training data, these new tools are prone to biases and most commonly provide correlational rather than causal insights. Results of these analyses have been difficult to validate, interpret, and explain to practitioners, and most genetic studies have struggled to encompass the full spectrum of human diversity. In this work, we summarize recent research trends in addressing these issues with examples from submissions to the "Computational Challenges and Artificial Intelligence in Precision Medicine" session at Pacific Symposium on Biocomputing 2021. We observe growing research interest in identifying biases, deriving causal and interpretable relations, tuning parameters of models for production, and using artificial intelligence for quality control. We expect further upsurge in work on interpretability and low-risk applications of advanced computational tools.

Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without replicates (TCWR) has been proposed, but not evaluated. Under TCWR conditions (e.g., unaffected tissue vs. tumor), differences of transformed expression of the proposed individualized DEG (iDEG) method follow a distribution calculated across a local partition of related transcripts at baseline expression; thereafter the probability of each DEG is estimated by empirical Bayes with local false discovery rate control using a two-group mixture model. In extensive simulation studies of TCWR methods, iDEG and NOISeq are more accurate at 5%90%, recall>75%, false_positive_rateThe proposed iDEG method borrows localized distribution information from the same individual, a strategy that improves accuracy to compare transcriptomes in absence of replicates at low DEGs conditions. http://www.lussiergroup.org/publications/iDEG

Objective To introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. Materials and Methods Within an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology “N-of-1-pathways.” The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided. Results Of the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). Discussion Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes. Conclusion The proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway–level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.

Background Gene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more ‘precision’ approach that integrates individual variability including ‘omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression needs to be interpreted meaningfully for single subjects. We propose an “all-against-one” framework that uses biological replicates in isogenic conditions for testing differentially expressed genes (DEGs) in a single subject (ss) in the absence of an appropriate external reference standard or replicates. To evaluate our proposed “all-against-one” framework, we construct reference standards (RSs) with five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2) and the remainder were treated separately as single-subject sample pairs for ss analyses (without replicates). Results Eight ss methods (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) for identifying genes with differential expression were compared in Yeast (parental line versus snf2 deletion mutant; n = 42/condition) and a MCF7 breast-cancer cell line (baseline versus stimulated with estradiol; n = 7/condition). Receiver-operator characteristic (ROC) and precision-recall plots were determined for eight ss methods against each of the five RSs in both datasets. Consistent with prior analyses of these data, ~ 50% and ~ 15% DEGs were obtained in Yeast and MCF7 datasets respectively, regardless of the RSs method. NOISeq, edgeR, and DESeq were the most concordant for creating a RS. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the RS method and dataset (> 90% in Yeast, > 0.75 in MCF7). Further, distinct specific single-subject methods perform better according to different proportions of DEGs. Conclusions The “all-against-one” framework provides a honest evaluation framework for single-subject DEG studies since these methods are evaluated, by design, against reference standards produced by unrelated DEG methods. The ss-ensemble method was the only one to reliably produce higher accuracies in all conditions tested in this conservative evaluation framework. However, single-subject methods for identifying DEGs from paired samples need improvement, as no method performed with precision> 90% and obtained moderate levels of recall. http://www.lussiergroup.org/publications/EnsembleBiomarker

BackgroundIn this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, when there are more features (i.e., transcript) than samples (i.e., mice or human samples) in a study, this poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest1 (RF) classifiers are widely used2–7 due to their flexibility, powerful performance, and robustness to “P predictors ≫ subjects N” difficulties and their ability to rank features. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions.MethodsbinomialRF treats each tree in a RF as a correlated but exchangeable binary trial. It determines importance by constructing a test statistic based on a feature’s selection frequency to compute its rank, nominal p-value, and multiplicity-adjusted q-value using a one-sided hypothesis test with a correlated binomial distribution. A distributional adjustment addresses the co-dependencies among trees as these trees subsample from the same dataset. The proposed algorithm efficiently identifies multiway nonlinear interactions by generalizing the test statistic to count sub-trees.ResultsIn simulations and in the Madelon benchmark datasets studies, binomialRF showed computational gains (up to 30 to 600 times faster) while maintaining competitive variable precision and recall in identifying biomarkers’ main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone.ConclusionbinomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers’ main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide path-way-level feature selection from gene expression input data.AvailabilityGithub: https://github.com/SamirRachidZaim/binomialRFSupplementary informationSupplementary analyses and results are available at https://github.com/SamirRachidZaim/binomialRF_simulationStudy

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis- and trans- relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene - and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49×10−8). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient’s specific polymorphisms.

Identifying functional elements and predicting mechanistic insight from non-coding DNA and noncoding variation remains a challenge. Advances in genome-scale, high-throughput technology, however, have brought these answers closer within reach than ever, though there is still a need for new computational approaches to analysis and integration. This workshop aims to explore these resources and new computational methods applied to regulatory elements, chromatin interactions, non-protein-coding genes, and other non-coding DNA.

Genes and pathways in which inactivation dampens tissue inflammation present new opportunities for understanding the pathogenesis of common human inflammatory diseases, including inflammatory bowel disease, rheumatoid arthritis and multiple sclerosis. We identified a mutation in the gene encoding the deubiquitination enzyme USP15 (Usp15L749R) that protected mice against both experimental cerebral malaria (ECM) induced by Plasmodium berghei and experimental autoimmune encephalomyelitis (EAE). Combining immunophenotyping and RNA sequencing in brain (ECM) and spinal cord (EAE) revealed that Usp15L749R-associated resistance to neuroinflammation was linked to dampened type I interferon responses in situ. In hematopoietic cells and in resident brain cells, USP15 was coexpressed with, and functionally acted together with the E3 ubiquitin ligase TRIM25 to positively regulate type I interferon responses and to promote pathogenesis during neuroinflammation. The USP15-TRIM25 dyad might be a potential target for intervention in acute or chronic states of neuroinflammation.

BackgroundGene expression profiling has benefited medicine by providing clinically relevant insights at the molecular candidate and systems levels. However, to adopt a more ‘precision’ approach that integrates individual variability including ‘omics data into risk assessments, diagnoses, and therapeutic decision making, whole transcriptome expression analysis requires methodological advancements. One need is for users to confidently be able to make individual-level inferences from whole transcriptome data. We propose that biological replicates in isogenic conditions can provide a framework for testing differentially expressed genes (DEGs) in a single subject (ss) in absence of an appropriate external reference standard or replicates.MethodsEight ss methods for identifying genes with differential expression (NOISeq, DEGseq, edgeR, mixture model, DESeq, DESeq2, iDEG, and ensemble) were compared inYeast(parental line versus snf2 deletion mutant; n=42/condition) andMCF7breast-cancer cell (baseline and stimulated with estradiol; n=7/condition) RNA-Seq datasets where replicate analysis was used to build reference standards from NOISeq, DEGseq, edgeR, DESeq, DESeq2. Each dataset was randomly partitioned so that approximately two-thirds of the paired samples were used to construct reference standards and the remainder were treated separately as single-subject sample pairs and DEGs were assayed using ss methods. Receiver-operator characteristic (ROC) and precision-recall plots were determined for all ss methods against each RSs in both datasets (525 combinations).ResultsConsistent with prior analyses of these data,~50% and ~15% DEGs were respectively obtained in Yeast and MCF7 reference standard datasets regardless of the analytical method. NOISeq, edgeR and DESeq were the most concordant and robust methods for creating a reference standard. Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the best median ROC-area-under-the-curve to compare two transcriptomes without replicates regardless of the type of reference standard (>90% in Yeast, >0.75 in MCF7).ConclusionBetter and more consistent accuracies are obtained by an ensemble method applied to singlesubject studies across different conditions. In addition, distinct specific sing-subject methods perform better according to different proportions of DEGs. Single-subject methods for identifying DEGs from paired samples need improvement, as no method performs with both precision>90% and recall>90%.http://www.lussiergroup.org/publications/EnsembleBiomarker

Background Reported coccidioidomycosis has increased with case rates of 198/100,000 in Arizona (2012). In California alone, 2000–2011 hospitalizations were $2.2B. Dissemination occurs in 8% of reports with significant morbidity and occasional deaths. DCM was found in 3 generations: grandmother (skin), mother (skin), and son (bone). Whole exome sequencing identified a heterozygous (het) STAT4 mutation (p.E626G) in all three. This mutation alters the phosphotyrosine binding pocket and is predicted to impair STAT4 function, interfering with (i) receptor binding and phosphorylation, (ii) nuclear localization, and/or (iii) transcription. Expression profiling of antigen-stimulated peripheral blood mononuclear cells from one patient showed dampening of known STAT4 targets compared with controls. Methods STAT4 p.E626G was generated and confirmed in C57BL/6NJ (WT) mice using CRISPR-Cas9. With continued breeding, neither homozygous (hom) nor het mice had gross abnormalities. There were normal spleen and lung lymphoid cell numbers. Thymus and bone marrow had normal development of lymphoid subsets. We performed intranasal infection with reduced virulence C. posadasii strain 1038 or with F. tularensis live vaccine- strain (LVS). Naïve or Δcps1-vaccinated mice were tested for resistance to C. posadasii strain Silveira. Results At day 21 post Cp 1038 infection, hom, het, and WT mice had similar lung fungal burdens (~104.7 cfu). All p.E626G mice died between days 31 and 39 with lung burden significantly higher (~9 × 106 cfu) than WT sacrificed on day 44 (7 × 105 cfu, P = 0.015). After LVS infection, p.E626G mice had increased lung bacterial cfu and all had dissemination to the spleen compared with WT lung bacterial burden and no splenic dissemination. Immunized het and WT mice all had significantly reduced lung cfu 14 days following C. posadasii infection compared with unvaccinated WT mice. Conclusion The STAT4 p.E626G mutated mouse recapitulated patients’ increased susceptibility to coccidioidal infection. The decreased fungal burdens seen in Δcps1-vaccinated mice suggest that vaccination may be effective in those persons genetically susceptible to DCM. Given the increasing frequency and economic burdens of coccidioidomycosis, pursuit of vaccination strategies should continue. Disclosures All Authors: No reported Disclosures.

Noncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology. We feature an invited speaker to how variation in transcription factor coding sequences impacts on sequence preference, along with submitted papers that span graph based methods, integrative analyses, machine learning, and dimension reduction to explore questions of basic biology, cancer, diabetes, and clinical relevance.

Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges. Results revealed reproducible and biologically meaningful signals from analysis of a single subject's response, opening the door to future transcriptomic studies where subject and resource availability are currently limiting. We used inbred mouse strains fed different diets to provide isogenic biological replicates, permitting rigorous validation of our method. We compared significant genotype-specific GO-BP term results for overlap and rank order across three replicate pairs per genotype, and cross-methods to reference standards (limma+FET, SAM+FET, and GSEA). All single-subject analytics findings were robust and highly reproducible (median area under the ROC curve=0.96, n=24 genotypes × 3 replicates), providing confidence and validation of this approach for analyses in single subjects. R code is available online at http://www.lussiergroup.org/publications/PathwayActivity.

Noncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology. We feature an invited speaker to how variation in transcription factor coding sequences impacts on sequence preference, along with submitted papers that span graph based methods, integrative analyses, machine learning, and dimension reduction to explore questions of basic biology, cancer, diabetes, and clinical relevance.

Motivation Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-1-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). Methods We propose a new N-of-1-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. Results In ∼9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment-specific pathways identified by kMEn correlate with therapeutic response (p-value Conclusion Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers.

SummaryLarge-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). The resulting catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We show that this catalogue can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 72% of which have no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes.

The Gene Expression Database (GXD; http://www.informatics.jax.org/expression.shtml) is an extensive and well-curated community resource of mouse developmental expression information. GXD collects different types of expression data from studies of wild-type and mutant mice, covering all developmental stages and including data from RNA in situ hybridization, immunohistochemistry, RT-PCR, northern blot and western blot experiments. The data are acquired from the scientific literature and from researchers, including groups doing large-scale expression studies. Integration with the other data in Mouse Genome Informatics (MGI) and interconnections with other databases places GXD’s gene expression information in the larger biological and biomedical context. Since the last report, the utility of GXD has been greatly enhanced by the addition of new data and by the implementation of more powerful and versatile search and display features. Web interface enhancements include the capability to search for expression data for genes associated with specific phenotypes and/or human diseases; new, more interactive data summaries; easy downloading of data; direct searches of expression images via associated metadata; and new displays that combine image data and their associated annotations. At present, GXD includes >1.4 million expression results and 250 000 images that are accessible to our search tools.

Functionally altered biological mechanisms arising from disease-associated polymorphisms, remain difficult to characterise when those variants are intergenic, or, fall between genes. We sought to identify shared downstream mechanisms by which inter- and intragenic single-nucleotide polymorphisms (SNPs) contribute to a specific physiopathology. Using computational modelling of 2 million pairs of disease-associated SNPs drawn from genome-wide association studies (GWAS), integrated with expression Quantitative Trait Loci (eQTL) and Gene Ontology functional annotations, we predicted 3,870 inter–intra and inter–intra SNP pairs with convergent biological mechanisms (FDR12). We additionally confirmed synergistic and antagonistic genetic interactions for a subset of prioritised SNP pairs in independent studies of Alzheimer’s disease (entropy P=0.046), bladder cancer (entropy P=0.039), and rheumatoid arthritis (PheWAS case–control P−4). Using ENCODE data sets, we further statistically validated that the biological mechanisms shared within prioritised SNP pairs are frequently governed by matching transcription factor binding sites and long-range chromatin interactions. These results provide a ‘roadmap’ of disease mechanisms emerging from GWAS and further identify candidate therapeutic targets among downstream effectors of intergenic SNPs.

Malaria is a disease that infects over 500 million people, causing at least 1 million deaths every year, with the majority occurring in developing countries. The current antimalarial arsenal is becoming dulled due to the rapid rate of resistance of the parasite. However, in populations living in malaria-endemic regions there are many examples of genetic-based resistance to the severe effects of the parasite Plasmodium. Defining the genetic factors behind host resistance has been an area of great scientific interest over the last few decades; this review summarizes the current knowledge of the genetic loci involved. Perhaps the lessons learned from the natural variation in both the human populations and experimental mouse models of infection may pave the way for novel resistance-proof antimalarials.

Cerebral malaria (CM) is an acute, generally lethal condition characterized by high fever, seizures and coma. The genetic component to CM can be investigated in mouse models that vary in degree of susceptibility to infection with Plasmodium berghei ANKA. Using survival time to measure susceptibility in an informative F2 cross (n=257), we identified linkage to chromosome 19 (Berr5 (Berghei resistance locus 5), LOD=4.69) controlling, in part, the differential response between resistant BALB/c and susceptible C57BL/6 progenitors. BALB/c alleles convey increased survival through the cerebral phase of infection but have no quantitative effect on parasitemia during the later, anemic phase. The Berr5 locus colocalizes with three other immune loci, including Trl-4 (tuberculosis resistance), Tsiq2 (T-cell secretion of IL-4) and Eae19 (experimental allergic encephalitis 19), suggesting the possibility of a common genetic effect underlying these phenotypes. Potential positional candidates include the family of Ifit1-3 (interferon-inducible protein with tetratricopeptide repeats 1-3) and Fas.

The Plasmodium parasite successfully infects and replicates in both human and insect vectors. Population studies in humans have long detected the enormous selective pressure placed by the parasite on its human host, revealing the footprints of co-evolution. Available complete genomic sequences for the human and insect hosts, and additional sequences from multiple field isolates of Plasmodium falciparum have identified a wide array of protein and gene families that play a crucial role at the interface of host–parasite interaction. Selected examples of such interactions will be reviewed herein.

Experimental infection of mice with Plasmodium berghei ANKA (PbA) provides a powerful model to define genetic determinants that regulate the development of cerebral malaria (CM). Based on the hypothesis that excessive activation of the complement system may confer susceptibility to CM, we investigated the role of C5/C5a in the development of CM. We show a spectrum of susceptibility to PbA in a panel of inbred mice; all CM-susceptible mice examined were found to be C5 sufficient, whereas all C5-deficient strains were resistant to CM. Transfer of the C5-defective allele from an A/J (CM resistant) onto a C57BL/6 (CM-susceptible) genetic background in a congenic strain conferred increased resistance to CM; conversely, transfer of the C5-sufficient allele from the C57BL/6 onto the A/J background recapitulated the CM-susceptible phenotype. The role of C5 was further explored in B10.D2 mice, which are identical for all loci other than C5. C5-deficient B10.D2 mice were protected from CM, whereas C5-sufficient B10.D2 mice were susceptible. Antibody blockade of C5a or C5a receptor (C5aR) rescued susceptible mice from CM. In vitro studies showed that C5a-potentiated cytokine secretion induced by the malaria product P. falciparum glycosylphosphatidylinositol and C5aR blockade abrogated these amplified responses. These data provide evidence implicating C5/C5a in the pathogenesis of CM.

Neural-tube defects such as anencephaly and spina bifida constitute a group of common congenital malformations caused by complex genetic and environmental factors. We have identified three mutations in the VANGL1 gene in patients with familial types (V239I and R274Q) and a sporadic type (M328T) of the disease, including a spontaneous mutation (V239I) appearing in a familial setting. In a protein-protein interaction assay V239I abolished interaction of VANGL1 protein with its binding partners, disheveled-1, -2, and -3. These findings implicate VANGL1 as a risk factor in human neural-tube defects.

The effect of a deficiency in the C5 component of complement on the pathophyisology of infection with the fungal pathogenCandida albicanswas studied by using the A/J inbred mouse strain and the BcA17 congenic mouse strain. Acute infection caused by intravenous injection ofC. albicansblastospores is associated with rapid fungal replication in the heart, brain, and, in particular, kidneys of C5-deficient mice. Histological studies and analysis of markers for tissue damage indicated that the heart is the organ that is most affected and that it ultimately fails in C5-deficient mice. In A/J and BcA17 mice, tissue damage is associated with (i) cellular infiltration in the heart, which is not seen in the kidney despite the higher fungal load in the latter organ, and (ii) a very strong inflammatory response, including elevated levels of many cytokines and chemokines. This results in cardiomyopathy, which is associated with elevated levels of creatine kinase and cardiac troponin I in the circulation. Damage to the cardiac muscle is associated with metabolic changes, including hypoglycemia, decreased lipid utilization resulting in elevated levels of cardiac triglycerides, and unproductive glucose utilization linked to a dramatic increase in the level of pyruvate dehydrogenase kinase 4 (Pdk4), a negative regulator of the pyruvate dehydrogenase complex.

We identify an N -ethyl- N -nitrosourea (ENU)-induced I23N mutation in the THEMIS protein that causes protection against experimental cerebral malaria (ECM) caused by infection with Plasmodium berghei ANKA. Themis I23N homozygous mice show reduced CD4 + and CD8 + T lymphocyte numbers. ECM resistance in P. berghei ANKA-infected Themis I23N mice is associated with decreased cerebral cellular infiltration, retention of blood-brain barrier integrity, and reduced proinflammatory cytokine production. THEMIS I23N protein expression is absent from mutant mice, concurrent with the decreased THEMIS I23N stability observed in vitro . Biochemical studies in vitro and functional complementation in vivo in Themis I23N/ + : Lck −/ + doubly heterozygous mice demonstrate that functional coupling of THEMIS to LCK tyrosine kinase is required for ECM pathogenesis. Damping of proinflammatory responses in Themis I23N mice causes susceptibility to pulmonary tuberculosis. Thus, THEMIS is required for the development and ultimately the function of proinflammatory T cells. Themis I23N mice can be used to study the newly discovered association of THEMIS (6p22.33) with inflammatory bowel disease and multiple sclerosis.

In humans, cerebral malaria is a rare but often lethal complication of infection with Plasmodium parasites, the occurrence of which is influenced by complex genetic factors of the host. We used a mouse model of experimental cerebral malaria (ECM) with Plasmodium berghei ANKA to study genetic factors regulating appearance of neurological symptoms and associated lethality. In a genome-wide screen of N-ethyl-N-nitrosourea-mutagenized mice derived from C57BL/6J (B6) and 129S1/SvImJ (129) mouse strains, we detected a strong interaction between the genetic backgrounds of these strains, which modulates ECM resistance. We have mapped a major gene locus to central chromosome 4 (log of the odds (LOD) 6.7; 79.6-97.3 Mb), which we designate Berr8. [corrected]. B6 alleles at Berr6 are associated with resistance, and are inherited in a co-dominant fashion. In mice heterozygous for Berr6 B6/129 alleles, resistance to ECM is strongly modulated by a second locus, Berr7, that maps to the proximal portion of chromosome 1 (LOD 4.03; 41.4 Mb). 129 alleles at Berr7 are associated with ECM resistance in a dosage-dependent fashion. Results are discussed in view of the possible role of this two-locus system in susceptibility to unrelated inflammatory conditions in mice and humans.

The host mechanisms responsible for protection against malaria remain poorly understood, with only a few protective genetic effects mapped in humans. Here, we characterize a host-specific genome-wide signature in whole-blood transcriptomes of Plasmodium falciparum -infected West African children and report a demonstration of genotype-by-infection interactions in vivo. Several associations involve transcripts sensitive to infection and implicate complement system, antigen processing and presentation, and T-cell activation (i.e., SLC39A8 , C3AR1 , FCGR3B , RAD21 , RETN , LRRC25 , SLC3A2 , and TAPBP ), including one association that validated a genome-wide association candidate gene ( SCO1 ), implicating binding variation within a noncoding regulatory element. Gene expression profiles in mice infected with Plasmodium chabaudi revealed and validated similar responses and highlighted specific pathways and genes that are likely important responders in both hosts. These results suggest that host variation and its interplay with infection affect children’s ability to cope with infection and suggest a polygenic model mounted at the transcriptional level for susceptibility.