The Drosophila Forkhead/Fox transcription factor Jumeau mediates specific cardiac progenitor cell divisions by regulating expression of the kinesin Nebbish.

Forkhead (Fkh/Fox) domain transcription factors (TFs) mediate multiple cardiogenic processes in both mammals and Drosophila. We showed previously that the Drosophila Fox gene jumeau (jumu) controls three categories of cardiac progenitor cell division-asymmetric, symmetric, and cell division at an earlier stage-by regulating Polo kinase activity, and mediates the latter two categories in concert with the TF Myb. Those observations raised the question of whether other jumu-regulated genes also mediate all three categories of cardiac progenitor cell division or a subset thereof. By comparing microarray-based expression profiles of wild-type and jumu loss-of-function mesodermal cells, we identified nebbish (neb), a kinesin-encoding gene activated by jumu. Phenotypic analysis shows that neb is required for only two categories of jumu-regulated cardiac progenitor cell division: symmetric and cell division at an earlier stage. Synergistic genetic interactions between neb, jumu, Myb, and polo and the rescue of jumu mutations by ectopic cardiac mesoderm-specific expression of neb demonstrate that neb is an integral component of a jumu-regulated subnetwork mediating cardiac progenitor cell divisions. Our results emphasize the central role of Fox TFs in cardiogenesis and illustrate how a single TF can utilize different combinations of other regulators and downstream effectors to control distinct developmental processes.

Two-dimensional enrichment analysis for mining high-level imaging genetic associations.

Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer's Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 25 significant high-level two-dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.

Structured sparse CCA for brain imaging genetics via graph OSCAR.

BACKGROUND: Recently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi-multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available. RESULTS: We propose a new structured SCCA model, which employs the graph OSCAR (GOSCAR) regularizer to encourage those highly correlated features to have similar or equal canonical weights. Our GOSCAR based SCCA has two advantages: 1) It does not require to pre-define the sign of the sample correlation, and thus could reduce the estimation bias. 2) It could pull those highly correlated features together no matter whether they are positively or negatively correlated. We evaluate our method using both synthetic data and real data. Using the 191 ROI measurements of amyloid imaging data, and 58 genetic markers within the APOE gene, our method identifies a strong association between APOE SNP rs429358 and the amyloid burden measure in the frontal region. In addition, the estimated canonical weights present a clear pattern which is preferable for further investigation. CONCLUSIONS: Our proposed method shows better or comparable performance on the synthetic data in terms of the estimated correlations and canonical loadings. It has successfully identified an important association between an Alzheimer's disease risk SNP rs429358 and the amyloid burden measure in the frontal region.

Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method.

MOTIVATION: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. RESULTS: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. AVAILABILITY AND IMPLEMENTATION: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/ CONTACT: shenli@iu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A New Statistical Image Analysis Approach and Its Application to Hippocampal Morphometry.

In this work, we propose a novel and powerful image analysis framework for hippocampal morphometry in early mild cognitive impairment (EMCI), an early prodromal stage of Alzheimer's disease (AD). We create a hippocampal surface atlas with subfield information, model each hippocampus using the SPHARM technique, and register it to the atlas to extract surface deformation signals. We propose a new alternative to standard random field theory (RFT) and permutation image analysis methods, Statistical Parametric Mapping (SPM) Distribution Analysis or SPM-DA, to perform statistical shape analysis and compare its performance with that of RFT methods on both simulated and real hippocampal surface data. The major strengths of our framework are twofold: (a) SPM-DA provides potentially more powerful algorithms than standard RFT methods for detecting weak signals, and (b) the framework embraces the important hippocampal subfield information for improved biological interpretation. We demonstrate the effectiveness of our method via an application to an AD cohort, where an SPM-DA method detects meaningful hippocampal shape differences in EMCI that are undetected by standard RFT methods.

GN-SCCA: GraphNet based Sparse Canonical Correlation Analysis for Brain Imaging Genetics.

Identifying associations between genetic variants and neuroimaging quantitative traits (QTs) is a popular research topic in brain imaging genetics. Sparse canonical correlation analysis (SCCA) has been widely used to reveal complex multi-SNP-multi-QT associations. Several SCCA methods explicitly incorporate prior knowledge into the model and intend to uncover the hidden structure informed by the prior knowledge. We propose a novel structured SCCA method using Graph constrained Elastic-Net (GraphNet) regularizer to not only discover important associations, but also induce smoothness between coefficients that are adjacent in the graph. In addition, the proposed method incorporates the covariance structure information usually ignored by most SCCA methods. Experiments on simulated and real imaging genetic data show that, the proposed method not only outperforms a widely used SCCA method but also yields an easy-to-interpret biological findings.

Two-dimensional Enrichment Analysis for Mining High-level Imaging Genetic Associations.

Enrichment analysis has been widely applied in the genome-wide association studies (GWAS), where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS-BC pair is enriched in a list of gene-QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer's Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 12 significant high level two dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.

Comprehensive gene- and pathway-based analysis of depressive symptoms in older adults.

Depressive symptoms are common in older adults and are particularly prevalent in those with or at elevated risk for dementia. Although the heritability of depression is estimated to be substantial, single nucleotide polymorphism-based genome-wide association studies of depressive symptoms have had limited success. In this study, we performed genome-wide gene- and pathway-based analyses of depressive symptom burden. Study participants included non-Hispanic Caucasian subjects (n = 6,884) from three independent cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Health and Retirement Study (HRS), and the Indiana Memory and Aging Study (IMAS). Gene-based meta-analysis identified genome-wide significant associations (ANGPT4 and FAM110A, q-value = 0.026; GRM7-AS3 and LRFN5, q-value = 0.042). Pathway analysis revealed enrichment of association in 105 pathways, including multiple pathways related to ERK/MAPK signaling, GSK3 signaling in bipolar disorder, cell development, and immune activation and inflammation. GRM7, ANGPT4, and LRFN5 have been previously implicated in psychiatric disorders, including the GRM7 region displaying association with major depressive disorder. The ERK/MAPK signaling pathway is a known target of antidepressant drugs and has important roles in neuronal plasticity, and GSK3 signaling has been previously implicated in Alzheimer's disease and as a promising therapeutic target for depression. Our results warrant further investigation in independent and larger cohorts and add to the growing understanding of the genetics and pathobiology of depressive symptoms in aging and neurodegenerative disorders. In particular, the genes and pathways demonstrating association with depressive symptoms may be potential therapeutic targets for these symptoms in older adults.

Protective variant for hippocampal atrophy identified by whole exome sequencing.

We used whole-exome sequencing to identify variants other than APOE associated with the rate of hippocampal atrophy in amnestic mild cognitive impairment. An in-silico predicted missense variant in REST (rs3796529) was found exclusively in subjects with slow hippocampal volume loss and validated using unbiased whole-brain analysis and meta-analysis across 5 independent cohorts. REST is a master regulator of neurogenesis and neuronal differentiation that has not been previously implicated in Alzheimer's disease. These findings nominate REST and its functional pathways as protective and illustrate the potential of combining next-generation sequencing with neuroimaging to discover novel disease mechanisms and potential therapeutic targets.

A novel structure-aware sparse learning algorithm for brain imaging genetics.

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.

Association of plasma and cortical amyloid beta is modulated by APOE ε4 status.

BACKGROUND: Apolipoprotein E (APOE) ε4 allele's role as a modulator of the relationship between soluble plasma amyloid beta (Aß) and fibrillar brain Aß measured by Pittsburgh compound B positron emission tomography ([(11)C]PiB PET) has not been assessed. METHODS: Ninety-six Alzheimer's Disease Neuroimaging Initiative participants with [(11)C]PiB scans and plasma Aß1-40 and Aß1-42 measurements at the time of PET scanning were included. Regional and voxelwise analyses of [(11)C]PiB data were used to determine the influence of APOE ε4 allele on association of plasma Aß1-40, Aß1-42, and Aß1-40/Aß1-42 with [(11)C]PiB uptake. RESULTS: In APOE ε4- but not ε4+ participants, positive relationships between plasma Aß1-40/Aß1-42 and [(11)C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aß1-40/Aß1-42 improved the explained variance in [(11)C]PiB binding compared with using APOE and plasma Aß1-40/Aß1-42 as separate terms. CONCLUSIONS: The results suggest that plasma Aß is a potential Alzheimer's disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aß levels.

Influence of genetic variation on plasma protein levels in older adults using a multi-analyte panel.

Proteins, widely studied as potential biomarkers, play important roles in numerous physiological functions and diseases. Genetic variation may modulate corresponding protein levels and point to the role of these variants in disease pathophysiology. Effects of individual single nucleotide polymorphisms (SNPs) within a gene were analyzed for corresponding plasma protein levels using genome-wide association study (GWAS) genotype data and proteomic panel data with 132 quality-controlled analytes from 521 Caucasian participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Linear regression analysis detected 112 significant (Bonferroni threshold p=2.44×10(-5)) associations between 27 analytes and 112 SNPs. 107 out of these 112 associations were tested in the Indiana Memory and Aging Study (IMAS) cohort for replication and 50 associations were replicated at uncorrected p<0.05 in the same direction of effect as those in the ADNI. We identified multiple novel associations including the association of rs7517126 with plasma complement factor H-related protein 1 (CFHR1) level at p<1.46×10(-60), accounting for 40 percent of total variation of the protein level. We serendipitously found the association of rs6677604 with the same protein at p<9.29×10(-112). Although these two SNPs were not in the strong linkage disequilibrium, 61 percent of total variation of CFHR1 was accounted for by rs6677604 without additional variation by rs7517126 when both SNPs were tested together. 78 other SNP-protein associations in the ADNI sample exceeded genome-wide significance (5×10(-8)). Our results confirmed previously identified gene-protein associations for interleukin-6 receptor, chemokine CC-4, angiotensin-converting enzyme, and angiotensinogen, although the direction of effect was reversed in some cases. This study is among the first analyses of gene-protein product relationships integrating multiplex-panel proteomics and targeted genes extracted from a GWAS array. With intensive searches taking place for proteomic biomarkers for many diseases, the role of genetic variation takes on new importance and should be considered in interpretation of proteomic results.

Network-Guided Sparse Learning for Predicting Cognitive Outcomes from MRI Measures.

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful.

PARP1 gene variation and microglial activity on [11C]PBR28 PET in older adults at risk for Alzheimer's disease.

Increasing evidence suggests that inflammation is one pathophysio-logical mechanism in Alzheimer's disease (AD). Recent studies have identifiedan association between the poly (ADP-ribose) polymerase 1 (PARP1) gene and AD. This gene encodes a protein that is involved in many biological functions, including DNA repair and chromatin remodeling, and is a mediator of inflammation. Therefore, we performed a targeted genetic association analysis to investigate the relationship between the PARP1 polymorphisms and brain micro-glial activity as indexed by [11C]PBR28 positron emission tomography (PET). Participants were 26 non-Hispanic Caucasians in the Indiana Memory and Aging Study (IMAS). PET data were intensity-normalized by injected dose/total body weight. Average PBR standardized uptake values (SUV) from 6 bilateral regions of interest (thalamus, frontal, parietal, temporal, and cingulate cortices, and whole brain gray matter) were used as endophenotypes. Single nucleotide polymorphisms (SNPs) with 20% minor allele frequency that were within +/- 20 kb of the PARP1 gene were included in the analyses. Gene-level association analyses were performed using a dominant genetic model with translocator protein (18-kDa) (TSPO) genotype, age at PET scan, and gender as covariates. Analyses were performed with and without APOE ε4 status as a covariate. Associations with PBR SUVs from thalamus and cingulate were significant at corrected p<0.014 and <0.065, respectively. Subsequent multi-marker analysis with cingulate PBR SUV showed that individuals with the "C" allele at rs6677172 and "A" allele at rs61835377 had higher PBR SUV than individuals without these alleles (corrected P<0.03), and individuals with the "G" allele at rs6677172 and "G" allele at rs61835377 displayed the opposite trend (corrected P<0.065). A previous study with the same cohort showed an inverse relationship between PBR SUV and brain atrophy at a follow-up visit, suggesting possible protective effect of microglial activity against cortical atrophy. Interestingly, all 6 AD and 2 of 3 LMCI participants in the current analysis had one or more copies of the "GG" allele combination, associated with lower cingulate PBR SUV, suggesting that this gene variant warrants further investigation.

Analysis of copy number variation in Alzheimer's disease: the NIALOAD/ NCRAD Family Study.

Copy number variants (CNVs) are DNA regions that have gains (duplications) or losses (deletions) of genetic material. CNVs may encompass a single gene or multiple genes and can affect their function. They are hypothesized to play an important role in certain diseases. We previously examined the role of CNVs in late-onset Alzheimer's disease (AD) and mild cognitive impairment (MCI) using participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and identified gene regions overlapped by CNVs only in cases (AD and/or MCI) but not in controls. Using a similar approach as ADNI, we investigated the role of CNVs using 794 AD and 196 neurologically evaluated control non-Hispanic Caucasian NIA-LOAD/NCRAD Family Study participants with DNA derived from blood/brain tissue. The controls had no family history of AD and were unrelated to AD participants. CNV calls were generated and analyzed after detailed quality review. 711 AD cases and 171 controls who passed all quality thresholds were included in case/control association analyses, focusing on candidate gene and genome-wide approaches. We identified genes overlapped by CNV calls only in AD cases but not controls. A trend for lower CNV call rate was observed for deletions as well as duplications in cases compared to controls. Gene-based association analyses confirmed previous findings in the ADNI study (ATXN1, HLA-DPB1, RELN, DOPEY2, GSTT1, CHRFAM7A, ERBB4, NRXN1) and identified a new gene (IMMP2L) that may play a role in AD susceptibility. Replication in independent samples as well as further analyses of these gene regions is warranted.

Amyloid pathway-based candidate gene analysis of [(11)C]PiB-PET in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

Amyloid imaging with [(11)C]Pittsburgh Compound-B (PiB) provides in vivo data on plaque deposition in those with, or at risk for, Alzheimer's disease (AD). We performed a gene-based association analysis of 15 quality-controlled amyloid-pathway associated candidate genes in 103 Alzheimer's Disease Neuroimaging Initiative participants. The mean normalized PiB uptake value across four brain regions known to have amyloid deposition in AD was used as a quantitative phenotype. The minor allele of an intronic SNP within DHCR24 was identified and associated with a lower average PiB uptake. Further investigation at whole-brain voxel-wise level indicated that non-carriers of the minor allele had higher PiB uptake in frontal regions compared to carriers. DHCR24 has been previously shown to confer resistance against beta-amyloid and oxidative stress-induced apoptosis, thus our findings support a neuroprotective role. Pathway-based genetic analysis of targeted molecular imaging phenotypes appears promising to help elucidate disease pathophysiology and identify potential therapeutic targets.

Hippocampal surface mapping of genetic risk factors in AD via sparse learning models.

Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.

Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net.

Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.

Expression profiles of pea pathogenicity ( PEP) genes in vivo and in vitro, characterization of the flanking regions of the PEP cluster and evidence that the PEP cluster region resulted from horizontal gene transfer in the fungal pathogen Nectria haematococca.

A cluster of pathogenicity genes ( PEP1, PEP2, PDA1, PEP5), termed the pea pathogenicity ( PEP) cluster and located on a 1.6-Mb conditionally dispensable (CD) chromosome, was identified in the fungal pathogen Nectria haematococca. Studies determined that the expression of PDA1 is induced in both infected pea tissues and in vitro by the phytoalexin pisatin. The present study reports the use of real-time quantitative RT-PCR to monitor the expression of each PEP gene and PDA1. In mycelia actively growing in culture, the mRNA levels of PEP1, PEP5 and PDA1 were very low and the PEP2 transcript was undetectable. In planta, PDA1 and PEP2 were strongly induced, while PEP1 and PEP5 were moderately induced. Starvation slightly enhanced the expression of PEP1, PDA1 and PEP5, while the expression of PEP2 remained undetectable. Exposure to pisatin in culture stimulated the expression of PDA1 and each PEP gene to a similar level as occurred in planta. In addition, all four pathogenicity genes displayed similar temporal patterns of expression in planta and in vitro, consistent with a coordinated regulation of these genes by pisatin during pea pathogenesis. In the flanking regions of the PEP cluster, six open reading frames (ORFs) were identified and all were expressed during infection of pea. Comparison of the codon preferences of these ORFs and seven additional genes from CD chromosomes with the codon preferences of 21 genes from other chromosomes revealed there is a codon bias that correlates with the source of the genes. This difference in codon bias is consistent with the hypothesis that genes on the CD chromosome have a different origin from genes of normal chromosomes, suggesting that horizontal gene transfer may have played a role in the evolution of pathogenesis in N. haematococca.

A diffusible signal attracts olfactory sensory axons toward their target in the developing brain of the moth.

The signals that olfactory receptor axons use to navigate to their target in the CNS are still not well understood. In the moth Manduca sexta, the primary olfactory pathway develops postembryonically, and the receptor axons navigate from an experimentally accessible sensory epithelium to the brain along a pathway long enough for detailed study of regions in which axon behavior changes. The current experiments ask whether diffusible factors contribute to receptor axon guidance. Explants were made from the antennal receptor epithelium and co-cultured in a collagen gel matrix with slices of various regions of the brain. Receptor axons were attracted toward the central regions of the brain, including the protocerebrum and antennal lobe. Receptor axons growing into a slice of the most proximal region of the antennal nerve, where axon sorting normally occurs, showed no directional preference. When the antennal lobe was included in the slice, the receptor axons entering the sorting region grew directly toward the antennal lobe. Taken together with the previous in vivo experiments, the current results suggest that an attractive diffusible factor can serve as one cue to direct misrouted olfactory receptor axons toward the medial regions of the brain, where local cues guide them to the antennal lobe. They also suggest that under normal circumstances, in which the receptor axons follow a pre-existing pupal nerve to the antennal lobe, the diffusible factor emanating from the lobe acts in parallel and at short range to maintain the fidelity of the path into the antennal lobe.