Additional file 2: Table 2. contains dataset for serum Aβ42, Aβ40 and Aβ42/Aβ40 ratio of F1 and F... more Additional file 2: Table 2. contains dataset for serum Aβ42, Aβ40 and Aβ42/Aβ40 ratio of F1 and F2 generation transgenic rats (see Fig. 3).
Additional file 1: Table 1. contains dataset for Northern blot analysis (see Fig. 2). PS1 mRNA ... more Additional file 1: Table 1. contains dataset for Northern blot analysis (see Fig. 2). PS1 mRNA level, serum Aβ42 and tissue are reported in the table.
Differentially expressed genes found the mouse knockout RNAseq analyses of Ugt8 in the CBM (Data ... more Differentially expressed genes found the mouse knockout RNAseq analyses of Ugt8 in the CBM (Data 1) and FC (Data 2), Cnp in the FC (Data 3), and Plp1 in the CBM (Data 4). For these differential expression analyses, we mapped RNAseq reads using TopHat, converted to count space using HTSeq, used voom to transform the read space data to log2 counts per million, and used limma for differential expression analysis. We also used the Ensembl database to identify the human gene with the highest homology percentage based on protein coding region DNA divergence, and report this homology percentage for each gene. Note that the differential expression signatures of Cnp in the CBM and Plp1 in the FC were not found not have any differentially expressed genes at FDR
Summary of read mapping from the three knockout mouse RNAseq experiments generated by TopHat. (XL... more Summary of read mapping from the three knockout mouse RNAseq experiments generated by TopHat. (XLSX 52Â kb)
Module membership file for the Mount Sinai Brain Bank (MSBB) proteomic coexpression network. The ... more Module membership file for the Mount Sinai Brain Bank (MSBB) proteomic coexpression network. The module label, a randomly chosen color name, is in the 1st column, while the protein name is in the 2nd column. (TSV 34Â kb)
Supplementary Experimental Procedures. Figure S1. The topological overlap matrix plot of the prot... more Supplementary Experimental Procedures. Figure S1. The topological overlap matrix plot of the protein co-expression network constructed from the proteomics data from the autopsied brains in the MSBB cohort, along with the dendrogram showing the tree cutting process used to define modules (above). Figure S2. Confirmation that the key driver knockouts abrogate gene expression of the key driver in the RNA-seq experiments. For each of the key driver knockouts whose genome-wide gene expression was profiled in this study using RNA-seq, we plotted the log10 counts overlapping that gene in both the wildtype (WT) and knockout (KO) samples. Notably, one of the matched samples from Cnp was detected as an outlier in both the CBM and FC brain regions (red), due to suspected mislabeling. These samples were removed prior to downstream differential expression analysis. (DOCX 161Â kb)
Table S3. GO Elite analysis of differentially expressed proteins in WT, LPS-treated WT and 5xFAD ... more Table S3. GO Elite analysis of differentially expressed proteins in WT, LPS-treated WT and 5xFAD mouse microglia. (XLSX 20 kb)
Supplemental Figures. Figure S1. Hierarchical clustering of proteins differentially expressed (p&... more Supplemental Figures. Figure S1. Hierarchical clustering of proteins differentially expressed (p<0.05) in either WT-LPS vs WT-control and 5xFAD vs WT comparisons. Figure S2. Pre-incubation of anti-Apoe antibody with fibrillar AĂ does not abolish plaque-like immunostaining for Apoe. (DOCX 761 kb)
Background: Family history of cardiovascular disease (CVD) is a readily available risk indicator ... more Background: Family history of cardiovascular disease (CVD) is a readily available risk indicator of future CVD, combining the influence of shared genetic, environmental and behavioral risk factors. Though CVD risk factors have been associated with an increased risk of cognitive impairment and decline, less is known about the association between family history of CVD and cognitive function. Evaluating this association may further elucidate the role of cardiovascular health in cognitive health. Methods: The Emory Healthy Aging Study isan ongoing prospective cohort study aimed at identifying predictors of healthy aging and age-related diseases. Participants are primarily residents of the Atlanta area, at least 18 years old, who completed an online baseline health survey. Multiple recruitment forums were used, including clinic waiting rooms, informational letters and emails, community events and online recruitment. Baseline information about demographic (age, race, gender), socioeconomi...
The biological processes that are disrupted in the Alzheimer's disease (AD) brain remain incomple... more The biological processes that are disrupted in the Alzheimer's disease (AD) brain remain incompletely understood. In this study, we analyzed the proteomes of more than 1,000 brain tissues to reveal new AD-related protein co-expression modules that were highly preserved across cohorts and brain regions. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism and a module related to the matrisome. The matrisome module was influenced by the APOE ε4 allele but was not related to the rate of cognitive decline after adjustment for neuropathology. By contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD. ResouRce NATuRE NEuRoScIENcE Results TMT consensus AD protein co-expression network. For this study, we analyzed a total of 516 dorsolateral prefrontal cortex (DLPFC) tissues from control, asymptomatic AD (AsymAD) and AD brains from the Religious Orders Study and Memory and Aging Project (ROSMAP, n = 84 control, 148 AsymAD and 108 AD) 8-10 and the Banner Sun Health Research Institute (Banner, n = 26 control, 58 AsymAD and 92 AD) 11 by TMT-MS-based quantitative proteomics (Fig. 1a and Supplementary Table 1). Cases were defined based on a unified classification scheme using semi-quantitative histopathological measures of Aβ and tau neurofibrillary tangle deposition 12-15 as well as cognitive function near time of death, as previously described 7. AsymAD cases were those with neuropathological burden of Aβ plaques and tau tangles similar to AD cases but without significant cognitive impairment near time of death, which is considered to be an early preclinical stage of AD 16. After data processing and outlier removal, a total of 8,619 proteins were used to build a protein co-expression network using the weighted gene co-expression network analysis (WGCNA) algorithm 17 (Fig. 1b, Extended Data Fig. 1 and Supplementary Tables 2-4). This network consisted of 44 modules or communities of proteins related to one another by their co-expression across control and disease tissues. Compared to our previous AD consensus network constructed using LFQ proteomic data, the TMT consensus network contained over five times as many proteins that could be assigned to a module (6,337 versus 1,205) as well as a larger fraction of quantified proteins that could be assigned to a module (73% versus 36%), highlighting the improved depth and coherence of the TMT data compared to the LFQ consensus data. Of the 13 modules previously identified in the LFQ consensus network 7 , every module except the smallest module (module 13 consisting of 20 proteins) was preserved in the TMT network (Extended Data Fig. 2a), also highlighting the consistency of the LFQ and TMT proteomic data. Because different network clustering algorithms can produce disparate networks, we tested the robustness of the TMT consensus network generated by the WGCNA algorithm by also generating a co-expression network using an independent algorithm-the MONET M1 algorithm. MONET M1 was identified as one of the top performers in the Disease Module Identification DREAM Challenge and is based on a modularity optimization algorithm rather than the hierarchical clustering approach used in WGCNA 18,19. We found that all 44 WGCNA modules were highly preserved in the MONET M1 network (Extended Data Fig. 2b), demonstrating the robustness of the TMT consensus network to clustering algorithm. The biology represented by each TMT consensus network module was determined using Gene Ontology (GO) analysis of its constituent proteins (Fig. 1b and Supplementary Data). Most modules could be assigned a primary ontology, and those that were ambiguous in their ontology were left unannotated or assigned as 'ambiguous' .
Flow cytometric confirmation of Kv1.3-channel specificity of the ShK-F6CA binding assay is shown ... more Flow cytometric confirmation of Kv1.3-channel specificity of the ShK-F6CA binding assay is shown in Figure S1. Comparison of ShK-F6CA labeling of Kv1.3 channels in splenic and brain-infiltrating macrophages is shown in Figure S2. Morphological changes induced by LPS, ShK-223, and LPS+ShK-223 treatment conditions are shown in Figure S3. Distribution of missing data in the proteomic data set is shown in Figure S4. Quantitative RT-PCR data showing validation of pro-inflammatory activation of BV2 microglia by LPS are shown in Figure S5. EHD1 upregulation by LPS and inhibition of EHD1 upregulation by ShK-223 is shown in Figure S6. (DOCX 1262 kb)
Demographic and biomarker information for cognitive normal subjects and MCI patients with CSF t-T... more Demographic and biomarker information for cognitive normal subjects and MCI patients with CSF t-Tau/Aβ42 < 0.39 in the ADNI and Emory cohorts. Table S2. Main effects from mixed linear modeling of CSF FH and C3 levels in ADNI. In each model, FH or C3 was entered as the dependent variable; age, gender, presence of APOE ε4 allele, diagnosis, Aβ42, t-Tau, p-Tau181, gender X age, presence of APOE ε4 allelle X age, and diagnosis X age were entered as fixed factors; and age was also entered as a random factor. Factors with main effect p > 0.10 were removed in a step-wise fashion to arrive at final model. See text and Fig. 2 for effects from different diagnostic categories. Table S3. Mixed linear modeling of PAD-based diagnostic classification and time (in months) on longitudinal memory and executive functions in the Emory validation cohort. A) Among patients initially classified as MCI with longitudinal follow-up (n=44), reclassification using PAD showed differences in absolute execu...
Figure S2. Protein Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between th... more Figure S2. Protein Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between the number of quantifiable proteins at a given threshold of missing values in the 47 brain samples from the BLSA cohort for TMT-LysC and LFQ-trypsin analyses is shown. The point at 23 samples and 6533 proteins represents the threshold used for the TMT-LysC analysis pipeline in this study. This point falls slightly below the TMT curve because 11 MCI samples were included in the TMT analysis workflow, for a total of 58 samples, but were later dropped from the analysis (see Methods). The increased number of samples when including the 11 MCI cases slightly reduced the number of quantifiable proteins at the ~ 50% missing value threshold. (PDF 79 kb)
Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue hom... more Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue homogenates from cases shown in Table S1 were analyzed by SDS-PAGE to assess sample integrity prior to TMT labeling and mass spectrometry analysis. Gels were stained with Coomassie Blue to visualize protein. AD, Alzheimer's disease; AS, asymptomatic Alzheimer's disease; CT, control; MCI, mild cognitive impairment. (PDF 22000 kb)
Table S6. Number of Alternative Exon-Exon Junctions in AD Risk Factor Proteins. From the twenty p... more Table S6. Number of Alternative Exon-Exon Junctions in AD Risk Factor Proteins. From the twenty proteins identified as risk factors for AD by GWAS at genome-wide significance [12], five had alt-EEjxn peptides that were observed and quantifiable in the BLSA-TMT analysis (observed). The number of observed and quantifiable alt-EEjxn peptides for each of these five proteins was a subset of the total number of alt-EEjxn peptides predicted to exist after LysC digestion (peptide database). This number was a further subset of the total number of alt-EEjxns observed for each of the five proteins from RNAseq data (transcript level). For details on generation of the peptide database and transcript level numbers, see Methods. (DOCX 28Â kb)
Table S5. Number of Alternative Exon-Exon Junction Peptides Identified by TMT-LysC and LFQ-trypsi... more Table S5. Number of Alternative Exon-Exon Junction Peptides Identified by TMT-LysC and LFQ-trypsin Approaches. The number of alt-EEjxn peptides identified by matching to the listed databases (Swiss-Prot, Trembl, or RNAseq) is shown, along with the number of quantifiable alt-EEjxn peptides. A peptide was considered quantifiable in this analysis if it had a minimum of 2 measurements in at least 2 different case groups. RNAseq data from control and AD patient brains (n = 6) were used to generate the RNAseq alt-EEjxn peptide database, as described in Methods. (DOCX 28 kb)
Table S4. List of Biological Terms for GO Network in Figure S8. GO, gene ontology; UP, UniProt; K... more Table S4. List of Biological Terms for GO Network in Figure S8. GO, gene ontology; UP, UniProt; KEGG, Kyoto Encyclopedia of Genes and Genomes; SMART, Simple Modular Architecture Research Tool; FDR, false discovery rate. (DOCX 35Â kb)
Additional file 2: Table 2. contains dataset for serum Aβ42, Aβ40 and Aβ42/Aβ40 ratio of F1 and F... more Additional file 2: Table 2. contains dataset for serum Aβ42, Aβ40 and Aβ42/Aβ40 ratio of F1 and F2 generation transgenic rats (see Fig. 3).
Additional file 1: Table 1. contains dataset for Northern blot analysis (see Fig. 2). PS1 mRNA ... more Additional file 1: Table 1. contains dataset for Northern blot analysis (see Fig. 2). PS1 mRNA level, serum Aβ42 and tissue are reported in the table.
Differentially expressed genes found the mouse knockout RNAseq analyses of Ugt8 in the CBM (Data ... more Differentially expressed genes found the mouse knockout RNAseq analyses of Ugt8 in the CBM (Data 1) and FC (Data 2), Cnp in the FC (Data 3), and Plp1 in the CBM (Data 4). For these differential expression analyses, we mapped RNAseq reads using TopHat, converted to count space using HTSeq, used voom to transform the read space data to log2 counts per million, and used limma for differential expression analysis. We also used the Ensembl database to identify the human gene with the highest homology percentage based on protein coding region DNA divergence, and report this homology percentage for each gene. Note that the differential expression signatures of Cnp in the CBM and Plp1 in the FC were not found not have any differentially expressed genes at FDR
Summary of read mapping from the three knockout mouse RNAseq experiments generated by TopHat. (XL... more Summary of read mapping from the three knockout mouse RNAseq experiments generated by TopHat. (XLSX 52Â kb)
Module membership file for the Mount Sinai Brain Bank (MSBB) proteomic coexpression network. The ... more Module membership file for the Mount Sinai Brain Bank (MSBB) proteomic coexpression network. The module label, a randomly chosen color name, is in the 1st column, while the protein name is in the 2nd column. (TSV 34Â kb)
Supplementary Experimental Procedures. Figure S1. The topological overlap matrix plot of the prot... more Supplementary Experimental Procedures. Figure S1. The topological overlap matrix plot of the protein co-expression network constructed from the proteomics data from the autopsied brains in the MSBB cohort, along with the dendrogram showing the tree cutting process used to define modules (above). Figure S2. Confirmation that the key driver knockouts abrogate gene expression of the key driver in the RNA-seq experiments. For each of the key driver knockouts whose genome-wide gene expression was profiled in this study using RNA-seq, we plotted the log10 counts overlapping that gene in both the wildtype (WT) and knockout (KO) samples. Notably, one of the matched samples from Cnp was detected as an outlier in both the CBM and FC brain regions (red), due to suspected mislabeling. These samples were removed prior to downstream differential expression analysis. (DOCX 161Â kb)
Table S3. GO Elite analysis of differentially expressed proteins in WT, LPS-treated WT and 5xFAD ... more Table S3. GO Elite analysis of differentially expressed proteins in WT, LPS-treated WT and 5xFAD mouse microglia. (XLSX 20 kb)
Supplemental Figures. Figure S1. Hierarchical clustering of proteins differentially expressed (p&... more Supplemental Figures. Figure S1. Hierarchical clustering of proteins differentially expressed (p<0.05) in either WT-LPS vs WT-control and 5xFAD vs WT comparisons. Figure S2. Pre-incubation of anti-Apoe antibody with fibrillar AĂ does not abolish plaque-like immunostaining for Apoe. (DOCX 761 kb)
Background: Family history of cardiovascular disease (CVD) is a readily available risk indicator ... more Background: Family history of cardiovascular disease (CVD) is a readily available risk indicator of future CVD, combining the influence of shared genetic, environmental and behavioral risk factors. Though CVD risk factors have been associated with an increased risk of cognitive impairment and decline, less is known about the association between family history of CVD and cognitive function. Evaluating this association may further elucidate the role of cardiovascular health in cognitive health. Methods: The Emory Healthy Aging Study isan ongoing prospective cohort study aimed at identifying predictors of healthy aging and age-related diseases. Participants are primarily residents of the Atlanta area, at least 18 years old, who completed an online baseline health survey. Multiple recruitment forums were used, including clinic waiting rooms, informational letters and emails, community events and online recruitment. Baseline information about demographic (age, race, gender), socioeconomi...
The biological processes that are disrupted in the Alzheimer's disease (AD) brain remain incomple... more The biological processes that are disrupted in the Alzheimer's disease (AD) brain remain incompletely understood. In this study, we analyzed the proteomes of more than 1,000 brain tissues to reveal new AD-related protein co-expression modules that were highly preserved across cohorts and brain regions. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism and a module related to the matrisome. The matrisome module was influenced by the APOE ε4 allele but was not related to the rate of cognitive decline after adjustment for neuropathology. By contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD. ResouRce NATuRE NEuRoScIENcE Results TMT consensus AD protein co-expression network. For this study, we analyzed a total of 516 dorsolateral prefrontal cortex (DLPFC) tissues from control, asymptomatic AD (AsymAD) and AD brains from the Religious Orders Study and Memory and Aging Project (ROSMAP, n = 84 control, 148 AsymAD and 108 AD) 8-10 and the Banner Sun Health Research Institute (Banner, n = 26 control, 58 AsymAD and 92 AD) 11 by TMT-MS-based quantitative proteomics (Fig. 1a and Supplementary Table 1). Cases were defined based on a unified classification scheme using semi-quantitative histopathological measures of Aβ and tau neurofibrillary tangle deposition 12-15 as well as cognitive function near time of death, as previously described 7. AsymAD cases were those with neuropathological burden of Aβ plaques and tau tangles similar to AD cases but without significant cognitive impairment near time of death, which is considered to be an early preclinical stage of AD 16. After data processing and outlier removal, a total of 8,619 proteins were used to build a protein co-expression network using the weighted gene co-expression network analysis (WGCNA) algorithm 17 (Fig. 1b, Extended Data Fig. 1 and Supplementary Tables 2-4). This network consisted of 44 modules or communities of proteins related to one another by their co-expression across control and disease tissues. Compared to our previous AD consensus network constructed using LFQ proteomic data, the TMT consensus network contained over five times as many proteins that could be assigned to a module (6,337 versus 1,205) as well as a larger fraction of quantified proteins that could be assigned to a module (73% versus 36%), highlighting the improved depth and coherence of the TMT data compared to the LFQ consensus data. Of the 13 modules previously identified in the LFQ consensus network 7 , every module except the smallest module (module 13 consisting of 20 proteins) was preserved in the TMT network (Extended Data Fig. 2a), also highlighting the consistency of the LFQ and TMT proteomic data. Because different network clustering algorithms can produce disparate networks, we tested the robustness of the TMT consensus network generated by the WGCNA algorithm by also generating a co-expression network using an independent algorithm-the MONET M1 algorithm. MONET M1 was identified as one of the top performers in the Disease Module Identification DREAM Challenge and is based on a modularity optimization algorithm rather than the hierarchical clustering approach used in WGCNA 18,19. We found that all 44 WGCNA modules were highly preserved in the MONET M1 network (Extended Data Fig. 2b), demonstrating the robustness of the TMT consensus network to clustering algorithm. The biology represented by each TMT consensus network module was determined using Gene Ontology (GO) analysis of its constituent proteins (Fig. 1b and Supplementary Data). Most modules could be assigned a primary ontology, and those that were ambiguous in their ontology were left unannotated or assigned as 'ambiguous' .
Flow cytometric confirmation of Kv1.3-channel specificity of the ShK-F6CA binding assay is shown ... more Flow cytometric confirmation of Kv1.3-channel specificity of the ShK-F6CA binding assay is shown in Figure S1. Comparison of ShK-F6CA labeling of Kv1.3 channels in splenic and brain-infiltrating macrophages is shown in Figure S2. Morphological changes induced by LPS, ShK-223, and LPS+ShK-223 treatment conditions are shown in Figure S3. Distribution of missing data in the proteomic data set is shown in Figure S4. Quantitative RT-PCR data showing validation of pro-inflammatory activation of BV2 microglia by LPS are shown in Figure S5. EHD1 upregulation by LPS and inhibition of EHD1 upregulation by ShK-223 is shown in Figure S6. (DOCX 1262 kb)
Demographic and biomarker information for cognitive normal subjects and MCI patients with CSF t-T... more Demographic and biomarker information for cognitive normal subjects and MCI patients with CSF t-Tau/Aβ42 < 0.39 in the ADNI and Emory cohorts. Table S2. Main effects from mixed linear modeling of CSF FH and C3 levels in ADNI. In each model, FH or C3 was entered as the dependent variable; age, gender, presence of APOE ε4 allele, diagnosis, Aβ42, t-Tau, p-Tau181, gender X age, presence of APOE ε4 allelle X age, and diagnosis X age were entered as fixed factors; and age was also entered as a random factor. Factors with main effect p > 0.10 were removed in a step-wise fashion to arrive at final model. See text and Fig. 2 for effects from different diagnostic categories. Table S3. Mixed linear modeling of PAD-based diagnostic classification and time (in months) on longitudinal memory and executive functions in the Emory validation cohort. A) Among patients initially classified as MCI with longitudinal follow-up (n=44), reclassification using PAD showed differences in absolute execu...
Figure S2. Protein Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between th... more Figure S2. Protein Quantitation in TMT-LysC and LFQ-trypsin Analyses. The relationship between the number of quantifiable proteins at a given threshold of missing values in the 47 brain samples from the BLSA cohort for TMT-LysC and LFQ-trypsin analyses is shown. The point at 23 samples and 6533 proteins represents the threshold used for the TMT-LysC analysis pipeline in this study. This point falls slightly below the TMT curve because 11 MCI samples were included in the TMT analysis workflow, for a total of 58 samples, but were later dropped from the analysis (see Methods). The increased number of samples when including the 11 MCI cases slightly reduced the number of quantifiable proteins at the ~ 50% missing value threshold. (PDF 79 kb)
Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue hom... more Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue homogenates from cases shown in Table S1 were analyzed by SDS-PAGE to assess sample integrity prior to TMT labeling and mass spectrometry analysis. Gels were stained with Coomassie Blue to visualize protein. AD, Alzheimer's disease; AS, asymptomatic Alzheimer's disease; CT, control; MCI, mild cognitive impairment. (PDF 22000 kb)
Table S6. Number of Alternative Exon-Exon Junctions in AD Risk Factor Proteins. From the twenty p... more Table S6. Number of Alternative Exon-Exon Junctions in AD Risk Factor Proteins. From the twenty proteins identified as risk factors for AD by GWAS at genome-wide significance [12], five had alt-EEjxn peptides that were observed and quantifiable in the BLSA-TMT analysis (observed). The number of observed and quantifiable alt-EEjxn peptides for each of these five proteins was a subset of the total number of alt-EEjxn peptides predicted to exist after LysC digestion (peptide database). This number was a further subset of the total number of alt-EEjxns observed for each of the five proteins from RNAseq data (transcript level). For details on generation of the peptide database and transcript level numbers, see Methods. (DOCX 28Â kb)
Table S5. Number of Alternative Exon-Exon Junction Peptides Identified by TMT-LysC and LFQ-trypsi... more Table S5. Number of Alternative Exon-Exon Junction Peptides Identified by TMT-LysC and LFQ-trypsin Approaches. The number of alt-EEjxn peptides identified by matching to the listed databases (Swiss-Prot, Trembl, or RNAseq) is shown, along with the number of quantifiable alt-EEjxn peptides. A peptide was considered quantifiable in this analysis if it had a minimum of 2 measurements in at least 2 different case groups. RNAseq data from control and AD patient brains (n = 6) were used to generate the RNAseq alt-EEjxn peptide database, as described in Methods. (DOCX 28 kb)
Table S4. List of Biological Terms for GO Network in Figure S8. GO, gene ontology; UP, UniProt; K... more Table S4. List of Biological Terms for GO Network in Figure S8. GO, gene ontology; UP, UniProt; KEGG, Kyoto Encyclopedia of Genes and Genomes; SMART, Simple Modular Architecture Research Tool; FDR, false discovery rate. (DOCX 35Â kb)
Uploads
Papers by James Lah