One of the aims of high-throughput gene/protein profiling experiments is the identification of biological processes altered between two or more conditions. Pathway analysis is an umbrella term for a multitude of computational approaches used for this purpose. While in the beginning pathway analysis relied on enrichment-based approaches, a newer generation of methods is now available, exploiting pathway topologies in addition to gene/protein expression levels. However, little effort has been invested in their critical assessment with respect to their performance in different experimental setups. Here, we assessed the performance of seven representative methods identifying differentially expressed pathways between two groups of interest based on gene expression data with prior knowledge of pathway topologies: SPIA, PRS, CePa, TAPPA, TopologyGSA, Clipper and DEGraph. We performed a number of controlled experiments that investigated their sensitivity to sample and pathway size, threshold-based filtering of differentially expressed genes, ability to detect target pathways, ability to exploit the topological information and the sensitivity to different pre-processing strategies. We also verified type I error rates and described the influence of overexpression of single genes, gene sets and topological motifs of various sizes on the detection of a pathway as differentially expressed. The results of our experiments demonstrate a wide variability of the tested methods. We provide a set of recommendations for an informed selection of the proper method for a given data analysis task. High-throughput gene expression technologies (microarrays or next-generation sequencing) allow the estimation of the expression levels of thousands of genes in a single experiment. Often these experiments are just a first step in a broader biological investigation and serve generating hypotheses based on identified differentially expressed genes and pathways. A biological pathway is a collection of genes or molecules that act synergistically by means of chemical reactions, molecule modifications or signal transduction to execute a biological function. Thus, from a computational analysis perspective, a pathway is a set of genes (proteins) and their associated pairwise interactions. Pathway analysis aims to discover those pathways whose activation/inactivation is associated with a group of interest. This type of analysis requires integrating information about gene ontology and pathway structure.Generally, there are two main approaches: one that relies only on the expression levels of the constituent genes (of the pathway)—and is epitomised by the GSEA family of methods—and a second one that additionally exploits the pathway topology. The second group of methods represents a more recent evolution of pathway analysis methods that try to improve both specificity and sensitivity of the findings.The application of topology-based methods is facilitated by the existence of public databases which gather information about gene/protein interactions, such as the well-known Kyoto Encyclopedia of Genes and Genomes (KEGG) database which provides access to hundreds of pathways representing state-of-the-art knowledge about molecular interactions. Prior to performing a topology-based pathway analysis, the pathway of interest must be pre-processed into a simple interaction network.Each new topology-based pathway method usually compares its performance to an enrichment-based method (most often GSEA ) on a seGiven the proliferation of methods , where V is a set of vertices/nodes represented by products of genes and E is a set of edges representing interactions between them; (iii) the pathways are modeled and analyzed individually (without cross-pathway interactions). The typical input data for these methods consists of a gene expression data matrix , group membership labels (as a vector) and the list of pathway topologies. Based on these criteria we selected the following methods: SPIA [p-value to each pathway  and pathways with extreme test-statistic or low p-value are called‘differentially expressed’.In this work, we consider a number of parameters that influence the quality of the results obtained by topology-based pathway analysis. These parameters are varied in controlled experiments in order to study the sensitivity of the methods and—when possible—to quantify it. These experiments are performed on both artificial and real-world data, thus resulting in a comprehensive characterisation of the behaviour of each considered method. From the beginning, we did not expect to identify a single method that would fit all possible applications, thus, in our investigations, we tried to capture most of the standard scenarios. The methods under investigation were selected based on the following criteria: (i) the aim is to detect differentially expressed pathways (DEPs) between two groups of interest based on gene expression data; (ii) the pathway topology is a priori known and is modeled as simple interaction network or graph ds: SPIA , PRS [10ds: SPIA , TAPPA [ds: SPIA , Topologds: SPIA , Clipperds: SPIA  and DEGrds: SPIA . Each meWe performed eight distinct experiments to provide comprehensive insight into the topology-based pathway analysis methods , S1 TextThe first group of parameters are data set-centric  and helped us to describe the performance of a method under various conditions and to guide the selection of the optimal method for a specific dataset. The methods’ ability to control type I error was studied in Experiment 2. The influence of overexpression of particular gene(s) (Experiments 3-5), the influence of discarding the topological information (Experiment 7) and the effect of the pre-processing of pathway topologies (Experiment 8) tested the topology-based nature of the methods. If no effects were observed, the method should not be considered as topology-based pathway analysis method. The increased sensitivity and specificity expected from the incorporation of the topological information were examined by the identification of biologically relevant pathways (Experiments 6-8) since no proper method for identifying truly differentially expressed pathways is known.competitive and self-contained); (ii) the (non)identification of differentially expressed genes (DEGs) prior pathway analysis  and functional class scoring (FCS)) and (iii) the number of variables in the model (univariable and multivariable)  the null hypothesis  [ToPASeq package [ToPASeq package in our work.For each experiment we applied selected methods  on gene ramework  and Biocramework . There aDEGraph)  and  which is directly related to the particular disease and hence very likely to be differentially expressed. These datasets were used in Experiments 1, 6, 7 and 8.In our study we used real gene expression microarray datasets from three public collections: Gene Overexpression Data Collection , 24, BreSince the proper statistical distribution of the pathway expression data is unknown, we decided to use a real dataset (a dataset from Breast Cancer Data Collection denoted as VDX) as a base for the generation of simulated data. It contains 344 expression profiles of breast tumours obtained on an Affymetrix Human Genome U133A Array platform with 22 283 probesets corresponding to 13 091 unique Entrez IDs. We used estrogen receptor status as the main parameter dividing samples into two clinical groups. The simulated datasets were used in all experiments. The datasets for particular experiments were generated as shown in graphite’s pre-processed pathways as a default set of pathway topologies for the following reasons: (i) they are claimed to be superior to original implementation [graphite implementation is readily available and widely used. In ToPASeq one can choose either graphite pre-processed pathways (+GPT) or pathway pre-processing as in the original implementation (MSPT) (if available) and hence evaluate the effect of different pre-processing strategies. The +GPT topologies were used in all experiments, and the MSPT was used in Experiment 8 only. In Experiment 7 we also used non-topological variants of the compared methods corresponding to pathway topologies without interactions (-GPT). To reduce computational complexity, we filtered out pathways with more than 150 genes and with less than two genes with available expression data.We used human pathways from the KEGG database as the source of pathway topologies. For our comparison we used entation ; (ii) thSimilar observations were made in the analysis of real datasets from the three real data collections. Across all data collections, the highest proportion of DEPs was observed in Clipper (median: 92.5%), followed by TopologyGSA (median: 73.7%), DEGraph (median: 48.0%) and TAPPA (median: 36.1%). CePa, SPIA and PRS reported the smallest median proportion of DEPs . Results for individual disease collections are shown in . Althougcompetitive methods , the statistical significance of the differential expression of a pathway depends on the set of genes measured in the experiment. A smaller number of genes outside a pathway leads to reduced variability of the random sets of DEGs which results in lower probability of extreme pathway-statistic and, as consequence, higher p-value. Hence, we split the collection into low-, medium- and high- density platform datasets, based on the number of unique EntrezIDs their probes mapped to  . For atforms) . Indeed,p < 0.05, p < 0.01 and p < 0.001) on the proportion of DEPs (p < 0.01 and p < 0.001), in all methods, the number of DEPs increased with increasing sample size, as one would expect based on statistical properties of hypothesis testing. For p < 0.05, however, this trend holds only until a breakpoint in sample size, which is method specific: between 85-120 samples in CePa, between 222-257 samples in PRS and between 257-291 samples in SPIA. After the breakpoint, the number of DEPs rapidly decreases for p < 0.05.The Disease-Control Data Collection contained small to medium size datasets in which patients with various diagnoses were compared to healthy controls. The proportion of DEPs varied greatly between datasets from this collection . However of DEPs . For strp-value of pathways within each group as a function of dataset sample size for individual methods. Large pathways achieved lower median p-values in comparison to small pathways, independently on the dataset sample size, except PRS. In PRS, we observed the opposite effect starting at 137 (40%) samples. In multivariable methods, median p-values decreased very rapidly with increasing sample size, dropping below 0.01 at 33 (10%) and 51 (15%) for Clipper and TopologyGSA.To study the effect of pathway size, we divided pathways into small (<35 nodes) and large (≥35 nodes) (following ). S3 FigFor all methods, the observed type I error rate was close to the expected 5% threshold, except for CePa (12.8%), see In this experiment, we studied the effect of group-specific increase of expression of single genes in three selected pathways (increments of 0.1 to 2 in log2 fold change with step size 0.1 in 200 simulated datasets). The influence of a gene was quantified as a proportion of identified differentially expressed pathways across all simulations and increments. For simplicity, we divided the gene influence into five categories: very low influence (0%-20% DEPs), low influence (20%-40% DEPs), medium influence (40%-60% DEPs), high influence (60%-80% DEPs) and very high influence (80%-100% DEPs) , respectAn induced change in a single gene had a much stronger influence on the results of multivariable methods than on the results of univariable methods. The median proportion of DEPs  for multivariable methods was 82.5% for TopologyGSA, 82.3% for Clipper and 42.7% for DEGraph, compared to 29.3% for PRS, 25.9% for CePa, 15.4% for TAPPA and 12.8% for SPIA.We further examined the effect of relative change of gene expression between the groups, the effect of gene mean expression and the effect of gene topology in a pathway .The influence of gene topology was in agreement with methods’ algorithms . In TopoHere, we assessed the combined impact of overexpression of multiple genes (gene sets), regardless of the possible topological motif. In all methods, the number of DEGs in a pathway positively correlated with the number of DEPs. Within the same gene set size, the influence of a gene set increased with the cumulative effect of individual genes as measured in Experiment 3 .In this experiment, we overexpressed three, four and five genes, respectively, representing one of the 18 topological motifs present in the Non-small cell lung cancer pathway see . SimilarFor the multivariable methods, we did not observe the influence of the motif on the proportion of DEPs when compared to the gene set effect from Experiment 4 . In all The effect of the motifs in the context of previous findings and the motifs’ properties  is shown as a heatmap with information from Experiment 3 overlaid , Fig 5. p-value in comparison to other pathways.In this experiment, for each real dataset we identified a pathway that was related to the disease or a biological problem and, in an ideal situation, this pathway should be detected as differentially expressed with very low During the analysis, we encountered multiple method-specific problems that resulted in the impossibility to analyse all available pathways. First, TopologyGSA requires the dataset to have more samples than the number of genes in the largest clique of the pathway and this condition was met only by several pathways. For large datasets, such as SUPERTAM_HGU133A from the Breast Cancer Data Collection (N = 856 expression profiles), we were unable to run TopologyGSA on 80GB RAM machine. DEGraph encountered similar but less frequent problems due to the singularity of pooled covariance matrices.p-values and ranks in the Disease-Control Data Collection and Breast Cancer Data Collection. The results from the Gene Overexpression Data Collection can be found in p–values and ranks to the target pathways than univariable methods. In the Disease-Control Data Collection, the target pathway was tested by TopologyGSA in only ten out of 36 datasets, of which nine times it was reported as differentially expressed. In contrast, the ranks from the DEGraph method were the highest in multivariable methods and the second largest in all methods. PRS and CePa reported consistently low median p-values  and low median ranks . Amongst univariable methods, the highest median p-value and rank of target pathways were observed in SPIA and TAPPA. In the Breast Cancer Data Collection datasets, the aim was to detect differentially expressed pathways between the estrogen receptor positive (ER+) and estrogen receptor negative (ER-) group. The set of target pathways therefore comprised of four pathways with estrogen receptor genes: Endocrine and other factor-regulated calcium reabsorption, Estrogen signalling pathway, Prolactin signalling pathway and Thyroid hormone signalling pathway. Since estrogen receptor plays different roles in these pathways and therefore harbours different topological ‘importance’, results for individual pathways from topology-based pathway analysis may vary. For all these pathways, all multivariable methods  again reported very low p-values and ranks. From the univariable methods, TAPPA returned the lowest median p-values  and the highest ranks . The lowest median p-values and ranks of all target pathways amongst remaining univariable methods were observed in CePa. SPIA reports lower p-values and ranks than PRS only for the Endocrine and other factor-regulated calcium reabsorption pathway. Estrogen receptor is one of the root nodes and has a medium influence on this pathway in SPIA (47% DEPs) and only low influence in PRS (22% DEPs). On the other hand, Prolactin signalling pathway is the least significant by SPIA, and the estrogen receptor is a leaf node in this pathway with very low influence (3.5% DEPs). In the original experiments of the Gene Overexpression Data Collection, an overexpression of three genes  was induced experimentally via adenoviral infection. The fold change of the perturbed genes ranged from 2.38 to 5.29  and real (from Experiment 6) datasets and Non-small cell lung cancer pathway was used as a model pathway for simulated data. Then we quantified the effect of genes in simulated datasets and computed the corresponding riment 6 .p-values and ranks for target pathways in PRS and CePa and decreased p-values and ranks for DEGraph and SPIA. No effect of exclusion of topological information was found in TAPPA, TopologyGSA and Clipper. Note that the median p-value of the target pathway was below 0.2 in all methods regardless pathway topologies. In PRS, the median p-value raised from 0.031 in the topological variant to 0.055 in the non-topological variant. In the Breast Cancer Data Collection  and decreased in other pathways. In all estrogen receptor containing pathways, we observed increased p-values in CePa and decreased in PRS. No difference was observed in multivariable methods.The effect of the individual genes in simulated data is shown in llection , we obsegraphite pre-processing +GPT), we first compared effects of the individual genes in model pathways  aim to detect differentially expressed pathways between two groups of interest, (ii) use a priori known pathway structures (topologies) and (iii) model each pathway separately. We described the performance of the selected methods on both simulated and real datasets.We studied the methods’ behavior from several perspectives: the sample size, pathway size, platform density, effect size, number of differentially expressed genes, gene topologies, platform density, gene sets and their topological motifs, the inclusion of topology information in the method’s algorithm and different strategies for pre-processing of pathway topologies. The influence of the tested variables was assessed by comparison of the proportion of differentially expressed pathways, their p-values and ranks.p-values than small pathways. Also, as expected, when a pathway contained more differentially expressed genes it was more often detected as differentially expressed. The number of differentially expressed genes usually surpassed their topological influence. None of the methods showed a preference for a particular differentially expressed topological motif.In all the compared methods, large pathways (> 35 genes) were assigned lower T2 statistic, they differed significantly in their performance. TopologyGSA and Clipper assigned very low p-values and ranks to all the target pathways. However, this seems to be the result of overall low specificity, since they reported many other pathways  as differentially expressed. These methods were also sensitive to the increase in the sample and pathway size, the number of differentially expressed genes and the mean gene expression. The higher the increase, the lower the p–values and the larger the proportion of differentially expressed pathways, independent of the platform density. These findings indicate that in the scenario where (i) many differentially expressed genes are expected (e.g. cancer-related experiments); (ii) the dataset contains more than a few tens of samples (> 68 samples in our experiments); (iii) a pathway contains a gene with at least a subtle random change in the expression, the pathway will be identified as significant. This behavior agrees with the self-contained nature of the methods, which is known to have higher sensitivity. However, many differentially expressed pathways identified by these methods might be false positives and therefore not useful for selection of biological hypotheses for further research. Interestingly, in TopologyGSA and Clipper, the exclusion of the topological information made no difference in the results. Therefore, despite well-established mathematical background , these methods do not appear to fit the definition of topology-based methods for identification of differentially expressed pathways.The most striking difference was found between multivariable and univariable methods. Multivariable methods  overall reported larger proportions of differentially expressed pathways in comparison to univariable methods . Although all tested multivariable methods are derived from Hotelling’s In contrast, DEGraph detected fewer differentially expressed pathways compared to TopologyGSA and Clipper, suggesting higher specificity. At the same time, in DEGraph the influence of individual genes was related to the pathway topology. DEGraph was less sensitive to sample size, pathway size or the number of differentially expressed genes. The performance of the non-topological variant of DEGraph was similar to the TopologyGSA and Clipper with or without topology. Different pathway pre-processing strategies had only limited influence on both DEGraph and Clipper (not assessed for TopologyGSA).p-values only to some of the target pathways depending on the topological properties of differentially expressed genes in the pathway. This behaviour suggests higher specificity and stronger dependency on the topological information. These methods were less sensitive to the effects of sample size, pathway size, number of DEGs or thresholds used to identify differentially expressed genes. However, with increasing number of differentially expressed genes in a pathway, the effect of gene topology became less important. Due to the competitive nature of SPIA, PRS and CePa, these methods reported less differentially expressed pathways on low-density platforms. The univariable methods also exhibited higher sensitivity to the pre-processing of pathway topologies. Hence they can be considered true representatives of the topology-based pathway methods. Pre-processing of protein complexes, gene families and interactions involving non-gene products (metabolites such as PIP3) was the key factor in methods’ performance and influence of the individual genes. Although, our results suggest that, for PRS and CePa, the method-specific pathway pre-processing seams to be more appropriate and should be preferred to graphite’s approach, further research is needed to identify an optimal pre-processing strategy for the compared methods. For instance, gene family members may be incomplete, and thus the observed increased influence of a gene which is a member of two different gene families may not be biologically sustained. Also, members of a gene family are seen as interchangeable regarding signal transduction, while each subunit of a protein complex is necessary for complex assembly and biological function. Therefore the unified approach, as used in method-specific pathway pre-processing, may not be optimal. The TAPPA [Univariable ORA methods SPIA, PRS and CePa, assigned low he TAPPA  method sThe increased sensitivity of multivariable methods (mainly TopologyGSA and Clipper) makes them ideal candidates for pathway analysis of experiments, where subtle changes in expression or a small number of differentially expressed genes between two conditions are expected—e.g. as in the case of tumor samples which contain a significant proportion of non-tumoral tissue (such as supporting stroma), thus confounding and diminishing measured signal of the gene expression. Since multivariable methods do not use lists of differentially expressed genes based on pre-defined thresholds but work with a complete list of the tested genes, they can be applied even in cases where none or very few genes are significant after statistical testing . The results of these methods, however, must be taken with caution and the significance of a pathway of interest must be interpreted in the context of all the results to ensure it is not just a consequence of overall low specificity of the method. To control for low specificity of the result, we recommend using DEGraph.Univariable methods are not sensitive to the sample size or the number of differentially expressed genes in the datasets. Their ability to identify particular pathway as differentially expressed is highly dependent on the topological properties of the deregulated genes, the inclusion of the topological information and the pre-processing of the pathway topologies. Univariable methods are recommended in most applications and especially when the biological hypothesis aims at a pathway where genes of certain topological properties  are expected to be affected (see below and graphite pre-processing of topologies (+GPT). In SPIA, CePa, TAPPA and DEGraph, we colored all the genes with the highest influence as defined in Experiment 3 for each method. Details of the topological as well as biological properties of the most influential genes are described in the Based on our results we propose some guidelines for optimal method selection based either on (i) design of the experiment —The observed differences between topological methods should be considered when the results of pathway analyses are to be compared across experiments in which different methods were used to detect differentially expressed pathways. Currently, SPIA is the most often cited method (282 citations from Web of Science Core Collection as of 14 February 2017). The other compared methods were mainly used in methodological publications, in which the general concepts were compared to the new method and only very rarely in applications.We performed one of the largest studies of topology-based pathway analysis methods published to date. In this study, we compared seven methods that aim to detect differentially expressed pathways from expression data employing a priori known pathway topologies in their algorithm. The methods were ranked according to their sensitivity to sample and pathway size, ability to detect target pathways, the proportion of differentially expressed pathways, benefit from incorporating topological information and sensitivity to different pathway pre-processing strategies. We also verified type I error rates and described the influence of overexpression and topological properties of a single gene or gene sets on the detection of a pathway as differentially expressed by the selected methods.We demonstrated that multivariable self-contained methods are very sensitive to the changes in gene expression within a pathway leading to the uninformative identification of over 90% pathways as differentially expressed. As a consequence, a significant result can be easily obtained for a particular pathway. On the other hand, univariable methods (mostly competitive) were less sensitive to subtle changes in gene expression but exhibited stable performance over a wide range of scenarios and benefited from the inclusion of topological information.Finally, we proposed guidelines for method selection based on a number of variables connected to experimental design as well as biological hypotheses. Overall, we recommend any of the multivariable approaches to be used mainly for applications with small sample size and subtle changes in gene expression, whereas univariable methods should be preferred for genome-scale applications with large changes in gene expression. The pre-processing strategy for pathway topologies must be carefully considered for univariable methods, and further research is required to identify an optimal pre-processing strategy.S1 Text(PDF)Click here for additional data file.S1 FigProportion of DEPs depending on the number of Entrez IDs for datasets from Breast Cancer Data Collection. Each point represents one dataset.(PDF)Click here for additional data file.S2 FigProportion of DEPs depending on the number of DEGs for datasets from Disease-Control Data Collection. Each point represents one dataset.(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 FigDependence of the proportion of DEPs on the difference in expression induced between groups, the gene mean expression and its postion. In SPIA, neutral interactions were drawn in grey.(PDF)Click here for additional data file.S7 FigSets of 2, 3, 4 and 5 genes were randomly selected from Non-small cell lung cancer pathway. Each circle represents one of those sets. Expression of genes in the set was modified with increments of 0.1 to 2 with step size 0.1 in 200 simulated datasets. Border color indicates number of genes in the set. Vertical axis shows combined influence of the genes . Horizontal axis corresponds to sum of the influence of individual genes. Pie color (from grey to blue and red) represents the influence of a single gene (see Experiment 3 for details).(PDF)Click here for additional data file.S8 FigHeatmaps of the proportion of DEPs for all compared methods.(ZIP)Click here for additional data file.S9 Figp-values and rank of the estrogen receptor-containing pathways in Disease-Control Data Collection. Ranks are based on p-values. Pathway with the lowest p-value has rank 1. All pathways with the same p-value recieved same rank. The rank was incremented by one between subsequent p-values.Boxplots of (PDF)Click here for additional data file.S10 Figp-values and rank of the estrogen receptor-containing pathways in Breast Cancer Data Collection. Ranks are based on p-values. Pathway with the lowest p-value has rank 1. All pathways with the same p-value recieved same rank. The rank was incremented by one between subsequent p-values.Boxplots of (PDF)Click here for additional data file.S11 Figp-values of the overexpressed oncogene-containing pathways in Gene Overexpression Data Collection. Pathways are ordered by the number of methods in which they are differentially expressed (p < 0.05).Heatmaps of (PDF)Click here for additional data file.S12 Fig(A) Proportion of differentially expressed pathways for different genes and the difference in expression induced between groups. (B) Dependence of the proportion of differentially expressed pathways on the difference in the gene position. In the non-topological variants of the methods (-GPT) we observed reduced proportion of differentially expressed pathways and loss of its dependence on gene postion in all methods except TopologySGA an Clipper.(PDF)Click here for additional data file.


Unlike most pathway analysis applications, our approach allows replication in an independent cohort.Children exposed to maternal smoking during pregnancy exhibit increased risk for many adverse health effects. Maternal smoking influences methylation in newborns at specific CpG sites (CpGs). Here, we extend evaluation of individual CpGs to gene-level and pathway-level analyses among 1062 participants in the Norwegian Mother and Child Cohort Study (MoBa) using the Illumina 450 K platform to measure methylation in newborn DNA and maternal smoking in pregnancy, assessed using the biomarker, plasma cotinine. We used novel implementations of bioinformatics tools to collapse epigenome-wide methylation data into gene- and pathway-level effects to test whether exposure to maternal smoking q < 0.25). Six of these 15 genes  contained individual CpGs that were differentially methylated with regards to cotinine levels (p < 1.06 × 10−7). Nine of the 15 genes  were associated with cotinine at the gene-level (q < 0.25) but had no genome-wide significant individual CpGs (p > 1.06 × 10−7). Pathway analyses using gene scores resulted in 51 significantly associated pathways, which we tested for replication in an independent cohort (q < 0.05). Of those 32 replicated in an independent cohort, which clustered into six groups. The largest cluster consisted of pathways related to cancer, cell cycle, ERα receptor signaling, and angiogenesis. The second cluster, organized into five smaller pathway groups, related to immune system function, such as T-cell regulation and other white blood cell related pathways.Data on 485,577 CpGs, mapping to a total of 20,199 genes, were used to create gene scores that were tested for association with maternal plasma cotinine levels using Sequence Kernel Association Test (SKAT), and 15 genes were found to be associated  contains supplementary material, which is available to authorized users. Previously, Joubert et al. collected genome-wide methylation data from 1062 MoBa mother-offspring pairs and demonstrated that maternal smoking, assessed objectively by cotinine levels, is significantly associated with 1) differential DNA methylation in genes involved in metabolism of tobacco smoke compounds, and 2) novel genes involved in diverse developmental processes not previously linked to tobacco response . For each gene, the CpG data was combined into a gene-level ted in R , 13. Thep-values) were used for pathway-level analysis. Genes were grouped into a priori pathways (gene sets) using the Molecular Signatures Database v4.0 (MSigDB) [Homo sapiens, 2) the number of genes in a pathway cannot exceed 250 genes, 3) at least one gene in the pathway must be present in the list of available gene scores, and 4) pathways representing positional gene sets (C1), motif gene sets (C3), and computational derived gene sets (C4) were excluded. This resulted in a total of 5836 pathways for analysis. These pathways came from the either curated gene sets (C2), GO gene sets (C5), oncogenic signatures gene sets (C6), or the immunologic signatures gene sets (C7) collections in MSigDB [The results from the SKAT gene-level association analysis  . MSigDB (MSigDB) . A subsen MSigDB . Each pap-value across all gene-level results from the SKAT analysis. There are a number of approaches for combining p-values, but most assume that the individual p-values are not correlated. Pathway analysis actually relies on the fact that genes scores within a pathway are correlated, so a collapsing approach that explicitly takes that into account was used. More specifically, the individual gene scores were combined into pathway-level scores using the correlated Lancaster method in Dai et al. (TA) [p-value for each pathway from MSigDB. It is important to note that this combined p-value represents a self-contained pathway analysis, where the null hypothesis is that gene sets are not more strongly associated than expected by chance. Because of the large number of pathways tested, we controlled for multiple comparisons using a conservative Bonferroni correction. We chose a conservative approach, even though the p-values from each pathway are not independent, since genes appear in multiple pathways. Pathways with a corrected p < .05  were considered statistically significant in the discovery cohort.The pathway-level score was calculated from the individual gene scores that overlapped with the genes in each pathway gene set. The pathway level score is the combined al. (TA) . This rep < 8.6 × 10−6) were tested for replication using MoBa2. The CpG values were combined for genes that occurred in significant pathways in MoBa1, using SKAT as described above. Gene scores were then combined using the Lancaster approach to calculate a pathway-level score for the replication cohort. Pathways p values were adjusted using both an FDR and a more conservative Bonferroni approach and were considered to be successfully replicated with an FDR q < 0.05  were tabulated. All gene-pathway combinations were recorded as either a “1” if the pathway contained the gene or a “0” if the pathway did not contain the gene. Clustering was then performed using Euclidean distance and Ward’s method. The resulting dendrogram , nine of which were not detected by only testing individual CpGs. Furthermore, pathway analysis revealed significant associations with 51 pathways, 32 of which replicated in an independent cohort of 685 participants. Significantly associated pathways, that replicated in the independent cohort, represent diverse biological processes including cancer, cell cycle, ERα receptor signaling, angiogenesis, and immune system function. This approach may provide new insight into the biological mechanisms that may lead to adverse health effects from exposure to tobacco smoke in utero.


P < 0.05) significantly associated with bladder cancer risk. Five of the most promising pathways (P ≤ 0.001 in any of the three GSEA methods) among the 18 pathways included two cell cycle pathways and neural cell adhesion molecule (NCAM), platelet-derived growth factor (PDGF), and unfolded protein response pathways. We validated the candidate polymorphisms in the NCI GWAS and found variants of RAPGEF1, SKP1, HERPUD1, CACNB2, CACNA1C, CACNA1S, COL4A2, SRC, and CACNA1C were associated with bladder cancer risk. Two CCNE1 variants, rs8102137 and rs997669, from cell cycle pathways showed the strongest associations; the CCNE1 signal at 19q12 has already been reported in previous GWAS. These findings offer additional etiologic insights highlighting the specific genes and pathways associated with bladder cancer development. GSEA may be a complementary tool to GWAS to identify additional loci of cancer susceptibility.Genome-wide association studies (GWAS) are designed to identify individual regions associated with cancer risk, but only explain a small fraction of the inherited variability. Alternative approach analyzing genetic variants within biological pathways has been proposed to discover networks of susceptibility genes with additional effects. The gene set enrichment analysis (GSEA) may complement and expand traditional GWAS analysis to identify novel genes and pathways associated with bladder cancer risk. We selected three GSEA methods: Gen-Gen, Aligator, and the SNP Ratio Test to evaluate cellular signaling pathways involved in bladder cancer susceptibility in a Texas GWAS population. The candidate genetic polymorphisms from the significant pathway selected by GSEA were validated in an independent NCI GWAS. We identified 18 novel pathways ( Urinary bladder cancer is the fourth most common cancer in men in U.S. Estimates in 2015 indicate urinary bladder cancer affects 56,320 males and 17,680 females, and will lead to 16,000 deaths in the U.S. , geneticSLC14A1, intron), 8q24.21 , 4p16.3 , 22q13.1 , 19q12 , 8q24.3 , 3q28 , 2q37.1, and 5p15.33  [GSTM1, deletion) and 8p22  were further validated in the GWAS studies [Approaches towards mapping cancer susceptibility regions have undergone an evolution due to the recent annotation of variation across the genome as well as technical advances in single nucleotide polymorphism (SNP) arrays . The can intron) . In addi studies , 19, 20.http://www.genome.gov/gwastudies/). They may be highly correlated with the variants directly associated with bladder cancer susceptibility [Recent GWAS have provided valuable insights into the genetic basis of human disease but GWAS do not fully explain heritability in sporadic cancers . So far,tibility .Gene set enrichment analysis (GSEA), also known as pathway based analysis, examines whether the test statistics for a group of common genetic variants that map to predefined gene sets  support the possibility of disease association . To datehttp://www.genome.jp/kegg/), Biocarta (http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways), and Reactome (http://www.reactome.org/) databases were included in the GSEA of bladder cancer GWAS (versus expected chi2 test statistics showed no evidence for inflation in the Texas population (inflation factor 0.995) significantly associated with bladder cancer risk in all three GSEA methods (Table P ≤ 0.001 in any of the three GSEA methods) among the 18 pathway included two cell cycle pathways involved in the G1/S transition , neural cell adhesion molecule (NCAM) pathway , platelet-derived growth factor (PDGF) induced intracellular pathway  and unfolded protein response pathway  Figure , 44 sign5)Figure , and 68 5)Figure . The reshttp://www.biocarta.com/pathfiles/h_RacCycDPathway.asp) contains 300 SNPs from 26 genes, and “BIOCARTA_SKP2E2F_PATHWAY” (http://www.biocarta.com/pathfiles/h_skp2e2fPathway.asp) consists of 115 SNPs from 10 genes. Five genes  overlapped in these two pathways (Table P < 0.05) in the NCI bladder cancer GWAS for validation , rs997669 , and rs4804903 , which were not in linkage disequilibrium and were the overlapping SNPs co-existed in the two cell cycle pathways   and SKP1 rs7701836 (Chr5: 133539947)  with CCNE1 rs8102137 and SKP1 rs10491321, a region already identified in previous GWAS [The two significant pathways, “BIOCARTA_ RACCYCD_PATHWAY” and “BIOCARTA_SKP2E2F_ PATHWAY”, were both related to cell cycle, specifically genes critical for G1 and S phase Table . In our ys Table . We also) Figure , which ahttp://www.reactome.org/entitylevelview/PathwayBrowser.html#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=375165&ID=375165) was composed of 2,232 SNPs from 69 genes, and platelet-derived growth factor (PDGF) induced intracellular pathway (“REACTOME_SIGNALING_BY_PDGF”: http://www.reactome.org/entitylevelview/PathwayBrowser.html#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=186797&ID=186797) consisted of 1,423 SNPs from 64 genes  with the validated SNPs rs2239117 and rs2239118  pathway,  with 257 SNPs from 19 genes showed strongest enrichment signal based on Aligator (P = 0.0008) and SNP Ratio Test (P = 0.0009), and ranked top 8 in Gen-Gen (P = 0.007) .Unfolded protein response (UPR) pathway (REACTOME_UNFOLDED_PROTEIN_RESPONSE: 7) Table . HoweverCCNE1, RAPGEF1, SKP1, HERPUD1, CACNB2, CACNA1C, CACNA1S, COL4A2, SRC, CACNA1C, appear to be associated with bladder cancer risk and were subsequently observed in the NCI bladder cancer GWAS.In this study, we pursued a pathway approach in the Texas bladder cancer GWAS data using three GSEA methods including Gen-Gen, Aligator, and SNP Ratio Test. We identified 18 promising pathways out of 781 predefined gene-sets, which were associated with bladder cancer risk according to our screening criteria. The top five significant pathways involved cell cycle control at G1 and S phase, NCAM and PDGF induced intracellular signaling, and unfolded protein response. From these top five pathways, 17 SNPs from CCNE1 rs8102137, which maps to the chromosome 19q12 at the 5′ flanking region of CYCLIN E1 gene with P value of 1×10−6  independent of the above two SNPs.The pathways highlighted were two cell cycle related pathways “BIOCARTA_RACCYCD_PATHWAY”, and “BIOCARTA_SKP2E2F_PATHWAY” at G1 and S phase . The G1/s Figure . There wIn the “REACTOME_NCAM_SIGNALING_FOR_ NEURITE_OUT_GROWTH” pathway , the neuRAPGEF1 rs7040470 was significantly associated with bladder cancer risk and the significance level reached 1.2×10−5 in the pooled analysis  resident protein which is up-regulated in response to ER stress [HERPUD1 have been associated with the metabolic syndrome in GWAS [UPR contributes to a critical decision point between homeostasis or apoptosis of cell . During 02 Table . HERPUD1R stress . Interes in GWAS –49.CCNE1 from previous GWAS, but also highlighted several novel SNPs, genes, and pathways potentially involved in bladder cancer tumorigenesis (Tables 1-3). Since GSEA grouped millions of SNPs into hundreds of gene sets, the burden of multiple comparisons have been greatly reduced. In addition, incorporation of the biological knowledge into statistical analysis renders our finding more relevant to biological interpretation. The biggest challenge for GSEA is how to define a gene set as misclassification leads directly to a loss of power. Due to the complexity of cell biology, some of the gene sets will be inevitably redundant or overlapping. Thus, associations could be driven by significant genes that are overlapped in different pathways. Another major limitation of GSEA is that it can only assess SNPs in or near gene regions so non-genic variants are not considered. Furthermore, the analysis assumes SNPs having only local cis-effects, an assumption that may be limiting. Finally, we only validated the genes and SNPs but not the pathways associated with bladder cancer risk since the NCI data was derived from multiple GWAS genotyping panels  compared to the MD Anderson data of using a single beadchip (HumanHap610). Differences in gene coverage and the selection of tagSNPs for each gene region in the various Illumina GWAS panels precluded us from confirming the results at the pathway level.The GSEA method is an attractive approach for identifying additional susceptibility signals but it does require both larger sample sizes and independent replication sets to conclusively establish novel loci. GSEA can detect evidence for subtle effects of multiple SNPs in the same gene set, though it does not dissect pleiotropy in a given region. In our GSEA, we not only validated one SNP at In summary, we implemented three different GSEA methods as internal validation to identify the biological pathways consistently associated with bladder cancer risk and also validated our results in an independent NCI population, which may reduce false discovery in our findings. GSEA is a complementary tool to identify additional genetic contributions to the heritability of bladder cancer, and may also be applicable to clinical outcome studies  by incorP < 0.0001. These procedures left 556,429 SNPs for the final analysis.Study population was derived from our previous published GWAS 14), which included a total of 969 Caucasian cases and 957 Caucasian controls. Cases were recruited from MD Anderson Cancer Center and Baylor College of Medicine between 1999 and 2007. They were newly diagnosed bladder cancer patients, histologically confirmed, and previously untreated (ICD codes 188.1- 188.9). There were no restrictions on age, sex, ethnicity, and cancer stage in case recruitment. Control subjects were recruited from Kelsey Seybold clinics and were frequency-matched to cases by age (±5 years), sex and ethnicity. We restricted our analysis to Caucasians, due to the small number of minority participants in our population. All epidemiology data were collected by trained interviewers after signing of the consent form by study participants. The study was approved by the institutional review boards of MD Anderson Cancer Center, Baylor College of Medicine, and Kelsey-Seybold Clinic. No inflation was found in the study population structure , which i. We randThe validation population consists of the primary scan of the NCI bladder cancer GWAS (available on dbGaP), which includes five studies with 3,532 cases and 5,120 controls of European ancestry , 34. Thehttp://www.broadinstitute.org/gsea/msigdb/) from Broad Institute was used to define gene sets/pathways, which were composed of positional gene sets, curated gene sets, motif gene sets, computational gene sets and GO gene sets. We downloaded the 880 canonical pathways for GSEA. To avoid the overly narrow and broad definition of a biological pathway, we confined the input pathway to contain 10-100 genes per pathway, resulting in 781 pathways in our GSEA analysis (http://www.genome.jp/kegg/), 214 were from Biocarta (http://www.biocarta.com/), 377 were from Reactome (http://www.reactome.org/), and 39 were from other resources. Biocarta generally has the smallest pathway size in terms of the number of gene in each pathway, with a median gene number of 18 per pathway. In contrast, KEGG pathway has the largest size with a median gene number of 44 per pathway. The significant pathways selected by GSEA were input into the Ingenuity Pathway Analysis tools (http://www.ingenuity.com/index.html) for functional annotation.The molecular signature database . SNPs were mapped to gene region and ±20KB upstream and downstream of gene boundaries to cover the gene coding region and most of the regulatory components.Gene information was downloaded from NCBI dbSNP build 36.3. SNP information was from the Illumina HumanHap610 chip and validated by USCS genome browser (http://www.bioconductor.org/packages/2.3/bioc/html/snpMatrix.html) and hexbin (http://cran.r-project.org/web/packages/hexbin/index.html). Deviation of observed data from expected results might indicate the possibility of population stratification, inadequacy of case-control matching, or differential genotyping in cases and controls. We randomly permuted the case-control status 1000 times in order to test the presence of differential genotyping. In each permutated data the same number of cases and controls was generated and an allelic 1 d.f. test statistic and P value was re-calculated for each SNP using the permuted case-control status. We applied three GSEA methods for comparison to determine which pathway(s) associated with bladder cancer were likely true findings, not derived by chance. For all methods, pathways with P values < 0.05 were considered significant.To assess the association between each SNP and disease status, we built a 2×2 contingency table by counting the number of times each possible allele appears in a case or control and allelic 1 degree of freedom (d.f.) test implemented in PLINK was performed similar to the primary GWAS analysis . We cond(1),…, r(N)) for all N genes in the GWAS dataset. For any given gene set S, composed of HN genes, a weighted Kolmogorov- Smirnov-like running-sum statistic was calculated that reflects the overrepresentation of genes within the set S at the top of entire ranked list of genes in the genome:The statistic value of each gene was represented by the highest statistic value among all SNPs mapped to the gene and then sorted from largest to smallest /(N+1), where s is the number of simulated datasets that produce a ratio greater than or equal to the original ratio, and N is the total number of simulated datasets.For a given pathway W, the SNP ratio rvs. controls in the validation group is consistent with the discovery population (same direction of change), and also both their associations with bladder cancer risk are significant at P < 0.05. A meta-analysis of the Texas and NCI populations was also used to further support the findings. Imputation of the SNPs at gene region of interest for Texas bladder cancer GWAS data was conducted by Impute 2 software (The University of Oxford http://mathgen.stats.ox.ac.uk/impute/impute_v2.html) using 1000 genomes data (http://www.1000genomes.org/) as reference panel.We selected SNPs identified in the pathway analyses above for validation in a second dataset from the already published NCI GWAS . The P v


Autism spectrum disorder (ASD) is sexually dimorphic in brain structure, genetics, and behaviors. In studies of brain tissue, the age of the population is clearly a factor in interpreting study outcome, yet sex is rarely considered. To begin to address this issue, we extend our previously published microarray analyses to examine expression of small noncoding RNAs (sncRNAs), including microRNAs (miRNAs), in ASD and in the control temporal cortex in males and females. Predicted miRNA targets were identified as well as the pathways they overpopulate.After considering age, sexual dimorphism in ASD sncRNA expression persists in the temporal cortex and in the patterning that distinguishes regions. Among the sexually dimorphic miRNAs are miR-219 and miR-338, which promote oligodendrocyte differentiation, miR-125, implicated in neuronal differentiation, and miR-488, implicated in anxiety. Putative miRNA targets are significantly over-represented in immune and nervous system pathways in both sexes, consistent with previous mRNA studies. Even for common pathways, the specific target mRNAs are often sexually dimorphic. For example, both male and female target genes significantly populate the Axonal Guidance Signaling pathway, yet less than a third of the targets are common to both sexes.Our findings of sexual dimorphism in sncRNA levels underscore the importance of considering sex, in addition to age, when interpreting molecular findings on ASD brain.The online version of this article (doi:10.1186/s13229-017-0117-0) contains supplementary material, which is available to authorized users. Autism spectrum disorder (ASD) is one of a number of neurodevelopmental disorders that display sexual dimorphism, occurring more frequently in males, which affect brain structure, gene expression, pathways, function, and ultimately behaviors that will require individualized treatments –3. ExtenFew molecular studies of ASD brain tissue to date have considered potential sexual dimorphism often because of limited tissue availability of female cases. In typical brain development, male-biased gene expression changes are enriched for extracellular matrix, immune response, chromatin, and cell cytoskeleton pathways that have been implicated in ASD . Sex difHere, we extend our analyses of previously published microarray data , 18 to eThe methods used in the current study are similar to our recent publications , 18 , which is suitable for unbalanced designs . REML optimizes the parameter estimates for the effects in the model. We used repeated measures to account for multiple regions measured within the same subjects (STS and PAC), and we made the SubjectID a random effect. Considering that our sample size was unbalanced between males and females, some identified differences may be due to this limitation. Statistical significance was defined as P < 0.05 was used for determining significance of pathway enrichment. The sexually dimorphic predicted mRNA target genes and pathways were compared to three other data sets: (1) autism risk genes from SFARI using the hypergeometric probability function phyper in R with population size set to all protein-coding human genes of 20,687 ,, 26,3, 2There are a number of sexually dimorphic miRNAs from our study that have been associated with ASD-relevant diseases and processes Table . For exaIn STS of ASD female compared to control female, miR-219 and miR-338 showed the highest level of downregulation. Both miRNAs are involved in regulating oligodendrocyte differentiation and likely myelin production . This wamiR-219 and miR-338 both promote oligodendrocyte differentiation –41. HoweIt is important to note that sex differences in the miRNAome are prominent in health and disease –47. SeveAs we have reported previously , there aspecific miRNA regulated in ASD and control brains in this study and the miRNA described by Ziats and Rennert for typically developing human brain [In general, there is little overlap of the an brain . Howeveran brain  and a tran brain  and our There are important limitations to consider when interpreting this data that are common to studies of postmortem human brains, including small sample size and variation in cause of death, postmortem interval, age at death, agonal state, postmortem RNA integrity, and tissue preparation at different brain banks. In addition, the changes in miRNA and predicted targets and pathways studied here could lead to some aspects of ASD, but they could just as well be a consequence of the condition and even represent compensatory mechanisms. Future studies will need to assess both miRNA and mRNA in the same samples, so that one can determine if specific miRNA target mRNA are present and are regulated in an inverse direction as occurs for most miRNA-mRNA interactions. Small sample size is also limitation of this report and findings need to be confirmed in larger cohorts and with alternative gene expression profiling techniques such as qRT-PCR. However, this study clearly indicates that sex needs to be considered when interpreting data on postmortem human brains in ASD.Our own molecular studies, and many others, have demonstrated clear age-related changes in the brains of both typically developing and autistic individuals , 24, 56.Additional file 1:Supplementary Data Tables. Table S1. sncRNA differentially expressed in ASD-STS vs CTRL-STS. Table S2. sncRNA differentially expressed in ASD-PAC vs CTRL-PAC. Table S3. Female (F) ASD Regional Analysis. F_ASD-STS vs F_ASD-PAC. Table S4. Female (F) controls (CTRL) regional analysis. F_CTRL-STS vs F_CTRL-PAC. Table S5. Male (M) ASD regional analysis. M_ASD-STS vs M_ASD-PAC. Table S6. Male (M) controls (CTRL) regional analysis. M_CTRL-STS vs M_CTRL-PAC. Table S7. Summary of putative target analyses. Table S8. Pathway analysis of putative targets of the mature miRNA from the PAC analysis in females. Table S9. Pathway analysis of putative targets of the mature miRNA from the PAC analysis in males. Table S10. Pathway analysis of putative targets of the mature miRNA from the STS analysis in females. Table S11. 227 common targets between female and male regional analyses. Table S12. Pathway analysis of putative targets of the mature miRNA from the female regional analysis. Table S13. Pathway analysis of putative targets of the mature miRNA from the male regional analysis. Table S14. Pathway analysis of putative targets of the mature miRNA from the female regional analysis: immune pathways. Table S15. Pathway analysis of putative targets of the mature miRNA from the male regional analysis: immune pathways. Table S16. Pathway analysis of putative targets of the mature miRNA from the female regional analysis: Neurotransmitter and other nervous system signaling pathways. Table S17. Pathway analysis of putative targets of the mature miRNA from the male regional analysis: neurotransmitter and other nervous system signaling pathways. Table S18. Pathway overlapping between our male regional analysis and the male-enriched pathways from Ziats and Rennert (Ref 15). Table S19. Seventy-four ASD-implicated genes amongst the 1382 putative targets of the miRNAs from the female regional analysis. Table S20. Canonical pathway enriched in the 74 ASD-implicated genes from the predicted targets in the female regional analysis Table S21. Targets overlapping between our analysis and the sexually dimorphic mRNA from Werling et al, Nature Communications, 2016; Supplementary Material: ncomms10717-s2. Table S22. Pathway overlapping between our male and female regional analysis and pathways were derived based on combined sexually dimorphic mRNAs from Werling et al, Nature Communications, 2016; Supplementary Material: ncomms10717-s2. (XLSX 130 kb)Additional file 2:P of overlap = 0.0034. B Overlap between all our sexually dimorphic pathways (290 pathways) and Ziats and Rennert, Mol. Psychiatry, 2014, sexually dimorphic pathways (204 pathways); P of overlap < 0.00001. C Overlap between all our sexually dimorphic pathways (287 pathways) and Werling et al, Nat. Communications, 2016, sexually dimorphic pathways (14 pathways). P of overlap between Werling et al, 2016 and our regional analysis = 0.098, P of overlap between Werling et al, 2016, and our PAC analysis = 0.092. D Overlap between the predicted targets from our female regional analysis (STS vs PAC) (1382 targets) and SFARI ASD-implicated genes (768). P of overlap < 0.001. (TIF 48 kb)Supplementary Figure. Overlap with relevant studies. A Overlap between our male regional analysis (118 pathways) and Ziats and Rennert, Mol. Autism, 2013, male-enriched pathways (10 pathways);


Vitamin D (VD) is a fat-soluble steroid essential for life in higher animals. It is technically a pro-hormone present in few food types and produced endogenously in the skin by a photochemical reaction. In recent decades, several studies have suggested that VD contributes to diverse processes extending far beyond mineral homeostasis. The machinery for VD production and its receptor have been reported in multiple tissues, where they have a pivotal role in modulating the immune system. Similarly, vitamin D deficiency (VDD) has been in the spotlight as a major global public healthcare burden. VDD is highly prevalent throughout different regions of the world, including tropical and subtropical countries. Moreover, VDD may affect host immunity leading to an increased incidence and severity of several infectious diseases. In this review, we discuss new insights on VD physiology as well as the relationship between VD status and various infectious diseases such as tuberculosis, respiratory tract infections, human immunodeficiency virus, fungal infections and sepsis. Finally, we critically review the latest evidence on VD monitoring and supplementation in the setting of infectious diseases. VD2 is most commonly added to foods given the paucity of naturally occurring VD rich foods, whereas VD3 is mainly synthesized in the skin but can be also found in animal-based foods [Vitamin D (VD) is a fat-soluble steroid essential for life in higher animals. VD is primarily produced in the skin by the direct action of ultraviolet (UV) sunlight, with a much smaller proportion coming from dietary intake . The twoed foods .3 [3 subsequently passes through an immediate non-enzymatic temperature dependent isomerization to form VD3 [Regarding the VD dermal synthesis pathway, cutaneous-derived 7-dehydroxycholesterol undergoes photolytic conversion by UV sunlight to form previtamin D3 ,3. Previform VD3 . The pat2-VD), the biologically active metabolite of VD [Regardless of its source, VD requires a number of steps to become calcitriol te of VD . The firte of VD . VD thente of VD .2-VD by the enzyme 1α-hydroxylase  [The final activation step of VD occurs primarily in the kidney, involving a second hydroxylation reaction in which 25(OH)-VD is converted to 1,25(OH)CYP27B1) .VD sufficiency is defined as having a serum 25(OH)-VD level equal or greater than 75 nmol/L (30 ng/mL) measured at the end of winter or early spring . VD insuVDD is a major public healthcare burden worldwide with estimates that between 20% and 100% of community-dwelling North American and European elderly present with VDD . VDD is VDD is directly involved in the pathogenesis of rickets, osteoporosis and osteomalacia. Nevertheless, there is a growing body of clinical and laboratory evidence that supports its association with many other vital biological processes, which is also known as the “non-classical” effects of VD . For exa2-VD [The immune response to infections is a complex and dynamic process involving multiple cell types and soluble factors such as cytokines, chemokines and hormones . Numerou2-VD ,20.2-VD has been found to influence cell differentiation within the innate immune system [2-VD promotes the differentiation of monocytes into macrophages in both mouse and human cells [2-VD suppresses the differentiation of monocytes into dendritic cells when using in vitro human peripheral blood monocytes exposed to bacterial lipopolysaccharides (LPS) [2-VD may inhibit the production of interleukin (IL)-12 and IL-10 by LPS-treated monocytes leading to decreased functional capacity of dendritic cells [1,25(OH)e system . Previouan cells . Lyakh ees (LPS) . Furtheric cells . It is, ic cells ,22.2-VD production after incubating pulmonary alveolar macrophages with interferon gamma (IFN-γ) [Recent studies have also highlighted the role of VD in enhancing mechanisms of pathogen elimination ,23. Apar (IFN-γ) .M. tuberculosis by phagocytosis or by expressing pathogen-recognition receptors (PRRs) [M. tuberculosis [M. tuberculosis lipoprotein leading to transcriptional induction of VDR and 25(OH)-VD-1-α-hydroxylase [2-VD in the mitochondria, binds to the VDR and ultimately acts as a transcription factor for human cathelicidin—an antimicrobial peptide [M. tuberculosis.Rook et al. also demonstrated that monocytes incubated with VD metabolites have increased anti-tuberculosis activity . In facts (PRRs) . Among trculosis . A heterroxylase . Moreoveroxylase . Hence,  peptide ,28,29. I peptide . Figure 2-VD is not evolutionary conserved in mice or rats [2-VD, providing specific mechanisms for humans and primates to modulate their immune response and counteract pathogen-mediated suppression [Recent research has shown that regulation of the cathelicidin antimicrobial peptide (CAMP) gene by both the VDR and 1,25(OH) or rats ,30. It i or rats ,30. Howe or rats ,31. CAMPpression . Therefo2-VD bound to the VDR induces transcription of an antimicrobial peptide through a VDRE within the promoter region of human gene DEFB4 for defensins β-defensin 2 and 4A [Furthermore, several studies suggest that VD might also have a role in the defense against viral infections ,33,34. H2 and 4A ,33. Defe2 and 4A . Defensi2 and 4A .2-VD’s anti-inflammatory effect in human T cells is partially mediated by NFκB [2-VD had an inhibitory effect on NFKB by reducing its protein expression in both the cytosolic and nuclear compartments of T cells [2-VD has been reported to shift the T helper (Th) cell response from Th1 to Th2 [2-VD inhibited the development of Th1 mediated immunity, which is required for the induction of cellular responses [2-VD treatment suppressed the Th1 response and protected animals from experimental autoimmunity [2-VD might act in reducing Th1-mediated tissue damage via inhibition of the Th1 cytokine IFN-γ and an increase in the Th2 cytokines IL-4, IL-5 and IL-10 [1,25(OH) by NFκB . NFκB is by NFκB ,37. Yu e1 to Th2 . Multiplesponses ,30,38,39immunity . Therefond IL-10 ,41. Over2-VD may have direct effects on B cell homeostasis by inhibiting its differentiation into plasma cells and modulating the production of immunoglobulins [Human B cells much like their counterpart T cells also express the VDR . Additiolobulins ,41.TB is a multi-systemic bacterial infection caused by Mycobacterium tuberculosis . The latTB is an infectious disease known to have a strong association with poverty . Over 95Micronutrients are essential elements to modulate various body reactions such as cell growth and repair, thereby contributing to homeostasis and disease prevention . Previou2 in the management of cutaneous TB [The first attempt to use VD supplementation in treating TB was reported by Williams in 1849 ,55. He dneous TB .Over the past decade eight RCTs and one complementary report of an RCT comprising over 1700 patients have been performed aiming to evaluate the effects of adjuvant VD in the setting of pulmonary TB in adults  56,57,5,62,63,64Taken together, results from these studies must be interpreted with caution since they present several concerns and limitations. Firstly, the doses of VD employed as well as the primary outcomes were strikingly different among the studies. Additionally, four studies did not evaluate VD levels after supplementation and one study did not provide VD baseline levels ,59,60,63Evidence supporting the hypothesis that VDD may predispose to influenza infection derives from observational studies, highlighting the seasonal influence of low sun exposure in both conditions ,66. Berr3 orally with different protocols of administration: bolus doses every month to every three months , weekly doses , daily doses (12 studies using doses ranging from 300 to 4000 IU/day) and a combination of bolus and daily doses . High quality evidence from 25 RCTs with moderate heterogeneity revealed that VD3 supplementation reduced the risk of experiencing at least one RTI (number needed to treat (NNT) to prevent one episode of RTI = 33, 95% CI 20–101). Furthermore, subgroup analysis of 15 RCTs indicated that daily or weekly VD3 administration protected against RTI , while no protective effect was seen in the 10 RCTs that used bolus doses of VD3. Moreover, VD3 supplementation was effective in preventing RTIs irrespective of baseline 25(OH)-VD levels, albeit the protective effects were greatest among subjects with more pronounced VDD [Clinical trials evaluating the effect of VD supplementation in preventing RTIs have reported contrasting protocols and results. Bergman et al. conducted a systematic review with meta-analysis assessing the effect of VD supplementation on the risk of RTI . Pooled nced VDD .3 supplementation may represent a novel and safe indication for preventing RTIs [Taken together, the RCT data currently available suggest that VDing RTIs . In addiing RTIs ,71. Finaing RTIs ,71.VDD is a common finding in HIV-infected patients. Coelho et al. found that 63 out of 97 adult patients on antiretroviral therapy presented with insufficient levels of VD . The inv3 [In a prospective study of 398 adult patients with Acquired Immune Deficiency Syndrome (AIDS) on highly active antiretroviral therapy (HAART), those with VDD showed less recovery of CD4+ cells after 18 months of HAART . Coelho 3 . Hence, 3 . Therefo2-VD in symptomatic HIV-infected patients when compared to asymptomatic HIV-infected subjects [2-VD levels also had shorter survival times, although serum 1,25(OH)2-VD was not correlated with 25(OH)-VD levels in this study [To date, four cohort studies have investigated the association between VD status and mortality ,77,78,79subjects . The invis study . MoreoveHIV-infected individuals have lower bone mineral densities and are at higher risk for bone fractures when compared to the general population ,81,82. A2-VD [Efavirenz, a non-nucleoside reverse transcriptase inhibitor, has also been associated with low bone mineral density ,87. In a2-VD .Similarly, tenofovir disoproxil fumarate (TDF) is another antiretroviral agent from the nucleotide reverse transcriptase inhibitor class and is associated with hypophosphatemia secondary to proximal renal tubular dysfunction, hyperparathyroidism and increased bone loss . MoreoveVDD may lead to worse outcomes in patients with AIDS through a number of different mechanisms including decreasing bone mineral density, altering immunologic state, as well as increasing the risk for adverse renal and cardiovascular adverse events. Although RCTs are needed, we propose that VD levels should be carefully monitored in AIDS patients, particularly for those on TDF-containing HAART. AIDS patients with VDD might benefit from VD supplementation to improve long-term outcomes.C. albicans, C. glabrata, C. parapsilosis, C. tropicalis and C. krusei [Candida spp. have been reported as the fourth and seventh most common cause of nosocomial bloodstream infection in the US and Europe respectively [Candida infection was reported as 7.28/100,000 inhabitants between 1992 and 1993 [There has been an increase in the incidence and prevalence of fungal infections worldwide over the past three decades . Fungal . krusei ,94,95,96ectively ,97. Accoand 1993 . AnotherCandida infections [C. albicans and C. tropicalis became resistant to fluconazole leading to clinical therapeutic failure [Fluconazole is the most commonly prescribed antifungal drug employed to treat fections . Howeverfections ,103. Rep failure . Moreove failure ,104.Despite its high toxicity, amphotericin B (AmB) remains the gold standard antifungal agent for the treatment of systemic mycoses, especially due to its greater activity against the fungal ergosterol membrane . RegardiCandida-infected mice treated with low-dose 1,25(OH)2-VD had reduced fungal burden and better survival when compared with untreated mice [2-VD (0.1 and 1 μg/kg) had poorer outcomes [2-VD  [C. albicans stimulation showed that 1,25(OH)2-VD induced an anti-inflammatory profile of cytokines as well as mediated inhibition of TLR2 and TLR4 [Candida blood stream infection had lower levels of 25(OH)-VD when compared with 78 hospitalized patients and 30 healthy volunteers [In another experimental study, ted mice . Howeveroutcomes . The aut1 μg/kg) . Furtherand TLR4 . In the lunteers .Cryptococcal meningitis (CM) has been described as one of the most important opportunistic infections related to HIV and the leading cause of death in AIDS patients living in low-resources areas ,112,113.At present, many questions regarding the role of VDD as a potential risk factor for fungal infections remain unanswered. Further research could explore the mechanisms of VD in modulating the inflammatory response, the association between VDD and fungal infections, as well as whether VD supplementation improves outcomes in those patients with fungal infections.Sepsis is a clinical syndrome that has physiological, pathological and biochemical abnormalities induced by infection . BacteriDespite significant advances in modern medicine, sepsis remains a major healthcare burden worldwide . It is bRecent research has shown an increased incidence and mortality from sepsis during the winter months . While t3  versus placebo in a small sample of subjects  within 24 h of new-onset severe sepsis or septic shock [2-VD (two mcg intravenously) to 37 critically ill patients with severe sepsis or septic shock versus placebo (n = 31) [2-VD showing no difference in clinical outcomes  [ClinicalTrials.gov identifier NCT02684487) of which the results are not yet available.There is a relatively small body of literature evaluating VD supplementation in the context of sepsis. Currently, seven RCTs have been published on VD levels in the adult ICU setting, of which only two evaluated the effects of supplementation in those patients with sepsis . Quraishic shock . The autic shock . Similar(n = 31) . Clinicartality) . Additiortality) ,126. We Further research should be undertaken before introducing VD supplementation into the routine clinical care of sepsis. It is yet to be determined the type of VD metabolite, the appropriate dose and the timing of administration that would improve clinical outcomes in those patients with sepsis. Another challenge might be normalizing VD levels in critically ill patients, as well as maintaining optimum VD levels in the general population, with the aim of preventing sepsis.3 supplementation as prophylaxis for acute RTIs, especially in individuals with more severe VDD. This benefit may also extend to subjects at higher risk for RTIs, such as those patients with asthma [3 dose of 4000 IU as the upper intake level for adults [In summary, the studies reviewed here highlight the role of VD beyond being only a simple bystander in various infectious diseases. VDD might in fact contribute to the pathogenesis of several infectious diseases by negatively modulating vital processes such as the innate and adaptive immune response. Currently the strongest available evidence supports the use of daily or weekly VDh asthma . For theh asthma . While sr adults ,128. Nevr adults . Finally