AB) that revealed 9 out of 96 FAM13A interacting partners identified by affinity purification assays were significantly close to the initial network neighborhood. Moreover, compared to a similar method , the CAB approach predicts low-degree genes as potential candidates. The candidates identified by the network-based closeness approach were combined with the initial network neighborhood to build a comprehensive disease network module (163 genes) that was enriched with genes differentially expressed between controls and COPD subjects in alveolar macrophages, lung tissue, sputum, blood, and bronchial brushing datasets. Overall, we demonstrate an approach to find disease-related network components using new laboratory data to overcome incompleteness of the current interactome.The polygenic nature of complex diseases offers potential opportunities to utilize network-based approaches that leverage the comprehensive set of protein-protein interactions (the human interactome) to identify new genes of interest and relevant biological pathways. However, the incompleteness of the current human interactome prevents it from reaching its full potential to extract network-based knowledge from gene discovery efforts, such as genome-wide association studies, for complex diseases like chronic obstructive pulmonary disease (COPD). Here, we provide a framework that integrates the existing human interactome information with experimental protein-protein interaction data for FAM13A, one of the most highly associated genetic loci to COPD, to find a more comprehensive disease network module. We identified an initial disease network neighborhood by applying a random-walk method. Next, we developed a network-based closeness approach (C Furthermore, similar to other complex diseases, it has been challenging to identify systematically the likely multiple genetic risk factors for COPD. Genome-wide association studies (GWAS) can identify specific genetic loci consistently associated with disease in an unbiased manner and have reported hundreds of associations between complex diseases and traits7. However, for the vast majority of such genome-wide “hits”, specific causal mechanisms remain uncertain. There is increasing evidence supporting the hypothesis that the onset and progression of complex diseases like COPD arise from the interplay between a number of interconnected causative genes in a manner compounding the effects of any one variant10. Indeed, integrating GWAS data with molecular interaction networks and gene expression information facilitates a better understanding of disease pathogenetic mechanisms14. A variety of approaches have been developed to infer relationships between genes showing genome-wide significant evidence of association within the human interactome—the comprehensive set of molecular relationships between cellular proteins17. For example, we showed that a disease network module is enriched for disease susceptibility variants in asthma10. A GWAS of inflammatory bowel disease used DAPPLE, which is based on the observation that truly causal genes tend to link to each other in the human interactome, to prioritize potential disease candidates18. Since combinations of genetic alterations associated with a disease might affect a common component of the cellular system, module-centric approaches might be helpful in finding the disease-related components in the interactome19. Yet, the output of these approaches can be strongly influenced by (i) the incompleteness of the pre-specified interactome , and (ii) false-positive errors in the interactome. The impact of the incompleteness could result in failure to identify network relationships for genes implicated by GWAS. Thus, integrating the module-centric approach with targeted interaction analysis  of GWAS genes might be helpful in discovering the functional relationships of these genes with a disease of interest. In this work we combine new experimental protein-protein interaction data with the existing human interactome to enhance our understanding of the genes involved in COPD. The objective relies on the “local impact hypothesis,” which assumes that if a few disease components are identified, other components are likely to be found in their vicinity of the human interactome12. Moreover, if a disease gene is not mapped in the interactome, it is possible that its neighbors detected by targeted interaction analysis might indicate its biological function. Hence, we first identify the disease-related network neighborhood including known COPD disease genes (seed genes) in the interactome by applying a degree-adjusted random-walk algorithm20 (DADA), which is a guilt-by-association approach. Next, we test whether experimentally determined links (pull-down assay) for a single, consistently associated COPD gene (FAM13A) not mapped on the human interactome could enhance our knowledge about functional implications of FAM13A in COPD pathogenesis. The approach first aggregates the network neighborhood around the COPD ‘seed’ disease genes using DADA20. Further, to define a boundary of the disease network neighborhood, we use the sub-genome-wide significant association signals from the COPD GWAS  to predict FAM13A partners significantly close to the initial COPD localized neighborhood. Overall, our approach enhances our understanding about the COPD disease network module and predicts new candidate genes and pathways influencing COPD pathogenesis.Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, and recently it was estimated that COPD cases in developed countries would increase by more than 150% from 2010 to 203012. Selection of the seed genes strongly influences the interpretation of such a module-centric approach, and therefore we restricted our analysis to only high-confidence COPD disease genes from GWAS and Mendelian syndromes  . We first generated a single genetic association p-value for each gene in the interactome using VEGAS with the default all snps test21, and then plotted p-values of the added DADA genes vs. the background p-value distribution  and moreover, no edges were reported in Rolland et al.24 high-quality human binary protein-protein interactions and BioGRID interaction data (2014)25. Thus, we performed a pull-down assay using affinity purification-mass spectrometry, which identified 96 interacting partners of FAM13A23. We measured the likelihood of having a protein with at least 96 interacting proteins in the interactome. Among 14,280 genes in the interactome, 581 genes had a degree of 96 or greater ). Further, we tested whether the FAM13A interacting partners are closer to each other within the interactome than a same-sized set of randomly selected proteins. Based on 10,000 simulations, we observed significant closeness (Zscore = −9.685) among FAM13A partners  that compares the weighted distance between FAM13A partners (A) and proteins in the COPD localized network neighborhood (B) to random expectation in order to compute the Z-score  method that utilizes topological information  to predict the proximity of dysregulated genes to corresponding drug targets26. In our case, we measured the closeness of FAM13A partners (96 genes) with the COPD disease neighborhood (150 genes) by applying the LR method. In CAB the confidence scores of the edges play an important role to either shorten or increase the distances. Thus, to carefully claim that a gene is close to the COPD network neighborhood, we not only ensured that the gene is topologically close to the neighborhood but also considered the strength of each interaction based on different sources of evidence for the existence of such a path. As compared to top CAB genes, the nine highest score genes by LR were enriched in hubs. As a consequence, the average degrees <k> between these two methods were significantly different   related to COPD pathogenesis. Furthermore, it has also been observed that changes in gene expression predominantly occur in the genes (nodes) with low connectivity, but not in the superhubs27.We compared the CAB considers all of the possible paths between AB genes28. We observed a single network module consisting of CAB genes and COPD network neighborhood genes with only four intermediate genes . Of interest, including these linker genes provided connections to the network module for the two COPD seed genes, RIN3 and HHIP, that were not part of the original largest connected component of 129 genes. Our resulting expanded set of 163 connected genes, including all of the 11 seed genes . As shown in Table We tested the relevance of the COPD disease network module by evaluating fold change of differentially expressed module genes in COPD-specific gene expression data sets. We compared the fold change  of differentially expressed module genes to all other differentially expressed genes with unadj.p < 0.05 in eight COPD-specific gene expression data sets Table . We obse4BAP3D1 (adj.p-0.038) and IL32 (adj.p-0.001) were up-regulated and MMP12 (adj.p = 0.042) was down-regulated in non-smoking controls vs. COPD subjects in alveolar macrophages29 (Alveolar macrophage I). In lung tissue, we found TGFB2 (adj.p = 0.014) and CAT (adj.p = 0.03) were down-regulated in control vs. COPD subjects30 (Lung I). Twenty COPD disease module genes were differentially expressed in GOLD stage II vs. GOLD stage IV subjects in ECLIPSE induced sputum data31. CTGF (adj.p = 0.047), GSDMB (adj.p = 0.044) and CHRNA7 (adj.p = 0.043) were up-regulated between current smokers with no COPD vs. current smokers with COPD in bronchial brushing samples32  , we found 36 COPD disease module genes differentially expressed in different COPD-related datasets. For example, 33. In the COPD network module, 34 genes representing the ECM pathway were connected to each other , negative regulation of muscle cell differentiation (ANKRD17), negative regulation of cell migration (OSBPL8), regulation of alpha-beta T cell activation (AP3D1) and response to decrease in oxygen levels (AP3D1). Gene expression analyses in cell lines from several tissues have demonstrated an increase in FAM13A levels in response to decrease in oxygen levels34. It has been suggested that lower oxygen tension might modulate FAM13A activity35, however, the exact mechanism has not been explained. In the COPD disease network module, AP3D1 (CAB gene) interacts with FAM13A and is an immediate neighbor of the CTGF gene, which is part of the hypoxia pathway (decrease in oxygen levels). Thus, the connection of FAM13A to CTGF reveals a potential mechanism by which FAM13A could contribute to the hypoxia response .Among the biological pathways most significantly enriched in the COPD disease network module were inflammatory response, collagen catabolic process, regulation of TGFB-receptor signaling pathway, and extracellular matrix organization pathway Table . Alterather Fig. . Moreoveher Fig. . CAB gennse Fig. .Table 3B36  of the COPD disease network module with the Inflammasome (see methods)OLFM2 was among the genes within the COPD protein-protein interaction network built with a greedy search algorithm38. SOD3 is known to attenuate emphysema and reduces oxidative fragmentation of ECM in mouse lung39. In addition, TGFB1 and its pathway members have been frequently implicated in COPD pathogenesis40. The novel CAB measure assisted in constructing a more comprehensive COPD disease network module including FAM13A and its relevant partners, eventually connecting all of the 11 COPD seed genes into a single connected component comprising 163 genes/proteins  was upregulated in smokers with COPD and directly interacts with RIN3, a COPD GWAS gene41. TNFAIP1 has been reported to be crucial for the induction of apoptosis42, indicating a potential role of RIN3 in apoptosis. Furthermore, AP3D1 was upregulated in COPD subjects29 (Alveolar macrophage I) and directly interacts with FAM13A in our pull-down assay. The new alliance of FAM13A to the COPD disease network module via AP3D1 connects it to the hypoxia pathway  by combining new experimental protein-protein interaction data with the existing human interactome. Identifying causal genes for complex diseases like COPD, which are likely influenced by many genetic factors of modest effect size, is a major bottleneck in understanding the biological mechanisms leading to these diseases. A complete and accurate map of the human interactome could have tremendous impact on our ability to understand the molecular underpinnings of human disease. Yet, such a map is far from completion, which makes it currently impossible to evaluate precisely how far a given disease network module is from completion. Here, we showed that despite its incompleteness, a systematic network-based approach could help us to understand the connectivity of disease genes in COPD. Our initial analysis provided a set of 140 potential candidate genes that were part of three connected components in a disease network neighborhood. Interestingly, the largest connected component of this set of genes included 8 of the seed genes, which showed substantial network coherence and localization. Some of these 140 candidate genes have been previously implicated in COPD. For example, ins Fig. . Overallway Fig. , which rFAM13A, a key COPD gene not present in the current human interactome. We were able to connect FAM13A individual interactions to a localized network neighborhood by developing a new metric of network closeness, CAB. With the current thrust to understand GWAS genes with the help of incomplete protein interaction networks, our approach provides an alternative to connect targeted interaction and interactome data to identify a disease network module.Our approach adds a new dimension to the current causal gene identification approaches in complex diseases using the human interactome. Moreover, we were able to localize the network neighborhood of COPD and try to address (at least in part) the shortcomings of interactome incompleteness by providing new experimentally derived interactions for HHIP43; 2) FAM13A44; 3) IREB245; and 4) MMP1246. In addition, several other COPD GWAS loci have strong candidate genes, such as the nicotinic acetylcholine receptor genes that have been related to nicotine addiction (CHRNA3 and CHRNA5) and TGFB2 (part of the TGFBeta pathway). Thus, we contend that most of our selected seed genes are likely related to COPD pathogenesis. We also acknowledge that protein-protein interactions observed during in vitro experiments like yeast two-hybrid or affinity purification assays may not actually occur due to the absence of cellular co-localization or gene expression in the tissue of interest. COPD is a heterogeneous disease, and it is possible that different subtypes of COPD patients could have different disease network modules. Since linker genes connected the three COPD disease components in the COPD network neighborhood into a single disease network module, it could be possible that these are really three different COPD network modules. Thus, future research to identify network modules related to specific COPD subtypes is warranted.We focused on only a small set of seed genes for COPD, and that could be one of the limitations of the work. Moreover, since the disease-related gene within each COPD GWAS locus has not been definitively proven, we selected those genes that had the most compelling evidence for a role in COPD pathogenesis. For example, murine models of emphysema have demonstrated a smoking-related phenotypic effect for genes in four of the COPD GWAS loci that we included: 1) Overall, the disease network module approach that we applied is generic and can be applied to other diseases; this type of approach may be of broad use in disease gene identification in complex diseases in the coming era of network medicine.HHIP, CHRNA3/CHRNA5/IREB2, and FAM13A. We added recently described genome-wide significant associations to moderate-to-severe COPD or severe COPD, including RIN3, MMP12, and TGFB251. We also considered genes causing Mendelian syndromes which include emphysema as part of their syndrome constellation: alpha-1 antitrypsin deficiency (SERPINA1) and cutis laxa (ELN and FBLN5)53. These 11 genes, in toto, were subsequently used as seed genes for network analyses. We included several genes from the chromosome 15q25 locus, since previous work from our group has suggested that there are likely at least two COPD genetic determinants in this region—both related to nicotine addiction (nicotinic acetylcholine receptor genes CHRNA3 and CHRNA5) and unrelated to nicotine addiction (IREB2)54. Of note, HHIP, FAM13A, and IREB2 are also supported by animal models of emphysema. In addition to these five COPD GWAS genes, we added MMP12, which was associated with COPD before it was discovered by GWAS50 and which is also supported by an animal model of emphysema, as well as TGFB2 and RIN3. TGFB2 and RIN3  were also strongly supported by the recent International COPD Genetics Consortium GWAS7.Starting with previous GWAS for COPD susceptibility, and with specific genes implicated by eQTL or functional studies within GWAS regions, we identified a set of well-established genes associated with COPD: 55. Physical protein interactions were assigned a confidence score between 0 and 1 using the interaction confidence-scoring tool (IntScore)56. We relied only on physical interaction data in ConsensusPathDB, obtaining M = 150,168 links between N = 14,280 genes encoding these proteins with mean degree <k> of 21.03 and average clustering coefficient <C> of 0.141.We compiled the physical protein-protein interactions from the ConsensusPathDB database58. However, many existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of protein-protein interaction (PPI) networks, as well as ascertainment bias in available interaction and disease association data. To enhance our understanding regarding the local neighborhood of seed genes in the network, we applied the degree aware algorithm (DADA)20 to compute the proximity of the selected COPD seed genes to their neighbors by exploiting the global structure of the network. Several studies59 have shown global approaches like random walk outperform other local approaches like shortest path distances, and therefore we focused on the global method. The final ranking for 14,280 genes encoding proteins included in the network was achieved by merging the random walk restarts output and statistical adjustment models. We used the results from a COPD GWAS of 6,633 cases and 5,704 controls from 4 cohorts to define a boundary for the most promising DADA-ranked genes41. We assigned significant SNPs to genes using 50 kb boundaries, and generated gene-based p-values using VEGAS21.The concept that proteins located close to one another in the human interactome may cause similar diseases is becoming an increasingly important factor in the search for complex disease genes. Different approaches tackle this problem of predicting complex disease susceptibility genes using different kinds of integrative data, but all of them involve superimposing a set of candidate genes alongside a set of known disease genes in some physical or functional networkFAM13A gene resides at a locus associated with COPD and with lung function in the general population by GWAS61. FAM13A contains a Rho GTPase-activating protein-binding domain, inhibits signal transduction, and responds to hypoxia; however, its primary function in the lung remains to be determined. The pull-down assay using affinity purification-mass spectrometry was performed previously23 and resulted in 96 interacting proteins, establishing 96 edges for FAM13A in the interactome.The FAM13A interactions and the COPD disease network neighborhood, we introduce the Cab minimum weighted distance, which we define as follows:To quantify the network-based separation between the identified l and m we define the Cab distance as:dlm depends both on the total number of network-based edges one needs to traverse from node l to node m and also the confidence scores of these weights, while parameter p tunes the relative contribution of these two factors.For any two nodes p = 1 case, dlm depends only on confidence scores of edges connecting two nodes:In particular, in the wuv are regarded as independent probabilities for the edges to be present in the network, then the product in Eq. , diabetes (islet), obesity, stroke, neuropathic pain, inflammation pain and sarcopenia)36. We used the total of 2,483 inflammatory signature genes previously reported from mouse models and converted them to their human orthologs, obtaining 2,331 genes in our analysis. Mouse to human orthologs were extracted from the Mouse Genome Informatics (MGI) database (http://www.informatics.jax.org).Since clinical COPD is influenced by inflammationSingh 2014: Peripheral blood gene expression samples from 171 subjects from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study (GSE54837). Differential expression analysis was performed between control (n = 6)  vs. severe COPD (n = 13)63.Singh 2011: Induced sputum gene expression from 148 COPD subjects in the ECLIPSE study, with 69 Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage 2, and 71 GOLD stage 3 & 4 subjects (GSE22148). Gene expression differences between GOLD 2 and GOLD 3&4 were analyzed31.Shaykhiev 2009: Transcriptional profiling of alveolar macrophages obtained by bronchoalveolar lavage of 24 healthy nonsmokers and 12 COPD smokers (GSE13896)29.Bahr 2013: Expression data from peripheral blood mononuclear cells (PBMC) generated from 136 subjects from the COPDGene study (GSE42057), which consisted of 42 ex-smoking control subjects and 94 subjects with varying severity of COPD64.Tedrow 2013: Microarray data from whole lung homogenates of subjects undergoing thoracic surgery from the Lung Tissue Research Consortium (LTRC). These subjects were diagnosed as being controls or having COPD as determined by clinical history, chest CT scan, and surgical pathology. We considered 220 COPD subjects and 108 controls with no chronic lung disease by CT or pathology. These subjects went for surgery typically to investigate a pulmonary nodule and normal lung tissue was obtained for differential expression analysis (GSE47460)30.Bhattacharya 2009: Gene expression patterns in lung tissue samples derived from 56 subjects (GSE8581). Cases (n = 15) were defined as subjects with FEV1 < 70% predicted and FEV1/FVC < 0.7 and Controls (n = 18) as subjects with FEV1 > 80% predicted and FEV1/FVC > 0.765.Poliska 2011: Gene expression data from alveolar macrophage samples from 26 COPD and 20 healthy control subjects (GSE16972)66.Steiling 2013: Bronchial brushings obtained from current and former smokers with and without COPD (GSE37147). Data from 238 subjects was used in the analysis to determine the association of gene expression with COPD-related phenotypes32.Our disease network module approach selects genes based on their topological closeness to the COPD seed genes. To evaluate COPD-specific relevance of genes localized around the seed genes, we extracted significantly differentially expressed genes  from eight publicly available COPD-specific gene-expression datasets and assessed for each case the fold change difference between genes present in the COPD disease module compared to non-module differentially expressed genes. We used the limma R package (ver 3.10.1) for differential expression analysis. The 8 datasets are as follows:Supplementary MaterialSupplementary Table 1Supplementary Table 2


Unlike healthy cells, tumor cells have their own unique metabolic features Condensation of MGO with reactive amino acid residues via the Maillard reaction thereby forming stable adducts is a major characteristic of glycation The core histone protein , which spool eukaryotic DNA into a chromatin structure, contains an unstructured N-terminal tail that extends away from the nucleosome core particle (NCP). It undergoes a variety of post-translational modifications (PTMs), including methylation, acetylation and ubiquitination by a range of chromatin effectors that can read, write and erase these modifications In a study recently published in Nature Communicatios, titled “Reversible histone glycation is associated with disease-related changes in chromatin architecture”, Zheng et al. In this study, the authors conducted a comprehensive analysis at both low and high resolutions utilizing a variety of methods examining the occurrence as well as the local and global chromatin changes induced by histone glycation in physiological and breast cancer samples. Since MGO is an important glycolysis by-product, they applied a range of MGO concentrations corresponding to different sites: MGO ratios to test the reactivity of MGO and chromatin components in vitro. They found that chromatinized histone MGO glycation disrupts global histone enzymatic PTM levels. H3R8me2 showed higher sensitive to MGO treatment than lysine methylation of H3K4 and H3K9, which corresponds with the higher reactivity of MGO towards arginine over lysine.To test whether MGO adducts could have a significant effect on nucleosome stability, the authors performed a “one-pot” nucleosome assembly in the presence of unmodified or glycated histones. The results indicate that high concentrations of MGO can induce both histone-histone and histone-DNA cross-linking in chromatin, potentially having harmful effects on its dynamic nature.Furthermore, they utilized reconstituted nucleosomal arrays composed of 12 repeats of 601 DNA assembled in a similar manner to NCPs and found that glycation-induced DNA-histone cross-linking on nucleosomes can affect the architecture of chromatin on both a local and global scale.DJ-1 has emerged in recent years as a protector of metabolism-associated cellular stress, reversing primarily ROS damage in neurons but also protein and DNA glycation under a variety of conditions Finally, the authors examined histone glycation and sensitivity to MGO in breast cancer cells because breast cancer was shown to have a particularly high metabolic rate and ROS levels. Their results indicated that all tested cell lines have both higher basal H3 glycation as well as sensitivity to MGO treatment. Consistence with the data observed in cell culture, analyzing histones extracted from breast cancer xenograft tumors revealed most of them contain high basal histone MGO glycation levels. Interestingly, the expression level of DJ-1 was positively correlated with the histone MGO glycation levels in cell lines and xenograft tumors.Taken together, these findings reveal the pathophysiological accumulation of histone glycation and identify an additional molecular mechanism linking metabolic perturbation with epigenetic misregulation in cancer. This study uncovered a novel mechanism for cellular metabolic damage through epigenetic perturbation and proposed at therapeutic avenue, targeting DJ-1 as a key gatekeeper of cancer cell survival.


Low bone mineral density (BMD) is established as a primary predictor of osteoporotic risk and can also have substantial implications for athlete health and injury risk in the elite sporting environment. BMD is a highly multi-factorial phenotype influenced by diet, hormonal characteristics and physical activity. The interrelationships between such factors, and a strong genetic component, suggested to be around 50–85% at various anatomical sites, determine skeletal health throughout life. Genome-wide association studies and case–control designs have revealed many loci associated with variation in BMD. However, a number of the candidate genes identified at these loci have no known associated biological function or have yet to be replicated in subsequent investigations. Furthermore, few investigations have considered gene–environment interactions—in particular, whether specific genes may be sensitive to mechanical loading from physical activity and the outcome of such an interaction for BMD and potential injury risk. Therefore, this review considers the importance of physical activity on BMD, genetic associations with BMD and how subsequent investigation requires consideration of the interaction between these determinants. Future research using well-defined independent cohorts such as elite athletes, who experience much greater mechanical stress than most, to study such phenotypes, can provide a greater understanding of these factors as well as the biological underpinnings of such a physiologically “extreme” population. Subsequently, modification of training, exercise or rehabilitation programmes based on genetic characteristics could have substantial implications in both the sporting and public health domains once the fundamental research has been conducted successfully. Low bone mineral density (BMD) is established as a primary predictor of osteoporotic risk and can have substantial implications for athlete health and injury risk in the elite sporting environment. BMD is a highly multi-factorial phenotype influenced by diet, hormonal characteristics as well as physical activity  receptor (VDR) FokI rs2228570 polymorphism, whilst lower total BMD was only observed in the FF (− 4.5%) genotype when comparing swimmers with a control group , human TNF receptor superfamily member 11a (TNFRSF11A) and sclerostin (SOST) genes have been associated with stress fracture in elite athletes  provide a critical review of the current literature on the influence of physical activity on BMD, particularly in athletic populations such as endurance runners; (2) provide an overview of genetic associations with BMD and highlight studies that have assessed this association in athletic populations; and (3) explore gene–BMD–physical activity interactions and identify future applications these might have in both the public health domain and elite sporting environment.2) or volumetric (g/cm3) BMD, depending upon the measurement methodology used  is extremely problematic. Many investigations have used self-report activity questionnaires rather than more direct measurements, such as via accelerometers or pedometers , although this is not always the case due to other variables, such as low energy availability  and oestrogen receptor 1 (ESR1) (Gong and Haynatzki TNFRSF11B) gene encodes the protein osteoprotegerin (OPG), which regulates bone resorption by inhibiting differentiation and activation of osteoclasts. OPG-deficient mice have been found to develop early-onset osteoporosis, and increased tissue mRNA expression has been observed in participants who possess specific haplotypes accompanied with reduced BMD, which may be due to increased expression resulting in stimulated osteoclast activity  and JunB proto-oncogene, AP-1 transcription factor subunit (JUNB) were both upregulated within 4 h after loading, whilst expression of Wnt/β-catenin signaling genes SOST and secreted frizzled-related protein 4 (SFRP4) was also altered in the synthetic phase of bone formation  signalling pathways, which results in decreased osteoblast OPG expression (and a decreased OPG/RANKL ratio) and enhanced matrix metallopeptidase (MMP) production, consequently increasing bone resorption  gene in an Icelandic population  with BMD completed, which identified many more potential candidate genes and SNPs  rs2887571 to influence bone mineral content in males and nominal interactions with physical activity were also observed with Wnt family member 16 (WNT16) rs3801387, axin 1 (AXIN1) rs9921222, SOST rs4792909 and stAR-related lipid transfer domain containing 3N-terminal-like (STARD3NL) rs6959212. Sclerostin has a negative effect on bone formation by inhibiting canonical Wnt signalling in osteoblasts and also stimulates osteoclastic bone resorption by increasing the RANKL/OPG ratio (via enhanced RANKL expression)  gene associated with differences in BMD, which were dependent upon volume of physical activity completed. The TT genotype of both the rs3736228 and rs2396862 SNPs was associated with lower BMD in more physically active men, but with higher BMD in less physically active men. Thus, the authors hypothesised that the substitution of a C with a T allele in the rs3736228 SNP could alter LRP5-mediated Wnt signalling in the case that the catabolic signals induced from the mechanical loading prevail over anabolic signalling. This was also the case when expressing alleles as a haplotype in vitro, where the T allele was associated with a decreased response to canonical Wnt3a signalling in comparison to the C allele. Activation of Wnt/β-catenin  signaling increases the sensitivity of osteoblasts to mechanical loading, which can occur via Wnt binding to low-density lipoprotein receptor-related proteins 5 and 6 co-receptors  val158met (rs4680) SNP has been reported to influence the association between physical activity and BMD suggesting that certain variants may be particularly important for BMD in individuals with low physical activity levels. Higher total BMD was observed in individuals completing greater levels of physical activity (> 4 h) compared to those undertaking lower activity (< 4 h) for GA and AA (lower enzyme activity) but not GG (higher enzyme activity) genotypes  is another potential candidate gene with a number of functional polymorphisms, suggested as candidates associated with BMD and/or osteoporosis. Meta-analysis revealed an association between the GG genotype in the IL6 − 174G/C (rs1800795) polymorphism and low BMD, as well as increased risk of osteoporosis, in a Caucasian population  following meta-analysis . National Health and Nutrition Examination Survey (NHANES) data have previously demonstrated that BMD did not differ between males who reported completing four–six times more physical activity than the recommended guidelines  genotype when comparing 48 swimmers with a control group  that are instrumental for this calcium absorption and bone formation  and Ff (6.9%) but not ff (1.8%) genotypes of the .7% and FVDR with BMD and/or fracture has also been supported across a number of different SNPs  in various cohorts, such as pre- and postmenopausal women , TNFSF11 (RANKL) and TNFRSF11B (OPG) SNPs rs9594738, rs1021188 and rs9594759  using pQCT analysis. However, these associations were only observed before, but not after, a 12-week period of increased football training volume and thus no association between genotype and changes in bone parameters over time was observed. These variants included CTR) rs1801197 and LRP5 rs2277268 polymorphisms, which were associated with femoral neck stress fractures in 72 Finnish military recruits  gene repeats (> 16) were more common in Israeli military personnel who had suffered stress fractures (23%) than those who had not suffered this injury (13%)  as mentioned above, alongside TNFRSF11A rs3018362, were associated with stress fracture incidence in the Stress Fracture in Elite Athlete (SFEA) cohort. However, a gain of function P2RX7 SNP (rs1718119) was associated with multiple stress fracture occurrence. Functional expression of purinergic receptor P2X7 primarily regulates configuration of osteoclasts  introduced by incorporating athletes from different sports into one investigation, would be advantageous.There are numerous polymorphisms that need further exploration vis-à-vis BMD. In particular, gene–environment  interactions are likely to contribute substantially to inter-individual differences in BMD throughout the human lifespan. Exciting findings have been observed in regard to gene–physical activity interactions and genetic associations with stress fracture, particularly in variants of pathways involved in the adaptation of bone to mechanical loading, such as the RANK/RANKL/OPG system.Therefore, the study of specific cohorts, who experience unusually high mechanical loads and who may display unusual bone phenotypes and/or possess genetic characteristics that differ from the norm, may provide novel insight into the area. Such individuals include elite athletes, who are at the extremes of human physiological capability, experience much greater environmental  stress than most and might possess a genotype particularly suitable to tolerate those stresses.GWAS or ideally whole genome sequencing (WGS) studies using athletic populations with their differentiating extreme phenotypes are, in principle, the next logical steps to identify key polymorphisms. Detailed study of gene function can follow. However, most GWAS designs cannot account for gene–gene/gene–environment interactions and only analyse SNPs with minor allele frequencies of more than 1%, not rare variants that may lie between 0.1–1% or even lower. Thus, GWAS is appropriate for the discovery of common variants that may confer low/moderate risk but are underpowered for the detection of rare variants, which may have a large influence on a complex phenotype according to the common disease/rare variant hypothesis  can be used to test hypotheses about genetic variants emerging from GWAS in relevant clinical populations. Assessing bone and injury phenotype data in those athletes will also enhance understanding of any observed genotype–phenotype relationship (Wang et al.


PHOX2B gene mutations are considered as responsible for CCHS. Most of patients with CCHS are heterozygous for polyalanine expansion mutations (PARMs) in exon 3, but 10% of patients with classic CCHS are heterozygous for non-polyalanine expansion mutations (NPARMs) of the PHOX2B gene.Congenital central hypoventilation syndrome (CCHS) is characterized by alveolar hypoventilation increasing during sleep and affected patients are unable to perceive and respond to hypercarbia with increased ventilation and arousal during sleep. Data are collected on 3 patients affected by CCHS who referred to the Paediatric Pulmonology Unit of Bambino Gesù Children’s Hospital  for a multidisciplinary follow-up program between 2000 and 2017.PHOX2B gene were detected.We describe three cases of patients affected by CCHS for which two novel mutations on exon 3 of The description of these novel mutations and related clinical phenotypes allows to expand the knowledge into NPARM spectrum. Since the presence of Hirschsprung disease is related to NPARMs and the number of alanine repeats, we suggest performing CCHS genetic investigation and periodical assessment also in patients without a clear history of CCHS but affected by Hirschsprung disease.Data are retrospectively collected. Congenital central hypoventilation syndrome (CCHS) is a rare life-threatening disorder characterized by autonomic dysregulation and alveolar hypoventilation often requiring lifelong ventilatory assistance. As a result of hypoventilation, affected subjects become hypoxemic, hypercarbic and unable to perceive and respond to hypercarbia with increased ventilation and arousal during sleep . More sePHOX2B gene mutations are considered as responsible for CCHS [PHOX2B mutation-confirmed CCHS are reported [PHOX2B gene is reported as 20/20 polyalanine repeat numbers. It encodes a transcription factor responsible for the regulation of genes involved in the development of the autonomic nervous system [PHOX2B gene producing genotypes of 20/24 to 20/33 [s system . Over 90to 20/33 . Most ofto 20/33 .PHOX2B gene. Most of these mutations are de novo and approximately 10–25% of asymptomatic parents are responsible for transmission to their offspring [The remaining 10% of patients with classic CCHS are heterozygous for non-polyalanine expansion mutations (NPARMs) founded at the end of exon 2 or in exon 3 of the ffspring .PHOX2B gene expanding the knowledge into NPARM spectrum.The purpose of our study is to describe two novel mutations on exon 3 of Data are retrospectively collected on 3 patients affected by CCHS who referred to the Paediatric Pulmonology Unit of Bambino Gesù Children’s Hospital  for a multidisciplinary follow-up program between 2000 and 2017.Demographic data, medical histories and results of diagnostic testing were obtained from electronic medical record review with approval from the institutional review board.Nocturnal polysomnography, transcutaneous capnography, echocardiography were performed at baseline in patients with history of apnea, reduction in blood oxygen saturation and cyanosis on falling asleep in order to detect central hypoventilation or heart diseases.Neurological, ophthalmological, maxillofacial, psychologist and swallowing evaluations were also considered in our analysis.Blood samples were obtained with informed consent of the probands or their parents to perform genetic analysis when central hypoventilation and hypercarbia were confirmed on nocturnal polysomnography and transcutaneous capnography.PHOX2B gene was performed by Sanger sequencing (primer sequence and PCR conditions are available upon request) on a genomic DNA sample extracted from whole blood of patients. The identified mutations were checked in parents for segregation by direct sequencing.Molecular analysis of Once the diagnosis was obtained, patients were included in a follow-up program providing ventilatory assessment during awake and asleep states, neurocognitive assessment, electroencephalography, continuous ECG monitoring and echocardiogram every 4 months in infants under the age of two years and every 6 months in those under the age of 3 years; then, all the investigations were annually performed when clinical stability was present .Abdominal ultrasound, urine cathecholamines and fecal occult blood testing were carried out every 4 months in first 2 years, then every 6 months in infant under the age of 3 years; then investigations were annually performed to identify neuroblastomas [A male caucasian child, born at 37 weeks of gestational age with birth weight 2980 g.PHOX2B gene but allowed to identify a novel likely deleterious heterozygous variant in exon 3 corresponding to the frameshift mutation c.255_256delCT (p.Phe86HisfsTer91), resulting in a premature stop codon. Genetic analysis, extended to child’s parents, confirmed that his mother carried the same mutation. To exclude the presence of a mosaicism, the variant has been confirmed, on a maternal DNA sample isolated from buccal mucosa, in heterozygous state with a ratio between mutant and normal allele comparable to that observed on the blood DNA. During hospitalization, the child started Non-Invasive Ventilation (NIV) with consequent clinical and gas exchange improvement.He had family history of Hirschsprung disease (HSCR) occurring in his mother. His past medical history was positive for delayed meconium and distended abdomen in the second day of life. A barium contrast enema study has been performed and the result suggested the hypothesis of HSCR then confirmed by rectal suction biopsy and acetylcholinesterase immune-histochemical examination. The infant underwent to surgical repair through a laparoscopic pull through procedure. During hospitalization in Surgical Department, the patient had recurrent apneas and cyanosis during sleep requiring investigations as brain and heart ultrasound and chest X-ray that were all normal. Due to family and personal history of HSCR and reported respiratory complaints, the child referred to our Pulmonology Unit when he was 2-months in order to exclude the diagnosis of CCHS. Nocturnal polysomnography detected central hypoventilation and transcutaneous capnography revealed increased levels of CO2 . Sequencing analysis revealed a normal number of alanine repeats (20/20) in Currently, no other clinical problems are emerging during periodical follow-up program.A 34-year-old caucasian female with past medical history of surgically treated HSCR.PHOX2B gene was carried out and the same mutation (c.255_256delCT) was singularly detected. Even though the same mutation was detected, the phenotype observed in her son was more severe with early onset of symptoms.No other significant symptoms were reported. As her son received the diagnosis of CCHS (see the case described above), she underwent to nocturnal polysomnography that was normal. Nevertheless, the sequencing analysis of the A female caucasian child, born at 36 weeks of gestational age with birth weight 2760 g.Her mother had history of several miscarriage. She had neonatal hypoxia and respiratory failure at birth needing hypothermia treatment and invasive mechanical ventilation (IMV) in the first days of life. Because weaning from mechanical ventilation failed, tracheostomy has been positioned in Neonatal Intensive Care Unit (NICU). Because of recurrent apneas and cyanosis during sleep despite tracheostomy, polysomnography was performed and showed central hypoventilation. In addition, she had delayed meconium suggestive of HSCR, subsequently confirmed by rectal suction biopsy and acetylcholinesterase immune-histochemical examination.The patient showed recurrent seizures with negative neurological examination . Electroencephalography (EEG) was normal and brain MRI showed a mild brain injury due to neonatal hypoxia. Therefore, glucose monitoring was considered and very low glucose levels were observed during all critical episodes that were not related to meals. Glycogen storage disease (GSD), GH deficiency and adrenal insufficiency were excluded because the absence of chetonuria. Normal values of urine organic acid, serum amino acid panel, plasma acylcarnitine profile, insulin and C-peptide, allowed to exclude respectively beta oxidation disorders and hyperinsulinism.Because of dysphagia, gastrostomy was required and continuous enteral nutrition was started, even though recurrent hypoglycemic events continued to be present.Moreover, the baby presented several episodes of breathing hold spell triggered by pain, frustration and fear occurring also during mechanical ventilation.PHOX2B gene but revealed a novel likely deleterious heterozygous variant in exon 3, c.780dup leading to 44 amino acids elongated protein. The presence of the mutation was excluded in healthy patient’s parents by direct sequencing. During hospitalization the child started weaning from IMV and spontaneous awake breathing has been reached.Due to the described clinical picture, genetic examination was performed to detect CCHS when she was three months old. Sequencing analysis excluded an alanine expansion (20/20) in PHOX2B gene expanding the knowledge into NPARM spectrum: c.255_256delCT and c.780dup.In this study we describe two novel mutations on exon 3 of The first one has been detected in a child and his mother. As most of NPARMs, both patients had history of HSCR. Nevertheless, despite the same detected mutation, the clinical course was different and characterized by delayed meconium and recurrent apneas and cyanosis in the first month of life with abnormal nocturnal monitoring in the affected child. Differently, his mother reported only intestinal involvement and no hypoventilation was actually detected on nocturnal monitoring. This observation suggests that environmental factors can contribute to the variability of clinical expression of the disease. Moreover, as we consider the benign evolution of the ventilation in patient 2 as uncommon feature of described disease, we suggest, in any case, yearly assessment of asymptomatic patients in order to identify the late onset symptoms.In fact, a genotype-phenotype correlation is well described and symptoms severity – including continuous ventilatory dependence, heart rhythm disorders, association with HSCR and neural crest tumors – is closely related to NPARMs and to the number of alanine repeats among subjects with PARMs .Indeed HSCR – that occurs among 20% of cases of CCHS – is more prevalent among NPARMs subjects 87–100%) than among those with PARMs (13–20%) 7–100% th.The incidence of HSCR is approximately one in 5.000 live births in general population , while tTumors derived from the neural crest  occur more frequently among individuals with NPARMs (50%) than among those with PARMs (1%) . Among PEven if classic CCHS presentation is typically in the neonatal period, rarely, a milder later-onset (LO-CCHS) presentation is described in toddlers, children and adults.PHOX2B mutations with the mild genotypes 20/24 and 20/25 or, rarely, NPARM requiring an environmental cofactor to unmask the phenotype [LO-CCHS reflects the variable penetrance of The second described mutation has been firstly reported: even if respiratory failure at birth was probably influenced by neonatal hypoxia, the subsequent recurrent and severe apnea ad cyanosis episodes were attributable to CCHS. The presence of severe hypoglycemia associated to seizures as well as the breathing hold spells occurring also during mechanical ventilation and dysphagia were interpreted as autonomic nervous system impairment yet described in NPARMs carrier patients , 14, 15.To the best of our knowledge, literature suggests that CCHS has to be suspected in cases of centrally mediated alveolar hypoventilation and/or cyanosis or seizures arisen after anesthetics or CNS depressants administration, recent severe lung infection or treatment of obstructive sleep apnea , 7. Our


Treatment of cancer with chemo-radiotherapy causes severe side effects due to cytotoxic effects towards normal tissues which often results in morbidity. Therefore, developing anticancer agents which can selectively target the cancer cells and cause less side effects are the main objectives of the new therapeutic strategies for treatment advanced or metastatic cancers. Newcastle disease virus strains AF2240 and V4-UPM were shown to be cytolytic against various cancer cells in-vitro and very effective as antileukemicagents. 45 rats at 6 weeks of age, were randomly assigned to nine groups with 5 rats in each group, both azoxymethane (AOM) and 5-Fluorouracil (5-FU) were given to rats according to the body weight. NDV virus strains (AF2240 and V4-UPM) doses were determined to rats according to CD50 resulted from MTT assay. After 8 doses of NDV strians and 5-FU, tissue sections preparations and histopathological study of rats’ organs were done. In this article morphological changes of rats’ organs, especially in livers, after treatment with a colon carcinogen (azoxymethane) and Newcastle disease virus strains have been recorded. We observed liver damage caused by AOM evidenced by morphological changes and enzymatic elevation were protected by the oncolytic viruses sections. Also we found that combination treatment NDV with 5-FU had greater antitumor efficacy than treatment with NDV or 5-FU alone. We noted morphological changes in liver and other rats’ organs due to a chemical carcinogen and their protection by NDV AF2240 and NDV V4-UPM seems to be most protective. Newcastle disease virus (NDV) is a member of the genus Avulavirus in the family Paramyxoviridae . NDV is classified as a virus with inherent oncolytic properties which anti-cancer effect was first reported by Cassel and Garret in 1965 . Since then NDV has been investigated for its anti-cancer effects and scientists are interested in NDV because it can replicate up to 10,000 times better in human neoplastically transformed cells than in most normal human cells . Two Malaysian NDV strains that have antineoplastic properties were discovered ; AF2240 and V4-UPM . These two strains are currently being tested as anticancer agents in vivo and in vitro against different types of cancers such as leukemia, brain and breast cancer . Azoxymethane (AOM) is an agent widely used in experimental models of colorectal carcinogenesis in rodents, it is highly specific indirect colorectal carcinogens that induce the initiation and promotion steps of colorectal carcinogenesis yielding colorectal tumors lesions in a dose-dependent manner in rats, mice and hamsters .Animal experiment protocolThe experimental protocol was approved by Animal Ethics Committee, University Sultan Zainal Abdin (UniSZAAEC); with an ethic no. (UniSZA/AEC 02/09/03). At 6 weeks of age, 45 rats were randomly assigned to nine groups with 5 rats in each group so that there was no difference in body weight among the groups. Both azoxymethane (AOM)  and 5-Fluorouracil (5-FU)  were given to rats according to the body weight, AOM dose was 15 mg/kg and FU dose was 12 mg/kg. Hundred mg of AOM ampoule was diluted in 10 mL sterile normal saline (0.9%) and stored at -20°C until used. NDV virus strains (AF2240 and V4-UPM) doses were determined according to CD50 resulted from MTT assay. Each low and high dose of NDV strains was diluted in 500 µL sterile normal saline (0.9%) before injection.The 9 groups of rats were as follows; a negative control (NC) served as normal control and were administrated with normal saline only, a positive control (PC) which rats were administrated subcutaneous injections of 15 mg/kg AOM and not treated with NDV, group III which received low doses of NDV AF2240 (AFLD), group IV which was given low doses of NDV V4-UPM (V4LD), group V that received high doses of NDV AF2240 (AFHD), group VI which received high doses of NDV V4-UPM (V4HD), group VII received both NDV AF2240 and 5-Fluorouracil (FU) (AFFU), VIII received both NDV V4-UPM and 5-Fluorouracil (FU) (V4FU) and the last group that treated with 5-Fluorouracil (FU).All rats (except the negative group) were then administered a subcutaneous injection of 15 mg/kg AOM with a second dose of 15 mg/kg AOM administered one week later. Four weeks post AOM treatment; rats administrated intraperitoneal injections of 8 doses of NDV and 5-FU. th, 8th, 10th, and 12th day . A week later, those rats were fasted for 12h overnight. After euthanasia, rats were sacrificed and then organs were removed and kept in 10% neutral formalin until used. Doses were administrated once daily for 4 successive days; then if no toxicity was observed doses were given on the 6Tissue sections preparations and histopathological study of rats’ organsFor histological observation, fresh tissue pieces of colon, liver, kidney and spleen from the 9 groups of rats were taken. All samples were placed in tissue embedding cassettes and fixed 10% buffered formaldehyde solution for minimum 48h. Following fixation, the specimens were dehydrated in ascending grades of alcohol by Automatic Tissue Processer  for 18-24 h. Tissues were embedded in paraffin wax and blocks were made by using Tissue Embedding Center . Sections were made 5–7µm thick by Semi-Motorized Rotary Microtome  and were double stained with hematoxylin and eosins (HE) stain. All prepared histological sections were viewed in a light microscope equipped with an Olympus digital camera  . Histopathological study was done by a histopathologist (3rd author) and the classification of colon tissue sections was based on three types of dysplasia; Mild, Moderate and Severe dysplasia .The discussion on production of dysplasia, prevention by newcastle virus is presented in an article by the authors elsewhere. This article pertains to organ changes in liver, kidneys and spleens. Histological sections were studied for signs of abnormalities or cancer metastasis. However, when reviewing liver sections it was noted that there were morphological changes which the authors felt that they should be recorded. These rats (except negative control) were treated with azoxymethane to induce dysplasia in colon and to observe effect of NDV strains on these dysplasia, incidentally these two agents did cause some morhological changes especially in livers.Statistical analysisThe statistical significance was assessed using one analysis of variance (ANOVA). All values were expressed as mean ± SE and a value of P ≤ 0.05 was considered significant as compared to the respective control groups using IBM SPSS 20 statistical package for Microsoft Windows .The morphological changes observed in liver sectionsPositive control rats liver hematoxylin and eosins (HE) stained sections reveals the least number of hepatocytes among all the groups of rats suggesting AOM has deleterious effect on liver cells. Supported also by three times elevation in aspartate transaminase and two times elevetion in alanine transaminase according to blood tests profile (not shown) in the same group.Kupfer cells are also decreased except in AFHD group . IncreasIn this aspect low and high doses of NDV AF2240 were less effective in preventing damage but low and high doses of NDV V4-UPM were better, however, AFLD, AFHD,V4LD all showed eosinophilia of hepatocyte cytoplasm which is a morphological evidence of hepatocyte damage .In V4HD rat group showed no enzymatic and morphological changes so we may assume it is most protective, also it is well known that cytoplasmic eosinophilia is caused by ischemic canges due to ATP depletion, the action may be drug induced here. The combination of NDV V4-UPM and 5-FU shows more protective than NDV AF2240 and 5-FU. The damage (necrosis) observed in AFLD and FU groups are suggesting that combination of low dose NDV AF2240 and 5-FU are not able to protect the damage caused by AOM, but low dose NDV V4-UPM is better than 5-FU in this aspect.In AFHD rats showed kupfer cell hyperplasia it may have irrtating effect/reactive change on kupfer cells in this dose. High dose NDV AF2240 has more irritative effect than low dose NDV AF2240. Necrosis is found only in FU and AFLD rat groups  which isNDV AF2240 may have different effect based on dosage former acting on kupfer cells and latter on hepatocytes. AFHD group has characteristc microvescicular fat change in cytoplasm .The morphological changes observed in Kideny and spleen sectionsHistopathological study of kidney HE stained sections revealed no metastasis. There were no evidence of glomerular damage except for the finding of 14 glomeruli per one high power field in PC group as against 18 in all other samples was statistically non-significant, so it is concluded that there were no significant kidney changes.Sections from spleen HE stained sections showed no metastasis, there were occational reactive hyperpalsia, this is not considered significant in lymphoid tissues.Many reports showed the possibility of NDV as a therapeutic agent in cancer treatment, from studies both in animal models and in human clinical trials which showed favorable results .In this study, we examined rats HE stained sections of liver, kidney and spleen. However, the most significant cell changes were found in livers compared treated with AOM to those treated with NDV strains. Positive control rats liver HE stained sections reveals the least number of hepatocytes among all the groups of rats suggesting AOM has deleterious effect on liver cells . Supported also by three times elevation in aspartate transaminase and two times elevetion in alanine transaminase according to blood tests profile. Kupfer cells are also decreased except in AFHD group. Increase in portal triads can be attributed to decrease in size of lobule bringing together closeness of portal tracts. 12 fat cysts also evidence of greater damage to liver suggesting that AOM in the doses used to induce dysplastic changes induce changes in liver. We observed liver damages caused by AOM evidenced by morphological changes and enzymatic elevation were protected by the oncolytic viruses. These results were also shown in previuos studies which mentioned that NDV AF2240 and NDV V4-UPM have cytolytic effect on different types of cancer cells, also no toxicity was recorded in these studies of even high doses of NDV strains . This study confirmed that combination of NDV and 5-FU had greater antitumor efficacy than NDV or 5-FU alone. These results were in agreement with a previous study which showed the synergistic effect of combination of NDV strain LaSota with Methotrexate on three types of cancer cell lines . Moreover, NDV combination with vinorelbine and carboplatin was tested on non-small cell lung cancer (NSCLC) and the results showed significantly enhanced efficacy, reduce toxicity, synergistic anticancer chemotherapy . The mechanisms of synergistic activity in the combination of 5-FU with NDV is thought to be that maybe NDV enhance the antitumor activity of 5-FU by increasing cellular sensitivity to it then enhance induction of cells apoptosis which is previously proved to be induced by NDV .In conclusion, we noted morphological changes in liver due to a chemical carcinogen and their protection by NDV AF2240 and NDV V4-UPM seems to be most protective. The results also proved that combination of NDV and 5-FU had greater antitumor efficacy than treatment with NDV or 5-FU alone. All these results confirmed that NDV strains are good antitumor agents in-vivo and can be used in preclinical studies.These subjective observations in our small number of samples need to be confirmed by larger cohort of animals. The number of samples is small and the conclusions derived are subjective so further studies with large number of samples and morphometry is suggested.


Human and animal studies suggest an intriguing relationship between the immune system and the development of depression. Some peripherally produced cytokines, such as TNF-α, can cross the blood brain barrier and result in activation of brain microglia which produces additional TNF-α and fosters a cascade of events including decreases in markers of synaptic plasticity and increases in neurodegenerative events. This is exemplified by preclinical studies, which show that peripheral administration of pro-inflammatory cytokines can elicit depression-like behavior. Importantly, this depression-like behavior can be ameliorated by anti-cytokine therapies. Work in our laboratory suggests that TNF-α is particularly important for the development of a depressive phenotype and that TNF-α antagonists might have promise as novel antidepressant drugs. Future research should examine rates of inflammation at baseline in depressed patients and whether anti-inflammatory agents could be included as part of the treatment regimen for depressive disorders. Depression remains the most common psychiatric disorder, affecting approximately 350 million people worldwide. Accordingly, depression now ranks as the top cause of global disability in terms of years lost due to disability. The defining characteristics of depression include anhedonia, a loss of interest in pleasurable activities, and lowered mood . DepressThe notion that inflammation could be a contributing factor in the pathogenesis of depression is not a new one. Work in the 1980s’s first revealed that some patients with heightened immune activity  displayed the hallmark features of depression  . This inStress is also known to transiently elevate the expression of pro-inflammatory cytokines. Unsurprisingly, pro-inflammatory cytokines are efficient activators of the hypothalamic-pituitary-adrenal (HPA) axis , which oper se produced by a spinal cord injury can lead to the development of depression-like behaviors. Cytokines are increasingly being recognized as having a contributive role in the development of depressive symptoms. Treatment with proinflammatory cytokines, including IL-1, IL-6, or TNF-α, or lipopolysaccharide (LPS), induces sickness behavior and corresponding depression-like behavior on the forced swim test . Yang etAlthough the release of pro-inflammatory cytokines can contribute to the development of depression-like behavior, TNF-α in particular is receiving considerable attention due to its prominent roles in promoting inflammation and its dampening effects on synaptic plasticity . It is iTNF-α is a protein that is initially released as a soluble cytokine (sTNF-α) after being enzymatically cleaved by its cell surface bound precursor (tmTNF-α) by TNF-α converting enzyme (TACE)  and is tOne of the main roles of TNF-α is in maintaining inflammation during times of proinflammatory conditions. During proinflammatory events, TNF-α production is induced by other cytokines  and microglia. Once released, TNF-α stimulates the production of other proinflammatory cytokines, including IL-1 and 6, and it increases the production of reactive oxygen intermediates, including nitric oxide . It is ePreclinical studies corroborate the role of TNF-α in depression-like behavior. Peripheral administration of TNF-α can produce anhedonic behavior in rodents . LikewisGiven that cytokine treatment produces depression-like behavior and reducing cytokine levels alleviates this, the question that arises is whether or not antidepressants can influence inflammation. Interestingly, SSRIs and tricyclic antidepressants are known to reduce levels of TNF-α and other proinflammatory cytokines and increase anti-inflammatory cytokines, including IL-10 . Two potConsiderable attention has been dedicated to the antidepressant potential of ketamine. Ketamine rapidly reverses depression-like behavior in both clinical and preclinical subjects, in a timeframe of hours . An impoIn terms of clinical studies, a large body of evidence supports the role of TNF-α in depression. Endotoxin administration in control subjects produces an increase in TNF-α in addition to depressed mood and cognitive impairment . MicroarSome interesting extensions of these clinical studies have been observed in patients with treatment-resistant depression. per se can cross the blood brain barrier by a receptor mediated mechanism  causes a functional decrease in peripheral TNF-α with only an indirect effect on central TNF-α expression, as drugs like etanercept cannot cross the blood brain barrier . Therefoechanism , and wheechanism . Peripheechanism . As statOne should therefore expect that etanercept injections would prevent some of the central effects of protracted release of central TNF-α, such as its effects on hippocampal activity and neurogenesis see . In lineAnother intriguing hypothesis about the mechanism by which etanercept might enhance hippocampal neurogenesis comes from a separate set of studies we conducted that focus on the extracellular matrix protein reelin. Reelin has been extensively studied for its role in guiding cell migration during development, but in the adult brain it is involved in the promotion of synaptic plasticity. Reelin binds to two receptors, the very-low-density lipoprotein receptor (VLDR) and apolipoprotein receptor 2 (ApoER2). Activation of these receptors by reelin ultimately excites downstream targets including mTOR and P13K . ImportaIt is therefore worth considering how etanercept might interact with the reelin signaling system in the dentate gyrus. Very little direct research has been done on this topic to date, but there could be a link through neuronal nitric oxide. TNF-α promotes the expression of nitric oxide synthase, and there are reports that some neuronal subtypes might reflect nitric oxide-mediated oxidative damage in response to increased levels of TNF-α  downregulates ApoE production . It is twhen etanercept actually interacts with reelin. In the per se. It will be important to conduct further studies on this issue, with etanercept given in different animal models of depression at different time periods during and after periods of stress, so that a more complete understanding of the beneficial effects of etanercept can emerge.Another question that remains to be answered is Numerous reports point toward the effectiveness of anti-TNFα drugs for some depression patients. Use of TNF-α antagonists as antidepressants may be particularly important for subpopulations of patients with treatment resistant depression that show high levels of expression of proinflammatory cytokines.All authors contributed to the ideas presented in this review and contributed to editing the manuscript. KB wrote the first draft and developed the figure. LK and HC finalized the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Neisseria meningitidis do occur. Rapid, early outbreak detection is important for timely public health response. In this study, we characterized U.S. meningococcal isolates (N = 201) from 15 epidemiologically defined outbreaks (2009–2015) along with temporally and geographically matched sporadic isolates using multilocus sequence typing, pulsed-field gel electrophoresis (PFGE), and six whole genome sequencing (WGS) based methods. Recombination-corrected maximum likelihood (ML) and Bayesian phylogenies were reconstructed to identify genetically related outbreak isolates. All WGS analysis methods showed high degree of agreement and distinguished isolates with similar or indistinguishable PFGE patterns, or the same strain genotype. Ten outbreaks were caused by a single strain; 5 were due to multiple strains. Five sporadic isolates were phylogenetically related to 2 outbreaks. Analysis of 9 outbreaks using timed phylogenies identified the possible origin and estimated the approximate time that the most recent common ancestor emerged for outbreaks analyzed. U.S. meningococcal outbreaks were caused by single- or multiple-strain introduction, with organizational outbreaks mainly caused by a clonal strain and community outbreaks by divergent strains. WGS can infer linkage of meningococcal cases when epidemiological links are uncertain. Accurate identification of outbreak-associated cases requires both WGS typing and epidemiological data.Although rare in the U.S., outbreaks due to  Neisseria meningitidis (Nm), with a case fatality ratio of 10–15%; 11–19% of survivors experience long-term sequelae1. Most meningococcal disease cases are sporadic in the United States. However, a number of outbreaks occurred in different settings, such as the recent meningococcal serogroup B (NmB) outbreaks on university campuses and meningococcal serogroup C (NmC) outbreaks among the general population and among men who have sex with men (MSM)8. Large meningococcal outbreaks and epidemics have also been reported in other countries15.Meningococcal disease is a serious and life-threatening bacterial infection, caused by 16. Many meningococcal reference laboratories also utilize multilocus sequence typing (MLST)17 and genotyping of outer membrane proteins (PorA18 and FetA19) to determine the lineage and genotype of the outbreak strains20. However, disadvantages have been described for both methods. PFGE is time consuming and labor intensive. Standardization between laboratories is challenging, leading to irreproducibility of PFGE data21. The interpretation of strain relatedness based on PFGE patterns can be subjective and discordant because it lacks the discriminatory capacity and phylogenetic basis of more advanced methods22. Isolates with high genetic similarity may appear different in PFGE analysis24. While Sanger sequencing-based typing using MLST genes and genes coding for outer membrane proteins is useful to define bacterial molecular epidemiology and population structure, it does not provide sufficient discriminatory power to differentiate outbreak strains from sporadic strains or strains with high genetic similarity. Analyses of MLST data often assume that isolates sharing the same sequence type constitute an evolutionary unit, but this assumption is over simplified for species such as Nm, which has higher rates of recombination26.Rapid and early detection of outbreaks is critical to guide prompt and appropriate public health interventions to control the outbreaks and prevent additional cases. Molecular typing has played an important role in outbreak investigations, identifying outbreak strains by assessing their genetic relatedness and providing evidence to understand case transmission and linkage. Historically, typing with pulse field gel electrophoresis (PFGE) has been the gold standard for many infectious diseases, including meningococcal disease29. WGS can distinguish strains based on differences in single nucleotide polymorphisms (SNPs) or multilocus genes across the whole genome, an essential feature needed in Nm outbreak investigations when strains show high genetic similarity. WGS has been successfully used to elucidate the evolution of some Nm subtypes35, to assess the epidemiological association between Nm carriage and disease-causing isolates36 as well as for outbreak investigations for Nm32 and other pathogens such as N. gonorrhoeae37.With rapid advances in next-generation sequencing (NGS) during recent years, the cost of whole genome sequencing (WGS) has dropped dramatically. The rapid turnaround time and availability of automated WGS analysis tools make it possible to use this technology for outbreak investigation and surveillance of infectious diseaseBefore implementing WGS in routine surveillance and outbreak investigations for Nm, it is essential to evaluate WGS-based methods in comparison with traditional typing methods to establish the suitable scheme for the outbreak investigation of meningococcal diseases. This study aimed to re-characterize NmB and NmC outbreaks in the United States during 2009–2015 using WGS-based methods and to develop an optimal WGS-based typing scheme for Nm outbreak investigations.porA and fetA among outbreak isolates; these changes have been previously reported to occur in NmB and NmC isolates38A total of 19 unique strain genotypes, serogroup:MLST sequence type (ST)/clonal complex (CC):PorA type:FetA type, were detected among isolates from 15 outbreaks: 10 genotypes among 8 NmB and 9 among 7 NmC outbreaks  between outbreak and sporadic isolates were estimated. The mean genetic distances  between outbreak isolates from a single phylogenetic clade and any sporadic isolates ranged between 209 and 1418 SNPs/alleles, depending on the method  but gave discordant results on the other 6 outbreaks  and methods that do not account for recombination (kSNP and cgMLST). We focused on one recombination-adjusted method (SNIPPY) and a method that does not account for recombination (cgMLST), and compared them with PFGE. cgMLST is widely used for typing meningococcal isolates, and uses t on 2 OB5 and OB3t on 2 OB5 and OB3t on 2 OB5 and OB3t on 2 OB5 and OB3The results of PFGE and WGS were in good agreement on 3 NmC outbreaks  but discordant on 4 Fig. . Outbrea−6 (95% highest posterior density (HPD) ranging from 2.2 × 10−6–7.8 × 10−6) and 4.9 × 10−6 (95% HPD ranging from 3.1 × 10−6–6.9 × 10−6) substitutions/site/year respectively, which was similar to previous estimates in N. meningitidis35 and the related species N. gonorrhoeae37. The timed phylogeny for the 24 NmB CC32 isolates from outbreaks  and sporadic cases is shown in Fig. In order to assess the evolutionary relationship between strains causing different outbreaks, and to determine potential linkage between outbreaks, we performed BEAST analysis on a subset of NmB and NmC isolates and reconstructed a timed phylogeny to estimate the time to the most recent common ancestor from which each isolate per outbreak was derived. From the BEAST analysis, we estimated the substitution rate for NmB and NmC isolates as 4.6 × 10The timed phylogenetic tree reconstructed using BEAST for CC11 NmC isolates in the US MSM population, along with phylogenetic related sporadic and outbreak isolates, is shown in Fig. 40. With the addition of FetA and PorA types to the 7 gene MLST scheme, US outbreak strain genotypes matched isolates associated with outbreaks reported in Italy and Brazil42. Some were seen among a small proportion, 0.6–8.5%, of US sporadic isolates collected through Active Bacterial Core Surveillance (2000–2014).While the incidence of meningococcal disease is at a historic low in the US, outbreaks occurring in communities and organizations remain a public concern. To better understand the genetics of US meningococcal outbreak strains, we characterized the isolates from 15 US outbreaks during 2009–2015 using different molecular typing methods. The majority of the meningococcal outbreak isolates belonged to hyper-invasive lineages CC32, CC41/44, CC269, and CC11Information on the genetic relatedness of meningococcal outbreak strains is used to determine whether an outbreak was caused by clonal or divergent strains, and to predict potential linkage of suspected outbreak-related case when an epidemiological link is not certain. WGS demonstrated high resolution and discriminatory power to assess genetic similarity compared to PFGE and other typing methods. Isolates of a single phylogenetic cluster had multiple PFGE patterns, and phylogenetically distinct isolates had the same PFGE pattern, suggesting PFGE is unable to conclusively establish the genetic relatedness of meningococcal isolates and has limited reliability to assist with epidemiological investigation of meningococcal outbreaks. WGS-based phylogenetic analysis revealed that most of the US meningococcal outbreaks, either organization- or community-based, were caused by clonal strains with a low rate of genetic variation. However, two meningococcal outbreaks (OB7 and OB8) among MSM were each caused by divergent strains. The isolates from each of the 2 MSM outbreaks showed a high degree of intra-outbreak genetic variation compared to non-MSM outbreaks.Including geographically matched sporadic isolates in the analysis allows for the understanding of the genetic diversity of meningococcal outbreak strains in relation to non-outbreak strains. Most of the sporadic isolates were not phylogenetically related to the outbreak isolates; however, 3 and 2 sporadic isolates were observed to be tightly clustered with isolates from OB7 and OB14, respectively. These sporadic isolates were collected either during the outbreak or a month before the outbreaks were reported, suggesting some outbreak strains or their common ancestors, once emerged, may be very stable and stay in circulation for a long period of time. In addition, sporadic isolates that are phylogenetically similar to outbreak isolates should be further investigated in order to determine whether they are associated with an outbreak. Using timed-phylogenetic analysis, we were able to trace the potential origin of meningococcal outbreak strains, determine the potential linkage of 2 outbreaks, and understand the possible transmission path of the meningococcal strains. For an example, recent NmC outbreaks among MSM  did not result from the direct transmission of the 2003 MSM outbreak isolates, although they might share the same common ancestor that emerged around 1994. The 2015 NmB OB12 was either caused by a strain derived from the strain causing the 2013 NmB OB11 in a neighboring state or a strain that diverged from their common ancestor that emerged in 2008.43. A recent study43 indicated that k-mer–based methods especially MASH might suffer from relying on missing k-mers; due to erroneous de novo assembly artifacts, the presence of mobile elements or contamination is treated as informative and, thus, will result in biased distance estimates. In addition, model-based recombination detection is not feasible for kSNP core SNP alignment, which may overestimate the number of SNP differences between isolates. Furthermore, unlike MASH, kSNP is not scalable for analyzing thousands of genomes simultaneously43.The performance of each WGS-based typing method was evaluated for ability to clearly distinguish outbreaks from sporadic cases. WGS analysis methods resulted in very comparable phylogenetic topologies and were highly sensitive in distinguishing multiple distinct Nm outbreak clusters. To apply these methods effectively for disease surveillance and outbreak investigation, each method was evaluated for processing time, analysis capacity, and ability to assess genetic diversity introduced through homologous recombination. As homologous recombination is the major driving force for genetic variation and sequence diversity in meningococcal strains, assessment of genetic similarity and transmission of meningococcal strains must consider homologous recombination. Although the topologies of phylogenetic trees by different WGS analysis methods are similar for this dataset, with or without correction for homologous recombination, the differences in wRF tree distance measures could be due to the inflated branch lengths introduced by recombination. A good understanding of strain transmission requires a method that is able to detect genetic recombination and accounts for its evolutionary effects and thereby accurately reconstruct the clonal relationship between meningococcal isolates during an outbreak. Both k-mer–based methods, MASH and kSNP, seem feasible; they are extremely fast and require fewer computational resources. However, k-mer–based methods have inherent drawbacksde novo assembly (Parsnp) and those using gene annotations inferred from de novo assemblies (cgMLST and Roary) also showed high accuracy. However, methods that rely on de novo assemblies to generate the whole genome core alignment  may have lower resolution for SNP detection, because mutations are less likely to emerge in core genes compared to the accessory genes and intergenic regions; these methods therefore may not provide the same resolution as reference-based mapping45. While Parsnp generated a whole genome alignment quickly, its scalability to analyze thousands of genomes at once is limited43. cgMLST treats all genetic variation within a single locus count as a single allelic difference and lacks the implementation of model-based recombination detection. The genetic similarity between any 2 isolates can be based on the allele differences obtained from cgMLST or the SNP differences obtained from SNP-based analysis; however, the quantified differences detected by these methods are relative and cannot be compared. A recent evaluation of phylogenetic reconstruction methods using bacterial genomes suggested cgMLST-based methods had limited resolution and inference of intra-cluster distances46. In general, SNP-based typing provides higher resolution than annotation-based methods and is faster to implement. However, the cgMLST scheme allows standardization of sequencing data. Roary, unlike cgMLST, which requires a curated set of reference core genes, identified the core genes present among all the Nm isolates analyzed and generated a core gene alignment. SNIPPY, the reference-based short read mapping method, is fast and accurate, and identifies high-quality SNPs. Short-read mapping provides greater sensitivity and specificity than SNPs called from assembled de novo contigs because the variant call is based on the full information available in the reads, rather than on the assembler and consensus base caller47. Systematic evaluation of various phylogenetic reconstruction methods among bacterial genomes showed that the most accurate phylogenetic reconstruction was obtained from an alignment generated by mapping the sequencing reads to a closely related reference genome46. It is also shown that phylogeny inferred from an alignment generated by short read mapping to a less closely related de novo assembled genome within the group as a reference was similar to the phylogeny using a closely related completed genome44. SNIPPY also generates a whole genome alignment that includes both the SNPs identified as well as the invariant sites, which enabled us to perform model-based recombination detection analysis. This was very important to account for accurate phylogenetic analysis of Nm isolates, due to the inherent high recombination rate.Methods based on 48, it is rather complex to determine a meningococcal outbreak strain or clone based solely on differences among the number of specific alleles/SNPs. In most cases, isolates from a single outbreak form a tight phylogenetic cluster, with only a few allele/SNP differences within an outbreak; the minimum inter-outbreak allele/SNP difference is much higher than the maximum intra-outbreak difference. However, several outbreaks showed lower minimum inter-outbreak differences than the maximum intra-outbreak difference, increasing the difficulty in establishing an allele/SNP threshold for outbreak strain or clone. In addition, numbers of allele/SNP differences may vary depending on analysis methods, the reference genome selected, SNP calling parameters, and sequencing metrics44. Threshold established for strain definition warrants reevaluation over time when any of these parameters change. Outbreak time span is also a factor, as genetic variations accumulate in Nm isolates over time. Therefore, defining an outbreak strain or clone should integrate both allele/SNP differences and phylogenetic analysis. In addition, the timed phylogenies using evolutionary “molecular clock” models will be useful in situations where there is high similarity  among isolates in different outbreak clusters; translation of the branch lengths to time points informs the estimated time when the most common recent ancestor emerged and sheds light on potential transmission paths49.Even though some studies have attempted to establish a threshold based on allele/SNP differences to define an outbreak strain or clone50, WGS data are not required to define an outbreak because not all cases will have an available isolate for WGS. However, WGS may be used to exclude cases caused by strains that are genetically different from the predominant outbreak strain50.For rapid analysis of meningococcal strains, we propose a simple comparison method (e.g. a pairwise distance measure derived from kSNP or MASH) as a first step to provide a quick assessment of genetic relatedness between the newly identified isolates and a comprehensive reference collection of meningococcal isolates. An in-depth phylogenetic analysis using an alignment generated based on short read-mapping to the closest reference meningococcal genome will be conducted to compare the new isolates with the ones that are identified as most closely related in the first step, and accounting for homologous recombination. While it is difficult to establish an allele/SNP difference threshold to define an outbreak strain or clone, the differences obtained from the defined outbreaks in this study can be used as reference values for classifying outbreak strains. According to the recently published “Guidance for the Evaluation and Public Health Management of Suspected Outbreaks of Meningococcal Disease”Meningococcal disease surveillance is conducted through the Nationally Notifiable Diseases Surveillance System (NNDSS). As part of routine surveillance, state health departments are requested to send isolates from meningococcal disease cases in addition to epidemiological data to CDC for further analyses.1. Following this initial call for cases, four additional outbreaks from January 1, 2014 to December 31, 2015, were reported to CDC. Here, we analyzed 15 outbreaks where ≥50% of cases had viable isolates, including 8 serogroup B outbreaks (NmB = 32 isolates) and 7 serogroup C outbreaks (NmC = 52 isolates) from 10 states. A subset of randomly selected isolates (61 NmB and 56 NmC isolates) from “sporadic” cases collected during 2009–2015 in the same states where the 15 outbreaks occurred were included for the comparison between WGS-based methods; the same number following “SP” or “OB” identifiers was used to indicate the same geographic location. Cases were considered sporadic if health departments did not report the case as outbreak-associated, and the case is without any known epidemiological links to the outbreak cases. Isolate information and sequencing statistics are shown in Supplementary Table 6 and one in Toronto Canada 2001 (n = 6)51, were included in the molecular clock analysis along with the isolates of 4 NmC outbreaks from 2009–2015 in this study.In 2014, CDC requested that state and local health departments review all meningococcal disease cases reported to NNDSS from January 1, 2009 to December 31, 2013, to identify cases that occurred as part of an “outbreak”. Outbreaks are defined as 2 or more primary cases of the same serogroup in an organization  in less than three months, or an increase in disease rates in a communityporA and fetA were sequenced using Sanger sequencing or whole genome sequencing. For Sanger sequencing, either the Meningococcal Genome Informatics Platform52 or SeqMan Pro  was used to analyze the sequencing data. Genomes of meningococcal isolates were sequenced on Illumina sequencer as described below in “whole genome sequencing.” The MLST alleles, porA, and fetA were identified by BLAST search of alleles in PubMLST against the assembled genomes53. Outbreak strain genotype was defined as serogroup:MLST ST/CC:PorA type:FetA type.Seven MLST loci, NheI  as described by Popovic et al.16, with one modification: the exclusion of sodium dodecyl sulfate in the plug agarose without causing differences in PFGE patterns54. TIFF images were normalized by aligning the gel standard M413 with the database standard using BioNumerics Fingerprinting software version 6.6.11 16. After normalization, bands were marked within the range of the standard. Dice coefficients with a 1.5% position tolerance and 1.0% optimization, and unweighted pair group with arithmetic averages (UPGMA) were used to determine the level of similarity between PFGE patterns. Computer analysis was followed by and confirmed with visual inspection. PFGE patterns with the same number of bands were considered indistinguishable if band positions were within 2 kilobases of each other. When a specific PFGE pattern is distinguishable from any existing patterns, a new PFGE pattern type was designated. UPGMA dendrograms based on Dice coefficients were constructed to present PFGE analysis of the outbreak isolates; similarity matrices from these dendrograms were exported for further analysis. Cophenetic correlation was used to determine the reliability of clusters within these dendrograms.Outbreak isolates (n = 81) and a subset of sporadic isolates (n = 51), matched on timeframe and state to outbreak isolates, were prepared for PFGE using 55 for sequencing on Pacific Biosystems  and/or Illumina platforms ; genomic DNA was further prepared for Illumina sequencing with the dual-index NEBNext Ultra DNA library preparation kits 56. Genomes generated using PacBio were processed as previously described56. The Illumina reads were trimmed with cutadapt57 to remove adaptor sequences and the reads below Q28 and 75 bp. De novo short read assembly was carried out using SPAdes58 with the “-careful” option. The de novo assemblies were improved by scaffolding the best contigs using SSPACE59 and sequence gaps filled using GapFiller60 using short read sequences. This step was implemented using the assembly_improvement pipeline61.Genomic DNA was prepared for sequencing using the 5 Prime ArchivePure DNA Purification kit , Ampure  as previously describedSix WGS-based analysis methods were evaluated using the genomic sequences from meningococcal outbreaks and sporadic isolates described in strain collection, above, and are summarized in Table 62. The kSNP3 program identifies high-confidence SNPs by matching unique nucleotide sequences (k-mers) from each genome that vary only at the middle site. A k-mer size of 25 bp was implemented and the core SNP alignments from NmB and NmC genomes were used to reconstruct an ML phylogenetic tree using RAxML63 with a general time reversible (GTR) nucleotide substitution model, with 100 bootstrap estimates.The whole genome core SNPs were extracted from the assemblies using kSNP v3.0MASH (v1.1) was used to calculate distances between the trimmed read sequences of each genome, with parameters k-mer (k) = 32, s = 100000, m = 5, and c = 75. The target coverage (c) and minimum copies (m) of each k-mer were selected to assure that each sketch had an estimated genome size between 2.0 and 2.2 megabases.64, which uses BWA-MEM v0.7.12 for short read mapping. SNPs were called using Freebayes v1.0.2, requiring a minimum read coverage of 10X, and a 90% read concordance at a locus for an SNP to be reported. Snippy generates a core SNP alignment as well as a whole genome SNP alignment. The whole genome alignment was used to infer the ML phylogenies using RAxML as described above. Recombination-adjusted phylogenies for NmC and NmB isolates were generated using ClonalFrameML (CFML)65.Trimmed and quality filtered Illumina sequencing reads from NmB and NmC isolates were mapped to serogroup B isolate M18678 and serogroup C isolate M21427, respectively. Both were outbreak isolates included in this study and sequenced using PacBio. Variants in the form of SNPs were called using Snippy v3.166. The resultant core genome alignment was used to generate ML-based phylogenies using RAxML and for generating the recombination-adjusted phylogenies using ClonalFrameML as described above.Whole genome core alignments were generated for both NmC and NmB isolates using the Parsnp whole genome aligner available through the Harvest suite67, and the resultant gff3  files were used to perform the pan-genome analysis using Roary68 for both NmC and NmB genomes. Roary extracted the coding regions from the input gff3 files and converted them to protein sequences, removed partial sequences and iteratively pre clusters sequences with CD-HIT69, which resulted in a reduced set of protein sequences. Then an all-against-all comparison was performed with BLASTP with 95% sequence identity on the reduced protein sequences. Sequences were then clustered with a Markov cluster (MCL) algorithm70 based on the BLASTP similarity score. Finally, the pre-clustering results from CD-HIT were merged together with the results of MCL. From the final clustering results, homologous groups containing paralogs were identified based on conserved gene neighborhood information and removed to obtain the true orthologs68. In the final step of Roary, core genes were identified and a concatenated core gene alignment was created using PRANK71; this alignment was used to generate the ML phylogeny using RAxML and for ClonalFrameML analysis as described above.All the genomes were annotated using Prokka v1.853. These WGS data were compared using the PubMLST Genome Comparator tool that implements a “gene-by-gene” comparative analysis. In brief, a specified set of loci (core genes) among the isolates were compared sequentially to pre-indexed allele identifiers at each core gene and a matrix was generated that contained the allelic distances between NmB and NmC isolates, along with the concatenated core gene alignment73. The core gene alignment was used to generate the ML phylogeny using RAxML as described above.Genomes of NmB and NmC isolates were uploaded to the Bacterial Isolate Genome Sequence (BIGSdb) genomics platform hosted on the web74. wRF takes into account both the topological similarities as well as branch lengths while calculating the tree distance. All pairwise tree distances were generated using the R package treespace75.In order to quantify similarities between phylogenetic trees reconstructed using various WGS methods, pairwise tree distances were estimated using weighted Robinson – Foulds (wRF) metric76, with genomes of all 24 CC32 NmB and a subset of 71 closely related CC11 NmC isolates that were associated with MSM population. Multiple outbreak strains that belonged to these two CCs caused organizational or community outbreaks over multiple years, providing sufficient temporal signal for molecular clock analysis (root-to-tip correlations estimated using root-to-tip linear regression were 0.3036 for NmB and 0.6842 NmC). The 24 CC32 NmB isolates included all isolates from outbreak OB11 and OB12, 2 OB6 isolates, and 12 sporadic isolates. The 71 CC11 NmC genomes included all isolates from OB7, OB8, and OB14, and 2 OB9 isolates. Six genomes from a well-characterized NmC CC11 outbreak at Toronto in 200151 (obtained from PubMLST77) and 5 genomes (sequenced at the CDC) from a MSM outbreak at Chicago in 20036 were also included in the NmC BEAST analysis. For BEAST analysis, first recombination was detected using ClonalFrameML65, and the SNP sites determined to be outside of the predicted recombinant segments were used. We used BEAST v1.8.2 with the HKY 78 substitution model, the coalescent model with constant population size, and a uncorrelated relaxed clock model with an exponential distribution as the prior for the clock rate. Two independent chains of 200 million steps were run for both NmC and NmB alignments, and each was sampled every 1000 steps to ensure good mixing. The results were compared using Tracer to confirm convergence, and the maximum clade credibility tree was generated using Tree Annotator; both tools are provided with the BEAST package79.A timed phylogeny was constructed using BEAST Supplementary FiguresSupplementary Table 1Supplementary Table 2Supplementary Table 3Supplementary Table 4