The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI  contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included. Metabolomics is the comprehensive study of small molecules present in cells, tissues and body fluids. Advances in metabolic profiling have led to discoveries of biomarkers in a variety of medical conditions using metabolomics and lipidomics approaches, including the vision to utilize metabolomics for precision medicine ,2,3. UntCurrently, two major analytical platforms are used in the small molecule identification process. Nuclear magnetic resonance (NMR) is a powerful structure elucidation technique and it has a significant advantage due to its nondestructive and noninvasive characteristics of analysis. However, this method lacks the sensitivity needed for the simultaneous analysis of thousands of metabolites observed in biological samples ,9. High Classical structure elucidation using NMR commonly elucidates the full structure using de-novo approaches . The natA number of reviews have been published that cover many diverse metabolomics topics including chromatography, data processing and statistics in great detail ,22,23,24The chemical space of small molecules currently covered in databases such as PubChem, ChemSpider or the Chemical Abstracts Database is larger than 120 million compounds  see Tab. The numDuring the structure elucidation process, small molecule databases serve as a foundation of known and well-researched metabolites see . Enzyme http://minedatabase.mcs.anl.gov/). MINES covers over 500,000 substances derived from KEGG and other pathway databases by applying known enzymatic transformation rules + have different CCS values, differing on average ±7 Å2 based on values obtained from [2 measured on a drift tube IMS (DTIMS) [2 measured on a TWIMS setup, and the predicted value is 197.7 Å2 and therefore falls outside the 3% median prediction error [Several considerations have to be taken into account when working with CCS values and predictive databases. CCS values of individual compounds depend on many additional parameters such as buffer gas, solvents, temperature, pH, ion activation voltage and conformer/rotamer ensembles ,159. Forned from . This isned from . Drugs sned from . The difned from ,162 and ned from , the exp (DTIMS) ; the samon error .Because of the IMS capability of separating stereoisomers and other isobaric compounds, the routine use of CCS values will become more and more important. The excellent experimental reproducibilities of CCS measurements compared to retention times will also improve identification rates. Once larger CCS datasets become publicly available, they can be combined, average consensus values can be calculated and CCS prediction methods can be retrained with larger compound numbers and therefore will automatically become more accurate. Technological advances such as printed circuit board (PCB)-based devices led to ion elevators and escalators in multilevel structures . Therefo1 peak relationships such as adducts and neutral losses, the inclusion of MS/MS data and biochemical knowledge as well as modelling of retention times as an orthogonal filter. A knowledge-based workflow for metabolite annotations that includes ionization rules, adduct formation rules and retention time rules was described in [The following section discusses some general compound identification workflows as well as a few selected cases of single compound identification examples via mass spectrometry. Workflows are important for highly reproducible and repeatable metabolomics analysis. Among those are Galaxy workflows  such as ribed in .However, even in-source fragmentation LC-MS mass spectra when used together with retention times of authentic compounds can be sufficient for ‘Level 1’ annotations in metabolomics . A pipel1-acetylisoputreanine and N1-acetylisoputreanine-gamma-lactam by metabolic profiling and used custom synthesis to confirm the commercially unavailable metabolite [4) and custom synthesis to identify and confirm N,N,N-trimethyl-l-alanyl-l-proline betaine in human plasma. Novel glycolipids were found in yeast annotated by combining multiple mass spectrometric platforms and chiral chromatography to ascertain stereoisomer configuration [Full structure elucidation of single novel compounds with chromatography and mass spectrometric analysis is possible but is harder than with the isolation of compounds and NMR analysis. A clear benefit of LC-MS/MS approaches is the limited amount of material needed, in comparison to LC-MS/MS-NMR methods. A recent report annotated Ntabolite . Anotherguration . Anotherguration . Naturalguration , but sucguration . Finallyhttp://www.casmi-contest.org) has been held since 2012 as a worldwide scientific competition to determine the best approaches for identifying small molecule structures directly from mass spectra [The CASMI  contest  .The publication of tools or databases that are neither publicly nor commercially available should be avoided. Such opaque software does not contribute much to the field and cannot be validated independently. We therefore mostly refrained from referencing such publications or tools in this review. Software tools should be validated on public, large and diverse datasets before making claims that they outperform any other tool.Computational metabolomics strategies for compound identification have gained increased attention in the community. Unknown metabolite signals cannot easily be used for biological interpretations , and incBioinformatics researchers have helped the proteomics and genomics community over many years to solve problems in their domain. However, the bioinformatics community had a smaller impact on the small-molecule community due to the chemical structure-centric approaches that are needed for structure elucidation in metabolomics. To this end, the much smaller cheminformatics community still struggles to provide adequate support simply due to its much smaller size and impact. Therefore, collaboration with researchers from scientific branches such as machine learning and the quantum chemistry community need to be actively embraced. The computational metabolomics community is a quite small but innovative community, and many more research groups worldwide contribute now in friendly competition.nMSMultiple stage mass spectrometryCASMICritical Assessment of Small Molecule IdentificationCCSCollisional cross-sectionCFM-IDCompetitive Fragmentation Modeling for Metabolite IdentificationFAHFAsFatty Acid ester of Hydroxyl Fatty AcidsFragmentation treeMass spectral fragmentation pathway of a compoundGNPSGlobal Natural Products Social molecular networkingHMDBHuman Metabolome DatabaseIMIon mobilityInChIKeyHash key or short unique structure codeLipidBlastIn silico generated database for lipid identificationMassBankMass spectral databaseMetaboBASEMass spectral library developed by BrukerMoNAMassBank of North AmericaNISTNational Institute of Standards and TechnologyNMRNuclear Magnetic ResonanceReSpectRIKEN MSn spectral database for phytochemicalsSPLASHHashed code or unique identifier for mass spectra


Over the last two decades, neuroscientists have used antidepressant placebo probes to examine the biological mechanisms implicated in expectancies of mood improvement.However, findings from these studies have yet to elucidate a model-based theory that would explain the mechanisms through which antidepressant expectancies evolve to induce persistent mood changes. Compared to other fields, the development of experimental models of antidepressant placebo effects faces significant challenges, such as the delayed mechanism of action of conventional antidepressants and the complex internal dynamics of mood. Still, recent neuroimaging studies of antidepressant placebo effects have shown remarkable similarities to those observed in other disciplines , such as placebo-induced increased µ-opioid signaling and blood-oxygen-level dependent (BOLD) responses in areas involved in cognitive control, the representation of expected values and reward and emotional processing. This review will summarize these findings and the challenges and opportunities that arise from applying methodologies used in the field of placebo analgesia into the field of antidepressant placebo effects. Antidepressant placebo effects — averaging 31–45%, compared to ∼50% response rates to conventional antidepressant medication — pose significant challenges for drug development , a proceFunctional neuroimaging studies, stemming primarily from the area of placebo analgesia, have rapidly advanced our knowledge of the mechanisms underlying placebo effects in pain using sophisticated experimental approaches . Howeverpriors on experiences, whereas sensory input forms the likelihood. Very strong expectancies are represented by priors with low variance, which in Bayesian updating means that incoming information  has little effect; the opposite is true of weak or uncertain expectancies. Therefore, strong expectancy priors about the effect of a placebo will reduce the amount of learning that occurs from experience. In an experimental test of this model, Grahl et al.  and the rostral ventromedial medulla. This study showed that pain perception results from the integration of expectancies, in the form of priors, and sensory information, in the form of likelihoods, and the relative variances of these distributions affects placebo learning at behavioral and neural levels.Classical theories of the placebo effect, informed predominantly by placebo analgesia experiments, posit that placebo responses are explained by expectancy and conditioning mechanisms . While tl et al.  fit a Baprediction error (PE), which signals the mismatch between what it is expected  and what it is perceived (the reward). This PE is then scaled by the learning rate, a parameter controlling the speed of updating of new sensory evidence and added to the expected value of the next experience. In standard RL, expectations not confirmed by experience are extinguished. However, emerging evidence from placebo analgesia experiments suggests that placebo analgesia arises from mechanisms implicated in self-reinforcing expectancies, such as confirmation biases, where expectancies are selectively reinforced by predictive cues  only when new experience confirms prior expectations, or discount new evidence otherwise . These models, and in particular variants of the Rescorla-Wagner model , proposetherwise . Alterna of RPEs .These different theoretical frameworks have been embedded in many experimental designs of placebo analgesia since its early stages, leading to substantial progress in identifying the cognitive, neural and molecular bases of placebo analgesia. While it remains largely unknown whether similar conceptual frameworks can be applied to the formation of placebo responses across disorders, these experimental approaches have the potential to illuminate new insights into our understanding of antidepressant placebo effects.The very first neuroimaging study of placebo analgesia measured regional cerebral blood flow (rCBF) with positron emission tomography (PET) to compare the effects of the short-acting µ-opioid receptor agonist remifentanil or a placebo under expectations of analgesia. This study revealed increased brain activity in the rostral anterior cingulate cortex (ACC) for both remifentanil and the placebo conditions. Placebo, but not remifentanil, further increased the connectivity between the rostral ACC and the PAG . Since tparallel group designs or open versus hidden drug design), this trial-by-trial manipulation of expectancies and sensory inputs  has been an essential feature of experimental neuroimaging models of placebo analgesia, which has allowed a rapid understanding of the behavioral, neural, molecular, and computational bases of placebo analgesia. Most commonly, neuroimaging experimental designs of placebo analgesia involved verbal instructions of pain relief  along with an inert treatment , compared to a control condition—the same inert treatment without expectations of pain relief. During an associative learning phase, the placebo is paired with a low-intensity painful stimulus and the control condition is paired with a high-intensity painful stimulus. Finally, during the test phase — usually conducted during a functional MRI scanning session — both the control and the placebo conditions are paired with a painful stimulus of the same intensity. Under these circumstances, experimenters can test whether pain reports and brain responses are modulated by the patient’s beliefs about the treatment . Alternain vivo receptor binding of μ-opioid receptors  , 25, as eceptors , 31 haveeceptors , and moreceptors , consisteceptors  and moodeceptors . Nowadayeceptors  and otheThe experimental manipulation of expectations of mood improvement as well as its conditioning posits significant challenges. For example, the delayed action of conventional antidepressants limits the possibility of manipulating expectancies acutely. Furthermore, mood, unlike pain—which reliably emerges in response to specific stimuli—is a latent state with complex internal dynamics. For these reasons, most neuroimaging studies have used placebo-induced neuroimaging changes in the context of randomized clinical trials (RCTs) , pre- a. Here, wIn the very first study that examined the neural correlates of antidepressant placebo effects, Leuchter et al. , used quSoon after, Mayberg and colleagues examined the neural correlates of antidepressant placebo effects using fluorodeoxyglucose (FDG) and PET before and after 1 and 6 weeks of fluoxetine or placebo . This stThese two studies represented a major step forward in the investigation of antidepressant placebo effects. Interestingly, and despite using very different neuroimaging modalities with different temporal and spatial resolution, both studies found overall increases in prefrontal activity in response to the drug or the placebo treatments. In the context of RCTs, parallel group designs often assess symptom stability using a placebo lead-in phase. During this phase, subjects who meet initial screening criteria, but exhibit a 20–25% reduction in symptoms, are usually excluded from participation in the post-randomization phase of the trial .Biomarker studies have used placebo lead-in designs to examine the relationship between neural changes during the placebo lead-in period and the endpoint clinical outcome. An example of this kind of experimental design is the one published by Hunter et al., where they examined the neural responses during a placebo lead-in phase . In this11C] carfentanil after the ‘active’ and an ‘inactive’ oral placebo treatment. In addition, during the PET scanning session, but only after the active placebo condition, participants were administered 1 mL of isotonic saline intravenously, with instructions of fast-acting antidepressant effects. This study had several interesting findings. First, higher baseline opioid receptor binding in the nucleus accumbens (NAc) was associated with a better treatment response during the 10-week open label antidepressant treatment. Second, clinical responses to the ‘active’ placebo treatment, compared to the ‘inactive’, were associated with increased placebo-induced μ-opioid neurotransmission in the subgenual ACC, NAc, midline thalamus and amygdala. Finally, we found that placebo-induced opioid neurotransmission was associated with better antidepressant treatment response, predicting 43% of the variance in symptom improvement at the end of the antidepressant trial  followed by a 10-week open-label antidepressant treatment . In thisnt trial . 2/3 receptor-selective radiotracer [11C] raclopride after each 1-week inactive and active oral placebo treatment. Here, we found that, compared to a matching sample of healthy controls, patients with depression showed greater D2/3 receptor availability in the bilateral ventral pallidum/NAc, and the right ventral caudate and putamen. D2/3 receptor availability in the ventral striatum correlated positively with high anxiety  and negatively with anhedonia . Furthermore, we observed increased placebo-induced DA neurotransmission in the ventral striatum. However, these changes were not correlated with the patient’s levels of expectations of improvement or their mood improvement after the I.V. or the oral placebo nor the treatment with 10 weeks of antidepressants  after each of two different ‘active’ and ‘inactive’ placebos . In thisWe recently developed a new Sham Neurofeedback fMRI Task . This taResults from this study in 20 patients with MDD demonstrated the feasibility of manipulating fast-acting antidepressant effects. As expected, patients reported higher expectancy ratings during the placebo infusion condition (expecting a drug infusion as opposed to no infusion), and higher mood ratings during the drug infusion cue, compared to the no-infusion cue, and following the display of positive sham neurofeedback, compared to negative. Furthermore, the positive effect of neurofeedback on reported mood was enhanced when expectancies were high, as reflected in a positive two-way interaction. The presentation of neurofeedback of greater magnitude recruited greater blood-oxygen-level dependent (BOLD) responses in the bilateral ventrolateral and dorsolateral PFC. Furthermore, greater increases in β-endorphin plasma levels during the task were associated with higher expectancy ratings during the placebo condition, compared to the no-infusion condition, and higher mood ratings during positive neurofeedback, compared to negative.Qt + 1(s) = Qt + 1(s) + αδt, where Qt (s) is the learned expected value of improvement at trial t, α is a learning rate, and δ is the difference between the actual and expected outcome (RPE): δt = rt – Qt(s), where, rt is the actual reward outcome (positive vs. negative neurofeedback). These values are used to make choices (such as ratings of expectation of improvement) according to a sigmoid choice rule with two free parameters: β (stochasticity) and K (choice bias). The estimation of such parameters and derived values  — which cannot be accessed with descriptive approaches alone — can then be mapped onto the neural response during the Sham Neurofeedback fMRI Task. This trial-by-trial information is likely to provide new opportunities to disambiguate placebo responses. Furthermore, this transdiagnostic RL framework may apply to other clinical conditions where placebo effects are also prevalent, notably anxiety disorders, Parkinson’s Disease, and various forms of persistent pain, but also schizophrenia, substance use disorders and surgeries  is observed. This updating is based on the following equation: urgeries , 45.Whereas computational theories have not yet been applied to models of antidepressant placebo effects, recent evidence supports a relationship between RL and mood, which opens the possibility that antidepressant placebo effects might indeed result from RL mechanisms , 47. Expprimary sensitivity to rewards (reduced consummatory anhedonia) and/or alterations in their ability to learn from positive or reward feedback. Furthermore, patients with depression might show exaggerated processing of negative or aversive feedback  or prediction errors (by estimating separate learning rates for positive versus negative prediction errors).RL models of antidepressant placebo effects are therefore likely to be influenced by features frequently affected in patients with depression. For example, patients with depression may show a reduction in the feedback . These abidirectional influences between mood and learning. This kind of biases create a feedback loop where initial improvements in mood, through biasing learning in positive direction, lead to more positive future mood states, providing a potential mechanism for the perpetuation of placebo responses.Finally, improved mood may increase processing and learning from positive outcomes, biasing learning towards more positive learning with initial improvements in mood . TherefoThis review has identified several challenges and opportunities that have emerged from early research investigating the neurobiology of antidepressant placebo effects and new computational approaches. As discussed, much can be learned from experimental approaches extensively used by other disciples.translational opportunities to promote treatment response by stimulating placebo-related networks as new targets for mood improvement. From the perspective of drug and therapy development, inhibiting placebo responses could help separate drug-specific and “non-specific” treatment effects. Higher signal and less noise in RCTs would, therefore, result in substantial savings by reducing the samples sizes necessary to achieve significant differences between active and inactive treatments. As discussed, a first step towards this aim is the use of model-based experimental approaches that disentangle the different elements involved in this complex phenomenon, including those shared by other disorders and those that are mood specific. In the future, the formalization of computational models of antidepressant placebo effects and other psychiatric conditions may provide a fruitful approach to map learning-based models of antidepressant placebo effects onto the underlying neural mechanism. The delineation of such a computational framework and associated neural circuits and neurotransmitters systems will open new https://www.thevirtualbrain.org/tvb/zwei ). Such neurocomputational models could potentially be used to help identify key subject-specific mechanisms of placebo responses that might impact treatment response broadly. This approach is more likely to account for individual differences in placebo responses, a phenomenon that is subject to both intra-individual and inter-individual variability. Consistently, recent evidence suggests that functional organization within individual subjects is idiosyncratic and relatively robust to changes in brain state and provides meaningful information beyond group averages  and a T32 MH019986 (VB).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.


To report a case of a new complication following complementary and alternative medicine chiropractor neck manipulation with multiple preretinal hemorrhages.A 59-year-old Caucasian female presented with the acute, painless constant appearance of 3 spots in her vision immediately after a chiropractor performed cervical spinal manipulation using the high-velocity, low-amplitude technique. Examination multiple unilateral preretinal hemorrhages with no retinal tears. The preretinal hemorrhages were self-limited and resolved by 2 months later.Chiropractor neck manipulation has previously been reported leading to complications related to the carotid artery and arterial plaques. This presents the first case of multiple unilateral preretinal hemorrhages immediately following chiropractic neck manipulation. This suggests that chiropractor spinal adjustment can not only affect the carotid artery, but also could lead to preretinal hemorrhages. Stroke,2A 59-year-old Caucasian female presented with the acute, painless constant appearance of 3 spots in her vision. She described the spots as “tadpoles” that were constantly present in her vision. She noted the first spot while driving home immediately following a chiropractor neck adjustment, and became more aware that there were 2 additional spots the following day. She received cervical spinal manipulation using the high-velocity, low-amplitude (HVLA) technique on the posterior neck. She also received twisting of the neck that day where it was twisted both to the left and to the right. She had a history of headaches, psoriasis, and restless leg syndrome for which she takes Topamax (topiramate), Stelara (ustekinumab), and Flexeril (cyclobenzaprine), respectively, but denied any further medical history. She denied recent trauma or surgeries.Visual acuity was 20/20 OU with minimal refractive error, full extraocular movements, and no ptosis. Neurologic examination was unremarkable. Blood pressure was 123/79, and work-up was negative for diabetes or blood dyscrasia. Slit lamp examination of the right eye demonstrated multiple unilateral preretinal hemorrhages with 3 present inferiorly along with a hemorrhage over the optic nerve A and a s3,,Complementary and alternative medicine (CAM) is being increasingly utilized by patients for a host of medical conditions, often without informing physicians.Upper spinal manipulation with the HVLA technique involves high velocity, low-amplitude thrusts on the cervical spine administered posteriorly. No other etiology of the preretinal hemorrhages was found on work-up . The temporal association immediately while driving home from the chiropractic procedure makes other causes less likely, although we cannot exclude Valsalva retinopathy or progressive posterior vitreous detachment. Given the lack of any retinal vessel abnormalities or plaques along with the temporal association, we postulate that the chiropractor neck manipulation itself induced vitreo-retinal traction that likely led to preretinal hemorrhages which were self-limited. It is also possible that the HVLA technique could have mechanically assisted with induction of a posterior vitreous detachment.The ophthalmic manifestations of chiropractor neck manipulation has primarily been viewed previously from its effect on the carotid artery and subsequent central retinal artery along with adjacent neurons, but this case demonstrates that it can also lead to multiple preretinal hemorrhages.4Chiropractor neck manipulation has previously been reported leading to complications related to the carotid artery  and arterial plaques. This presents the first case of multiple acute, unilateral preretinal hemorrhages immediately following cervical spinal manipulation using the high-velocity, low-amplitude technique on the posterior spine that spontaneously resolved.The patient consented to the submission and publication of this case report with documentation on file.National Eye Institute Michigan Vision Clinician-Scientist Development Program 4K12EY022299 (YMP).Funding was provided in part by the None of the authors has a conflict of interest with the submission.All authors attest that they meet the current ICMJE criteria for authorship.


Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we performed whole-exome sequencing (WES) on three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA for which 82% of the somatic variants were detected reliably, only 36%–78% were found consistently in technical replicate pairs. Overall, 34%–80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low-mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of sub-clonal mutations by WES in the absence of orthogonal validation remains unreliable.  Shi et al. report that standard coverage whole-exome sequencing and bioinformatics pipelines cannot discriminate between genuine intratumor genetic heterogeneity and sequencing artifacts. Although aggressive minimum depth filtering would not improve the false detection rate of subclonal mutations, excluding mutations in low-mappability regions or in certain mutational contexts could help. Intratumor genetic heterogeneity (ITGH), typically defined as the coexistence of genetically distinct but clonally related cancer cells within the same patient , can manThe assessment of ITGH, by definition, involves the detection of subclonal, low-frequency variants that are not uniformly present in all cancer cells and is made possible by the availability of bioinformatics tools to detect low-frequency somatic mutations with high sensitivity . HoweverGiven the implications in prognosis and treatment response, ITGH is an important consideration in the clinical setting. Because the cost of WES and complex informed consent requirements, the inclusion of matching normal samples still represents a limitation in the sequencing of tumor samples in some large clinical trials . WhetherIn this study, we aimed to assess the reliability of somatic variant detection from whole-exome sequencing (WES) in the context of ITGH  and S1A.We performed WES on DNA extracted from three distinct regions of the primary tumor and the matching normal blood cells from six breast cancer patients, including four with estrogen receptor-positive and two with triple-negative cancers . The bioTo quantify the technical reliability of somatic mutation detection by the matched-normal WES pipeline, the approach that is most frequently used in the research setting to assess ITGH, we compared the somatic mutations identified in the six pairs of technical replicates. In this experiment, tumor samples and normal samples were sequenced to a median coverage of 1843 (range 92–211) and 903 (range 80–138), respectively . We idenGiven that both technical replicates used the same input DNA, we anticipated detecting nearly identical somatic mutations in each pair of replicates. However, only a subset of the somatic SNVs and INDELs were consistently identified in technical replicate pairs, with median Jaccard distances  of the concordant variants were confirmed to be genuinely somatic in at least one of the two technical replicates, compared with a median 33% (range 14%–48%) of the discordant variants . Of the Taken together, these results suggest that WES performed at typical sequencing depth may be inadequate for detecting ITGH, particularly when the tumor cell content is less than 50%, as only 62% (range 36%–76%) of the somatic mutations were detected consistently in the technical replicate pairs by WES, with the remaining mutations falsely appearing as discordant.Next, we quantified ITGH by comparing somatic variant calls between the geographically distinct biopsies from the same cancer. In this analysis, we also examined how the three different WES analytic approaches differed in the quantification of ITGH . Tumor cTo assess the reliability of the different WES pipelines in detecting ITGH, we compared the WES candidate mutations with the “gold-standard” AmpliSeq-validated somatic variants from the same sample. A median of 62% (range 50%–98%) of the candidate somatic variants detected by the tumor-only pipeline were germline variants, and only 28% (range 2%–45%) were validated as true somatic mutations, highlighting the challenges posed by this commonly used approach in clinical practice. By contrast, a median of 79% (range 59%–91%) and 84% (range 61%–92%) of the variants defined by the cohort-normal and the matched-normal pipelines, respectively, were true somatic mutations , and S1ENext, we estimated the apparent ITGH on the basis of mutations detected by the three WES calling pipelines using the Jaccard distance as the metric of ITGH. Mutations identified as somatic in one or two of the three biopsies and as germ-line-like or absent in the remaining biopsies contributed to ITGH. The tumor-only pipeline had a median Jaccard distance of 0.34 (range 0.19–0.96) compared with the cohort-normal pipeline of 0.70 (range 0.53–0.91) and matched-normal pipeline of 0.60 (range 0.44–0.89) . The appCDC27 (p.Cys71*) was identified as heterogeneous (in one of the three biopsies) by the matched-normal WES pipeline but was not validated by AmpliSeq. False-positive heterogeneous variants were mostly not actionable, however. genes. For example, a deleterious stop-gain branch mutation in CDC27 (p.Cys71*) was identified as heterogeneous (in one of the three biopsies) by the matched-normal WES pipeline but was not validated by AmpliSeq. False-positive heterogeneous variants were mostly not actionable, however.Taken together, these results suggest that the tumor-only WES pipeline misidentifies a substantial proportion of germline variants as somatic mutations. Even when using the matched-normal DNA for mutation detection, the extent of ITGH defined solely on the basis of WES performed at typical sequencing depth is overestimated, potentially affecting actionable cancer genes. For example, a deleterious stop-gain branch mutation in To identify the characteristics of the putative mutations identified by WES that were subsequently found not to be truly somatic variants, we examined the alternative coverage , the variant allele frequency (VAF), and the total depth of coverage of the candidate somatic mutations identified by the three WES pipelines from the intratumor biopsies. The tumor-only pipeline reported a median of 2 variants (range 0–5) with VAF < 10% because of reduced sensitivity of the single-sample mutation detection algorithm at low VAF and the strict filtering imposed to remove potential germline variants  and 3B. Given that the majority of the ITGH studies carried out to date  used matBecause subclonal mutations are expected to be the predominant contributors to ITGH, we inferred the clonality of all mutations identified by WES using ABSOLUTE . SubclonExamination of the genomic locations of mutations revealed that 41.1% of the artifactual somatic mutations occurred in regions of low mappability  comparedFinally, we considered whether applying filtering strategies to exclude mutations that occur in low-mappability regions or within sequence contexts enriched in artifactual mutations . The reliability of WES to detect low-frequency mutations, which often account for the majority of ITGH within a cancer, and therefore to resolve clonal architecture  of all variants identified by WES demonstrated that the majority of the false-positive somatic variants (1) displayed low VAF and were often detected as subclonal in one experiment but not in the other, (2) were in fact germline-like variants that appeared as heterogeneous somatic mutations , or (3) Our analysis of the mutational signatures between the validated mutations implicated in ITGH versus the false-positive mutations revealed striking differences. The heterogeneous mutations were enriched in a pattern typically associated with increased APOBEC activity, and this pattern has been previously shown to contribute to ITGH in breast and lung cancers . On the This study has several limitations. The sample size of 6 breast cancers with 18 biopsies may be too limited to allow generalizing our results on ITGH to all breast cancer subtypes or to other cancers. Of note, the unique nested experimental design incorporating within-sample technical replication, processed and sequenced in the same manner as the intratumor biopsies, provided an estimate of background discordance against which the ITGH results could be interpreted. Additionally, the extensive orthogonal validation by high-depth amplicon sequencing on an independent sequencing platform with very different chemistry from the platform used for WES adds rigor to our study. The sequencing depth attained in this study is comparable with that in previous studies using WES  for the genomic characterization of ITGH ; it is pIn summary, our study showed that WES at 184 mean depth of coverage in the tumor samples overestimates the extent of ITGH, and the technical noise associated with somatic mutation detection using WES alone can confound true ITGH. Our results also suggested that it is not possible to reduce the false-positive rate through more aggressive minimum depth filtering without affecting the sensitivity of detecting true somatic mutations in the 1%–5% VAF range, but excluding mutations that occur in low-mappability regions of the genome, or in certain mutational contexts could reduce artifactual somatic mutations and provide less biased estimates of ITGH. Nevertheless, orthogonal, high-depth validation experiments are highly desirable in the context of quantifying ITGH.Breast cancer samples were collected from patients with newly diagnosed invasive breast cancer with tumor size > 2 cm at the Yale Cancer Center. Tumor tissues were obtained with three punch biopsies at least 1 cm apart from three different regions of the tumor after pathologic gross examination. Six of these tumors with high enough cellularity (>50%) and high DNA quality from all three biopsies and with matched blood DNA were selected for this study.DNA was extracted and library was prepared using standard protocols, and the exome was captured using the NimbleGen SeqCap EZ Human Exome Kit version 2.0. Sequencing was performed on the HiSeq 2000 in paired-end 75-cycle mode at the Yale Center for Genome Analysis. We used three different analytical pipelines for detecting variants. A single-sample “tumor-only” pipeline, a “cohort-normal” pipeline using an in-house normal reference obtained from ten unrelated normal blood DNA samples, and a “matched-normal” pipeline using the matched-normal DNA from each patient as reference. Further details are provided in Variants identified by WES were subjected to validation with high-depth amplicon sequencing using custom AmpliSeq panels on the same tumor and matched normal DNA samples. Amplicon sequencing was performed to a median depth of 6003. Further details are provided in Mutational signature analyses comparing the validated variants that contribute to ITHG and the WES false-positive calls were performed for individual tumor samples and for the pooled mutations over all samples using the R package deconstructSigs . Mappabi123456


Vertical slab-tearing has been widely reported in modern convergent settings profoundly influencing subduction and mantle dynamics. However, evaluating a similar impact in ancient convergent settings, where oceanic plates have been subducted and the geological record is limited, remains challenging. In this study, we correlate the lower mantle structure, which retained the past subduction configuration, with the upper-plate geological record to show a deep slab rupture interpreted as a large-scale tearing event in the early Mesozoic beneath southwestern Gondwana. For this purpose, we integrated geochronological and geological datasets with P-wave global seismic tomography and plate-kinematic reconstructions. The development of a Late Triassic-Early Jurassic slab-tearing episode supports (i) a slab gap at lower mantle depths, (ii) a contrasting spatiotemporal magmatic evolution, (iii) a lull in arc activity, and (iv) intraplate extension and magmatism in the Neuquén and Colorado basins. This finding not only has implications for identifying past examples of a fundamental process that shapes subduction zones, but also illustrates an additional mechanism to trigger slab-tearing in which plate rupture is caused by opposite rotation of slab segments. While horizontal oceanic slab-tearing is expected after continental collision4, vertical slab tearing is detected in convergent settings that undergo non-uniform slab retreat2 or in zones at the edges of active margins accommodating trenchward slab motion1. In addition, vertical tearing is locally observed where aseismic ridges5, mantle plumes7 or oceanic fracture zones interact with active margins8.Tear in subducting plates, linked to vertical or horizontal slab ruptures, are outstanding tectonic features described in some modern subduction9, the fingerprints of vertical slab tears are more difficult to detect. Identification of this process demands joint evidence from independent methodologies, such as geophysics, geochemistry and structural geology (e.g.11). This task is more difficult in ancient convergent settings where oceanic slabs have been fully subducted, and the geological record is cryptic and severely overprinted by shear zones. As a consequence, the tectonomagmatic characteristics of this process have only been accurately described in active margins in current subduction zones  and relatively young convergent margins (no older than the early Cenozoic19). Geophysical and geological observations and numerical and analog modeling studies have shown that slab-tearing directly influence along-strike subduction morphology and related mantle flow patterns10. Geological consequences of this process include acceleration of trench retreat20, creation of slab gaps and consequent mantle flow21, anomalous arc-backarc magmatism10, thermal perturbations22, and upper-plate segmentation and extension13. Slab gaps created by slab tears differ from those produced by subduction of spreading centers, which result from continued sublithospheric divergence of oceanic plate boundaries (i.e. not slab rupture) leaving a geological fingerprint widely detected in ancient settings. This process is commonly identified by its unique tectonomagmatic record associated with anomalous magmatism and hydrothermal activity in the forearc, a transient arc shut-off in the orogenic sector and extensive alkalic and/or tholeiitic OIB plateau basalts in the backarc area23.In contrast to horizontal tearing leading to slab break-off, often invoked to explain ancient post-collisional tectonomagmatic events26, opens new possibilities for understanding complex geodynamic processes at ancient convergent settings. We build on this key finding to track a major slab tear event beneath Gondwana during the Mesozoic  along the southwestern active margin of Gondwana  to analyze mantle metasomatism in different geodynamic settings along the Tyrrhenian Sea and Apennine belt. Variable La/Nb values were found in mixed slab tear-arc domains and slab break-off geodynamic settings. In contrast, low values were associated with asthenospheric upwelling through vertical slab tears producing a lesser degree of subduction-related mantle metasomatism in the arc2.To test the influence of the development of a slab gap in the magmatism of the Neuquén basin, we compare geochemical data from synextensional volcanic rocks of the Pre-Cuyano deposits57 for discerning a slab break-off environment  do not indicate a dominant role for this process in the Pre-Cuyano synextensional magmatism  mainly ranging from 1.05 to 4.98. The samples plot across different possible geodynamic setting (see methods section for details) Fig. . We obseBased on model predictions of sub-vertical slab gaps and observations in current settings, we suggest that creation of this discontinuity could have directly influenced the dynamics of the southwestern Gondwana margin. This process would have triggered mantle flow as a combination of toroidal flow through the slab gap and upwelling induced by the contemporaneous slab steepening north of 41°S, ultimately leading to accelerated rollback, a localized arc disruption and intraplate extension in the Neuquén and Colorado basins  do not tend to develop slab window opening59.As slab-tear events and ridge subduction share similar tomographic signatures and geological effects associated with arc shut-off and anomalous magmatism in the backarc area16. An alternative explanation is the subduction of an aseismic ridge5 or oceanic plateau60; however, there is no geological evidence of accreted seamounts that would support this past interaction61.Another potential mechanism is the interaction with an oceanic fracture. However, recognizing the past existence of these features is difficult, unless a remnant of the fracture zone is still preserved onboard the oceanic plate, which is not the case here. Besides, oceanic fractures do not always result directly in a slab rupture; instead, these mostly act as plate weaknesses that are eventually exploited by processes that induce slab rupture62  from Carboniferous to Jurassic plutonic and volcanic rocks with a subduction-related geochemical signature along southern South America and the Antarctic Peninsula. For clarity in our time-space diagrams, we plotted magmatic stages immediately before the arc shifting stages. Hence, arc position north of 41°S starts in the Carboniferous, while in the south it starts in the Permian. Geochronologic data and arc distances used in the space-time diagrams in Fig. The position from the trench of the Carboniferous-Permian to Middle Jurassic magmatic arc in southwestern Gondwana as plotted in Fig. et al. for the different Italian magmatic environments linked to slab-tear, mixed slab-tear with subduction, slab break-off and undisturbed arc. In Fig. et al. do not explain why slab break-off and slab tear yield different La/Nb ratios. In this regard, numerical modeling and geochemical studies indicate that these two processes result in contrasting dynamics in the subduction system, mantle flow pattern, and related mixing of the mantle. In slab break-off settings, dehydration and melting of the tip of the detaching slab is more likely the main cause for magmatism57, which may explain the more variable La/Nb ratio2 in this setting. On the other hand, in slab-tearing settings the amount of mantle material exchanged between the arc and sub-slab mantle is more significant than in slab break-off events67, which probably explains the more marked asthenospheric signature in the latter setting2.We extracted La/Nb ratios from Rosenbaum In our plot, we differentiate samples with basic to intermediate composition to provide a closer idea of the magma source, as isotopic data from the Pre-Cuyano synrift units is limited. Geochemistry data used in the Nb vs. La/Nb diagram in Fig. Tomotectonic and geochemical analysesDataset 2Dataset 1


As the first line of host defense, the innate immune system utilizes germline-encoded receptors named pattern-recognition receptors (PRRs) to detect invading pathogens. PRRs recognize conserved molecular structures of pathogens known as pathogen-associated molecular patterns (PAMPs) to initiate immune responses that counteract pathogen infection. The immunostimulatory feature of exogenous nucleic acids, such as viral DNA and RNA, has been known for more than half a century, but the mechanism by which they function as an immune stimulant remained unclear for a long time. The past two decades have witnessed tremendous progress in understanding the signaling mechanisms of innate immune networks and established the retinoic acid inducible gene-I (RIG-I)/melanoma differentiation associated gene 5 (MDA5)–mitochondrial antiviral-signaling protein (MAVS) axis and cyclic GMP-AMP synthase (cGAS)–stimulator of interferon genes (STING) axis as the major sensing pathways for cytosolic RNA and DNA, respectively . HowevercGAS binds to cytosolic double-stranded DNA (dsDNA) from various sources, including bacteria, DNA viruses, and retroviruses, in a sequence-independent but length-dependent manner , 2. FollThe question of whether cGAS and STING are also engaged in antiviral responses to RNA viruses has been asked since the very beginning. A quick answer to this question would be yes because studies have shown that deficiency of cGAS or STING in cells or mice greatly facilitated replication of several RNA viruses, such as vesicular stomatitis virus (VSV), Sendai virus (SeV), dengue virus (DENV), and West Nile virus (WNV) –11. HoweSTING was found to interact with RIG-I and MAVS , 16, whiDNA virus infection leads to quick ubiquitination and phosphorylation of STING, which is required for its trafficking and subsequent degradation in the perinuclear lysosomal compartment , 20. HowConsidering the central role of cGAS and STING in the innate DNA sensing pathway, it’s not surprising to find that many DNA viruses have evolved effective strategies to antagonize the function of cGAS and STING in order to facilitate their replication in host cells  Fig 1)..Fig 1). Emerging evidence also indicates that RNA viruses have developed their own strategies to antagonize cGAS and STING activity . The nonAlthough it is clear that cGAS is essential in cytosolic recognition of DNA viruses, its role in restricting RNA virus infection is still inconclusive. On the other hand, it is now apparent that STING is required for host responses against both DNA and RNA viruses. Studies of mechanisms employed by STING to restrict RNA virus infection have only just begun. The study which unraveled STING’s function in protein translation inhibition has shed light on our understanding of how the cGAS–STING pathway functions in RNA virus restriction . However


Dissecting cellular differentiation hierarchies in the mammary gland is a prerequisite for understanding both normal development and malignant transformation during tumorigenesis and tumor cell-of-origin. To achieve these goals, several recent papers utilized single cell RNA-seq and lineage tracing to improve our understanding of the composition of the mammary epithelium at different developmental stages. It is a unique organ that completes its development and differentiation during puberty and adulthood, orchestrated by ovarian and pituitary hormones, to fulfill its main function of milk production during lactation. The mammary epithelium consists of two differentiated cell types organized into two cell layers, an inner layer of luminal epithelial and an outer layer of myoepithelial cells in direct contact with the basement membrane  to the mammary gland in human5 and in mice7, while another study used lineage tracing to follow the fate of Blimp1+ stem cells8.The mammary gland is a branching epithelial structure composed of ducts and alveoliane Fig. . FunctioDefining the cellular composition of a solid organ is a challenging task requiring optimized methods to ensure reproducibility. First, the tissue has to be dissociated into single cells fairly rapidly, to minimize perturbation of cellular features. Second, the accurate detection of minor subpopulations, present as low as 1 in a 1000 cells frequency, requires the portrayal of thousands of cells. The characterization of the mammary gland is even more challenging as it undergoes dramatic changes during postnatal development and more subtle variations during menstrual/estrus cycles in response to ovarian and pituitary hormones.7 characterized the mouse mammary epithelium at the single cell level at four developmental stages, pre-puberty, mid-puberty, adult virgin, mid-pregnant, and also at different phases of the estrus cycle. Similarly, Bach et al.6 profiled mammary epithelial cells (MECs) in mice at four developmental stages: adult virgin, mid-gestation pregnant, day 6 lactating, and 11 days post involution. The two groups have largely overlapping, but also some seemingly discordant findings, potentially due to differences in cell purification and data analysis procedures. Pal et al. concluded that basal gene expression occurs throughout all developmental stages, with a particularly distinct and homogeneous profile in the pre-pubertal gland, whereas luminal expression is only detected at puberty through adulthood. This suggests that there may be a hormone-responsive luminal progenitor that subsequently gives rise to both hormone-responsive and non-responsive luminal epithelial cells or that a subset of basal cells responds to ovarian hormones and generates luminal progeny. The authors also identified one basal, and several distinct luminal cellular expression clusters; some were expected based on prior studies like mature luminal (ML) cells and luminal progenitors (LP), while others were novel like a luminal intermediate (a transit population between ML and LP cells), and a mixed-lineage subpopulation expressing both luminal and basal markers.To tackle these challenges, Pal et al.6 reached somewhat differing conclusions finding that mammary epithelial cells display a differentiation continuum rather than clearly defined clusters, suggesting that a common luminal progenitor cell gives rise to intermediate, restricted alveolar, and hormone-sensitive progenitors. The authors divided the cells into 11 luminal and 4 basal clusters (based on the expression of known marker genes), proposing a putative differentiation tree. The basal cluster was further subdivided into differentiated myoepithelial, and stem cell-like basal, and Procr+ cells, while the luminal compartment was classified into hormone-sensing cells  and cells expressing low levels of hormone receptors. Using diffusion maps, the authors reconstructed the differentiation states in the mammary gland showing luminal and basal clusters clearly segregated but with states transitioning between the secretary alveolar lineage and hormone-sensing luminal cells implying origination from the same progenitor. The authors provide an interactive online presentation of the expression data, making it available to the community and allowing the easy interrogation of the expression of particular genes of interest. Nevertheless, a limitation of this study is that too few basal cells were captured such as to be able to define a hierarchy. Contrary to Pal et al.7, no mixed-lineage population with both basal and luminal markers was detected, either because this population is not present, or as a technical consequence of the different sorting and data analysis strategies employed.Bach et al.9 focused on the normal human breast and analyzed breast epithelial cells isolated from reduction mammoplasties of healthy adult pre-menopausal women. Cells were sorted prior to RNA-seq based on known cell surface markers into luminal and basal subsets . The analysis identified three main clusters essentially in line with previously described groups of basal/mammary stem cells (CD49fhiEPCAM−), luminal progenitors (L1:CD49f+EPCAM+), and mature luminal (L2:CD49f−EPCAM+) cells. Pseudotemporal reconstruction of differentiation trajectories produced one continuous lineage connecting the basal and the two differentiated luminal branches. The authors further subdivided these three main clusters into subclusters showing some inter-individual variability, which were interpreted as cell states, not distinct cell types. The three basal subclusters were characterized as high in inflammatory mediators, myoepithelial cell markers, and specific keratins. An intriguing finding of this study is the detection of replicating cells in all three main clusters  based on the expression of proliferation markers in the scRNA-seq data (confirmed by immunofluorescence). This finding implies that each cluster may be maintained by its own stem/progenitor cell population with proliferative capacity.Nguyen et al.8 applied lineage-tracking to examine the relationship between Blimp1-expressing cells and previously described luminal progenitor subpopulations. Blimp1 is a repressor that governs cell fate decisions in embryonic and adult tissues and is robustly induced in alveolar cells during pregnancy10. The authors identified very rare Blimp1+ lineage-restricted, unipotent luminal progenitor cells that maintain their identity through adult life, display extensive self-renewal capacity, and play essential roles in duct formation, homoeostasis and alveologenesis during pregnancy. Specifically, Blimp1+ cells give rise to highly proliferative Elf5+ERα-PR−luminal progenitors that expand during pregnancy to promote alveologenesis, and the Blimp1+ cell population present during pregnancy is derived exclusively from the rare Blimp1+ cells originally labelled during puberty. The experiments presented show no evidence that Blimp1+ progenitor cells emerge de novo in the post-natal gland, but rather that Blimp1+ cells are maintained as a constant pool of long-lived alveolar progenitors throughout pregnancy-associated mammary gland remodeling. These data also suggest that hormone-responsive luminal epithelial and hormone receptor-negative alveolar cells represent different lineages, which is in line with results of prior lineage tracking studies using different markers to follow the cells13.Elias et al.14, which profiled all cells of the mammary gland without any purification, or be used by the Human Cell Atlas15 initiative to provide informative experimental design frameworks. Overall both the scRNA-seq and the lineage tracking data support a model whereby in the post-pubertal mammary gland differentiated luminal epithelial and myoepithelial cells are maintained by lineage-restricted progenitors and bipotential cells are present through embryonic development up to puberty (Fig. The studies briefly discussed here represent valuable contributions to our understanding of the cellular composition of the mammary epithelium at different reproductive stages in mice and human, providing a useful resource easily accessible to the public. Since all three scRNA-seq papers used different types of sorting for the cell populations profiled and focused on epithelial cells, it would be important to compare these results to that of the Mouse Cell Atlasrty Fig. . Lineage


Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes. To elucidate the molecular basis of cancer we examine the variation and dynamics characterizing the flow of information from epigenome to the transcriptome and proteome. Conducting the first genome wide analysis of epigenome-proteome associations, we present a MethylMix analysis that leverages protein abundance data taking advantage of recent high-throughput proteomic data generated using mass-spectrometry technology to elucidate the role of DNA methylation in cancer. By integrating across molecular data types, we confirm the benefit of using protein abundance data to provide additional insights into pathways and processes involved in oncogenesis and how they manifest as clinical phenotypes. Applying our method across three large cancer cohorts including breast cancer, ovarian cancer and colorectal cancer, MethylMix identifies key genes and describes molecular features and subtypes in these cancers. Genomic characterization can elucidate underlying biology, disease etiology and reveal biomarkers of cancer development and progression; however, each molecular feature is susceptible to different sources of biological and technical measurement noise and provides only one view on the cell state. Therefore, comprehensive studies are needed to understand the molecular basis of disease. Toward this end a multi-institutional consortium, The Cancer Genome Atlas (TCGA), has extensively characterized numerous cancer sites producing genome wide data for mutations, copy number alterations (CNA), RNA expression, microRNA expression, and DNA methylation –5. As paTo best leverage this new technology comparative analysis between protein abundance and RNA expression can highlight factors influencing concordance and inform how to best interpret proteomic data . For exaThe proteome represents the final link from genotype to molecular phenotype, so proteins are of special importance among molecular features and likely provide a more accurate depiction of cell state; this enhanced view on disease can be leveraged to assess functional effects of upstream aberrations, such as epigenetic modifications. Multi-level epigenetic features such as DNA methylation and histone modification work in concert to regulate gene expression. DNA-methylation, the covalent addition of methyl groups to CpG dinucleotides to form 5-methylcytosine (5mC), is catalyzed by DNA methyltransferases, and is influenced by both environmental and hereditary factors . PreviouTo elucidate the role of DNA methylation in disease, our goal is to investigate whether linking proteomic data with DNA methylation data identifies key genes, describes molecular features and subtypes in cancer. Previously we presented MethylMix an algorithm that formalizes the identification of DNA methylation driver genes using a model-based approach –23. RecoWe applied MethylMix to identify differentially methylated genes in two ways: once with gene expression data defined as MethylMix-GE and once with protein abundance data defined as MethylMix-PA –22 acrosFor each cohort both models identify genes that are 1) differentially methylated when compared to normal adjacent tissue and 2) functionally predictive of downstream effects at the level of RNA expression in the case of MethylMix-GE or protein abundance in the case of MethylMix-PA . Among aFor each cancer cohort using protein abundance data also identifies a few unique driver genes, the majority of which have documented roles in carcinogenesis. Explanative mechanisms by which the effect of DNA methylation may be undetected at the RNA level but functional at the protein level are further addressed below in the discussion.FSTL1, an autoantigen that promotes immune response. This candidate tumor suppressor, FSTL1, has also been shown to mediate tumor immune evasion in nasopharyngeal cancer through hyper-methylation silencing [DHX40 which has an unclear link to cancer; although it is of note that RNA splicing proteins—like DHX40 –are highly stable, perhaps explaining the particularly stronger effect of DNA methylation on protein abundance than mRNA [In breast cancer we discovered 19 novel differentially methylated genes of diverse biological functions. MethylMix-PA identified hyper-methylation of ilencing . MethylMhan mRNA   status . SimilarAmong colorectal samples we are able to confirm the CpG island methylator phenotype (CIMP) . ClusterExamining subtypes in ovarian cancer our MethylMix-PA clusters agree well with molecular subtypes and are significantly correlated . ClusterEpigenetic aberrations contribute to oncogenesis, where DNA hypermethylation inactivates tumor suppressor genes, while hypomethylation is known to promote genomic instability and activate oncogenes ,20. TherEHF, FSTL1, PTPRC, S100A9, LTF, EVL, and TSTA3. Importantly, in all these cases the type of DNA methylation is consistent with gene function, where known tumor-suppressors are hyper-methylated and oncogenes are hypo-methylated at regions where DNA methylation negatively regulates transcription.MethylMix-PA identifies oncogenes and tumor suppressors and—with the exception of a few genes—returns a subset of MethylMix identifications, where often the effect of DNA Methylation does not propagate to the proteome . In otheTaken together MethylMix-PA genes highlight important features in cancer related to tumor features and subtypes. MethylMix-PA genes also capture oncogenic biological processes based on enrichment analysis showing key aspects of cancer development such as processes related to EMT, immune function, and proliferative signaling . MethylMOverall MethylMix-PA shows practical utility for improving nominations of cancer driver genes and elucidating new mechanisms of cancer development missed by our previous model. More broadly our study supports using proteomic data to better understand how epigenetic deregulation promotes cancer. Similar approaches have been applied and found to potentially improve aspects of patient care. For example, a retrospective analysis of outcomes in an oncology trial for glioblastoma—which tested efficacy of different temozolomide regiments—found that updating the clustering model to incorporate MGMT protein expression and c-MET protein abundance provided better separation of overall survival prognostic groups than incorporating MGMT promoter methylation alone . These fAlthough milestone initiatives like TCGA and CPTAC provide valuable date for the acceleration of discovery and research in cancer, we acknowledge the limitations of this study and further work required. A barrier to translation, the number of specimens used here is insufficient to draw conclusive clinical correlations and require replication of these results by independent studies. Importantly molecular measurements used here are also subject to sources of technical and biological bias. For example, it is known that bulk measurements obscure the complex nature of tumor microenvironment which includes many cell types including vascular, lymphatic, and immune cells. This confounding effect is compounded considering that each molecular feature was measured using different tumor fragments, which may have very different cellular compositions due to intra-tumor heterogeneity. Additionally, we recognize further characterization of genome wide proteomic studies is required to fully understand possible biases, such as worse detection of highly hydrophobic and hydrophilic peptides, or low-abundance peptides co-eluting with very high-abundance peptide . MoreoveThe complex nature of disease development and interplay between interacting biological aberrations—genetic, epigenetic, somatic or germline—often makes it difficult to elucidate causal mechanisms of cancer development. Furthermore, there is still much work in multi-omics to elucidate causal flows of information influencing cellular physiology and pathology and to discriminate how separate phenomena are linked to create cancer ,5,42,44.All data used in this study is third party data, and is available from the following articles ,10,45–47Molecular data were produced from tissue bio-specimens from three cancer cohorts: breast invasive carcinoma (BRCA), colorectal adenocarcinoma (COADREAD), and ovarian serous cystadenocarcinoma (OV) .CpG site methylation levels/percentages were measured using Illumina Infinium Human Methylation 27k and 450k BeadChip Platforms . We limiWe used transcriptomic data in MethylMix produced by RNA sequencing technology –47. We lProteomic data used in MethylMix-PA was provided by CPTAC ,9,10. PaThe BRCA and OV workflows used iTRAQ-labeling to increase throughput, where 3 patient samples are isotopically labelled and analyzed against a common reference standard and describe relative ion intensities. Quantities are recorded after taking the log2 ratio of the abundances. Alternatively, measurement of COADREAD samples used label free MS technology and are reported as absolute counts, which were transformed to relative quantities by taking the log2 of quantile normalized values using the limma R package . OV sampTo remove samples compromised by protein degradation we filtered samples using the QC method described by Mertins et al. : we calcAs described earlier methylation levels are recorded as beta values or values ranging from 0 to 1 representing the percentage of methylation and therefore gene values across all samples are beta distributed. MethylMix identifies subgroups of patients with a distinct methylation pattern or state by finding the beta mixture model with the number of components that best describe the data. To map samples to subgroups we iteratively add components requiring that each additional component improve the Bayesian Information Criterion (BIC) to avoid overfitting. To define the most descriptive subgroups we include methylation measurements across all samples, however our model integrates epigenetic data with proteomic and transcriptomic data using only the subset of these samples with available matched data .To identify differentially methylated CpG clusters we compare the mean methylation level—the mean value of the beta mixture component—to the mean methylation level of normal samples. To measure if an observed difference is significant we perform a Wilcoxon rank sum test with a Q-value cutoff of 0.05, using both p-value multiple testing correction with False Discover Rate (FDR). As an additional measure, we require a minimum difference of 0.10 based on the platform sensitivity . If signNext, we filter our set of genes, requiring that genes be not only differentially methylated when compared to normal but also predictive of gene expression or protein abundance. Hyper-methylation should lower gene expression and corresponding protein abundance when compared to the normally methylated samples, therefore we only accept genes that have a negative correlation between methylation level and downstream gene products. Note this assumption is only explanatory of methylation at promoter regions and does not necessarily apply to methylation at the gene-body or 3’ and 5’ untranslated regions (UTRs). To assess the likelihood that methylation events are functional, MethylMix-GE uses the relationship between methylation and gene expression, whereas MethylMix-PA examines the effect on protein abundance. In both cases, we perform a linear regression between methylation levels and RNA expression or protein abundance data respectively. We use the R-square statistic to estimate the magnitude of the correlation and used cutoffs at R-square greater than 0.05 and a Q-value of 0.01 using FDR multiple testing correction.Applying the procedures outlined above for MethylMix-GE and MethylMix-PA each produces a list of candidate cancer drivers (referenced as MethylMix-GE and MethylMix-PA genes) and a corresponding matrix of DM-values for identified CpG clusters across all samples. All MethylMix genes have the following statistical properties: (i) a DNA methylation difference based on the beta mixture model that is significantly different from normal that is > = 0.1 based on the platform sensitivity , and  terms in MethylMix-GE and MethylMix-PA genes. This analysis was implemented using the PANTHER Classification System’s statistical overrepresentation tool  with theWith the matrices of DM values for our CpG clusters we performed consensus clustering to identify robust groupings of patients based on epigenetic signatures . Our anaWe compared MethylMix-GE and MethylMix-PA genes by investigating their enrichment in genes related to disease progression. We used correlation of gene expression with cancer stage and tumor size to identify potential genes capturing disease progression. We took the spearman correlation between gene expression levels and these clinical variables using all available samples. We selected genes using a p-value cutoff of 0.05 and biased for genes with greater likelihood of relevance by taking top 50th quantile in sample variance. Next, we filtered for only relationships that can be explained by methylation, such that genes identified as hyper-methylated in cancer tissue were required to show a negative correlation between gene expression and disease progression (tumor-suppressor genes) and hypo-methylated genes positively correlated (oncogenes). To assess each models’ likelihood in picking up genes related to disease progression we examined the overlap between these genes and the MethylMix-PA and MethylMix-GE genes, using Fisher’s exact test to evaluate significance.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


A 59-year-old male who presented with a nonspecific headache at the vertex, resembling retrobulbar optic neuritis, was treated as such but did not show any improvement. Although optic nerve compression from sphenoid mucocele was finally discovered, the delayed diagnosis and improper treatment led to a permanent visual loss. Optic neuritis could be caused by a common problem, “mucocele/sinusitis,” but might be easily overlooked in general practice. Rhinogenic optic neuropathy should, therefore, be considered in every case of optic neuritis whenever atypical presentation occurs or is unresponsive to high-dose steroid treatment. A 59-year-old healthy male presented with a nonspecific headache at the vertex followed by sudden painless visual loss of the right eye. Ophthalmologic examination showed visual acuity of no light projection with a relative afferent pupillary defect. No diplopia was found. The intraocular pressure, optic disc appearance, and other neurological assessments were normal. Routine blood tests and echocardiogram were unremarkable. Magnetic resonance imaging (MRI) of the brain and orbit performed 2 days after the headache incident revealed minimal fluid surrounding the optic nerve sheath suggestive of mild swelling of the optic nerve sheath papilledema. A follow-up computerized tomography (CT) of the brain 6 days after the onset was unremarkable. With the initial diagnosis of right optic neuritis, he was given intravenous dexamethasone 96 mg/day for 2 days and then switched to oral prednisolone for 3 days.Because of no improvement within one month, he sought for the second opinion, which revealed persistently no light perception of the right eye with the temporal pallor of the optic disc but normal macula and vascular trunks. Complete blood count, lipid panel, hepatitis profile, anti-HIV, thyroid function, erythrocyte sedimentation rate, C-reactive protein level, autoimmune screening tests, and AQP4-antibody were all unremarkable. Intravenous methylprednisolone 1 g/day for 3 days was given but no improvement was observed.The previous MRI brain and orbit images were reviewed . Axial STwo weeks later, another MRI of the brain and orbit demonstrated focal hyposignal T1/hypersignal T2 retention cyst, adhering to the right lateral sphenoidal wall, around the right optic nerve canal with mild surrounding enhancement, along the mucosa and around the optic nerve canal, and focal mild swelling with ill-defined hypersignal T2 change and faint enhancement of the corresponding intracanalicular portion of the right optic nerve.The multidetector computed tomography (MDCT) of the paranasal sinuses illustrated a nonenhanced soft tissue density lesion in the right sphenoid sinus and no visualization of the superior wall of the right sphenoid sinus, adjacent to the right optic canal, as well as a wall that became thinner by either pressure effect of right sphenoid lesion or bony destruction . There iRight posterior ethmoidectomy, sphenoidotomy, and draining the content in the Onodi cell  were perThe Onodi cell or sphenoethmoidal cell, first described by Adolf Onodi in 1904 –6. IdentOur report showed an immunocompetent male patient with atypical unilateral painless retrobulbar neuritis with rapid onset but unresponsive to high-dose steroids. MRI of the orbit and CT of paranasal sinus showed soft tissue density lesion with a very thinning wall of the right sphenoid lesion suggestive of bony destruction of the same side of the suspected ON. The surgical drainage of the right Onodi cell revealed noninfectious discharge; the compression optic neuropathy associated with mucoceles with or without direct spreading from suppurative sinusitis should, therefore, be the pathophysiologic causes.Two cases were reported from Spain : (1) 21-Even the rare occurrence of rhinogenic optic neuritis, sinusitis of the Onodi cell causing optic neuropathy, should be considered as a possible cause in all cases of atypical optic neuritis. The recommended imaging to analyze the sphenoid sinus and its surrounding structures is the CT paranasal sinuses including axial, coronal, sagittal, and sagittal oblique  views. Proper treatments with antibiotics and surgical intervention at the right time are crucial in order to prevent the irreversible optic nerve damage.Rhinogenic optic neuritis should be one of the differential diagnoses in patient presenting with atypical neuritis.Optic neuritis could be caused by a common problem “mucocele/sinusitis” but might be easily overlooked in general practice.Delayed diagnosis and improper treatment could lead to permanent visual loss.The appropriate imaging should be done with fine cut or appropriate sequences if sinusitis of the Onodi cell causing optic neuropathy is suspected.Rhinogenic optic neuropathy should be considered in every case of optic neuritis whenever atypical presentation occurs or is unresponsive to high-dose steroid treatment.


Cellular metabolism is a dynamic system in which metabolic nutrients are being constantly consumed and catabolized to generate energy Fig. a. ProlifA major complication in cancer metabolic research is that, unlike the concentration of mRNA, proteins, and metabolites, metabolic flux, which reflects the cellular metabolic phenotype, is not a directly measurable quantity Fig. b. HoweveThe most direct approach for interrogating intracellular metabolic flux in cancer cells is isotope tracing –10. ThisAnother commonly used flux inference approach is COnstraint-Based Reconstruction and Analysis (COBRA), enabling flux assessment through genome-scale metabolic networks. COBRA has traditionally been utilized to model microbial metabolism for biotechnological and bioengineering purposes –15. MoreHere, we provide a brief overview of how COBRA and 13C-MFA work  for which a simulated isotopic labeling pattern of metabolites optimally matches experimental measurements [13CFlux2 [13C-MFA calculations require a metabolic network model consisting of a set of biochemical reactions, with information on the mapping of atoms between the substrate and product metabolites  . When an13C-MFA has been frequently used for investigating cellular metabolic rewiring in response to genetic mutations in cancer, revealing the link between signaling circuitry and cancer metabolism. For example, oncogenic activations of Ras , 44, AktEmploying 13C-MFA to probe flux alterations following genetic silencing of metabolic enzymes provided means to explore enzyme importance and mechanisms: depletion of MTHFD1L, an enzyme in the mitochondrial folate cycle that produces formate, was shown to repress mitochondrial one-carbon metabolism and lead to reduced cancer invasion . DeletioMetabolic rewiring due to non-genetic factors such as the tumor microenvironment has also been investigated utilizing 13C-MFA. Hypoxia promotes tumor cell reliance on reductive glutamine metabolism for lipogenesis , 53 and Identifying and characterizing metabolic rewiring with 13C-MFA in specific cancer cells not only contributes to our understanding of metabolic regulation but can also lead to the discovery of novel targets for anticancer drugs. For example, applied to studying the effect of PHGDH amplification in breast cancer cells, 13C-MFA revealed that de novo serine biosynthesis is responsible for up to half of the total anaplerotic flux of glutamine into the TCA cycle, suggesting that targeting the serine synthesis pathway may be therapeutically valuable in breast cancers with elevated PHGDH expression . LikewisA major limitation of flux inference via isotope tracing coupled with 13C-MFA regards the inference of metabolic flux in specific organelles H). Metabolite uptake and secretion rates across the NCI-60 cancer cell lines collection were used to model fluxes in these cells, exploring different metabolic strategies used by cells to generate energy and redox cofactors and explaining the abilities of different cell lines to support respiration [Several studies have utilized COBRA to explore the production and consumption of central energy (ATP) and redox cofactors (NAD(P)piration . An analpiration . The potpiration . Fan et piration  demonstrSeveral studies used COBRA to explore overflow metabolism in cancer—i.e., excess consumption and non-efficient utilization of metabolic nutrients, including for glucose , glutamiWhile measured metabolite uptake and secretion rates in a given cell line provide readily usable constraints for flux analysis by COBRA, utilizing abundant transcriptomic, proteomics, and metabolomics datasets (available for large collections of cell lines) as input for flux prediction is highly challenging. This is due to metabolic flux being regulated at multiple levels and depending on the concentration of the active enzyme , the concentration of reactants and allosteric regulators, and complex enzyme kinetic mechanisms . Numerous computational techniques have been proposed to generate metabolic network models for specific tumors . Specifically, these methods aim to identify a subset of enzymes from a genome-scale metabolic network that is expected to be active based on the mRNA, protein, and metabolite concentrations, enzyme-specific biochemical or genetic measurements, and known cell line-specific metabolic functions. Various methods such as GIMME , iMAT 8, 90, MBAPredictions of cell line-specific gene essentiality derived with cell line-specific metabolic network models were shown to correlate significantly with measured growth response to CRISPR-based gene knockouts , achieviWhile predicting cell line-specific response to genetic silencing or chemical inhibition is technically difficult, identifying enzymes whose inhibition selectively affects cancer cells while sparing normal cells is even more challenging. This was previously addressed by searching for enzymes whose inhibition would prevent cell proliferation, while not affecting basic metabolic functionality such as ATP production . AdditioAnother appealing approach for identifying selective anti-cancer metabolic targets is based on the concept of synthetic lethality . SpecifiIsotope tracing is widely used to probe intracellular metabolic activities in cancer cells. However, most studies still rely on manual assessment of measured metabolite isotopic labeling to qualitatively infer metabolic activities , while 1While both 13C-MFA and COBRA were demonstrated to be highly useful in cancer metabolic research, there are inherent limitations and complications with each approach. We provide a brief comparison of the two modeling approaches in terms of scope, required experimental data, and possible output . Analyzing genome-scale metabolic networks enables COBRA to reveal non-canonical pathways with an important contribution to some cancer cells. However, it can falsely predict flux through enzymatic reactions that were included in the model based on weak biochemical evidence. Further work by the metabolic modeling community is needed to further refine and extend the existing genome-scale metabolic network reconstructions based on accumulating knowledge of enzymatic activities in human cells. An important future challenge for COBRA methods is improving the reliability of biochemical enzymatic activities that are included in the model. With 13C-MFA, on the other hand, it is challenging to determine the boundaries of the analyzed metabolic system, while reactions that are left out of the model could potentially bias flux estimation. Applying 13C-MFA for larger scale networks is an experimentally challenging task which requires the measurement of metabolite isotopic labeling outside the central metabolism. Furthermore, it is highly computationally challenging to apply 13C-MFA for genome-scale networks, though some attempts in this direction have been made , 111. FuWhile both 13C-MFA and COBRA rely on measurements of metabolite uptake and secretion rates for flux estimation, 13C-MFA that relies on isotope tracing measurements is more experimentally demanding. Omics data, and specifically genomics, transcriptomics, proteomics, and metabolomics, can be utilized as input by COBRA methods, though this typically relies on simplified heuristics that do not account for the complexity of regulatory and enzyme kinetic mechanisms. A major open challenge in COBRA is developing improved methods for utilizing quantitative proteomics and metabolomics data for flux inference via enzyme-mechanistic models accounting for kinetic and thermodynamic considerations.In terms of the ability to uniquely infer flux, this is typically possible with 13C-MFA applied to analyze flux in central metabolism, rigorously evaluating flux confidence intervals. With COBRA, over-simplified optimality assumptions are typically employed to derive unique fluxes . SubcelIn terms of common applications, both COBRA and 13C-MFA enable the inference of flux in cells based on measurements performed under a specific genetic and cell culture condition. Derived flux maps by these approaches provide a holistic understanding of metabolic processes, while changes in flux due to genetic or environmental perturbations provide means to examine metabolic regulation. The identification of induced flux through specific enzymes in cancer cells reveals the increased dependence on metabolic transformations that could be therapeutically targeted. Note that unlike 13C-MFA, COBRA can further address the more challenging task of predicting how metabolic flux will be rewired in response to genetic or pharmacological interventions in silico, providing means to investigate potential anti-cancer drug targets.Overall, COBRA and 13C-MFA provide complementary capabilities for understanding the rewiring of metabolic flux in cancer. While 13C-MFA analyzes isotopic tracing measurements to provide an accurate quantitative view of flux through central metabolic pathways, COBRA analyzes flux through genome-scale metabolic networks based on physicochemical constraints and ‘omics’ data integration. In some cases, isotope tracing is used to quantify specific fluxes in human tissues under different physiological conditions, while these are used as inputs for COBRA-based flux analysis on a genome scale , 113. In