SERPINE1 and inactivation of TP53-family tumor suppressor gene TP73.Many biological datasets are high-dimensional yet manifest an underlying order. In this paper, we describe an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses influence between the variables of the given set. The technique involves network geometry employing the Wasserstein distance, global spectral analysis in the form of diffusion maps, and topological data analysis using the Mapper algorithm. The prototypical application is to gene expression profiles obtained from RNA-Seq experiments on a collection of tissue samples, considering only genes whose protein products participate in a known pathway or network of interest. Employing the technique, we discern several coherent states or signatures displayed by the gene expression profiles of the sarcomas in the Cancer Genome Atlas along the TP53 (p53) signaling network. The signatures substantially recover the leiomyosarcoma, dedifferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, and they also include a new signature defined by activation and inactivation of about a dozen genes, including activation of serine endopeptidase inhibitor  We apply the technique to gene expression profiles along gene sets participating in known pathways. We discern several coherent states or signatures displayed by the gene expression profiles of The Cancer Genome Atlas (TCGA) sarcoma project along the TP53 signaling network. The signatures substantially recover the leiomyosarcoma, dedifferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, and they also include a new signature defined by activation and inactivation of about a dozen genes, including activation of serine endopeptidase inhibitor SERPINE1 and inactivation of TP53-family tumor suppressor gene TP73.Modern biological investigations often result in dense, high-dimensional datasets describing genes, proteins, mutations, or other variables. A near universal problem arises as the dimensionality of the data grows: how can the data be investigated in a relatively unbiased manner, to expose underlying clusters and relational structure? To date, there is a lack of robust techniques for exposing the structure of biological data in an unbiased, agnostic fashion. Biological relevance is maintained in this work by considering known pathways as an underlying guide. The technique we describe involves network geometry via the Wasserstein distanceThe mechanisms that intervene between DNA sequence genotype and overall cell phenotype are complex, including the presence of transcription factors, the chemistry of the cell microenvironment, and epigenetic factors like phosphorylation and methylation. We focus on the determination of transcriptomic molecular phenotypes. The simplest molecular phenotypes are defined by single marker genes, like the estrogen receptor (ER), progesterone receptor (PR), or human epidermal growth factor receptor 2 (HER2/ERBB2) status of breast carcinomas. In general a comparatively large number of genes must be considered simultaneously.Methods falling under the heading of Genome Wide Association Studies  or Gene Set Enrichment Analysis  take into account data concerning a large number of genes to ascertain statistical significance with respect to given known outcomes or endpoints such as disease states. In general, they do not attempt to discern coherent states in gene expression quantification profiles in an “unsupervised” manner. That is, these methods do not ascertain existing apparent molecular phenotypes, but rather impose or design molecular phenotypes specifically to serve as predictors for variables of ultimate interest like prognosis.5 employ degree 0 persistent homology towards the end of unsupervised analysis, leveraging the robustness of Topological Data Analysis (TDA) techniques for unsupervised clustering. One could also use various established unsupervised clustering algorithms such as hierarchical clustering or k-mean optimization methods, optionally preceded by dimensional reduction techniques like Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), or Multi-Dimensional Scaling (MDS). Note, however, that hierarchical clustering has the drawback that the output of the algorithm strongly underdetermines the usual heat map visual representation. Every branch of the hierarchy tree creates an ambiguity in the order in which the samples are displayed.Seemann, Shulman, and Gunaratne6 advocate “higher order” methods, e.g. degree 1 persistent homology, extracting 1-dimensional features in the space charted by the data points, roughly in order to take account of the relations between categories and not just the categories themselves. One major difficulty with this approach is that homology classes are defined by cycles; topological features which are not cycles, such as relative cycles or branches, are not detectable with existing tools . A second major difficulty is that homology classes do not have canonical representative cycles. This means that in theory an almost arbitrary subset of the points of a point cloud can appear along the path of a cycle belonging to an observed persistent 1-homology class. In other words, while persistent homologies are certainly evidence of important dataset-specific global features, there is an unsolved problem of interpretability of such features.From the topological point of view, however, any method within the clustering paradigm is order 0 in the sense that it summarizes a dataset in terms of a finite/discrete set of disjoint categories, a “space” of dimension 0. Lockwood and Krishnamoorthy9 calculate persistent 1-homologies in evolutionary/phylogenetic data, surmounting both of these difficulties simultaneously by interpreting the presence of non-trivial cycles (closed loops), and not the internal structure of their representative cycles per se, as an indication of the presence of genetic recombination events.Camara, Emmett, and Rabadanet al.4 in that we use the Mapper algorithm to map our point clouds onto summary spaces of dimension 1, graphs or networks. This algorithm can be regarded as a discrete version of the Morse-theoretic analysis of a smooth manifold with respect to a height function . Nicolau et al. heavily de-sparsify the point clouds, in order to avoid the normal preprocessing step of dimensional reduction , and employ a carefully designed deviation-from-normal filter function in accordance with what they call the Progression Analysis of Disease paradigm. We take a slightly different tack: First, we perform a biologically-motivated intermediate-scale dimensional reduction by considering only those genes participating in well-known pathways (we use the Kyoto Encyclopedia of Genes and Genomes). Next, we replace the ordinary Euclidean distance metric between gene expression profiles with alternative metrics, especially a version of the Wasserstein 1-metric which takes account of curated knowledge of the network structure linking the genes (coordinates). Then we employ the dimensional reduction and analysis technique of diffusion maps3 to regularize the point cloud with respect to intrinsic or characteristic global geometry. We have found that this process results in datasets with favorable properties for the application of Mapper and the interpretation of its resultant graph summaries.We largely follow Nicolau G relating the N genes which label the coordinates. Optionally, we include an additional control dataset We take as our primary input a gene expression quantification sample set, as a point cloud Normalize the values ofSRestrict/projectSto the genes appearing in the gene networkGCalculate a network-based distance metric between samplesEvaluate a diffusion mapPerform the Mapper algorithm on the re-mappedExtract and process the state graphPlot heat maps and discern coherent statesThe output is a list of coherent states or molecular phenotypes, characterized by activated, inactivated, and equivocally-activated genes. We now enumerate the steps of our pipeline. Details will be given in subsequent sections.S represent gene expression quantification, for example, FPKM (Fragments Per Kilobase Million) or TPM (Transcripts Per Kilobase) values as a result of a high-throughput sequencing pipeline. These values correspond roughly to the concentration of RNA transcripts in the tissue samples, typically across many cells for each sample (bulk sequencing) though sometimes for single cells.We must ensure that the values of N genes, we replace the values of S in the corresponding coordinate by a truncated translated z-score, μ and σ are the mean and standard deviation of the values for this coordinate and x is a typical value of this coordinate. The resulting values will be substantially normalized to lie on a scale from 0 to 1 with mean 0.5.Optionally, for comparison between genes and for the purposes of image-rendering, for each of the G whose nodes correspond to genes whose presence or absence constitutes participation in a coordinated function or process of interest, and whose edges represent the coordinating relationships. Omit the expression values for genes not participating in G. The networks we consider are the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways concerning cell cycle regulation, senescence, proliferation, apoptosis, and TP53 signaling.We select a network s ∈ S, define a probability distribution ps on the set of nodes of G by interpreting the values of s (divided by their sum) as a probability density function. Alternatively, use a distribution on the nodes which is the invariant measure for a Markov chain stochastic process inferred from the values of the sample s, in the manner of10. It can happen that G is disconnected into several components, with no edges/links between components, in which case one should define separate distributions for each component. Note that this disconnection may be either genuine or an indication of missing biological knowledge, so that a network-connection inference method may be useful. For each component c, calculate the Wasserstein 1-metric or Earth Mover’s Distance dc between each pair ps and ps′ with respect to the path-length metric on c . Intuitively, the Earth Mover’s Distance measures the total effort needed, in the best case scenario, to displace one distribution into another, taking account of the ground point-to-point distance. This distance is illustrated for networks in Fig. 1. We use the “direct sum” formula to amalgamate these distances across components c into a single distance for each pair of samples :For each sample 2.The Wasserstein 1-metric employed in this way is perhaps the simplest alternative to the standard Euclidean metric for which a network or pathway structure relating the coordinates is in some way taken into account. The principal benefit of this metric is that it greatly increases the distance between two samples in comparison with the Euclidean metric in case the main activity of one sample takes places in an area of the network very far from the area of main activity of the other sample. One conceivable disadvantage is that isolated changes to a given sample, say in the expression of a single gene, can have an outsized effect on the Wasserstein 1-distance of the displacement. We also caution that since the KEGG database networks are enriched with nodes for various compounds and macromolecules in addition to protein gene products, an analysis which considers only gene expressions will not take advantage of the full KEGG pathways and may have some misleading consequences. To compute the Wasserstein distance, we used the Hungarian algorithmS, while preserving key information for subsequent analysis, we apply diffusion maps11. This manifold learning technique provides a global parametrization of a low-dimensional, possibly nonlinear manifold on which the high-dimensional data is assumed to lie. Such an embedding is obtained by spectral properties  of the graph Laplacian on a certain weighted graph with nodes S. Its eigenvectors can be used as a coordinate system on the dataset S, which is justified by the fact that they approximate the eigenvectors of the Laplace-Beltrami operator of the underlying manifold12. See also some recent work13 for employing diffusion map techniques for data whose “points” are weighted graphs.In order to reduce the dimension and complexity of the dataset 11, for data samples si, sj ∈ S we define a connectivity matrix W using a Gaussian kernel:d is the Wasserstein 1-metric as defined in1 and ε is the kernel scale parameter. The kernel is intended to capture the features of the underlying dataset and it is therefore reasonable to choose the metric d and the scale parameter ε based on the application. The parameter ε defines a local connectivity scale, in the following sense: If sj is in the ε-ball around si, the kernel induces high weight between si and sj. Otherwise the weights are negligible. We can choose ε to be almost any value between the minimum and maximum among the pairwise squared distances )2.FollowingD the diagonal matrix N = #S. We use the adapted kernel  dividing the point cloud into overlapping “slices” by binning the values of a chosen filter function This algorithm results in a simplicial complex, in some sense modeling the mesoscopic-scale topology of the support space for the collection of samples or states C, roughly as in Nicolau et al.4, e.g. the Mahalanobis distance function adapted to C in case the size of C is sufficiently large in comparison to the dimension M. We often use the general-purpose network centrality measure available in Daniel Müllner’s Python Mapper implementation (http://danifold.net/mapper/).A reasonable choice for the filter function is a deviation function devised in comparison with a control dataset nf of filter-level-set bins and a threshold t for the single-linkage clustering algorithm applied to each slice. One should select these parameter values intermediate between the extreme values which completely divide the sample set into isolated clusters and those which completely merge the sample set into a single cluster. In practice a narrow range of such values exists.The algorithm requires the choice of certain resolution or scale parameters: the number ad hoc (case by case) form of ascertaining persistent topological features. Persistence is meant in roughly the same sense as the technique of persistent homology. Though persistent homology would ordinarily determine preferred values for parameters like nf, existing persistent homology tools are seemingly inapplicable to our setting. This is because it is multi-dimensional in that the simplicial complexes of interest depend on multiple parameters nf and t, and because a well-defined relation of containment or mapping between the complexes across parameter values is not apparent. Nevertheless appropriate values for nf and t are normally apparent.Applying the Mapper algorithm in this way is an Next, we consider the graph which is the 1-skeleton of the simplicial complex resulting from the Mapper algorithm. We decompose it into linear paths, and concatenate these paths for display. An example state graph is shown in Fig. S along one or both axes of a two-dimensional plot of:the expression valuesthe correlations with subpopulations defined by discrete covariates1-Wasserstein distance matrixdiffusion map Euclidean distance matrixWe order the samples within each node of the state graph according to the filter values. This ordering is combined with the concatenated linear path structure for an ordering of the samples Salient coherent states may appear in the expression heat map defined by patterns of activation and inactivation of particular genes, especially near the two extreme values for the filter function. This may require approximate dichotomization of the expression values .From the point of view of topological data analysis, the most interesting states are ones which are not separated by the chosen filter function alone, but are nevertheless distinguished by branching of the state graph. We caution that although the topological aspect of this pipeline has the benefit of insensitivity to dimensionality, functioning well even in very high-dimensional settings, it sometimes provides little insight beyond that already provided by the spectral analysis or diffusion map when the number of samples is small. On the other hand, Mapper seems to have the potential to function well even without diffusion maps preprocessing or gene network analysis, provided that a filter function can be chosen that brings sufficiently rich outside information into the analysis.The application of the network-metric/diffusion-map/Mapper pipeline to the 265 gene expression profiles of the samples of the TCGA sarcoma project demonstrates the basic efficacy of the method. The state graph is shown in Fig. We refer to the KEGG TP53 signaling pathway and indicate in parentheses the gene names appearing there, following official HUGO gene symbols. Italics indicate protein products.MDM2 are all negatively regulating TP53, which is not substantially activated. Arrest of the G1 and G2 phases should be frequently triggered since CDK2, CCNB1 (cyclin B), and CDK1 (CDC2), are all activated. Although FAS, PIDD1 (PIDD), PMAIP1 (NOXA), and SIAH1 (SIAH) are substantially activated, the apoptosis pathway for which they are precursors is not, including low levels of BAX, the death receptor protein TNFRSF10B (DR5), BID, CYCS (cytochrome c), and all caspases. Apoptosis seems to have been largely evaded.The state PS#1  consists almost entirely of samples tagged for leiomyosarcoma, meaning that tissue pathology determined a derivation from smooth muscle cells. As expected, high levels of CCNG1 (cyclin G), PPM1D (Wip1), and TP73 (P73), as well as TP53 is strongly activated, and its negative regulator MDM2 seems to be repressed by CDKN2A (P14ARF). A large number of the elements of the normal apoptosis signaling pathway are activated: FAS, BAX, TNFRSF10B, BID, ZMAT3 (PAG608), and SIAH1. The downstream caspase CASP3 is substantially activated. Apoptosis may occur in dedifferentiated liposarcomas at comparatively high rates. Alternatively, see16 for a discussion of situations where normally apoptotic caspases are non-lethal to the cell. Substantial activation of CDKN1A (P21) is not inhibiting CDK4/6 or CDK2 as expected; rather CDK4/6 over-expression is the most salient characteristic of state PS#3. According to Binh et al.17, over-expression of CDK4 and MDM2 is known to be a reliable diagnostic marker for well-differentiated liposarcoma (not represented in the TCGA sarcoma project).PS#3 consists almost entirely of samples tagged for dedifferentiated liposarcoma (DDLPS), and conversely almost all of the dedifferentiated liposarcomas among the 265 samples display state PS#3. We emphasize for clarity that all of the states were determined in an entirely unsupervised manner, with no input from the histological classification. https://cancergenome.nih.gov/cancersselected/Sarcoma). Nevertheless the coherent states PS#1 and PS#3 show that the expression profiles for these subtypes are more organized than their mutational profiles. We remark that ordinary unsupervised hierarchical clustering substantially reproduces these results, with somewhat less coherence among the apparent states.Note that both leiomyosarcoma and DDLPS subtypes are known to exhibit complex karyotypes, with no apparent characteristic mutation. This seems to be part of the reason why they were selected for inclusion in the TCGA sarcoma project  potentially indicating DNA damage. Nearly all of the markers for TP53 negative feedback regulation are strongly inactivated, including CCNG1, PPM1D, TP73, and MDM2. CDKN1A inactivation is consistent with the appearance of CCND1 (cyclin D) and CDK4/6. With respect to the upstream elements of the apoptosis signaling pathway, we observe in state PS#2 the opposite behavior from the state PS#1, namely that FAS, PIDD1, SIAH1, and possibly PMAIP1 are absent, but BAX, PERP, TNFRSF10B, and BID transcripts are all present.Finally, we consider the state PS#2. It does not consist mainly of any one histopathological subtype. The most obvious feature is that 19 query of the TCGA sarcomas displaying the TP73-/SERPINE1+ expression pattern of PS#2 reveals high probability of loss of some portion of chromosome 18q. SERPINB3, which is located on 18q, and TP73 are both associated with negative regulation of JUN kinase activity (GO:0043508) according to the human Gene Ontology Annotation database20.A cBioPortalSERPINB3 on chromosome 18q disrupts serine/cysteine-type endopeptidase inhibitor activity, which is then restored by SERPINE1 upregulation by some intermediate process. Normal SERPINB3 would in addition inhibit JUN kinase activity, but this inhibitory function is not restored by SERPINE1 upregulation. TP73 also normally inhibits JUN kinase activity. Unchecked JUN kinase activity may then be the main driver of tumor cell proliferation and transformation21 in state PS#2 since effective TP73 and SERPINB3 both seem to be absent.This suggests the following narrative to explain the molecular mechanisms driving the cancers in state PS#2. Damage to CCND1 activity of PS#2 is consistent with this hypothesis, since JUN induces transcription of CCND122.The high degree of 23. In this specific case, our finding answers the call of Messoussi et al.23 to delineate patients that would potentially benefit from JNK  inhibitors. In approximately 10% of soft-tissue sarcomas, largely irrespective of histological subtype and possibly independent of JUN amplification status, JNK inhibitors that can replace the inhibitory function no longer provided by SERPINB3 may restore JNK activity to normal condition.Note that a proto-oncogenic role for JUN has long been suspected, and its actual function is complex, including alternately pro- and anti-tumor behaviors depending on contextThe network-metric/diffusion-map/Mapper pipeline uncovered some latent features of high-dimensional genomic sarcoma data in a relatively robust way. One promising direction for future research is the inference of phylogenetic trees via mutational data like Single Nucleotide Polymorphism (SNP) calls or gene amplifications and deletions in the evolutionary context. This context could be spatially dense single-tumor samples or single-patient metastasis or micrometastasis samples. Mapper would be especially adapted to the elucidation of branching/inheritance structures when the filter function is a suitable quantification of the deviation of a sample from a founder population. For example, a Hamming-type distance in the case of SNP sequences, which could also be used for the intrinsic metric between SNP sequences. As an alternative to the naive Hamming distance, a network-enriched distance could be obtained by means of linkage disequilibrium calculations.


Natural killer (NK) cells have increasingly become a target of interest for immunotherapies1. NK cells express killer immunoglobulin-like receptors (KIRs), which play a vital role in immune response to tumors by detecting cellular abnormalities. The genomic region encoding the 16 KIR genes displays high polymorphic variability in human populations, making it difficult to resolve individual genotypes based on next generation sequencing data. As a result, the impact of polymorphic KIR variation on cancer phenotypes has been understudied. Currently, labor-intensive, experimental techniques are used to determine an individual’s KIR gene copy number profile. Here, we develop an algorithm to determine the germline copy number of KIR genes from whole exome sequencing data and apply it to a cohort of nearly 5000 cancer patients. We use a k-mer based approach to capture sequences unique to specific genes, count their occurrences in the set of reads derived from an individual and compare the individual’s k-mer distribution to that of the population. Copy number results demonstrate high concordance with population copy number expectations. Our method reveals that the burden of inhibitory KIR genes is associated with survival in two tumor types, highlighting the potential importance of KIR variation in understanding tumor development and response to immunotherapy. MHC molecules, which are expressed on nearly all nucleated cells, can present pathogenic or tumorigenic peptides on the cell surface for recognition by T cells. In order to evade the immune system, malignant cells often down regulate expression of MHC molecules3. However, KIR on NK cells are able to respond with an immune attack if they can recognize that the expression of MHC deviates from normal4. This dual system allows “no way out” for cancerous cells -- either the MHC presents the neo-peptides or the MHC is downregulated and NK cells attack the cell5. However, the efficiency of this process depends greatly on the ability of the KIR expressed on NK cells to bind to the MHC receptors.Killer Immunoglobulin-like receptors (KIRs) are cell-surface receptors expressed by Natural Killer (NK) cells and some T cells. KIRs bind to other naturally occurring immune receptors, including Major Histocompatibility Complexes (MHCs), to inhibit or activate immune cell activity7. Associations have also been found between specific KIR genes and susceptibility to several cancers 11. Furthermore, the strength of HLA-KIR interactions plays a functional role and can influence disease susceptibility12.The impact of these NK cell mechanisms in response to malignancies has been validated through the several associations found between KIR genotype and cancer phenotypes. The presence of certain KIR genes can predict response to immunotherapy treatment and survival outcomes in chronic myeloid leukemia and acute myeloid leukemiaHowever, all of these studies have been performed on cohorts of low sample size due to the difficulty of studying the highly variable KIR region. KIRs are encoded by a cluster of genes on chromosome 19q13.4. Individuals vary widely in the number of KIR genes they carry and in the allelic variation within those genes. The region can contain up to 16 genes but sometimes has as few as four gene, each one with up to 100 known allelic variants.13. Another uses sequence specific oligonucleotides as a first pass and then sequences specific exons to identify allelic variation14. Sanger sequencing can also provide long enough reads to cover several genes at a high resolution15. However, all of these techniques require KIR specific techniques in the data gathering stage. Only two computational alternatives exist that do not require KIR specific techniques in the data gathering stage. KIR*IMP imputes the KIR region from SNP genotype data16 and PING predicts KIR copy number from NGS data17. However, KIR*IMP cannot be applied to large exome datasets and PING requires time consuming read mapping, a potentially biased normalization and manual curation step.The highly homologous nature of the KIR genes hampers usage of conventional, computational copy number technologies for short read Next Generation Sequencing (NGS) data. However, the interesting immune implications of the region have led to the development of several experimentally based techniques. One approach uses polymerase chain reaction to amplify the sequences and sequence specific primers to detect particular allelesTo achieve the computational speed and accuracy required for inferring the KIR types of nearly six thousand cancer patients in order to study tumor phenotypes, we implemented an unsupervised, k-mer based algorithm that leverages large populations to determine copy number . Using t2.2.118. Population KIR allele frequencies were obtained from The Allele Frequency Net Database on February 22, 201719.Exome sequencing, transcriptome sequencing and clinical data from The Cancer Genome Atlas was downloaded from the National Cancer Institute’s Genomic Data Commons on August 3rd, 2018. All disease types were obtained. KIR alleles were downloaded from the Immuno Polymorphism Database on October 6th, 20162.218. The number of unique k-mers for each gene is shown in A set of k-mers were selected to represent each KIR gene -- these k-mers are referred to as unique k-mers. The criteria for the unique k-mers are as follows: a unique k-mer, or its reverse complement, must appear in (1) every allele of a specific KIR gene and (2) no alleles of any other KIR gene. Unique k-mers of lengths 10, 15, 20, 25, 30, 35 and 40 were collected based on the KIR reference from the Immuno Polymorphism Database (IPD)2.320 to a reference that is constructed of all the KIR alleles for each KIR gene from IPD and each of the 100 random reference genes. All reads that mapped in the reference at least once are again stripped and then searched for the set of unique k-mers and occurrence counts are stored for each k-mer. The pipeline concludes with each patient having a vector of occurrence counts for every unique k-mer.The genomic region encoding the KIR locus (GRCh38:chr19:54025634–55084318) and the regions encoding the 100 random genes were extracted from the exome sequencing bam files from the TCGA. The unmapped reads of the exome sequencing bam files were also pulled from the exome sequencing bam files. All of these genomic regions were merged together into a single bam file. Then, the reads were stripped into a fastq file and realigned using Bowtie22.421. To further understand the relationship between sequencing depth and clusters of samples, we plotted the distribution of k-mer counts in the set of 100 random genes and also k-mer counts in the KIR region. To reduce noise from outliers, only the samples from the largest cluster of the t-SNE (Agilent Sureselect capture kit) were selected and all samples with < 40,000 k-mer coverage in the set of 100 random genes and < 20,000 k-mer coverage in the set of KIR genes were excluded. After applying these filters, a total of 4,717 samples remained.To identify substructure in the dataset that might indicate problematic samples, the k-mer frequency for each of a set of 100 random genes for all patients in TCGA are visualised with a t-SNE plot2.5Since every sample will have different sequencing depth, the k-mer counts must be normalized before being compared between samples. Furthermore, there are several lengths of k to choose between. We evaluated normalization methods and lengths of k based on reduction in variance of k-mer counts associated with KIR3DL3 which is known to be almost universally diploid. We tested each length of k  against each of the following normalization approaches: (1) the mean of the number of k-mers mapped to the set of 100 random genes, (2) the mean of the number of reads with at least one k-mer mapping to the set of 100 random genes, (3) the median of the number of k-mers mapped to the set of 100 random genes and (4) the median of the number of reads with at least one k-mer mapping to the set of 100 random genes. In the end, we used a k of 30 and normalized with option (1) for the remainder of the analysis.2.6KIR genes have varying numbers of unique k-mers . After cThese kernel density plots can be used to assign gene copy numbers in an unsupervised manner. First, the genes are divided into three categories based on the documented ploidy of the gene: anchor genes that are present in two copies for most individuals , high frequency non-anchor genes that are present at least once in most individuals  and low frequency non-anchor genes that are present less than once in most individuals . Second, peaks and valleys are called for each kernel density plot by finding local minima of the second derivative. Third, we map the highest peak to the most common ploidy based on the documented copy number variant frequency in the population and determine cutoffs by selecting the valleys surrounding that peak. For anchor genes, the highest peak is determined to be two copy numbers. Samples beyond either edge of the peak (as determined by a second derivative close to 0) are assigned a copy number of 1 or 3+. Instead of looking for subsequent minima, we used the width of the highest peak to create a new threshold for samples with 0 copies to the left of the region for 1 copy. For high frequency non-anchor genes, three peaks are usually observed and thresholds are defined as the valleys between them. The left-most and shortest peak corresponds to 0 copies, the middle peak to 1 copy and the right-most and highest peak to 2 copies. All samples beyond the the third peak correspond to 3+ copies. For low frequency non-anchor genes, typically only two peaks are observed. The left-most and highest peak is assigned 0 copies and the second peak is assigned 1 copy. The distance between these peaks is used to denote thresholds for the samples that had 2 copies or 3+ copies. Each sample was assigned copy numbers at each KIR gene according to where their k-mer count fell in the distribution. However, patients that fell very close to the cutoff boundaries for a gene  were excluded for that gene. All of the genes that do not have any unique k-mers are known to co-segregate with other KIR genes. Thus, we inferred copy number for these genes from the copy number of the co-segregating gene as follows: individuals typically have as many copies of KIR2DS1 as they do KIR3DS1, KIR2DL2 as KIR2DS2, KIR3DL1 as KIR2DS4 and KIR2DL5A as the combined total of KIR2DS3 and KIR2DS5. Furthermore, individuals typically have an inverse number of KIR2DL3 as KIR2DS2 .2.7KIR gene counts for TCGA patients of a specific ancestry are expected to follow the documented distribution of the corresponding population. To validate this assertion, KIR gene frequencies for a European ancestry population from IPD were compared to predicted KIR gene frequencies for the European ancestry patients in TCGA. The correlation between individual gene frequencies was determined using a Pearson correlation.2.8For each tumor type, we divided patients into two sets: those that had the median number of inhibitory genes or fewer and those who had greater than the median number of inhibitory genes. We calculated the survival difference between the two cohorts using the Kaplan Meier and the log rank test as implemented by the lifelines python library. P-values were adjusted with Bonferroni correction. The two tumor types with different survival outcomes, cervical squamous cell carcinoma (CESC) and uterine carcinosarcoma (UCS), were combined because of their similar physical location, immune infiltration profiles and rates in order to increase statistical power.2.922. Then, we checked the relationship between inhibitor gene count with infiltration of CD8+ T cells and NK cells for the tumor types where significant survival differences were found. P-values were calculated with a Mann-whitney U test between the patient set with high and low inhibitory gene counts. Furthermore, we calculated MHC-I PHBR scores  for each patient’s observed driver mutations as outlined in Marty et al.23 and compared the PHBR scores for CESC and UCS patients with all other patients using a Mann-whitney U test.We used RNA-seq data from TCGA to obtain immune infiltration predictions with EPIC3.3.1all alleles of a specific gene but in no alleles of any other gene. The IPD contains all observed alleles of each KIR gene. Using this reference, we searched each gene for unique k-mers and found that all KIR genes either have unique k-mers , a large set of cancer patients  with germline exome sequencing to learn the relationship between k-mer counts and gene copy number. We first evaluated the implication of technical covariates for our analysis. The majority of patients in TCGA had their exome captured with an Agilent capture kit; however, there were several other capture kits used for subsets of patients . We sele3.3Next, we searched the reads for each patient mapping to the KIR reference for unique k-mers. Since every patient will have a different sequencing depth, we had to normalize the k-mer counts before comparing them among individuals. Furthermore, we gathered k-mer counts for several lengths of k and wanted to choose the optimal value. Thus, we swept the parameter space, evaluating several normalization techniques and several values for k . We evalAfter establishing the normalization technique, we calculated the normalized k-mer count over all of the unique k-mers for every KIR gene of each patient. The frequencies were combined across the population to construct density curves showing the proportion of individuals with similar frequencies. Each KIR gene shows a smooth density curve with peaks that correspond to gene copy number. Anchor genes that are present in all patients have a single peak while the non-anchor genes that are present mostly at 0, 1 or 2 copies have three peaks . From th3.42 = .999) between ancestry-matched population frequencies of KIR haplotypes in TCGA and a recent study that used an experimental approach for typing24  and uterine carcinosarcoma (UCS), with unadjusted p-values of less than 0.05 . In both of these tumor types, patients with high numbers of inhibitory genes had lower survival rates, suggesting that NK cells were unable to defend against the tumor in these patients. Since these tumor types are physically co-localized and have similar immune infiltration profiles and survival rates (KIR genes are divided into two functional categories: activating genes and inhibitory genes. Inhibitory genes bind to specific MHC-I ligands to inhibit the NK cell from attacking the MHC-I expressing cellal rates , we analal rates .23. Patients with CESC and UCS had better presentation of observed driver mutations to the immune system than other tumors (P=0.0034, 3 or loss of heterozygosity in the HLA region26, allowing perpetuation of the tumor despite high affinity of observed drivers for the MHC-I. If MHC-I presentation on the cell surface is altered and T cells become less relevant, we expect that individuals with higher inhibitory KIR gene counts would have less ability to initiate an NK based attack against the tumor. These observations suggest that when NK cells are called to action, patients with higher NK cell inhibition may be less able to attack the cancer cells, resulting in a shorter survival time.To investigate why we found a significant survival difference in these two tumor types as compared to others, we explored the ability of their MHC-I to present observed driver mutations for recognition by the immune system5.Though natural killer cells are increasingly being considered as targets for immunotherapy, little is understood about the role of KIR, their main receptor family, on tumor development. Here, we describe our effort to evaluate the copy number of KIR genes in a large cancer cohort to learn about their influence in relationship with MHC on tumor development. We demonstrate the value of algorithmically learning KIR copy number in a large population by uncovering a survival difference in CESC and USC based in the number of inhibitory genes carried by an individual. Due to batch effects in exome sequencing, the current method must be retrained on each new cohort of individuals. This limitation leaves us unable to validate many of our methods experimentally. Furthermore, our method does not provide allele calls and cannot be used to determine the copy number of small cohorts or individual patients. However, our analysis highlights the importance of KIR variability to tumor development and warrants further study of this complex locus.2


Action potentials trigger neurotransmitter release at active zones, specialized release sites in axons. Many neurons also secrete neurotransmitters or neuromodulators from their somata and dendrites. However, it is unclear whether somatodendritic release employs specialized sites for release, and the molecular machinery for somatodendritic release is not understood. Here, we identify an essential role for the active zone protein RIM in stimulated somatodendritic dopamine release in the midbrain. In mice in which RIMs are selectively removed from dopamine neurons, action potentials failed to evoke significant somatodendritic release detected via D2 receptor-mediated currents. Compellingly, spontaneous dopamine release was normal upon RIM knockout. Dopamine neuron morphology, excitability, and dopamine release evoked by amphetamine, which reverses dopamine transporters, were also unaffected. We conclude that somatodendritic release employs molecular scaffolds to establish secretory sites for rapid dopamine signaling during firing. In contrast, basal release that is independent of action potential firing does not require RIM. In addition to secretion from axonal nerve terminals, many neurons release neurotransmitters or neuromodulators from their somata and dendrites . ImportaA prominent example for somatodendritic secretion is the release of dopamine in the ventral midbrain. Subsequent activation of dopamine receptors is important for regulating neuronal excitability, for the response to drugs of abuse, and for the control of motor function . SomatodElectrophysiological recordings from midbrain dopamine neurons revealed that somatodendritic dopamine release evokes a D2 receptor mediated inhibitory postsynaptic current (D2-IPSC) that is mediated by GIRK channels and rises in 200 ms and decays in 500 ms . The D2-The goal of this study was to identify molecular machinery that could provide for precise targeting of somatodendritic secretion of dopamine, focusing on the active zone organizer RIM. RIM localization within neurons is thought to be restricted to axons, where it is present in small clusters within active zones and organizes these release sites . Recent DA mice, in which RIM1 and RIM2 were specifically removed in dopamine neurons by crossing conditional RIM ‘floxed’ alleles to DATIRES-cre mice . We prepared acute brain slices from these mice and recorded from individual substantia nigra dopamine neurons. There were no differences in cell capacitance, resistance, spontaneous firing rates, Ih current, or the current measured immediately following break-in between RIM cKODA and RIM control mice  and to determine the current density. There was no significant difference between RIM control and RIM cKODA animals  and Rims2 (‘floxed RIM2’) genes  were used for all experiments, and mice were either littermates from the same litter or age-matched controls from the same intercrosses. All procedures and experiments were approved by and done in accordance with the policies of the IACUC at Oregon Health and Science University and at Harvard University.RIM cKO’) genes  with DATcre mice . RIM cKO2, 2.4 CaCl2, 1.4 NaH2PO4, 25 NaHCO3, 11 D-glucose, along with 10 µM MK-801 and continuous bubbling with 95%/5% O2/CO2. Following recovery, slices were placed in a recording chamber and perfused with Krebs buffer at a rate of 3 ml/min and maintained at 34–36° C. For all experiments DNQX (10 µM), picrotoxin (100 µM), and CGP55845 (300 nM) were included in the solution to block AMPA, GABA-A, and GABA-B receptors. In all experiments except those involving L-DOPA, sumatriptan (1 µM) was also included in the bath. In all experiments, a gigaohm seal was made on a dopamine neuron in the subatantia nigra with a glass electrode (1.3–1.8 megaohm resistance) filled with an internal solution containing (in mM) 10 BAPTA (4 k), 90 K-methanesulphonate, 20 NaCl, 1.5 MgCl2, 10 HEPES (K), 2 ATP, 0.2 GTP, and 10 phosphocreatine. In this configuration, the firing rate of the cell was measured. All cells were firing in a pacemaker fashion between 0.75 and 4 Hz and the firing frequencies were recorded. Then the seal was broken and whole-cell voltage clamp recordings (held at − 60 mV with an axopatch-1D amplifier) were achieved. The cell capacitance, input resistance, and series resistance were documented. Ih currents were measured using a 60 mV hyperpolarization. D2-IPSC measurements were conducted in voltage clamp and continuously recorded and monitored using Chart 7 . To test for D2 receptor IPSCs, a glass electrode filled with Krebs buffer was lowered into the slice 20–50 µM away from the cell of interest. To begin, five 0.5 ms pulses at 40 Hz were applied with a stimulus isolator (World Precision Instruments) once every minute and recorded in an episodic manner with AxoGraph software . The stimulation intensity was started at 1 µA. If no IPSC was produced, the stimulation was gradually increased and the stimulation electrode was repositioned. The stimulation intensity of 12 µA was never exceeded, as this would often cause loss of recording or unclamped depolarization. In some experiments, the stimulation was reduced to a single pulse. To measure cell excitability, recordings were done in current clamp. A current was injected such that the membrane potential was − 70 mV to quiet pacemaker firing, and current steps were applied in + 50 pA intervals (from + 50 – + 250 pA for 500 ms each) over the injected holding current, and the number of action potentials during each step was recorded. The application of drugs was done using bath superfusion in all experiments. For the L-DOPA experiments, 10 µM L-DOPA was applied for 10 min, and then removed for 10 min before applying sumatriptan (1 µM). For testing D2 receptor sensitivity, 1 µM dopamine was applied until a peak current was achieved and then removed. Once the current returned to a steady baseline, 100 µM dopamine was applied until a new peak was achieved. For amphetamine experiments, 10 or 30 µM were applied for 5 min (Mice were deeply anesthetized with isoflurane and decapitated. Brains were rapidly removed and slices (222 µM thick) containing the substantia nigra were taken in the horizontal plane using a vibrating blade microtome and placed in a recovery chamber for > 30 min prior to experimentation. All preparation was done in warm (32–34° C) Krebs buffer containing (in mM) 126 NaCl, 1.2 MgClor 5 min , followeor 5 min . All eleDA cells, not shown). Slices were then incubated for 30–45 min in extracellular solution at 35 °C prior to fixation. Slices were fixed for 30 min in PBS  + 4% paraformaldehyde (PFA) at room temperature and washed 3 × 15 min in PBS. Slices were incubated in PBS + 0.5% fish skin gelatin (FSG) + 0.3% Tween 20 with streptavidin Alexa Fluor 568 (at 1:1000) and washed 3 × 15 min in PBS at room temperature. Sections were mounted on glass slides with coverglasses with a 1.5 refractive index using fluorescent mounting medium. Laser-scanning confocal images were acquired on a ZEISS LSM880 with airyscan, with laser excitation at 560 nm at 20 x magnification with Z steps of 1 µm. The images were acquired through the Z plane such that the whole cell and all processes in the slice were captured. The Z-stacks were collapsed with z-project using Fiji for analysis. Individual neurons in confocal images were then traced manually. Sholl analysis of each traced neuron was performed using the ImageJ Sholl analysis plug-in. The cell body was selected, and the number of neurite crossings of concentric circles around the center of the cell body was measured at increasing radii . Cell filling, image acquisition and quantification were performed by experimenters blind to the genotype.Dopamine neurons for morphological analyses were first identified and characterized electrophysiologically. Whole cell recordings were made from one cell per slice using an internal solution that contained neurobiotin (0.05%). Cells were recorded for a minimum of 15 min prior to removal of the electrode, and capacitance and resistance were recorded  following the application of cocaine (300 nM) and forskolin (1 µM). For analysis, recordings were filtered at 1 kHz and decimated (averaging 10 points). Spontaneous IPSCs were automatically detected using a sliding template procedure in AxoGraph. The template was generated by averaging multiple events that conformed to previously published kinetic analysis . SpontanDrugs were bath applied. MK-801, forskolin, CGP55845, and picrotoxin were acquired from Hello Bio Princeton, NJ. CNQX, sulpiride, L-DOPA, were acquired from Sigma-Aldrich. Amphetamine was acquired from NIH NIDA and sumatriptan was acquired from Glaxo Wellcome Inc. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Eve Marder as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Nicolas Tritsch (Reviewer #1); Pablo E Castillo (Reviewer #2); Louis-Eric Trudeau (Reviewer #3).Thank you for submitting your article "RIM is essential for stimulated but not spontaneous somatodendritic dopamine release in the midbrain" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The study by Robinson et al. shows that RIM proteins are required for evoked somatodendritic release of dopamine from midbrain DA neurons in mice. Specifically, selective removal of RIM from dopamine midbrain DA neurons abolished D2 receptor mediated-IPSCs induced by electrical bulk stimulation, whereas spontaneous release of dopamine remained intact. Together the data indicate that the mechanisms underlying evoked and spontaneous release of dopamine differ.Essential revisions:1) Validation of DA neurons:- What midbrain dopamine neurons were analyzed in this study ?- Are dopamine neurons in cKO mice just as likely to be stimulated electrically to release dopamine than control neurons? Please compare spontaneous firing rates and action potential waveforms, and then demonstrate that electrical stimulation equally recruits dopamine neurons in both genotypes.- Is the global density of dopaminergic dendrites was altered in these KO mice (suggested experiment:TH immunostaining in the substantia nigra)?2) D2-IPSCs: To more directly address the possibility of changes in D2 receptor responsiveness of dopamine neurons in the RIM KO mice, please measure membrane currents evoked in dopamine neurons by local application of a D2 agonist.The kinetics of spontaneous D2-IPSCs and electrically evoked D2-IPSCs seem to differ as seen in Figure 3B. Please measure and analyze the frequency, rise-time and decay of evoked and spontaneous D2-IPSCs.3) RIM-dependent synchronization:Can action potentials triggered by direct depolarization of DA neurons induce DA-IPSCs? If so, the authors may want to use this single-cell approach to confirm that RIM-dependent synchronization is stimulation protocol-independent and can be observed with a more subtle manipulation.4)Rim localization:The authors need to state more explicitly what is known about the localization of RIM1-2 in neurons and cite the relevant work. Can the authors show the presence of RIM proteins in the somatodendritic domain of dopamine neurons? Can the authors detect RIM in dendrites ?Reviewer #1:This brief report makes a simple point, and does so clearly and well: it reveals that the presynaptic protein RIM is essential for electrically evoked, but not spontaneous dendritic release of dopamine in the mouse midbrain. This paper comes on the heels of a compelling report from the same last author, showing that the active zone proteins RIM 1 and 2 distribute to presynaptic terminals of midbrain dopaminergic neurons in striatum, and that mice in which these proteins are conditionally deleted (cKO) from dopaminergic neurons are unable to release dopamine when measured with amperometry . Interestingly, these mice did not appear to suffer from gross locomotor defects and microdialysis experiments revealed trace amounts of extracellular dopamine in striatum, suggesting that spontaneous release may be intact. Here, the authors build upon this work, comparing dendritic release of dopamine in of control and RIM cKO mice using whole cell recordings of D2 receptor-evoked GIRK currents in midbrain dopamine neurons. They report the absence of electrically-evoked GIRK currents in RIM cKO mice, and perform a series of control experiments to rule out insufficient stimulation, or changes in D2 receptor expression. Interestingly, spontaneously occurring D2 receptor mediated GIRK currents appear intact in these mice, harking back to their earlier speculation that spontaneous release may occur in a RIM-independent fashion.I only have 2 points that should be clarified, or excluded as potential confounds to confirm their conclusions:- In Figure 2, the authors use amphetamine to test whether the cKO mice have a reduced number of vesicles (as opposed to a defect in release). They report no differences in current amplitude compared to WT mice, but a difference may only be revealed using this approach if GIRK conductances are not saturated. Can the authors rule this out? A direct measure of large dense core vesicles with EM would obviously be a better experiment, but probably not a resource available to either lab.- In Figure 1—figure supplement 1, the authors make the case that the intrinsic properties of dopamine neurons are identical in both genotypes. But what the authors really need to show is that dopamine neurons in cKO mice are just as likely to be stimulated electrically to release dopamine than control neurons. The authors could start by comparing spontaneous firing rates and action potential waveforms, and then demonstrate that their electrical stimulation paradigm equally recruits dopamine neurons in both genotypes.Reviewer #2:In this study Robinson et al. report that RIM proteins are required for evoked somatodendritic release of dopamine from midbrain DA neurons in mice. The key finding is that selective removal of RIM from these neurons abolished D2-IPSCs induced by electrical bulk stimulation, whereas spontaneous release of dopamine remained intact. These observations strongly suggest that the mechanisms underlying evoked and spontaneous release of dopamine differ. The need for spatiotemporal precision of dopamine release and the associated secretory machinery, as seen for fast neurotransmitters, is unclear to this reviewer. In any event, the experiments are well designed and the results support the claims that are made. I only have a few suggestions that may strengthen the authors' conclusions.1) "RIMs scaffolding functions may allow for the synchronization of release from multiple sites or multiple vesicles". Synchronization may also result from the stimulation method (extracellular bulk stimulation). Can action potentials triggered by direct depolarization of DA neurons induce DA-IPSCs? If so, the authors may want to use this single-cell approach to confirm that RIM-dependent synchronization is stimulation protocol-independent and can be observed with a more subtle manipulation.2) The comparison between single stimulus and five stimuli IPSCs can only provide a very indirect estimation of Pr. Yet, the authors conclude that evoked somatodendritic release of DA has a high initial Pr and that a scaffolding mechanism to tether dopamine-laden vesicles to calcium channels and other secretory machinery are essential. Repetitive, bulk stimulation could release modulators that reduce transmitter release that can mistakenly interpreted as high initial Pr.3) Why does somatodendritic DA release require the spatiotemporal precision that is typically observed in fast neurotransmission? Evoked DA-IPSCs are extremely slow -as compared to fast synaptic transmission- and do not seem to require (pre-postsynaptic) nanodomains. This issue should be discussed more thoroughly.4) "In dopamine axons, RIM was only present in ~1/3 of the varicosities…". Can the authors detect RIM in dendrites?5) The authors claim that spontaneous D2-IPSCs were kinetically similar to electrically evoked D2-IPSCs. However, consistent with Gantz et al., 2013 their kinetics seem to differ as seen in Figure 3B. Maybe the authors want to measure and analyze the rise-time and decay of evoked and spontaneous D2-IPSCs.Reviewer #3:In this manuscript by Robinson and colleagues, the authors explored the implication of the active zone proteins RIM1-2 in somatodendritic dopamine release in mice. The work is quite original as the molecular mechanisms of somatodendritic dopamine release are still incompletely characterized and whether the release machinery and scaffolding proteins involved are similar or different from the machinery in axon terminals in an important question. Globally, the work presented in this very short paper is quite convincing. The block of dendritic dopamine release is quite striking. However, a few issues need to be addressed.1) The authors need to state more explicitly what is known about the localization of RIM1-2 in neurons and cite the relevant work. Obviously, it would add significantly to the paper to demonstrate the presence of RIM proteins in the somatodendritic domain of dopamine neurons. The authors should at least explain why this was not done in the present work 2) The Introduction should make it clearer that somatodendritic dopamine release is well established to be vesicular in origin and to be sensitive to cleavage of SNARE protein by clostridial toxins.3) The data on the frequency-dependence of dendritic dopamine release presented in Figure 1—figure supplements 2 and 3, while interesting, are not really relevant to the main point of the paper. Also, the conclusions drawn from these results (that dendritic dopamine release employs scaffolds and that RIM heterozygosity did not strongly impair dendritic dopamine release) are not really supported by this data. I would suggest removing this data from the manuscript.4) The experiments carried out with L-DOPA are presented as if they were performed to determine whether the loss of dendritic dopamine release in RIM KO mice is due to reduced levels of dopamine D2 receptors. In fact, the results do not really address this question, which is otherwise important. The fact that L-DOPA induces a D2R-dependent response which apparently comes from serotonin axon terminals is quite interesting in itself, but only supports the idea that some dopamine D2 receptors remain in the somatodendritic compartment of dopamine neurons in the KO mice. It does not allow saying whether there are less or more D2R. Also, in control mice, it is surprising to note that there was no difference between the baseline response and the response after L-DOPA and sumatriptan. This would tend to suggest that L-DOPA did not boost dendritic dopamine release at all. Also, the sumatriptan-sensitive component is bigger in RIM KO mice compared to the control mice. What this means is unclear. Was there some compensatory adaptation in the 5-HT terminals in response to abrogation of axonal and dendritic dopamine release in these mice? Globally, these experiments with L-DOPA raise more questions than anything else.5) To more directly address the possibility of changes in D2 receptor responsiveness of dopamine neurons in the RIM KO mice, it seems to me that it would be better to simply measure membrane currents evoked in dopamine neurons by local application of a D2 agonist. This would nicely complement the experiments of Figure 2 with amphetamine.6) The experiments of Figure 3 are quite interesting. The authors should also provide the frequency of these spontaneous events in control and KO neurons.7) The absence of RIM starting from the late embryonic period could have perturbed the development of dopamine neurons, potentially leading to altered dendritic development. The capacitance results from Figure 1—figure supplement 1 argue that there were no major changes in the size of dopamine neurons in the KO mice. However, do the authors have the result of a TH immunostaining experiment in the substantia nigra to determine whether the global density of dopaminergic dendrites was altered in these KO mice? 1) Validation of DA neurons:- What midbrain dopamine neurons were analyzed in this study ?We apologize that we did not make this clear. All analyzed neurons were dopamine neurons in the pars compacta of the substantia nigra. This is now mentioned at various places including the results, the legend of Figure 1, and the Materials and methods section.- Are dopamine neurons in cKO mice just as likely to be stimulated electrically to release dopamine than control neurons? Please compare spontaneous firing rates and action potential waveforms, and then demonstrate that electrical stimulation equally recruits dopamine neurons in both genotypes.DA neurons . We have then performed experiments to assess whether stimulation recruits the RIM knockout neurons efficiently. We have quantified the number of action potential spikes induced by current injections in dopamine neurons of RIM control and RIM cKODA mice , and found that stimulation equally recruits dopamine neurons in both genotypes. The observations that pacemaker firing, firing in response to depolarization, and input resistance are unchanged strongly indicate that excitability is normal in RIM cKODA neurons.We have performed the requested experiments. First, we have quantified spontaneous firing rates and there was no difference between RIM control and RIM cKODA.An additional way to assess this would be to determine the somatic action potential waveforms of dopamine neurons. We have not attempted this here because 1) all recordings were done with amplifiers that cannot truly follow rapid voltage changes and hence action potential shape may be distorted  and 2) the spike and afterhyperpolarization of these neurons include many conductances, some of which would only cause subtle changes in the action potential waveform if affected. A recent review on dopamine neurons points out this complexity . We strongly feel that any change could easily be missed and would prefer not to reach conclusions about the many important conductances based on simply assessing somatic waveforms. Nevertheless, the observations that spontaneous firing, recruitment upon depolarization and input resistance are unchanged exclude that excitability defects account for the strong impairment in release in RIM cKO- Is the global density of dopaminergic dendrites was altered in these KO mice (suggested experiment:TH immunostaining in the substantia nigra)?DA mice in preliminary experiments. While the overall morphology and TH density appeared unaffected, dendrite and cell shapes could not be analyzed with certainty because the dendrites are dense and for many dendrites it is not possible to follow and assign them to a given cell.We thank the reviewers for bringing this up and have performed experiments to address this point. First, we have stained TH neurons in RIM control and RIM cKODA slices by confocal microscopy, and performed Sholl-analyses  to analyze their branch structure. RIM knockout dopamine neurons appear identical to the control neurons and electrophysiological properties of the neurons were unchanged .Instead, we performed a full experiment in which we identify individual dopamine neurons electrophysiologically in acute brain slices and then dye-filled these neurons. We then fixed the slices and imaged individual dopamine neurons in RIM control and RIM cKOThese data establish that there are no strong effects of RIM deletion on dopamine neuron cell size and shape.2) D2-IPSCs: To more directly address the possibility of changes in D2 receptor responsiveness of dopamine neurons in the RIM KO mice, please measure membrane currents evoked in dopamine neurons by local application of a D2 agonist.DA mice. While a robust response is present in both genotypes upon application of 100 µM dopamine, there may be a non-significant trend towards a reduction in RIM cKODA mice in 1 µM dopamine. We note that these currents are small and unreliable. However, overall our data strongly support that D2 receptor localization and function is not significantly impaired, because the D2 responses to L-DOPA  and amphetamine  are unchanged, and most importantly the amplitude of spontaneous D2 IPSCs is unaffected by RIM cKODA .We thank the reviewers for bringing this up and have performed this experiment as instructed . We have measured D2 receptor currents in response to bath application of 1 µM followed by 100 µM dopamine. There is no significant difference in the currents induced by dopamine between RIM control and RIM cKOThe kinetics of spontaneous D2-IPSCs and electrically evoked D2-IPSCs seem to differ as seen in Figure 3B. Please measure and analyze the frequency, rise-time and decay of evoked and spontaneous D2-IPSCs.DA animals. We note that these experiments are laborious given the low amplitude and overall frequency of the events and the difficulty of their detection. Our analysis reveals that the frequency of spontaneous events is not significantly changed in the RIM cKODA mice, but there is a sizable trend towards an increase in spontaneous event frequency . We also provide a new analysis of the 20% peak width of the spontaneous events that establish that the kinetics of the spontaneous events are unchanged in RIM cKODA mice.We have performed new experiments and analyses of the frequency and kinetics of spontaneous events in RIM control and RIM cKOThe kinetics of spontaneous and evoked D2-IPSCs and how they relate to one another have been extensively studied before . As presented by these studies and in the previous version of our manuscript, the tail of the evoked D2-IPSC is somewhat longer than that of the spontaneous D2-IPSC. It has also been shown in these previous studies that the spontaneous D2-IPSCs scale to evoked D2-IPSCs, and the decay is most likely dependent of downstream GPCR signaling with a decay time constant of 300-350 ms rather than the properties of dopamine release. While interesting, we strongly feel that a detailed analysis and discussion of these points is marginal to the manuscript and distracts from the main point. In the interest of keeping this brief report concise and focused on the key point (the mechanisms of somatodendritic dopamine release), we have removed the comparison of the spontaneous and evoked D2-IPSCs, but instead refer to the relevant literature.3) RIM-dependent synchronization:Can action potentials triggered by direct depolarization of DA neurons induce DA-IPSCs? If so, the authors may want to use this single-cell approach to confirm that RIM-dependent synchronization is stimulation protocol-independent and can be observed with a more subtle manipulation.We have pursued experiments to test whether direct depolarization of a DA neurons triggers DA IPSCs in that neuron in previous experiments. Unfortunately, we have never succeeded in using a single neuron as a “sensor” for its dopamine release. Hence, this experiment is not possible. We have adjusted the text around the hypothesis of synchronization and have included the possibility suggested by reviewer 3  to better express the hypothetical nature of these models and their underlying mechanisms.4)Rim localization:The authors need to state more explicitly what is known about the localization of RIM1-2 in neurons and cite the relevant work. Can the authors show the presence of RIM proteins in the somatodendritic domain of dopamine neurons? Can the authors detect RIM in dendrites ?We thank the reviewers to raise the point of RIM localization and we now address it . RIM localization has been best characterized in hippocampal neurons. There, RIM is predominantly found in axons. Within axons, it is highly concentrated at active zones , as best assessed by STED microscopy . Two reports, however, proposed that RIM also may have dendritic functions. Specifically, it was proposed that RIM1α may support postsynaptic trafficking in dendrites , and RIM3 and 4, small RIM version, localize in dendrites and control their growth .In the dopamine neurons, RIM localization has previously only been assessed in striatal axons of substnatia nigra and VTA dopamine neurons, where it is clustered in sparse, active zone-like release sites . Recently, we have put significant effort into attempting to localize RIM in somata and dendrites of dopamine neurons with several super-resolution microscopic methods. Unfortunately, we have remained uncertain about RIM localization in these compartments. The key challenge is that dopamine somata and dendrites receive dense presynaptic inputs from other neurons, and hence somata and dendrites are densely decorated with RIM, but it is impossible to tell whether some  of these RIM clusters are within somata and dendrites . Hence, despite significant effort, we have not been able to establish or exclude somatic and dendritic localization of RIM at release-site like hotspots.This point also relates to the broader question of whether there are defined release sites (for example generated or marked by RIM) in dopamine neuron somata and dendrites. While our data suggest that secretory hotspots in dopamine neurons are close to dopamine receptors on receiving cells, it has remained uncertain how this secretory pathway is organized. For example, fundamental aspects such as the nature of the membranous compartment that mediates somatodendritic release  has remained uncertain. In respect to RIM, it is possible that it is clustered at release sites, decorates the target membrane broadly, is instead associated with the vesicular compartment that contains dopamine, or is a soluble, cytosolic release factor to support release. While all data support the model that RIM is sparse in somata and dendrites, it is currently impossible to distinguish between these different models.We hope that the better description of what is known about RIM localization in the text and the explanations provided here are sufficient to clarify this point. We will continue to study the structures that underlie somatodendritic dopamine exocytosis. We also hope that our findings motivate other laboratories to join the important effort to understand the structure and organization of the somatodendritic secretory pathway in dopamine neurons.Reviewer #1:[…] I only have 2 points that should be clarified, or excluded as potential confounds to confirm their conclusions:- In Figure 2, the authors use amphetamine to test whether the cKO mice have a reduced number of vesicles (as opposed to a defect in release). They report no differences in current amplitude compared to WT mice, but a difference may only be revealed using this approach if GIRK conductances are not saturated. Can the authors rule this out? A direct measure of large dense core vesicles with EM would obviously be a better experiment, but probably not a resource available to either lab.As outlined above in the response to the editorial decision, we have ruled out that D2 GIRK conductances are saturated .DA mice, but we note that despite many years of research it has not been established what vesicle dopamine is released from in wild type animals. We hope that the reviewer understands that we cannot solve this problem in the context of a revision.We also agree that it would be great to have an electron microscopic assessment of the vesicular compartment for dopamine release in the RIM cKO- In Figure 1—figure supplement 1, the authors make the case that the intrinsic properties of dopamine neurons are identical in both genotypes. But what the authors really need to show is that dopamine neurons in cKO mice are just as likely to be stimulated electrically to release dopamine than control neurons. The authors could start by comparing spontaneous firing rates and action potential waveforms, and then demonstrate that their electrical stimulation paradigm equally recruits dopamine neurons in both genotypes.We have now included these experiments as outlined in the response to the editorial decision letter (major point 1) in Figure 1—figure supplement 1 and in Figure 2.Reviewer #2:[…] I only have a few suggestions that may strengthen the authors' conclusions.1) "RIMs scaffolding functions may allow for the synchronization of release from multiple sites or multiple vesicles". Synchronization may also result from the stimulation method (extracellular bulk stimulation). Can action potentials triggered by direct depolarization of DA neurons induce DA-IPSCs? If so, the authors may want to use this single-cell approach to confirm that RIM-dependent synchronization is stimulation protocol-independent and can be observed with a more subtle manipulation.Unfortunately, the single cell approach has not been used successfully. In the past, this experiment has been taken on by many researchers in each of the monoamine nuclei, locus coeruleus, dorsal raphe, substantia nigra and VTA. Pharmacological experiments did further not find any evidence of autoreceptor dependent inhibition. Similarly, paired cell recordings have been the source of failed experiments in both the locus coeruleus and VTA. At this point, the site(s) of dendritic connections are not known. A similar experiment can be done in autaptic culture, but in this preparation the release is most likely axonal rather than dendritic. Finally, experiments using the intracellular application of reserpic acid, a membrane impermeant version of reserpine, did not block spontaneous IPSCs suggesting that at least the spontaneous events were not true autoreceptor dependent processes . We hope that given these explanations, the reviewer agrees that this experiment is currently not possible.Instead, we have toned down the Discussion and also express that synchronization may arise from synchronization of different neurons. We note that the RIM-dependent release machinery would also be well suited to do this as it time-locks release with firing. Hence, if dopamine neurons receive timed depolarizing inputs, the fast secretory machinery may serve to convey these signals as well.2) The comparison between single stimulus and five stimuli IPSCs can only provide a very indirect estimation of Pr. Yet, the authors conclude that evoked somatodendritic release of DA has a high initial Pr and that a scaffolding mechanism to tether dopamine-laden vesicles to calcium channels and other secretory machinery are essential. Repetitive, bulk stimulation could release modulators that reduce transmitter release that can mistakenly interpreted as high initial Pr.We agree to this point and thank the reviewer for pointing this out. Given this comment and the comments of the other reviewers, we have removed this figure. We further note that previous literature has established a high Pr , and we simply refer to this previous literature (which contains a much more extensive characterization than the one we provided in the previous version of the paper) in the interpretation of our data.3) Why does somatodendritic DA release require the spatiotemporal precision that is typically observed in fast neurotransmission? Evoked DA-IPSCs are extremely slow -as compared to fast synaptic transmission- and do not seem to require (pre-postsynaptic) nanodomains. This issue should be discussed more thoroughly.It has remained uncertain what the true time scales of dopamine coding are and why such fast machinery has been selected through evolution to mediate its release. Independent of the rapidity, it is important that D2 receptors require a relatively high concentration of dopamine for activation , and the machinery we describe here likely accounts for this. Finally, we would note that secretory machinery like RIM may also be well suited for regulation. We now better discuss this in the manuscript with a specific statement referring to the need for fast signaling.4) "In dopamine axons, RIM was only present in ~1/3 of the varicosities…". Can the authors detect RIM in dendrites?We have addressed this question in the response to the editorial decision major point 4, we hope that this clarifies what is known at this point.5) The authors claim that spontaneous D2-IPSCs were kinetically similar to electrically evoked D2-IPSCs. However, consistent with Gantz et al., 2013 their kinetics seem to differ as seen in Figure 3B. Maybe the authors want to measure and analyze the rise-time and decay of evoked and spontaneous D2-IPSCs.We thank the reviewer for this comment and have addressed it by removing the claim of kinetics. As explained above and as noted by the reviewer, this point has been addressed in previous literature, and we refer to this body of literature now.More generally, the differences between evoked and spontaneous transmitter release are subject of considerable discussion  and the dopamine neurons are not alone. The Gantz et al., 2013 paper did find a difference in the time course of the IPSCs in that the evoked IPSCs had a somewhat longer tail. This was observed in experiments with minimal stimulation, and stimulation was most often done near the cell soma. However, the location of the site(s) of spontaneous or evoked release are not known and could account for the difference, but in the end the reason is not known.Reviewer #3:[…] A few issues need to be addressed.1) The authors need to state more explicitly what is known about the localization of RIM1-2 in neurons and cite the relevant work. Obviously, it would add significantly to the paper to demonstrate the presence of RIM proteins in the somatodendritic domain of dopamine neurons. The authors should at least explain why this was not done in the present work We have addressed this point in detail in the response to the editorial letter, major point 4.2) The Introduction should make it clearer that somatodendritic dopamine release is well established to be vesicular in origin and to be sensitive to cleavage of SNARE protein by clostridial toxins.We have addressed this in the Introduction and cite the relevant literature.3) The data on the frequency-dependence of dendritic dopamine release presented in Figure 1—figure supplements 2 and 3, while interesting, are not really relevant to the main point of the paper. Also, the conclusions drawn from these results (that dendritic dopamine release employs scaffolds and that RIM heterozygosity did not strongly impair dendritic dopamine release) are not really supported by this data. I would suggest removing this data from the manuscript.We thank the reviewer for bringing this up and fully agree, we have removed these data as also explained in the response to the editorial letter.4) The experiments carried out with L-DOPA are presented as if they were performed to determine whether the loss of dendritic dopamine release in RIM KO mice is due to reduced levels of dopamine D2 receptors. In fact, the results do not really address this question, which is otherwise important. The fact that L-DOPA induces a D2R-dependent response which apparently comes from serotonin axon terminals is quite interesting in itself, but only supports the idea that some dopamine D2 receptors remain in the somatodendritic compartment of dopamine neurons in the KO mice. It does not allow saying whether there are less or more D2R. Also, in control mice, it is surprising to note that there was no difference between the baseline response and the response after L-DOPA and sumatriptan. This would tend to suggest that L-DOPA did not boost dendritic dopamine release at all. Also, the sumatriptan-sensitive component is bigger in RIM KO mice compared to the control mice. What this means is unclear. Was there some compensatory adaptation in the 5-HT terminals in response to abrogation of axonal and dendritic dopamine release in these mice? Globally, these experiments with L-DOPA raise more questions than anything else.DA neurons have D2 dopamine receptors to sense dopamine. The striking absence of an IPSC cannot be explained by the complete or near complete loss of receptors.We fully agree that the L-DOPA experiments do not indicate that the D2 receptor expression is identical in the two genotypes, and we currently do not have an approach to count D2 receptor numbers. We think that the data are important and would prefer to leave them in the manuscript because they show that RIM cKODA animals was a gratifying confirmation of the severity of loss of dopamine exocytosis in these animals!The release of dopamine from 5-HT terminals and the augmentation of dopamine release from dopamine neurons has been studied in wild type animals before . Those results show that the major increase in the IPSC results from the 5-HT terminals. There is also an increase in dendritic dopamine release that is relatively small compared to that from the 5-HT terminals. The new results in the control animals (which are heterozygous for RIM) mirror experiments in these wild type animals, and we hope that the reviewer concurs that there is value in having this comparison in the paper given that it was not possible to have wild type littermates as controls included. To us, the observation that sumatriptan completely blocked the IPSCs in the RIM cKO5) To more directly address the possibility of changes in D2 receptor responsiveness of dopamine neurons in the RIM KO mice, it seems to me that it would be better to simply measure membrane currents evoked in dopamine neurons by local application of a D2 agonist. This would nicely complement the experiments of Figure 2 with amphetamine.We thank the reviewer for suggesting this experiment. We have performed it and included it in the revised manuscript . We have addressed the point in detail in the response to the editorial decision (major point 2) and also note that the spontaneous event amplitude does not change. Together, these observations make a strong effect on dopamine receptor localization or function very unlikely.6) The experiments of Figure 3 are quite interesting. The authors should also provide the frequency of these spontaneous events in control and KO neurons.We thank the reviewer for proposing this experiment. It is a laborious experiment given the low frequency and difficult detection, but we think that the new data added in response to this suggestion make the manuscript significantly stronger. This point is also addressed in the response to the editorial decision, major point 2.7) The absence of RIM starting from the late embryonic period could have perturbed the development of dopamine neurons, potentially leading to altered dendritic development. The capacitance results from Figure 1—figure supplement 1 argue that there were no major changes in the size of dopamine neurons in the KO mice. However, do the authors have the result of a TH immunostaining experiment in the substantia nigra to determine whether the global density of dopaminergic dendrites was altered in these KO mice?We have addressed this point with new experiments and described in the response to the editorial decision letter major point 1, and the new data are shown in Figures 2A and 2B. There is no detectable effect on dendritic development.


Records from 17 prior admissions documented elevations of WBC and LDH and included an abnormal dexamethasone suppression test (DST) which normalized with electroconvulsive therapy. Two later admissions showed CK and WBC elevations. We propose that these abnormalities reflect different aspects of catatonic biology: (1) the serum CK, the severity of muscle damage probably exacerbated by the menses; (2) the hypercortisolemia, the associated fear; (3) the leukocytosis with neutrophilia, the hypercortisolemia; and (4) the LDH elevations, which appear to be influenced by other biological abnormalities. Twentieth-century literature was reviewed for (1) menstrual exacerbations of catatonia, (2) biological abnormalities related to periodic catatonia, and (3) familial periodic catatonia.Kahlbaum first described catatonia; later Kraepelin, Gjessing, and Leonhard each defined periodic catatonia differently. A 48-year-old female with catatonia, whose grandmother probably died from it, was prospectively followed for >4 years in a US psychiatric state hospital. Through 4 catatonic episodes (one lasting 17 months) there were menstrual exacerbations of catatonia and increases in 4 biological variables: (1) creatine kinase (CK) up to 4,920 U/L, (2) lactate dehydrogenase (LDH) up to 424 U/L, (3) late afternoon cortisol levels up to 28.0 mcg/dL, and (4) white blood cell (WBC) counts up to 24,200/mm Catatonia is one of the six types of psychiatric disease described by Kahlbaum . After tKraepelin and two other lesser known Europeans, Gjessing and Leonhard, are associated with the concept of periodic catatonia. Kraepelin  describeRolv Gjessing  made manAfter reviewing 13 articles –21 and aKraepelin  was the In 1991, Rosebush and Mazurek  first suThe supplemental material presents in On her first admission to our state psychiatric hospital, the patient was a 48-year-old single white female. The patient signed a consent form for a pharmacogenetic research study approved by two institutional review boards in which she provided permission for publication of her data and clinical information. Beginning at age 34, she had had 17 admissions to private psychiatric hospitals  lorazepam. She was then transferred to an intermediate care unit from day 6 to day 136. All psychiatric medications except lorazepam were initially discontinued. The patient began to make improvements and olanzapine 20 mg/day was added. She had a sudden worsening of agitation on day 120 for no apparent reason, but the chart had no information on the menses. This episode of agitation was treated primarily with lorazepam as needed. On day 132, the treating psychiatrist reported that the episodes of agitation responded well to lorazepam and so he decided to add clonazepam 1 mg/day to the treatment.On day 137, the patient was transferred to the long-term unit managed by the senior author. She exhibited no clear catatonic signs and was being treated with 1 mg/day of clonazepam and 20 mg/day of olanzapine.On day 194, the patient started her menses. She appeared to have “overwhelming anxiety” and reported “anxiety in her body”. She was treated with 2 mg of oral lorazepam. She also had problems with eye contact, answering questions, and non-goal-directed hyperactivity, but the senior author did not realize these were initial catatonic symptoms. On day 196, the senior author realized that the patient definitively had catatonic signs  but after the treatment she became extremely agitated and had to be moved to the emergency room (ER) of the academic hospital, where the senior author was called in to manage her. She was treated with lorazepam 8 mg IV and then sent to the state psychiatric hospital. Over the next 10 days she received 3 ECT treatments plus the 6 mg/day of oral lorazepam. On day 1286, the patient returned to baseline and was placed on convalescent leave.The patient experienced one final brief episode of catatonia. On day 1563, the psychiatrist who provided ECT reported that the patient had been vomiting for 3 days and was unable to take her lorazepam, resulting in combative behavior that was managed by the family. There were no labs drawn because the patient was on convalescent leave and the catatonic episode resolved after the ECT was moved forward to day 1564 and another ECT on day 1567 was added. After those two ECTs she returned to baseline. After this last catatonic episode, the ECT was scheduled every 3 weeks and then moved to every 2.5 weeks to avoid paranoid ideation between ECT sessions. On day 1599, the patient was discharged on lorazepam 6 mg/day and ECT every 2.5 weeks and never readmitted.The data on the less important biological abnormalities has been moved to Supplementary Once the relationship between the menses and the first catatonic episode was established, the family indicated, after questioning, that the patient had suffered multiple previous catatonic episodes associated with the onset of her menses. As a matter of fact, within 30 days before admission to the state psychiatric hospital  was not enough to prevent a catatonic relapse. Catatonia resolved by day 11 of this menstrual cycle after treatment with lorazepam. She was placed on convalescent leave and had no relapses for three menstrual cycles.The second catatonic episode, in which catatonia reemerged and become refractory to high doses of benzodiazepines, started after the patient stopped taking lorazepam and began her menses the next day. The episode persisted for 17 months, including three regular menstrual cycles and times of irregular menses. Her menstrual cycles were sometimes three weeks apart, and other times more than sixty days apart. At other times the patient had light flow or menses for only two days at a time. Due to the association of the onset of catatonia with the menses, oral medroxyprogesterone was started in an attempt to regulate the female sexual hormone fluctuations associated with the menses. This medication was increased to 12 mg/day over the course of three months in the context of low serum progesterone levels. The patient did not show any improvement and medroxyprogesterone was stopped. The catatonic episode resolved when the patient finally agreed to proceed with ECT treatment, which appeared to help recover her response to benzodiazepines .During the 12 months of convalescent leave between the second and third catatonic episodes, the menses were not associated with catatonic symptoms while the patient was on maintenance treatment with both lorazepam and ECT. The patient had a sudden lorazepam withdrawal episode due to lack of a prescription which manifested as a withdrawal seizure rather than catatonia, but this event did not coincide with the menses.The third catatonic episode resulted from the combination of menses onset and a missed ECT treatment the next day due to a hospital misunderstanding. She recovered in 14 days after very aggressive treatment.During the nine months of convalescent leave between the third and fourth catatonic episodes, the menses did not exacerbate any catatonic symptoms while she was on maintenance treatment with lorazepam and ECT. The last menstrual period started on day 1479 and after that she did not have any more menstrual periods. After 2.5 months of being menopausal she had a short final catatonic episode after 3 days of vomiting and probably not absorbing the oral lorazepam, which resolved with ECT.In summary, the menses led to (1) the first catatonic episode when the patient was taking a low dose of benzodiazepines, (2) the second catatonic episode only after the patient also stopped taking lorazepam, and (3) the third catatonic episode only after the patient also was delayed in receiving ECT.The visual inspection of means and percentage of abnormal values  to 159 U/L on the tenth day of the episode (day 203). On the tenth day, the CK value was not yet normal despite the fact that the catatonic symptoms were no longer present. Due to the abnormal laboratory findings during this time, particularly the CK elevation of 1957 U/L, the patient was sent to the academic general hospital for further testing. She was returned to the state psychiatric hospital on day 197, with the comment that myocardial infarct had been ruled out.The second catatonic episode lasted 17 months and was characterized by an irregular oscillating pattern of CK values. The highest value was 4920 U/L on day 372, 32 days after the onset of this second episode. From day 483 to day 721, the patient had catatonic symptoms but the CK value was within normal limits. There were no CK values during the first days of the third catatonic episode but the CK value was normal on the eleventh day of this episode (day 1282). There were no CK values taken during the fourth catatonic episode because she was not admitted to the state psychiatric hospital.There were no LDH elevations during the first and third catatonic episodes, which were relatively short-lived. The first catatonic episode lasted only 10 days and LDH was always normal despite the peak CK elevation of almost 2000 U/L. The third catatonic episode lasted only 14 days; both the two LDH values and the CK value were normal. No CK or LDH values were studied during the fourth catatonic episode. During the 17 months of the second episode, two occurrences of LDH elevation were detected. The first occurrence of LDH elevation lasted 20 days and started on day 371, 30 days after the first CK elevated value, and at the same time CK peaked . The second occurrence lasted only during day 411, which was the same day that CK again was > 1,000 U/L.A multiple linear regression model with LDH as the dependent variable and 3 independent variables, CK, WBC, and cortisol, showed good adjustment (adjusted R square =.687). The 3 variables were significant after adjusting by the others: CK (p<0.001), WBC (p=0.002), and cortisol (p=0.027). The standardized residuals showed no significant autocorrelation and partial correlations did not reach the upper and lower limits of the confidence intervals.After the arrival of the documents from most of the 17 admissions to private psychiatric hospitals, it was clear that serum CK was never measured; however, LDH elevations were described in the seventh and tenth of these 17 admissions  of ANC and 64% (33/52) of WBC were above the normal limits. There were two periods in which the ANC increased to >16 x 103/mm3 on day 1276 which was the third day of catatonic symptoms  and erythrocyte sedimentation rate (ESR) were described  that the senior author saw the patient experiencing the first signs of catatonia, he misdiagnosed her with overwhelming anxiety of unknown cause and treated her with oral lorazepam. This is not surprising since there is consistent literature on the association between catatonia and fear, reviewed by Moskowitz . In his Rosebush and Mazurek  suspect The hypothesis that catatonia is a fear response is especially applicable to our patient because there is definitive agreement in the literature that psychological stressors induce cortisol release, particularly when a test subject feels a situation is uncontrollable or that they are being judged and evaluated by others . The fea3/mm3 with an ANC of 19.7 x 103/mm3. A few hours later at the state psychiatric hospital the WBC had decreased to 16.2 x 103/mm3 with an ANC of 12.0 x 103/mm3.Although there is debate, glucocorticoid-induced elevation of ANC is thought to occur through multiple mechanisms including enhanced mobilization of neutrophils from the bone marrow, demargination of neutrophils into active circulation, and prolonged intravascular neutrophil half-life . A recen3/mm3.As (1) the literature definitively supports the concept that hypercortisolemia can increase WBC and (2) in this patient, during periods of high cortisol, mean and abnormal WBCs appeared higher  she suffered from a lapse in treatment with either benzodiazepines or ECT. As detailed in We suggest a two-hit hypothesis of the menses and catatonia: the patient suffered from episodes of catatonia when (1) she had onset of her mensesKraepelin , BrockinWe suspect that heredity contributed to the patient's catatonic episodes. Her paternal grandmother probably died of severe agitation during an episode of excited catatonia. Her death occurred in her 30s, which is the same age that the patient started having catatonic episodes. The father would not be expected to demonstrate catatonia exacerbated by the menses, and the siblings were only half-siblings. The patient did not have any children. Leonhard  and his This study is limited due to the uniqueness of this case and the lack of comparison; as a matter of fact, a thorough literature review failed to reveal any other case reports or case series with comparable (1) long-term follow-up and data collection, (2) presentation of catatonia exacerbated by the menses, (3) LDH elevations in catatonia, and (4) unexplained platelet elevations in catatonia. Therefore, while this case demonstrates some unprecedented findings, we have over 1,000 days of close follow-up and over 1,500 days of inpatient monitoring. Similarly, the laboratory and clinical data available from other psychiatric hospitals are certainly limited, but we have gathered information over 25 years and 20 hospitalizations.In the late 1990s, catatonic patients were not studied for risk of pulmonary embolism using fibrin D-dimer . CurrentWe have presented years of clinical observations and drawn from hundreds of biological measurements in a patient with familial periodic catatonia. This case may have implications for those patients with (1) familial periodic catatonia, (2) fear as a salient feature of catatonia, and (3) any type of catatonia.Although familial periodic catatonia is probably rare, catatonia in general is underdiagnosed and undertreated , so it iIf fear is present in at least a significant number of patients with catatonia, hypercortisolemia may be more common than is recognized in patients with catatonia. As After a review of the literature from the early 20th century forward, we find that (1) menstrual exacerbations of catatonia, (2) biological abnormalities of periodic catatonia, and (3) familial periodic catatonia support the uniqueness of this case. Its originality is demonstrated by the long-term follow-up, including 4 years of prospective data including 4 catatonic episodes (the second one lasting 17 months) and an exhaustive study of biological abnormalities. Besides the 4-year admission at the state hospital, we gathered data from 14 years of previous admissions and from the 7 years following; these limited laboratory data also suggested that other catatonic episodes had similar biological abnormalities. Exacerbations of catatonia with the menstrual cycle have been described before but no previous description had similar detail. Moreover, during the second catatonic episode, we observed a significant CK elevation during the menses. Prior descriptions of catatonic patients included leukocytosis by Gjessing , 10 and