In the developing central nervous system, pre-myelinating oligodendrocytes sample candidate nerve axons by extending and retracting process extensions. Some contacts stabilize, leading to the initiation of axon wrapping, nascent myelin sheath formation, concentric wrapping and sheath elongation, and sheath stabilization or pruning by oligodendrocytes. Although axonal signals influence the overall process of myelination, the precise oligodendrocyte behaviors that require signaling from axons are not completely understood. In this study, we investigated whether oligodendrocyte behaviors during the early events of myelination are mediated by an oligodendrocyte-intrinsic myelination program or are over-ridden by axonal factors.To address this, we utilized in vivo time-lapse imaging in embryonic and larval zebrafish spinal cord during the initial hours and days of axon wrapping and myelination. Transgenic reporter lines marked individual axon subtypes or oligodendrocyte membranes.In the larval zebrafish spinal cord, individual axon subtypes supported distinct nascent sheath growth rates and stabilization frequencies. Oligodendrocytes ensheathed individual axon subtypes at different rates during a two-day period after initial axon wrapping. When descending reticulospinal axons were ablated, local spinal axons supported a constant ensheathment rate despite the increased ratio of oligodendrocytes to target axons.We conclude that properties of individual axon subtypes instruct oligodendrocyte behaviors during initial stages of myelination by differentially controlling nascent sheath growth and stabilization. Ensheathment of nerve axons with myelin is an essential process during vertebrate neural development. Oligodendrocytes are specialized central nervous system (CNS) ensheathing cells that extend multiple membrane processes to sample candidate axons, initiate and perform spiral axon wrapping, then extend along axons to form a mature myelin sheath . AltThe discoveries that cultured oligodendrocytes form immature myelin onto chemically fixed axons and synthetic fibrils indicate that oligodendrocytes possess an intrinsic ensheathment program that is capable of executing at least some of the cell behaviors and stages of myelination –19. The The CNS comprises many distinct neuronal subtypes, each possessing specific genetic profiles, structural morphologies, and functional connectivity. Oligodendrocytes stereotypically myelinate some axon subtypes but not others, but the cellular interactions forming this heterogenous myelin landscape are unknown. In this study we provide evidence that the earliest steps of sheath formation, stabilization, and growth are influenced by properties of individual axons. Using in vivo imaging in the larval zebrafish spinal cord, we found that distinct axon subtypes differentially controlled the rate of nascent sheath growth and initial stabilization. Consequently, the myelin ensheathment of distinct axon subtypes proceeded at different rates during the 48 h following initial wrapping. When a subset of target axons was ablated, remaining axon subtypes maintained consistent ensheathment rates despite the increased ratio of oligodendrocytes to myelin-competent axons. Taken together, these findings support the hypothesis that properties of individual axons are sufficient to influence or override the oligodendrocyte-intrinsic ensheathment program by regulating initial sheath growth and stabilization at the onset of myelination in vivo.°C in egg water  and staged according to hours post-fertilization or morphological criteria. The sex of animals was not determined. Transgenic lines used in this study included Tg(sox10:RFP-CaaX)vu234, Tg(sox10:TagRFP-T)co26, Tg(nkx2.2a:EGFP-CaaX)vu16, Tg(phox2b:EGFP)w37, Tg(phox2b:GAL4)co21, Tg(tbx16:EGFP)uaa6/812c, Tgzf234, and Tg(pitx2c:EGFP)zy8.All animal work performed in this study was approved by the Institutional Animal Care and Use Committee at Winona State University. Zebrafish embryos were raised at 28.5 Embryos or larvae were embedded in 1% low-melt agarose using tricaine (MS-222) anesthesia . All images were acquired or rotated to show lateral views with anterior left and dorsal up. Confocal microscopy was performed using an Olympus IX-81 equipped with a disk spinning unit (DSU), 60 × 1.3 NA silicone immersion objective (unless otherwise stated), LED illumination with narrow bandpass filters, and a Hamamatsu Orca-R2 CCD camera. Z-intervals were 400 nm and individual camera pixel sizes were 107.5 nm, or if 2 × 2 binning was required for fluorescence detection, 215 nm. All image analysis was performed using CellSens (Olympus) and NIH ImageJ software. Images were deconvolved using 3D iterative deconvolution  and all projection images were produced using maximum intensity.°C while embedded in 1% low-melt agarose under tricaine anesthesia. Sheath growth rate measurements were restricted to sheaths that were present at both time points. A pruning event was defined as a full retraction such that at t = 3 h no interaction remained, or the remaining interaction lacked both parallel processes and a lumen.To determine overall ensheathment rates, a baseline time point was first defined for each individual axon subtype as a specific spinal cord segment where axon wrapping had initiated in the majority of larvae imaged. This baseline (t = 0) was determined by the presence of a low but consistent level of sheath initiation on transgene-labeled axons. Subsequent images were acquired 24 and 48 h later within the same spinal cord segment. For each image, the percent of individual axon length ensheathed was determined by dividing the sum of all sheath lengths  by the total axon length within a field of view. For measurements of individual sheath growth and pruning, we collected images of the same nascent sheath at t = 0 and t = 3 h. A nascent sheath was defined as a structure with processes wrapped around the entire circumference of an axon in x-y and orthogonal views, containing a clearly defined lumen devoid of oligodendrocyte-channel fluorescence, and myelinating internodes 2–5 μm in length. During the 3-h period between image acquisitions, fish were incubated at 28.5 isl1+ RB is dorsal/medial). Next, we determined the lengths of all sheaths within the resident domain. We separated these values into two categories (yes/no) based on whether the sheath exhibited RFP/GFP colocalization with an axon marked by the subtype-specific reporter line. The total length of sheaths with RFP/GFP colocalization was then divided by the combined length of all sheaths within the resident domain. Images were acquired at 98–105 hpf. Images of ventral axon subtypes were acquired at somite 27, whereas images of dorsal/medial subtypes were acquired at somite 21 to account for the slight developmental delay of myelination within the dorsal spinal cord.We first measured the percent of all myelin associated with the Mauthner axon, dorsal to the Mauthner, and ventral to the Mauthner axon. To accomplish this, the combined sheath lengths within the three aforementioned dorsal-ventral domains were summed and then divided by the combined lengths of all sheaths within the spinal hemisegment. Images were acquired in somites 24–25 (restricted to larvae possessing 10–50 sheaths per domain) at 96 hpf (hours post-fertilization). Individual sheaths within each of the three domains were next assigned to individual axon subtypes. First, axon subtypes were assigned a domain of residence based upon their anatomical position (e.g. °C. Behavior was assessed at 24 h post-injury (hpi) and immediately prior to data acquisition. Animals exhibiting any signs of spontaneous swim in a period of 20 min, response to light stimulation, or response to touch (pin tool) near the otolith were discarded. Unless otherwise indicated, the Tg(pitx2c:EGFP) reporter was crossed into the background of all animals used for experiments to visually verify the absence of distal reticulospinal axon segments in the region of interest prior to data acquisition. Because the absence of spontaneous swim behavior and complete morphological reticulospinal axon ablation corresponded , behavioral criteria were used to select animals for imaging of isl1[ss]+ Rohon-Beard (RB) axons because nkx2.2a:EGFP-CaaX+ cells prevented observation of GFP+ reticulospinal axons in the ventral spinal cord. Non-injured, stage-matched siblings were used as controls.A sharp-edged ablation needle was generated from pulled capillary glass and adjusted to 60 μm diameter using #5 forceps. Zebrafish embryos staged at 2–3 days post-fertilization (dpf) under tricaine anesthesia were transferred atop Sylgard-coated petri dishes and aligned anterior left and dorsal up, with minimal egg water, under a dissecting microscope. The ablation needle was held roughly vertical and perpendicular to the embryo (60–75 degree angle) and inserted through the spinal cord at somite 7. Care was taken to avoid desiccation of the animal or injury to the notochord. Animals were immediately transferred to egg water and incubated at 28.5 tbx16+ CoPA axon, whose diagonal ascending portion traversed the medial domain then projected horizontally along the dorsal domain, and by sheaths wrapping the Mauthner axon, which separated the medial domain (sheaths above) from the ventral domain (sheaths below). Sheaths wrapping the Mauthner axon were excluded from analysis. Local spinal neuron cell body and axon counts were performed at 96 hpf (24 hpi) and images were acquired at or posterior to somite 20 for isl1[ss]+ neurons and within somites 16–17 for tbx16+ neurons.Oligodendrocyte-lineage cell counts and myelin sheath counts were performed at 96 hpf (corresponding to 24 hpi), and images were acquired within somites 24–26 and 24–25, respectively. For oligodendrocyte-lineage cell counts, images were acquired using a 20 × 0.7 NA objective. The domains were delineated by sheaths wrapping the isl1[ss]+ RB sensory axon ensheathment, larvae were imaged at 96 hpf (24 hpi). All sheaths at or posterior to somite 21 were counted and imaged. Selection of somite 21 was based on the absence of sheath initiation at this segment at the time of injury (72 hpf), enabling later assessment of sheaths that had formed in an environment lacking descending reticulospinal axons. For assessment of tbx16+ CoPA axon ensheathment, the distribution of tbx16+ CoPA neurons necessitated alterations in somite of data acquisition. Because the Tg(tbx16:EGFP) reporter line marks relatively few CoPA axons in posterior spinal cord somites 24–26 . Larvae were imaged at 72 hpf and 84 hpf (24 hpi and 36 hpi). To verify injury performed at the earlier 48 hpf time point still resulted in the complete absence of reticulospinal axons within the posterior spinal cord at both 24 and 36 hpi, the Tg(pitx2c:EGFP) reporter line was crossed into all animals for internal validation. Zebrafish embryos have not initiated myelination by 48 hpf, therefore any myelin that we observed had formed in the absence of reticulospinal axons.For assessment of For acquisition, an individual axon was located and an image series comprising 100 single z-plane images with minimal time delay (rate of ~ 12 frames per second) was obtained by standard widefield fluorescence microscopy. A corresponding single z-plane GFP/RFP image depicting the location of myelin on the axon was also obtained for reference. The 100 frame series was imported into Fiji and processed using the NanoJ-SRRF plugin with TRA and TRPPM temporal settings . Fourierp-values. Fisher’s exact test was used to determine whether the presence of barriers differed across axon subtypes, with Bonferroni correction to adjust for multiple pairwise comparisons. Simple linear regression was used to determine whether sheath growth rate correlated with change in axon diameter. All statistical analyses were performed using Prism 6 (GraphPad), JMP Pro 14.0, or R Studio 1.1.453. In all figures, error bars represent mean ± standard error of the mean (SEM).In instances where the data were normally distributed, we used an unpaired, two-tailed t-test for hypotheses concerning differences between two means. In instances where data were not normally distributed, we preferentially utilized non-parametric tests to compare distributions: the Mann-Whitney test when comparing distributions of two groups or the Kruskal-Wallis test for comparing distributions of more than two groups. When the Kruskal-Wallis test was significant, Dunn’s method was used to adjust for multiple comparisons to determine where differences existed. A chi-square test was used to determine if individual axon subtypes supported sheath pruning versus stabilization (binary outcomes) by comparing percentages of outcomes across groups. Tukey-adjusted pairwise comparisons were then used to determine where differences existed. In order to determine if myelination rate differed across the axon subtypes, an analysis of covariance (ANCOVA) was fit, allowing for an interaction between time and axon subtype. Tukey-adjusted pairwise comparisons were used to assess potential myelination rate differences between individual subtypes and generate Tg(tbx16:EGFP) reporter expression restricted to CoPA neurons + RB sensory axons (41/41)  .Previous studies indicate that some initial axon wrapping attempts can undergo retraction and pruning, motivating us to ask whether the outcome of sheath stabilization versus pruning could be axon subtype-dependent –5. Durinisl1[ss]+ RB sensory axons over the 48-h period post initial wrapping  during the same 48-h period post initial wrapping  based on the earliest appearance of nascent sheaths within a defined spinal cord segment, and subsequently acquired images within the same spinal segment 24 and 48 h later. Oligodendrocytes did not increase the overall percent ensheathment of The initiation of myelination requires a threshold axon diameter to be met . This lepitx2c = 0.75 μm, phox2b = 0.71 μm, and isl1[ss]+ 0.61 μm). These observations are compatible with the possibility that comparatively large diameters of CoPA axons could cause their more rapid myelination. However, using the same logic, these findings cannot explain why reticulospinal axons become myelinated more rapidly than RB sensory axons, because SRFF imaging did not detect a significant diameter difference between these subtypes . In comparison, other subtypes examined were of slightly lower caliber  myelin sheaths  to the oligodendrocyte reporter Tg(nkx2.2a:EGFP-CaaX). At 72 hpf, oligodendrocytes within and posterior to somite 21 had not initiated myelination in the DLF, where RB axons resided . We therefore performed anterior spinal cord injuries at 72 hpf and observed RB axon ensheathment in all somites within and posterior to somite 21 at 96 hpf. Because ensheathment had not commenced at the time of ablation, all sheaths observed and counted within and posterior to somite 21 had formed after ablation of reticulospinal axons. During the 24-h period from 72 to 96 hpf, we observed an average of 2.8 ± 0.5 new sheaths formed onto RB axons in control animals  and lacks inhibitory signals , 22, revtbx16+ CoPA) supported more rapid sheath growth rates in comparison to sensory (isl1[ss]+) and reticulospinal axons (pitx2c+ and phox2b+). Commissural and sensory axons consistently instructed nascent sheath stabilization, but in comparison, sheaths wrapping reticulospinal axons pruned more frequently. We interpret these observations to mean that axon subtype-specific factors over-ride the oligodendrocyte ensheathment program during the earliest steps of myelination. Because we directly observed the behaviors of nascent sheaths within their initial few hours after formation, if a purely oligodendrocyte-intrinsic program does temporally precede the emergence of axonal influences, our data indicate that these control mechanisms would be separated by only a few hours or less. One alternative explanation could be that the axon subtypes observed are surrounded by distinct extracellular matrix molecules, which could thereby influence the behavior of oligodendrocyte processes and nascent sheaths in an axon-independent manner. Although this is possible, one reason this may be unlikely is that commissural and sensory axons are juxtaposed within the same dorsal spinal cord tract (DLF), yet became myelinated at different rates and to different extents.If the initial growth and stabilization of nascent sheaths is controlled by an oligodendrocyte-intrinsic program, we predicted that nascent sheath behavior would be independent of the axon subtype being myelinated. Instead, we observed that both nascent sheath growth and stabilization depended on the axon subtype being myelinated. For instance, spinal cord commissural axons  and oligodendrocytes . In contOur studies of how ensheathment of individual axons is influenced by changes to the availability of other target axons also has implications for myelin plasticity. Are the early phases of myelination mediated by hard-wired developmental processes that are controlled exclusively by gene regulatory networks intrinsic to neurons and oligodendrocytes? Alternatively, could experience-based changes modify gene regulatory networks and pathways controlling initial sheath growth, stabilization, and the extent and rate that individual axons become myelinated? These questions are incompletely resolved, but accumulating evidence indicate that multiple forms of distinct adaptions can occur, and that such changes may depend on developmental stage, anatomical region, and axon subtype identity , 43, 44.If axons provide critical information that instructs early myelin sheath growth and stabilization, this suggests that strategies and regenerative therapies focused solely on manipulating oligodendrocyte biology may be inefficient or insufficient to induce remyelination. Instead, if the properties of demyelinated axons have changed, these axonal alterations may represent important targets in addition to promoting oligodendrocyte differentiation and myelinating potential. For example, emerging evidence indicates that axonal galectins mark non-myelinated axon segments, and that galectins may be mis-expressed within Multiple Sclerosis lesions , 47. NumThis study provides insight into how individual axons obtain distinct myelin profiles during development, and how neuronal diversity contributes to early myelin patterns across the CNS. Our findings demonstrate that the oligodendrocyte intrinsic propensity to myelinate is overridden or modified by properties of individual axons in vivo. This axon-dependent regulation occurs from the onset of myelination. Taken together, our data support a model whereby axons are not passive substrates during initial myelination, but instead instruct oligodendrocyte behaviors to control early sheath growth, stabilization, and pruning.Additional file 1: Figure S1. Characterization of transgenic reporter lines used. Figure S2. Ablation removes posterior reticulospinal axon segments without altering the number of CoPA and RB local spinal cord axons. Figure S3. Effects of reticulospinal ablation on posterior spinal cord glia.


Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers. Predicting an individual's response to therapy is an important goal for precision medicine. Here, the authors use an algorithm that takes into account the interaction type and directionality of signalling pathways in protein–protein interactions and find that their pathway analysis can predict essential genes, which may be a target for therapy. However, identifying high-quality biomarkers that are accurate and robust across different experimental conditions is challenging.The principle of precision medicine is to optimize medical care by tailoring the treatment of patients based on their genetic and molecular characteristics. To achieve this goal, it is necessary to discover and develop biomarkers that help decide which patients to treat (prognostic biomarkers) and which therapy is most likely to be effective (predictive biomarkers)5), partly because measuring somatic mutations has less noise and batch effects compared to measuring gene expression8, and somatic events represent changes specific to cancer cells. Biomarkers based on gene expression levels, on the other hand, are less common because they are much more prone to batch effects, can vary greatly among different cell types, and are affected by the expression levels of genes in both cancer and non-cancer cells. Despite these challenges, a few expression-based biomarkers were shown to improve predictions beyond the traditional clinical and pathological parameters in various cancer types13. Most of these biomarkers were based on using expression levels across multiple genes or gene pathways rather than single-gene biomarkers because they more reliably and robustly predicted response17. Pathway-level methods were also more robust against batch effects, thus enabling the aggregation of data from various cohorts, resulting in highly significant biomarkers22. Historically, identifying pathway-based expression biomarkers was limited due to the lack of large-scale gene expression data from many cancer cell lines coupled with their sensitivity to a large cohort of anti-cancer drugs. However, the recent technological advances in gene silencing and editing enabled the generation of large-scale gene essentiality data  in hundreds of cell lines derived from many different tumor types. This source of gene dependency data may enable the discovery of previously unknown altered pathways that are predictive of a tumor’s response to a drug. This can potentially uncover important biomarkers for several cancer types and treatments that are not possible to find at the single-gene expression level or through somatic DNA alterations.For biomarkers that predict a cancer’s susceptibility to a drug, detection of either (i) the presence of somatic DNA alterations or (ii) specific gene expression patterns have been used. Of these detection methods, the most common has been detection of somatic DNA alterations in single genes . Indeed, accounting for the directionality of signaling pathways may increase both the accuracy and robustness of the analysis over other publicly available pathway-level tools that evaluate only gene sets. Here, we integrate gene expression and drug sensitivity datasets measured for hundreds of anti-cancer drugs across 460 cancer cell lines from 10 tissue types , ovary, pancreas, skin, stomach, and upper aerodigestive tract). To ensure robust findings, we use pathway activity levels generated from both microarray and RNA-seq data and tested their association with sensitivity measured in two different drug screening efforts, the Cancer Target Discovery and Development (CTD2)24 and Genomics of Drug Sensitivity in Cancer (GDSC)25. Furthermore, we (i) use highly conservative analytical methods and (ii) validate our predictive biomarkers using independent datasets as well as in patient-derived xenograft models (PDX) and fresh human tumors grown ex-vivo. Overall, our work illustrates the utility of identifying pathways whose activity levels can be used to predict, and potentially control, the response to specific compounds.In this study, we aimed to identify novel pathway-based predictive biomarkers across multiple tumor types for the response to different compounds by taking advantage of these recently available large datasets. To analyze this wealth of data, we chose to use a publicly available tool called PathOlogist17. Previous studies suggest that pathway-based biomarkers may have a higher reproducibility than individual gene-based biomarkers19. This makes sense from both biological and statistical standpoints since biological processes often affect many genes simultaneously, offering the opportunity to aggregate readouts across many genes and thus extract a more stable metric from the “noisy” expression pattern of individual genes. While a common way to calculate a score for a pathway is to average the expression level of its member genes, here we employed the PathOlogist tool23, as it uses the structure of gene relationships within the pathway rather than treat the genes as simply a uniform set of entities. For each interaction node in a pathway, the PathOlogist tool calculates its potential to occur by determining the expression of its input proteins. An activity score of ‘1’ for an interaction indicates that all positively regulating proteins are being highly expressed, while inhibitory proteins are not expressed. The activity score of each of the pathways (ranging from 0 to 1) is then calculated by averaging the activity scores of all interactions within a pathway. A more detailed explanation of how pathway activity scores are being calculated can be found in the 23. Importantly, taking into account the structure of the pathway may have a major effect on the final pathway activity score as compared to averaging the genes’ expression levels. For example, the positive or negative regulation that each protein has on every interaction in the pathway is not reflected by averaging gene expression. Also, hub proteins that influence multiple interactions in a pathway will affect the PathOlogist activity scores of all of these interactions, as opposed to giving the same weight to all genes, if we simply average the expression of all genes in the pathway  plus (ii) an additional 449 pathways that we added from PharmKGB27, Wikipathways28, and SignaLinks29 (see “Methods” section). In order to confirm the robustness of pathway-level activities, we evaluated the consistency of gene expression and pathway activities between experiments and platforms. We first took advantage of gene-expression microarray data that was generated for the same 438 cell lines at two different research centers: the GDSC project from the Wellcome Trust Sanger Institute and Massachusetts General Hospital30, and the Cancer Cell Line Encyclopedia (CCLE) project from the Broad Institute and Novartis32. To compare the stability of expression values of genes and the stability of pathway-level activities, we studied the distributions of Euclidean distances, a formal measure of similarity, between the corresponding values for each gene and pathway in these two datasets (see “Methods” section). Indeed, the distances were much lower for pathways than they were for individual genes  the original 579 pathways already in the PathOlogist dataset including PID pathwaysnes Fig. , reflectnes Fig. .Fig. 1Pa33 to visualize the data and observed that, when using individual genes, the samples correctly partitioned according to the different tumor types but did not clearly distinguish between the normal and tumor states. In contrast, the t-SNE of pathway activities clearly separated the normal and cancer samples, as well as the different subtypes in breast cancer patients , for which higher values indicate more robust clustering . Here again, we observed that pathway activity levels cluster the samples more robustly  that had a large number of tumor and tumor-adjacent normal samples. We used t-SNE dimensionality reductionnts Fig. . To eval2 project. We additionally selected 254 and 307 cell lines for which the essentiality scores of more than 10,000 genes were tested by CRISPR or RNAi screens, respectively, in the Achilles project39. The association between activity levels and response was calculated within each tissue type separately to allow the detection of cancer type–specific biomarkers.To see the possible benefit of using pathway activity levels as biomarkers, and to see the differences between pathway-based biomarkers and single genes or gene sets biomarkers, we initially selected as input 460 cell lines from 10 cancer types for which we had available drug-response sensitivity data to 481 compounds from the CTDz-score values derived from the area under the dose–response curve . We used (i) single-gene expression for 10,225 genes; (ii) GSEA  with 50 hallmark and 4762 curated gene sets from the Molecular Signatures Database (MSigDB41); (iii) GSVA  using the same gene sets as for GSEA, and (iv) PathOlogist pathway activity levels for 1028 pathways  and 31 near-significant (0.05 < q ≤ 0.25) pathways in both microarray and RNA-seq datasets. Within those identified, 25 pathways overlapped between the two platforms (q < 0.25) increased with the number of cell lines analyzed . Interestingly, it has been suggested that a CREB-dependent mechanism may cause resistance to BRAF–MEK inhibition in some melanoma patients43 and that NFAT is activated by oncogenic BRAFV600E via canonical MEK/ERK signaling in melanoma cell lines44. These results are thus consistent with our observation that the CREB and NFAT pathways are highly significant and robust biomarkers for predicting the response to BRAF/MEK inhibitors. In addition, we also identified the activity of ‘MAPK inactivation of SMRT corepressor pathway’ as a good predictive biomarker for the sensitivity of EGFR-activated NSCLC cell lines to EGFR inhibitors. As expected, this pathway correlated with the presence of activating mutations in EGFR , lack of specific predictive biomarkers for their use has limited their utility. Therefore, there is a clear unmet need for predictive biomarkers for these drugs46. In the nucleus, activated STAT5 dimers bind to specific DNA-response elements located in the promoters of target genes to regulate various cellular responses, including growth and survival47. STAT5 is constitutively activated in several solid tumors, including prostate cancer48, breast cancer49, nasopharyngeal carcinoma50, and head and neck squamous cell carcinoma51. However, the precise role of STAT5 in epithelial carcinogenesis remains incompletely understood.Here, we identified the activity of the ‘IL2 signaling events mediated by STAT5’ pathway Fig.  as a robBCL2 and BCL2L1) whose expression is directly induced by STAT5. Low pathway levels were significantly associated with sensitivity to ABT-263 and ABT-737 in both small-cell (SCLC) and non-small-cell lung cancer (NSCLC) cell lines but not in any other cancer type, while high pathway activity levels were associated with lack of sensitivity  are used in the treatment of many solid and hematologic malignancies59. Here, we identified the activity of the apoptosis-inducing factor (AIF) pathway as a strong predictor for response to four different MTIs in both SCLC and NSCLC , block AIF1 translocation by directly sequestering members of the pro-apoptotic proteins like Bax. Interestingly, MTIs were shown to promote the release of Bax from Bcl-xL by inducing phosphorylation of Bcl-xL60. We reason that the high activity level of the AIF pathway reflects the pro-apoptotic pull in the delicate balance between anti- and pro-apoptotic forces. Treating cells that have high AIF pathway activity with MTIs may cause a drastic shift in this delicate balance by releasing large amounts of sequestered Bax proteins and tipping the balance into apoptotic cell death.Microtubules (MTs) are important for many cellular functions involving the cytoskeletonCLC Fig. . The AIFInterestingly, the expression levels of each of the three genes in the AIF pathway poorly classified the lung cancer cell lines as responders or non-responders to MTI , one partially resistant cell line (HCC2935), and one cell line that was highly sensitive (H211). We measured the expression levels of the pathway genes using RT-qPCR and found, as expected, that the sensitive cell line H211 had a higher AIF pathway activity level than the four resistant cell lines  model. PDX tumors were removed and sectioned into 250 μM slices (see “Methods” section) and then treated ex vivo with (i) DMSO, (ii) Navelbine (vinorelbine), (iii) ABT-263, or (iv) a combination of the two drugs. Four biological repeats were carried out, each using four PDX tumors, and tissue viability was assessed by H&E staining Fig. . In agreTaken together, our results highlight (i) the sensitivity of lung cancer cells with high activity of the AIF-apoptosis pathway to MTIs, and (ii) the synergistic effect of Bcl-xL and MTIs in lung tumors with initial low activity level of the AIF-apoptosis pathway.67. While specific inhibitors are available for some of these targets, many others are still lacking clinical-grade small-molecule inhibitors. The presence of predictive biomarkers for the essentiality of specific genes may prioritize the development of such small molecules or RNAi interventions that can target these genes.Over the last decade, multiple genome-wide functional genomic screens identified genes that were essential for the proliferation and survival of cancer cellsngenes = 32; Supplementary Fig. BRAF in melanoma cell lines . For example, the ‘IL2 signaling events mediated by PI3K’ pathway  at the mitotic spindle, and the appearance and disappearance of CLTC along with TUBB occurs during spindle activity70. Inhibition of CLTC was previously shown to induce apoptosis in colon cancer cell lines by disrupting bipolar spindle formation71. As such, it may play an important role in determining cellular sensitivity to chemotherapeutic agents72.Clathrin heavy chain 1 (CLTC) is an important component of the cytoplasmic face of intracellular organelles. CLTC also plays an important role in mitotic spindle assembly and chromosome congression in the metaphase plate through a microtubule-mediated mechanismCLTC inhibition by RNAi  and in > 500 TCGA patients  a p53 signature that can refine the selection of patients that will likely respond to p53–MDM2 inhibitors83.By incorporating multiple available novel datasets and tools that take into account the combined influence of different components of a pathway, we were able to find a large number of novel biomarkers as well as the following other important advances in discovering biomarkers based on pathway-based analyses: (1) our results demonstrate that relatively large cohorts of cell lines from each cancer type are needed in order to be powered enough to detect statistically significant biomarker pathways  can commonly affect protein activity levels and the resulting pathway activities. For pathways in which the activity level is not reflected by changes in the expression patterns of the pathways’ members, our analysis will be underpowered and likely miss a potential association with response, resulting in false-negative results. We speculate that the activity of pathways controlled by transcription factors may be easier to detect by gene expression data. Nevertheless, even classic signaling pathways that propel the signaling flux along the pathway via post-translational modification events commonly affect downstream transcriptional activity, and thus may also be detected by mRNA expression data.It should also be noted that while the PathOlogist tool takes into account prior data about protein–protein interactions, as well as the interaction type and directionality, these connections can change among different tissues and cancer types. Future availability of tissue-specific connections may result in even better and more accurate predictive biomarkers. Still, our ability to find and validate multiple novel biomarkers speaks to the fact that, at least in some, the pathway expression data coupled with prior knowledge of the interactions between the proteins in the pathway and their directionality is reflective of the pathway’s activity.In summary, personalized predictive biomarkers can help us identify which tumors will be sensitive to a certain drug. The work presented here provides evidence that our novel, stringent methods can confidently identify pathways that can serve as robust and powerful biomarkers. Based on our findings, which we validated in several independent datasets and in human-derived cell lines and tumors, we propose a way of identifying new and clinically relevant therapeutic combinations that take into acccount a patient’s specific molecular expression pattern. Finally, based on our sensitivity analyses, we observe a rapid increase in the number of pathway-level biomarkers with the number of cell lines of a given tumor type (or subtype); hence, we expect additional biomarkers to be found with larger drug-sensitivity and gene-dependency datasets.All cells were maintained in Dulbecco’s Modified Eagle’s Medium (DMEM)  containing 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin with glutamine . For green fluorescent protein (GFP) expression in the cancer cell lines, lentiviral transduction was carried out using the pLex_TRC206-GFP plasmid.Mouse experiments were performed in accordance with the Institutional Animal Care and Use Committee (IUCUC) guidelines. Patient-derived xenograft (PDX) models were purchased  by the Weizmann Institute and licensed for institutional use.2, and the tumor tissues were removed and placed in ice-cold PBS. Tumors were sliced to 250-μM thick slices using a vibratome , placed in 6-well plates on titanium grids  with 4 mL of DMEM/F12 medium . Tissue was cultured at 37 °C, 5% CO2, and 80% O2, on an orbital shaker  at 70 rpm. The following day, tissue was treated with drugs as indicated for 96 h, followed by formalin-fixed paraffin embedding (FFPE) after overnight fixation.Mice were euthanized with CO3 was taken and placed in ice-cold PBS for immediate transfer to the lab for ex-vivo organ culturing. All experiments were performed in accordance with Medical Ethics Committee approval and after obtaining informed consent. Specimen was coded anonymously prior to its arrival to the lab.Fresh lung tumor tissue was obtained from an untreated 71-year-old lung cancer patient undergoing partial lung resection for cancer treatment at Kaplan Medical Center . After resection, the tissue was directly transported to the pathology department. A small sample of approximately 1 cmThe human experiment was authorized by the Kaplan Medical Center (0097-18-KMC). IACUC authorization for the mice experiments was given by the IACUC committee of the Weizmann Institute.2) data portal . These data represented dose–response curve AUC measures for 887 cell lines across 481 compounds. Gene expression (Affymetrix HG-U133_Plus_2 Array Plate Set) and RNA-Seq of these cell lines was downloaded from the CCLE cell lines portal . The expression data were normalized by quantile normalization to produce RMA expression values from the Affymetrix CEL files84. RNA-sequencing data were downloaded as RPKM (Reads Per Kilobase of transcript per Million mapped reads) levels from the CCLE data portal.Drug sensitivity measurements were downloaded from the Cancer Target Discovery and Development (CTDThe GDSC database is a resource for biomarker discovery for the development of therapeutics for cancer cells. It contains information from 138 anticancer drugs across 696 cell lines. These data were generated from high-throughput screening performed by the Cancer Genome Project at the Wellcome Trust Sanger Institute (WTSI) and the Center for Molecular Therapeutics at Massachusetts General Hospital. Gene-expression was quantified using an Affymetrix U219 mRNA expression array and was downloaded from the same portal. The expression data were normalized by quantile normalization to produce RMA expression values from the Affymetrix CEL files.Project Achilles uses genome-scale RNAi and CRISPR-Cas9 genetic perturbation reagents to silence or knock out individual genes and identify those genes that affect cell survival. Gene essentiality profiles for 11,408 genes over 307 cell lines as well as 18,377 genes across 227 cell lines from genome-wide RNAi and CRISPR-Cas9 screens, respectively, were measured using the DEMETER algorithm, which aims to isolate the effects of gene knockdown from off-target effects. CERES is used to detect dependencies from the CRISPR data. Cell lines were designated as ‘sensitive’ or ‘not sensitive’ based on their Z-transformed RNAi and CRISPR essentiality levels.Euclidean distance (ED) metric was applied to define the distance between the coordinates of any two genes or pathways in linear space. Hence, the smaller the distance between two genes or pathways, the more similar they are. Here, we calculated the similarity between the same platforms in different centers and between two different platforms by measuring the ED between the normalized gene expression or pathway-level activities across the cell lines in the corresponding datasets.26, PharmGKB27, Wikipathways28, and SignaLinks29.Network information was obtained from the National Cancer Institute’s Pathway Interaction DatabaseTo calculate pathway activity metrics, we used the PathOlogist tool, which translates gene expression levels (in the manner detailed below) to a metric that provides information about the interactions within a pathway. This pathway activity is provided per sample, for each of the included pathways.To use gene expression, PathOlogist first calculates, for each gene in each sample, the probability for that specific gene to be expressed in that specific sample. An expressed gene is called an “up” gene, and an unexpressed gene is called “down”. A probability for a specific gene to be in the “up” state is calculated using the distribution of the expression level of the gene across all samples.To be able to accommodate a multitude of probability distributions, the algorithm uses gamma distributions as the family of functions that describes the gene expression. In the set of samples, the gene could be either expressed (“up”) or unexpressed (“down”) in each sample. Across samples, we assume that the collection of unexpressed instances follows an exponential distribution, which is one particular case of a gamma distribution. The collection of expressed instances is assumed to be distributed in a normal manner, which is well approximated by a larger mean gamma distribution. For each gene, we use an expectation-maximization (EM) algorithm to iterate over the data such that the likelihood of fitting these data by the distribution’s increases. The EM algorithm finally provides us with the most likely parameters of the modeled distributions and with the mixture weights of the two distributions. Once these two distributions are in place, we can calculate the probability of each gene in a sample to belong to one of the two distributions. That is, we obtain the probability of the gene to be in the up state. This probability is the (Up Down Probability) UDP measure that is used in the pathway score. Since a gene, in principle, could be in an “up” state or in a “down” state across all samples, we need to determine if the best fit is a single gamma distribution or a mixture of two gamma distributions. To compare these two models, we use the Bayesian information criterion (BIC).U-test is performed based on the sensitive/non-sensitive groups of cell lines in order to determine the dependency and significance of the association of the pathway activity scores with response to treatment.Once we have the set of gene probabilities for each gene in each sample, we continue to calculate the pathway activity metric, which is calculated for 1028 pathways. PathOlogist treats the pathway as a network of interactions and assigns the network a score based on the expression levels of the interacting genes and on the quality of the interaction. The analysis also takes into consideration the specific type of interaction (inhibition or promotion). The activity of each interaction is calculated by multiplying the probability of the genes to be “active” (based on the UDP matrix). Then, the final pathway activity score is calculated by averaging all the interaction activity scores in the pathway. Once the pathway activity scores are calculated, a Mann–Whitney 41 was applied to the microarray and RNA-seq data using the hallmark gene sets (n = 50) and the curated gene set (n = 4762) to discover statistically significant gene sets that can predict drug sensitivity.GSEA85 calculates gene set enrichment scores per sample in an unsupervised manner . GSVA was applied to both the microarray and RNA-seq data using the hallmark gene sets (n = 50) and the curated gene set (n = 4762). Mann–Whitney U-test was then applied on the GSVA results with the response groups, and FDR correction for multiple hypotheses was performed (as we did with the PathOlogist pathway activity scores). While MSigDB curated gene set (C2) did not yield any significant results, MSigDB Hallmark set yielded 654 significant results for the microarray dataset and 213 significant results for the RNA-seq dataset—none of which overlapped between the two sets.Similar to PathOlogist, GSVASPP1, high expression levels were associated with sensitivity to GDC-0879, a BRAF inhibitor, in skin cancer cell lines; q = 0.228), and this gene did not belong to any of the significant pathways.To test if the larger number of hypotheses generated by genes as compared to pathways was the reason that we did not detect any single gene as a predictive biomarker Fig. , we re-rn = 106) and skin (n = 50) subsets of cell lines. Specifically, we explored how the number of significant results decreased with sample size by repeating the analysis on smaller random subsets of the cell lines, reducing the subset to as few as 10 cell lines per tumor type. As expected, for both sets of cell lines, the number of significant pathways (q < 0.25) increased with the number of cell lines analyzed  reference gene in the same sample.Total RNA was purified using a Qiagen RNeasy mini-prep kit (product #74104) according to the manufacturer’s protocol. A 0.5-μg aliquot of total RNA from each sample was reverse transcribed using PCRBIO cDNA Synthesis kit (product #PB30.11-10). qRT-PCR was performed on the Applied Biosystems qPCR system using KAPA SYBR Green Fast ABI Prism qPCR kit  at 94 °C for 3 min to denature RNA, with 40 cycles of amplification at 94 °C for 15 s, 50 °C for 30 s, and 72 °C for 30 s; Data analysis was performed according to the ΔΔCt Method by normalizing the expression level of each gene to that of a glyceraldehyde 3-phosphate de-hydrogenase (AIFM: 5′-AAGGGCAATGCAGACTACAGA (F) and 5′-GGAACCATCATGTGCCCAAAG (R); BCL2L1: 5′-ATTGGTGAGTCGGATCGCAG (F) and 5′-CCACAAAAGTATCCCAGCCG (R); PARP1: 5′-CGAGTCGAGTACGCCAAGAG (F) and 5′-CATCAAACATGGGCGACTGC (R); and GAPDH: 5′-ACCCACTCCTCCACCTTTG (F) and 5′-CTCTTGTGCTCTTGCTGGG (R).The following primers were used for qRT-PCR: On day 0, GFP-labeled cancer cells (6000 cells/well in 120 μL) were plated in 96-well clear-bottom plates . On day 1, the cells were treated with 15 μL 10× of drug A and 15 μL 10× of drug B or DMSO  staining was performed using an automated stainer. Cleaved Caspase-3 staining ; 1:1000 dilution) was performed using an automated stainer .Human tissue was scored using pathological criteria of response to treatment commonly used after neoadjuvant therapy as recommended by the College of American Pathologists (Modified Ryan Scheme for Tumor Regression Score). A score of 0 represents no viable cancer cells, and a score of 3 represents extensive residual cancer with no evident regression (poor or no response).2 dataset were calculated by fitting a curve through viability readouts across 16 different concentrations of a compound in a given cell line. A dr-AUC value of 0 represents complete cell death at all drug concentrations, while a dr-AUC value of 15 represents a “flat” curve . dr-AUC values greater than 15 indicate an increasing curve . The dr-AUC values were further centered and normalized to yield Z-scores for every compound across all cell lines. The Z-scores were then used to define relatively sensitive and non-sensitive cell lines: cell lines with a Z-score < −1.5 were designated as sensitive to the particular compound, and cell lines with a Z-score > 0 were tagged as non-sensitive. Cell lines with a Z-score between −1.5 and 0 were excluded from further analysis in order to avoid contaminating the sensitive and non-sensitive groups with intermediate cases and thus obscure the signal. Eighteen cell lines that were sensitive to more than 20% of the compounds were excluded from the analysis. In addition, pathways that were not variable across the cell lines were excluded from further analysis . We removed pathways with a range of activity scores ≤ 0.1 across samples (note that the activity scores can range between 0 and 1).Dose–response AUCs (dr-AUC) for 481 compounds from the CTDU-test was used to identify pathways whose activity level was significantly associated with the sensitivity of a specific cancer type to a specific compound or essentiality of a specific gene (obtained by RNAi/CRISPR-Cas9). In every tissue type, compounds with minimal AUC level greater than 8 were omitted from further analysis. This was done in order to only include compounds with actual response.The data were partitioned according to cancer type , and a two-sided Mann–Whitney P value) for every pair of pathway and compound , and for every pair of pathway and RNAi/CRISPR-Cas9 gene . We then corrected for multiple hypothesis testing all P values per tissue type using the Benjamini–Hochberg FDR procedure42 . Results with FDR q < 0.05 were considered as significant, and those with 0.05 < q < 0.25 were tagged as near-significant.For each tissue type, we calculated the significance  that lie on the y = x line follows the null hypothesis, while dots that lie above the diagonal represent significant results.We produced a quantile–quantile (QQ) plot Fig.  of P valTo analyze the dependency between the number of significant results and the number of cell lines in the dataset, we performed down-sampling analysis on NSCLC, and skin cell lines, separately. We repeated our analysis pipeline by randomly subsampling the data using different numbers of cell lines, beginning at a minimum of 10 cell lines. Subset sizes were chosen on the basis of sampling at ascending order from 10 cell lines to the final number of cell lines in the given tissue type. For each of the random subsets, we repeated the full analysis 10 times.33 was used for dimensionality reduction with the default perplexity parameter of 30. Of note, t-SNE was used only for visualization and not for clustering.The t-Distributed Stochastic Neighbor Embedding (t-SNE) method35 index, which calculates the proportion between the dissimilarity (or the distance) between clusters and tightness or the dissimilarity within the cluster. (ii) The Dunn index36 aims to identify sets of clusters that are compact with a small variance between the members and are well separated. (iii) Silhouette scores34 calculate, on average, how members within a cluster are closely grouped and, at the same time, how loosely these members belong to neighboring clusters.To assess the robustness of the clusters, we used the following three internal clustering validity indices. (i) The Calinski–HarabaszFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Code 1Reporting Summary


In this work, an original methodology was developed that quantifies bioenergetically and physiologically feasible net ATP yields for large numbers of microbial metabolic pathways and their variants under different conditions. All variants are evaluated, which ensures global optimality in finding the pathway variant(s) leading to the highest ATP yield. −6 to 10−2 M). The complete understanding of syntrophic propionate oxidation remains elusive due to uncertainties in pathways and the mechanisms for interspecies electron transfer (IET). Several million combinations of pathway variants and parameters/conditions were evaluated for propionate oxidation, providing unprecedented mechanistic insight into its biochemical and bioenergetic landscape. Our results show that, under a scenario of optimum environmental conditions for propionate oxidation, the Smithella pathway yields the most ATP and the methylmalonyl-coenzyme A (CoA) pathways can generate sufficient ATP for growth only under a cyclical pathway configuration with pyruvate. The results under conditions typical of methanogenic environments show that propionate oxidation via the lactate and via the hydroxypropionyl-CoA pathways yield the most ATP. IET between propionate oxidizers and methanogens can proceed either by dissolved hydrogen via the Smithella pathway or by different mechanisms  if other pathways are used.In this work, a systematic methodology was developed  for the automated feasibility evaluation and net ATP yield quantification of large sets of pathway variants. Possible pathway variants differ in their intermediate metabolites, in which electron carriers are involved, in which steps are consuming/producing ATP, and in which steps are coupled to (and to how many) proton  translocations. A pathway variant is deemed feasible, under a given set of physiological and environmental conditions, only if all pathway reaction steps have nonpositive Gibbs energy changes and if all the metabolite concentrations remain within an acceptable physiological range  leading to the highest ATP yield. The methodology is designed to be especially relevant to hypothesize on which microbial pathway variants should be most favored in microbial ecosystems under high selective pressure for efficient metabolic energy conservation. Syntrophic microbial oxidation of propionate to acetate has an extremely small quantity of available energy and requires an extremely high metabolic efficiency to sustain life. Our results bring mechanistic insights into the optimum pathway variants, other metabolic bottlenecks, and the impact of environmental conditions on the ATP yields. Additionally, our results conclude that, as previously reported, under specific conditions, IET mechanisms other than hydrogen must exist to simultaneously sustain the growth of both propionate oxidizers and hydrogenotrophic methanogens. G01 = +76.1 kJ/mol propionate ) the results of the positive net ATP yield of the feasible propionate oxidation pathways for different values of the physiological parameters and environmental conditions around the default values  is the one yielding the most net ATP. It appears that a higher or lower ΔGATP value would lead to lower efficiency of the P7 pathway. The lactate and hydroxypropionyl-CoA pathways appear to be those capable of producing the most ATP for the oxidation of propionate under the stoichiometry of 5 in 1 and P3) appear feasible only for ΔGATP values of −55 kJ/mol.The value of the H+/ATP ratio (+/ATP ratio of 13/3) could favor the efficiency of the propionate oxidation pathways (1–6) with highly efficient energy conservation from the total available in the lactate pathway (see Smithella pathway (P7) appears to yield diminishing net ATP production when the quantum of energy becomes smaller. The concentration values of free CoA (2) and the Smithella (P7) pathways. Values for intracellular pH (2) and the Smithella (P7) pathways at a neutral pH, while the rest of the configurations (P4–6) appear to be unaffected by pH. The detailed effect of the intracellular pH on the lactate pathway can be seen in Results under the scenario Opt  show thaTP ratio  shows thpathways  (P1–6) wfree CoA  do not alular pH  appear tH2  remains the most exergonic with respect to the other pathways (P1–6) and is favored for higher net ATP yield production , the lower its concentration, the more catabolic energy is available for pathways P1–6. However, under the environmental conditions of scenario Opt, the net ATP yield remains unaffected for all pathways. It appears that even at high CO2 concentrations the carboxylation step (R4) does not proceed; therefore, methylmalonyl-CoA pathways (P1 and P3) do not yield any net ATP. For propionate oxidation via methylmalonyl-CoA to be feasible, the configuration needs to involve no carboxylations, as is the case in the cyclical configuration (P2 and R5).Analogously, for the dissolved external COThe impact of temperature  can be oa values) for propionate and acetate  (Smithella pathway (P7) due to the impact on the bioenergetics of the reaction of the protons  , while iMet  as the ones biochemically and thermodynamically capable of yielding the most ATP from propionate oxidation under the methanogenic conditions. Only for ΔGATP values of −65 kJ/mol (2) could yield a similar net ATP.The results consistently present the lactate and the hydroxypropionyl-CoA pathways (P5 kJ/mol  does it +/ATP ratio (+/ATP ratio of 14/3) could favor the efficiency of the lactate pathway. The concentration of free CoA (4–6) appear to be unaffected by intracellular pH within the range covered.The HTP ratio  shows thfree CoA  does notOpt. Lower values for PH2 , such as those found in methanogenic environments, the energy available is below the energy threshold for one net proton translocation. As in scenario Opt, CO2 concentration does not impact the net ATP yield for the pathways considered .Analogously to scenario ario Met  can be oa values) for propionate and acetate .The extracellular pH  shows al4), at three different partial pressures of hydrogen (PH2).To enable detailed pathway and bottleneck analyses, the intermediate metabolite concentration profiles of all feasible reactions are provided by the automated method developed for the pathway evaluation. In H2 values evaluated. As the catabolic energy decreases with increasing product concentration (PH2), less net energy in the form of translocated protons can be recovered by the cell, particularly in the reoxidation of FADH2. For those reactions with products potentially exceeding the maximum physiological concentrations thermodynamically , energy is dissipated . H2 value.In GATP only impacts the size of the energy quantum in reactions with proton translocation. As shown in GATP in the range from −45 to −60 kJ/mol, while an energy quantum of 40 kJ/mol or smaller appears to allow for one additional net proton translocation. At the default reference ΔGATP, the optimum H+/ATP ratio appears to be 11/3. Intracellular pH values lower than 7 appear to decrease the net ATP, while no effect is shown from the concentrations of CoM or H4MPT within the evaluated ranges.During hydrogenotrophic methanogenesis, no ATP is produced by substrate-level phosphorylation, and the value of Δ2 . Since hydrogen and CO2  and g arH2. All other default parameters as per Opt , widely accepted as the syntrophic link for interspecies electron transfer (IET). The energetically equivalent values for alternative possible IET mechanisms are also shown, namely, the concentration of formate and the electron potential for any possible direct IET. The values shown for both are in thermodynamic equilibrium with the corresponding P per Opt  and scenOpt  between 1.2 and 100 Pa. A complete evaluation of all possible pathway variant feasibilities shows that, within this syntrophic PH2 range, propionate oxidation can generate net ATP only via the lactate or the hydroxypropionyl-CoA pathway (P4–6). The methylmalonyl-CoA pathway (P2) was shown to be able to generate only net ATP at PH2 of 3.6 Pa or below.Although in scenario H2 for syntrophic growth coexistence correspond to dissolved hydrogen concentrations between 10−8 and 10−6.1 M, below the defined minimum physiological limit of 10−6 M. This poses a fundamental problem if we consider that, for a bacterial cell volume of circa 1 μm3, the number of hydrogen molecules present inside a cell within this concentration range would be as few as 6 to 480. Such small numbers imply a kinetic impossibility for methanogenesis to actually occur. This supports the idea that IET between syntrophic partners should occur through alternative or additional mechanisms other that via dissolved hydrogen. Sustained growth for the methanogenic syntrophic partner, if based solely on dissolved hydrogen as the electron donor, appears theoretically impossible according to this analysis.The very low values of PPelotomaculum  are shown in omaculum  are in somaculum .H2 range if the propionate oxidation takes place via the Smithella pathway (P7) is, however, much wider, reaching feasible PH2 values of up to 11,000 Pa. This corresponds to dissolved hydrogen concentrations up to 10−4 M, well within physiological limits.The syntrophic growth coexistence PSmithella stoichiometry and pathway (P7).These results indicate that the IET mechanism for syntrophic propionate oxidation under the stoichiometry from Met (less favorable for propionate oxidation). The results under these conditions are shown in The syntrophic coexistence niche was also evaluated under the typically observed conditions in methanogenic environments of scenario Met, the conditions are so restricted energetically that the net ATP from all pathways is substantially lower. As opposed to the scenario Opt, shown in H2 range becomes even narrower, and the Smithella pathway (P7) does not even seem to be possible.Interestingly, under scenario Smithella pathway (P7) never makes any net ATP feasible due to the catabolic energy available being lower than the minimum required for single-proton translocation.Propionate oxidation seems possible only via hydroxypropionyl-CoA and syntropy via IET, unlike dissolved hydrogen. The These dramatically different results between the conditions of the two scenarios suggest that the actual concentrations encountered under local conditions  must fall between both or have a spatial variability and differ substantially from those measured in the bulk liquid of anaerobic digestion reactors.The automated pathway analysis method developed in this work provides an unprecedented capability to evaluate large numbers of pathway configurations. This allows for the evaluation of any known, and even postulated, biochemistry to theoretically determine the feasible pathways  with the highest ATP yield. The method, entirely mechanistic and largely founded on first principles, brings insights for the study of energy-limited microbial metabolisms.Smithella pathway yields the most ATP, and the methylmalonyl-CoA pathways can generate sufficient ATP for growth only under a cyclical pathway configuration with pyruvate (P2). Under a scenario of typical methanogenic conditions, the oxidation of propionate via the lactate and via the hydroxypropionyl-CoA pathways appears to yield the most ATP.Propionate oxidation was evaluated in the entire domain of possible pathway variations within the known biochemistry and the thermodynamic and physiological feasibility, applied to all reaction steps and all metabolite concentrations. Under a scenario of optimum environmental conditions, the oxidation of propionate via the H2 values  appear to be required to sustain syntrophic growth coexistence with methanogens if propionate is oxidized to acetate and three hydrogens (P1–6), while this is not observed for the Smithella stoichiometry and pathways (P7). This implies that IET via dissolved hydrogen is not feasible under pathways P1–6 and must occur via alternative mechanisms that could include formate or direct electron transfer . Conversely, dissolved hydrogen appears a feasible IET if the propionate oxidation goes via the Smithella pathway (P7). The very different results predicted under most favorable or methanogenic typical conditions suggest that local concentrations or spatial variability via microbial aggregates must be occurring to explain the literature observations for syntrophic propionate oxidation.Extremely low PSmithella propionica per The selection of the possible catabolic pathways for the oxidation of propionate to acetate with the overall stoichiometry shown in 2 has also been reported as a possible electron carrier for the same reaction step , lactate (P4), and hydroxypropionyl-CoA (P6), contain cyclic steps (Selected pathway reactions were cross referenced from the literature sources and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . Only reonicicum , 11, 42.cum 7 in  correspoopionica , 43. Proica 4 in  correspoica 4 in . Propionic steps . The com10.1128/mSystems.00814-20.5FIG S1FIG S1, TIF file, 0.6 MB.Schematic representations of the pathways selected for propionate oxidation, summarized in Copyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Opt and Met). Under scenario Opt, the most favorable environmental concentrations for a propionate oxidizer, i.e., the maximum permitted concentration (10−2 M) for propionate and minimum concentration (10−6 M) for acetate, were selected. Scenario Opt somehow assumes that those extremely favorable local concentrations could occur at some point for cells living, e.g., in aggregates (that can differ significantly from the ones measured in the anaerobic bulk environment). Under scenario Met, environmental conditions similar to those found in a typical stable methanogenic anaerobic digester under steady-state operation were selected  was set to a default of 1.62 Pa, assumed to be in equilibrium with its corresponding dissolved concentration. The evaluation of all pathway variants is conducted for each scenario independently under the indicated constant extracellular concentrations of substrates and products.The propionate oxidation pathways were evaluated under two different scenarios  . All these assumptions were made due to the lack of data regarding the concentrations of internal metabolites for propionate oxidizers. Experimental measurements of such concentrations would provide certainty in some of the assumptions followed and help in providing a better estimation of the pathways evaluated.Based on the values for intracellular metabolite concentrations reported in the literature  and on ta 1 μm3)  implies rameters , 48. The4MPT), or its related components (methyl-MFR). However, the evaluation of the hydrogenotrophic methanogenesis corresponds to the most exergonic scenario, as higher temperatures decrease the full catabolic energy available for the hydrogenotrophic methanogen reaction. Therefore, the evaluation of the pathway at 25°C provides an upper bound of the ATP that can be produced by this group of microorganisms.The thermodynamic values of Gibbs free energy  of formation, required for the thermodynamic calculations for each reaction step, were collected from the literature for each metabolite , 49–52. 49–10.1128/mSystems.00814-20.1TEXT S1Text S1, DOCX file, 0.1 MB.Information describing the algorithms used in Materials and Methods and the estimation of enthalpies of components. It also includes additional figures that support the Discussion. Download Copyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00814-20.2DATA SET S1Data Set S1, XLSX file, 0.02 MB.A spreadsheet that describes the calculation of the enthalpies of the components. In addition, the values estimated and used for the evaluation of the pathways can be checked in Copyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the All reactions identified to take place via membrane-bound enzymes were assumed to be capable of proton translocation through the cell membrane, either to directly recover energy as a proton-motive force (pmf) or to drive endergonic reactions in a pathway. Those energy conservation sites were identified both through previous literature , 53–58 a53–For each reaction step in which an electron carrier was involved, a set of possible electron carrier variants was defined. Additionally, for each reaction step with proton translocation capability, a range of possible numbers of proton translocations that can take place in that step were defined .All possible variants, combinatorial of all electron carrier variations with all possible numbers of proton translocations in the capable sites, were evaluated for each pathway. The feasibility of any given pathway variant is evaluated by seeking a zero or minimum Gibbs energy dissipation in all pathway reaction steps. This corresponds to minimum energy dissipation and maximum catabolism efficiency of the pathway. This criterion allows for the sequential calculation of the subsequent product concentrations at each pathway step given that of the substrate from the previous step.−2 M) while still showing ΔG of <0, then the concentration must sit at the upper physiological limit and energy must dissipate and be lost. The evaluation of a pathway variant that is feasible concludes with the quantification of its overall net ATP yield.The evaluation of a pathway variant consists first of the determination of its feasibility. A pathway is feasible only if all reaction steps have a zero or negative Gibbs energy change and all intermediate metabolite concentrations can still remain within the physiological limits. A pathway variant is deemed unfeasible and is discarded if any of the metabolite concentrations must fall below the lower physiological limit in order to thermodynamically enable a preceding reaction to occur. In the opposite case, if a reaction step is highly exergonic and allows for the produced metabolite to take concentration values higher than the upper physiological limit  indicate the logarithmic concentration of the metabolite as labeled in the upper axis. Concentration range outside the physiological limits is the shaded red area. Green and red bars (middle) indicate energy conservation reactions in which either energy is recovered or consumed to fuel a reaction via proton translocations. Darker green bar indicates a reaction in which ATP is produced by substrate-level phosphorylation. Yellow bars (bottom) indicate Gibbs free energy dissipation (loss) at each reaction step in the pathway. The physiological parameters and environmental conditions (other than for the H+/ATP ratio), set as a reference in FIG S4, TIF file, 0.4 MB.Pathway metabolite concentrations in the propionate oxidation via the lactate pathway (P4a) at different ratios of HCopyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00814-20.9FIG S5FIG S5, TIF file, 0.4 MB.Pathway metabolite concentrations in the propionate oxidation via lactate pathway (P4a) at different pH values. Symbols in gray (top) indicate the logarithmic concentration of the metabolite as labeled in the upper axis. Concentration range outside the physiological limits is the shaded red area. Green and red bars (middle) indicate energy conservation reactions in which either energy is recovered or consumed to fuel a reaction via proton translocations. Darker green bar indicates a reaction in which ATP is produced by substrate-level phosphorylation. Yellow bars (bottom) indicate Gibbs free energy dissipation (loss) at each reaction step in the pathway. The physiological parameters and environmental conditions , set as a reference in Copyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00814-20.10FIG S6GATP values. Symbols in gray (top) indicate the logarithmic concentration of the metabolite as labeled in the upper axis. Concentration range outside the physiological limits is the shaded red area. Green and red bars (middle) indicate energy conservation reactions in which energy is either recovered or consumed to fuel a reaction via proton translocations. Darker green bar indicates a reaction in which ATP is produced by substrate-level phosphorylation. Yellow bars (bottom) indicate Gibbs free energy dissipation (loss) at each reaction step in the pathway. The physiological parameters and environmental conditions , set as a reference in FIG S6, TIF file, 0.5 MB.Pathway metabolite concentrations in propionate oxidation via methylmalonyl-CoA pathway (P2) at different ΔCopyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the The combinatory set of possible pathway variants as defined above becomes very large  for each set of physiological parameters and environmental conditions as defined in GATP hydrolysis under physiological conditions have been reported that range from as small as −45 or −50 kJ/mol  concentrations have been previously reported to be as high as 10 mM  and measGR of ≤0 plus all metabolites within physiological limits), only those combinations with a positive net ATP yield are presented and discussed. All other pathway variants are deemed either unfeasible or unable to sustain microbial growth under the given conditions.A total of 32 parameter set configurations were evaluated for all the pathway variants (which corresponded to 2.5 million pathway variant-parameter set scenarios). Within this evaluation space and among the feasible pathway variants . It provides an overview of the results obtained with the framework presented in the manuscript. Download Data Set S2, XLSX file, 5.2 MB.A spreadsheet with the simulation results for the scenario Copyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00814-20.4TABLE S1Table S1, DOCX file, 0.02 MB.Name of the metabolites and the thermodynamic properties used. Estimated enthalpy values are shown in blue. Download Copyright © 2020 Patón et al.2020Patón et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the


GBA2 c.1780G > C (p.Asp594His) missense variant as the cause of spastic ataxia in a Cypriot consanguineous family, and more recently the biochemical characterization of this variant in patients’ lymphoblastoid cell lines. GBA2 is a crucial enzyme of sphingolipid metabolism. However, it is unknown if GBA2 has additional functions and therefore additional pathways may be involved in the disease development. The current study introduces bioinformatics approaches to better understand the pathogenetic mechanisms of the disease. We analyzed publicly available human gene expression datasets of diseases presented with ‘ataxia’ or ‘spasticity’ in their clinical phenotype and we performed pathway analysis in order to: (a) search for candidate perturbed pathways of SA; and (b) evaluate the role of sphingolipid signaling pathway and sphingolipid metabolism in the disease development, through the identification of differentially expressed genes in patients compared to controls. Our results demonstrate consistent differential expression of genes that participate in the sphingolipid pathways and highlight alterations in the pathway level that might be associated with the disease phenotype. Through enrichment analysis, we discuss additional pathways that are connected to sphingolipid pathways, such as PI3K-Akt signaling, MAPK signaling, calcium signaling, and lipid and carbohydrate metabolism as the most enriched for ataxia and spasticity phenotypes. Spastic ataxia (SA) is a group of rare neurodegenerative diseases, characterized by mixed features of generalized ataxia and spasticity. The pathogenetic mechanisms that drive the development of the majority of these diseases remain unclear, although a number of studies have highlighted the involvement of mitochondrial and lipid metabolism, as well as calcium signaling. Our group has previously published the  Spastic ataxia (SA) is a term used to describe a group of rare neurodegenerative diseases of the central and peripheral nervous system, characterized by a combination of clinical features of cerebellar ataxias and spastic paraplegias. Their main characteristics include gait ataxia, spasticity, and weakness in the limbs. Other neurological or non-neurological features may be present, such as neuropathy, pyramidal and extrapyramidal involvement, oculomotor abnormalities, cognitive involvement, seizures, retinopathy, and hypogonadism . The strSACS, FXN, SPG7, POLR3A, NKX6-2, and GBA2 [GBA2 gene (c.1780G > C (p.Asp594His)) as the cause of SA in a Cypriot consanguineous family [GBA2 gene encodes for the enzyme non-lysosomal β-glucosidase 2, known for its catalytic function in the sphingolipid metabolism [GBA2 variants in COS7 and HeLa cells including the Cypriot variant, has shown reduction of the GBA2 enzyme activity and differential protein expression for the majority of variants [The genetic causes of SA include genes such as and GBA2 ,6,7,8. O> C p.Asp94His) asvariants .Thus far, studies of gene expression at the RNA level have been performed in human tissues and different ataxia mouse models. These studies concluded to a number of candidate involved biological pathways, including glutamate signaling, calcium signaling, synaptic transmission, DNA repair pathways, cell cycle, metabolic processes, and receptor-mediated signaling pathways ,15,16,17In this work, we analyzed publicly available human gene expression datasets for diseases that include ataxia or spasticity in their phenotype, and identified biological changes at the pathway level that may contribute to the development of SA. We focused on datasets with neuronal samples of affected individuals, since neurons are primarily affected in the studied neurodegenerative disease and therefore are possibly the most representative of the disease pathogenetic mechanisms. We then included additional datasets derived from other tissues, some of which are more easily accessible to use for future functional investigation or biomarkers discovery. This approach would enable us to examine whether differential gene expression and pathway analysis findings in neurons are tissue specific or whether these are represented in additional tissues. It could also possibly reveal additional interesting findings not detectable in neurons, either due to tissue specific gene expression, limitations in the number of the analyzed datasets, or even the specific phenotype of the patient they were collected from. Overall, we highlighted candidate SA pathways and evaluated the role of sphingolipid signaling and metabolism pathways in the pathogenesis of the studied disease. We used two different pathway analysis approaches regarding the analyzed gene expression datasets; (a) a bottom-up approach where differentially expressed genes (DEGs) were investigated through enrichment analysis, in order to provide lists of SA candidate pathways; and (b) a top-down approach where two selected pathways, the sphingolipid signaling pathway and the sphingolipid metabolism were evaluated by selecting and analyzing the corresponding DEGs. Our results further support the already established association of lipid, carbohydrate, and protein metabolism as well as signal transduction pathways with SA pathogenesis, and also reveal a number of traced biological pathways for SA. In addition, we detect consistent differential expression in key genes of the sphingolipid signaling pathway and sphingolipid metabolism, thus supporting the association of these pathways in SA development. We discuss the central role of the sphingolipid signaling pathway in association to several biological processes and pathways, such as PI3K-Akt signaling, MAPK signaling, apoptosis, insulin signaling, and calcium signaling. Our investigation encourages further exploration of the sphingolipid pathways in relation to SA pathogenesis, in order to conclude to more specific mechanisms that lead to the development of the disease.A total of 22 human microarray gene expression datasets derived from various tissues , were anEnrichment analyses initially focused on neuronal datasets, provided 39 KEGG 2019 and 76 Reactome 2016 pathways that consistently appeared as a result in at least one-third of the ‘ataxia’ neuronal datasets . Of thosThe statistically significant DEGs were also used for gene set enrichment analysis (GSEA) based on gene ontology biological process using the GeneTrail3 tool , in ordeInitial analysis of neuronal datasets indicated biological processes, such as “RNA processing”, “mRNA metabolic process”, “DNA metabolic process”, “heterocyte metabolic process”, “nucleobase-containing compound metabolic process”, and “RNA metabolic process” in “ataxia” neuronal datasets. Further analysis in other tissue datasets indicated the aforementioned processes in peripheral blood and fibroblasts of “ataxia” datasets as well . The GO p-value < 0.05 were used and a combined fold change was calculated for each of the two pathways based on rateFC and normMeanFC values  and Sphingolipid metabolism (hsa00600) in association to the development of SA at the gene expression level, we used the PathExNet tool in the 16 “ataxia” and 6 “spasticity” analyzed human gene expression microarray datasets. PathExNet is a web tool that can be used for the characterization of pathways using differential expression datasets. It allows the generation of pathway expression networks based on the DEGs that participate in each pathway using a number of freely available databases, thus providing knowledge on a disease or other biological condition of interest. We used PathExNet with KEGG as the selected database for the characterization of the sphingolipid pathways, in order to examine alterations at the expression level of genes that participate in the selected pathways, in samples of individuals with “ataxia” or “spasticity” in their phenotype, compared to controls. The lists of DEGs with a C values . The firC values . TherefoC values . The gen dataset G–H, in oPIK3R1 and SPTLC1 were found in at least one-third of the datasets from all the above-mentioned tissues . The common DEGs between datasets of the neuronal tissue with peripheral blood and fibroblasts are demonstrated in The sphingolipid signaling pathway (hsa04071) consists of 119 unique genes out of which 67 in total were differentially expressed in at least 2 datasets of at least one of the three tissues: neurons (22 genes), fibroblast (28 genes), and peripheral blood (26 genes) A,C,E. InIn order to further explore the role of sphingolipid signaling pathway and sphingolipid metabolism genes with consistent differential expression across datasets, we performed separate PathWalks  analysesIn neurons “ataxia” datasets, the Sphingolipid signaling pathway had the third highest OR score and Sphingolipid metabolism was also found in the top 15 pathways based on OR. The two pathways were also scored as the most visited pathways in the fibroblasts “ataxia” and peripheral blood “ataxia” datasets, followed among others by TGF-beta signaling pathway, AMPK signaling pathway, mRNA surveillance pathway, neurotrophin signaling pathway, FoxO signaling pathway and insulin signaling pathway . For thePathway clusters were also generated through the PathWalks analysis . The patThe genes that participate in the two selected pathways  with consistent expression changes (over- or under-expression) in at least one-third of the datasets from the same tissue  were mapped to the protein–protein interaction (PPI) network , in ordeIn this study we analyzed 22 publicly available human gene expression datasets from GEO corresponding to samples of affected individuals with ataxia or spasticity as one of their clinical features, against controls. We used the results of the gene expression analyses in order to investigate possible candidate pathways through enrichment analysis. We also evaluated the sphingolipid signaling pathway and the sphingolipid metabolism as candidates for the development of SA.GBA2, coding for the non-lysosomal glucosylceramidase, an enzyme that catalyzes the hydrolysis of GlcCer to glucose and ceramide, have been associated with spastic paraplegia and SA, while variants of GBA, coding for the lysosomal glucosylcermidase have been associated with Gaucher’s disease and Parkinson’s disease [GBA2 in sphingolipid metabolism and the association of sphingolipid pathways with neurodegenerative diseases, we chose them as candidates for evaluation in relation to SA pathogenesis through targeted pathway analysis.Previous studies have described the involvement of sphingolipids in neurodegeneration. As mentioned in  disease . Ceramid disease , while s disease . In the  disease . BecauseGBA2 gene, which was under-expressed in patient samples in one-third of neuronal datasets is included. The differentially expressed PP2A, AKT1/2, MAP3K5, MAPK11/12, and CTSD genes, are associated with the regulation of apoptosis via BAX and BCL-2 [AKT2 and CTSD genes also showed similar differential expression (over- and under-expression respectively) in the ‘ataxia’ fibroblast datasets  and Ceramide-1-Phosphate (Cer-1-P), which are both inhibitors of apoptosis [The results have demonstrated consistent differential expression of key genes of the sphingolipid signaling pathway and sphingolipid metabolism between affected individuals with “ataxia” or “spasticity” compared to controls in datasets of the neuronal tissue, peripheral blood, and fibroblasts. nd BCL-2 . From thdatasets . Furtherpoptosis ,27.PIK3R1 gene was under-expressed in neuronal datasets as well as in fibroblasts and peripheral blood, while AKT2, PPP2R5C, and SPTLC1 were found over-expressed in neuronal and fibroblast datasets of the “ataxia” phenotype. The “ataxia” neuronal and peripheral blood datasets also share common DEGs, such as ASAH1 and MAP3K5. All the remaining genes with consistent differential expression across tissues are highlighted in In addition to the neuronal dataset findings, analysis of other tissue datasets revealed consistent differential expression in key genes of the sphingolipid pathways, which are also demonstrated in ASAH1 gene encodes for the enzyme acid ceramidase, which is implicated in ceramide metabolism through the hydrolysis of fatty acids from ceramide and the production of sphingosine [Furthermore, our produced PPI networks demonstrate the connection of GBA2 with the encoded proteins of the consistent DEGs of sphingolipid pathways . Specifiingosine . The aboIn addition to sphingolipid pathways, our most significant findings that have been supported by different approaches performed in the three main tissues  include: the PI3K-Akt signaling pathway, Calcium signaling, cAMP signaling, MAPK signaling pathways, and Insulin signaling pathway. According to PathexNet data analysis, a number of genes with consistent expression change across the analyzed datasets, such as PI3K regulatory subunits (PIK3R1-3), AKT1/2, PKCA/E, PPP2A regulatory subunits and others, participate in the PI3K-Akt signaling pathway . This paCalcium signaling, cAMP signaling and MAPK signaling pathways were highlighted in both KEGG 2019 enrichment  and PathGBA2 mutants localized at mitochondria instead of plasma membrane, thus leading to mitochondrial fragmentation and loss of mitochondrial transmembrane potential [GBA2 associated SA with mitochondrial-related functions. Previous reports have also described the association of mitochondrial dysfunction, oxidative stress, and disruption of cell energetics with ARSACS and other recessive ataxias with spasticity [The possible association of the disease with mitochondrial dysfunction is supported by additional literature data. A recent in vitro study investigating the effect of several missense and nonsense variants on the protein cellular localization, has demonstrated that the truncated otential ; therefoasticity ,36,37,38asticity , and theasticity . Low gluasticity . Not onlasticity , furtherOf our most significant findings, the insulin signaling pathway has been also recently reported to be associated with sphingolipids and their role in aging and neurodegeneration . More spAll of the above considered, we hypothesize that disruption in the sphingolipid pathway alone or in a synergistic manner with the abovementioned related pathways may contribute to the development of SA, either through the disruption of cell energetics or neuroinflammation, thus leading to cell death and neurodegeneration. In conclusion, our bioinformatics-based work shows a consistent disruption of the sphingolipid signaling pathway and sphingolipid metabolism across affected individuals with “ataxia” or “spasticity” in their clinical phenotype as compared to non-affected controls, and provides novel information on the association of the sphingolipid pathway with SA. Furthermore, it highlights meaningful candidate pathways and synergies that could be further examined through experimental procedures and functional studies in SA patient samples or disease models.In this study, we analyzed human microarray gene expression datasets that include samples of affected and control individuals for diseases with ataxia or spasticity in their clinical phenotype. Differential expression analysis was performed and the results were used for the discovery of SA-related candidate pathways and for the evaluation of two pathways of interest, the sphingolipid signaling pathway and the sphingolipid metabolism. We initiated our analysis using datasets with neuronal samples of affected individuals and controls and then included additional datasets from other tissues. A flowchart of our work is presented in The Gene Expression Omnibus was accessed in February 2020 and searched for human gene expression datasets under the search ID “ataxia” or “spastic/spasticity”. The datasets chosen included samples from tissues of affected and control individuals for diseases with ataxia or spasticity as one of their clinical features and were available as pre-processed Series Matrix Files (expression quantification matrices).A total of 22 datasets were downloaded across 8 different tissues of affected and control individuals: 16 human gene expression microarray datasets were retrieved from Gene Expression Omnibus (GEO)  under th2 transformation. For datasets downloaded as log2 transformed matrices, reversed-transformation was applied followed by quantile normalization and re-transformation to log2 as described above in order to harmonize all the datasets for further processing. Differential expression analysis was performed using the empirical Bayes statistics of the Limma package [2 transformed and the lm.series function was applied that performs a straightforward least squares fitting of a linear model for each gene. Due to the limited number of genes remaining after a 0.05 p-adjusted value cutoff, a 0.05 p-value cutoff was used on the results and any remaining genes that had multiple sign probes (up- and down-regulation at the same time) were removed. In the case of duplicate, same-sign gene entries, we kept the result with the lowest p-value score. The DEG lists were compared across intra-tissue datasets, and Venn diagrams showing the DEG overlap are provided in The pre-processed series matrix files of the data provided by each experiment were analyzed for differential expression. Only samples from affected and healthy individuals were used (carrier samples were excluded). In the case of negative values in the datasets, a positive shift was applied to the data . The normalizeQuantiles function was applied to the datasets using the Limma package of Bioconductor  followed package . The lmFp-value < 0.05 and absolute log2FC > 0.585 were selected and the top-500 DEGs based on absolute log2FC were used for enrichment analysis using the EnrichR web-server [p-value < 0.05 significance were selected. The datasets for each tissue were then compared in order to study the intersection of pathways. In addition, enrichment analysis was performed based on gene ontology (GO) biological process using the tool GeneTrail3 [2FC values. The unweighted version of the gene set enrichment analysis (GSEA) was used, which is a non-parametric hypothesis test that sorts the input list of genes based on their expression values. It then evaluates which GO biological process each gene corresponds to, and tests if the genes in the set that belong to the particular GO biological process are uniformly distributed or accumulated on top, or on bottom of the sorted input list, in order to provide a number of hits score, an expected score and a p-value.The results of differential expression analysis of each dataset were used: (A) for the discovery of candidate pathways for SA through enrichment analysis; and (B) for the evaluation of sphingolipid signaling pathway and sphingolipid metabolism as candidates for the development of SA phenotype. The DEG sets were first filtered to keep only the protein coding transcripts. Non-coding RNA, microRNA, miscRNA, small nuclear RNA, small nucleolar RNA, and uncharacterized genes with prefixes LOC, FLJ, and RP were excluded. The lists of DEGs with a b-server  based onneTrail3 , which thttp://bioinformatics.cing.ac.cy/PathExNET). PathExNet was used to calculate a combined fold change for each of the two pathways based on the expression change of the genes that participate in the respective pathways. PathExNet was executed for each dataset separately. The PathExNet tool was also used to isolate the genes that participate in each of the selected pathways based on KEGG 2019 along with the logFC and p-value metrics, as provided by the differential expression analysis. A comparative analysis was then performed in order to find the intersection of genes that participate in each of the two pathways for at least two datasets regarding each tissue.For the second analysis, two KEGG pathways were selected, (a) hsa04071-Sphingolipid signaling pathway and (b) hsa00600-Sphingolipid metabolism, and were evaluated using the PathExNet tool  across two or more datasets per tissue were further investigated using PathWalks , in ordeOR values greater than one were visualized as a network using R’s igraph package [Pathways with  package , highligThe lists of genes with consistent over- or under-expression participating in hsa04071 and hsa00600 were also used for the generation of protein interaction pairs using the protein–protein interaction (PPI) database STRING . The PPI