Finding prey is essential to survival, with marine predators hypothesised to track chemicals such as dimethyl sulfide (DMS) while foraging. Many predators are attracted to artificially released DMS, and laboratory experiments have shown that zooplankton grazing on phytoplankton accelerates DMS release. However, whether natural DMS concentrations are useful for predators and correlated to areas of high prey biomass remains a fundamental knowledge gap. Here, we used concurrent hydroacoustic surveys and in situ DMS measurements to present evidence that zooplankton biomass is spatially correlated to natural DMS concentration in air and seawater. Using agent simulations, we also show that following gradients of DMS would lead zooplankton predators to areas of higher prey biomass than swimming randomly. Further understanding of the conditions and scales over which these gradients occur, and how they are used by predators, is essential to predicting the impact of future changes in the ocean on predator foraging success. Kylie Owen et al. sample concurrent prey biomass and natural dimethyl sulfide (DMS) concentration, and show that these variables are correlated in air and seawater. Agent simulations show that following fine-scale gradients of DMS would lead zooplankton predators to higher prey biomass, shedding light on how marine predators may use these cues for foraging. The chemical compound that has received the most attention as a potential foraging cue is dimethyl sulfide (DMS). DMS in the ocean (DMSaq) and atmosphere (DMSg) are produced by the breakdown of dimethylsulfoniopropionate (DMSP), which is synthesised by phytoplankton4. DMS is the largest natural source of atmospheric sulfur and it plays an important role in cloud formation and likely in climate regulation5. Data collected in the context of climate science has shown that high concentrations of DMS occur in regions characterised by high productivity, being correlated to multiple factors such as chlorophyll-a concentration and mixed layer depth7. However, no previous research has determined whether natural DMS concentrations could accurately convey information on prey patch quality to a predator.Marine predators face the challenge of finding prey resources that are patchy and ephemeral in nature. Odour landscapes have been proposed as a strategy that animals may use to locate prey in a vast and largely featureless ocean7, with changes shown in response to meteorology, solar radiation, and phytoplankton photophysiology8. Many biotic  and abiotic (e.g. temperature changes) factors are known to influence DMS production10. In addition, grazing by microzooplankton has also been shown to play a large role in DMS concentration in surface waters in some regions11. These species are too small to be prey for many large predators such as marine mammals, seabirds, and turtles that have been the centre of most behavioural research on the role of DMS in foraging. The high number of factors that influence DMS production, and large spatial and temporal variation in DMS concentration, may result in DMS being an uninformative foraging cue for predators. Despite all of the biological and oceanographic drivers of variability in DMS, for zooplankton predators to rely on DMS as an accurate foraging cue, an association between DMS concentration and prey biomass is essential.DMS concentration has been shown to be highly variable over both space and time14. In addition, many marine predators, such as fish15, turtles16, marine mammals17 and seabirds18 have been shown to be attracted to artificially released DMS. This has led to the theory that zooplankton predators use this chemical to locate prey1 and the hypothesis that the attraction of predators to areas of DMS concentration may be the link between the sulfur, carbon and iron cycles in high latitude oceans19. However, the majority of studies on the behavioural response of predators to DMS have been completed in captivity or involved artificially introducing DMS into the environment19. To the best of our knowledge, the relationship between prey biomass and natural DMS concentration has never been compared in the field.Zooplankton grazing on phytoplankton has been shown to accelerate the rate of DMS release in laboratory experimentsg and DMSaq) and prey biomass (zooplankton and fish), at a scale that is likely to be relevant to a foraging predator. Our aim was to determine whether there was a correlation between prey biomass and DMS and whether gradients of DMS existed at fine-scales, that could be utilised by predators to increase the prey biomass encountered while foraging. We found that zooplankton biomass and DMS concentrations in air and seawater are correlated and that smooth gradients of DMS exist at fine spatial scales that would lead zooplankton predators to areas of higher prey biomass in the ocean.Here, we present concurrent high-resolution measurements of multiple phases of DMS (DMSaq and DMSg) and prey (zooplankton and fish) biomass during surveys conducted during day time hours from Chatham Harbor, Cape Cod, MA, USA. These surveys covered a total of 220.2 km over 5 days in June 2019 ), and the DMSaq concentration ranged from 1.3 to 19.5 nM (mean 8.0 ± 5.6 nM)   for zooplankton and 55–910 m2 nm−2 (mean 153 ± 141 m2 nm−2) for fish. The concentration of both DMSaq  and DMSg  were found to be positively correlated to the biomass of zooplankton , there were rarely times when zooplankton biomass was low (close to zero)  and DMSg   ranged from 0.0 to 464.3 mton Fig. . Althougro) Fig. . In contro) Fig. . The bio05) Fig. .Fig. 4ThIn order to test whether DMS gradients could be used by predators to locate areas of higher zooplankton biomass, despite the high spread in the data, we utilised agent simulations, where agents acted as pseudo-predators. There were two scenarios tested: (1) a tracking DMS experiment, where each agent was instructed to follow the DMS gradient, moving to the neighbouring cell with the highest DMS until a local maximum was reached, and (2) a random movement experiment, where agents were instructed to move randomly for the same length distribution as agents in the tracking DMS experiment. The distribution of the track lengths for the random movement experiment were limited to the same distribution as the tracking experiment for two reasons: (1) to ensure that the search effort was the same for both the random movement and tracking DMS experiments, and (2) as the maximum prey biomass encountered along each track was selected, having no limits on the length of the random movement tracks would allow all random agents to ultimately find the cell with the highest prey biomass in the entire area. The highest prey biomass encountered along all of the tracks was selected, as this is more likely to reflect true predator behaviour, with animals ending their search and beginning to feed as soon as prey is located. For the random tracking experiment, 1000 iterations were completed, and the mean of the maximum prey encountered across the 1000 tracks was compared to the tracking DMS experiment. Only one iteration was needed for the tracking DMS experiment as this is deterministic.aq concentrations lead agents to areas that had significantly higher prey biomass compared to when the agents moved randomly   .Tracking DMS16) Fig. . However74) Fig. . When tr74) Fig. . Across 10, and that DMS was sampled at a single depth while prey biomass was integrated over a larger depth range. Nonetheless, the occurrence of higher zooplankton biomass in regions of higher DMS suggests that natural concentrations of this chemical would allow DMS to act as a valuable foraging cue for zooplankton predators. Many marine predators, such as fish20, marine mammals21 and sea birds22 forage primarily on zooplankton, ranging from copepods to krill. Previous research has shown that attraction towards areas of DMS release is stronger in zooplankton predators compared to higher trophic level predators23. The lack of a positive correlation between fish biomass and DMS concentration may explain why higher trophic level predators show a weaker or no response to the chemical23. It is possible that time lags between DMS production and the build-up of fish biomass, or the diffusion of the chemical both spatially and temporally, may result in DMS being a valuable foraging cue only to zooplankton predators that will find higher prey biomass in areas of higher DMS concentration.We show that a correlation between zooplankton biomass and the concentration of DMS in air and seawater occurs in the ocean. Although it has been suggested that DMS is likely to be a useful foraging cue for marine predators, to the best of our knowledge, no previous research has demonstrated that higher concentrations of DMS are linked to higher prey biomass. The observed correlation between prey biomass and DMS is notable, especially given the many factors that influence the production and concentration of DMS24, likely relying on a combination of senses to assist with finding the highest density prey patches in the localised area25. It is unlikely that DMS is used by predators in isolation, with predators using information from a number of sensory cues at different scales to reduce foraging effort. Examining how animals respond to the gradients of this chemical could provide much-needed information on the sensory capabilities of many marine predators. In addition, understanding the scales over which these gradients provide useful information on prey quality could also increase understanding of the scales at which different senses are used for foraging.Higher prey biomass is likely to be a high source of DMS, which would then cause DMS to diffuse into the surrounding area and make the area of elevated DMS larger than the prey biomass area. We observed with the agent simulations that tracking gradients of DMS lead predators to areas that were characterised by higher DMS, but not necessarily the highest prey biomass in the area. Once in an area of high DMS and elevated prey biomass relative to the surrounding area, zooplankton predators may then initiate area-restricted search behaviourg in the air as they forage over the ocean surface26. Here we have shown that tracking DMSaq concentration would lead predators to areas of higher zooplankton biomass, but no significant benefit of tracking DMSg was found. We also observed that DMSaq lead agents to significantly higher prey biomass than DMSg, which was unexpected given the similarity in the strength of the correlation observed between prey biomass and both phases of DMS. We speculate that the difference in the success of DMS as a foraging cue in seawater vs. air is the result of the higher mobility of DMSg in wind, relative to the movement of DMSaq in seawater, making the spatial overlap between DMSg and prey biomass less reliable. In addition, the data used for creating the DMS maps took 8 h to collect, thus, especially for DMSg, it cannot be considered an instantaneous measurement. On the day of sampling, the wind direction was from west to east, which is evident in the eastward shift in the distribution of DMSg relative to DMSaq  tropical open oceans30. However, DMSg concentration has been reported to be higher at night32, likely due to photochemical decomposition of DMSg during the daytime. Our findings of a correlation between zooplankton biomass and DMS concentration suggest that variation in zooplankton grazing pressure in surface waters during the day and night may also influence DMS concentration throughout the day. Higher DMS concentrations at night could be a benefit to predators that may rely on vision to forage during the day and then switch to a stronger reliance on chemical cues in the dark. If animals can sense DMSaq, then the deeper exploratory dives completed by marine predators may be a mechanism to assist predators to locate zooplankton at depth. Understanding whether the exploratory diving behaviour of marine predators is also linked to DMS concentration may then provide insight into the energetics of marine predator foraging, particularly for air-breathing predators that may choose to continue or stop an exploratory dive based on the chemical information obtained during the dive.In this study, we focused on surface prey (in the upper 10 m) based on an assumption that prey in the upper water column would be most strongly associated with surface DMS25. It is worth noting that the results of this study were based on 5 days of field research in one location. Therefore, a priority for further research should be collecting additional data on concurrent DMS concentration and prey biomass in order to validate that the results are consistent in other conditions and locations. Much further research is required to fully understand the conditions under which these gradients occur, and the full range of oceanographic and biological factors that influence the strength of the correlation. For example, phytoplankton species composition is known to influence DMS production, and this factor may explain why in some areas prey biomass was high, while DMS concentration remained low. Alternatively, time lag effects could also play a role in explaining this pattern, with zooplankton (being longer-lived) biomass often remaining high long after phytoplankton has been consumed, and DMS has presumably dispersed. The variation in DMS concentrations observed in this study was relatively high despite the small observation area. This highlights the need for further fine-scale measurement of these spatially and temporally dynamic chemicals in order to fully understand their role in foraging ecology. Measuring the movement and distribution of marine predators in relation to natural DMS concentrations would provide some insight into whether predators respond to these gradients. Given the assumed link between DMS and global climate, understanding the role that DMS plays in predator foraging behaviour is essential to achieving an understanding of how the foraging success of predators may vary in changing oceans.We show that the link between DMS concentration and prey biomass that is necessary for predators to use DMS as an accurate foraging cue occurs in the ocean. This is a necessary condition to investigate which predators use these gradients to find prey and the scales over which DMS may be important to predators. How the use of these gradients interplays with other sensory modalities also needs further investigation, as it is likely that different senses are used at different scalesMV Noah departing from Chatham Harbor, Cape Cod, MA, USA, during daylight hours from the 23 June to 28 June 2019. This is a known area of high primary productivity and a hotspot for baleen whale foraging activity33.Data collection occurred over 5 days on board the aq) and air (DMSg) were conducted using a sequential vapour generation chemiluminescence instrument34. Water was collected from 1 m depth and approximately 1 m beside the vessel into an overflow tank with an exchange rate of 12 L min−1. We used a high flow-through pump and water in the overflow tank was replaced every couple of seconds. Air samples were collected via an inlet filter and a Teflon tube, attached to the roof of the pilothouse, well in front of the exhaust at the stern, in order to avoid contamination. Water samples were analysed for DMSaq and gas samples were analysed for DMSg automatically as distinct samples at a 7.5 min interval following a previously developed protocol34.Measurements of DMS in seawater  six transects in a lawn mowing pattern to obtain a 2D distribution of DMS concentration in order to conduct agent simulations, (2) targeting high and low productivity patches (based on echosounder data and observations of predators foraging in the area) in an attempt to cover a full range of possible prey biomass values in the area, (3) a drifting experiment using a sea anchor to stay in the same water mass and (4) a long-transect offshore and back. All data were used to assess the overall correlation between DMS and prey biomass.35.Concurrent prey measurements were conducted using a multi-frequency down-looking echosounder . The EK80 was operated in narrowband mode and both the EK80 and ES60 echosounders operated with ping rates of 0.5–2.0 Hz (depending on bathymetry), pulse lengths of 256 microseconds, and output power of 500, 250 and 100 W  during the surveys. Echosounders were mounted on the port side of the vessel with the transducer faces at a depth of 0.5 m. All frequencies were calibrated during the study using a 38.1 mm diameter Tungsten carbide sphere37. To ensure that our acoustic measure of small zooplankton biomass was not including backscatter from individual or aggregations of fish, we identified regions of backscatter consistent with fish (volume backscattering strength > −70 dB) using the 38 and 200 kHz echograms, and then removed those same regions from the 710 kHz echogram before additional analysis. This procedure may lead to underestimates of zooplankton biomass as we may exclude some regions that had both copepods and fish present.Because different organisms, such as small zooplankton and fish, scatter sound with different efficiencies depending on the acoustic frequency used, we used data from all three frequencies to measure the backscatter from small (<5 mm length) crustacean zooplankton such as copepods. In general, 38 kHz systems can detect fish with swim bladders, 200 kHz systems can detect fish without swim bladders and large mesozooplankton (such as krill > 20 mm), but neither can detect small copepods. However, small zooplankton produces measurable backscatter at 710 kHz2 nm−2, a parameter widely used in fisheries acoustics as a proxy for zooplankton or fish biomass38), for each 8 m vertical by 100 m horizontal bin. Fish biomass was similarly estimated using the 38 kHz vertically integrated NASC data. We conducted three net tows using a 50 cm diameter, 2.5 m long ring net with 200-micron mesh towed 1–2 m below the surface for 2–5 min duration to collect ground-truth data on the fish and mesozooplankton present. Small copepods (lengths < 3 mm) were the most abundant taxa, although cladocerans, fish eggs and other small crustaceans were also caught. Pelagic fish aggregations were mostly mackerel which were caught by hook and line during breaks in the vessel survey operations.Because of this approach, we were limited in analysing data from regions where we had both 38 and 710 kHz data. The 38 kHz system has a larger nearfield region (roughly 2 m from the transducer) so we were limited to analysing backscatter data from 3 m and deeper. Since DMS measurements were made in the upper water column, we analysed acoustic data from 3 to 10 m water depths to try to best match the sampling region while also measuring sub-surface zooplankton aggregations. Backscatter at 710 kHz was binned  to roughly match the DMS sampling resolution and then vertically integrated (depths from 3 to 10 m) and converted to a zooplankton biomass value as measured by the Nautical Area Scattering Coefficient . As the prey biomass data were not normally distributed, correlations were computed using a two-sided Spearman’s rank-order correlation within the corr function in Matlab (version R2019a). Significance was determined at 2 area was sampled as a series of six transects and interpolated to a grid cell size of 0.0025°, equal to 208 m. Agent simulations were also computed in Matlab using the DMS maps and were used to represent pseudo-predator movement through the area39. We completed two experiments: (1) a tracking DMS experiment, and (2) a random movement experiment. As a starting point, one agent was placed in every grid cell (n = 783) across the whole area. In the tracking DMS experiment, each agent was instructed to move from their initial position to an adjacent grid cell with the highest DMS concentration (DMSaq or DMSg) out of the eight grid cells adjacent to its current position. This behaviour was repeated until they reached a local maximum, where all of the adjacent grid cells had a lower DMS concentration than the current grid cell. We expect real predators would stop their search behaviour, with their movements no longer following the DMS signal, when prey is located, as opposed to the agents that continued searching until a local maximum in DMS was reached. As a result of this, the grid cell along the path that had the highest prey biomass was labelled and used to derive a frequency distribution of maximum prey biomass encountered when tracking DMS. In addition, a random movement experiment was completed, where an agent started in each grid cell but was instructed to move randomly. The length of the tracks for the random movement experiment was pulled from a distribution that was equal to the distribution of track lengths in the tracking DMS experiment. This was to prevent random agents from searching the entire area and always encountering the highest prey biomass in the area and to ensure that the search effort by agents was consistent in both experiments. The tracking DMS experiment was only run once, as it is deterministic, however, the random experiment was completed 1000 times from each grid cell. The maximum prey biomass encountered was recorded for each of the 1000 random tracks from each cell. The mean of the maximum prey encountered over these 1000 iterations was then compared to the maximum prey encountered in the tracking DMS experiment.DMS and prey biomass maps (Supplementary Data g and DMSaq (n = 783). In addition, the same test was used to compare the mean of the maximum prey biomass encountered when the agents followed DMSg to the mean of the maximum prey biomass encountered when following DMSaq.Since prey biomass measurements are likely to be spatially correlated and thus individual grid cells are not necessarily independent, and agents beginning in the same grid cell (random movement and tracking DMS) could be considered paired samples, non-parametric Wilcoxon signed-rank tests were completed in R Statistical Software (version 3.6.1) to compare the mean of the maximum prey biomass encountered by agents moving randomly, to the mean of the maximum prey biomass encountered when the agents followed either DMSSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


The “distal axonopathy” hypothesis in amyotrophic lateral sclerosis (ALS) proposes that pathological changes occur at the neuromuscular junction (NMJ) early in the disease. While acetylcholinesterase (AChE) plays an important role in the functionality of the NMJ, its potential role in ALS remains unexplored. Here, we identified AChE as a limiting factor regulating muscle/motor neuron connection in a vertebrate model of ALS. Knockdown of the TAR DNA-binding protein 43 (TDP-43) orthologue in zebrafish resulted in early defects of motor functions coupled with NMJ disassembly. We found that a partially depleted tdp-43 caused a decrease of ache expression. Importantly, human AChE overexpression reduced the phenotypic defects in the tdp-43 loss of function model, with amelioration of post- and pre-synaptic deficits at the NMJ. In conclusion, our results provide a better understanding of the role of TDP-43 in the NMJ organization and indicate AChE as a contributing factor in the pathology of ALS. In particular, it may be implicated in the early defects that characterize NMJs in this major neurodegenerative disorder. TARDBP [TARDBP mutations in 2008 [Amyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem disease caused by progressive loss of both upper and lower motor neurons . The patTARDBP , in the TARDBP ,4. The d in 2008 ,6 brough in 2008 ,8 able t in 2008 . Togethe in 2008 ), TDP-43G93A mouse, which replicates several key features of ALS, reduced NMJ complexity at presymptomatic stages has been described, corresponding to an augmented number of disassembled acetylcholine (ACh) receptor clusters on muscle from the mutant SOD1 transgenic mice [Increasing evidence shows early morphological and biochemical changes at the neuromuscular junction (NMJ) before complete muscle fiber denervation and functional symptom onset in ALS patients ,12. In pnic mice .Structurally, the NMJ is a highly complex cholinergic synapse where the majority of the molecules responsible for its formation are also crucial for its stability and function at the adult stage .In this context, the enzyme acetylcholinesterase (AChE) is a master regulator of the signal transduction. In mammals, AChE exists as three distinct variants coming from alternative exon splicing  ,16,17. TIn addition, the enzyme has several non-classical roles in neuronal regeneration and development, as demonstrated in dopaminergic neurons ,25; in rDanio rerio) permit the overcoming of these issues since this vertebrate does not appear to express a functional bche and has not been reported to undergo alternative splicing at the respective 5′ or 3′ ends [In mammals, our understanding of AChE cellular mechanisms is complicated due to the presence of butyrylcholinesterase (BChE), another enzyme able to hydrolyze ACh, although less effectively , and thr 3′ ends . Thus, i 3′ ends ,36, consSeveral studies on pathological tissues ,38,39 anData summary: In this study, we describe for the first time a functional link between tdp-43 and ache in zebrafish. For this purpose, we employed the zf model previously described by our team , where kDanio rerio) were maintained at the ICM  fish facilities and bred according to the National and European Guidelines for Animal Welfare. Experiments were performed on wild-type embryos from AB and TL genetic strains. Zebrafish stored in incubators at 28.5 degrees remain at embryonic stages until approximately the end of the third day post-fertilization, before they hatch out of the chorion [Adult and larval zebrafish  Nonidet P−40/0.5% (w/v) Triton X-100 supplemented with a cocktail of protease inhibitors . Fish were then sonicated and centrifuged at 14,000× g at 4 °C for 20 min. Supernatants were collected and frozen at −80 °C until biochemical analysis.A morpholino, binding to the ATG sequence, responsible for translational initiation, was designed to knockdown (KD) tardbp  and AChE gene . A control morpholino (mis-std), containing mismatch nucleotides and not binding anywhere in the zebrafish genome, was used to assess the specificity of the observed phenotype (5′-CCTCTTACCTCAgTTACAATTTATA-3′). Human TARDBP wild-type (WT) mRNA (WT-TDP-43) was transcribed from NotI-linearized pCS2þ using SP6 polymerase with the mMESSAGE Machine Kit . AChE-T plasmid (WT-AChET) under the cytomegalovirus (CMV) promoter–enhancer was a generous gift from Dr. J. Saez Valero  . MicroinZebrafish embryos at 48 hpf were analyzed to identify any major morphological abnormalities . Subsequently, embryos that did not display any developmental abnormalities were touched lightly at the level of the tail with a pipette tip in order to evaluate their locomotor behavior. TEER episodes were performed only in zebrafish that appeared morphologically normal and were recorded with a Grasshopper 2 Camera  at 30 Hz. The videos were then analyzed using the manual tracking plugin of ImageJ 1.45r software as previously described .Animals were fixed in 4% paraformaldehyde for 3 h at room temperature. After fixation, the embryos were rinsed several times with PBS and then incubated in PBS containing 1 mg/mL collagenase  to remove skin. The collagenase was washed off with PBS Triton X-100  and heads were cut away. After an incubation of 30 min in blocking solution , the embryos were incubated overnight at 4 °C in synaptic vesicle 2  antibody diluted in blocking solution. The embryos were then washed and incubated for 30 min in PBST containing α-bungarotoxin conjugated to Alexa 488 . The embryos were rinsed several times with PBST and then incubated in freshly prepared block solution containing a secondary antibody  for 3h at RT before mounting on glass slide in 50% glycerol. The NMJs were visualized using a Leica SP8 Inverted scanning confocal microscope. All captured images of stained embryos were processed using Imaris Image Analysis software and ImageJ.4, and 5 mM potassium ferricyanide (pH 6.0). The reaction was stopped with washes in PBS. Then, the sections were dehydrated, cleared with xylene, coverslipped, and imaged with the Nanozoomer scanner .The Karnovsky and Roots method adapted for zebrafish was used . OptimalA modified micro assay version of the colorimetric Ellman’s method was used to measure AChE . A totalTotal RNA was isolated from injected fish using TRIzol Reagent (Sigma) according to the manufacturer’s protocol. First-strand cDNAs were obtained by reverse transcription of 1 μg of total RNA using the High-Capacity cDNA Reverse Transcription Kit (Roche), according to the manufacturer’s instructions. Quantitative PCR amplification was performed with SyBer2X Gene Expression Assays using the primers listed in p < 0.05.All data values for the zebrafish and cells experiments are expressed as mean with error bars representing standard errors of mean (SEM) with significance determined using one-way ANOVAs. Differences between groups were identified via Bonferroni post hoc comparisons. All analyses were performed using Prism 5.0 . Significance level was set at n = 12 for tdp-43 KD, vs. 15.82 ± 3.786, n = 10 for mis-CTRL, p < 0.05; sv2 orphans: 47.67 ± 4.206, n = 9 for tdp-43 KD, vs. 31.41 ± 4.311, n = 9 for mis-CTRL, p < 0.05; Several genes mutated in ALS patients and implicated in motor neuron degeneration have been studied in zebrafish, including superoxide dismutase (SOD1) , alsin . Then, tSigns of endplate denervation in ALS have been observed in patients ,54 as weachRα) and γ (achR γ), and for the muscle histone deacetylase 4 (hdac4), already described as molecular markers of denervation intensity [achRα was increased  that can be rescued by the expression of the corresponding human gene (hTDP-43) . Because of the impossibility to extract proteins specifically from muscle, we used Karnovsky and Roots reaction on sagittal embryo sections to detect ache activity in muscles .n = 11 for tdp-43 KD, vs. 99.99 ± 23.44, n = 11 for mis-CTRL, p < 0.05; Furthermore, we performed qPCR analysis in order to quantify ache expression at the transcript level. After KD of tdp-43, endogenous ache transcript was found to be reduced by 40%  or TARDBP (hTDP-43) genes after KD of either of these genes in zebrafish.To determine whether a functional interaction occurs between ache and tdp-43, we performed rescue experiments by co-injecting the human n = 27 for tdp-43 KD + hAChE-T, vs. 7 ± 3.613, n = 12 for tdp-43 KD vs. 100 ± 10.19, n = 26 for mis-CTRL; velocity: 91.65 ± 6.572, n = 27 for tdp-43 KD + hAChE-T, vs. 23.27 ± 4.229, n = 12 for tdp-43 KD vs. 100 ± 3.924, n = 26 for mis-CTRL; total time: 103.4 ± 12.09, n = 27 for tdp-43 KD + hAChE-T, vs. 21.43 ± 5.14, n = 12 for tdp-43 KD vs. 100 ± 10.62, n = 26 for mis-CTRL; Expression of hAChE-T in tdp-43 knockdown fish (orange) significantly improved the swimming behavior at 48hpf  gradually lost motility up to 48 hpf . The oveSince tdp-43 knockdown in zebrafish induces ach receptors reorganization, reminiscent of the diffuse achRs distribution previously seen in mutant zebrafish (achesb55) lacking of ache activity , we evalIn both double injections (KD tdp-43 + hAChE-T and KD ache + hTDP-43), we did not detect any pre/post synaptic orphans as compared to tdp-43 or ache KD conditions B,C. FurtThese data suggest the existence of a functional relationship where ache and tardbp operate and confirm the important role that ache has during development and NMJ stability.In this study, we extended our understanding of a major pathological factor in ALS, TDP-43, and its potential contribution in this neuromuscular disorder. Here, we established that tdp-43 plays an important action in regulating ache expression and levels in zebrafish. We demonstrated that ache levels that remain membrane-bound are decreased upon tdp-43 knockdown. Importantly, motor phenotype and the NMJ’s defects due to the tdp-43 depletion were rescued upon restoration of ache levels and activity.Drosophila [In the overwhelming majority of ALS cases, TDP-43-mediated neurodegeneration is primarily caused by the loss of normal function of TDP-43 in nuclei without being mutated with a consequent accumulation in the cytosol resulting in pathological aggregation ,59,60. Hosophila  as well osophila . Our finThe relevance of these findings is evident when taking into consideration the role that AChE displays during neuronal development and NMJ synapse stabilization independently of its activity in cholinergic neurotransmission . Thus, pDespite AChE being considered an “old actor”  in scienIn the mature NMJ instead, the AChE classical role involves the hydrolysis of ACh , and hasachR α subunits are increased and hdac4 expression augmented. The increase of these factors is particularly interesting since they are clearly upregulated in ALS-denervated muscle [Similarly, the zebrafish model lacking ache catalytic activity presents smaller and fragmented ach receptors clusters, reminiscent of the anatomical abnormalities observed in muscle/NMJ biopsies from ALS patients ,78. Mored muscle ,56,79,80AChE mRNA or rather indirectly TDP-43 and AChE can exert a role in NMJ maintenance through trans-acting factors [In our tdp-43 KD model, NMJs clearly lose their integrity, and ache down-expression can be one of the major causes of NMJ dysfunctions. The discovery that tdp-43 LoF decreases ache expression in zebrafish could provide a novel mechanistic insight for nerve-muscle integrity disruption in ALS pathogenesis. Since TDP-43 downregulation appears to be an early defect in ALS, AChE expression could also be altered at early pre-symptomatic stages of the disease, leading to NMJ remodeling. Indeed, to reinforce these findings, a recent study that performed spatial transcriptomic analysis in ALS pathological tissue observed reduced AChE levels . Since N factors .Certain limitations arise from the usage of zebrafish models in this current study. Importantly, compared with other vertebrates, zebrafish embryos appear to be devoid of ache splicing . For thi


Purpose: Diffuse large B cell lymphoma (DLBCL) is one of the most common B cell lymphomas, which displays heterogeneous pathologies. Programmed cell death 1/ programmed cell death ligand 1 (PD-1/PD-L1) plays an essential role in immunosuppression in multiple malignancies. Signal transducer and activator of transcription 3 (STAT3)-positive patients also have an independently inferior clinical outcome. However, there are no reports on the effect of plasma soluble PD-L1 (sPD-L1) combined with plasma STAT3 on the prognosis of DLBCL. In this study, we investigate the relationships between plasma sPD-L1 combined with STAT3 and clinical prognosis of DLBCL.Methods: Levels of plasma sPD-L1 and STAT3 were quantified using ELISA in eighty-seven DLBCL patients. Multiplexed immunofluorescence staining was performed to visualize the expression of PD-L1 in twenty-nine matched FFPE specimens from all patients.Results: The survival analysis revealed that the progression-free survival (PFS) and overall survival (OS) in high sPD-L1 level group were poorer than that in low sPD-L1 level group . Similarly, the PFS and OS in high STAT3 level group were also poorer than that in low STAT3 level group. Multivariate cox regression analysis showed that both high sPD-L1 and high STAT3 levels were the independent prognostic factors negatively affecting survival. In addition, patients with DLBCL having high levels of both sPD-L1 and STAT3 had a worse outcome than those patients having any one high or low levels of both (P < 0.001).Conclusions: We therefore revealed that high levels of plasma sPD-L1 and STAT3 are associated with inferior outcome for DLBCL patients, suggesting that combined measurement of their levels in plasma may be a promising prognostic strategy for DLBCL patients. Diffuse large B cell lymphoma (DLBCL) is one of the most common B-cell lymphomas, accounting for approximately 30-40% of non-Hodgkin's lymphoma (NHL) PD-L1, also known as B7-H1/CD274, is an immune inhibitory receptor expressed on the surface of T cells, B cells, and monocytes upon activation. It is a member of the B7 family and an important ligand of PD-1  Three oncogenic pathways have been observed in DLBCL including the constitutively activated NF-κB, JAK/signal transducer and activator of transcription (STAT), and PI3K/AKT/mTOR pathways Although high level of plasma sPD-L1 is associated with poor prognosis in DLBCL patients Our study included eighty-seven patients with de novo DLBCL whose peripheral blood was available and received R-CHOP  or R-CHOP-like regimen. Plasma samples from the peripheral blood of all patients were collected before systematic treatment and frozen at -80 °C until further analyses. Meanwhile, 29 matched formalin-fixed paraffin-embedded (FFPE) tissue from patients before treatment were collected. This study was approved by the Clinical Research Ethics Board of Tianjin Medical University Cancer Institute and Hospital . All patients provided written informed consent, and this study was conducted in accordance with the principles of Declaration of Helsinki and Good Clinical Practice guidelines.Levels of plasma sPD‐L1 and plasma total STAT3 were measured using enzyme linked immunosorbent assay (ELISA) kits namely, Human PD-L1 ELISA kit  and Human Signal Transducer and Activator of Transcription 3 (STAT3) ELISA Kit  according to the manufacturer's instructions. Each sample was analyzed in duplicates. The intra-assay and inter-assay coefficients of variation were below 20%.+. The mean value of PD-L1 positivity across images acquired from 20 fields of view was calculated. Phenotyping and quantification were carried out by a researcher who was blinded to the sample identities and clinical outcomes.In order to precisely detect the PD-L1 and PAX-5 (paired box 5) expression, multiplexed immunofluorescence staining was performed in twenty-nine matched formalin-fixed paraffin-embedded (FFPE) tissues from patients before treatment according to the Opal immunostaining protocol, as described in our previous study P < 0.05 was considered statistically significant.We used SPSS 25.0 statistical software  and Graphpad Prism 8.0  to perform all statistical analyses. Pearson's chi-squared test was used to analyze the correlations between plasma sPD-L1 and plasma STAT3 levels. The relationships between clinicopathologic parameters and sPD-L1 or STAT3 levels were analyzed via chi-square test. The association between plasma sPD-L1 and PD-L1 expression in tumor tissue was assessed by linear regression analysis. Progression-free survival (PFS) was calculated from the date of diagnosis to that of disease progression, relapse, or death from any cause. Overall survival (OS) was calculated from the date of diagnosis to that of last follow-up or death. PFS and OS were estimated using the Kaplan-Meier method and log-rank tests were performed. Table P = 0.013), elevated LDH (P = 0.012) and β2-M levels (P = 0.001), international prognostic index (IPI) score > 2 (P = 0.003), non-GCB subtype (P = 0.042), B symptom (P = 0.012), DEL (P = 0.021), and poor clinical response (P = 0.002). High STAT3 levels were related to the advanced stage (P = 0.041) and IPI score > 2 (P = 0.044). Furthermore, we also found that there were no correlations between plasma sPD-L1 level and plasma STAT3 level .Using a cut-off value of median sPD-L1 level, 1.21 ng/ml, we separated the patients into two groups, which is similar to the previous study P < 0.001, Figure P < 0.001, Figure P = 0.005, Figure P = 0.013, Figure P = 0.046, Figure P = 0.002, Figure P = 0.013, Figure P = 0.053, Figure Median follow-up duration was sixty range: 2-106 months) months. All eighty-seven patients were available for the 3-year PFS and OS analysis. We found that 3-year PFS and 3-year OS rates were 74.7% and 79.3%, respectively. The survival analysis revealed that patients with high sPD-L1 level in the entire cohort , β2-M , IPI scores , B symptom , DEL , high sPD-L1 level , high STAT3 level  were associated with poor prognosis in patients with DLBCL. Multivariate cox regression models were further performed to determine the prognostic value of sPD-L1 and STAT3 level. We found that IPI score , high sPD-L1 level , and high STAT3 level  were the independent prognostic factors negatively affecting survival.Subgroup analyses were performed according to gender, age, clinical stages, LDH level, β2-M level, IPI grades, B symptoms, sPD-L1 level, and STAT3 level. Table 2χ = 26.289, P < 0.001; Figure P = 0.107). However, on comparing groups 1 and 2, we found that in patients with high sPD-L1 level, those with high STAT3 level had poorer survival rates than patients with low STAT3 level (P = 0.013). These findings suggested that if high STAT3 level led to the upregulation of sPD-L1 level, patients had the worst prognosis. If high STAT3 level did not lead to the upregulation of sPD-L1 level, there was no significant difference in patient survival (P = 0.107). According to present reports, overexpression of STAT3 may upregulate the expression of PD-L1 and relate to poor overall survival Among all patients, twenty-five displayed high sPD-L1 and high STAT3 levels (group 1), nineteen showed high sPD-L1 and low STAT3 levels (group 2), nineteen exhibited low sPD-L1 and high STAT3 levels (group 3), and twenty-four showed low sPD-L1 and low STAT3 levels (group 4). We performed survival analyses to compare these four groups according to their combination of sPD-L1 and STAT3 levels. Kaplan-Meier survival curves showed that group 1 patients had the worst OS compared with other groups ] and high STAT3 levels  were the independent prognostic factors negatively affecting survival. We also found that the PFS and OS for high sPD-L1 level group was poorer than that for low sPD-L1 level group in all patients as well as in the non-GCB and GCB-DLBCL subgroups. As demonstrated previously P = 0.041) and IPI score (P = 0.044). These findings were consistent with previous reports P < 0.001) compared with all other patients. Interestingly, we also found that there was no significant difference in OS in patients with DLBCL having low sPD-L1 levels and high or low STAT3 levels (P = 0.107). However, the patients with high sPD-L1 and STAT3 levels had poorer OS compared with those having high sPD-L1 levels and low STAT3 levels (P = 0.013). These findings also suggest that we can stratify DLBCL patients according to plasma sPD-L1 and STAT3 levels into different risk groups.In our study, we showed that high sPD-L1 levels [HR (95% CI) = 6.284 (1.390-28.397),  4.158 1.2-14.627,2R = 0.3787, P = 0.0004, Figure The correlation between PD-L1 expression in the tumor tissue and plasma sPD-L1 is controversial In summary, the levels of plasma sPD-L1 and plasma STAT3 were independent prognostic factors negatively affecting survival, and high levels of both showed a significantly worse survival rate compared with that by any one high or low level of both. In the era of personalized diagnosis and treatment, combined measurement of the levels of plasma sPD-L1 and STAT3 may be a promising prognostic strategy for DLBCL patients.


They demonstrate cell type-specific vulnerability to both seeded aggregation (induced by post-mortem tissue extracts) and upon treatment with highly purified recombinant TDP-43 oligomers.Smethurst  Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by motor neuron loss, resulting in muscle wasting, paralysis and eventual death. A key pathological feature of ALS is cytoplasmically mislocalized and aggregated TDP-43 protein in >95% of cases, which is considered to have prion-like properties. Historical studies have predominantly focused on genetic forms of ALS, which represent ∼10% of cases, leaving the remaining 90% of sporadic ALS relatively understudied. Additionally, the role of astrocytes in ALS and their relationship with TDP-43 pathology is also not currently well understood. We have therefore used highly enriched human induced pluripotent stem cell (iPSC)-derived motor neurons and astrocytes to model early cell type-specific features of sporadic ALS. We first demonstrate seeded aggregation of TDP-43 by exposing human iPSC-derived motor neurons to serially passaged sporadic ALS post-mortem tissue (spALS) extracts. Next, we show that human iPSC-derived motor neurons are more vulnerable to TDP-43 aggregation and toxicity compared with their astrocyte counterparts. We demonstrate that these TDP-43 aggregates can more readily propagate from motor neurons into astrocytes in co-culture paradigms. We next found that astrocytes are neuroprotective to seeded aggregation within motor neurons by reducing  cytoplasmic TDP-43, TDP-43 aggregation and cell toxicity. Furthermore, we detected TDP-43 oligomers in these spALS spinal cord extracts, and as such demonstrated that highly purified recombinant TDP-43 oligomers can reproduce this observed cell-type specific toxicity, providing further support to a protein oligomer-mediated toxicity hypothesis in ALS. In summary, we have developed a human, clinically relevant, and cell-type specific modelling platform that recapitulates key aspects of sporadic ALS and uncovers both an initial neuroprotective role for astrocytes and the cell type-specific toxic effect of TDP-43 oligomers. Amyotrophic lateral sclerosis (ALS) is caused by the selective degeneration of motor neurons in the brain and spinal cord, which results in rapidly progressive paralysis and invariable death, commonly by respiratory failure. ALS has a lifetime risk of ∼1 in 400 in individuals with European ancestry . Ten perAn increasing number of neurodegenerative diseases are considered to have ‘prion-like’ characteristics , which iAgainst this background, we and others have shown that TDP-43 pathology can be directly and reliably reproduced in HEK293 and NSC-34 cells via a templated seeding reaction from human sporadic ALS post-mortem brain and spinal cord tissue .Here we have induced a pathological TDP-43 seeded aggregation reaction in control human iPSC-derived motor neurons and astrocytes on an endogenous background level of TDP-43 expression using sporadic ALS spinal cord tissue extract serially passaged in cell culture (spALS). These TDP-43 aggregates were phosphorylated and morphologically representative of ALS post-mortem tissue. We found that astrocytes could also be seeded with TDP-43 aggregates but at lower frequency than motor neurons, implying cell-type specific differences in vulnerability. We found that proteasomal inhibition could both exacerbate seeded TDP-43 aggregation and significantly increase cell death in motor neurons when compared to astrocytes. We were also able to demonstrate that TDP-43 pathology spreads from motor neurons to astrocytes preferentially, but could also be observed spreading from astrocytes to motor neurons. We next found that astrocytes—both by physical co-culture and conditioned medium experiments—are neuroprotective to seeded aggregation within motor neurons by reducing  cytoplasmic TDP-43, TDP-43 aggregation and cell toxicity. Furthermore, we detected TDP-43 oligomers in these spALS spinal cord extracts, and as such demonstrated that highly purified recombinant TDP-43 oligomers can reproduce this observed cell type-specific toxicity, providing further support to a protein oligomer-mediated toxicity hypothesis in ALS.Detailed methods are provided in the Initial sarkosyl-insoluble extracts from frozen CNS tissue for seeding were prepared as previously described  and from Days 7 to 14   C. Howeve < 0.01) D.P < 0.001) compared to treatment with the proteasome inhibition alone (∼4%) (P < 0.01) compared to proteasome inhibition alone (∼6%) (P < 0.001) (P < 0.01) than treatment with MG132 alone  E and F. ne (∼6%) G, suggesne (∼6%) H. Treatm< 0.001) I and J w< 0.001) I. After < 0.001) J. The fr32 alone J. Howeve32 alone K and L.P < 0.001). After this time point the percentage of astrocytes containing TDP-43-positive aggregates significantly decreased from 27.3% to 11.3% (P < 0.001) indicating a clearance or dilution of TDP-43 aggregates over time [To determine if TDP-43 aggregates could spread between motor neurons and astrocytes, we cultured control untreated astrocytes together with motor neurons that had been treated with our optimized seeded aggregation paradigm described above for 3, 7 and 14 days [P < 0.05) and again to 1% at Day 14 (P < 0.01) [Next, we investigated the propagation of TDP-43 aggregates from astrocytes to motor neurons by performing seeded aggregation in astrocytes and then co-culturing with control untreated motor neurons for 3, 7 and 14 days [P < 0.01) and 20.4% (ACM) (P < 0.01) (P < 0.01) (P < 0.01) with astrocyte co-culture and 32.2% to 19.3% (P < 0.01) with ACM treatment  treatments significantly reduced the percentage of cells containing TDP-43-positive aggregates from 37.8% to 23.4% (astrocyte co-culture) ( < 0.01) A and B.  < 0.01) C. Finallreatment D. These reatment .There is now accumulating evidence to support the notion that soluble oligomeric species of misfolded proteins are the cause of toxicity in several neurodegenerative diseases with prominent protein misfolding . We nextP < 0.001) compared to the control  after 24 h, detected via an increase in activated Casp3 immunolabelling  for 24 h . We have previously demonstrated that serial passage of TDP-43 pathology further enhances its potency and the increased abundance of seeded aggregation demonstrated in the experiments performed in The number of TDP-43 aggregates reproducibly and significantly increased in frequency with longer incubations, which is consistent with either The lack of toxicity in human iPSC-derived motor neurons treated with seeded aggregation alone in this study likely reflects low frequency and abundance of TDP-43 inclusions observed. However, upon proteasomal inhibition and subsequent seeded aggregation, the number of TDP-43 aggregates and activated Casp3-positive cells significantly increased compared to proteasome inhibition alone suggesting that such TDP-43 seeded aggregation is indeed toxic under defined cellular states. The sequential proteasome inhibition and inoculation of the spALS spinal cord extract resulted in the increased formation of TDP-43 aggregates in both human iPSC motor neurons and astrocytes. Indeed, a mouse motor neuron-driven expression of proteasome dysfunction, but not autophagy, resulted in a robust model of TDP-43, FUS, ubiquilin-2 and optineurin mislocalization and aggregation with a resultant ALS phenotype  in primary neuron and astrocytes co-culture happens in a cell contact dependent manner via tunnelling nanotubes , the UK Medical Research Council (FC010110), and the Wellcome Trust (FC010110). R.P. holds an MRC/MND Association Lady Edith Wolfson Senior Clinical Fellowship (MR/S006591/1) and is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.The authors report no competing financial interests.awz419_Supplementary_InformationClick here for additional data file.


Noonan and LEOPARD syndromes (NS and LS) belong to a group of related disorders called RASopathies characterized by abnormalities of multiple organs and systems including hypertrophic cardiomyopathy and dysmorphic facial features. There are no approved drugs for these two rare diseases, but it is known that a missense mutation in PTPN11 genes is associated with approximately 50% and 70% of NS and LS cases, respectively. In this study, we implemented a hybrid computational drug repositioning framework by integrating transcriptomic and structure-based approaches to explore potential treatment options for NS and LS. Specifically, disease signatures were derived from the transcriptomic profiles of human induced pluripotent stem cells (iPSCs) from NS and LS patients and reverse correlated to drug transcriptomic signatures from CMap and L1000 projects on the basis that if disease and drug transcriptomic signatures are reversely correlated, the drug has the potential to treat that disease. The compounds that were ranked top based on their transcriptomic profiles were docked to mutated and wild-type 3D structures of PTPN11 by an adjusted Induced Fit Docking (IFD) protocol. In addition, we prioritized repositioned candidates for NS and LS by a consensus ranking strategy. Network analysis and phenotypic anchoring of the transcriptomic data could discriminate the two diseases at the molecular level. Furthermore, the adjusted IFD protocol was able to recapitulate the binding specificity of potential drug candidates to mutated 3D structures, revealing the relevant amino acids. Importantly, a list of potential drug candidates for repositioning was identified including 61 for NS and 43 for LS and was further verified from literature reports and on-going clinical trials. Altogether, this hybrid computational drug repositioning approach has highlighted a number of drug candidates for NS and LS and could be applied to identifying drug candidates for other diseases as well. NS has tistics) . The mististics) . Mutatiotistics) . The curtistics) . No drugin silico drug repositioning as a parallel approach to exploring potential opportunities for rare disease treatment   , stimulaNovel high throughput screening platforms such as LINCS1000  and Tempin silico drug repositioning approaches varies among different rare diseases based on data availability  and six common down-regulated genes  were found in the transcriptomic signatures of NS and LS. An analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways highlighted two over-represented KEGG for NS included Ribosome (KEGG id: hsa03010) and Biosynthesis of antibiotics (KEGG id: hsa01130). Meanwhile, a total of 14 KEGG pathways were over-represented for LS, which consisted of several cardiovascular-related pathways such as Hypertrophic cardiomyopathy (KEGG id: hsa05410) and some cancer-related pathways such as PI3K-Akt signaling pathway (KEGG id: hsa04151)  between NS and LS transcriptomic signatures, indicating the power of transcriptomic profiles to differentiate between NS from LS  from the STRING database. The top three clusters of PPIs were extracted using Cytoscape (plugin MCODE), and the over-represented KEGG pathways were enriched using the Database for Annotation, Visualization and Integrated Discovery (DAVID)  and compared them with the NS and LS signatures. There was little overlap of genes among the different studies, possibly due to the high heterogenicity of HCM patients. The number of overlapping genes with HCM studies were 26 (10 up-regulated and 16 down-regulated) and 28 (16 up-regulated and 12 down-regulated) for NS and LS, respectively  for each disease were extracted. We noticed that some repositioning candidates were enriched across multiple assay conditions. For example, CGP-60474, an inhibitor of a cyclin-dependent kinase, was identified as a potential treatment for NS based on its transcriptomic profiles in 12 different cell line/concentration/duration combinations. Repositioning candidates were also identified using CMap02 data sets. We collapsed these drug transcriptomic profiles generated under different assay conditions such as cell line/concentration/duration into one prototype ranking list (PRL) to generate its signature. The top 50 repositioning candidates were prioritized based on ranked GSEA scores for NS and LS, respectively. Together, we obtained 74 repositioning candidates for NS and LS from L1000 and CMap data sets, respectively (see Repositioning candidates were enriched by using two large drug transcriptomic data sets including CMap02 and LINCS 1000 (L1000) . The ratp values were less than 2.2×10-16. Both statistical measures indicated that all the enriched repositioning candidates were not attributable to chance.To investigate whether the enriched repositioning candidates were better by chance, we conducted a random test by using a pseudo disease signature to query the two drug transcriptomics datasets. The process was repeated 50,000 times. L-Antineoplastic and immunomodulating agents dominated all the therapeutic classes, corresponding to 22% and 32% of repositioning candidates for NS and LS, respectively. It was interesting that 11 repositioning candidates for NS were from J-Antiinfectives for systemic use and 10 for LS belonged to N-Nervous system, highlighting the divergence of the underlying mechanisms of the two diseases. It was notable that the 8 dermatological drugs were enriched for NS, perhaps correlating with facial deformaties in NS patients.The therapeutic class distribution of enriched repositioning candidates is shown in PHE113, PRO215, LEU216, LEU233, LEU236, LEU254, and PRO491 and the hydrophobic area of 6CMR (NS) consisted of the residuals PHE113, LEU117, LEU125, LEU233, LEU236 and ALA237 (Three 3D protein structures of PTPN11 including wild type (PDB ID: 4DGP), LS-Associated SHP2/T468M mutant (PDB ID: 4OHL), and NS-associated SHP2/E76D mutant (PDB ID: 6CMR) were used. The crystal structure of PTPN11 consisted of N- and C-terminal SH2 domains, and a PTP domain . The topd ALA237 Table 3.Materials and Methods section). We found that the IFD scores of LS were higher than that of their wild type, while the IFD scores of NS were lower than their wild type (see The Schrödinger Induced Fit Docking (IFD) protocol was implemented to dock the repositioning candidates to the optimal binding site of 4DGP (wild type), 4OHL (LS), and 6CMR (NS), respectively. Some repositioning candidates  failed to dock into protein binding pockets. Building on this, compounds with positive IFD score values were also considered as docking failures. Overall, the success in the execution of IFD to generate repositioning candidates  resulted in multiple docking poses for each repositioning candidate. We selected the best docking pose based on the lowest IFD score for each repositioning candidate. We implemented an adjusted IFD score strategy by combining the IFD scores of ligands in both the mutated and wild type (see HIS114 (polar), PHE113 (hydrophobic), and GLU249 (negative charge). Repositioning candidates belonging to Phosphoinositide 3-kinases (PI3Ks) inhibitors  and non-selective, irreversible monoamine oxidase (MAOI) inhibitors  were clustered together (shown in red), where more frequently binding residuals included ARG111 (positive charge), LEU216 (hydrophobic), THR218 (polar), and THR219 (polar). There was another group of repositioning candidates with diverse modes of action (shown in green). The average and standard deviation values of adjusted IFD scores for the three clusters  were not statistical significantly different. For LS, repositioning candidates were also divided into three clusters (PHE113 (hydrophobic), THR218 (polar), GLU249/250 (negative charge), and ARG111 (positive charge) were more frequently bound. Two heat shock protein 90 inhibitors  clustered together (shown in green) and key residuals including ARG111 and HIS114 were highlighted. Repositioning candidates that are MOA-dependent formed another cluster (shown in blue). Furthermore, the average adjusted IFD scores of the cluster including heat shock proteins  was higher than that of the other two clusters (1111.96± 16.60 for the HDAC cluster and -1110.78 ± 15.18 for the MOA cluster).To further investigate the key residuals involving in the ligand binding process, we analyzed the ligand-residual binding interactions for 4OHL (LS) and 6CMR (NS) with hierarchical cluster analysis (HCA). For NS, the repositioning candidates were categorized into three clusters based on their binding residuals . The prioritized repositioning list for NS and LS by proposed combined ranking strategy are listed in To take advantage of both transcriptomic and structure-based approaches for repositioning candidates prioritization, we developed a consensus ranking strategy  inhibitors has been the focus of considerable research efforts in light of their diverse role in cancer and RASopathies disorders . HoweverA large proportion of enriched repositioning candidates for LS and NS has anticancer activity. In our previous study, we have proposed to reuse oncology drugs for rare disease treatment development based on the similarity of the two types of diseases at the molecular level . The mutOne of the most common phenotypic characteristics of NS and LS is hypertrophic cardiomyopathy (HCM). The LS patients involved in this study had a clear HCM phenotype. Multiple small molecule inhibitors of histone deacetylase (HDAC) have been shown to be efficacious in blocking pathological cardiac hypertrophy, and in improving cardiac function in various rodent animal models . SeveralOur data also suggested several heat shock protein 90 (HSP90) inhibitors including BRD-K41859756, geldanamycin, radicicol for potentially treating LS. HSP90 has been widely studied given its essential role in cancer etiology . AlongsiSeveral areas would benefit from further verification of the strategies and results suggested from this study. First, due to the limited samples and data of NS , the transcriptomic signatures may suffer a degree of bias leading to unspecific functional analysis results. Second, the adjusted IFD model is time-consuming due to the need for precise docking for both mutated and wild types, limiting its broader application for screening purposes. However, the adjusted IFD model could easily be migrated to the Glide docking framework to address this limitation. Finally, no single bioinformatics approach offers a one-stop solution for exploring rare disease treatment options. Each research question requires a “fit for purpose” approache based on biological understanding and data availability. More importantly, the prioritized repositioning list serves as a starting point for further treatment development, and wet lab experiment verification and real-world evidence are imperative .It is worthwhile to consider additional experimental studies to confirm the findings from our study. The current study set the focus on the novel bioinformatics approaches by integrating genomics and structure information to generate testable hypotheses and provide viable leads for other investigators to follow up on. However, the clinic validity of enriched candidates could be only established with further wet-lab experiments and clinical trials, which is out of the scope of the current study. Specifically, a more profound transcriptome analysis should be conducted to show that candidate drugs might influence the transcriptome in such a way that the NS and LS cells could come back to normal profile. Moreover, hiPS cells with NS and LS syndromes should be analyzed for cardiac defects. Then, the proposed drugs should be demonstrated to functionally rescue these defects. Furthermore, some experimental validation of the cellular activity of the proposed drugs against the disease targets  could be helpful to elucidate the pharmacological effects of enriched candidates. In our future work, we will further consider how to enhance biological validation for a better verification of proposed in silico approaches.In conclusion, we have developed a computational drug repositioning framework that integrates transcriptomic and structural approaches to find novel potential NS and LS treatment options. The framework developed is robust and relatively straightforward to use as such it may be equally applicable to the search for treatments for other rare diseases.https://www.ncbi.nlm.nih.gov/geo/) by using keyword “Noonan syndrome” AND (“LEOPARD syndrome” OR “Noonan syndrome with multiple lentigines”). Consequently, two GEO datasets GSE54538  included NS patients, juvenile myelomonocytic leukemia (JMML) patients, NS/JMML patients, and healthy donors. Here, NS patient samples with E76D mutation in PTPN11  and four control samples from healthy donors were employed for further analysis.The experiment samples of LS (GSE20473) consisted of two iPSC lines of two LS patients with T468M mutation in PTPN11, normal or patient Fibroblasts, and HES2 human ES cell line. In this study, two iPSC lines from each LS patient  and two controls including the HES2 human ES cell line and a wild-type iPSC line and BJ-iPSB5 derived from a normal human fibroblast line (BJ) was used.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE54538. By comparing NS patient samples to the controls, the fold changes and p values were generated using R limma package. We further mapped the probe sets into gene symbols using R biomaRt packages. Then, the probes that could not be converted to gene symbols were excluded. Final, we ranked genes based on their fold change values and selected the top/down 250 genes as NS signatures (see For NS dataset (GSE54538), Illumina HumanHT-12 V4.0 expression BeadChip was used, which targets more than 47,000 probes derived from the National Center for Biotechnology Information Reference Sequence (NCBI) RefSeq Release 38. The preprocessed data by Robust Multi-array Average (RMA) was downloaded from https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE20473&format=file. First, the RAW CELL files were processed with alternative chip definition files  from Brainarray (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/CDF_download.asp) and RMA used to summarize the intensity ratios at probe set level to obtain expression values per gene. Like the procedure used for NS data, the fold change and p values were generated by using R limma and limited to gene levels. Finally, genes were ranked by their fold changes values, and the top/down 250 genes were extracted as the LS signatures (see For LS dataset (GSE20473), the experiment was conducted using Affymetrix Human Exon 1.0 ST Array. The Raw CELL files were downloaded from p-value less than 0.05 were considered to be statistically significant.The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was carried out with the obtained disease signatures including 500 genes  for NS and LS by using the Database for Annotation, Visualization and Integrated Discovery  software . The pathttps://string-db.org/) (Protein-protein interactions (PPI) among NS and LS signatures were further determined by human PPI data extracted from the STRING v11.0 database (db.org/) . Only thdb.org/) .in silico miRNA prediction algorithms including miRWalk, miRDB, PITA, MicroT4, miRMap, RNA22, miRanda, miRNAMap, RNAhybrid, miRBridge, PICTAR2, and Targetscan were employed. We considered the mRNA-miRNA relationships that were predicted as positive by 10 of 12 algorithms. The calculation was carried out through miRNA 2.0   .limma packages.One of the most common phenotypes of NS and LS are cardiovascular disorders such as hypertrophic cardiomyopathy (HCM). We collected four transcriptomic profiles of HCM  by searching against the GEO database see Table 1.http://www.lincsproject.org/)   and Connct.org/)  were emp2) was used to reversely compare the NS and LS transcriptomic signatures to the drug transcriptional signatures in LINCS project for repositioning candidate enrichment. The top 50 candidates based on cosine similarity scores were extracted as the repositioning candidates. For CMap 02 dataset, 6,100 experiments were conducted in six or seven different cancer cell lines using 1,309 compounds with two Affymetrix platforms. We followed the standard procedures to process and normalize the dataset  for each drug-transcriptomic data set. Then, the pseudo enrichment scores were calculated. This process was repeated N= 50,000 to remove the potential bias in the gene selection process, and a null distribution of pseudo enrichment scores was obtained. Finally, the Repositioning candidates obtained from the transcriptomics-based approach were further prioritized by using molecular docking. Since the patients involved in the transcriptomic studies of NS and LS has PTPN11 mutants , the 3D structures of PTPN11 including wild type, T468M mutation, and E76D mutation were retrieved from RCSB PDB . The 3D Molecular docking was carried out by Schrödinger . All protein structures were prepared with Protein Preparation Wizard in the Schrödinger Suite. The structural integrity was checked, and missing site chains and loops segments were added using Prime. Hydrogen atoms were added without deleting the original ones. All ligands of repositioning candidates based on transcriptomics-based approach were prepared with LigPrep  in the Schrödinger Suite with the default settings. The four poses with minimal energy were kept.The potential binding sites of the 3D structure of mutated and wild type PTPN11 were predicted by the SiteMap program in Schrödinger software. The predicted binding sites for the PTPN11 with E76D mutations were compared with the pocket where the SHP099 inhibitor was located. The binding site with the highest score was selected as the most probable binding sites for further docking.Induced fit docking (IFD) predicts optimal ligand-binding modes and concomitant structural movements in the receptor using Glide and Prime modules. In IFD, when a ligand binds to the receptor, it undergoes side-chain or backbone conformational changes or both in many proteins. These conformational changes permit better binding to the receptor according to the shape and binding mode of the ligand. Docking was performed using the Schrödinger Induced Fit Docking (IFD) protocol, consisting of GLIDE docking with the rigid protein followed by energy minimization of the side chain orientations with Prime of those residues within 7.5 Å of the ligand. In the first Glide docking round, the ring containing carbons 1, 2, 3, 4, 5, and 10, and the hydroxyl at C3 of the estradiol core were restricted to an RMSD of 1.0 Å from the crystal pose. A final round of GLIDE docking was then performed towards the optimized receptor structures, from which final Induced Fit Docking (IFD) scores for each ligand-receptor complex were calculated.We developed an adjusted IFD score to reflect the specificity of compounds binding to the structure of mutated SHP-2 with the following equation:c, the adjusted parameter, is normalized the difference of IFD scores between mutated and wild type protein structures for compound i. The c was used to measure the specificity of compound binding to mutated PTPN11 structure.Where To combine the transcriptomic-based approach with molecular docking for generating a consensus ranking list, we developed a combined score as listed below,Publicly available datasets were analyzed in this study. This data can be found here: GSE54538; GSE20473;GSE89714;GSE68316; GSE36961; GSE32453.ZL and WT conceived and designed the study. LZ and ZL performed data analysis and wrote the manuscript. RH, MX, and JZ designed and conducted the wet-lab experimental verification. LZ, RR, BD, RH, ZL, and WT revised the manuscript. All authors contributed to the article and approved the submitted version.The views presented in this article do not necessarily reflect current or future opinion or policy of the U.S. Food and Drug Administration and/or the U.S. National Institute of Health. Any mention of commercial products is for clarification and not intended as an endorsement.RR is co-founder and co-director of ApconiX, an integrated toxicology and ion channel company that provides expert advice on non-clinical aspects of drug discovery and drug development to academia, industry and not-for-profit organizations.