Glacier- and snowpack-derived meltwaters are threatened by climate change. Features such as rock glaciers (RGs) are climatically more resilient than glaciers and potentially contain hydrologically valuable ice volumes. However, while the distribution and hydrological significance of glaciers is well studied, RGs have received comparatively little attention. Here, we present the first near-global RG database (RGDB) through an analysis of current inventories and this contains >51,000 RGs. Using the RGDB, we identify key data-deficient regions as research priorities . We provide the first approximation of near-global RG water volume equivalent and this is 62.02 ± 12.40 Gt. Excluding the Antarctic and Subantarctic, Greenland Periphery, and regions lacking data, we estimate a near-global RG to glacier water volume equivalent ratio of 1:618. Significant RG water stores occur in arid and semi-arid regions . These results represent a first-order approximation. Uncertainty in the water storage estimates includes errors within the RGDB, inherent flaws in the meta-analysis methodology, and RG thickness estimation. Here, only errors associated with the assumption of RG ice content are quantified and overall uncertainty is likely larger than that quantified. We suggest that RG water stores will become increasingly important under future climate warming. Described as the world’s natural “water towers”4, glacier- and snowpack-derived meltwater are critical to ecological, social and economic systems in these regions. Additionally, mountain water stores provide buffering capacity for surrounding lowlands5. Elevation dependent warming  suggests that high-altitude environments will likely experience comparatively faster warming than lower altitude areas6. Furthermore, high-altitude hydrological resources are highly sensitive to environmental change7. Indeed, between 2003 to 2009 glacier volume loss globally was estimated to be −259 ± 28 Gt yr−1 8. With projected atmospheric warming, long-term glacier and seasonal snowpack changes are expected to impact significantly hydrological resources stored within high mountain systems9. Small and low-lying glaciers are particularly likely to be sensitive to warming, with many disappearing12. In the short-term glacier shrinkage results in increased runoff. However, following “peak non-renewable water”13, summer runoff will significantly reduce in semi-arid and arid regions15. Additionally, a warming-induced precipitation shift from snowfall to rainfall16 combined with a temporal shift towards earlier snowpack melt17, will further lead to runoff reduction.In semi-arid and arid high mountain systems glaciers and seasonal snowpack form natural buffers to hydrological seasonality, as seasonal meltwater contributions smooth the effects of highly variable summer precipitation and associated irregular runoff18, that of rock glaciers (RGs) has received comparatively little attention19. RGs are cryospheric landforms that are formed by gravity-driven creep of accumulations of rock debris supersaturated with ice. They are characterised by a seasonally frozen, clastic-blocky surficial layer ~0.5 to 5 m thick that thaws each summer (this is known as the active layer)20. RGs are described as active or inactive if they contain ice beneath the active layer. These are described collectively as intact RGs. Those containing no or minimal ice content are termed relict RGs21. RGs are thermally decoupled from external micro- and meso-climates because of the insulating effect of the active layer, which is shown to slow the rate of ice melt within RGs20. Consequently, RGs respond to climate change at comparatively longer time scales than glaciers22. Therefore, RGs are more climatically resilient than glaciers and form frozen water stores of potentially significant hydrological value23. Indeed, under future climate warming RGs are expected to form a larger component of base flow to rivers and streams24. RGs containing ice generally display slow movement rates (mm yr−1 to cm yr−1) because of the gravity-driven creep of the ice-supersaturated RG body. This movement creates distinctive morphometric characteristics that enable feature identification and activity classification (i.e. intact or relict)  glacier inventory. To date RG distribution has only been researched at regional scales . Although described as the “most pressing need” in RG research, as yet no global-scale RG inventory exists27. This prevents full assessment of RG distribution and their hydrological value as water stores, and forms the motivation for this study.Ubiquitously distributed through the world’s high mountain systemsAs the primary objective of this study, we present the first near-global-scale RG database (RGDB). The RGDB is shared as a Microsoft Excel database available in the supplementary information online. We argue that data dissemination forms a positive step towards scientific transparency and open-access research, benefitting both the scientific and local/regional communities. The RGDB was developed through meta-analysis of systematic inventory studies published prior to October 2017 (see Methods). In this study, ‘systematic inventory studies’ refer to the strategic and complete mapping of RG features within a study area. We identified these using ISI Web of Science, Scopus, ProQuest Dissertations and Theses, Google Scholar, and National Snow and Ice Data Center (NSIDC) search tools (Supplementary Table H = cAβ where mean RG thickness (H) is calculated as a function of surface area (A) and two scaling parameters (c and β), were applied. The scaling parameters applied here were derived from the empirical rule established by Brenning28  released December 201432 “provides a globally complete set of outlines for all [ice- and debris-covered-] glaciers outside the two ice sheets Greenland and Antarctica”33. For each glacier of the RGIv4.0, Huss and Hock34 calculated glacier volume and ice thickness distribution through the application of an ice-thickness distributed model35 (see Methods). These global-scale data were used within this study. To better enable regional assessment, the RGDB was divided into RGIv4.0 adapted regions  scaling relations, i.e. equation  in Metho36, an increased interest in RG research in the last decade. After duplicate RG data, i.e. overlapping study areas, were excluded, 76 studies formed the final RGDB. Systematic RG inventories forming the RGDB were predominantly generated using expert photomorphic mapping from remote sensing image data, with landforms manually identified and digitised based upon geomorphic indicators 38;provided the opportunity for large-scale geomorphological surveys. For instance, fine spatial satellite image data are accessible freely through Google Earth, including SPOT and DigitalGlobe . The RGDB includes studies that have used the Google Earth platform to compile systematic RG inventories(ii)39. This is important as relict landforms strongly influence catchment hydrology40; andprovided open-access to <1 m resolution airborne laser scanning (LiDAR) data, enabling relict RGs covered by dense vegetation to be systematically mapped(iii)et al.41).provided accessible interferometric synthetic aperture radar (InSAR) data . In the context of this study, InSAR has enabled systematic RG mapping through the investigation of surficial kinematics (i.e. feature movement). Subsequently, activity status classification can be defined with greater accuracy . Study density is highest in Central Europe (n = 27), followed by South America (n = 17) and North America (n = 7)  covering an estimated area of ~6,300 km2 globally34. Furthermore, Huss and Hock34 estimated glaciers to contain 138,074 Gt of ice, equating to an estimated water volume equivalent of 124,266 Gt  and 1:18,395 , excluding the Antarctic and Subantarctic and Greenland Periphery RGI regions. However, continental-extent ratios are not reflective of national-level or regional-level ratios. For example, RG to glacier water volume equivalence ratios of 3:1 (semi-arid Chilean Andes [29°–32°])46 and 1:3 38 have been reported.Excluding those RGI regions where no systematic RG inventory studies have been undertaken , the estimated ratio of RG to glacier water volume equivalence is 1:1,482. For completeness we also excluded the Antarctic and Subantarctic and Greenland Periphery RGI regions, similar to other studies47. The term paraglacial is defined as “…nonglacial earth-surface processes, sediment accumulations, landforms, landsystems and landscapes that are directly conditioned by glaciation and deglaciation”49. Modification of rock slopes (through rock slope failures [RSFs]) dominate the rock slope paraglacial system, as high mountain systems respond to deglacial unloading or debuttressing following the exposure of glacially steepened rockwalls by glacier downwastage and retreat48. This may subsequently increase glacier surface insulation through enhanced debris-supply. Therefore, frozen water store preservation may occur, as glaciers transition to RG forms50. Sasaki et al.51 estimated global supraglacial debris-cover to be ~43,750 km2  and also provided regional estimations. For example, significant debris-cover in Central Asia , South Asia East , and South Asia East  suggests that potentially ‘suitable habitats’ for RG development exist within these locations. We suggest, therefore, that these are priority regions for future systematic RG inventory studies. Furthermore, given the global glacier volume loss projections of 25–48% between 2010–210034, we further suggest that RGs will play an increasingly important future role in regional water supply.A number of RGI regions are underrepresented within the RGDB Table . ImportaInherited errors within the RGDB: Whereas automated and semi-automated techniques have enabled the mapping and monitoring of clean-ice glaciers from optical satellite data52, these approaches are generally unsuitable for mapping debris-covered glaciers . This is because both supraglacial-debris (upon the glacier) and debris at the glacier margins share a common source, and thus spectral similarity of features “render them mutually indistinguishable”52. This limitation is also applicable to RGs . Therefore, manual RG identification and digitisation using geomorphic indicators . Furthermore, Whalley et al.56 have previously highlighted the problem of mapping ‘hidden’ ice with respect to RGs. The RGDB presented here includes only meta-analysis derived data from the available systematic RG inventory studies. As such, any errors present (and where those errors are quantified or unquantified) in the original studies will be present here.ors Fig.  remains The meta-analysis methodology: The RGDB was developed through meta-analysis of systematic RG inventory studies published prior to October 2017, using ISI Web of Science, Scopus, ProQuest Dissertations and Theses and NSIDC search tools. Therefore, the RGDB comprehensiveness is predominantly governed by the availability of openly accessible academic information on the topic. As such, studies outside of these research-bounds, in particular those published in non-ISI indexed journals, may have been missed. Furthermore, integration of systematic RG inventory data into the RGDB was hampered by: (i) non-standardised inventory datasets ; (ii) non-English language writing; (iii) the absence of an accessible open-access database (only 43 of studies in the full RGDB [~33%] had linked databases); and (iv) incomplete inventory data. With regards to (ii), we used Microsoft Translator/PROMT Translator to increase the accessibility of non-English manuscripts, and thus increased the completeness of the RGDB. Studies where Microsoft Translator/PROMT Translator was used are noted as such within the RGDB files available in the supplementary information. Additionally, a possible source of error occurs in situations where systematic RG inventory data are either incomplete or unknown ([iii] and [iv]). As previously mentioned, in these situations we chose to implement a three-step approach to determine RG activity status, area, and ice volume  glacier- and (b) rock glacier-hydrological stores: Regarding (a), within this study results provided in Huss and Hock34 were adopted. For each glacier of the RGIv4.0 Huss and Hock34 calculated glacier volume and ice-thickness distribution through the application of their model (herein HF-model), updating the previous results of Huss and Farinotti35 that relied upon RGIv2.0 data (released June 2012). “[T]he RGI is intended to be a snapshot of the world’s glaciers as they were near the beginning of the 21st century ”57. Indeed, within the RGIv4.0 released in December 201432, the average satellite acquisition date (±1 standard deviation) of inventoried glacier outlines within each of the 19 first-order regions ranges from 1970 ± 19 (North Asia) to 2009 ± 2 (Alaska)34. Given that glacier volume loss globally was estimated to be −259 ± 28 Gt yr−1 between 2003 to 20098, RGI-derived data may overestimate glacier area. With regards to the HF-model, previously uncertainty assessments have been undertaken, the results of which have been summarised and discussed in detail58. Lastly regarding (a), results within Huss and Hock34 were presented as sea-level equivalent (SLE) assuming an ice density of 900 km m−3 and an ocean area of 3.625 × 108 km2. For the purposes of this study, these results were converted from SLE to ice volume for each RGI first-order region. Converted ice volumes may slightly differ compared to the original dataset, as Huss and Hock34 reported SLE only to 2 decimal places.It is important to acknowledge possible sources of uncertainty within this study, particularly given the necessity to generalise with regards to RG ice volume and the associated water volume equivalent calculations. Possible sources of uncertainty are discussed below.56, generally, RG thickness and average ice content are unknown variables. Direct measurements of internal structure are limited, due to the practicalities of field-based research  in largely remote locations27. Indeed, regarding the paucity of such scientific investigations, it has been purported that “[m]embers of the mining community have had more opportunities to study RGs internally than have geomorphologists”59. Furthermore, unless in situ internal structure data are spatially distributed with good coverage of the entire feature extent, the ice-thickness and ice-debris ratio at any location remains an assumption60. Therefore, here T-A scaling relations, i.e. H = cAβ (see Methods), were applied. Scaling parameters derived from the empirical rule established by Brenning28 were used  and Janke et al.27 detail examples where quantitative field measurements indicate RG thicknesses far in excess of ~20 m. As such, application of this alternate approach may significantly underestimate RG thicknesses.With regards to (b), we acknowledge that the results presented here represent a first-order approximation. Although “[a] detailed examination of the surface features of a RG [as used in many studies included in the RGDB] may also give a general indication of the position, activity, and quantity of hidden ice”equation ). Accord64, enabling the calculation of lower (40%), average (50%), and upper (60%) estimates. Ice content within RGs is spatially heterogeneous. Therefore, the volumetric ice content varies strongly within a RG and between individual RGs. “The average volumetric ice content of rock glaciers is widely accepted to vary between approximately 40 per cent and 70 per cent…”65. This percentage array is consistent with field data from different climatic regions worldwide66. In this context, the assumption of an average 50% ice content is reasonable. Within the RGDB many studies assume RGs contain 40–60% ice content by volume; adoption of the same percentage array in this study enables inter-study comparative assessment. Furthermore, within the RGDB numerous studies classify RGs as intact, i.e. studies do not provide separate data for active and inactive RGs. Related to this, potential bias may be introduced by the typically lower ice contents of inactive RGs65. Additionally, information regarding RG genesis, i.e. permafrost origin or glacigenic origin, is predominantly absent within the RGDB despite strongly influencing ice content. Therefore, we acknowledge that due to the nature of the RGDB and the methodology, we cannot comprehensively account for regional specificities. Further research related to ice-thickness and ice content by volume is certainly needed.Further regarding (b), here we assume estimated ice volume is 40–60% by volumeThe significance of these results is fourfold. First, the systematic meta-analysis undertaken here has enabled the first near-global RGDB to be developed. This is based on the present state-of-knowledge of systematic RG inventory studies. Second, this work focuses on RGDB coverage and therefore enables identification of priority regions for systematic RG inventory studies, both at the RGI regional- and local- scales. Third, for the first time we present an assessment of water volume equivalents contained in the world’s observed RGs. These indicate that RG water stores are of potentially significant hydrological value (62.02 ± 12.40 Gt). In particular, our RGI regional approach indicates significant frozen water stores contained within RGs in arid and semi-arid high mountain systems facing potential future water scarcity . Fourth, the methodology presented here enable an approximate comparative assessment of the ratios of RG to glacier water volume equivalents at RGI regional- and near-global-spatial scales. Finally, we acknowledge and discuss the uncertainty associated with the results presented here. These results represent a first-order approximation; uncertainty in the near-global RG water storage estimates is due to several factors, e.g., inherited errors within the RGDB, inherent flaws in the meta-analysis methodology, and RG thickness estimation, but here only errors associated with the assumption on RG ice content are quantified. Therefore, overall uncertainty is likely larger than that quantified here. Importantly, a full understanding of all inputs to the high mountain system hydrological is critical for effective water resource management to mitigate or adapt to the impacts of climate change – this includes RGs.RG studies published prior to October 2017 were identified by means of journal search tools , online databases (NSIDC), and direct communication with academics involved in RG research. We searched the ISI Web of Science for peer-reviewed journal papers published between 1900–2017 using topic searches for “rock glacier” OR “rockglacier” OR “rock glaciers” OR “rockglaciers”. Scopus was searched for peer-reviewed journal papers with ‘Document Type’ restricted to ‘Article’, ‘Conference Paper’, ‘Review’, and ‘Article in Press’ and no time-period restriction, also using the search terms “rock glacier” OR “rockglacier” OR “rock glaciers” OR “rockglaciers”. ProQuest Dissertations and Theses was searched for publications with full-text availability, using the search terms “rock glacier” OR “rockglacier” OR “rock glaciers” OR “rockglaciers”. Note that dissertations and theses with research outcomes already published as journal papers, were not included in the RGDB. Lastly, Google Scholar and NSIDC searches for “rock glacier” OR “rockglacier” OR “rock glaciers” OR “rockglaciers” were undertaken. Search results from ISI Web of Science and Scopus were categorised into (i) systematic inventory resources or (ii) not relevant.et al.26 (European Alps) was partially excluded where the study area overlapped that of Winkler et al.40 .Excluding duplicate studies, a total of 131 systematic RG inventory studies resulted from the systematic meta-analysis. So as to avoid duplicate RG data, i.e. overlapping study areas, 55 studies were excluded from the RGDB where more comprehensive and/or up-to-date inventories included the same RGs. This process was undertaken through Google Earth  and ArcGIS . Partially overlapping study areas were partially excluded. For example, data from Cremonese The full RGDB structure required that the following fields be filled, where the data was available: (i) Source; (ii) Author(s) (including full citation); (iii) Study Location; (iv) Datasets Applied: (a) image dataset(s), (b) topographic dataset(s); (v) Inventory Validation: (a) Yes, (b) No, (c) NA (i.e. unknown); (vi) Number of Rock Glaciers: (a) total, (b) intact, (c) relict; (vii) Elevation : (a) mean(s), (b) minimum elevation at the front (MEF), (c) maximum elevation of the landform (MaxE); (viii) Area : (a) total, (b) mean(s); (ix) Length : (a) mean(s), (b) maximum(s); (x) Width : (a) mean(s), (b) maximum(s); (xi) Planform-shape ; (xii) Dominant Aspect(s); (xiii) Water Volume Equivalent (WVEQ); (xiv) Specific Density; (xv) Ratio of Rock Glacier WVEQ to Glacier WVEQ; (xvi) Additional Information. Note that where inventory data is missing but calculable , we reflect updated values in blue font within the full RGDB.32. Further information is available from the Global Land Ice Measurements from Space (GLIMS) website (for access: http://www.glims.org/RGI/). In compiling the RGDB, a decision to merge consensus areas was taken for two key regions because the systematic RG inventory studies could not be split easily to account for regional differences. Here, we combined the RGIv4.0 regions: (i) ‘01’ (Alaska) and ‘02’ (Western Canada and US) to create a new dataset for “North America”; and (ii) ‘16’ (Low Latitudes) and ‘17’ (Southern Andes) to create a new dataset for “South America”, where there are high concentrations of both RGs and glaciers. Regarding (ii), sites in Central America, Africa, and Southeast Asia, which contained relatively insignificant proportions of RGs or glaciers, were grouped within the “South America” category. Systematic RG inventories resulting from the meta-analysis were similarly divided into the 17 regions  is calculated as a function of surface area (A) and two scaling parameters (c and β), were applied. Scaling parameters derived from the empirical rule established by Brenning28 were used  versus those that support the glacier ice core model (i.e. glacier ice cored structure [glacigenic origin]) has previously been summarised and discussed in detail70. Note that discussion of RG genesis and evolution is beyond the scope of this study and is briefly highlighted here for completeness. Relatively few geophysical investigations of RG internal structure have previously been conducted. Those studies that exist often focus on quantifying ice presence opposed to ice content by volume. Therefore, here we assume estimated ice volume is 40–60% by volume64, enabling lower (40%), average (50%), and upper (60%) estimates to be calculated. Finally, water volume equivalent was calculated assuming an ice density conversion factor of 900 kg m−3.Estimations of RG water volume equivalent were calculated based upon assumed ice volumes stored within intact RGs. In order to place this work in the context of traditional glacier studies, the units of giga tons (Gt) are used. Here, T-A scaling relations, i.e. equation ). RG volWhere complete RG inventories were available, RG surface area data were extracted for each individual feature for use in the abovementioned T-A relationship and subsequently water volume equivalent calculation. A three-step approach to determine RG volume was applied where inventory data was incomplete or unknown  is calculated as a function of surface area (A) and two scaling parameters (c and γ), are frequently used approaches for volume estimations58. Indeed, previously V-A approaches have been used in rock glacier-glacier comparative studies68. Furthermore, V-A approaches have been applied to global-scale volume estimations of glaciers and ice caps71. Reports indicate, however, the potential of V-A approaches to systematically overestimate ice volume, particularly for large and/or relatively steep glaciers , generally yield comparatively lower results than V-A approaches58. Additionally, HF-model ice-thickness results have previously been validated, indicating good agreement with a comprehensive set of ice-thickness observations from almost all glacierized mountain ranges globally58. Direct validation cannot be undertaken for results derived from V-A relations58. Therefore, here we use the results of Huss and Hock34. For each glacier of the RGIv4.0, Huss and Hock34 calculated glacier volume and ice thickness distribution through the application of the HF-model35. Results within Huss and Hock34 were presented as SLE assuming an ice density of 900 km m−3 and an ocean area of 3.625 × 108 km2. As such, conversion of SLE to ice volume was necessary. When converting from cubic kilometres to gigatons, we assumed that 1 Gt of nonporous ice equated to a volume of 1.091 km³ 72.Regarding glaciers, volume-area (V-A) scaling relations, i.e. The datasets (RGDB) generated during and/or analysed during the current study are available in the supplementary information online.Supplementary InformationDataset 1


Motor inhibitory control is a central executive function, but only recently the importance of perceptual mechanisms for these processes has been focused. It is elusive whether basic mechanisms governing sensory perception affect motor inhibitory control. We examine whether sensory lateral inhibition (LI) processes modulate motor inhibitory control using a system neurophysiological approach combining EEG signal decomposition with source localization methods in a somatosensory GO/NOGO task. The results show that inter-individual variations in the strength of LI effects predominantly affect processes when information needs to be integrated between cerebral hemispheres. If information needs to be integrated between hemispheres, strong sensory suppression will lead to more impulsive errors. Importantly, the neurophysiological data suggest that not purely perceptual or motor processes are affected. Rather, LI affects the response selection level and modulates processes of stimulus categorization. This is associated with activity modulations in the posterior parietal cortex. The results suggest that when sensory suppression is high and when information needs to be integrated across hemispheres, these processes are less efficient, which likely leads to worse motor inhibitory control. The results show how basis principles modulating perceptual processes affect subsequent motor inhibitory control processes. While it is obvious that to successfully deploy inhibitory control processes, perceptual and attentional processes are essential3, the role of perceptual processes during response inhibition processes has only recently been focused regarding motor inhibitory control6, but several lines of evidence suggest that perceptual processes predict response inhibition performance7. While this underlines the importance to consider perceptual processes during motor inhibitory control, we are only at the verge to understand the role of perceptual processes during response inhibition. The relevance of core mechanisms governing perceptual processes has not been investigated for motor inhibition processes. One such basic principle modulating perceptual processes is lateral inhibition10. Lateral inhibition emerges when „a local network of inhibitory interneurons connects adjacent cortical neusrons so that firing of one cortical neuron tends to lead to inhibition of its neighbors”8. Since lateral inhibition mechanisms do therefore play a central role in the processing of incoming stimuli that have been shown to impact motor response inhibition11, we hypothesize that modulations of lateral inhibition mechanisms may well affect motor inhibitory control. Especially processing of stimuli within the somatosensory modality strongly depends on mechanisms of lateral inhibition14. Because the somatosensory modality has been shown to be more powerful than the visual modality to trigger response inhibition processes11, it seems particularly suited to examine the effect of sensory lateral inhibition processes on motor inhibitory control in the current system neurophysiological study.Response inhibition constitutes a central executive function16. For example, it has been shown that simultaneous stimulation within one hand leads to attenuated perceptual processes as compared to the stimulation of both hands, suggesting that lateral inhibition effects are evident in the within-hand condition, but not in the between-hands condition12. We therefore compare response inhibition processes between one condition in which lateral inhibition can influence response inhibition (i.e. the within-hand condition) with a condition in which lateral inhibition effects do less occur due to the involvement of both hemispheres (i.e. the between-hands condition). We hypothesize that strong lateral inhibition processes are associated with better motor inhibitory control; i.e. response inhibition performance is better in the within-hand condition, than in the between-hands condition. This is because in response inhibition, the frequent “GO” stimulus leads to prepotent response tendencies18. Since lateral inhibition leads to a weaker representation of the stimuli, the activation of the associated prepotent response is weaker in the within-hand condition. This likely entails better response inhibition performance and would suggest that fundamental principles governing cortical stimulus representation affect subsequent motor response inhibition processes. To examine the strength of lateral inhibition on motor inhibitory control in more detail, we split the sample of participants in a “high suppression” (HS) and a “low suppression” (LS) group using a median split (refer methods section for details).It is well-known that the distance between stimulation sites determines lateral inhibition effects with less distance leading to stronger lateral inhibition effects20. However, ERP measures are usually composed of various amounts of signals from different brain regions and therefore reflect a mixture of processes and information being processed. To dissociate these different kinds of information we applied residue iteration decomposition (RIDE)25 on the EEG data and combine this with source localization analyses . RIDE decomposes EEG data on the basis of their timing variability properties. This decomposition reveals the S-c luster referring to processes related to the stimulus, the R-cluster depicting processes related to the response and the C-cluster representing intermediate processes between S and R 23. Using RIDE it has been shown that response inhibition mechanisms are reflected by the S-cluster and the C-cluster22. Both of these clusters may therefore be modulated by the experimental manipulation of lateral inhibition processes. However, since the critical aspect in response inhibition is the inhibition of a predominant transition of stimuli into a response, we hypothesize that it is especially likely that the C-cluster is modulated. This is because the C-cluster has been shown to reflect processes linking stimulus processing and responding26, and lateral inhibition may lead to a weaker representation of the stimuli that consequently entails a weaker activation of the associated prepotent response (see above). Moreover, the C-cluster has been shown to reflect mechanisms similar to the Nogo-P3 ERP component, which has frequently been linked to the inhibition of the motor response28. Such a result would suggest that lateral inhibition effects at the perceptual level spill over to the response selection level.The underlying neurophysiological mechanisms can be examined using EEG recordings and the analysis of event-related potentials (ERPs) in combination with source localization analyses (sLORETA). Using ERPs, effects of lateral inhibition are likely to lead to modulations in the Nogo-N2 reflecting pre-motor processes like conflict monitoring or, to modulations in the Nogo-P3 reflecting the motor process of inhibition11. Moreover, they are involved during sensorimotor integration30, also during response inhibition when sensory information is difficult to categorize but essential for behavioral control33. This may be the case due to the effects of lateral inhibition.Regarding the functional neuroanatomical structures underlying modulations of response inhibition processes by lateral inhibition mechanisms, we expect that aside the usual response ‘inhibition network’ in the prefrontal cortex  also parietal structures become involved. This is due to specific stimuli applied and the emphasis on lateral inhibition processes. These parietal regions have already been shown to be involved in response inhibition processes using somatosensory stimulip = 0.134; η² = 0.088], nor a main effect “group” , but a significant interaction of “condition × group” . However, post-hoc tests did not withstood Bonferroni-correction . The analysis of reaction times (RTs) in GO trials revealed faster responses in the between-hands (458 ms ± 19) than in the within-hand condition (482 ms ± 20) . The interaction of “condition” × “group”  as well as the between-subject factor “group”  were not significant.The mixed effects ANOVA for GO hit rates revealed neither a main effect of “condition” . Importantly, the interaction of “condition × group” reached significance , which is shown in Fig. t(25) = −0.14; p = 0.892]. In the between-hands condition the high suppression group had a significantly higher false alarm rate (16 ± 3) than the low suppression group (8 ± 2) . The between-subject factor “group” was not significant .Concerning false alarms in NOGO trials a main effect of “condition” revealed a significantly higher false alarm rate in the between-hands 12 ± 2) than in the within-hand condition (10 ± 2) . As can be seen in the scalp topography plots, the P3 ERP was evident at electrode FCz and P3. Therefore, we added the factor “electrode” as an additional within-subject factor to statistical model. The mixed effects ANOVA revealed a main effect electrode  showing that the P3 was larger at electrode P3 (3.21 ± 2.03) than at electrode FCz (14.47 ± 2.13). The main effect “GO/NOGO”  showed that the P3 was larger in NOGO (14.90 ± 2.84) than in GO trials (2.79 ± 0.88). There was no main effect “group” . There was an interaction “electrode × GO/NOGO” , showing that the P3 difference between GO and NOGO trials was larger at electrode FCz (22.23 ± 4.05) than at electrode P3 (1.93 ± 1.85) . No other main or interaction effects were significant .The N2 and P3 ERP components are shown in Fig. p < 0.001; η² = 0.491] showed that the S-cluster amplitude was stronger in NOGO (−13.82 μV/m² ± 3.26) than in GO trials (−0.86 μV/m² ± 1.25). Furthermore, a main effect of “condition” was found  with a larger amplitude in the between-hands (−8.57 μV/m² ± 2.18) than in the within-hand condition (−6.11 μV/m² ± 2.13). There were no further main or interaction effects .The S-cluster was maximal at electrode FCz  than in GO trials (6.59 μV/m² ± 1.51). The main effect of “condition” was not significant . Importantly, there was a significant interaction “GO/NOGO × condition × group” . Subsequent ANOVAs revealed that there was an interaction “condition × group” for NOGO trials , but not for GO trials . Post-hoc independent-samples t-tests revealed that this interaction originates from the difference in C-cluster amplitude between the high suppression group (23.4 μV/m² ± 2.79) and the low suppression group (10.55 μV/m² ± 5.94) in the between-hands condition . The sLORETA analysis revealed that activity differences were located in the right posterior parietal cortex (BA7). For the within-hand condition, the groups did not differ in the C-cluster amplitude . There were no other main or interaction effects in the C-cluster .The C-cluster is also shown in Fig. p = 0.005; η² = 0.188] showing that the R-cluster was largest (most negative) at electrode FC1 (−9.21 μV/m² ± 2.02), followed by electrode FC3 (−6.17 μV/m² ± 1.37) and C3 (−3.73 μV/m² ± 0.90). Only electrode FC1 differed from electrode C3 (p = 0.021), no other differences between electrodes were obtained.The R-cluster is shown in Fig. The current study was designed to investigate the effects of sensory lateral inhibition mechanisms on motor response inhibition processes. Therefore, a task using vibrotactile information as GO and NOGO stimuli was employed. To examine the system neurophysiological mechanisms modulated we combined ERP recordings with source localization analyses (sLORETA) in combination with temporal data decomposition methods (RIDE).16, resulting in a reduction of neuronal stimulus representation caused by lateral inhibition when nearby somatotopic regions are stimulated13. This suggests that it is easier to withhold a prepotent response under the influence of lateral inhibition, compared to a condition where lateral inhibition processes are unlikely to occur, since simultaneously delivered information is processed in different hemispheres. The possible reason for this may be as follows. A high rate of GO stimuli leads to a strong response tendency18, which may be particularly so in case of somatosensory stimuli, because of strong structural connections of the primary somatosensory and motor cortices34. Since lateral inhibition effects may attenuate cortical representation of sensory stimuli, the activation of a response and the response tendency is also weaker, making it easier to withhold the dominant response. Consequently, less false alarms occur. This interpretation is underlined by RT data in GO trials, which are slower in the with-hand condition. Yet, it is important to note that this main effect of condition and hence its interpretation has to be handled with caution since there is a higher order interaction with the factor “group”. Notably, lateral inhibition does not affect the execution of an action per se but specifically unfolds its beneficial effects when motor plans have to be revised, because no condition effects were observed in GO trials.The results revealed that response inhibition performance was better in the within-hand than in the between-hands condition as reflected by a significantly lower false alarm rate. Due to the somatotopic organization of the primary somatosensory cortex, it is assumed that receptive fields of neighboring fingers overlapHowever, to maximize the lateral inhibition effect in order to examine the neurophysiological processes being modulated by the effects of lateral inhibition, participants were assigned to the low or high suppression group. A significant interaction of group and condition was obtained. Interestingly, no group differences were evident in the within-hand condition. In the between-hands condition the high suppression group showed a higher false alarm rate than the low suppression group. This pattern of results suggests that in conditions where lateral inhibition is possible (i.e. the within-hand condition), even small lateral inhibition effects are sufficient to reach the performance level of the group showing marked lateral inhibition effects. Obviously, the beneficial effect of lateral inhibition on response inhibition is so strong that even a small degree is enough to notably enhance performance. Yet, in the condition in which the advantage of lateral inhibition cannot be used (i.e. in the between-hands condition), the inhibition performance of the group showing largest lateral inhibition effects significantly drops. The neurophysiological data provide insights to the cognitive subprocesses related to this effect.35. Of note, this decomposition reveals three clusters of components with dissociable functional relevance23: the S-cluster refers to processes related to the stimulus (like perception and attention), the R-cluster refers to processes related to the response (like motor preparation and execution) and the C-cluster refers to intermediate processes between S and R 23. Interestingly, an effect paralleling the obtained interaction was not found for the S-cluster. Even though lateral inhibition effects are a mechanism well-described for the level of perceptual processing16, the RIDE data show that modulations at the stimulus processing level do not underlie the effects of lateral inhibition processes during motor inhibitory control. Since no modulations paralleling the behavioral data were obtained for the R-cluster, also pure motor processes do not seem to be important. Rather, the results suggest that effects of lateral inhibition are protracted to the response selection level, since an interaction effect paralleling the behavioral data was found in the C-cluster. There were significant differences in C-cluster amplitudes for the NOGO but not the GO trials. The C-cluster amplitude in NOGO trials was larger in the high than in the low suppression group and consequently higher in the group also showing more false alarms. The C-cluster was maximal in the P3 time window. Usually a higher P3 in NOGO trials is associated with better behavioral performance and in these cases the medial frontal cortical areas are involved20. The C-cluster, however, has been suggested to reflect processes related to stimulus-response transition26. In case of the current study, amplitude modulations in the C-cluster between the low and high suppression group were related to activation differences in the right posterior parietal cortex (PPC) activity (BA 7) as indicated by the sLORETA analysis. The posterior parietal cortex has also been linked to sensorimotor integration and hence the transformation of incoming sensory information into appropriate behavior29. Yet, opposed to medial frontal areas, the superior parietal cortex (BA7) has less frequently been reported to be involved in response inhibition37. However, BA7 is involved in response inhibition whenever information is complex and probably difficult to categorize but essential for behavioral control33. This is also likely the case in the current study, and especially so in the between-hands condition, because information from both hands need to be integrated. The results therefore suggest that in the high suppression group these processes are intensified and less efficient than the low suppression group. This relative lack of efficacy to categorize stimuli essential for behavioral control, compared to the low suppression group, may lead to the higher false alarm rate in the high suppression group. Corroborating this interpretation, it has already been shown that if stimuli are presented to a hemisphere that is not optimized for processing this information due to functional cerebral asymmetries, the efficacy of response inhibition processes is compromised32. Due to the effect of perceptual processes on motor inhibitory control described in the current study, one can only speculate about the potential relevance of structural connectivities between somatosensory and prefrontal areas. It has been shown that the primary, but also the secondary somatosensory cortex is directly connected to areas that are part of a motor inhibitory network39, although the connections between the SI and prefrontal cortex are more pronounced40. The SII, however, is engaged in the cognitive processing of tactile information41. So, future studies should be designed to assess the contribution of each area to response inhibition considering the fact that both areas are involved in the processing of different kinds of vibrotactile stimuli43. Furthermore, the current study investigated sensorimotor integration processes on a correlative level. One possibility to evaluate whether there is a causal relationship between somatosensory and motor areas is the application of transcranial direct current stimulation which has already been shown to modulate activity in the somatosensory cortex46.For the neurophysiological data, no effects paralleling the behavioral results were found on the basis of traditional ERPs, but only after applying RIDE. RIDE is a temporal decomposition method that reduces intra-individual variability in the dataTo summarize, the study examined in how far lateral inhibition effects modulate motor inhibitory control processes. Motor inhibitory control is better when (somatosensory) stimuli, which are presented in parallel, are processed in the same hemisphere. This likely reflects the result of lateral inhibition processes. However, inter-individual variations in the strength of lateral inhibition effects do predominantly affect processes when information needs to be integrated between cerebral hemispheres. If information needs to be integrated between hemispheres, strong sensory suppression will lead to more impulsive errors. Importantly, the neurophysiological data suggest that not purely perceptual or motor processes are affected. Rather, effects of lateral inhibition are protracted to the response selection level and seem to affect processes of stimulus categorization associated with activity modulations in the posterior parietal cortex . The results suggest that when sensory suppression is high and when information needs to be integrated across hemispheres, these processes are intensified and less efficient, which likely leads to worse motor inhibitory control. The results show how basis principles modulating perceptual processes affect subsequent motor inhibitory control processes.N = 27 participants  took part in this study. Their age ranged from 19 to 30 years . They were all right-handed and reported no psychiatric or neurological disorders. Prior to testing, written informed consent was obtained from the subjects. The study and experimental protocols were approved by the institutional review board of the Medical faculty of the TU Dresden. All methods were performed in accordance with the relevant guidelines and regulations. To analyze the effects of lateral inhibition, i.e. to maximize the effects of lateral inhibition in the statistical analyses, the difference between false alarms in the between-hands and the within-hand condition was calculated for each participant. Using this calculated parameter, a median split of the sample was conducted. Participants with values below or equal to the median were assigned to the “high suppression group” (N = 14), whereas participants with values above this median were assigned to the “low suppression group” (N = 13).http://www.dancerdesign.co.uk) that were connected to the “main module” of the stimulation device . The experimental setup is shown in Fig. 14. In the within-hand condition (right hand stimulation) the middle and ring finger of the right hand were stimulated simultaneously (with corresponding frequencies). Simultaneous stimulation of separate sites at the same hand causes lateral inhibition due to the activation of overlapping receptive fields14. These fingers were chosen, because the middle and ring finger stimulation is more effective than for example the middle and small finger stimulation because in case stimulation sites are closer to another interference is more pronounced than with greater distance between them16. Lateral inhibition is not relevant in the between-hands condition since only if the same stimulus (GO or NOGO stimulus) is simultaneously delivered to two fingers with overlapping receptive fields lateral inhibition can occur. The receptive fields of the right and left hand ring fingers cannot overlap because they are in differed hemispheres.To investigate the effects of lateral inhibition on response inhibition processes a tactile Go/Nogo task was employed. Vibrotactile stimuli were presented using small electromagnetic stimulators . The participants were acquainted with these stimuli, before the experiment was started. During the experiment, participants were asked to look at a white fixation cross in the middle of a black screen. This was done to prevent the subjects from closing their eyes and getting tired or moving their eyes excessively. In order to prevent the processing of any auditory feedback generated by the conductors, the subjects wore earplugs. The participants were asked to respond as fast as possible to the occurrence of the slow frequency by pressing a button with the right hand and to avoid responses to the fast frequency. In sum, 832 trials assigned to 4 blocks were presented. The trials were pseudorandomized to avoid sequence effects in the stimulation. The inter-trial interval was jittered between 700 and 1100 ms so that stimulus onsets were not predictable. To foster a premature response tendency, GO stimuli were presented in 70% of trials and a NOGO stimulus in 30% of trials. Conditions were blocked in order to prevent the participants from getting confused by constantly changing stimulation sites across the between-hands and within-hand conditions. Every subject received two within-hand and two between-hands blocks with breaks in between and the block sequence was counterbalanced across participants. With “A” representing the within-hand condition and “B” the between-hands condition, subjects either received the ABBA or BAAB sequence. The described block sequences were employed to reduce expectancy effects in regard to the upcoming block.2. We used 4 splines and 10 polynominals. CSDs work as a spatial filter48, which accentuates electrode sites and makes it easier to identify electrode sites that best reflect relevant neuronal activity. This was followed by a baseline correction from −200 ms to 0 (time point of stimulus presentation). Finally, trials in the different conditions were averaged on a single-subject level. Grand averages for the two conditions (within/between) for GO and NOGO trials were computed separately for both groups. Scalp distribution and the course of averaged data was inspected visually before decisions about the to be analyzed components and electrodes of interest were made. The N2 and P3 components were evident at electrode FCz. In GO and NOGO trials the time interval from 305 to 315 ms was chosen in the high suppression group in the within-hand and between-hands condition. Correspondingly, this was also done for the low suppression group. The P3 was quantified in the range from 395 to 405 ms in the high and low suppression group for both conditions in GO and NOGO trials. Comparing the difference waves  of the low and high suppression group revealed that the P3 ERP was also evident at P3 electrode. Therefore, the mean amplitude of the P3 component at electrode P3 for NOGO trials was computed for the within-hand and between-hands conditions. Correspondingly, this was also done for the GO trials. In both groups, the mean P3 amplitude was quantified based on the time interval from 415 to 425 ms after stimulus onset.60 passive Ag/AgCl ring electrodes at equidistant positions connected to a QuickAmp amplifier (BrainProducts Inc.) were used to record EEG data. The ground and reference electrode were placed at coordinates theta = 58, phi = 78 and theta = 90, phi = 90, respectively. The sampling rate was 500 Hz and the impedances were kept below 5 kΩ. After acquisition of data the sampling rate was changed to 256 Hz and an IIR band-pass filter from 0.5 Hz to 20 Hz using a slope of 48 db/oct as well as a notch filter at 50 Hz was applied to the un-epoched data set. Infrequent  artifacts were discarded during a first manual raw data inspection. Afterwards, an independent component analysis was applied  to detect blinks, eye movements or pulse artifacts. ICA components revealing horizontal and vertical eye movements, blinks and pulse artifacts were manually rejected. The ICA was run for all blocks combined. Subsequently, the data was segmented in GO and NOGO trials for the between-hands and the within-hand condition according to the presentation of the stimuli. A segment started −200 ms before stimulus presentation and ended 1300 ms after it. Only trials answered correctly were included in analysis. In GO trials, the correct answer was defined as pressing the specified button in the time interval between target presentation and up to 900 ms after it. In NOGO trials, no registration of button press was defined as correct. Then, an automated artifact rejection procedure excluded trials in which a maximal value difference of 200 μV in a 200 ms period was exceeded. Furthermore, trials with amplitudes below −200 μV and above 200 μV as well as amplitudes below 0.5 μV in a 100 ms interval were rejected. To eliminate the reference potential from the data and to re-reference the data, we applied a current source density (CSD) transformation which results in values for amplitudes in μV/m26 the RIDE toolbox (available on http://cns.hkbu.edu.hk/RIDE.htm) was employed to conduct RIDE analysis using MATLAB . In the following the RIDE method is briefly described. Further information concerning the RIDE decomposition algorithm are stated in Ouyang et al.21. RIDE decomposes ERP components applying L1-norm minimization  and therefore minimizes residual error due to temporal variability in single trials21. RIDE decomposition is based on latency variability and is applied to each electrode separately regardless of scalp distributions or waveforms35. Therefore, the conducted CSD transformation does not affect the results. The decomposition of the ERPs into S- and R-cluster (components) is based on stimulus onsets and response times, respectively, while the time markers for deriving the C-cluster are estimated and iteratively improved. It is assumed that there is a C-cluster with variable latency over single trials which is defined as being fully locked neither to stimulus onsets nor to reaction times, and that the latency of this C-cluster may be initially estimated in each single trial as reflecting some global waveform26. Based on the information given by the estimated latency of the C-cluster, by the time markers of stimulus onsets and by reaction times RIDE uses a self-optimized iteration scheme for latency estimation through which the latency estimation of the C-cluster is improved. The RIDE algorithm uses a time window function to extract the waveform of each RIDE component. Each time window is assumed to cover the range within which each component is supposed to occur and the specific values should be adjusted to fit the data on application21. For the current study this was from −200 to 800 ms for the S-cluster, from 200–800 ms for the C-cluster and 300 ms around the response trigger (−300 to 300 ms) for the R-cluster in GO trials. For further details on the method see refs Based on previous workIn correspondence to the steps described above for the original ERPs the computation of the RIDE components was preceded by the visual examination of difference waves at relevant electrodes. Again, groups were compared in the within-hand and between-hands condition calculated on the basis of NOGO trials. The S-cluster was maximal at electrode FCz. Mean amplitudes of the S-cluster were computed in the interval from 305 to 325 ms for GO and NOGO trials in both conditions. The C-cluster was maximal at the parietal electrode P3. The mean amplitude was calculated for GO and NOGO trials in the within-hand as well as between-hands condition. For the GO trials the time interval from 520 to 530 ms was chosen for both groups and conditions. In NOGO trials within the between-hands condition, the period from 425 to 435 ms was used for quantification in the low suppression group. In the high suppression group the interval ranged from 465 to 475 ms. For both groups the C-cluster was calculated between 450 and 460 ms in the within-hand condition. For GO trials, the R-cluster was quantified in amplitude in the time window corresponding to RT in the between-hands and the within-hand conditions in the high and low suppression group; i.e. the negativities between 360 ms to 580 ms were quantified.49 was used. As a basis for the source localization analysis we used the estimated RIDE clusters. sLORETA provides a single solution to the inverse problem51. For sLORETA, the intracerebral volume is partitioned into 6239 voxels at 5 mm spatial resolution. Then, the standardized current density at each voxel is calculated in a realistic head model52 based on the MNI152 template53. It has been mathematically proven that sLORETA provides reliable results without a localization bias51. Moreover, there is evidence from EEG/fMRI and neuronavigated EEG/TMS studies underlining the validity of the sources estimated using sLORETA54. The voxel-based sLORETA images were compared across conditions  using the sLORETA-built-in voxel-wise randomization tests with 2000 permutations, based on statistical nonparametric mapping (SnPM). Voxels with significant differences  between contrasted conditions were located in the MNI-brain.For the source localization analysis sLORETA (standardized low resolution brain electromagnetic tomography)Behavioral data, i.e. hit rates in GO trials and hit reaction times as well as false alarms in NOGO trials were examined by means of mixed effects ANOVAs. “Group” (low/high suppression group) was set as between-subject factor and “condition” (within/between) as within-subject factor. Mixed effects ANOVAs were also applied for neurophysiological data so that “group” constituted the between-subject factor and “condition” and “trial type” (GO/NOGO) the within-subject factors. Greenhouse-Geisser correction was applied for all tests. Bonferroni correction was conducted for all post-hoc tests. All variables were normal distributed as indicated by Kolmogorov Smirnov tests . Mean values and the standard error of the mean (SEM) are given for the descriptive statistics.The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request


WEAX inhibited α-glucosidase activity only when maltose was used as a substrate but not sucrose. The IC50 values of WEAX (4.88 ± 0.3–10.14 ± 0.5 mg/mL) were highly correlated to ferulic acid content (R = −0.89), arabinose to xylose ratio (R = −0.67), and relative proportions of xylose being unsubstituted (R = 0.69), disubstituted (R = −0.63), and monosubstituted (R = −0.76). The Lineweaver–Burk plot suggested an uncompetitive enzyme inhibition mode. Thus, our results suggest that antiglycemic properties of WEAX may be derived from direct inhibition of α-glucosidase activity.The studies on the effects of arabinoxylan (AX) polysaccharides on postprandial glucose response have resulted in contrasting results owing to the diversity in AX structures. Four water extractable AX (WEAX) extracts obtained from wheat aleurone and bran were used to investigate (a) the effect of AX on activities of  The prevalence of type 2 diabetes is increasing globally. Diabetes is a chronic disease epitomised by high circulating plasma glucose. Thus management of postprandial glucose is critical in prevention and treatment of type 2 diabetes patients. Human intervention studies have shown that consumption of an arabinoxylan- (AX-) rich diet attenuates postprandial blood glucose levels in healthy, impaired glucose tolerance, and diabetic subjects –5. In coβ-D-xylopyranosyl (Xylp) residues with α-L-arabinofuranosyl (Araf) residues linked to the xylan backbone at C(O)-2 and C(O)-3 and/or at both C(O)-2 and C(O)-3 positions )∗100. IC50 value was determined from the plot of %  α-glucosidase inhibition against sample concentration. Inhibition of rat intestinal α-glucosidase with acarbose (a known α-glucosidase inhibitor) was also done for comparison purpose. Acarbose concentrations of 1.625, 3.25, 4.9, 6.5, 9.8, and 13 μg/mL were used instead of sample.The i et al.  was usedp ≤ 0.05. Correlations among parameters were computed using Pearson's correlation test.All analyses were performed in sextuplicate (unless indicated otherwise) and all statistics were calculated using one way analysis of variance (ANOVA) on a JMP 12 statistical software . Sample means were compared using Tukey HSD method and significant differences determined at α-amylase in the presence or absence of WEAX fractions. Addition of WEAX fractions numerically decreased the amount of glucose produced compared to control treatment. However, statistical comparisons of treatment groups and control showed that the mean difference was not significant (p < 0.05) for WA-f50, WA-f75, and WB-f75 regardless of WEAX concentration. The presence of 0.5% WB-f50 resulted in significant decrease in amylolysis compared to control (p < 0.05). However, our observations for alpha amylase activity contrasted other reports in literature possibly due to concentration and type of AX. Amylolysis of starch was performed in the presence of 1 and 2% AX and the AX used was devoid of ferulic acid [The effects of WEAX on starch hydrolysis are presented in lic acid . We usedlic acid .α-glucosidase activity in the presence of sucrose or maltose as substrate. The data is presented as IC50 which is the concentration of the inhibitor resulting in 50% inhibition of α-glucosidase activity. The IC50 values ranged from 4.88–10.14 mg/mL against α-glucosidase activity with maltose as substrate. However, no inhibition was observed when sucrose was used as a substrate. The inhibitory potency of WEAX against intestinal maltase was 1000–2000 times less compared to acarbose (a positive control). It is widely hypothesised that viscosity may be the cause of the effect of AX on postprandial glucose [α-amylase and α-glucosidase activity studies and yet only the later was inhibited. This may imply that the effect of WEAX fractions on α-glucosidase activity may not have been a consequence of viscosity but rather substrate-enzyme-inhibitor interaction. The same is supported by the observation that sucrose activity of α-glucosidase was not affected by the presence of WEAX in our study. Thus it is possible that the antiglycemic effect of AX may be due to inhibition of α-glucosidase activity. Even though its potency on α-glucosidase activity is 1000–2000 times less compared to acarbose, IC50 concentration is achievable upon consumption recommended dietary requirement of fiber (21–38 g/day). glucose . Thus Vop < 0.05) among the WEAX samples with WA-f50 being the most potent. WEAX isolated at 50% ammonium sulphate saturation exhibited higher inhibition capacity compared to their corresponding fractions obtained at 75%. A Pearson correlation analysis (R = −0.89) of their inhibition potency. Antidiabetic properties of plant extracts have been associated with their total phenolic compound content [50 and WB-f75 had significantly different inhibition potency towards α-glucosidase activity despite having similar relative proportions unsubstituted (un-Xylp), monosubstituted, (2-Xylp or 3-Xylp), and disubstituted xylose residues  and degree of substitution but different ferulic acid content.The inhibition potency varied significantly  was observed between DS and IC50. This could have been a consequence of increased solubility of AX due to high DS. Thus highly substituted WEAX seems to have lower IC50 value (high inhibition potency). The same observation was supported by negative association between inhibition potency and relative proportion of unsubstituted xylose residues. The apparent di-Xylp to mono-Xylp residues ratio did not seem to have influence but the extent of xylose substitution showed an effect. Thus it is likely that the effect on α-glucosidase activity may emanate from arabinose residues of WEAX. There have been reports on arabinose inhibiting α-glucosidase activity [p at C (O)-2 was a major determinant compared to mono-Xylp at C (O)-3 suggesting that inhibition potency was beyond the mere presence of arabinose residue. There was a strong correlation between mono-Xylp at C (O)-2 and ferulic acid content (R = 0.99). Thus it is possible that the observed influence of DS could have been derived from that of ferulic acid.Arabinose to xylose ratio is a measure of degree of substitution (DS). A strong negative linear association (activity . AttemptVmax) and Michaelis–Menten constant (Km) for α-glucosidase activity on maltose in the presence and absence of WEAX. The effect of WEAX fraction on Vmax and Km was analysed to determine the type of inhibition. Vmax and Km of α-glucosidase for maltose in absence of WEAX fractions were 17.5 μg glucose per minute and 5.99 mM, respectively. Vmax and Km values suggesting that WEAX inhibited α-glucosidase activity through an uncompetitive mode. A typical characteristic of uncompetitive inhibition is that both Vmax and Km decrease in the presence of inhibitor. Thus it is plausible that WEAX fractions bind to enzyme substrate complex thereby decreasing both Vmax and Km. Arabinose was also found to inhibit α-glucosidase activity through an uncompetitive mode [The Lineweaver–Burk plot  was usedive mode .Our results may provide an explanation on the inconsistency observed in literature about the effect of AX on postprandial glucose level. Feeding of Zuker diabetic rats with AX (Ara/Xyl = 0.9) supplemented bread resulted in a significant decrease in postprandial blood glucose level . In contα-glucosidase activity but not amylase activity. The potency of water extractable AX on α-glucosidase activity was influenced by ferulic acid contents arabinose to xylose ratio and pattern of xylose substitution. The findings also suggest that inhibition of α-glucosidase activity occurs through uncompetitive mechanism. Thus, consumption of diet rich in water extractable AX may attenuate postprandial blood glucose level.The results of this study indicated that antiglycemic effect of arabinoxylans may be derived from inhibiting intestinal


Glioblastoma (GBM) is an aggressive, malignant brain tumor that inevitably develops resistance to conventional chemotherapy and radiation treatments. In order to identify signaling pathways involved in the development of radiation resistance, we performed mass spectrometry-based phospho-proteomic profiling of GBM cell lines and normal human astrocytes before and after radiation treatment. We found radiation induced phosphorylation of a number of proteins including calpastatin, specifically in GBM stem cells (GSCs). Herein, we focused on calpastatin, an endogenous inhibitor of calpain proteases. Radiation-induced phosphorylation of calpastatin at Ser-633 within the inhibitory domain was validated with a phospho-specific antibody. In order to test the functional significance of phosphorylated calpastatin, we utilized site-directed mutagenesis to generate phospho-inactive (Ser633Ala) and phospho-mimetic (Ser633Glu) mutant calpastatin. GBM cell lines stably expressing the mutant calpastatin showed that phosphorylation was necessary for radiation-induced calpain activation. We also showed that casein kinase 2, a pro-survival kinase overexpressed in many cancer types, phosphorylated calpastatin at Ser-633. Our results indicate that calpastatin phosphorylation promotes radiation resistance in GBM cells by increasing the activity of calpain proteases, which are known to promote survival and invasion in cancer. Glioblastoma (GBM) is the most prevalent and aggressive primary malignant brain tumor in adults, accounting for 60–70% of malignant gliomas. GBM is a grade IV malignant glioma with 17,000 new cases diagnosed each year in the United States . GBMs arIn this study, we used label-free, mass spectrometry (MS)-based phospho-proteomic profiling to identify proteins involved in early response to radiation treatment in GBM. We focused on phosphorylation because it is the most common mechanism of regulating protein function and signal transduction, providing fast and highly regulated responses to stimuli. Phospho-proteomic profiling can reveal alterations in protein phosphorylation that have a direct effect on protein function and would not be detected by genomic or transcriptomic profiling. We profiled an established GBM cell line as well as primary GSCs before and after radiation treatment in order to identify treatment-induced changes in phosphorylation. GSCs represent a distinct population of tumor cells that possess the ability to proliferate, self-renew, differentiate, and initiate and maintain tumors. A growing amount of data suggests that GSCs are highly resistant to conventional treatments compared to the bulk of differentiated tumor cells , 9. We pCalpastatin is an endogenous inhibitor of the calpain family of cysteine proteases. Calpains regulate a diverse range of signaling pathways by limited proteolysis of target proteins, including cell cycle (cyclin D1 and cyclin E), cell survival (nuclear factor-κB), migration , and apoptosis (BCL-2 family and caspases)  Supplem. Out of Altered regulation of the calpastatin-calpain proteolytic system is associated with several pathological disorders, including cancer . The conp value less than 0.05.In order to identify radiation-induced changes in protein phosphorylation, we first performed phospho-proteomic profiling of normal human astrocytes (NHAs) before and after radiation treatment. Post-radiation time-points of 30 seconds and 4 hours were chosen for profiling based on western blots of proteins in the DNA damage response pathway showing the temporal dynamics of phosphorylation Figure . Cells wWe then performed phospho-proteomic profiling of 4 GBM cell lines with the same radiation treatment and time-points as used above for NHA. One differentiated cell line (U87 monolayer) and 3 primary GSC lines  were profiled in a single batch, with biological triplicates of each condition. Mass spectrometry results showed a phospho-peptide enrichment efficiency of 95%. We quantified 6,398 phospho-peptides mapping to 2,416 unique proteins that were common across all 36 samples. This includes 5,574 serine, 913 threonine, and 156 tyrosine residues that were found to be phosphorylated. To validate our phospho-proteomics data for GBM cell lines, we performed a Western analysis of SMC1A, a protein involved in DNA repair and known to be phosphorylated at serine-957 by ATM following radiation treatment . An incrAs described above, statistical analysis was performed to identify significant changes in phosphorylation at 30 s and 4 h post-radiation. The number of phospho-peptides with significant fold-changes in each cell line ranged from 105 to 349 at the 30 s time-point, and from 216 to 251 at the 4 h time-point. Because GSCs are typically more resistant to radiation treatment than bulk tumor cells, we compared phospho-peptides that are altered post-radiation in the 3 GSCs but not U87. Conversely, we also identified phospho-peptides altered in U87 but not GSCs because these changes might uncover pathways that were improperly silenced in GSCs following treatment. Ingenuity pathway analysis of the phospho-peptides differentially regulated in GSCs versus U87 following radiation treatment was performed.The top pathway altered specifically in GSCs following radiation treatment was “regulation by calpain protease”. Within this pathway, we found that calpastatin protein is consistently phosphorylated across the 3 GSCs. Two different calpastatin peptides showed significant increases in phosphorylation at one or both time-points across all GSCs Table . Fold chCalpastatin was chosen for further validation and mechanistic studies because it functions upstream of multiple signaling pathways implicated in cancer, is altered in all four GSC lines post-radiation treatment, and has not been previously reported to be involved in radiation resistance mechanisms. Further analysis of the two significantly different calpastatin phospho-peptides revealed that the phosphorylated residues were serines 351 and 633 . Interestingly, these two residues are located at homologous sites within subdomain C of inhibitory domains 2 and 4, respectively Figure  12]..12].In order to validate the radiation-induced calpastatin phosphorylation observed by MS, phospho-specific antibodies were generated for serines 351 and 633. The phospho-antibodies were tested on U87 cell extracts treated with or without calyculin A, a serine/threonine phosphatase inhibitor that enriches for phosphorylated calpastatin by stabilizing the modification. Western analysis with the calpastatin-pS633 antibody revealed a distinct band upon calyculin A treatment at 120 KDa, which corresponded to the molecular weight of calpastatin . HoweverS633A) and a phospho-mimetic mutant . Serine 633 was chosen for functional studies because phosphorylation of this residue was validated by Western analysis. Stable cell lines overexpressing myc-tagged calpastatinWT, calpastatinS633A, or calpastatinS633E were generated in two established GBM cell lines, LN18 and U87  of calpastatin were phosphorylated in GSCs following radiation treatment. The calpastatin inhibitory domains have conserved sequences and can each bind and inhibit one calpain molecule. Although phospho-proteomic profiling did not reveal phosphorylation of the homologous serines in inhibitory domains 1 and 3 , we propose that all four sites are phosphorylated and have identical functions. Phospho-proteomic profiling by mass spectrometry is a sampling technique that cannot comprehensively identify every phosphorylation event in the cell, which may explain the absence of phospho-peptides spanning serines 214 and 494. Also, our analysis here of radiation-induced changes in phosphorylation is limited to two early time-points, and it is possible that additional calpastatin residues are phosphorylated later than 4 hours post-radiation.S633A mutant cell line prevented an increase in calpain protein levels . A preclinical study showed that CX-4945 treatment suppresses prosurvival pathways and decreases migration of GBM, demonstrating the potential utility of targeting the CK2 pathway in GBM patients [Clinical trials are in progress to test the efficacy of the casein kinase inhibitor CX-4945 in multiple cancer types such as multiple myeloma and cholangiocarcinoma  for 30 minutes, and then harvested for western blot. For combination calyculin A and CX-4945 treatment, cells were treated with 10 μM CX-4945 (Selleckchem) for 24 hours, followed by 100 nM calyculin A for 30 minutes.U87 cell line was purchased from ATCC and maintained in DMEM (Life Technologies), supplemented with 10% FBS (Sigma-Aldrich), and 1% antibiotic-antimycotic (Life Technologies). The 4 primary glioblastoma stem cell lines  were isolated from glioblastoma patient tissues and authenticated by a neuropathologist at The Ohio State University. GSCs were maintained in DMEM:F12 (Life Technologies) supplemented with B27 supplement (Gibco), recombinant human FGF (Gibco), recombinant human EGF (Gibco), 10% FBS and 1% antibiotic-antimycotic (Life Technologies). Normal human astrocytes were obtained from Lonza and maintained in Clonetics AGM Astrocyte Growth Medium (Lonza) supplemented with 10% FBS and 1% antibiotic-antimycotic (Life Technologies). All cells were cultured at 37°C under a gas phase of 95% air and 5% CO6 cells) were frozen in liquid nitrogen.U87 cells were plated to be ~80% confluent on the day of radiation. GSCs were plated as single cell suspensions and incubated for 5 days to form neurospheres, and were plated at various cell numbers in order to have similar cell densities on the day of radiation. Cells were irradiated with a 10 gray dose using a RS-2000 biological irradiator (RadSource). At 30 seconds or 4 hours following treatment, cells were centrifuged at 4°C and washed twice with ice cold PBS. Cell pellets (containing 3–4 × 102 phospho-peptide enrichment kit (ThermoFisher Scientific). LC-MS/MS data collection was then performed for each sample using a UPLC system  interfaced to an Orbitrap ProVelos Elite MS system (ThermoFisher Scientific). All peptide precursor ions across all chromatographic analyses were clustered using the peak alignment algorithm of Rosetta Elucidator software. Automated differential quantification of phospho-peptides was accomplished using downstream quantitative analysis modules of the Rosetta Elucidator. Peptide and protein identifications were integrated from the protein database search engine output  with these quantifications.Cell pellets were thawed on ice and resuspended in 2% SDS containing1% v/v protease inhibitor cocktail (Sigma-Aldrich), 1% v/v phosphatase inhibitor cocktail 2 (Sigma-Aldrich), and 1% v/v phosphatase inhibitor cocktail 3 (Sigma-Aldrich). Following a 30 minute incubation on ice, cells were sonicated on ice with a probe sonicator (Branson) at 10% power for 30 seconds. Lysates were vortexed until completely solubilized. Filter-Aided Sample Preparation (FASP) clean-up was then performed according to Wisniewski  to removN = 3) were performed for each fold change. Data was filtered for peptides with a fold change > 1.5 in either direction (corresponding to a ratio of < 0.66 or > 1.5), and a p-value < 0.05. Significant peptides were imported into Ingenuity Pathway Analysis (Qiagen). A core analysis was performed to identify the top canonical pathways with differential phosphorylation.Within each batch, data was quantile normalized to remove overall sample effects. For each peptide within each cell line, geometric fold changes were calculated for both time-points . Paired t tests  was purchased from Origene.QuikChange II XL Site-Directed Mutagenesis Kit (Agilent Technologies) was used to introduce point mutations into calpastatin. Mutagenesis primers for calpastatin S633A mutant are CCC ATT GAT GCT CTC GCA GGA GAT CTG GAC AGC (forward) and GCT GTC CAG ATC TCC TGC GAG AGC ATC AAT GGG (reverse). Mutagenesis primers for calpastatin S633E mutant are CCC ATT GAT GCT CTC GAA GGA GAT CTG GAC AGC TG (forward) and CAG CTG TCC AGA TCT CCT TCG AGA GCA TCA ATG GG (reverse).WT, calpastatinS633A, calpastatinS633E, and pCMV6-Entry empty vector (Origene) were transfected into U87 and LN18 cells using Lipofectamine 3000 (Invitrogen). Selection was performed using 0.5 mg/mL Geneticin (Gibco) until no cells remained on the empty vector plate. Calpastatin protein expression was assessed by Western analysis following completion of selection. Stable cell lines were maintained with 0.5 mg/mL Geneticin.DNA plasmids for calpastatinProtein was extracted from cells using RIPA buffer (Sigma-Aldrich) with 1% v/v protease inhibitor cocktail (Sigma-Aldrich), 1% v/v phosphatase inhibitor cocktail 2 (Sigma-Aldrich), 1% v/v phosphatase inhibitor cocktail 3 (Sigma-Aldrich), and 1 mM PMSF (Sigma-Aldrich). Protein lysates were centrifuged at 12,000 rpm at 4°C for 15 minutes to remove cell debris. Total protein concentration was determined using the Pierce BCA protein assay kit (Thermo Scientific). Following SDS-PAGE, proteins were transferred to PVDF membranes. Membranes were blocked with Tris-buffered saline containing 0.1% Tween-20 (TBST) and 5% milk for 60 minutes at room temperature. The following antibodies were diluted in TBST containing 0.5% milk, and incubated with membrane overnight at 4°C: calpastatin-pS633, calpastatin-pS351 (custom ordered from Genscript), SMC1A, SMC1A-pS957, beta-actin, tubulin, calpastatin, calpain-1, calpain-S1, myc-tag, ATM-pS1982, ATM, Chk2, and Chk2-pT68 . Mouse or rabbit IgG-HRP conjugate secondary antibodies  and an enzyme-linked chemiluminescence kit (Pierce) were used to detect proteins.


This review summarizes our knowledge of HPV16 cis-acting RNA elements and cellular and viral trans-acting factors that regulate HPV16 gene expression at the level of splicing and polyadenylation.The human papillomavirus type 16 (HPV16) life cycle can be divided into an early stage in which the HPV16 genomic DNA is replicated, and a late stage in which the HPV16 structural proteins are synthesized and virions are produced. A strong coupling between the viral life cycle and the differentiation state of the infected cell is highly characteristic of all HPVs. The switch from the HPV16 early gene expression program to the late requires a promoter switch, a polyadenylation signal switch and a shift in alternative splicing. A number of  Homo sapiens in the world . ,20. 3,20Although HPV16 uses the cellular transcription-, splicing- and polyadenylation-machineries to express viral genes, there are a number of differences between HPV16 genes and cellular genes. The nucleotide composition of the HPV16 genome is skewed towards A and T, and consequently mRNAs are more AU-rich than the average cellular mRNAs and may attract different sets of RNA binding proteins and/or may be differently processed. Secondly, the majority of the transcribed region of the HPV16 genome is protein coding, often with overlapping coding regions . The earThe upstream region of the HPV16 genome contains two 5′-splice sites named SD226 and SD880, and three 3′-splice sites named SA409, SA526 and SA742  3,10]. . 10]. WiThe middle region of the HPV16 genome contains splice sites and a polyadenylation signal (pAE) that are subject to regulation. At an early stage of the HPV16 life cycle, the HPV16 3′-splice site SA3358 connects with the early polyadenylation signal pAE to produce all early mRNAs except E1 and E2 mRNAs, thus producing E6, E7, E4 and E5 mRNAs . As the The HPV16 3′-splice site SA3358 has an upstream polypyrimidine tract that is highly interrupted by purines and as such is predicted to be inefficient [The HPV16 3′-splice site is controlled by strong splicing enhancers located downstream of the splice site itself  34]. De. De34]. The HPV16 late 5′-splice site SD3632 is suppressed at the early stage of the HPV16 life cycle , includiRegulation of the HPV16 early polyA signal pAE is key to the entry into the late stage of the HPV16 life cycle since inhibition of pAE allows read-through into the L2 and L1 coding regions and polyadenylation at the late polyA signal pAL . MutatioRNA sequences downstream of HPV16 pAE also control the activity of the pAE ,47. ThisPerhaps most importantly, the HPV16 E2 protein itself is involved in the control of pAE  22]. Th. Th22]. , interactions that are lost when the AU-richness is reduced by mutagenesis and splice site SA5639 is activated, suggesting that hnRNP A1 and A2/B1 are major suppressors of HPV16 late 3′-splice site SA5639 (HPV16 late 3′-splice site SA5639 is located immediately upstream of the L1 ATG and is the most important splice site for generation of mRNAs encoding the major HPV16 capsid protein L1  3,10]. . 10]. The SA5639  [58,59,6e SA5639 .The vast majority of all HPVs appear to be endowed with a negative regulatory element in the late untranslated region ,63. IndeChromatin associated factors are in an excellent position to control splicing and polyadenylation of HPV16 mRNAs as they are synthesized by RNA polymerase II. Since the HPV16 E2 protein is chromatin associated and possesses DNA binding activity with specific binding sites in the HPV promoter region, one may speculate that E2 binding to the HPV promoter affects the conformation of the transcription complex, in particular its association with various RNA processing factors ,19,53. H


Mycobacterium tuberculosis remain mostly unknown. In this work our aim was to construct fluorescent fusions of M. tuberculosis Rpf proteins as tools to investigate their function.Resuscitation promoting factor proteins (Rpfs) are peptidoglycan glycosidases capable of resuscitating dormant mycobacteria, and have been found to play a role in the pathogenesis of tuberculosis. However, the specific roles and localisation of each of the 5 Rpfs in We found that Rpf-fusions to the fluorescent protein mCherry are functional and able to promote cell growth under different conditions. However, fusions to Enhanced Green Fluorescent Protein (EGFP) were non-functional in the assays used and none were secreted into the extracellular medium, which suggests Rpfs may be secreted via the Sec pathway. No specific cellular localization was observed for either set of fusions using time-lapse video microscopy.M. tuberculosis Rpfs fused to mCherry, which are functional in resuscitation assays, but do not show any specific cellular localisation under the conditions tested. Our results suggest that Rpfs are likely to be secreted via the Sec pathway. We propose that such mCherry fusions will be useful tools for the further study of Rpf localisation, individual expression, and function.We present the validation and testing of five The online version of this article (10.1186/s12866-018-1165-0) contains supplementary material, which is available to authorized users. Micrococcus luteus [Mycobacterium tuberculosis, which can reactivate growth of dormant mycobacteria [M. tuberculosis, little is known about their individual, specific roles, or localisation in the cell. Structural studies on RpfB, E and C catalytic domains shows a high degree of similarity, but also differences among these five proteins [Resuscitation promoting factor proteins (Rpfs) are peptidoglycan glycosidases able to resuscitate dormant bacteria. After the first description in s luteus , five hobacteria , 3. Theybacteria . Knockoubacteria –7. The mbacteria , 9 may sbacteria , 10–12. proteins –16 . Allproteins , possiblproteins –22, suggM. tuberculosis [M. tuberculosis RpfA, RpfB, RpfD and RpfE overproduced in M. smegmatis were detected in the culture supernatant by ELISA [Putative signal sequences at the amino terminus suggests they function extracellularly . RpfB anrculosis , 29, shorculosis  suggestiby ELISA .M. tuberculosis Rpfs to two different fluorescent proteins, EGFP and mCherry. We found that all fusions to mCherry, but none of the EGFP fusions, were functional. These results make Rpf-mCherry fusions interesting tools for studying resuscitation in mycobacteria.Fusion of Rpfs to fluorescent proteins would help localise them within the cell and give indications about function and possible distinct roles. To date only the localisation of RpfB fused to RFP has been communicated . In the rpf genes from M. tuberculosis H37Rv and constructed C-terminal translational fusions to EGFP. This way, the N-terminus of each rpf gene was unmodified, maintaining putative signal sequences. We transformed M. smegmatis mc2155, a fast growing non-pathogenic mycobacteria used as a surrogate for M. tuberculosis, with plasmids harbouring each of the five constructs. The strains producing Rpf-EGFP fusions grew at the same rate as those expressing EGFP alone  under control of a tetracycline inducible promoter.We amplified the five n = 219), 12.7% for RpfC-EGFP (n = 134) and 11.5% for RpfE-EGFP (n = 139)), raising the question whether this was due to over-production of the fusion proteins, although RpfB has been reported to localise to the septum [Using static microscopy the five fusion proteins appeared distributed along the cell body, but in a fraction of cells RpfB-EGFP, RpfC-EGFP and RpfE-EGFP localised at the tips and septum, the only areas of peptidoglycan production and growth in mycobacteria Fig. . Howevere septum .Fig. 1RpWe were unable to see any evidence of localisation of the proteins using time-lapse microscopy of cells growing in a microfluidic chamber; instead fluorescence was distributed along the cell body . RpfE-EGFP is shown as an example; all the Rpfs fused to EGFP present widespread localisation. (AVI 168 kb)Additional file 3:Movie S2. Putative RpfB-EGFP inclusion bodies in M. smegmatis (time lapse microscopy). (AVI 150 kb)All Rpfs have predicted signal peptides at the N-terminus , and arerpf genes were fused to the N-terminus of mcherry and the fluorescent fusion proteins produced from replicative plasmids pMEND - rpfA/B/C/D/E-mcherry, under control of a tetracycline inducible promoter.The M. smegmatis transformed with each of the plasmids grows at the same rate as the control strain . RpfE-EGFP is shown as an example; all the Rpfs fused to mCherry present widespread localisation. (AVI 351 kb)Western blot was used to check the presence of fusion proteins in soluble extracts and precipitated fractions of disrupted cells, and in culture supernatants. Using an anti-mCherry antibody, we were able to detect proteins bands in soluble extracts and the precipitated fraction that matched the expected size for each fusion Fig. . Some ofThe detection of RpfA-mCherry and RpfE-mCherry in culture supernatants strongly suggests that these mCherry-fusions are efficiently translocated to their correct localisation in the cell. In contrast, when analysing Rpf-EGFP fusions, none were detected in the culture supernatant Fig.  and neitWe tested the functionality of the fusions by checking the ability of each strain to resume growth after being subjected to nutritional shift-down, adapted from a published protocol . BrieflyIn addition to demonstrating that Rpf-mCherry fusions are functional in a nutritional shift-down assay, we also tested if their overproduction could resuscitate cells after they had been subjected to different stresses. We aimed to determine if specific Rpfs were required for particular stresses, or whether there are functional overlaps that might form a hierarchy.Suboptimal growing conditions or the presence of antibiotics produce non-platable mycobacteria, which resume growth in a Rpf-dependent manner , 31, 34.As expected, cells under acidic, osmotic stress and nutrient starvation exhibit a less significant loss of viability (tested by CFUs) than those exposed to antibiotic stress , with a MPN and CFU values were analysed after stress. Cells previously exposed to acidic conditions (pH 4.5) show a tendency to increased capacity to resume growth of non platable cells when overproducing any of the Rpf-mCherry fusions compared to mCherry alone Fig. , with thM. tuberculosis remains unknown. As a prelude to investigating this, we have constructed and tested the functionality of two sets of Rpf fluorescent fusion proteins. Rpf-EGFP fusions gave a diffuse pattern of localisation when analysed using microscopy, with some of the proteins  showing a tendency to aggregate. None of the five EGFP- fusion proteins were functional, with none of them secreted into the extracellular medium; this suggests that Rpfs are secreted by the Sec pathway and therefore folded in the periplasmic area, where EGFP is known to be misfolded [The cellular localisation of four of the five Rpfs in isfolded .Rpf-mCherry fusions also did not show specific localisation in the cell, but in contrast to EGFP-fusions, none formed aggregates. Western blot showed all Rpf-mCherry proteins were found in the soluble and insoluble extracts of disrupted cells, with RpfA-mCherry and RpfE-mCherry also found in the culture supernatant. However, we were unable to detect any other Rpf proteins in the supernatant, contrary to published studies , 29, 31.Rpf-mCherry proteins were all functional in terms of equal capacity to promote growth-recovery in a nutritional shift-down assay. Since all the Rpf-fusions were functional, we tested their capacity to promote growth of the non platable cells created under stress conditions. RpfE-mCherry and RpfD-mCherry seem to be most effective after pH 4.5 stress, and RpfE-mCherry after starvation stress; it has been suggested that this type of activity could be associated with tuberculosis reactivation . The losIn conclusion, the Rpf-mCherry fusions described here are a useful tool for the study of mycobacterial resuscitation factors, and their role in the biology of these organisms.Mycobacterium smegmatis mc2155 and derivatives were grown in defined liquid medium, either Middlebrook 7H9 , Sauton’s medium  or Hartmans-de Bont minimal medium , all supplemented with 0.025% tyloxapol (Sigma). Liquid cultures were grown aerobically at 37 °C in an orbital shaker at 180 rpm. For growth on solid medium mycobacteria were grown on Middlebrook 7H11 agar  incubated at 37 °C. When needed, hygromycin (50 μg/ml) and kanamycin (20 μg/ml) were used for plasmid selection and maintenance. Tetracycline (5 ng/ml) was used to induce mCherry (and fusion derivatives) production. Escherichia coli DH5α strain was used as a host for cloning. It was grown in LB medium at 37 °C in an orbital shaker at 180 rpm. LB agar plates were used. Hygromycin (150 μg/ml) and kanamycin (50 μg/ml) were used for plasmid selection and maintenance.The strains and the plasmids used in this work are listed in Additional file rpf gene fused to EGFP were constructed by amplifying each of the five rpf genes in M. tuberculosis H37Rv genome (rpfA (Rv0867c), rpfB (Rv1009), rpfC (Rv1884c), rpfD (Rv2389c) and rpfE (Rv2450c)) and cloning them in the EcoRI site of plasmid pST5552 [rpfA/B/C/D/E-egfp.Plasmids harbouring each  rpfD Rv29c and rp pST5552  in the Brpfs fused to mCherry protein, the five amplified genes were cloned into the BamHI–NdeI sites of pMEND-mCherry [rpf start codon. The resulting plasmids pMEND-rpfA/B/C/D/E-mcherry have the fusion genes under the control of a tetracycline promoter.For the generation of plasmids with -mCherry , which pM. smegmatis mc2155 as described [Plasmids were electroporated into competent escribed .600 of 0.1 from mid-exponential cultures in Hartmans-de Bont medium, and cultured at a continuous flow rate (1 psi) in a temperature-controlled chamber at 37 °C. Fluorescent fusion proteins were induced with tetracycline before loading the cells in the microfluidic chamber, where they continued to be perfused with the inducer. Images were captured every 15 min using a Zeiss Axiovert 200 inverted widefield microscope fitted with an EM-CCD (C9100–02) camera (Hammamatsu) controlled by HCImage software, using a 63X objective. Z-stacks were collected at 1 μm intervals to ensure in-focus images were collected. Images were analysed using Fiji image processing software [Microscopy was performed in the Facility for Imaging by Light Microscopy (FILM) at Imperial College London. Time-lapse live cell microscopy was performed in B04A plates with an ONIX flow-control system (Merck-Millipore). Cells were loaded in the chamber at an ODsoftware .For static microscopy, 20 μl samples of growing cultures were mounted on slides using Mowiol 4–88  previously to their visualization.600 of 1. The cultures were then centrifuged and resuspended in 500 μl PBS+ protease inhibitor  and disrupted in a water bath sonicator. The soluble extract and the precipitated fraction were subsequently separated by centrifugation and protein concentration in the soluble extract was determined using the Pierce™ BCA Protein Assay Kit (Thermo Fisher). An equal amount of soluble proteins for all the samples was loaded in the gels for western blotting. The precipitated fractions were resuspended in the same volume for all the samples and a fixed volume was loaded on the gels. Western blots were performed using the NuPAGE Western blotting system (Thermo Fisher). SeeBlue Pre-stained Protein Standard (Thermo Fisher) was used as size standard. As a primary antibody, polyclonal rabbit anti-EGFP antibody  or polyclonal rabbit anti-mCherry antibody  were used, and as a secondary antibody we used goat anti-rabbit IgG-HRP . The blots were developed using the SuperSignal West Femto Substrate Trial Kit (Thermo Fisher) in a LAS-3000 Fuji Imager.For western blots of soluble extracts and precipitated fraction of disrupted cells, 10 ml cultures of the strains were grown in Hartmans-de Bont broth with the inducer tetracycline up to an OD600 of approximately 0.6. Protease inhibitor was added to the cultures 30 min before collecting, centrifuging and filtering the supernatants (0.22 μm). The proteins in the supernatants were concentrated by Ion-Exchange chromatography, adapting a published protocol [For the western blots of filtrated culture supernatants, 100 ml of each strain were grown in Hartmans-de Bont broth without tween 80 and with the inducer tetracycline up to an ODprotocol . The pI protocol . It was The theoretical molecular weight of the fusion proteins was calculated using the Compute pI/Mw tool on the ExPASy Server .600 measured over time. Three biological replicates of each strain were analysed.Strains were grown in 7H9 broth for 4 days in shaking, ensuring they reached stationary phase. They were then subcultured in 10 ml Hartmans-de Bont broth in the presence of the inducer tetracycline. The number of bacteria inoculated in Hartmans-de Bont was checked by CFUs counting and was determined to be between 100 and 600 cells/ ml in all cases. Cultures were grown in shaking and ODM. smegmatis strains harbouring each Rpf-mCherry or m-Cherry alone were grown in shaking up to mid-log phase (OD600 ≈ 0.6) in Sauton’s medium before being exposed to different stress conditions. For osmotic stress, a NaCl solution in water was added to the cultures at a 1 M final concentration. For acidic stress, cultures were washed in pH 4.5 Sauton and resuspended in the same medium. For nutrient starvation, cultures were washed twice in PBS-0.025% Tyloxapol (Sigma) and resuspended in the same solution. Samples for CFU counts were taken at this point and subsequently, the cultures were incubated for 6 days at 37 °C in static. After the incubation, the cultures were washed and resuspended in Sauton’s medium, and samples were taken for CFU and MPN counts and resuscitation index calculations.Assessment of CFU counts was performed in serial dilutions of cultures in triplicate by using the standard droplet method  and subs10(MPN)-log10(CFU) [For MPN counts, serial dilutions of 200 μl cultures samples were incubated in 96-well plates in Sauton’s medium containing the inducer tetracycline. Three wells were used for each dilution. The plates where incubated in static at 37 °C for 2 weeks and MPN calculated . Three ig10(CFU) . StatistAdditional file 1:Figure S1. Growth of M. smegmatis Rpf-EGFP and Rpf-mCherry producing strains in 7H9. (a) Growth curve of strains overproducing Rpf-EGFP proteins and a control strain overproducing EGFP. (b) Growth curve of strains overproducing Rpf-mCherry proteins and a control strain overproducing mCherry. Growth curves were made with data collected from 3 biological replicates. Error bars indicate standard deviation. (PDF 16 kb)Additional file 5:Table S1. Bacterial strains and plasmids used in this study. (PDF 27 kb)Additional file 6:Table S2. Primers used in this study. (PDF 14 kb)