The female reproductive tract (FRT) is the most common site of infection during HIV transmission to women, but viral remodeling complicates characterization of cells targeted for infection. Here, we report extensive phenotypic analyses of HIV-infected endometrial cells by CyTOF, and use a ‘nearest neighbor’ bioinformatics approach to trace cells to their original pre-infection phenotypes. Like in blood, HIV preferentially targets memory CD4+ T cells in the endometrium, but these cells exhibit unique phenotypes and sustain much higher levels of infection. Genital cell remodeling by HIV includes downregulating TCR complex components and modulating chemokine receptor expression to promote dissemination of infected cells to lymphoid follicles. HIV also upregulates the anti-apoptotic protein BIRC5, which when blocked promotes death of infected endometrial cells. These results suggest that HIV remodels genital T cells to prolong viability and promote viral dissemination and that interfering with these processes might reduce the likelihood of systemic viral spread. Sexual contact remains the most common mode of HIV transmission, and more than half of new worldwide infections occur in women. Most new cases arise in sub-Saharan Africa, where in 2018 four out of every five newly infected individuals aged 15–19 were female . The typOnce infected, susceptible cells from the FRT become the founder population that eventually leads to systemic spread of the virus. A limited number of studies have queried the features of these initial HIV targets. Implementation of the FACS-based virion fusion assay – which identifies cells that HIV has entered  – on endbefore infection, or changed by HIV via remodeling after infection. Indeed, HIV and other viruses markedly remodel cells by up- or down-regulating a variety of cell-surface receptors  does not require ex vivo stimulation  . This viTo characterize the HIV-susceptible cells without the confounding effects of HIV-induced remodeling, we identified the Predicted Precursor (‘PRE’) cell for every HIV-infected cell using PP-SLIDE . This meAfter converting all the infected cells to their corresponding PRE cells, we assessed what T cell subsets they belonged to: memory CD4+ T cells (Tm), naïve CD4+ T cells (Tn), memory CD8+ T cells, or naïve CD8+ T cells. The PRE cells from both PBMCs and ETs were almost exclusively Tm cells . Among tAlthough the HIV-susceptible cells in blood and the endometrium both belonged primarily to the Tem subset, they differed markedly in their phenotypes . CompareWe next implemented a complementary approach that incorporates all phenotyping parameters instead of manual gating in order to define cellular subsets. Using FlowSOM  to clustWe then assessed to what extent each of the identified clusters was enriched among the PRE cells relative to Tm. A cluster was considered to be enriched when the ratio of its frequency in PRE versus Tm cells was >1, with a higher ratio indicating a greater enrichment . Analysis of the data from Donor 1 revealed that while PBMCs had ten enriched clusters, ETs only had five. More importantly, the PBMC clusters were enriched up to more than 40-fold, while the ET clusters were all enriched less than 1.5-fold . SimilarWe next addressed whether the high permissivity of ET Tm cells was associated with a heightened state of T cell activation. Compared to their blood counterparts, ET Tm cells expressed higher levels of multiple activation markers  and moreWe next determined to what extent the HIV-susceptible ETs were enriched among the canonical subsets of differentiated CD4+ T cells. F4.HSA-infected ETs from 4 additional donors, for a total of 8, were analyzed by CyTOF to ensure a more robust statistical analysis. The new datasets were subjected to PP-SLIDE to identify the PRE cells . Manual Having described the phenotypes of HIV-susceptible ETs, we next assessed how HIV modulates expression of specific receptors on these cells during infection by comparing antigen expression in PRE versus infected cells . For exaUsing such an approach, we first confirmed the expected pattern of CD4 expression. PP-SLIDE revealed, as expected, that PRE cells had a higher CD4 MSI than the total population of uninfected T cells, suggesting that HIV selects cells expressing high levels of CD4 for infection. The PRE cells also had higher CD4 MSI than the infected cells, consistent with downregulation of CD4 upon infection . We thenlow cells for infection, followed by further down-regulation of α4 after infection , and remodels these cells in ways that can promote viral dissemination, may help explain the generally poor effectiveness of oral PrEP or microbicides in protecting women from sexual transmission of HIV. However, certainly other factors including adherence and genital drug levels also play important roles . Our fin2+ and Mg2+ supplemented with 3.4 mg/ml collagenase type 1  and 100 U/ml hyaluronidase (Sigma-Aldrich H3631). Digested cells were then filtered through a Falcon 40 μm cell strainer and the filtrate was washed with R10 media (RPMI 1640 containing 10% FBS and 1% P/S). The cells were then cultured in 96-well U-bottomed polystyrene plates at a concentration of 106 cells / well in 200 μl. Of note, endometrial cells were never stimulated with a mitogen at any point during in vitro culture.Fresh human endometrial biopsies from HIV seronegative women were obtained from the University of California, San Francisco (UCSF) (IRB # 14–15361) and the Women’s Health Clinic of Naval Medical Center Portsmouth (NMCP) in Virginia (CIP # NMCP.2016.0068), under standard operating procedures . Donor iPBMCs were isolated from reduction chambers obtained from Vitalant Research Institute or the NMCP. All blood products were first diluted at a 1:1 ratio using FACS buffer (PBS supplemented with 2% FBS and 2 mM EDTA). Ficoll (Stemcell Technologies) was then slowly added at the bottom of the tube at a ratio of 2:1. The samples were then centrifuged at 931 g for 30 min. After centrifugation, the PBMC layer underlying the top supernatant was transferred to a new tube and cells were washed 3x in FACS buffer. Where indicated, PBMCs were activated by stimulation for 3 days with 5 μg/ml PHA (Sigma-Aldrich) in the presence of 100 IU/ml IL-2 (Life Technologies). Activated PBMCs were always maintained in R10 media containing 20 IU/ml IL-2. For sorting experiments, memory CD4+ T cells (Tm cells) were purified using the EasySep CD4 negative selection enrichment kit (Stem cell Technologies) followed by depletion of naïve T cells using CD45RA beads (Miltenyi Biotec) prior to antibody staining.2 pieces and pushed through a 40 μm cell strainer using a syringe plunger. The cells were then filtered through a second 40 μm cell strainer and cultured in 96-well U-bottomed polystyrene plates at a concentration of 106 cells / well in 200 μl tonsil media , 1% Glutamax (Thermo Fisher), and 1% Fungizone (Invitrogen)).Human tonsils were obtained from the Cooperative Human Tissue Network (CHTN). To generate HLACs, tonsils were dissected into ~1 cmGag Rapid Titer Kit (Clontech). PBMCs (unstimulated or PHA-stimulated), unstimulated tonsillar HLACs, or unstimulated endometrial cells were inoculated with the viral stocks. Because recent studies demonstrated that fibril-based enhancers of HIV infection, including those naturally present in human semen . This corresponded to a final p24Gag concentration of 10–20 ng/ml. Infection was then allowed to proceed for 2 hr, after which cultures were replaced with fresh media and allowed to incubate for another 3 days. Control cells were incubated with media for 3 days and harvested at the same time as the infected culture. Both EF-C and SEM86 markedly enhanced infection by F4.HSA , PE/Cy7-CD4 , APC-CD8 , FITC-CD24 , and the LIVE/DEAD Zombie Aqua Fixable Viability Kit (Biolegend). After 3 additional washes with FACS buffer, cells were analyzed by flow cytometry on an LSRFortessa or LSR II (BD Biosciences).low, CCR7medium, and CCR7high populations, along with total Tm, were infected with F4.HSA for 3 days and then analyzed by FACS as described above.For sorting experiments, purified Tm cells were washed once with FACS buffer and stained at room temperature for 15 min with a 1:200 dilution of the LIVE/DEAD Zombie Aqua Fixable Viability Kit (Biolegend). After washing 1x with FACS buffer, cells were stained for 30 min at 4°C with a cocktail of antibodies diluted in a 1:1 mixture of FACS buffer and the Brilliant Stain Buffer (BD Biosciences). The antibody cocktail consisted of APC/Cy7-CD3 , BD Horizon BV650-CD8 , BD Horizon BUV737-CD4 , FITC-CD45RO , BD OptiBuild BUV395-CD45RA , and PE/Dazzle 594-CD197 . The cells were then washed once in FACS buffer, resuspended at a concentration of 10 million cells / ml, and sorted using an AriaII flow cytometer (BD Biosciences). Fluorescence minus one (FMO) controls for CCR7 staining and compensation controls using Comp Beads (BD Biosciences) were acquired at the same time. Sorted CCR7For this study, we designed a 38-parameter CyTOF panel that includes markers of T cell differentiation states, activation markers, transcription factors, and homing receptors, along with an antibody against HSA to identify productively-infected cells . AntibodFor live/dead discrimination, 1–6 million cells were washed 1x with contaminant-free PBS (Rockland) supplemented with 2 mM EDTA (PBS/EDTA), incubated for 60 s at room temperature with 25 μM cisplatin in 4 ml PBS/EDTA, and then quenched with CyFACS  supplemented with 0.1% bovine serum albumin and 0.1% sodium azide). Cells were then fixed with 2% PFA in metal contaminant-free PBS (Rockland), washed 3x with CyFACS, and frozen at −80°C until CyTOF staining.5/ ml in EQ calibration beads (Fluidigm) diluted in Maxpar Cell Acquisition Solution. Cells were acquired at a rate of 250–350 events/sec on a CyTOF2 instrument (Fluidigm) at the UCSF Flow Cytometry Core.To stain multiple samples in the same reaction, barcoding of cells was conducted using the Cell-ID 20-Plex Pd Barcoding Kit according to manufacturer’s instructions (Fluidigm). Briefly, for each sample, 1–3 million cells were washed 2x with Barcode Perm buffer (Fluidigm), and then the appropriate barcode was added at a 1:90 ratio and incubated with the cells for 30 min. Cells were then washed with 0.8 ml Maxpar Cell Staining buffer (Fluidigm), followed by with CyFACS. Barcoded samples were then combined and blocked for 15 min on ice with sera from mouse (Thermo Fisher), rat (Thermo Fisher), and human  in Nunc 96 Deep-Well polystyrene plates (Thermo Fisher). Cells were then washed 2x with CyFACS buffer, and stained for 45 min on ice with the cocktail of CyTOF surface staining antibodies  in a totThe new CyTOF panel was validated on paraformaldehyde (PFA)-fixed cells by confirming expected expression patterns of each antigen on different subsets of cells . The abiData were normalized to EQ calibration beads and de-barcoded with CyTOF software (Fluidigm). Normalized data were imported into both FlowJo (BD) and Cytobank for gating and further analysis. To identify total T cells, sequential gating was used based on DNA content, viability, cell length, and a CD3+CD19- gate . t-SNE pIdentification of predicted precursor (PRE) cells by PP-SLIDE has been previously described . In our CD3+CD19- T cells from the uninfected culture and HIV-infected cells from the infected culture were gated on and exported using Flowjo10. To prepare for PP-SLIDE, the following parameters, which do not contain useful information for identifying the original cell type, were removed from the analysis:Raw expression values  of selected markers from each cell in the exported files were transformed by the inverse hyperbolic function (arcsinh) transformation as follows:This transformation is used to standardize the diverse range of raw expression level scales for the measured parameters, and minimizes the effect of outliers and extreme numbers.F_Ud) between each infected cell F and each uninfected cell U was calculated as follows:i refers to the parameter being analyzed. For example, for parameter 1, i – UiF would correspond to the value of parameter 1 on the infected cell minus the value of parameter 1 on the uninfected cell.The Euclidean distance  = [(number of cells in Cluster X) / ] * 100For each sample, the proportions of each cluster at a frequency >1% are shown .To explore whether PRE cells preferentially resided within any of the clusters, or alternatively whether the PRE cells were stochastically distributed, we compared the frequency of each cluster among total uninfected Tm cells versus PRE cells by calculating chi-square values (see below). To examine the extent of enrichment of each cluster among PRE cells, we divided the frequency of each cluster among the PRE cells by the frequency of that same cluster among total uninfected Tm cells to obtain the ratio of cluster frequency:Ratio(Cluster X) = Frequency(Cluster X among PRE) / Frequency(Cluster X among uninfected)The ratios are reported in The expression level of each parameter was reported by its median signal intensity (MSI). Student’s two-sided paired (by donor) t-tests were used to test for differences in MSI of each parameter among uninfected cells, PRE cells, and infected cells, or among PRE cells from ETs and PBMCs. P-values were adjusted for multiple testing using False Discovery Rate (FDR) via the Benjamini-Hochberg method. FDR adjusted P-values that were less than 0.05 were considered as significant.SLIDE analysis was conducted using the R package SLIDE . The metp(np) and Se(ne) were respectively the chi-square statistic values from the PBMC and ET comparisons based on np and ne groups, then, for testing the alternative hypothesis that PBMC chi-square values were significantly higher than the corresponding ET values, the P-value was computed based on Chi-squared test statistics values from the FlowSOM analyses were generated based on a goodness of fit test between the distribution of clusters between uninfected Tm and the PRE cells. To determine whether the chi-squared values between PBMCs and ETs were different, a conservative asymptotic test incorporating degrees of freedom correction was implemented. If S In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Marta Rodriguez-Garcia (Reviewer #2).Thank you for submitting your article "HIV efficiently infects T cells from the female genital tract and remodels them to promote systemic viral spread" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:In this manuscript, Ma and colleagues evaluate the susceptibility of endometrial CD4+ T cells to HIV infection. They perform in-depth phenotypic analyses of HIV-infected T cells isolated from the female reproductive tract (FRT) using a Mass Cytometry approach (CyTOF).The authors find that endometrial T cells are more susceptible to infection than blood T cells, and that the initial targets in the endometrium are phenotypically more diverse compared to blood T cells. Additionally, they identify specific modifications induced by productive HIV-infection that may result in increased cell survival, impaired antigen recognition and viral dissemination of HIV-infected cells to lymphatic tissues. Finally, they observed that HIV infection upregulates the anti-apoptotic protein BIRC5 also in T cells from FRT as previously observed in CD4 T cells form peripheral blood .The major conclusion is, thus, that HIV reprograms the phenotypic profile of the infected cells in favor of the virus, by e. g. manipulating cell trafficking and cell survival mechanisms/cell surface markers. The study takes advantage of impressive, recently-developed technologies, including biocomputational algorithms for identifying "predicted precursors" of infected cells — a technique to infer the phenotypic profile of cells prior to infection.In conclusion, the work is original, novel and presents three key components that differentiate this study from previous work: the use of transmitter/founder viruses, the inclusion of semen component in the infection protocol and the focus on endometrium. All three reviewers express high enthusiasm for the presented work, which represents a very notable advance to the field. The reviewers identified two very novel and unique findings that need additional work: 1) the high susceptibility of endometrial cells compared to blood cells and 2) the modification of cells following infection.Essential revisions:1) One of the most important findings of this manuscript is that non-stimulated endometrial CD4 T cells are much more permissive to HIV infection than activated CD4 T cells from blood. This striking finding needs be analyzed in more detail — can the authors be more specific what mechanisms are responsible for this difference? Consideration should be given to using alternative viral species (e. g. X4-tropic or VSV-G pseudotyped viruses). The authors should also show the percentage of naÐ¯ve, CM and EM in in both PBMC and T cells from endometrial biopsies. These data would give the information on how many T cells from FRT are potentially targets for infection.2A) The key experiment missing from the study is a control demonstrating that blocking infection abrogates remodeling. The study compares uninfected vs. infected T cells, which are analyzed only at day 3 post-infection. A control including T cells exposed to HIV in the presence of antiviral drugs that prevent productive infection (blocking RT for example) would demonstrate that productive infection is necessary for the remodeling effects observed.2B) The authors conclude that the differences between infected and uninfected cells are due to remodeling induced by productive infection. But the data is generated by pre-gating the HIV+ population and comparing it to uninfected cells, which were not exposed to the virus. How would the t-SNE analysis look like if the whole population of cells that was exposed to the virus was included in the analysis? Are the HSA negative cells also modified? This would indicate an effect of viral exposure or indirect effects induced by other cells present in the mixture instead of direct modification by productive infection.2C) The authors should also discuss the mechanisms leading to "remodeling" or reprogramming of phenotypic properties of infected cells more clearly — is this related to cell-intrinsic innate immune recognition? Is it related to cytopathic effects? Is it related to DNA damage responses resulting from chromosomal integration?3) It is unclear whether PBMCs and tonsil cells were incubated with SEM86 for comparisons, and whether uninfected controls were treated with SEM86. How long was SEM86 incubation? Did it modify the phenotype of the cells by itself in the absence of virus? These controls need to be shown. This is a key difference in this study compared to other studies characterizing HIV infection in the genital tract, and it should be mentioned in the Results as well.4) The analysis done to identify the PRE cells considers the HIV-infected cells three days after infection and analyzes the uninfected control  to find the original preferential cell infected. How is this model accounting for the potential effects of culturing the cells? The uninfected control three days after culture is probably different than the initial population of cells present when infection was performed on day 0. What if specific T cell subsets survive culture conditions better than others? Essential revisions:1) One of the most important findings of this manuscript is that non-stimulated endometrial CD4 T cells are much more permissive to HIV infection than activated CD4 T cells from blood. This striking finding needs be analyzed in more detail — can the authors be more specific what mechanisms are responsible for this difference? Consideration should be given to using alternative viral species (e. g. X4-tropic or VSV-G pseudotyped viruses).We have addressed the reviewer’s suggestion to better characterize the features associated with the high permissivity of endometrial CD4+ T cells to infection, by first comparing the expression levels of 8 different activation markers and the CCR5 co-receptor on Tm cells from the endometrium vs. unstimulated PBMCs for the four donors we had characterized in Figure 2. We found that relative to their blood counterparts, ET Tm cells expressed significantly higher levels of six of the eight activation markers , and more frequently expressed the CCR5 co-receptor . These results imply that both activation state and co-receptor expression may contribute to the increased permissivity of the ET Tm cells.To assess whether the activation state of T cells alone can account for the high susceptibility of ETs to infection, we conducted an experiment where unstimulated ETs, unstimulated PBMCs, and PHA-stimulated PBMCs (as an abundant source of highly activated T cells) were exposed to F4.HSA, phenotyped by CyTOF, and analyzed by PP-SLIDE. Of note, the ETs and PBMCs in this experiment were all from the same donor. We found the following: (1) infection rates were higher in the unstimulated ETs than the PHA-stimulated PBMCs; (2) the phenotypes of the HIV-susceptible cells in unstimulated ETs were distinct from those in stimulated PBMCs; (3) multiple activation markers were expressed at higher levels on the stimulated PBMCs than the unstimulated ETs, despite the former being relatively less susceptible to infection; and (4) CCR5 levels were higher on the unstimulated ET Tm cells than stimulated PBMC Tm cells. These data are presented as the new Figure 2—figure supplement 7C-G. Together, these results suggest that T cell activation state does not fully account for the high susceptibility of ETs to infection. Instead, the high levels of CCR5 expression, and other unique aspects described in this manuscript, appear to be more important.The authors should also show the percentage of naïve, CM and EM in in both PBMC and T cells from endometrial biopsies. These data would give the information on how many T cells from FRT are potentially targets for infection.The requested analysis has now been conducted and is presented as the new Figure 2—figure supplement 5C. While the proportion of Tn and Tcm cells was significantly lower in the endometrium than in blood, Tem cells showed the opposite pattern. These results suggest that one mechanism rendering ET cells more susceptible than their blood counterparts is the higher frequency of Tem cells.2A) The key experiment missing from the study is a control demonstrating that blocking infection abrogates remodeling. The study compares uninfected vs infected T cells, which are analyzed only at day 3 post-infection. A control including T cells exposed to HIV in the presence of antiviral drugs that prevent productive infection (blocking RT for example) would demonstrate that productive infection is necessary for the remodeling effects observed.We have conducted a modified form of the suggested experiment. Instead of just adding ART to our infection assay, we first set up cultures of endometrial cells mock-treated or infected for 3 days with F4.HSA. We then took the supernatants from these two cultures and used them as a source of conditioned media, which we added to target cells isolated from the same endometrial biopsies, but in the presence of suppressive ART. The rationale for using conditioned media containing HIV, as opposed to free HIV virions, is that the conditioned media contains infection-associated factors such as cytokines and other inflammatory mediators, that may induce phenotypic changes in cells. In this experimental system, our target cells are exposed to both virions and conditioned media from infected cells, but cannot become infected with the virus due to ART. The results of these experiments, conducted on two donor specimens, revealed that endometrial Tm cells exposed to conditioned media from infected cultures in the presence of ART are not remodeled, as they exhibit lower SLIDE scores than productively-infected cells , and appear in similar regions of t-SNE space as Tm cells exposed to conditioned media from uninfected cultures .2B) The authors conclude that the differences between infected and uninfected cells are due to remodeling induced by productive infection. But the data is generated by pre-gating the HIV+ population and comparing it to uninfected cells, which were not exposed to the virus. How would the t-SNE analysis look like if the whole population of cells that was exposed to the virus was included in the analysis? Are the HSA negative cells also modified? This would indicate an effect of viral exposure or indirect effects induced by other cells present in the mixture instead of direct modification by productive infection.We have included this suggested analysis as the new Figure 2—figure supplement 4A. In contrast to the productively-infected HSA+ cells, the bystander (HSA-) Tm cells resided in regions of the t-SNE similar to regions occupied by Tm cells in the uninfected sample. Furthermore, the SLIDE scores for the HSA+ cells were significantly higher than that of the HSA- Tm cells , confirming that remodeling is primarily driven by productive infection.2C) The authors should also discuss the mechanisms leading to "remodeling" or reprogramming of phenotypic properties of infected cells more clearly — is this related to cell-intrinsic innate immune recognition? Is it related to cytopathic effects? Is it related to DNA damage responses resulting from chromosomal integration?As described above, in contrast to productively-infected cells, bystander cells in infected cultures exhibit limited remodeling. These results suggest that the extensive remodeling that we observe requires proviral integration. The fact that bystander cells exhibit limited remodeling, and that addition of supernatants from infected cultures whilst blocking productive infection with antiretroviral drugs does not induce remodeling in the Tm cells (see above), suggests that the inflammatory cytokine milieu is not sufficient to remodel cells. Rather, it is likely that cell-intrinsic innate immune recognition and/or DNA damage responses, as well as viral-induced remodeling through the activity of viral accessory genes, drives the remodeling. Cytopathic effects are unlikely contributing as we had gated on live cells in our analyses. These concepts are now brought forth in the Discussion (paragraph three).3) It is unclear whether PBMCs and tonsil cells were incubated with SEM86 for comparisons, and whether uninfected controls were treated with SEM86.In the study comparing PBMCs and tonsil cells'responses to SEM86, all samples (including the uninfected ones) were treated with SEM86. This has now been clarified in the Results section (paragraph one).How long was SEM86 incubation?We apologize for the lack of details on the experimental protocol implemented for SEM86 treatment. We had incubated the virus for 15-30 minutes with 100 μg/ml SEM86, followed by dilution of this viral stock 10-fold onto target cells. The details of the protocol have now been added to the Materials and methods section (subsection "Virus Preparation and infection assays").Did it modify the phenotype of the cells by itself in the absence of virus?We believe that SEM86 did not remodel the cells independent of virus, because limited remodeling was observed in the bystander cells in the culture that were exposed to the same amount of SEM86 (see above). Consistent with this notion, in our FACS experiments the mean fluorescence intensity (MFI) of CD3 and CD4 was not significantly different between T cells treated or not with SEM86. These data are shown in These controls need to be shown. This is a key difference in this study compared to other studies characterizing HIV infection in the genital tract, and it should be mentioned in the Results as well.The addition of SEM86 to our cultures has now been mentioned in the Results section (paragraph one).4) The analysis done to identify the PRE cells considers the HIV-infected cells three days after infection and analyzes the uninfected control  to find the original preferential cell infected. How is this model accounting for the potential effects of culturing the cells? The uninfected control three days after culture is probably different than the initial population of cells present when infection was performed on day 0. What if specific T cell subsets survive culture conditions better than others?The uninfected control was indeed mock-treated for 3 days. This has now been clarified in the Materials and methods (subsection "Virus Preparation and infection assays"). We opted to not compare the infected cells from day 3 to uninfected cells from day 0, since if that were done the phenotypic changes in the infected cells would reflect not only changes induced by infection, but also phenotypic changes that incur during ex vivo culture of primary cells, a process known as phenotypic drift. Such changes could include, as the reviewer mentions, the preferential survival of particular T cell subsets. To confirm that drift did occur in ETs, we conducted CyTOF analysis on an endometrial specimen immediately after generating the single-cell suspension of the freshly isolated specimen (day 0), and after 3 days of culturing the specimen in media (day 3). A t-SNE analysis of the resulting CyTOF datasets demonstrated differences between Tm cells of the two specimens, as revealed by the cells occupying unique regions of the t-SNE space, see These results suggest that drift does indeed occur in ETs, but is an unavoidable issue with any kind of ex vivo viral infection experiments using primary cells targets. Of note, the phenotypic differences between the Day 0 and Day 3 specimens may also be in part due to the lack of time for the Day 0 specimen to re-express antigens following collagenase digestion. To recognize drift as an unavoidable limitation of our experimental model, we have now brought up the issue in the Discussion, as well as how we tried to account for it by always comparing infected samples to uninfected ones cultured for the same period of time (paragraph eight).


Meningiomas, the most common adult brain tumors, recur in up to half of cases. This requires timely intervention and therefore accurate risk assessment of recurrence is essential. Our current practice relies heavily on histological grade and extent of surgical excision to predict meningioma recurrence. However, prediction accuracy can be as poor as 50% for low or intermediate grade tumors which constitute the majority of cases. Moreover, attempts to find molecular markers to predict their recurrence have been impeded by low or heterogenous genetic signal. We therefore sought to apply systems-biology approaches to transcriptomic data to better predict meningioma recurrence. We apply gene co-expression networks to a cohort of 252 adult patients from the publicly available genetic repository Gene Expression Omnibus. Resultant gene clusters (“modules”) were represented by the first principle component of their expression, and their ability to predict recurrence assessed with a logistic regression model. External validation was done using two independent samples: one merged microarray-based cohort with a total of 108 patients and one RNA-seq-based cohort with 145 patients, using the same modules. We used the bioinformatics database Enrichr to examine the gene ontology associations and driver transcription factors of each module. Using gene co-expression analysis, we were able predict tumor recurrence with high accuracy using a single module which mapped to cell cycle-related processes . This module remained predictive when controlling for WHO grade in all cohorts, and was associated with several cancer-associated transcription factors which may serve as novel therapeutic targets for patients with this disease. With the easy accessibility of gene panels in healthcare diagnostics, our results offer a basis for routine molecular testing in meningioma management and propose potential therapeutic targets for future research. For over half a century, prediction of meningioma recurrence has relied solely upon histological features2  and degree of surgical excision (Simpson grade). While about 70–80% of completely excised WHO grade I meningiomas do not recur, the remaining 20–30% do, with half of these recurrences occurring before the tenth year of follow-up3. For completely excised WHO grade II lesions, histology predicts a recurrence of 50% over 5 years and a disease-specific survival rate of 69% over 10 years. Given this lack of predictive accuracy, such patients require lifelong monitoring with MRI imaging and clinical follow-up. Not only does this represent a significant financial burden but the strain on the psychological well-bring of patients can be substantial given the associated ongoing uncertainty. It is therefore critical to develop more precise adjunctive methods of assessing recurrence risk that take into account the molecular biology of meningioma.Meningiomas constitute approximately 34% of all brain tumors and affect approximately 3% of the adult population4, mutations in genes TERT5, AKT16, SMO7, DREAM complex repression8, and DNA methylation11 as correlates of tumor aggressiveness or recurrence, though translation into routine clinical practice remains limited to date. We add to the growing body of literature seeking to describe meningiomas in molecular terms by leveraging transcriptomics methods rooted in systems biology to capture small additive biological effects and relate them to complex and multifactorial clinical traits14. Such methods may help extract translatable markers of meningioma recurrence and avoid the potential stochasticity of using individual genes.The genetic landscape of meningiomas is the next frontier to our understanding of its biology and its relation to disease recurrence, and many studies have begun to elucidate important molecular associations with aggressiveness and recurrence. For example, previous studies have identified chromosomal rearrangements15. This technique establishes gene similarity profiles based on shared connectivity profiles and clusters individual genes into co-expressed  modules. Expression of each module can be represented using principle component analysis to define a module meta-gene in order to reduce the noisy effects of individual genes. This approach seeks to holistically and robustly represent individual biological processes. Importantly, it is being increasingly used to identify novel disease-associated gene programs that would not be identified using single-gene approaches alone. Examples include the identification of gene programs underlying mouse weight12, glioblastoma13, and Huntington’s disease14. With the affordability of gene expression profiling, meta-gene-based prediction of tumor recurrence becomes feasible.Gene co-expression networks are used to detect patterns in transcriptomic data by incorporating additive signal from relatively low gene expression levelsIn the current study, we hypothesized the existence of gene modules that predict tumor recurrence with high accuracy. By annotating modules correlated with recurrent phenotypes we aim to provide further insight into the underlying biology driving tumor recurrence and identify potential new avenues for molecular therapeutics.Gene Expression Omnibus (GEO)16 which contained human meningioma tissue transcriptomics and which were, at minimum, annotated with WHO grade and recurrence/progression. We define recurrence as the re-appearance of tumor on imaging after complete excision or the measurable progression of tumor after subtotal resection. Four of these studies use microarrays ) and one uses RNA-sequencing (GSE1366618) . GSE16181 constituted the discovery cohort and external validation was done using a merged dataset from the remaining microarray-based series  which was batch-corrected with ComBat, a well-established Bayesian batch correction tool20, and the RNA-sequencing series GSE136661 .We identified five datasets in the publicly available repository 13. Modules were discovered using the discovery cohort. We first constructed an adjacency matrix with gene–gene Pearson correlations. Soft-thresholding was introduced by raising correlations to a common power, selected as the lowest natural number for which the network approached scale-free topology (r2 ≥ 0.9). The adjacency matrix was subsequently converted into a biologically-inspired topological overlap map (TOM), wherein pairwise gene similarities are computed based their shared connectivity profiles within the network, a more meaningful measure than direct correlation21. Hierarchical clustering was done on the TOM and clusters (gene modules) selected with hybrid adaptive tree cut22, an unsupervised dendrogram cutting function which considers not only the degree of correlation but also the shape of the dendrogram to determine clusters. Finally, representative module “meta-genes” for each sample are taken as the projections of a sample’s modules’ gene expression values on their respective first principal components. The validity of this approach is demonstrated in Supplemental Fig. Our analysis was based on the well-established Weighted Gene Correlation Network Analysis (WGCNA), which has been described in detail elsewherep < 0.05) in the discovery cohort. In order to probe the biological function of relevant modules, we used the well-established and open-source Enrichr24 to analyze their constituent gene lists. Highly associated GO Biological Processes, GO Molecular Functions, and transcription factors (from the CHEA/ENCODE consensus list) are shown in Fig. We first selected modules with significantly different meta-gene expression between recurrent and non-recurrent tumors  and WHO grade (ANOVA p < 0.05): a “blue” module containing 220 genes, and a “turquoise” module containing 299 genes , and E2F4, FOXM1, TRIM28, KAT2A, SIN3A to be strongly associated with the turquoise module . Annotating with biological processes and molecular functions yield several mechanisms related to cell cycle and transcription  while the E2F4/FOXM1-enriched module has a strong negative correlation  . The E2F4/FOXM1-enriched module meta-gene remains a predictor of recurrence when correcting for WHO grade in all cohorts, and in the RNA-seq validation cohort it remains independently predictive when also correcting Simpson grade . It also performs similarly to both of these models when applied to validation cohort 2. The meta-gene from sparse module 2 performs significantly better than the E2F4/FOXM1-enriched module in validation cohort 2 (DeLong’s p < 0.05), though the performance of the E2F4/FOXM1-enriched module and sparse module 2 in validation cohort 1 are similar  are examined in Fig. p > 0.05) in either of the validation cohorts, and DEPDC1, SUZ12, NFKB1, and RUNX1 is not significantly associated with WHO grade one of the validation cohorts.We also sought to characterize transcription factors (TFs) associated with the E2F4/FOXM1-enriched module using the Enrichr data (Fig. Finally, we performed a gene linkage analysis on module genes to assess the importance of chromosomal gains/losses in meningioma biology (Supplemental Fig. We have used a simple and robust approach to predict meningioma recurrence using gene modules derived from a list of cancer-associated genes, which yields high accuracy in two external validation cohort: one merged microarray-based cohort and one RNA-seq based cohort. Predictive ability of the E2F4/FOXM1-enriched module remained modest when controlling for WHO grade in all 3 cohorts. In the highly annotated validation cohort 2, this module remained predictive of recurrence when also controlling for Simpson grade. We also demonstrate the robustness of a module approach, showing that while predictive accuracies can be improved on an individual cohort by sparsifying the module into the its most individually predictive genes, any added performance is lost on other cohorts. This lends credence to the value of a gene program-based approach. Finally, we identify transcription factors which are strongly associated with the top-performing module and whose expression is significantly different between recurrent and non-recurrent tumors. In particular, FOXM1, DEPDC1, and SUZ12 are positively associated with recurrence while RUNX1, STAT5A, TWIST1, EP300 and NFKB1 are negatively associated.8. SUZ12 is known as an element in PRC2, which is involved in chromatin silencing and appears to be lost in a subset of comparatively indolent meningioma8. EP300, an apoptosis-associated transcription factor, has been shown to be downregulated in meningioma when compared to normal arachnoid27 though it’s role in meningioma prognostication is not well understood. While DEPDC1 has not been studied in meningioma, it is a known regulator of NFkB signaling and is overexpressed in multiple cancers such as glioma, breast cancer, and nasopharyngeal cancer, having been proposed as a potential therapeutic target in each30. Similarly, the roles of RUNX1, STAT5, and TWIST1 in meningioma remain poorly understood, and could represent novel therapeutic targets.These results lend to the value of this semi-supervised approach to genetic analysis and yields a panel of genes which could effectively be represented on an inexpensive mini-microarray chip for clinical use. Additionally, some of the associated transcription factors have previously been implicated in meningioma. For example, FOXM1 has been shown to be a critical driver of meningioma aggressiveness and is associated with the activating DREAM complex, thereby allowing cell-cycle progression and cell proliferationIn our study, we demonstrate the potential utility of a meta-gene based systems biology approach in meningioma prognostication. This principle can be used in marker discovery for other challenging diseases where conventional approaches such as differential gene expression analysis have been unsuccessful. Our results are derived from data obtained under diverse conditions and yet still produce an accurate classier which projects translational value in creating a diagnostic panel. The additional mapping of potentially key transcription factors lends to the therapeutic potential of this holistic approach as well, though validation studies would be needed to support this hypothesis.31 the relevance has been debated over the past decade and our classifier yields high accuracy purely with gene expression data. Additionally, in validation cohort 2, our classifier is predictive of recurrence independently of Simpson grade. While follow-up times are limited for some patients (Table Our study uses publicly available data and is thus limited by sparse annotation and variable follow-up times. The discovery cohort is not associated with a peer-reviewed citation, and thus the full extent of their methodology is not available. The extent of tumor excision is not known in all cohorts. Although this is conventionally held to affect recurrenceWe apply gene co-expression networks to a microarray-based cohort of 252 adult meningiomas and identify a gene module which predicts tumor recurrence with high accuracy in both microarray and RNA-seq external validation cohorts. Importantly, the module remains predictive when controlling for WHO grade in all three cohorts. With the wide accessibility of custom-made mini-arrays, our findings support, and should encourage, the shift to include this or a similar classifier in routine clinical care. Furthermore, the module’s strong association with key transcription factors may yield new therapeutic options for patients with meningioma.Supplementary Figures.Supplementary Information.


Rainwater samples are derived from three precipitation collectors installed at different altitudes . Surface water samples were collected at three sampling sites located along the Del Azul Creek and six wetlands . Groundwater samples were collected from 17 piezometers with depths ranging between 3 and 10 m, and from 12 piezometers of 30 m depth, all located throughout the entire basin . Sampling campaigns were performed during the austral dry (summer) and wet (spring) seasons. This dataset provides useful information to understand a) how water moves from recharge to discharge areas, b) how water acquires salinity, and c) how particular solutes of concern, such as arsenic and fluoride, are distributed in space and time across in an extensive plain.The Chaco-Pampean Plain (Argentina) is the strongest economic region and the most inhabited in the country, comprising approximately 66% of the country's population   Our data will allow the scientific community to understand and compare how water and solutes move across these unique environments, as well as enable better water and soil management decisions.•Our hydrological dataset from a large sub-humid plain located in South America will enhance modeling comparison exercises with other large plains across the world in order to categorize (similarities and differences) and understand the key hydrological processes governing the water quantity and chemical quality.•Our database offers an extensive series of chemical and isotopic rainwater data from a large sub-humid plain.•In a large sub-humid plain, the increase of salinity and alkalinity towards the flattest area is a relevant topic of environmental concern. Our dataset provides data of various chemical and isotopic analyses from rainwater, surface water and groundwater in order to analyze their spatial and temporal distribution.•In alkaline environments, both arsenic and fluoride are released into groundwater. At high concentrations, both elements can limit the water use 118O contents vs. elevation of the rainwater, creek, wetlands and groundwater in the Del Azul Creek basin.In addition, a table and a KMZ file were uploaded as supplementary data files to Mendeley repository 2n = 51) and physico-chemical analyses (n = 57).Three integrating wet/dry precipitation collectors and rain gauging stations were installed at different altitudes in the upper (LG), middle (IHLLA) and lower basin (LM), ranging from 221 to 72 masl, respectively. Passive precipitation collectors consisted of a 26.5 cm diameter plastic funnels connected to 20 L plastic containers. Containers with a mineral oil thin-layer were buried underground to avoid secondary evaporation . The samn = 6) along the Del Azul Creek in February 2018 (summer season) and November 2018 (spring season). The superficial flow rates were measured with an OTT MF pro-Water Flow Meter equipment (Site 1) and calculated by rating curves at control sections (Sites 2 and 3). Six physico-chemical analyses and six stable isotope analyses are also available. In addition, six wetlands were sampled (W1 - W6). In this case, six physico-chemical analyses and six stable isotopes analyses for October 2019 (spring season) are also provided in this dataset.Three surface water sites were sampled  are screened in their last meter, while in the deep piezometers (30 m) screens are located between 25 and 30 m depth. In each groundwater sampling campaign, piezometric levels were also measured. Groundwater samples were collected after purging at least three times the borehole water volume and after EC, pH and water temperature were stabilized The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


In the adult vertebrate brain, enzymatic removal of the extracellular matrix (ECM) is increasingly recognized to promote learning, memory recall, and restorative plasticity. The impact of the ECM on translaminar dynamics during cortical circuit processing is still not understood. Here, we removed the ECM in the primary auditory cortex (ACx) of adult Mongolian gerbils using local injections of hyaluronidase (HYase). Using laminar current-source density (CSD) analysis, we found layer-specific changes of the spatiotemporal synaptic patterns with increased cross-columnar integration and simultaneous weakening of early local sensory input processing within infragranular layers Vb. These changes had an oscillatory fingerprint within beta-band (25–36 Hz) selectively within infragranular layers Vb. To understand the laminar interaction dynamics after ECM digestion, we used time-domain conditional Granger causality (GC) measures to identify the increased drive of supragranular layers towards deeper infragranular layers. These results showed that ECM degradation altered translaminar cortical network dynamics with a stronger supragranular lead of the columnar response profile. El-Tabbal et al removed the extracellular matrix (ECM) in the primary auditory cortex of Mongolian gerbils and assessed both spontaneous and elicited neural activity. They show that ECM degradation alters translaminar cortical network dynamics and they consider its role in neural plasticity. The most prominent forms of the adult ECM in the brain are (i) perineuronal nets (PNN) named by their mesh-like appearance around cell bodies and proximal dendrites of mainly parvalbumin-positive (PV + ) interneurons, and (ii) the “loose” ECM found almost ubiquitously in the brain3. The ECM is formed during brain maturation implementing a switch from juvenile to adult forms of synaptic plasticity5. For instance, enriched PNNs have been linked to states of reduced structural plasticity7. In a seminal study, Pizzorusso and colleagues demonstrated, that enzymatic removal using chondroitinase ABC reinstalls critical period plasticity4. ECM removal within the cortex or hippocampus has further diverse effects on contextual fear conditioning9, object recognition10, extinction of fear or drug memories12, and auditory reversal learning13. During learning processes in the developing and adult brain, variability of ECM densities and dynamic remodeling has been shown to support learning-dependent plasticity15. In accordance, systemic injection of dopamine receptor D1 agonists in vivo promote rapid cleavage of the ECM protein brevican16. ECM weakening increases lateral diffusion of glutamate receptors at the synapse20 and alters the balance between inhibition and excitation21. However, the mechanistic understanding on how states of reduced ECM may affect circuit processing in a layered structure, such as the cortex, is rather elusive.While basic neuronal networks established during development must be conserved, their activity-dependent fine-tuning and modification form the basic mechanism for adult learning and memory. In the vertebrate brain, long-lasting structural tenacity of neuronal networks is supported by the extracellular matrix (ECM). It consists of the core backbone glycan hyaluronic acid that attaches chondroitin sulfate proteoglycans and other proteinsMeriones unguiculatus) by local microinjections of hyaluronidase (HYase). In parallel, we used laminar recordings of local field potentials and current-source density (CSD) analysis23 to quantify the spatiotemporal sequence of spontaneous and stimulus-evoked laminar synaptic activation. Local HYase injection led to an imbalance between the synaptic activity in supragranular layers I/II and infragranular layer Vb. The imbalance was mediated by a moderate increase in tone-evoked synaptic currents in supragranular layers I/II and a stronger stimulus-specific decrease in infragranular layer Vb. These changes in the spatiotemporal synaptic pattern were accompanied by an increase of lateral corticocortical input revealed by CSD residual analysis22. Utilizing a multitaper spectral analysis of tone-evoked oscillatory circuit responses24, we revealed a layer specific change of the oscillatory power in the beta band (25–36 Hz) within infragranular layers Vb. To investigate how these synaptic spatiotemporal changes are mediated by the translaminar dynamics of cortical network activity, we implemented time-domain Granger causality (GC) measures mapping the directional causal relationship between activity across cortical layers. After application of HYase, we found a significant increase in the GC from supragranular layers I/II onto infragranular layer VI. Additionally, there was a significant increase in the infragranular layer VI drive routing towards early infragranular layers Vb which decreased its drive towards granular layers III/IV. Our data thereby shows that enzymatic removal of the ECM acutely biases the columnar synaptic network processing towards stronger recruitment of supragranular circuits and enhancement of lateral, cross-columnar interactions along with stronger drive from supragranular layers I/II towards deep infragranular layers VI which also showed stronger drive towards layer Vb. Our study unraveled a mesoscopic cortical circuit mechanism of enhanced sensory integration in upper layers controlled by the ECM. These findings help to better understand existing behavioral findings linked to ECM modulation and may further allow to better understand certain pathological conditions of altered translaminar response characteristics, as for instance during sensory loss, and may help to optimize concepts for therapeutic approaches targeting the ECM.Here, we acutely weakened the ECM in the primary auditory cortex (ACx) of adult Mongolian gerbils  of the glass capillary, we recorded the laminar local field potential across all layers within the region of the significantly reduced ECM. CSD analysis was used to investigate how ECM removal acutely affects laminar tone-evoked processing and spontaneous activity. Specifically, analysis of the relative residual of the CSD, layer-specific multitaper spectral analysis, and Granger causality measures allowed us to identify enhanced corticocortical input in upper layers to dominate the translaminar processing dynamics within the cortical column25. Recordings showed a canonical feedforward processing pattern with main short-latent current sink components in granular layers III/IV and infragranular layer Vb most prominently for stimulation with the best frequency . Those short-latent sinks reflect the synaptic afferent input from the lemniscal auditory part of the thalamus, the ventral medial geniculate body (vMGB), with main projections to granular layers and collaterals within layer Vb28. This has been revealed by earlier studies showing persistent thalamocortical inputs in corresponding layers after intracortical silencing with the GABAA-agonist muscimol29 in accordance with reports by others30. Early sinks were followed by synaptic activation of supragranular (I/II) and deep infragranular (VI) layers . Before the start of the recording and tissue was allowed to recover from insertion for >1 h. After HYase injection we again waited for >2.5 h in order to let the enzyme effectuate the ECM in the proximity of the recording patch. Stability of the recording track along the cortical laminae over time has been controlled by a statistical correlation method, which assumes that the general profile of sinks and sources across recording channels should be stable irrespective of relative amplitude changes. Onset latencies within layers receiving short-latent thalamocortical input were stable across both conditions . No obvious differences were found for layers III/IV and VI. We stained brains after local unilateral HYase injection in temporal cortex against PV (red) and WFA (green) staining  amplitudes of individual cortical layer activity as a function of stimulation frequency Fig. . Pure-to26. HYase treatment, however, amplified this supragranular lead of current flow and LSI values exceeded values of +0.4. Correspondingly, a 2-way rmANOVA of the RMS amplitude-based LSI showed main effects for ‘Freq’ and ‘HYase’ without interaction  of current flow between cortical layers I/II and Vb . By calculating the LSI = (I/II – Vb)/(I/II + Vb), positive LSI indicate stronger activation of supragranular layers compared to infragranular layers. In untreated cortex, supragranular layers generally display larger evoked synaptic currents compared to infragranular layers with LSI-value around +0.2on Table . This sh33. We have previously developed a method that dissociates intracortically relayed contributions to cortical activity from local circuit activity in order to quantify the cross-columnar activity spread23. The rationale of this analysis is to analyze the relative residuum of sinks and sources of the extracellular electric field within the local integration cylinder along the linear electrode array radially penetrating the cortical layers. Non-zero values of relative residual CSD . The spectrum of spontaneous activity showed no significant difference before and after HYase injection.Next we were interested in the underlying oscillatory nature of the layer-specific synaptic activity. We found that ECM removal had a layer-dependent impact on the spectral power distribution within infragranular layer Vb. Figure We hypothesized that the temporal relationship between activity measured in cortical layers was changed after application of HYase. We further assume that the columnar response was modulated by a differential impact of supragranular and infragranular layers on the laminar response dynamics. To test this, we analyzed this dynamical relationship by employing time-domain Granger causality (GC) estimation on the single trial CSD traces from each cortical layer to generate a network model for each animal and then compared the granger causality matrices Eqs. –7. Figurn = 7) before and after HYase-induced removal of the ECM. CSD recordings in the untreated auditory cortex revealed the occurrence of spontaneous events of translaminar synaptic information flow, which we refer to as spontaneous columnar events (SCE). SCE’s showed a comparable, but distinct synaptic current flow compared to tone-evoked columnar feedforward activation patterns. While tone-evoked responses display earliest synaptic inputs in both granular layers IV and infragranular layer Vb due to afferent thalamocortical input, SCE’s showed initial synaptic activity in layer Vb initiating translaminar information flow34. Activity in layer Vb was followed by current sink translaminar synaptic activity across the entire column including infragranular and supragranular layers . For quantification of the frequency of SCE’s we used a peak detection analysis based on single-trial AVREC traces . After microinjection of HYase in the vicinity of the recording axis, spontaneous CSD profiles changed considerably. We still observed SCE’s, however, with an altered current flow of columnar activity. Individual SCE’s were shorter in duration and showed significantly reduced synaptic current flow across all layers  within infragranular layer Vb revealed by a multitaper spectral analysis of layer specific CSD activity. We then applied time-domain Granger causality measures to map the directional causal relationship between cortical layers. We found a significant increase in the GC from supragranular layers I/II towards the infragranular layer VI, and from infragranular layer VI to early infragranular layers Vb. We additionally found a significant decrease in the GC from layer Vb towards granular layers III/IV. Thereby, our data shows that enzymatic removal of the ECM in sensory cortex acutely alters the entire columnar synaptic network processing with stronger weight of supragranular layer activity. Columnar processing is thereby dominated by the integration of cross-columnar widespread activity in upper layers.35. This is in accordance with reports that sensory input in layer Vb is more vulnerable to juvenile forms of plasticity, as induced by monocular deprivation36. Thalamocortical collaterals terminating in infragranular layer Vb also transmit early subcortical inputs  and infragranular layer Vb  is one of the possible physiological mechanisms by which HYase is mediating its action. To further reveal potential network mechanisms of this newly proposed hypothesis, we continued with an analysis of layer-specific neuronal oscillations and translaminar network dynamics.With maturation of cortical circuits and phases of experience-dependent shaping of cross-columnar connectivity patterns, intracolumnar circuits stabilize subsequently in order to control its outputs to promote behavior later on44. In the human EEG and animal LFP literature, beta range fluctuations have been related to the deployment of top-down attention46, working memory allocation48, and stimulus salience50. Increased beta power in deep layers and reverse effects on high-frequency components in upper layers have been specifically linked to altered GABA levels in PV + interneurons in PV-GAD67 mice51, suggesting that PV cells, generally rich in ECM, might play an important role in balancing neuronal oscillations across cortical layers.We found that stimulus-evoked oscillations showed a layer-dependent shift of increased beta power (~25–36 Hz) selectively in infragranular layer Vb Fig. . Infragr44. Those neurons therefore might play a pivotal role in orchestrating the translaminar columnar activity. During ECM weakening in the adult cortex, these enhanced low-frequency oscillations may mimic to a certain degree the orchestration of internal and peripheral inputs present during critical periods of the developing cortex54.Further, increased layer Vb beta power has been attributed to relatively increased inputs on supragranular distal over infragranular proximal dendrites of intrinsic bursting infragranular neurons55. Thereby, ECM removal might induce a higher weight of local sensory input processing and its corticocortical integration and reduced activity of effector circuits towards downstream areas, as motor-related cortical outputs58 potentially supporting higher plastic adaptability towards altered sensory inputs.Based on the imbalanced synaptic spatiotemporal pattern and the shorter onset latencies of supragranular activity Fig. , we hypo5. ECM removal, therefore, has been interpreted as a temporally constricted period of neuronal “re-juvenation” promoting a higher level of structural, developmental plasticity60. States of weakened ECM have been also linked to other forms of plasticity, as for instance post-traumatic restoration, recognition memory, seasonal learning in songbirds, and cognitive flexibility during reversal learning15. Further, mouse models deficient in specific matrix components, as tenascin-R or brevican, have shown impairments in hippocampus-dependent spatial or contextual acquisition learning61. Recently, we showed in wildtype mice that brevican was downregulated 48 h after auditory training in the auditory cortex14. Recently, we further showed that such intrinsic reduction of the ECM integrity in the adult cortex can be linked to the stimulation of D1 dopamine receptors suggesting an activity-dependent and learning-related cellular mechanism16. Altogether, those studies enunciate the major impact of the integrity of the cortical ECM onto the delicate balance of plasticity and tenacity in the learning brain. Although the underlying physiological circuit mechanisms are still rather elusive, our study suggests that imbalanced supragranular and infragranular synaptic activity during states of weakened ECM may play a major role in promoting cross-columnar integration, which has been related to learning-related plasticity in several systems65.ECM removal has been linked to re-open ‘critical periods’ of juvenile plasticity unlocking the potential of considerable topographic map adaptations29. Particularly, ketamine-xylazine had only minor effects on the spectral beta range and off-BF-evoked activity, which showed strongest effects after HYase treatment. In this study, ECM removal specifically enhanced beta power in infragranular layer Vb, and minor effects on layer III/IV activity.In this study, we investigated the effects of ECM removal acutely and under ketamine-xylazine anesthesia. We have recently shown that, in addition to generally very similar spatiotemporal activity patterns, anesthesia mainly influences gain enhancement in layer III/IV at best frequency stimulation66, which we hence found to be reversed after “re-juvenation” by HYase . All experiments were conducted in accordance with the international NIH Guidelines for Animals in Research and with ethical standards for the care and use of animals in research defined by the German Law for the protection of experimental animals. Experiments were approved by an ethics committee of the state Saxony-Anhalt, Germany.Experiments were performed on 12 adult male ketamine-xylazine anesthetized Mongolian gerbils  were anesthetized by an initial dose of 50 mg/KG ketamine and 5 mg/KG xylazine intraperitoneal and kept under narcosis via an infusion (0.06 ml/h) of 45% ketamine , 5% xylazine  and 50% isotonic sodium chloride solution . Status of anesthesia was monitored, and body temperature was kept at 37 °C. The right ACx was exposed by craniotomy (~3 × 4 mm) of the temporal bone. Recordings were performed in an acoustically and electrically shielded recording chamber. Laminar profiles of local field potentials (LFP) were measured using linear 32-channel-shaft electrodes  inserted perpendicular to the cortical surface. Neuronal potentials were preamplified (500x), band-pass filtered between 0.7 and 170 Hz (3 dB cut-off frequency), digitized at 2 kHz  and averaged over 40–80 stimulus repetitions. The location of the field AI in primary ACx was identified by vasculature landmarks and physiological parameters22. In this study, we analyzed only data from recordings which exceed 90 min after implantation of the electrode and the glass capillary (see next section)–a state where the cortical responses under anesthesia have been largely stabilized as reported recently29.Surgical and experimental procedures have been described before29. Then, we recorded spontaneous and tone-evoked activity over a period of roughly 1 h. After that, we injected 500 nl of HYase-solution (500 units) in 22 steps (22.8 nl each) with 3 s pause in between13. Before we continued recordings, we waited at least 2.5 h until we continued with recordings after ECM removal.In direct proximity of the recording electrode, we inserted a thin glass capillary (<250 µm distance) approximately within the middle of the right ACx in depths of 500 µm in order to perform microinjections  of HYase at the cortical patch of recording in ACx. We started with recording of baseline activity 90 min after insertion of micropipette and electrode. All relaxation movements of the brain relative to the recording electrode and glass pipette should be completed in order to guarantee stabilized cortical responses79θ is the field potential, z the spatial coordinate perpendicular to the cortical laminae, Δz the spatial sampling interval (50 µm), and n the differentiation grid. LFP profiles were smoothed with a weighted average (Hamming window) of 7 channels . CSD profiles reveal the patterns of extracellular current influx (sinks) and efflux (sources) of positive charges. Positive charge influx results from the depolarization of postsynaptic neurons by synaptic populations in laminar neuronal structures, while sources to a large degree reflect passive return currents25. CSD activity thereby reveals the spatiotemporal sequence of neural activation across cortical layers as ensembles of synaptic population activity. Due to the extracellular negativity, current sinks are depicted in blue and current sources in red.One-dimensional current-source density (CSD) profiles were calculated from the second spatial derivative of the LFP:80. Layers above (I/II) or beyond (V–VI) the granular layers are generally referred to as supragranular and infragranular layers, respectively81. In our study, corresponding sink activity within cortical layers were referred to as granular activity (III/IV) supragranular activity (layer I/II), and infragranular activity (layer Vb and VI). Root-mean square (RMS) amplitudes of individual layers were determined for individual channels and then averaged.Main sink components were found to represent the architecture of primary sensory input from the lemniscal auditory part of the thalamus–the medial geniculate body (MGB). Lemniscal thalamocortical projections from the ventral division of the MGB terminate on pyramidal neurons with local dendritic arbors and ramifying axons in cortical layers III/IV, which are henceforth referred to as the granular input layers. Collaterals of these thalamocortical projections also target infragranular layer Vb79. The relative residue of the CSD (RelResCSD), defined as the sum of the non-rectified divided by the rectified magnitudes for each channel, was used to quantify the balance of the transmembrane charge transfer along the recording axis:22Based on single trial CSD profiles without spatial filtering we transformed the CSD by rectifying and averaging waveforms of each channel (n) comprising the laminar CSD profile (AVREC). The AVREC waveform provides a measure of the temporal pattern of the overall strength of transmembrane current flowWe have shown before that the RelResCSD provides a quantitative measure of the contributions of intercolumnar, corticocortical synaptic circuits to the overall measured local cortical activity. In short, the rationale is that corticocortical circuits would be more likely distributed beyond the integration cylinder surrounding the electrode array in which extracellular currents contribute to the measured LFP cf. Fig. . We quan23. The best frequency (BF) of the recording site was determined as the frequency evoking the highest RMS amplitude within the granular layer III/IV at 40dB above threshold. For estimation of pure-tone based frequency response curves, evoked RMS amplitudes of individual parameters were binned into frequency bins with respect to their distance to the best frequency of each individual recording . Cross-laminar activation patterns were quantified and statistically analyzed by introducing a Layer-Symmetry-Index (LSI). The LSI quantifies the total activity bias based on RMS values of the supragranular layer vs. infragranular layer Vb by calculating LSI = (I/II – Vb) / (I/II + Vb). A positive LSI indicates that columnar synaptic activity is biased towards stronger supragranular activity, while negative values indicate stronger recruitment of infragranular synaptic circuits. For pure tone processing, no hemispheric differences between left and right auditory cortex are expected for either evoked response properties, nor behavioral differences83.We presented pseudo-randomized series of pure tones . Stimuli were delivered via a programmable attenuator , a wide-range audio amplifier (Thomas Tech Amp75) and a loudspeaker (Tannoy arena satellite) positioned in 1 m distance to the animal’s head. The response threshold was determined as the lowest intensity eliciting a significant response at any frequency 2 SD over baseline (>5 ms). The sound level then was adjusted to 40 dB above threshold, which generally was at intensities around 65 dB SPL. Response bandwidths of AVREC and RelResCSD were quantified as the Q40dB-values corresponding to the number of octaves evoking a tone-evoked response over the threshold criterionWe recorded spontaneous data in 7 animals for at least 3 minutes before and after injection of HYase. Spontaneous CSD data was cut into 6 s traces and we analyzed RMS values of the AVREC, RelResCSD and layer dependent CSD traces of individual sink components (see above). Spontaneous events of translaminar synaptic information were referred to as spontaneous columnar events (SCE). The frequency of SCE’s was quantified by a peak detection of the AVREC trace based on crossings of 3 standard deviations (SD) above baseline  and at least 150 ms between individual peaks.http://chronux.org), as explained in detail elsewhere24. CSD-signals were transformed into the frequency domain by a multitaper FFT (600 ms epochs) and 5 tapers of 600 ms length with a time-bandwidth product of 3 and no padding were employed84. The stimulus evoked power spectrum was averaged by the complex valued spectra across trials before calculating the power as squared magnitude of the average. To normalize the power spectral values, we divided it by the sum of all values. As induced spectra might include background effects unrelated to the stimulus, we further calculated the power spectrum during spontaneous recordings. Finally, the mean and the standard error of the mean of the power spectra were calculated across subjects. Power spectral comparison before and after HYase administration was done across subjects, separately for the BF condition and layer . Comparisons were restricted to a frequency range from 1 to 100 Hz. To avoid biased determination of power spectrum bands, t-scores were calculated between recordings in untreated cortex and after HYase injection for each spectrum, frequency, and layer using sample mean and variance and the significance level was corrected using a Benjamin Hochberg procedure as a suitable method to adjust the false discovery rate when performing a large number of multiple comparisons85. Accordingly, we ranked individual p-values in an ascending order. Critical Benjamin-Hochberg (BH) p-values are calculated by p*= (ir/m)*Q, where ir is the individual p-value’s rank, m is the total number of tests, and Q is the false discovery rate (0.1). The critical BH p* value serves as adapted level of significance.Traditional Fourier spectral analysis uses a single windowing function, which yields large variance and biases especially at higher frequencies. We therefore used the multitaper Fourier transform employing multiple splenial tapers that are orthogonal to each other  assumes two time series  and provides information about the magnitude of their causal dependency. Briefly, the unconditional GC measures if past values of the time series Y provide information that predicts upcoming values X better than the past history of X itself. Mathematically, this is done by constructing and comparing two linear vector autoregressive models (VAR). the first model described as a full model where the value of X at particular time point is a linear weighted sum of both its own and Y’s past values according to the following equation:Axx,k and Axy,k are the full regression coefficient matrices and εx,t is the error or residual process. The second model is described as the reduced model where we only use X past values to predict X current values.To identify the dynamical nature of the directed connectivity between different cortical layers as defined by the CSD analysis, we calculated the multivariate conditional causality in the time-domain using MVGC Matlab toolbox86. We used the toolbox to estimate the conditional granger causality from the single trial evoked CSD dataset. To ensure that our data was stationary, we took the first time-derivative of CSD traces and then tested for stationarity using the Augmented Dickey Fuller test. After that, we estimated the order of our vector autoregressive (VAR) model using Akiko information criteria (AIC). The model order is then used to estimate the model parameters  using the “LWR algorithm” or the multivariate extensions to Durbin recursion. The autocovariance sequence derived from the full model parameters is estimated and used to derive the reduced VAR parameters using an algorithm to solve the Yule-Walker equation. The residual covariance matrices of both the reduced and full model are used to estimate the G-causal as in Eq. p-value is determined as a cut off for significance85.The multivariate granger causality toolbox adds to the standard G-causal estimation as it allows for reduced model parameter computation from the full model parameters for more computationally effective and accurate statistical estimatesn = 4) under anesthesia as explained above . We injected HYase in the ACx unilaterally and used 0.9% saline at the opposite side as a control. Animals were transcardially perfused with 20 ml phosphate buffered saline (PBS) followed by 200 ml paraformaldehyde (4% in PBS) 2.5 h after injections in order to assess the ECM removal at the earliest time point where recordings have been done in the experimental animals. Brains were removed, postfixated overnight, cryoprotected in 30% sucrose solution for 48 h, frozen, and cut in a series of horizontal sections (40 µm thickness) on a cryostat . After blocking (5% goat serum) and washing, the sections were stained using Wisteria floribunda agglutinin coupled to fluorescein  for 12 h (at 4 °C). Then, sections were incubated with anti-PV  and afterwards with a Cy3-conjugated anti-mouse secondary antibody . Finally, sections were mounted on gelatin-coated slides and coverslipped with MOWIOL . Images were taken using a confocal microscope  equipped with a 2.5x objective .To assess effects of HYase injection on the ECM, we applied microinjections in naive mature gerbils . Mean gray values of the staining intensities in the ACx  of the HYase-treated and the contralateral side injected with 0.9% sodium-chloride were measured in 6 sections of 200 µm interval of 4 animals using the Measurement Tool of ImageJ.Comparison of multiple groups was performed by multifactorial repeated-measures ANOVAs (rmANOVAs). For comparison between two groups, paired sample Student’s t-tests were used. Generally, a significance level of α = 0.05 was chosen. For multiple comparisons, we used Bonferroni-corrected levels of significance.Further information on research design is available in the Peer Review FileSupplementary InformationReporting Summary


Chikungunya fever (CHIKF) was first described in Tanzania in 1952. Several epidemics including East Africa have occurred, but there are no descriptions of longitudinal surveillance of endemic disease. Here, we estimate the incidence of CHIKF in coastal Kenya and describe the associated viral phylogeny.We monitored acute febrile illnesses among 3500 children visiting two primary healthcare facilities in coastal Kenya over a 5-year period (2014–2018). Episodes were linked to a demographic surveillance system and blood samples obtained. Cross-sectional sampling in a community survey of a different group of 435 asymptomatic children in the same study location was done in 2016. Reverse-transcriptase PCR was used for chikungunya virus (CHIKV) screening, and viral genomes sequenced for phylogenetic analyses.We found CHIKF to be endemic in this setting, associated with 12.7%  of all febrile presentations to primary healthcare. The prevalence of CHIKV infections among asymptomatic children in the community survey was 0.7% . CHIKF incidence among children < 1 year of age was 1190 cases/100,000-person years and 63 cases/100,000-person years among children aged ≥10 years. Recurrent CHIKF episodes, associated with fever and viraemia, were observed among 19 of 170 children with multiple febrile episodes during the study period. All sequenced viral genomes mapped to the ECSA genotype albeit distinct from CHIKV strains associated with the 2004 East African epidemic.CHIKF may be a substantial public health burden in primary healthcare on the East African coast outside epidemic years, and recurrent infections are common.The online version contains supplementary material available at 10.1186/s12879-021-05875-5. Togaviridae that was first isolated from a febrile patient in Tanzania in 1953 [Aedes aegypti and Ae. albopictus mosquitoes, with geographic spread and spillover from sylvatic transmission cycles in monkeys thought to account for the periodic re-emergence of human disease outbreaks [Chikungunya fever (CHIKF) is a mosquito-borne febrile illness characterised by acute, often chronic, debilitating polyarthralgia and polyarthritis that can last for months to years –3. The d in 1953 . CHIKV tutbreaks .Aedes albopictus mosquitoes [The CHIKV genome is approximately 11.8 kb in length and encodes four non-structural proteins (nsP1 to nsP4) required for viral replication, three major structural proteins , and two small polypeptides (6 K/TF and E3) . Exposursquitoes , 16.CHIKF epidemics are now frequently reported globally , 17, but2 with approximately 290,000 residents that are enumerated every 4 months at the household level [We conducted the study between March 2014 and October 2018 in two dispensaries, Ngerenya and Pingilikani, both located in Kilifi County along the northern coast of Kenya . Finger-prick whole blood samples were collected in EDTA vacutainers from all febrile children for the purpose of malaria rapid diagnostic testing as part of an ongoing malaria surveillance study [We drew a random sample of 3500 out of 5669 children, aged < 16 years, who visited the two dispensaries with fever (defined as axillary temperature of ≥37.5 °C) during the study period. Random sampling of individuals was done using the ce study . The remWe seperately conducted a community cross-sectional survey of 435 asymptomatic children aged < 16 years whose residence was within the catchment of the two dispensaries. Serum was obtained from these children and stored at − 80 °C until the day of viral RNA isolation. This cross-sectional community sampling was conducted between April and May 2016 and allowed estimation of the frequency of asymptomatic CHIKV infections in the study population. Ethical approval was provided by the Kenya Medical Research Institute Scientific and Ethics Review Unit and written informed consent obtained from parents or guardians of all study participants .FAM-CGCTGTGATACAGTGGTTTCGTGTG-TAMRA-3′). Briefly, 100 μl of finger-prick whole blood samples collected at presentation to the dispensaries was used for total RNA isolation using TRIzol™ Reagent (ThermoFisher) as per manufacturer instructions. Presence of CHIKV viral RNA was then determined using the QuantiFast RT-PCR Kit (Qiagen) in a 25 μl reaction with primers and probe at a final concentration of 100 nM and 20 nM, respectively, and 5 μl total RNA template. RT-PCR assays were done on a 7500 Real-Time PCR System (Applied Biosystems) with cycling conditions as follows: 50 °C for 20 min, 95 °C for 15 min, followed by 45 cycles of 94 °C for 15 s and 60 °C for 1 min. A second confirmatory screen was performed on some of the RT-PCR positive samples using an assay targeting the CHIKV nsP4 region as previously described [CHIKV infection was defined as detection of CHIKV viral RNA by reverse-transcriptase polymerase chain reaction (RT-PCR) using a published primer-probe set targeting the CHIKV non-structural protein 1 (nsP1) region , namely escribed . For botRT-PCR positive CHIKV samples were sequenced using the Nanopore MinION technology following PCR amplification using methods described in the PrimalSeq approach . Full deBased on previous studies on childhood malaria , we expeData from the dispensaries were linked to the larger KHDSS data using unique person identifiers allowing us to estimate disease incidence in the population, and to assess the influence of various demographic variables on CHIKF incidence. We decided, a priori, to restrict the incidence analyses to a 5 km radius around each dispensary which we assumed to be the catchment area. Because we only sampled from all the visits (with febrile illness) made to the dispensaries, we estimated what the expected cases of CHIKF  would be by multiplying the test positive rate from the sample by the total number of visits due to febrile illness. This was based on the assumption that the test positive rate among the randomly selected would hold true for the remaining unsampled visits due to febrile illness. Incidence rate was then computed as total number of expected CHIKF cases divided by total person years of observation (pyo) and was expressed per 100,000 pyo. Incidence rate ratios (IRRs) comparing incidence across various socio-demographic variables were computed using a univariate negative-binomial regression model. All statistical analyses were conducted using STATA/IC version 15.1 .Between March 2014 and October 2018, there were 29,819 visits by children aged less than 16 years to the two dispensaries , of which 13,696 were febrile , with 1701 being resident in Ngerenya and 1799 in Pingilikani, respectively . This yielded an overall clinical-to-asymptomatic CHIKV infection ratio of 18:1 during the epidemic year.We calculated the incidence of CHIKF restricting our analysis to children aged < 16 years whose residence was within the dispensary catchment area. This represented a total of 136,509 person-years of observation (pyo) over the study period at both locations.The overall CHIKF incidence during the study period was 314 cases/100,000 pyo  and the RT-PCR Ct values and clinical diagnoses were comparable between the index and recurrent CHIKF episodes . Of the 443 children, 170 presented to the dispensary with fever on at least one other occasion during the study period, contributing a total of 320 subsequent febrile dispensary visits with varying durations since the corresponding index CHIKF episode  distribution in this setting and how this influences risk of CHIKV infection remains to be determined [In summary, we find that CHIKF is endemic in coastal Kenya with the case burden being particularly high in young children aged < 1 year. Our population-based cohort approach in primary healthcare facilities could detect an increase in CHIKF cases during the 2016 epidemic, supporting the utility of such a surveillance framework in the early detection of CHIKF epidemics. We observed a substantial CHIKF incidence during the epidemic and non-epidemic years. However, a significant increase in CHIKF incidence over time was noted in Ngerenya while in Pingilikani (located approximately 40 km south of Ngerenya) incidence had been declining. CHIKF incidence was highest during the rainy season in Pingilikani and during the dry season in Ngerenya suggesting potential differences in the ecology of CHIKF in these locations as has been observed for malaria . Aedes stermined .None of the patients in this study had a clinical diagnosis of CHIKF, and in no instance was arthritis or arthralgia reported in the clinical assessment. This may reflect the rarity of CHIKF-associated joint involvement in children and is consistent with other studies in East Africa , 35. TheIt is plausible that CHIKV infections in children generate immune responses with variable efficacy against clinical disease that may underlie the recurrent CHIKF episodes observed in our setting. Neutralising antibodies that develop following CHIKV infection have been shown to correlate with decreased risk of clinical illness in a longitudinal study in the Philippines , 37, thoIn contrast to the 2016 CHIKF epidemic viruses from Mandera (northern Kenya) that formed a single well-defined clade, sequences from coastal Kenya were highly diverse and fell within multiple clades whose divergence increased over time. Together, these data suggest ongoing endemic CHIKV transmission at the Kenyan coast which may be sustained through genetically diverse CHIKV strains from local sylvatic transmission cycles [Our data and that from others in the region  further Additional file 1.


The domestication and breeding of crops has been a major achievement for mankind enabling the development of stable societies and civilisation. Crops have become more productive per unit area of cultivated land over the course of domestication supporting a current global population of 7.8 billion. Food security crops such as wheat and maize have seen large changes compared with early progenitors. Amongst processes that have been altered in these crops, is the allocation of carbon resources to support larger grain yield (grain number and size). In wheat, reduction in stem height has enabled diversion of resources from stems to ears. This has freed up carbon to support greater grain yield. Green revolution genes responsible for reductions in stem height are known, but a unifying mechanism for the active regulation of carbon resource allocation towards and within sinks has however been lacking. The trehalose 6-phosphate (T6P) signalling system has emerged as a mechanism of resource allocation and has been implicated in several crop traits including assimilate partitioning and improvement of yield in different environments. Understanding the mode of action of T6P through the SnRK1 protein kinase regulatory system is providing a basis for a unifying mechanism controlling whole-plant resource allocation and source-sink interactions in crops. Latest results show it is likely that the T6P/SnRK1 pathway can be harnessed for further improvements such as grain number and grain filling traits and abiotic stress resilience through targeted gene editing, breeding and chemical approaches. A series of papers in the late 1990s and early 2000s began to indicate a widespread role for trehalose metabolism in plants . Up untiE. coli trehalose phosphate synthase (otsA) or trehalose phosphate phosphatase (otsB) genes were very striking showing robust shoots with changes in leaf area and photosynthetic capacity and improvements in productivity [Heterologous expression of trehalose-synthesising genes in transgenic plants proved a turning point . This wauctivity , atypicaIt was subsequently shown that T6P, the direct precursor of trehalose, was indispensable for carbohydrate use in plants ; the indZmINCW1 and OsGRAIN INCOMPLETE FILLING1) are associated with seed size and weight [BT1, [Four food security cereals, rice, maize, wheat and sorghum have all showed a decisive contribution of T6P signalling in crop traits  and 2. Td weight . There iht [BT1,  involvedht [BT1,  have beeE. coli otsA and otsB transgene in rice under the control of tissue-specific or stress-inducible promoters. The purpose of using a TPS-TPP gene fusion is to increase trehalose without necessarily increasing T6P. Plants showed better performance under drought, salt and cold stress. Low amounts of trehalose were present and plants accumulated more soluble carbohydrate and had elevated photosynthesis. However, the transgenic lines in this study do not appear to have progressed to commercial production. Improved tolerance to drought, salinity and sodic conditions (soil with an exchangeable sodium of >6% of the cation exchange capacity) was also shown using an ABA-inducible promoter to target the transgene [OsTPP7, as the genetic determinant in qAG-9-2, a major quantitative trait locus (QTL) for anaerobic germination tolerance. In this case, it is thought that lowering of T6P levels in germinating embryo and elongating coleoptile enhances mobilisation of starch fuelling better germination under anaerobic conditions (OsTPS8 (OS08G0445700) was shown to confer salt stress tolerance by enhancing suberin deposition in roots [+ in shoots. It was proposed that OsTPS8 regulates suberin deposition through ABA signalling. Although given the known effect of T6P/SnRK1 on lipid metabolism [GROWTH REGULATING FACTOR (GRF4) is a transcription factor that promotes and integrates nitrogen assimilation, carbon fixation and growth, counterbalancing the inhibiting action of DELLAs of the gibberellin signalling pathway. It was shown that GRF4 up-regulates TPS and TPP genes and SWEET sucrose efflux transporters as part of the growth-promoting process [Rice was the first major crop where transgenic modification of the trehalose pathway showed a real possibility in crop improvement. Garg et al.  expresseransgene . In bothransgene  showed anditions . The ricin roots . Suberintabolism ,19 and t process . Such inRAMOSA3 controls inflorescence architecture in maize and was found to be encoded by a TPP gene which regulates meristem fate through catalytically inactive TPPs also including TPP4 [TPP4 has been put forward; the exact mechanism through which meristem fate is regulated by TPPs is not known. Whole-plant architecture in terms of apical dominance appears to have been strongly influenced in maize by the TEOSINTE BRANCHED (TB1) transcription factor. TB1 targets are TPS2, TPP1, RAMOSA3 and TREHALASE1 which were found to be altered in buds of a tb1 mutant. This could mean that in maize, as in pea [TB1 in maize [ing TPP4 ,31. No es in pea , T6P is in maize . In othein maize ,35 and sin maize  showing OsTPP1 gene in maize improved yield under a range of water availabilities. In this instance over expression of the TPP gene with a MADS6 promoter active during the flowering period in the phloem of female reproductive tissue, particularly companion cells, reduced T6P levels in pith tissue (tb1 maize (above). The important points [TPS13 (Zm00001d020396) [Nuccio et al.  and Oszvt points  are first points . Many ott points  and a std020396)  in confid020396)  showed dSorghum, grown in tropical and sub-tropical regions, is the fifth most important cereal worldwide after maize, rice, wheat and barley. Li et al.  publishe−1 fresh weight relative to Arabidopsis seedlings (0.01 nmol g−1 fresh weight in sugar-starved seedlings [−1 fresh weight in cold treated seedlings [−1 fresh weight) and paternal tissue (endosperm) (119 nmol g−1 fresh weight) at 7 days after anthesis (DAA), to high levels in endosperm only at 17 DAA during grain filling. High T6P levels in endosperm may be associated with driving starch synthesis during grain filling. In confirmation of this, Griffiths et al. [Martinez Barajas et al.  presenteeedlings  up to 10eedlings ) and secs et al.  sprayed s et al. . Interess et al.  also shos et al.  or cold s et al.  being re2, grains per m2 and grain-filling traits. Interestingly, some genes for traits were under positive selection indicating that genetic change in both TPS and TPP genes was being tolerated and selected into new material e.g. for grains per m2, plant height, infertile spikelets per spike and grain filling duration. Some genes also showed a high degree of heritability for grain per m2, grain per spike, grain filling duration, biomass and days to anthesis indicating a strong contribution of the pathway to traits. Not surprisingly in a pathway where TPS and TPP act together there were epistatic interactions between TPS and TPP genes e.g. between TPS1 and a TPP gene and also within TPS and TPP genes. The data indicated a considerable contribution of this regulatory pathway to phenotypic variation, showing historical selection for harvest index, final biomass, plant height and flowering time with further opportunities to improve traits that increase grain numbers per unit area of land and grain filling which would likely affect grain size. The paper provides a basis for greater in-depth characterisation of these genes in yield traits and for use in strategic crosses and is likely to be an important resource of information for the improvement of wheat and possibly other crops too.Trehalose pathway genes were listed as domestication improvement candidates in maize , howeverIt has taken 20 years of research to go from interesting observations in Arabidopsis and tobacco to a translation of the knowledge of T6P signalling into crop improvement. We are now at the point where research in crops can go to the next level through targeting T6P in different ways (genetics and chemistry) to achieve tangible benefits for both yield potential and yield resilience in a range of crops. It will be important to understand the mode of action of T6P action through the T6P/SnRK1 system i.e. the cells in which the mechanism achieves the effects on yield traits and the downstream gene targets and how this has been changed over the course of breeding and selection. TPP genes particularly in cereals appear to be very different from Arabidopsis . InteresImportance of T6P signalling in crops: For the improvement of crop yields there are likely to be few individual genes that can be targeted, yet for T6P signalling there are examples of several TPS and TPP genes that underlie yield traits [d traits ,25,43,45d traits . The reaA summary of current thinking: The inhibition of SnRK1 by T6P [1 by T6P  where suFuture directions: For crop improvement, there are numerous avenues for modifying and selecting T6P signalling in crops. Transgenic approaches have shown good effects in rice and maize. However, the difficulties and regulatory costs associated with getting new transgenic varieties to the marketplace and lack of wide acceptance may prove the transgenic approach to be difficult. Gene editing does offer new promise combined with selection of TPS and TPP genes positively linked to traits [in vivo at any developmental time point provide a new way to increasing both yield potential and resilience through growth recovery after stress [o traits . Additior stress . Mode of