Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.The online version of this article (10.1186/s12964-019-0381-z) contains supplementary material, which is available to authorized users. HOG1-GFP strain of the yeast strain-library available from Life Technologies  ). package . P-valueProtein-protein interaction network for all putative targets of Hog1 was created using STRING database (version 10.5) . All facU-test.The lower and the upper hinges of the boxes correspond to the 25% and the 75% percentile, and the bar in the box the median. The upper and lower whiskers represent the largest and lowest values, respectively (but at maximum 1.5 times the IQR). Points outside the whiskers are plotted individually. Tests were performed using a nonparametric Mann-Whitney Intensity, as defined per scan, (2) Score, as defined per scan, and (3) Length, as defined per peptide. For PD, the following parameters from the PSM-file were taken into account: (1) Intensity, as defined per scan, (2) Xcorr, as defined per scan, and (3) peptideLength, as defined per peptide. To compare the two respective distributions against each other, the Mann-Whitney U-test was used.For all comparisons between the search engines, we generally set all leucines in peptide sequences to isoleucine. We determined whether peptides identified with both search engines, PD and MQ, could be distinguished by their precursor intensity, scores or peptide length from the peptide identifications that were unique to either one of the programs. For each software, we compared the frequency distribution of intensities/scores/peptide lengths from the overlap against the unique set of peptide identifications, respectively. In the case of MQ, the following parameters from the evidence-file were taken into account: (1) Living cells integrate various physico-chemical stimuli via complex intracellular signaling systems, involving highly intertwined kinase- and phosphatase networks. Numerous studies aimed to unravel such signaling networks using quantitative mass spectrometry (MS)-based proteomics, often with the focus to comprehensively identify substrates of a given kinase or phosphatase at a specific condition , 22–27. Saccharomyces cerevisiae [Here we re-analyzed an extensive, quantitative MS-based phosphoproteomics dataset , previourevisiae , comprisrevisiae . In comprevisiae , 4, 13. Saccharomyces cerevisiae serves as a paradigm of complex signaling networks. Elevated extracellular osmolarity results in activation of the mitogen-activated kinase (MAPK) Hog1 which in turn propagates the stress signal by phosphorylating target proteins at specific motifs, characterized by a serine or threonine followed by a proline (S/T-P)  response in 2-based phosphopeptide enrichment and strong cation exchange (SCX) fractionation to enable in depth analysis of the phosphorylome  by Romanov et al. using MQ see ; Fig. 1cTo facilitate phosphorylation site analysis, we integrated peptides where the same residues are phosphorylated  to a so-called “phosphorylation site group” (PSG), similarly to procedures described in Romanov et al. . The eveR = 0.88), suggesting that both software tools almost equally quantify phosphorylation sites. When considering the MQ/PD-ratio for commonly quantified phosphorylation sites 93.43% were within a limit of +/− 1 (log2) quantification difference , with these phosphorylation sites found to be associated with signal transduction , cell cycle regulation, endocytosis, transport- and cytoskeleton-related processes, which is in line with the general understanding of the HOG response [p-value = 2.25 × 10− 3) were overrepresented in the MQ-derived dataset, such as conjugation, membrane and organelle fusion, as well as organelle inheritance and exocytosis, ultimately giving a potentially novel context to Hog1 signaling.To capture cellular processes affected by Hog1-activity, we performed a gene ontology (GO)-term analysis using the Hog1-dependent phosphorylation sites derived from the MQ- and the PD-based analysis, and a combination of the results Fig.  Table S2response , 39, 40.We next tested whether the list of putative direct Hog1-substrates was extended by the integration of results from both programs. For this purpose we selected sites phosphorylated at S/T-P motifs and, in addition, sites phosphorylated at S/T-S/T-P motifs, in order to prevent omission of targets due to incorrectly localized phosphorylation sites. 49 S/TP (or S/T-S/T-P) motifs (40 proteins) were found within the MQ-derived set of stress- and Hog1-dependent phosphorylation sites Fig. c. 28 of 625) or susceptibility to Hog1-inhibition (Ser723) [625 and Thr1073). A similar scenario occurred with Orm2 and Ppz1 both of which did not have sufficient coverage in the PD-analysis; the re-analysis, however, suggests a Hog1-mediated phosphorylation of Thr18 of Orm2 and Ser265 of Ppz1 .In total, our combined analysis revealed 15 novel putative Hog1-target proteins (10 via genuine S/T-P motifs), namely the kinases Kic1, Pkh1 and Ste20, the transcription factors Hac1, Hsf1 and Tgf1, the retrograde transport-associated proteins Gcs1, Vps53 and Ysp2, the mitochondria-associated proteins Mfb1 and Psp2, and the ubiquitin-specific protease Ubp13. Further putative targets include Far8 - a protein involved in the recovery from cell cycle arrest, Orm2 - a protein involved in sphingolipid homeostasis, Sog1 - a key-component of the RAM signaling network (and binding partner of kinase Kic1), and finally, Pmd1 and Sap1, two proteins of unknown function , Pbs2 (Ser514), and Rck2 (Ser520) [808 of Rgc2 [748, Ser1003 and Ser1253 of Pan1 [1307 of Ede1 [We therefore implemented seven additional experimental MS setups with 2–3 replicates for a reasonably fast probing of the kinetics of a large number of phosphorylation sites. In a quantitative SILAC-MS setup without prior SCX-fractionation see , global Ser520)   HOG signaling response and defined distinct clusters according to phosphorylation kinetic profiles . Applyin662 [To test whether the newly MQ-identified putative Hog1-targets indeed follow transient phosphorylation kinetics we first performed gel mobility shift assays. However, of all tested candidates (12) only Kic1 and Vps53 showed a subtle decrease in gel mobility at later time points of the osmostress response  kinetic patterns are captured independently of net phosphorylation effects. Given these criteria, a targeted MS approach was deemed appropriate to provide an accurate fingerprint of phosphorylation kinetics as a function of hyperosmotic stress and Hog1as-inhibitor susceptibility.625 and Thr1073 of Kic1 and Thr18 of Orm2), ii) S/T-S/T-P motifs (Ser790 of Vps53), iii) putative indirect target sites (Ser511 of Kic1), and finally iv) on phosphorylation sites that were differentially quantified by PD and MQ (Ser340 of Kic1). Hog1as cells expressing Kic1, Orm2, and Vps53 fused to a HB tandem affinity tag were treated with DMSO (mock) or 0.25, 0.5, 5 μM as-inhibitor (SPP86), respectively, followed by a 5 min exposure to increased extracellular salt concentrations , Ser790 of Vps53  in our MQ-derived re-analysis dataset, was weakly affected (~ 1.6-fold) by higher inhibitor-concentrations, confirming previous observations  affected by inhibitor treatment clearly rendering it Hog-independent  putative direct Hog1-target sites  but also to differences during pre-processing steps, including MS1 peak picking, MS1 m/z re-calibration and MS2 precursor re-evaluation. To exclude dataset-specific effects, we analyzed an MS test run of an HeLa cell extract with MQ (version 1.5.2.8) and PD with SEQUEST as search engine (PD version 1.4), and obtained a similarly low level of overlap  and release of Orm2-mediated inhibition of sphingolipid biosynthesis in response to heat stress [18 of Orm2, a site located on the N-terminus adjacent to the three nested consensus Ypk1 phosphorylation sites, and, interestingly, also of Ser513 of kinase Pkh1. Generally, disturbance of sphingolipid homeostasis has been shown to directly affect the generation of diverse pathological phenotypes in both, yeast and mammalians [Our findings regarding Kic1 and Sog2 highlight a previously unrecognized interconnection between the HOG and RAM (regulation of Ace2 activity and cellular morphogenesis) signaling network, which coordinates cell separation in revisiae –82  Histograms of MS-signal intensities of precursor ions (B), PSM scores (C), and peptide lengths of MQ-derived (top) and PD-derived (below) datasets. Light blue bins indicate the distribution of spectra identified solely by MQ. Yellow bins: PD. Green bins: overlap. P-values were calculated using the Mann-Whitney U-test. (E) Correlation of SILAC log2-ratios of mutually quantified phosphorylation sites of setup I + 5′S. (F) Histogram illustrating distribution of SILAC-ratio quantification difference  of mutually quantified phosphorylation sites of setup I + 5′S. Lines indicate limits of +/− 1 quantification difference. (G) and (H) Results obtained for setup I + 10′S are illustrated similarly to (E) and (F). (I) and (J) Setup I + 0′S. (K) Venn diagrams showing percentage and total number of peptide identifications (IDs) obtained from a MS test run of a HeLa cell extract sample (left) and the dataset described in [ribed in . Light bAdditional file 2:Figure S2. Related to Fig. hog1Δ (right) strain. (B) Similar to (A) except that results of Hog1-independent phosphorylation sites are shown. (C) Stratification of Hog1-dependent (first two pies) and -independent (last two pies) sites into clusters as defined by Kanshin et al., 2015 [l., 2015 .  and (F) Illustration of PRM-measured phosphorylation patterns for the Hog1-independent phosphorylation sites Ser9, Ser15, Ser22, Ser29, Ser31, and Thr36 of Orm2 (E) and Ser512 of Kic1 (F) upon hyperosmotic stress (+ 0.5 M NaCl) and inhibitor treatment (SPP86). The green box plots represent the (mean) normalized intensities for the respective phosphopeptide(s). The yellow box plots illustrate the normalized intensities for unphosphorylated counter-peptides. Significance was assessed by comparing intensities derived from all pooled inhibitor-treated samples with those from the mock sample (t-test). (PDF 1297 kb)Additional file 4:Figure S4. Related to Fig. 18 (A), Kic1 Thr625 (B) and Kic1 Tyr634 (C). Note: Transition product peaks of Orm2 Thr18 are well separated from peaks of peptide isoforms. For Kic1 Thr625 as well as Kic1 Tyr634, however, co-elution of respective phosphorylated peptide peaks hampers unambiguous peak assignment and quantification. #: phosphorylated amino acid. Indicative transitions used for quantification are shown in color-code. Precursor m/z is indicated in bold. (PDF 622 kb)Additional file 5:Figure S5. Related to Fig. Additional file 6:Table S1. Related to Fig. Additional file 7:Table S2. Related to Fig. p-value as well as the adjusted p-value. Finally, the tables also contain the GO level, giving the hierarchy level of the given ontology. (XLSX 60 kb)Additional file 8:Table S3. Related to Fig. Additional file 9:Table S4. Related to Fig. Additional file 10:Table S5. Related to Fig. Additional file 11:Table S6. Related to Fig. Additional file 12:Table S7. Related to Figs. Additional file 13:Table S8. Key resource table. (XLSX 36 kb)


Cassia obtusifolia Linn. have been used to improve vision, inflammatory diseases, and as hepatoprotective agents and to promote urination from ancient times. In the present study, we investigated the influence of glycosylation of components of C. obtusifolia and structure-activity relationships (SARs) with respect to the inhibition of acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1), which are related to Alzheimer’s disease (AD). All six C. obtusifolia-derived compounds, rubrofusarin (1), rubrofusarin 6-O-β-d-glucopyranoside (2), rubrofusarin 6-O-β-d-gentiobioside (3), nor-rubrofusarin 6-O-β-d-glucoside (4), isorubrofusarin 10-O-β-d-gentiobioside (5), and rubrofusarin 6-O-β-d-triglucoside (6) showed promising inhibitory activity against AChE/BACE1. Compounds 3 and 4 showed most significant inhibition against AChE and BACE1, respectively. The SARs results emphasized the importance of gentiobiosyl moiety in the rubrofusarin for AChE inhibition, whereas the presence of hydroxyl group at C-8 and the glucosyl moiety at the C-6 position in the nor-rubrofusarin appeared to largely determine BACE1 inhibition. Kinetics and docking studies showed the lowest binding energy and highest affinity for mixed-type inhibitors, 3 and 4. Hydrophobic bonds interactions and the number of hydrogen bonds determined the strength of the protein-inhibitor interaction. These results suggest that C. obtusifolia and its constituents have therapeutic potential, and that the SARs of its active components are further explored with a view towards developing a treatment for AD. Alzheimer’s disease (AD) is the most predominant neurodegenerative disorder that is found in the elderly. It has been reported that more than 46.8 million people suffer from AD, and that the associated annual medical costs exceed $818 billion. In the same report, it was estimated that more than 131.5 million individuals would have AD in the year 2050 ethyl}-6-phenoxyquinazolin-3-ium (QUD)  and 3,5,7,3′,4′-pentamethoxyflavone (PMF)  were used as positive ligands. QUD is the selective non-peptic BACE1 ligand reported till date according to the RCSB Protein Data Bank, and PMF is reported to be a noncompetitive inhibitor (IC50 = 59.8 µM)  × 100, where C60 was the fluorescence of the control  after incubation for 60 min, C0 was the initial fluorescence of the control, S60 was the fluorescence of the tested samples  after incubation for 60 min, and S0 was the initial fluorescence of the tested samples. To account for the quenching effect of samples, the sample solution was added to a separate reaction mixture C, and any reduction in fluorescence by the sample was investigated. The BACE1 inhibitory activity of compounds was expressed in terms of the IC50 value , as calculated from the log-dose inhibition curve. Quercetin was used as a positive control.Assays were performed according to the supplied manual (Pan Vera Co.) with selected modifications, as described previously . BrieflyO-β-d-gentiobioside/AChE and nor-rubrofusarin 6-O-β-d-glucoside/BACE1 interaction, we employed two complementary kinetic methods, namely Lineweaver–Burk and Dixon plots [3 and 4, respectively, were achieved in the presence of different substrate concentration . Inhibition constants (iK) were determined by the interpretation of Dixon plots, where the x-axis intercept was taken as iK. The enzyme assays were performed using previously mentioned methods.To examine the kinetics of rubrofusarin 6-on plots ,55. Spec1–3 were tested for AChE, whereas 2 and 4 into the binding site for BACE1 using the Autodock 4.2 software. The crystal structures of the AChE and BACE1 protein targets were acquired from the RCSB Protein Data Bank with the respective accession codes 1acj and 2wjo, respectively. The co-crystallized ligands, tacrine and QUD, were used to generate the grid box for catalytic inhibition mode, whereas reported allosteric inhibitors, donepezil and PMF, were used to generate the grid box for allosteric inhibition mode [+ were used for visualization and analysis of results.In our study, ion mode ,30, and t test  were used for the analysis of statistical significance. All of the results are expressed as the mean ± standard deviation (n = 3) of triplicate samples, and were repeated at three individual days.One-way ANOVA and Student’s 3 and 4 inhibited AChE and BACE1 more efficiently than the other examined derivatives. Our results show the presence of two glucose molecules, arrangement of pyrone at the γ-position of the naphthalene ring, and the substitution of methoxyl group, with hydroxyl at C-8 on the naphthopyrone significantly enhancing AChE inhibitory activity, whereas the presence of a single glucose, a pyrone ring at the γ-position of the naphthalene, and the presence of hydroxyl group at C-8 of the naphthopyrone ring are essential for BACE1 inhibitory activity. Hence, we have successfully identified AChE and BACE1 inhibitors, 3 and 4, which shows in vitro activity in micromolar range by means of combined ligand- and structure based virtual screening approach. Moreover, we suggest similar derivatives be synthesized and in vivo experiments be conducted to investigate the SARs in depth.The present study showed that


Mrakia stokesii was the most abundant fungal species found in the cave ice samples of all prospected ages, alongside other cryophilic fungi also identified in various glacial environments. Ice deposits formed during the Little Ice Age  appeared to have a higher fungal diversity than the ice layer formed during the Medieval Warm Period . A more complex fungal community adapted to low temperatures was obtained from all analyzed ice layers when cultivated at 4 °C as compared to 15 °C, suggesting the dominance of cold-adapted fungi in this glacial habitat. The fungal distribution in the analyzed cave ice layers revealed the presence of unique OTUs in different aged-formed ice deposits, as a first hint for putative further identification of fungal biomarkers for climate variations in this icy habitat. This is the first report on fungi from a rock-hosted cave ice block.Screening of 1,000-years old ice layers from the perennial ice block of Scărișoara Ice Cave (NW Romania) revealed the presence of fungal communities. Using culture-dependent methods and molecular techniques based on DGGE fingerprinting of 18S rRNA gene fragments and sequencing, we identified 50 cultured and 14 uncultured fungi in presently-forming, 400 and 900 years old ice layers, corresponding to 28 distinct operational taxonomic units (OTUs). The dominant ice-contained fungal OTUs were related to Ascomycota, Basidiomycota and Cryptomycota phyla. Representatives of Mucoromycota and Chytridiomycota were also isolated from recent and 400 years old ice samples. The cryophilic  In spite of low temperatures, life is not absent in frozen environments, and organisms, primarily bacteria, have found various ways to survive and colonize different icy habitats8. Such habitats include polar and high mountain ice caps, alpine glaciers, permafrost and rock-hosted cave glaciers. The later – hereafter called ice caves – refer to ice accumulations in caves, formed as seepage water freezes to form underground perennial ice bodies, some of them thousands of years old9. Apart from surface glaciers  that form as snow slowly metamorphoses into ice, most of cave ice deposits form from water that has percolated through soil and rock and ponded for days through months before freezing. As such, they could host most peculiar microbial assemblages, and are among the poorest investigated cold habitats10. The presence of bacteria in an alpine ice cave was first mentioned by Margesin and collaborators11 who isolated and characterized a psychrophilic Arthrobacter psychrophenolicus strain from the cave non-icy sediments. Bacterial strains belonging to the genera Pseudomonas, Acidovorax and Brevundimonas were isolated from ice deposits of Oregon Cascades lava tube12, while several profiles of prokaryotic communities from Antarctic volcanic ice caves  were recently described13. Moreover, the occurrence of Archaea in the sediments of an Austrian ice cave was also mentioned14. In Scărișoara Ice Cave (NW Romania), our previous investigations revealed the presence of diverse bacteria in recent ice stalagmites15, including phototrophic microbes in sunlight-exposed ice, and various types of cultured16 and uncultured17 bacteria in ice layers up to 900 years old from the perennial ice block harbored in the cave.Over the last decades, investigation of icy environments became a major scientific direction to unravel the presence, adaptation mechanisms and role of ice-contained microorganisms19. In the Arctic, various yeast species were detected in sub-glacial water, sea ice and sea water20, permafrost21, glacier surface and ice cores from Northern Greenland, at depths of about 2 km24. Fungi were also discovered in various Antarctic habitats such as permafrost26, decaying wood27, lake28, sub-glacial29 and sea water30, and ice cores as deep as 3.5 km, corresponding to 1 to 2 million years old ice31. While information on psychrophilic fungi from South American, Asian and Himalayan icy environments is rather scarce, several reports mentioned their presence in glacial habitats from Europe, including sediments from alpine glaciers and permafrost33, sub-glacial sediments and water, as well as glaciers ice cores34.In addition to prokaryotes, icy environments also contain complex fungal communities21, and the diversity of uncultured fungi was reported from an Antarctic volcanic ice cave35. Meanwhile, very few reports refer to fungi from perennial ice deposits in alpine caves. Among these, two psychrophilic Cryptococcus spp. strains and one heterobasidiomycete were isolated from an Austrian ice cave and characterized36. In Scărișoara Ice Cave, the presence of fungi in different layers of the perennial ice block was revealed by 18S rRNA gene amplification17.Ice cave habitats, mainly screened for prokaryotic organisms, also revealed the presence of uncultured and cultured fungi in ice sediments. Fungal strains were isolated from an Arctic glacier cave located in Svalbard archipelago37, no studies of the possible use of fungi as biomarker of past climatic changes have been carried out so far. Building on the unique records of past climatic and environmental changes derived from the ice in Scărişoara Ice Cave (Romania), identification of fungal diversity throughout the cave ice block could lead to a novel microbial climatic proxy. In this context, the current data, representing the first report on fungal diversity in an alpine ice cave, led to the identification of cultured and uncultured fungi in perennial ice layers up to 900 years old from Scărişoara Ice Cave, based on DGGE analysis and sequencing of 18S rRNA gene amplicons.While the presence of fungi in caves was reported worldwideDenaturing gradient gel electrophoresis (DGGE) profiles of 18S rRNA gene fragments retrieved from the 1-S, 1-L, 400-O, 900-O and 900-I cave ice samples Fig.  and from39. In addition to temperature, the higher fungal diversity identified in the 400-O ice sample could be related to the higher organic content of this ice layer as compared to that of the clear ice sample 900-I16. However, the cultured fungal communities at both 4 °C and 15 °C from 400-O ice sample appeared to be more diverse than those from the 900 years old samples (900-O and 900-I), independent of their organic content. Moreover, no significant differences in fungal amplicon profiles were observed between the 900 years old ice layers of high (900-O) and low (900-I) organic content. In this case, the cultured fungal community at 4 °C contained a larger number of amplicons (12.70 ± 3.76) relative to that grown at 15 °C (8.70 ± 3.73). As expected, the cold-active fungal community from Scărișoara Ice Cave enrichments appeared to be more diverse than that growing at higher temperature, similar to the cultured bacterial community from the same habitat16.Amplicon based analysis of the DGGE gels (data not shown) revealed a relatively higher number of fungal OTUs in the recent ice deposits 1-S and 1-L (13.22 ± 3.41) as compared to older ice strata (9.33 ± 3.94). Also, the fungal community profiles of the 400-O ice sample and enrichments appeared to be more complex (average OTUs number 10.44 ± 3.61) than those of the 900-O and 900-I samples (average OTUs number 7.11 ± 2.85). These latter ice deposits were formed during the Medieval Warm Period , that corresponded to warmer and wetter climate conditions than those formed 400 years ago (sample 400-O) during the cold and dry Little Ice Age (LIA), that lasted between AD 1,250 and 1,85040Mrakia stokesii was the most represented species (14 OTUs) in the analyzed environmental ice samples and in both 4 and 15 °C enrichment cultures, considering that Mrakia gelida is a synonymous species of M. stokesii41. Another cryophilic yeast, Teberdinia hygrophila, identified in alpine fen soil from Caucasus Mountains42, was present in the clear ice sample 1-L, and recovered from enrichment cultures of both presently-forming and 900 years old ice performed at 4 °C (1-S) and 15 °C   were found in recent ice and 900 years old ice layers  strain was identified in both the 1-S and 400-O samples originating from high organic content ice deposits , were described from Antarctic lakes43. A close relative of the SM.400-O.LBG-4b enrichment culture from 400 years old ice was Acidomyces acidothermus, previously identified in acidic soils from Czech Republic and Iceland44. Sequences of more ubiquitous fungi  were also identified in presently-forming and 900 years old ice samples, and recovered in all enrichment cultures at both 4 °C and 15 °C. Thirteen OTUs corresponded to unidentified fungi , while ers Fig. . The Aurits Fig. . TheleboGlomus mosseae [NG017178]45 as outgroup, revealed clearly separated corresponding clades of the fungal tree  and 400-O samples and recovered in enrichment cultures of ice of all ages. Species belonging to Cryptomycota phylum were identified also in Arctic and Antarctic ice cores from Svalbard47 and from Lake Vostok at 3.5-km depth31, respectively. Viable fungal spores were isolated from Antarctic ice cores more than 1.5 million years old and from Greenland ice cores 140,000 years old49. Likewise, the identified fungi in the perennial ice from Scărişoara cave could originate from the surface and trapped within the ice layers that succeeded every year, in the presence of organic debris accumulated on the bottom of supraglacial pond formed during summer time on top of the ice block, while engulfed permanently by the ice during winter51.The fungal strains were homologous to known OTUs belonging to five phyla Table . A phyloree Fig. . Among t50. In these settings, the microbial communities may form spores that allow them to endure for centuries26. Alternatively, these microorganisms may survive deep in the ice in micro-spaces surrounding various types of debris insoluble in frozen water. A thin film of unfrozen water may form around mineral particles allowing microorganisms to perform metabolic reactions in icy habitats4. To decipher their metabolic status, in the case of culturable fungi found in the cave ice block, further investigations are needed using high-throughput RNA sequencing, a study that is presently underway. Diversity of cultured and uncultured fungi present in perennial ice collected from layers of different ages in Scărişoara Ice Cave can provide evidence for climate change effects. Melting of the cave perennial ice due to global warming could release viable microorganisms trapped for the last hundreds of years in the ice block51. These microbes could be of significant value both for basic science and applications in bionanotechnologies and cryopreservation52.In Scărişoara Ice Cave, new layers of ice form on at the surface every year, trapping the microbial communities within the ice, with no further contact with the outer surface of the ice blockAureobasidium pullulans, also detected in Scărişoara ice samples, is known to produce various skin infections53.Ice melting could lead to revival of microorganisms considered extinct, and isolation and characterized of entirely new taxa entrapped in ice. In our study, 19 OTUs shared less than 97% identity with the closest known relatives in GenBank database, constituting putative novel strains. Such new fungi may have major influences of the existing environment by interfering with strains of same species that have evolved and adapted to different environmental conditions, while the new arrivals have rested for centuries of even millennia. The emergence of the new microbes could have a major impact on other organisms, including humans, possibly generating disease outbreaks. Among these, Our data not only provided a first glimpse on the fungal diversity in different aged ice layers of an ice cave, a pioneering study in this type of habitat, but led to isolation of a series of cultured cold-active fungal strains of putative applicative potential. The presence of some dissimilar fungal OTUs in different aged ice layers of Scărişoara ice block could constitute promising leads for future identification of fungal climate indicators. A deeper sequencing of an extended ice block chronosequence using NGS, currently underway, should provide more insight into the ice cave fungal diversity and the possible use of these microorganisms as a proxy for climate variation.3) and the oldest  perennial cave ice blocks in the world9. The cave is located in Bihor Mountains, NW Romania, , at an altitude of 1,165 m51. The ice block is formed of ice layers deposited annually by freezing of percolating water, containing debris with microorganisms carried to the cave from the surface56. As a result of this depositional mechanism, each annual stratum is formed by an upper layer of clear ice and a bottom layer of debris-rich ice. Further, two mechanisms are responsible for the formation of extremely rich layers of debris: during periods of enhanced melting, several annual layers of ice are melted and the debris they contain is concentrated in one single, thicker layer, while inflow of water from the surface during wetter periods lead to the formation of similarly thick layers of debris. The two can be distinguished by their content, with the latter being richer in macrofossils washed-in from the surface37. Five ice samples of variable age and sediment content were collected aseptically from newly formed ice (1-S and 1-L) corresponding to direct (S) and indirect (L) sunlight exposed areas, 400 (400-O) and 900 (900-O and 900-I) years old ice deposits, from organic-rich (O) and clear ice (I) layers of the ice block16.Scărişoara Ice Cave harbors one of the largest  and FR1-GC 58. PCR amplification was performed in a 50-μl reaction mixture containing 110 ng cave-ice DNA, 100 pmol of both forward and reverse primers, 0.2 mM dNTP, and 1.25 U MyTaq HS DNA Polymerase , in the presence of 1× MyTaq Reaction Buffer. Amplification was performed in a Thermal Cycler C1000™ , using an initial denaturation of 95 °C for 8 min, followed by 30 cycles of 95 °C for 30 s, 50 °C for 45 s and 72 °C for 2 min, and a final elongation step at 72 °C for 10 min.The enrichment cultures of melted ice samples in T1, T2, LB and LBG media−1 ethidium bromide, and the amplicon profile of the DGGE gels was used to compare the fungal diversity in different environmental samples and enrichment cultures, using statistical tests.DGGE was carried out using a DGGE-4801-220 system . PCR products were loaded onto 8% (wt/vol) polyacrylamide (ratio of acrylamide to bisacrylamide 37.5:1) gels. A 20 to 35% linear denaturing gradient was used, where 100% denaturant was defined as 7 M urea and 40% (vol/vol) formamide. Electrophoresis was performed in 1 × TAE buffer  at 200 V and 60 °C for 4 h. The gels were stained in 1 × TAE buffer containing 1 μg ml58. The DNA sequences were edited using CodonCode Aligner and BioEdit for eliminating the sequencing errors. The closest match of each OTU was determined using the BLAST-NCBI Megablast algorithm and the nucleotide collection database59. Sequence alignment was performed using ClustalW and the phylogenetic tree was generated using the maximum likelihood analysis, using the www.phylogeny.fr platform60. The 18S rDNA sequences were deposited in the GenBank DNA database under accession numbers KY614706-KY614719 (uncultured fungi) and KY614552-KY614602 (cultured fungi).18S rDNA amplicons were excised from the DGGE gels, incubated 48 h at 4 °C in 20 µl sterile water, and re-amplified by PCR as described above. Sanger sequencing was performed  using the FF390 primer


Detection, attribution and projection of mass loss from the Greenland Ice Sheet has been a central focus of the glaciological community, with surface meltwater thought to play a key role in feedbacks that could accelerate sea-level rise. While the prospect of runaway sliding has faded, much remains uncertain when it comes to the role of surface runoff and subglacial discharge in Greenland’s future. The remaining 40% is attributed to uncompensated ice-flow into the ocean, or dynamic mass loss. Observations in the early 2000s linked seasonal meltwater production to ice-sheet acceleration, disrupting the prevailing view of the Greenland Ice Sheet as unresponsive to surface melt. This intuitive link based on water reducing friction at the ice–bed interface, familiar from decades of work on alpine glaciers, led to proclamations of a looming positive feedback between surface melt and Greenland’s dynamic mass loss. Fifteen years of intensive research , its accelerating mass loss regularly captures headlines. About 60% of Greenland’s 1991–2015 mass loss (~0.47 ± 0.23 mm per year SLE) has been attributed to surface mass balance3 highlights a potential feedback termed cryo-hydrologic warming4, whereby latent heat released by refreezing meltwater warms the surrounding ice, accelerating its deformation. There is some observational evidence for this mechanism, for example, in anomalously warm ice found in western Greenland. However, the effect of cryo-hydrologic warming is expected to be limited at least on centennial timescales, with the Greenland Ice Sheet being more sensitive to climate forcing and bed conditions than internal thermal regime5. The more significant effect of refreezing may be its role in limiting the porosity and permeability of the firn6. Combined with reduced firn extent, various densification processes and diminished cold content, the firn aquifer’s future buffering capacity of Greenland’s contribution to sea level stands to be compromised. The firn aquifer may also serve to concentrate water supplies in a manner conducive to hydrofracture, allowing water stored in the near-surface to reach the base of the ice-sheet.With most of the Greenland Ice Sheet blanketed in snow and firn , and melting conditions reaching further inland as observed during the extraordinary summer of 2012, near-surface hydrology stands to play an increasingly important role in connecting surface melt and sea-level rise. The discovery of vast stores of liquid water in the porous near-surface firn7. Water impinging on the bed in this way has a short-lived effect on local ice flow8 that may or may not be significant when the cumulative impacts of numerous lakes are considered, but does facilitate the formation of longer-lived moulins. Seasonal cycles of surface meltwater supply compete with the evolution of subglacial drainage-system efficiency, with ice-flow speeds seemingly more dependent on meltwater variability than volume9. Even negative feedbacks between surface melt and annual ice motion have been documented in multi-year studies10, particularly at lower elevations where a well-developed drainage system permits efficient evacuation of water.Below the firn line, kilometre-scale sapphire-blue lakes, fed by networks of surface streams, dot the flanks of the Greenland Ice Sheet in summer. Their disappearance is one of the most striking features in time-lapse satellite imagery, prompting questions about their role in delivering water to the ice-sheet interior. Some of these lakes drain by overtopping into downstream crevasses and moulins . Others drain rapidly through in-situ fractures that may propagate all the way to the bed, in some cases associated with uplift and/or sliding of the ice sheet caused by distal drainage events11 and the importance of hydraulically isolated regions of the bed12. While persistent surface-to-bed connections appear to have little influence on long-term ice flux, the wider implications of supraglacial lake drainage remain murky. A Greenland-wide assessment revealed a regionally differentiated response, with fewer drainage events in areas experiencing the greatest dynamic mass losses13. Though lakes are projected to form further inland with rising temperatures and poised to deliver water to increasingly sensitive areas of the bed14, whether lake drainage will be effective at these inland sites is unclear.Just as the melting margins of Greenland were beginning to seem like an outsized alpine glacier with its seasonal see-saw between efficient and inefficient subglacial drainage, borehole studies highlighted limits to the inland extent of an efficient drainage system15. Complexities arise, however, from the particulars of fjord configuration, ice depth, subglacial discharge rates, the geometry of subglacial conduits and externally forced ocean currents and fjord stratification17.Most work investigating the direct impacts of surface meltwater has targeted land-terminating outlet glaciers. Marine-terminating glaciers are fundamentally different however, in that ice–ocean interactions permit highly non-linear and unstable behaviour with profound impacts on glacier retreat and the attendant export of ice to the ocean. Sediment-laden freshwater plumes can be seen rising from subglacial conduits to the fjord surface, implicating glacier melt in fjord circulation dynamics. These meltwater inputs draw warm ocean water at depth toward the ice front, where sub-marine meltrates can reach metres per day18, so the effects of dynamic thinning will be with us well into the future.Though it is too early to generalize on the importance of ice-sheet hydrology for processes at Greenland’s marine margins, current indications are that surface melt does have an important role in driving dynamics at the ice–ocean interface where marine-terminating glaciers are most sensitive. Although the contributions to Greenland’s dynamic mass loss since 2000 are concentrated in relatively few outlet glaciers, there may be other glaciers with geometries and fjords primed for retreat. In contrast to Antarctica, most of Greenland’s marine-terminating glaciers are ultimately destined to retreat onto dry land, spelling a less dynamic future ice sheet. In the meantime, even a decade of perturbation at the ice front can lead to a century of ice-sheet drawdown and committed sea-level rise19? How rapidly will this partitioning change as a function of snow/firn extent, porosity and permeability20? Where does this water access the bed21? How pervasive and persistent might efficient subglacial drainage become11, and what role do the bed areas isolated from the drainage system play in ice-sheet dynamics12? How important is ice-sheet hydrology in driving mass loss from marine-terminating outlets, both at the ice front and upstream22?While technological advances in observing systems allow unprecedented monitoring, there are fundamental questions that remain. How is surface meltwater partitioned between infiltration and runoffSatellite remote sensing is now able to provide regular ice-sheet-wide snapshots of ice-surface velocities and net mass changes (e.g. NASA MEaSUREs program), while ground-based and airborne geophysical data (e.g. through Operation IceBridge) also play an important role in spatially extended studies. But many hydrological variables remain challenging to observe, let alone predict. Supraglacial channels of several metres width are visible from space, but depths and water fluxes are difficult to obtain. Discrete injection of meltwater through moulins is a key feature of the drainage system, but predicting moulin formation can be challenging. The englacial and subglacial drainage systems are almost entirely obscured from view, directly observable only at the point-scale through labour-intensive borehole drilling or natural portals such as moulins. Models of ice-sheet hydrology, still in their infancy, tend to resolve individual conduit elements, making efficient coupling with large-scale ice-flow models still a thing of the future. They also require parameterization of metre-scale bed roughness and higher resolution bed topography than is widely available.23. Information on bed composition24 and roughness at multiple scales would help reduce unnecessary ambiguity for models. From surface to bed to ice front, we also need to exercise creativity in upscaling process-based studies, and vigilance in the unavoidable geographical biases imposed by field logistics. Productive partnerships between disciplinary specialists will move the science forward fastest, as demonstrated in grassroots initiatives like GRISO (https://web.whoi.edu/griso/).Continued progress will require sustained efforts to better image the ice-sheet interior and bed as well as coastal bathymetry, combined with data assimilation exercises such as BedMachine designed to produce seamless and self-consistent public datasetsAs a community, we have developed a nuanced understanding of the impact of surface meltwater on the dynamics of the Greenland Ice Sheet, but ice sheets are more than just their net contributions to global sea level. They sculpt landscapes, deliver sediment, solutes and nutrients to the ocean, gravitationally shape our coastlines and influence global climate. Although the threat of surface meltwater to Greenland’s stability does not appear as grave as it once did, the details of how and where meltwater is stored, how it is ultimately transported through the ice sheet to the ocean, and what physical, chemical and biological effects it has along the way should be priorities for future research. There we have barely scratched the surface.


We investigated afferent inputs from all areas in the frontal cortex (FC) to different subregions in the rostral anterior cingulate cortex (rACC). Using retrograde tracing in macaque monkeys, we quantified projection strength by counting retrogradely labeled cells in each FC area. The projection from different FC regions varied across injection sites in strength, following different spatial patterns. Importantly, a site at the rostral end of the cingulate sulcus stood out as having strong inputs from many areas in diverse FC regions. Moreover, it was at the integrative conjunction of three projection trends across sites. This site marks a connectional hub inside the rACC that integrates FC inputs across functional modalities. Tractography with monkey diffusion magnetic resonance imaging (dMRI) located a similar hub region comparable to the tracing result. Applying the same tractography method to human dMRI data, we demonstrated that a similar hub can be located in the human rACC. The anterior cingulate cortex (ACC) is composed of multiple regions that support a wide range of functions , and thus, is in a position to use value-related information to help regulate flexibility, adaptation and top-down control . This fuThe rACC sits at the connectional intersection of the motivation and action control networks. Thus, it is in an important position in the transition from valuation, to choice, to action. An important question is how the transitions occur within the rACC. One possibility is that information processing changes sequentially across subregions within the rACC, from valuation in regions close to the sACC, to cognition at the center of the rACC, and then to action in regions close to the dACC . AlternaTo determine whether a hub region exists in the rACC, we systematically placed tracer injections along the rACC in nonhuman primates (NHP), and quantified the input strength and patterns from the frontal cortex (FC) to each injection site. As expected, projections from the vmPFC were concentrated in the ventral rACC and those from motor control areas were found more dorsally and caudally. Projections from the vlPFC, dlPFC, dmPFC and OFC varied across sites. However, one site showed uniquely high degree-centrality with the FC, suggesting a hub region at this site. We verified that inputs to this site were from functionally diverse regions and were not a composition of inputs to the neighboring cytoarchitectonic areas. To test whether a similar connectivity pattern and the location of a hub could be identified in the human rACC, we used dMRI tractography to analyze connections between the FC and the rACC. Comparing two connectivity modalities (tracing and dMRI) in different species (NHP and humans) is problematic. We therefore first verified that dMRI correctly detected connectivity convergence in NHPs. Note: for two experiments, the tracer injections and dMRI were carried out in the same animals. We seeded each frontal area and, consistent with the tracing results, probabilistic streamlines converged in a similar location as the hub within the rACC in the NHP. We then seeded each frontal area in a dMRI dataset from the Human Connectome Project (HCP). As in the NHP results, the streamlines converged in a similar rACC location.Bidirectional tracer injections were placed systematically throughout the rACC. FC was divided based on cytoarchitectonics into areas  and retrMacaca mulatta and Macaca fascicularis). Bidirectional tracers were injected into the rACC. Details of the surgical and histological procedures have been described previously . After imaging, the brains were shipped back to University of Rochester Medical Center for histological processing. Serial sections of 50 μm were cut on a freezing microtome, and one series with sections 1.2 mm-apart was processed for subsequent retrograde tracing. The serial sections were processed free-floating for immunocytochemistry. Tissue was incubated in primary anti-LY , anti-FS , or anti-FR  in 10% NGS and 0.3% Triton X-100 (Sigma-Aldrich) in PB for 4 nights at 4°C. After extensive rinsing, the tissue was incubated in biotinylated secondary antibody followed by incubation with the avidin-biotin complex solution . Immunoreactivity was visualized using standard DAB procedures. Staining was intensified by incubating the tissue for 5–15 s in a solution of 0.05% DAB tetrahydrochloride, 0.025% cobalt chloride, 0.02% nickel ammonium sulfate, and 0.01% H2O2. Sections were mounted onto gel-coated slides, dehydrated, defatted in xylene, and coverslipped with Permount. In cases in which more than one tracer was injected into a single animal, adjacent sections were processed for each antibody reaction.After a survival period of 12–14 days, monkeys were again deeply anesthetized and perfused with saline, followed by a 4% paraformaldehyde/1.5% sucrose solution in 0.1 mol PB, pH 7.4. Brains were postfixed overnight and cryoprotected in increasing gradients of sucrose , vmPFC , OFC , vlPFC , dlPFC , dmPFC , frontal eye fields  and premotor cortex  see e.g. . The dem32P(a) and P24(a) are the probabilities of observing a labeled cell in area a according to the input distributions of areas 32 and 24, respectively. s4P(a) is the probability composed of 32P(a) and 24P(a) with weight w (0 ≤ w ≤ 1). When w is held constant across areas, w to each area. The values of w were randomly sampled from a uniform distribution over the  interval.Based on the analyses described above, site 4 stood out as having a uniquely diverse input pattern as compared to the other sites. Such connectivity property suggested that site 4 is a hub in the FC-rACC network. However, because site 4 was at the junction of areas 32 and 24, an important question is whether its input pattern could be explained by a composition of inputs to areas 32 and 24. We tested this alternative hypothesis by comparing the connectivity pattern of site 4four with mixtures of the patterns of areas 32 and 24 using the symmetric Kullback-Leibler distance. The percent cell counts for sites 1, 2 and 3 and those for sites 5 and 6 were averaged to represent the input distributions of areas 32 and 24, respectively. The composition of the two input distributions followed a weighted sum:max = 40,000 s/mm2, 514 gradient directions, matrix size 96 × 96 × 112, and 0.7 mm isotropic resolution. The human dMRI data used 35 healthy subjects, publicly available as part of the MGH-USC Human Connectome Project (HCP) (The dMRI data collection and preprocessing was previously described in ct (HCP) . Both thct (HCP) . Artifacct (HCP) . A crossct (HCP)  was fit fsaverage by FreeSurfer 4.5, fsaverage space to each individual’s diffusion space with nonlinear registration provided by FreeSurfer. The FC areas were used as seeds, and areas 32 and 24 were combined and used as the target. Each mask covers 3 mm thickness (two voxels) of white matter at the gray-white matter boundary. The fsaverage parcellation is available in Following preprocessing, probabilistic tractography  was perfFSL was used for probabilistic tractography for both NHP and human. Tractography was performed from each seed mask to the ipsilateral target mask. At each voxel, a sample was drawn from the orientation distribution of the anisotropic compartment with the closest orientation to the previously visited voxel . To exclInjection sites were labeled 1–6 based on the spatial order of their locations . We founNot all FC regions project equally to the rACC. Overall, retrogradely labeled cells in cases 1–3 were primarily located in more rostral regions of the FC, while those in cases 4–6 were primarily in more caudal regions . Case 1 An essential property of network hubs is their high degree of connections . To measure the degree-centrality of each site, we identified the number of areas that contributed to 50% and 75% of total inputs. This number varied extensively across sites . At siteFC areas were grouped into commonly referred FC regions. These included: FP , vmPFC , OFC , vlPFC , dlPFC , dmPFC , FEF , and premotor cortex . At each site, we identified the FC regions contributing 50% and 75% inputs . FP and H = 10.48, p<0.02). This trend indicated a decrease in projection strength of vmPFC and FP along a gradient from site 1 to 6. In contrast, the second trend demonstrated an increase of projection strength from site 1 to 6 of inputs from premotor cortex and FEF. These regions projected strongly to site 6, but weakly to the other sites. Indeed, the average contribution of an area in these regions was less than 1% to the inputs to sites 1–3,~2% to sites 4 and 5, and more than 5% to site 6 . Unlike projections from FP and vmPFC, this trend was less evenly distributed along a gradient from sites 1–6, as the projections are concentrated in site 6. The final projection pattern was a single-peak nonmonotonic gradient centered at site 4. The average input strength from dlPFC, vlPFC and dmPFC increased from site 1 (~1%) to site 4 (>5%) and then decreased from site 4 to site 6 (~2%). The change across sites was more gradual than that in the two monotonic trends. The between-site difference was statistically significant . Interestingly, OFC areas did not show a trend across the rACC  and those to area 24 (averaged percent scores of sites 5 and 6) . Areas 1Finally, in a reconstructed 3D model, we verified that the cortical locations of retrogradely labeled cells of site 4 was not a composition of the spatial patterns of labeled cells of its neighboring sites 3 and 5 . In the The FC of NHP dMRI images was parcellated into 27 areas that corresponded to the 27 areas used in the tracing analysis . Each arWe applied the same tractography method to human dMRI data. The human FC was parcellated into 25 areas following In this study we mapped the FC inputs to different subregions within the rACC and determined the strength of connections from each FC region and areas within each region. Site 4 showed the highest number of regions and areas within regions that contributed the strongest inputs, thus showing the highest degree-centrality. Site 4 was in the intermediate transition zone of two projection gradients across the six sites. It was also at the peak center of a third, nonmonotonic, gradient. Together with the degree-centrality, these connectivity patterns suggested that site 4 is a hub within the rACC. The dMRI tractography in the NHP demonstrated that streamlines from the FC converged in a location comparable to site 4. Using the same tractography methods in human, we found that streamlines from FC also converge in a similar position in the human rACC. Thus, using a cross-species, multimodal approach we located a hub in the rACC in NHPs and, in a similar position, a likely hub in human rACC.Qualitatively, our results were overall consistent to previous studies in the literature that demonstrated vmPFC, dlPFC and caudal OFC projections to the rACC . HoweverComparative studies have utilized spatial location, cytoarchitectonics, connectivity pattern and functional activation to relate homologous areas between human and NHP. The numerical designations of FC areas in early human and NHP atlases  have beeAn important issue is whether the hub is in a homologous region within the rACC. We base our assessment on the surface features and cytoarchitecture of the ACC. The cytoarchitectonic regions of the human cingulate cortex have been found broadly consistent with the NHP region . The twoComparing two connectivity modalities (tracing and dMRI) in different species (NHP and humans) is problematic. We therefore first compared the tracing results with dMRI scans in NHPs, two of which were in the same animals that received the tracer injection. This comparison was central for determining how accurately the dMRI method was able to replicate the anatomic findings. The most direct way to compare the methods was to place the dMRI seeds at the location of each injection site and follow the streamlines to the FC. The results showed that streamlines from these seeds could not be followed to the cortical areas, but rather remained within the cingulum bundle . This prWhile the concept of hubs emerged in early discussions of brain networks , recent In this study, site 4 showed two defining features of a hub: high degree-centrality and a position in the network that could facilitate cross-module integration. High degree-centrality was reflected by the number of areas with strong projections to this site –5. To deAn alternative to the hub hypothesis is that site 4 was at the border of areas 32 and 24, and thus, its diverse inputs could be a result of a composition of inputs to areas 32 and 24. The analyses of projection strength across FC areas and the spatial distribution of labeled cells argue against this alternative . 1. If tA main limitation of the current study is that experiments were performed in different animals. It is therefore difficult to assess individual variability of the input patterns for each site. Ideally, each animal would receive tracer injections along the rACC and this would be repeated across several animals. Unfortunately, technically this would be difficult to accomplish without, for example, tracer contamination. A second limitation is that the analyses cannot completely exclude the possibility that within-region microcircuitry heterogeneity leads to the observed patterns of a hub. The microstructure of a region between two cytoarchitectonic areas might involve a complex spatial composition of neurons from each area. The inputs to these neurons form heterogeneous microcirctuitries that are not visible in the tracing experiments. Thus, when observed as a whole, the region containing these neurons may appear as a hub.The rACC hub provides an alternative view to the dichotomous interpretation of ACC functions, such that emotional and cognitive influences affect the ventral and dorsal ACC separately . ClassicThe rACC hub suggests an alternative view on the functions of ACC subregions. In contrast to associating each subregion with one function to support serial computation, the ACC may be better understood through the functional integration by its subregions. Neuroimaging studies have reported high degree of structural and functional connectivity between the rACC and the rest of the brain . While sThe results in this study did not suggest that only one hub exists in the rACC. Despite the advantage of measuring projection strength at cellular level, this study – and generally tract tracing studies – have a limited number of cases. Unlike neuroimaging studies that can identify all hubs in the global network of the brain, we can only test for hub locations among the available injection sites. Thus, the experiment did not exhaustively cover the all ACC regions. It is possible, therefore, that additional hubs are located elsewhere. The dorsal bank of the cingulate sulcus at the rostro-caudal level between sites 5 and six is a region of particular interest as it is often the site for NHP electrophysiology experiments. These studies demonstrate the functional diversity of this region. To determine whether this area had the characteristics of a hub, we analyzed case 5b . We founFrom a network perspective, most psychiatric disorders are seen as a consequence of network imbalance rather than localized deficits . This vi In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by David Badre, as Reviewing Editor, and Michael Frank as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "A connectional hub in the rostral anterior cingulate cortex links areas of emotion and cognitive control" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision based on the reviewers' summary and comments to help you prepare a revised submission.Summary:The authors studied the inputs to six 'rostral anterior cingulate cortical' (rACC) areas with injection of tracers in two species of macaque monkeys and mapped the origin of projections from the frontal cortex. They then compared the data with findings obtained from diffusion MRI in monkeys and humans, using the injection sites as seeds to study pathways. They identified a non-monotonic connectional gradient and concluded that one site within rACC (their site 4) receives strong input from more areas than the rest, and so is a connectional hub.Essential revisions:The reviewers saw considerable value in this study. If verified, the gradient of connectivity and "hubness" of rACC would be an important and novel finding. Furthermore, there was an appreciation of the use of convergent methods between high resolution tracing and diffusion tractography for cross-species comparison.However, the reviewers were also in agreement about the primary weakness of the study: The number of injections was highly limited and so this has left open several alternatives to a hub in rACC. For example, beyond some basic methodological concerns, there could be sites in the caudal regions left untested, such as dorsal ones, that would show a similar hub-like pattern, or there could be more than one region being conflated in what is being called rACC (site 4). Thus, the overall consensus was that more injections are needed in order to draw the broad conclusion that rACC is a connectional hub. Here are specific concerns along these lines from reviewers:1) Three different tracers are used in the study, but it was not indicated which was injected where. Could the findings of strong labeling in more areas after a single injection in site 4 be attributed to a more efficient tracer, better uptake at the injection site, or a stronger antibody used in the processing of the tissue in each case? With one case per site and missing methodological detail, it is not possible to accept such a broad conclusion.2) The conclusion that only one site in the cingulate cortex is a connectional hub is based on injection sites located mainly in the ventral bank of the cingulate sulcus (sites 5 and 6), whereas the site in rACC covers both the ventral and dorsal banks. Although it has been suggested that the fundus of the cingulate sulcus might be the dorsal limit of the cingulate cortex , this dorsal limit has been challenged. There is functional and anatomical evidence that the dorsal bank of the cingulate sulcus belongs to the cingulate cortex . For example, all electrophysiological studies demonstrating activity related to cognitive control and performance monitoring have recorded within the dorsal bank of the cingulate sulcus in monkey. So, can the authors exclude that another hub may exist in the dorsal bank of the cingulate sulcus ?3) Site 4 was at the intersection of areas 24 and 32 and sites 1-3 and 5-6 are located in areas 32 and 24, respectively . Is it relevant that the injection in site 4 involves 2 areas and thus appears as a hub? The control case is intended to demonstrate that it is the location and not the size of the injection that matters, but the control case involves more posterior areas with less diverse profiles and number of connections. The fact that site 4 involves both areas 32 and 24 possess a problem since its "hubness" might be the result of the convolution of the connectivity profile of the two sites (3 and 5) with a high number of connections and diversity of connectivity profiles as Figure 4 indicates. The arguments against such an interpretation (subsection “Input patterns vs. cytoarchitectonic patterns”) are unconvincing. The first one does not provide evidence against the aforementioned interpretation. The second one assumes a simple sum of the connectivity profiles of two areas. But, this assumption is not necessary since there is connectional heterogeneity within areas . In other words, there is not pure addition of connectivity profiles.eLife. Your revised article has been favorably evaluated by Michael Frank as the Senior Editor, David Badre as the Reviewing Editor, and three reviewers.Thank you for resubmitting your work entitled "A connectional hub in the rostral anterior cingulate cortex links areas of emotion and cognitive control" for further consideration at The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:One of the main points raised previously was the claim that the "hubness" of site 4 could not reflect an injection involving 2 areas, that is, area 24 and 32. Though the control provided rules out an additive version of this account, there may be reason to doubt additivity of connectivity. Thus, reviewer 3 has suggested a set of additional control analyses to fully rule out this concern. These are appended in this reviewer's comments below. Reviewer 1 also has a suggestion regarding the strength of the conclusions that you should consider in your revision. In discussion, the reviewers were in agreement that both of these points should be addressed. Thus, I send this back to you for additional revision. The reviewers' specific comments follow.Reviewer #1:The authors addressed key points raised and concluded that due to technical limitations, there indeed could be other areas within the anterior cingulate region with strong connectivity resembling a 'hub'. In view of this conclusion, it would be consistent to also tone down the initial part of the discussion by removing words such as 'strongly' as in: "..the integrative nature of site 4 strongly supports that it is a hub in the frontal cortex..".The manuscript varies in style of expression. For example, the first paragraph of Discussion reads like a section from Results. There is variation in the style of the references in the text, missing references from the reference list and misspellings of some names.Reviewer #3:I would like to thank the authors for their clarifications.I would like to focus on one of the main points, namely, the claim of site 4 being a hub and that this is not explained by the fact that the injection involves 2 areas, that is, area 24 and 32.The authors performed a nice analysis showing that site 4 connectivity cannot be explained by a linear addition of the pattern of connections of areas 24 and 32.This is a basic demonstration of the fact that the assumption of "pure addition" of connectivity profiles does not necessarily hold. This was stated in my previous comment: "But, this assumption is not necessarily true since there is connectional heterogeneity within areas . In other words, there is not pure addition of connectivity profiles."So the authors indeed show that there is no pure linear addition as I indicated, but this is something that is distinct from claiming that the overlap might not contribute to the "overshoot" in the metrics that are used to classify sites as hubs.A more direct rigid control analysis addressing the hubness of sites should focus on the criteria used for hubness and if they could be synthesized and to what extend they resemble site 4 hubness by mixing connections from areas 24 and 32. Specifically, the following steps should be executed to clarify the issue:1) Perform the mixing analysis and generate "predicted site 4" connectivity from areas 24 and 32.2) Calculate ALL formal hubness metrics for this "predicted site 4 profile", namely, degree and entropy.3) Compare how well they fit to the actual site 4 metrics.4) If the "predicted site 4" hub metrics fit better to the actual site 4 than the controls of e.g., mixing area 24 with randomly selected areas  then the we have some evidence that the hubness DOES occur due to mixing, even if there is no pure, at least linear, addition of connectional profiles.Note, that KL-divergence between predicted site 4 and actual site 4 connectivity might be low but degree centrality similarity might be even higher (the same with entropy of connections), since all these metrics do not quantify the same aspects.Importantly, all the above controls cannot exclude that within area inhomogeneities lead to the observed hubness (stated in the previous round of revisions). This is impossible in my view, since we cannot know the structure and spatial periodicity of projection neurons within these areas across animals and how they are adding up from case to case when injections involve two areas and if they lead to what we observe from cases from different animals.I think that it is fair to run the controls that are mentioned above and, depending on the outcome, support site 4 as hub but also indicate the methodological limitations in the manuscript . It is more than fair to do so – invasive-tract tracing is the gold standard, but like any method, it has its limitations and we should operate with such limitations.I hope that the above comments are of help. Essential revisions:The reviewers saw considerable value in this study. If verified, the gradient of connectivity and "hubness" of rACC would be an important and novel finding. Furthermore, there was an appreciation of the use of convergent methods between high resolution tracing and diffusion tractography for cross-species comparison.However, the reviewers were also in agreement about the primary weakness of the study: The number of injections was highly limited and so this has left open several alternatives to a hub in rACC. For example, beyond some basic methodological concerns, there could be sites in the caudal regions left untested, such as dorsal ones, that would show a similar hub-like pattern, or there could be more than one region being conflated in what is being called rACC (site 4). Thus, the overall consensus was that more injections are needed in order to draw the broad conclusion that rACC is a connectional hub. Here are specific concerns along these lines from reviewers:1) Three different tracers are used in the study, but it was not indicated which was injected where. Could the findings of strong labeling in more areas after a single injection in site 4 be attributed to a more efficient tracer, better uptake at the injection site, or a stronger antibody used in the processing of the tissue in each case? With one case per site and missing methodological detail, it is not possible to accept such a broad conclusion.The tracers injected into sites 1–6 are: FR, LY, FS, FR, LY and FR, respectively. The experimental design took into account the variability of tracer efficiency, uptake level and antibody reactivity, described below in detail. The findings of extensive labeling of an injection into site 4 could not be attributed to these factors.”.First, the type of tracers is randomized among cases. Sites 1, 4 and 6 were injected with FR. In contrast to site 4, site 1 showed the most limited labeling among all six cases, with the fewest areas contributing to 50% of its inputs . Thus, the tracer FR is not more or less efficient than LY or FS. Moreover, this is consistent with our experience with these tracers. Our entire database of injection sites in non-human primates includes 54 cortical injections of LY, 34 of FR, and 29 of FS, all with outstanding transport. We find similar transport results regardless of the tracer used. We have included the tracer information for each site in the revised Results “OverviewSecond, although uptake and transport are difficult to quantify, the extensive labeling of case 4 is unlikely to be attributed to these factors for the following reasons: 1) All tracers were conjugated to dextran amine (provided by Invitrogen) to optimize the transport and stability  . 2) Prior to selecting a case for analysis, we first assessed its transport to their known and distant connections, including the posterior cingulate/retrosplenial cortex and the thalamus. All cases showed dense labeling in areas 23, 29 and 30, and in the AV, VA and MD nucleus in the thalamus. We have included additional details of the qualitative assessment in the revised Materials and methods and Results . 3) If the uptake level was particularly high in a case, it would result in a greater large number of labeled cells in that case. We did not observe that for case 4 compared with the other cases . 4) Importantly, if that had occurred, it would not affect the relative numerosity of labeled cells across areas. The input strength to each site is measured by the percentage of labeled cells to take into account possible variability of uptake.2) The conclusion that only one site in the cingulate cortex is a connectional hub is based on injection sites located mainly in the ventral bank of the cingulate sulcus (sites 5 and 6), whereas the site in rACC covers both the ventral and dorsal banks. Although it has been suggested that the fundus of the cingulate sulcus might be the dorsal limit of the cingulate cortex , this dorsal limit has been challenged. There is functional and anatomical evidence that the dorsal bank of the cingulate sulcus belongs to the cingulate cortex . For example, all electrophysiological studies demonstrating activity related to cognitive control and performance monitoring have recorded within the dorsal bank of the cingulate sulcus in monkey. So, can the authors exclude that another hub may exist in the dorsal bank of the cingulate sulcus ?We agree that there may be more than one hub within the ACC region. It was not our intention to conclude that site 4 is the only hub in the ACC. As suggested by the reviewers, we examined an injection site (control case 5b) in the dorsal bank of the cingulate sulcus at the level between sites 5 and 6. The degree-centrality measured by the number of regions was similar between site 5b and sites 1, 2 and 6. The limited degree-centrality of site 5b did not support the hypothesis that this site in the dorsal bank is a hub. The location and photo micrograph of site 5b have been added to Figure 1A and B. The stereology results have been included in the revised Results, “Projection patterns of the control cases”. Figure 7 has been updated accordingly.We have included additional discussion of this new result and the possibility of other hub sites in the ACC:“Potential hubs in other subregions of the rACC. The results in this study did not suggest that only one hub exists in the rACC. […] This heterogeneity is consistent with findings in our other cases and in other cytoarchitectonic areas , where inputs are derived from more than one functional region.”3) Site 4 was at the intersection of areas 24 and 32 and sites 1-3 and 5-6 are located in areas 32 and 24, respectively . Is it relevant that the injection in site 4 involves 2 areas and thus appears as a hub? The control case is intended to demonstrate that it is the location and not the size of the injection that matters, but the control case involves more posterior areas with less diverse profiles and number of connections. The fact that site 4 involves both areas 32 and 24 possess a problem since its "hubness" might be the result of the convolution of the connectivity profile of the two sites (3 and 5) with a high number of connections and diversity of connectivity profiles as Figure 4 indicates. The arguments against such an interpretation (subsection “Input patterns vs. cytoarchitectonic patterns”) are unconvincing. The first one does not provide evidence against the aforementioned interpretation. The second one assumes a simple sum of the connectivity profiles of two areas. But, this assumption is not necessary since there is connectional heterogeneity within areas . In other words, there is not pure addition of connectivity profiles.This is an interesting point. Indeed, several studies have demonstrated functional and connectional heterogeneity within cytoarchitectonic areas . Our results from case 5b  showed such heterogeneity in that it received inputs from both vlPFC and premotor areas. However, its degree-centrality did not suggest a hub. To address the issue more robustly, we performed additional analyses to examine: (1) whether the inputs to site 4 can be accounted for by a composition of the connectivity profiles of the cytoarchitectonic areas 32 and 24, and (2) whether the spatial pattern of inputs to site 4 is a composition of those of its neighboring sites 3 and 5. The results suggested “no” to both scenarios.If the projection pattern changes gradually from areas 32 to 24, then the strength of the major FC projections to site 4 would fall between the strength of these FC projections to areas 32 and 24. Moreover, if the input pattern of site 4 was a composition of inputs to areas 32 and 24 but one area contributed more than the other, then this inequality should be present consistently across FC areas. In other words, one would expect to see a closer approximation of projection strength between site 4 and either area 32 or 24 but consistently across all FC areas.To test these predictions, we compared the strength of projections to site 4 with the average strength of projections to area 32 (averaged percent scores of sites 1–3) and those to area 24 (averaged percent scores of sites 5 and 6). We found that 8 areas, 14O, 11, 47L, 9/46D, 9L, 9M, 9/32 and 8A showed stronger projections to site 4 than to either area 32 or 24 . Moreover, 14 out of the 27 FC areas showed more similar projection strength between site 4 and area 24 than site 4 and area 32 . The 14/27 ratio indicates that the similarity between site 4 and area 32 or 24 was not consistent across FC areas but a random pattern. These observations do not support the above predictions of the “composition” hypothesis.We further compared the projection pattern of site 4 with different weighted averages of the connection patterns of areas 32 and 24. We used the symmetric Kullback-Leibler distance as a measure of similarity between the normalized connectivity patterns. The best fit to site 4 was a combination of approximately 2/3 of area 24 and 1/3 area 32. However, the distance measures were indistinguishable from a situation where we have replaced one of the two areas’ connectivity patterns with a uniform distribution over the FC areas . In addition, the optimum mixture was inconsistent across different FC areas, and in many samples of different weights the connectivity of site 4 was higher/lower than predicted by the weighted average model . This inconsistency shows that site 4 connectivity is not a simple linear mixture of the input patterns of areas 32 and 24.In the second analysis, we compared the spatial patterns of labeled cells across sites 3, 4 and 5. In the reconstructed 3D model of the retrogradely labeled cells , site 4 showed a high number of cells in the caudal dlPFC and the principal sulcus, which were only sparsely covered by cells projecting to the neighboring sites 3 and 5. This result argues against the alternative interpretation of the "hubness" of site 4, that it is a result of the convolution of the connectivity profile of the two neighboring sites.Details of this analysis are included in the updated Materials and methods:s4(aP) is the probability composed of 32P and 24P with weight w (0 ≤ w ≤ 1).”“Comparison of the inputs to site 4 with a composition of inputs to areas 32 and 24. Based on the analyses described above, site 4 stands out as having a uniquely diverse input pattern as compared to the other sites[…] The method for 3D reconstruction is described as follows:“For each case, the stack of 2D coronal charts from StereoInvestigator were imported into IMOD, a 3D rendering program (Boulder laboratory for 3D Electron Microscopy). […] The 3D model of each case was then merged through spatial alignment to our in-house template brain as previously described .”The results described above are added to the Results section. Discussion of these results is added as follows:“A hub or inputs reflecting the junction of two cytoarchitectural areas?An alternative to the hub hypothesis is that site 4 was at the border of areas 32 and 24, and thus, its diverse inputs could be a result of a composition of inputs to areas 32 and 24. […] This cannot be predicted by the location of this site among the cytoarchitectonic areas.”The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:One of the main points raised previously was the claim that the "hubness" of site 4 could not reflect an injection involving 2 areas, that is, area 24 and 32. Though the control provided rules out an additive version of this account, there may be reason to doubt additivity of connectivity. Thus, reviewer 3 has suggested a set of additional control analyses to fully rule out this concern. These are appended in this reviewer's comments below. Reviewer 1 also has a suggestion regarding the strength of the conclusions that you should consider in your revision. In discussion, the reviewers were in agreement that both of these points should be addressed. Thus, I send this back to you for additional revision. The reviewers' specific comments follow.Reviewer #1:The authors addressed key points raised and concluded that due to technical limitations, there indeed could be other areas within the anterior cingulate region with strong connectivity resembling a 'hub'. In view of this conclusion, it would be consistent to also tone down the initial part of the Discussion by removing words such as 'strongly' as in: "..the integrative nature of site 4 strongly supports that it is a hub in the frontal cortex..".The manuscript varies in style of expression. For example, the first paragraph of Discussion reads like a section from Results. There is variation in the style of the references in the text, missing references from the reference list and misspellings of some names.Reviewer #3:I would like to thank the authors for their clarifications.I would like to focus on one of the main points, namely, the claim of site 4 being a hub and that this is not explained by the fact that the injection involves 2 areas, that is, area 24 and 32.The authors performed a nice analysis showing that site 4 connectivity cannot be explained by a linear addition of the pattern of connections of areas 24 and 32.This is a basic demonstration of the fact that the assumption of "pure addition" of connectivity profiles does not necessarily hold. This was stated in my previous comment: "But, this assumption is not necessarily true since there is connectional heterogeneity within areas . In other words, there is not pure addition of connectivity profiles."So the authors indeed show that there is no pure linear addition as I indicated, but this is something that is distinct from claiming that the overlap might not contribute to the "overshoot" in the metrics that are used to classify sites as hubs.A more direct rigid control analysis addressing the hubness of sites should focus on the criteria used for hubness and if they could be synthesized and to what extend they resemble site 4 hubness by mixing connections from areas 24 and 32. Specifically, the following steps should be executed to clarify the issue:1) Perform the mixing analysis and generate "predicted site 4" connectivity from areas 24 and 32.2) Calculate ALL formal hubness metrics for this "predicted site 4 profile", namely, degree and entropy.3) Compare how well they fit to the actual site 4 metrics.4) If the "predicted site 4" hub metrics fit better to the actual site 4 than the controls of e.g., mixing area 24 with randomly selected areas  then the we have some evidence that the hubness DOES occur due to mixing, even if there is no pure, at least linear, addition of connectional profiles.Note, that KL-divergence between predicted site 4 and actual site 4 connectivity might be low but degree centrality similarity might be even higher (the same with entropy of connections), since all these metrics do not quantify the same aspects.Importantly, all the above controls cannot exclude that within area inhomogeneities lead to the observed hubness (stated in the previous round of revisions). This is impossible in my view, since we cannot know the structure and spatial periodicity of projection neurons within these areas across animals and how they are adding up from case to case when injections involve two areas and if they lead to what we observe from cases from different animals.I think that it is fair to run the controls that are mentioned above and, depending on the outcome, support site 4 as hub but also indicate the methodological limitations in the manuscript . It is more than fair to do so – invasive-tract tracing is the gold standard, but like any method, it has its limitations and we should operate with such limitations.I hope that the above comments are of help.Regarding further control tests for “hubness”, i.e. whether the patterns of site 4 can be predicted by non-linear mixing of patterns of areas 32 and 24, we performed additional analyses following the steps suggested by reviewer 3. Below is the original comment quoted in italic followed by our response.1) Perform the mixing analysis and generate "predicted site 4" connectivity from areas 24 and 32.We generated 1000 samples of “predicted site 4” connectivity by mixing the connectivity profiles of areas 24 and 32. The mixing used varying weights across frontal cortical (FC) areas. Thus, the mixing resulted in non-linear combinations rather than a pure addition of the profiles of areas 24 and 32. The mixing procedure is included in the revised Materials and methods (subsection “Comparison of the inputs to site 4 with the composition of inputs to areas 32 and 24”.2) Calculate ALL formal hubness metrics for this "predicted site 4 profile", namely, degree and entropy.We calculated the number of FC areas that contributed to 50% and 75% of the total input  and entropy for each of the 1000 samples.3) Compare how well they fit to the actual site 4 metrics.Site 4 has 5 FC areas contributing 50% inputs, and 10 FC areas contributing 75% inputs . Among the 1000 generated samples, only 38 showed 5 or more FC areas contributing 50% inputs, and 63 showed 10 or more FC areas contributing 75% inputs. Moreover, 117 of the 1000 samples showed greater entropy than that of site 4.4) If the "predicted site 4" hub metrics fit better to the actual site 4 than the controls of e.g., mixing area 24 with randomly selected areas  then the we have some evidence that the hubness DOES occur due to mixing, even if there is no pure, at least linear, addition of connectional profiles.We mixed the connectivity pattern of area 24 or 32 with a uniform distribution, generating 1000 control samples in each case. In both control cases, more than 600 samples showed degree-centrality ≥ 5 at 50%, and more than 900 showed degree-centrality ≥ 10 at 75%. More than 900 samples showed greater entropy than that of site 4. Thus, the degree and entropy of site 4 were better predicted by the mixture with a uniform distribution than the combination of areas 32 and 24. Taken together, these results suggest that the hub properties of site 4 did not occur due to the mixing of connectivity patterns of areas 32 and 24.The above results are added to the subsection “Comparison of the inputs to site 4 with the composition of inputs to areas 32 and 24”. The Discussion is updated accordingly.We have also followed the suggestions by reviewers 1 and 3 to adjust the conclusion and state the methodological limitations. The conclusion is changed to “…the integrative nature of site 4 suggests that it is a hub in the frontal cortex.” In addition, a new subsection is added to the Discussion:“Methodological limitations.A main limitation of the current study is that experiments were performed in different animals. […] Thus, when observed as a whole, the region containing these neurons may appear as a hub.”Finally, following the suggestion by reviewer 1, we have revised the style of expression in the Discussion. The first two paragraphs are now merged into one in the revised version. The references are also reformatted to be consistent throughout the text.