Sea ice primary production is considered a valuable energy source for Arctic marine food webs, yet the extent remains unclear through existing methods. Here we quantify ice algal carbon signatures using unique lipid biomarkers in over 2300 samples from 155 species including invertebrates, fish, seabirds, and marine mammals collected across the Arctic shelves. Ice algal carbon signatures were present within 96% of the organisms investigated, collected year-round from January to December, suggesting continuous utilization of this resource despite its lower proportion to pelagic production. These results emphasize the importance of benthic retention of ice algal carbon that is available to consumers year-round. Finally, we suggest that shifts in the phenology, distribution and biomass of sea ice primary production anticipated with declining seasonal sea ice will disrupt sympagic-pelagic-benthic coupling and consequently the structure and the functioning of the food web which is critical for Indigenous Peoples, commercial fisheries, and global biodiversity. Koch and Brown et al. led a collaborative and comprehensive synthesis that shows the transfer of ice algal carbon is widespread throughout the Arctic marine food web and contributes to supporting organisms throughout the dark winter months Additionally, the early timing of ice algal blooms and high concentrations of polyunsaturated fatty acids make this a vital resource for many primary consumers in the spring3. These two microalgae communities display inherent similarities resulting from their dominance by photosynthetic diatoms4. However, until recently, the ability to differentiate and measure these two distinct carbon sources with other methods (e.g. stable isotopes and fatty acids) was somewhat limited in certainty owing to the lack of a specific biomarker for sea ice algae.Arctic marine food webs are classically viewed as being supported by two ecologically distinct types of primary production, effected in temporal sequence by ice-associated (sympagic) algae in spring and (pelagic) phytoplankton in summer. Each source contributes energy to the ecosystem with varying intensity, community composition, and nutritional quality5, understanding how this ecosystem is responding to ongoing climate change is of critical importance. Numerous species have evolved with the seasonal extremes of the Arctic, timing their migrations, foraging, and reproduction with the phenology of ice-associated blooms3. The loss of seasonal sea ice will likely not only affect the primary consumers of this resource but could also drive cascading effects within the food web, including impacting coastal ecosystem resources on which Indigenous Peoples rely. The consequences to these Arctic organisms’ life-history events and their ability to adapt to an alteration in the sea-ice primary production is uncertain7.With the steady decline of sea ice and projections of a seasonally ice-free Arctic Ocean within this century9 to undertake the most comprehensive and quantitative spatial and temporal assessment of carbon partitioning within the Arctic marine ecosystem to date. Since certain HBI lipids are only synthesized by a minority of sea ice diatom species12, while other HBIs are produced by a similar proportion of phytoplankton species14, these proxies now make it possible to differentiate between carbon derived from these two sources in the consumers9. We combined HBI biomarker values from sea ice- and phytoplankton-carbon sources into a single index, termed “H-Print”9. Based on a previously established calibration8, we were then able to convert H-Print values into estimates of sea-ice particulate organic carbon (iPOC) directly consumed by animals15 or transferred to higher trophic organisms. So far, this approach has resulted in comparable findings of carbon sources, examples including fatty acid markers18, bulk stable isotopes20 and compound-specific stable isotope analysis of amino acids (CSIA-AA)22 and CSIA of fatty acids (CSIA- FA)24. Stable isotope analysis of carbon for the primary ice-associated HBI, IP25, has also confirmed sea ice origins25. These multi-proxy approaches further validate the use of iPOC values as quantitative predictors of ice-algal carbon in Arctic food webs26. For example, iPOC and CSIA of a source amino acid  in Atlantic walrus tissues were significantly positively correlated, where high iPOC values were associated with more enriched 13C typical of sea-ice algae21. By quantifying the sea-ice algal HBI contribution (relative to phytoplankton) throughout food chains of the marine ecosystem, spanning several marginal seas and coastal areas of the Arctic, including both Pacific and Atlantic-influenced sectors, we reveal striking year-round consumption of sea ice-associated primary production. In doing so, we consider how the potential future decrease and redistribution of this carbon source will likely have dramatic consequences requiring a concerted conservation approach to mitigate this rapid biodiversity change.Here, we trace source-specific highly branched isoprenoid (HBI) diatom lipid biomarkers27 and extending from the sub- to the high-Arctic  of organisms measured, representing 143 species, contained HBI biomarkers of both sea ice- and phytoplankton-carbon origin. In contrast, the remaining individuals (n = 96), from 26 species, contained only phytoplankton HBIs. Several individual zooplankton samples  collected from late summer28 (15 of 54 samples) and winter29 (17 of 43 samples) had no measurable HBI biomarkers (sea ice or phytoplankton) and were therefore excluded, as an iPOC value cannot be calculated. iPOC revealed that 67% (n = 1568) of animals sampled had a stronger sea-ice carbon signature than phytoplankton , polar cod (Boreogadus saida) and sculpins (Cottidae). The mean iPOC values by month indicated reduced utilization of sympagic sources in May, September, and December, while the remainder of the year indicated strong sea-ice carbon utilization (>50% iPOC). Using multiple linear regressions, we tested several models to verify if iPOC values reflected an increased dependence on sea ice-organic carbon rather than an effect of sampling bias or variables relevant to prevailing sea ice conditions at time of sampling . The model inclusive of month, latitude, habitat and the interaction of month and habitat performed the best as indicated by the lowest Akaike Information Criterion (AIC) score. Habitat , latitude  and the interaction of month and habitat  were each significant (p < 0.001). Month was not significant (p > 0.05). Given the high variability within and among species, we conducted a sensitivity analysis of this model, using habitat as the treatment. Based on the low robustness value of our sensitivity analysis  unobserved parameters would likely have a minimal impact in explaining the variability of iPOC values.More than 96% n = 238 of or30, benthic invertebrates32, zooplankton33, to apex predators including polar bears15, seals34, walruses35, whales36, and seabirds37. Sea ice-derived particulate organic carbon was observed within 134 of 155 Arctic consumer species studied. There were 12 species of zooplankton . As indicated by our analysis, season cannot fully account for the low sea-ice carbon values observed but can very likely explain low to moderate values observed in species such as the ice or arctic cod , polar cod, and Calanus copepods. A low transfer of HBIs in the copepod-associated pelagic food web could also explain the surprisingly low iPOC values in polar cod and ice cod compared to other fishes  in the spring in the Barents Sea33, which contrasts with observations from the Central Arctic Ocean38 and those presented here from the Nansen Basin in July  from Cumberland Sound (n = 142 of 155), but we observed low iPOC levels (<25%) in nearly all samples collected in July from the eastern Beaufort Sea (n = 21 of 22). This may be a result of the Cumberland Sound beluga population that do not migrate and remain in icy Arctic environments year-round36, while the eastern Beaufort Sea population have long migrations and spend time foraging further south in marginal ice waters in the subarctic Bering and Chukchi Seas68. Additionally, similar variations in iPOC values were observed in Pacific walrus populations, which have largely sex-segregated migrations. The males remain in the Bering Sea year-round and reflected in low iPOC values, while the females and calves migrate with the ice edge north into the Chukchi Sea in the spring and summer, where we observed higher iPOC values35. Similarly in seabird populations, thick-billed murres (Uria lomvia) from Prince Leopold Island in Nunavut indicated greater reliance on ice algal carbon sources and sensitivity to changes in sea ice on an annual basis compared to northern fulmars , which have a greater foraging area less constrained by sea ice conditions37.There may also be population-level dynamics in relation to sea ice observed in the iPOC variation within migratory marine mammals and seabirds Fig. . For exa1, until relatively recently, it was assumed the dark Arctic winters were a period of biological dormancy69. We now find this idea is being challenged as wintertime studies are increasingly revealing significant biomass and biological activity72, despite extremely low levels of in situ primary production73. While sources of organic carbon, other than locally produced marine primary production, are available in the Arctic, including advected temperate phytoplankton74, terrestrial river discharges75, benthic algae76, or even recycled carbon from seabird guano77 or marine mammal defecation78, these are relatively localized. In contrast, sea-ice primary production is widespread79.In contrast to the established understanding that springtime is known for its significant productivity in the Arctic[ 80, and may occur at other locations on Arctic shelves. In support of this, we show that iPOC is present in winter (October – March) sampled animals . Feeding strategy likely also influences the amount of iPOC observed in benthic invertebrates  of sediment within the upper 70 mm  and total organic carbon from a box core collected in the Amundsen Gulf  in 2008 as part of the International Polar Year – Circumpolar Flaw Lead study85. Changes we are already witnessing include the shrinking and thinning of multiyear ice leading to greater areas of open water in summer86. Recent observations suggest that over the last several decades, this thinning of sea ice has resulted in greater potential habitat for ice algae in the near term87. Even if temporarily expanding, the redistribution of this habitat has major implications for underlying benthic ecosystems relying on this resource. A reversal of this trend in increasing first-year sea ice is expected in the coming decades with the gradual disappearance of seasonal ice in summer. Changes in the phenology and duration of Arctic sea-ice melt are also evident, with seasonal ice in some sub-Arctic regions melting up to seven days earlier each year88. The reduction of sea-ice habitat, currently occurring at the lower sub-Arctic regions , is suggested to result in a decoupling of sea-ice and benthic habitats. It is, therefore unclear if a future Arctic, with no summer sea ice and potentially reduced, or even absent, sea ice-benthic coupling in at least some regions, will provide sufficient primary production to sustain the sympagic ecosystem throughout the extended dark winter period. In a summer without sea ice, phytoplankton will be highly productive , resulting in partial remineralization in the water column and the remaining refractory organic matter settling to the seafloor. We clearly show here that a large component of the Arctic marine ecosystem relies on benthic carbon storage, adapted to ice algae production that allows for recycling through the winter dark period. Consequently, the major ecosystem changes will be twofold – first we will see a mismatch in life histories adapted to this carbon source that have evolved with seasonal sea ice; and second, there will be a redistribution of species and their habitats to respond to this shifting carbon source eventually leading to reduced Arctic and global biodiversity.The Arctic is rapidly changing, with recent estimates suggesting a warming upwards of four times faster than the rest of the planet39. Indeed, the ability of an extended phytoplankton season to suitably subsidize a loss of sea-ice organic carbon would likely depend upon a sufficient excess primary production that continues exceeding the grazing potential of pelagic consumers. Some suggest a more than three-fold increase in pelagic primary production is possible89, and that an increased proportion of thinner seasonal sea ice and/or less snow cover can transmit sufficient light for under-ice phytoplankton blooms to begin even before ice has melted91. There may also be an associated decline in ice algae and phytoplankton biodiversity and size92, outside the preferred size range of some primary consumers. However, these outcomes depend, in part, on sufficient over-winter nutrient availability and the ability of temperate-associated species to move in and compete for resources. Perhaps then, in the event that the availability of sea ice-algae is greatly reduced and phytoplankton cannot match the current capacity for year-round energy supply, the benthic retention of sea-ice carbon will not be sufficient to support organisms throughout the winter. The resulting poor understanding in organic carbon contributions has, in part, led to the Arctic Nations taking a proactive approach in declaring a moratorium on commercial fishing in Arctic high seas for a period of sixteen years, until 203793. Intended to protect Arctic marine species from potential overexploitation, this has also highlighted the urgent need for research into the future sustainability of the Arctic ecosystem in the face of continued climate warming and reducing sea ice94. This further emphasizes the critical role of and urgent need for increased biological monitoring programs and proactive approaches for sustainable commercial activities to allow a transition that minimizes species loss.It is difficult to accurately predict the type of Arctic ecosystem that will evolve without sea ice – one that depends on a large summer phytoplankton production and no stored energy during the winter. While the above mentioned changes in sea ice and the resulting extended open water period may provide a setting for a longer phytoplankton growth season, this must be accompanied by increases in the springtime nutrient load that can be sustained through summer37.A majority of samples and associated HBI data analyzed for this study were reported in previously published studies and recalculated as iPOC, including several datasets that were not included in the original publications , mixed with hexane (3 ×4 mL), then centrifuged . The hexane layer is then transferred to a clean vial and dried under a gentle N2 stream. Dried lipid extracts were resuspended and fractionated (5 mL hexane) using open-column silica gel chromatography . The non-polar lipids containing the HBIs were eluted while the polar compounds were retained on the column. The eluted compounds were dried under N2. 50 μL of hexane was added twice to the dried extract and transferred to amber chromatography vials for analysis.HBI extraction and analysis followed established protocols36. The HBIs identified and quantified included IP25 (m/z 350.3), HBI II (m/z 348.3) and HBI III (m/z 346.3).HBIs were analyzed by gas chromatography–mass spectrometry on an Agilent HP-5 ms column (30 m × 0.25 mm × 0.25 μm). The oven temperature was programmed to ramp up from 40 °C to 300 °C at 10 °C/min with a 10 min isothermal period at 300 °C. HBIs were identified and quantified by measuring the retention indices and mass spectral intensities for each HBI in selective ion monitoring (SIM) mode25: m/z 350.3, II; m/z 348.3 and III; m/z 346.3), according to Eq. 8.Estimates of iPOC were based on the H-Print which was calculated using the analytical intensities of three HBIs . This equation was based on a controlled feeding experiment using ice algae with known quantities of HBI II. HBI concentrations were measured throughout the experiment. The test organisms were ensured to have depurated any natural occurrence of HBIs before being fed the ice algae and measured repeatedly until all HBIs had been eliminated. Sea-ice carbon (iPOC) estimates are expressed here as mean values.Sea-ice carbon, as a proportion of marine-origin carbon within samples, was estimated using Eq. 99 and publications102. All analyses were conducted using R version 4.2.2 (www.r-project.org) and plots were produced using the package ‘ggplot2’. A significance level of α = 0.05 was used for all tests. Our model was designed with the following assumptions based on prior knowledge, 1) there are multiple confounders that can influence iPOC including time of sampling, time of ice retreat relative to sample location, feeding strategies, nutrient availability, ice algae species composition and production, and varying HBI retention time of individuals, 2) iPOC can vary widely between and within species, 3) all samples were collected north of the median maximum sea-ice extent and would therefore have the potential to consume ice algae or sea-ice derived carbon sources. Multiple linear regressions were used to explore correlations between sea ice algae carbon estimates and several predictor variables  and applicable interactions (month*habitat). AIC scores of each model were compared using package ‘AICcmodavg’. The model with the lowest AIC score (iPOC ~ month+latitude+ habitat+month*habitat) was further assessed using the ‘sensemakr’ sensitivity analysis package103 with habitat as the treatment and month as the confounder, as sea-ice retreat generally follows a pattern one could anticipate whether there would be open water (summer) or sea ice (winter). Samples without month recorded were excluded (n = 65). The partial R2 of habitat with the outcome was 0.02 and the robustness value was 0.05 at α = 0.05. We further explored the sensitivity by shifting each observation three months earlier. The result was similar to our original model, suggesting that there is minimal effect of any one of these environmental parameters alone in explaining the variability observed in our iPOC values, but is rather likely a combination of factors.Organisms were classified by their primary habitat and feeding strategies (benthic invertebrates only) using available resources, including various online species databasesSupplementary Information


Rapid identification of effective antibiotic treatment is crucial for increasing patient survival and preventing the formation of new antibiotic-resistant bacteria due to preventative antibiotic use. Currently utilized “gold standard” methods require 16–24 h to determine the most appropriate antibiotic for the patient’s treatment. The proposed technique of laser speckle imaging with subpixel correlation analysis allows for identifying dynamics and changes in the zone of inhibition, which are impossible to observe with classical methods. Furthermore, it obtains the resulting zone of inhibition diameter earlier than the disk diffusion method which is recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). These results could improve mathematical models of changes in the diameter of the zone of inhibition around the disc containing the antimicrobial agent, thereby speeding up and facilitating epidemiological analysis. Empiric antimicrobial treatment must be replaced by pathogen-directed therapy as soon as possible to reduce patient mortality risk. Early identification of the pathogen and determination of its susceptibility to antibiotics can provide targeted pharmacological intervention at the early stages of the disease, increasing patient survival chances. This issue has become particularly relevant in treating elderly and immunocompromised patients. The EUCAST-standardized phenotypic resistance tests, such as disk diffusion and E-test, require 16–24 h to obtain results . At the In previous studies using a non-contact optical technique called laser speckle contrast imaging, we determined bacterial growth after 3 h from the beginning of activity . In our We suppose that the laser speckle technique, combined with subpixel correlation analysis, can provide a sensitive and early detection method for assessing bacterial growth inhibition and the formation of a sterile zone in the presence of antibiotics. For this reason, the aim of the study is to assess the feasibility and effectiveness of the laser speckle technique combined with subpixel correlation analysis for detecting bacterial growth inhibition and the formation of a zone of inhibition in the presence of antibiotics. Additionally, the study aims to evaluate the spatiotemporal characteristics of the formation of zone of inhibition and compare the performance of the laser speckle technique with traditional methods for assessing bacterial growth inhibition.This study demonstrates the advantages of the proposed method - faster antimicrobial susceptibility results and, therefore, faster replacement of empiric antimicrobial therapy with pathogen-targeted therapy, which has greater potential to reduce treatment duration and costs, as well as to reduce mortality.2.Experiments were performed at the Pauls Stradins Clinical University Hospital Joint Laboratory on different bacteria and their corresponding antibiotics .2.1.The laser speckles were generated by a single mode 658 nm diode-pumped solid-state laser (output power 60 mW). Images were captured at 20-s intervals by a CMOS camera . To read2.2.Escherichia coli (E. coli) and Klebsiella aerogenes (K. aerogenes) bacteria were selected for the experiment. We chose cefotaxime disk  and ampicillin disk  for K. aerogenes and ciprofloxacin disk  for E. coli, and amikacin disk , for an experiment without bacteria. To avoid artifacts, the experiments were performed in a separate room, in an incubator at 37°C. The bacterial suspensions were made in saline to the density of a 0.5 McFarland turbidity standard. Culturing on the Petri dish was prepared according to a EUCAST standard procedure - bacteria were inoculated on Mueller-Hinton agar and then antibiotic discs were placed on the surface  and b toward x and y, respectively.where a and b are two adjacent frames in the sequence, The correlation peak shift in space characterizes the changes that occur between consecutive images.Interpolation around the correlation peak is performed to find a more accurate peak position .where The offsets obtained between each pair of adjacent images were accumulated and converted into a “time signal.”To avoid the influence of local transient spikes, a signal envelope within a certain window was used .where N is the length of the window, n is the current sample, k is the index running inside the window. Accordingly, N – the length of the window – is responsible for the degree of signal smoothing.An increase of signal values represents spatiotemporal activity patterns during colony growth .3.3.1.In this section, we compare the behavior of the zone of inhibition for raw speckle images and for speckle images processed using subpixel correlation analysis. Two cases of bacterial susceptibility were considered: (1) bacteria that are susceptible to the antibiotic  and (2) The subpixel correlation technique allows for the detection of submicron bacterial events  earlier than without the algorithm . It can The proposed subpixel correlation algorithm allows it to detect effects that are not observed without it , 2023.When the bacteria are completely resistant to the antibiotic, raw speckle images and speckle images after subpixel correlation analysis will show that the zone of inhibition is not observed. However, since subpixel correlation analysis is designed to detect activity, there will be noticeable differences in bacterial behavior. Raw speckle images do not yet demonstrate the presence of bacteria 3.5 h). Speckle images after subpixel correlation already detect it. That is, the activity of bacteria already exists. In the images showing data analysis using subpixel correlation analysis  lower than the signal from the zone where bacterial activity occurs. In the zone where bacterial activity occurs, we get increased signal values. Bacterial activity using the subpixel correlation method can be detected 3–4 h after inoculation , 2021a,bThis effect can be observed in a signal proportional to activity, i.e., after subpixel correlation analysis. If we observe raw speckle images, then this effect may not be noticeable since it is not the activity of bacteria that is observed there, but their presence. Bacteria are already present for several hours, and when the antibiotic disk is placed, they begin to “lose” activity. Hence, raw speckle images still do not demonstrate the formation of a zone of inhibition, while it is observed using subpixel correlation analysis of laser speckle images .Thus, using subpixel correlation analysis of laser speckle images, it is possible to measure the formation of a zone of inhibition already 1–1.5 h after the time when the antibiotic was placed on already grown bacterial cultures. And what is important – the zone of inhibition did not appear “suddenly,” but was formed with a small radius, continuously increasing, which can be explained by the spread of the antibiotic in the agar.3.3.It was obtained that after a few hours from the start of the experiment, a disc of the zone of inhibition begins to form around the antibiotic. The zone of inhibition becomes clearly visible within a couple of hours after the beginning of formation. The result was obtained significantly earlier than by the disk diffusion method for the same bacterial species . The forWhere Consider the time when the signal envelope starts to drop as a function of time for several received radii. Note that the beginning of signal envelope drop might indicate the appearance of a zone of inhibition in this place. Whereas in the situation where antibiotics are placed without bacteria such behavior was not found .Putting the averaged signal envelopes for all radii together, we get a spatio-temporal image. By creating this image, we can study the behavior of the zone of inhibition. By emphasizing the drop for each radius, we can obtain a certain curve, according to which the size of the zone of inhibition changes over time . This beFor reference, experiments were performed with antibiotic diffusion discs placed on agar without inoculated bacteria. In such cases, the zone of inhibition was not observed . This in4.The potential of laser speckle imaging techniques for antimicrobial susceptibility testing have been earlier demonstrated by many authors . AuthorsSimilarly, as other rapid antimicrobial susceptibility tests (RAST) the technology has several limitations. Firstly, the slow growth rate of certain bacterial strains can prolong the time required for conducting AST, which may limit the feasibility of obtaining results within a desired timeframe. Secondly, the presence of heterogeneous antibiotic resistance within bacterial populations can affect the accuracy of AST results, as different subpopulations may exhibit varying levels of susceptibility or resistance. However, in the context of presented study and the technology developed, it is important to consider the clinical timepoints for administering antibiotics. Many broad-spectrum antibiotics are given at intervals of 6 to 8 h, allowing clinicians to reassess the choice of antibiotics based on the patient’s condition. Future advancements in identification and antibiotic susceptibility testing (ID/AST) methods should focus on providing results within this time frame of less than 6 h, enabling timely adjustments in antibiotic therapy to optimize patient care .In this paper the clinically significant dynamics and changes of the zone of inhibition were evaluated by the processing of laser speckle images with the subpixel correlation analysis method. The zone of inhibition is the “absence of signal;” accordingly, it can be detected on the “signal presence” background, that is, only when the bacteria are already visible (after 3–4 h). The lag phase continues for up to 3–4 h, until the surface density of bacteria increases to the level necessary for observation. However, if the antibiotic is placed on the Petri dish 4–4.5 h after the bacteria inoculation, the bacteria are in sufficient concentration to be visible with the subpixel correlation method. In this case, we observe that the formation of the zone of inhibition is clearly visible almost immediately. While observing unprocessed (raw) speckle images in this case, the zone of inhibition is not immediately visible, but after a delay of several hours (approximately 5 h delay). A case was observed where bacteria determined to be fully resistant to antibiotics using the conventional method could show a formation of zone of inhibition when analyzed using subpixel correlation analysis on laser speckle images . This shOur proposed technology on one hand would fit well into the clinical microbiology automation offered by any company, where the determination of antimicrobial susceptibility is based on the disk diffusion test, and on the other hand, it would help to save time and obtain antimicrobial susceptibility results faster.5.In the present study, we have demonstrated the ability of subpixel analysis of laser speckle time series images for dynamic visualization of zones of inhibition produced by antimicrobials. The obtained results demonstrate the ability to follow changes in the radius of the zone of inhibition from early times when it cannot yet be detected by classical methods. Thus, this allows for a detailed analysis of the dynamics of the zone of inhibition, which in the future will make it possible to predict the diameter of the zone of inhibition around the antibiotic disk faster than using the disk diffusion method without laser speckle technology. The practical implementation of the obtained technology will allow the laboratory to send the results and their interpretation to the clinic much faster. This study will serve as a foundation for a deeper understanding of the zone of inhibition formation processes and the possibility of describing this zone using mathematical models, which will become the basis for further prediction of the zone of inhibition in our future studies.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.IB, AR, SK, DB, EP, AL, and IL: conceptualization, formal analysis, investigation and methodology, writing—review, and editing. AR, SK, DB, and EP: data curation. AR, SK, DB, AL, and IL: funding acquisition. AR, DB, AL, and IL: project administration. IB, AR, DB, and IL: resources. IB: software. IB, IL: supervision. IB and DB: visualization. IB, AR, and SK: writing—original draft. All authors contributed to the article and approved the submitted version.This work has been supported by the European Regional Development Fund project “Rapid assessment system of antibacterial resistance for patients with secondary bacterial infections” (No. 1.1.1.1/21/A/034).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Asthma is not well investigated in equatorial Africa and little is known about the disease-associated allergen molecules recognized by IgE from patients in this area. The aim was to study the molecular IgE sensitization profile of asthmatic children and young adults in a semi-rural area (Lambaréné) of an equatorial African country (Gabon), to identify the most important allergen molecules associated with allergic asthma in equatorial Africa.Dermatophagoides pteronyssinus (Der p), D. farinae (Der f), cat, dog, cockroach, grass, Alternaria and peanut. Sera were obtained from a subset of 35 patients, 32 with positive and 3 with negative skin reaction to Der p and tested for IgE reactivity to 176 allergen molecules from different allergen sources by ImmunoCAP ISAC microarray technology and to seven recombinant Blomia tropicalis (Blo t) allergens by IgE dot blot assay.Fifty-nine asthmatic patients, mainly children and few young adults, were studied by skin prick testing to Thirty-three of the 59 patients (56%) were sensitized to Der p and 23 of them (39%) were also sensitized to other allergen sources, whereas 9 patients (15%) were only sensitized to allergen sources other than Der p. IgE serology analyses (n=35) showed high IgE-binding frequencies to the Blo t allergens Blo t 5 (43%), Blo t 21 (43%) and Blo t 2 (40%), whereas the Der p allergens rDer p 2, rDer p 21 and rDer p 5  were less frequently recognized. Only few patients showed IgE reactivity to allergens from other allergen sources, except to allergens containing carbohydrate determinants (CCDs) or to wasp venom allergens .Our results thus demonstrate that IgE sensitization to mite allergens is very prevalent in asthmatics in Equatorial Africa with B. tropicalis allergen molecules representing the most important ones associated with allergic asthma. Asthma is a common chronic respiratory disease and an increasing health problem worldwide . The preBlomia tropicalis , cat, dog and grasses is a major risk factor for the development of asthma , 9. In topicalis , 11. Fewopicalis –14. Howeopicalis –19. In t species –22, but  species , 18, 23. species , 25. Ski species , 18, 26.Allergen microarrays can be used to determine patients’ IgE sensitization profiles to a panel of individual allergens and allow to distinguish between genuine and cross-sensitization to a certain allergen source .Blomia tropicalis but less to Dermatophagoides and their IgE-reactivity profiles were different from those found in the Western world and in other geographic/climate areas in Africa. Thus this is the first study demonstrating that genuine sensitization against Blomia tropicalis is responsible for allergic asthma in Africa.In this study, 59 children and young adults (2-18 years) from a semi-rural area of Gabon, located in Equatorial Africa, with confirmed asthma were skin prick tested with eight different allergen sources, which showed that 56% of the asthmatics were sensitized to HDMs. Sera of these patients were tested for IgE-reactivity to 176 allergens from different allergen sources Interestingly, the analysis with micro-arrayed allergen molecules revealed that asthmatics from Equatorial Africa were mainly sensitized to allergens from Dermatophagoides pteronyssinus and Dermatophagoides farinae), cat and dog extracts, cockroach, grass pollen extract, Alternaria and peanut extracts . Sera could be obtained from 35 of the individuals, 32 of them had perennial symptoms and positive skin prick test reactions to Dermatophagoides extracts, the other 3 (#33-35) were negative to Dermatophagoides . IgE reactivity to a panel of purified mite allergens as well as to allergens from different allergen sources was analyzed with a customized version of the ImmunoCAP ISAC microarray   . Asthma symptoms were confirmed by functional investigations (FEV1 measurements and reversibility after bronchodilator (except in 18 cases when the children did not perform the test because of lack of collaboration due to low age), oxygen saturation measurements). Severity of asthma was classified according to clinical criteria according to GINA recommendations. All participants were tested by skin prick testing (SPT) to 8 different allergen sources: 2 house dust mite extracts  . Table SPT were performed on the forearm, in the absence of antihistamine intake for at least one week . The test was considered positive if the greatest diameter of the wheal was >3 mm or >50% of the greatest diameter of the positive control (histamine solution at 1/1000) . Each poD. pteronyssinus (d1) (cut-off > 0.35 kU/L) were measured according to the manufacturer’s instructions on an ImmunoCAP 100 instrument .Specific IgE antibodies to D. pteronyssinus sensitization were: nDer p 1, rDer p 2, rDer p 4, rDer p 5, rDer p 7, rDer p 10, rDer p 11, rDer p 14, rDer p 15, rDer p 18, rDer p 21, rDer p 23 and rDer p 37; allergens indicative for grass pollen sensitization were: rPhl p 1, rPhl p 2, nPhl p 4, rPhl p 5b, rPhl p 6, rPhl p 7, rPhl p 11, rPhl p 12; allergens indicative for cockroach sensitization were: rBla g 1, rBla g 2, rBla g 5, nBla g 7; allergens indicative for cat sensitization were: rFel d 1, nFel d 2, rFel d 4; allergens indicative for dog sensitization were: rCan f 1, rCan f 2, nCan f 3, rCan f 4, rCan f 5, rCan f 6; and allergens indicative for peanut sensitization were: Ara h 1, rAra h 2, Ara h 3, nAra h 6, rAra h 8, rAra h 9. Detection of IgE reactivity was performed as described   for measuring allergen-specific IgE to 176 allergens from different allergen sources was used . For comescribed  and leveE. coli strain BL21 (DE3) and purified as described (E. coli strain Origami (DE3) under native conditions . Blo t 21.0101 (gbAAX34047.1) coding sequence was amplified from a cDNA library of B. tropicalis and cloned into pET100 plasmid. Protein expression was performed in E. coli BL21 DE3 cells. To obtain a soluble product, cells were resuspended in 50 mM Tris-HCl and 30 mM NaCl pH 7.8, lysed with an ultrasonic dismembrator FB 705  and centrifuged at 5000 rpm for 30 minutes at 4°C. The lysate was then incubated with Ni-NTA and purified under native conditions. Protein concentration was determined by densitometry using a BSA standard curve.Recombinant Blo t 5, Blo t 8, Blo t 10 and Blo t 13 were expressed in escribed –33. Blo nditions . Blo t 2nditions . Blo t 2Blomia tropicalis allergens, rBlo t 2, rBlo t 8, rBlo t 10, rBlo t 12, rBlo t 13 and rBlo t 21 (0.2 mg/ml) and, for control purposes, BSA were dotted onto nitrocellulose membrane strips . Nitrocellulose strips containing the dot-blotted proteins were blocked in buffer A  and incubated with sera (1:10 in buffer A) from the asthmatic patients and from one non-allergic individual. Bound IgE antibodies were detected with 125I-labeled anti-human IgE antibodies  and visualized by autoradiography (Kodak film).Two microliter aliquots of the The main demographic and clinical characteristics of the asthmatic children and young adults are summarized in Alternaria extracts, as these extracts were not available at the beginning of the study (Dermatophagoides species (56%), four of them were only sensitized to D. pteronyssinus, three only to D. farinae  were negative for D. pteronyssinus in the ImmunoCAP (D. pteronyssinus specific IgE levels (0.1 kUA/l) also had only tiny wheal diameters after testing with HDMs (< 3mm)  and 12 of the 13 patients with IgE reactivity to rBlo t 5 had high IgE-levels  to this allergen , whereas nDer p 1 and rDer p 23, which are known as major allergens in European populations, were only recognized by 11% and 14% of the patients. However, almost all of these patients had IgE-levels >15 ISU to nDer p 1 and/or rDer p 23 and the median IgE levels of nDer p 1 (21.65 ISU) and rDer p 23 (30.54 ISU) were comparable to that of rDer p 2 (26 ISU). The IgE-binding frequencies to other D. pteronyssinus allergens varied between 3% (rDer p 11 and rDer p 14) and 17% (rDer p 4) . Interestingly, the highest IgE-binding frequency was found for the allergen Figure 3Betulaceae and Oleaceae or for weed allergens   by dot blot assay  Figure 3When we compared patients with an asthma severity score of <2 with those with asthma severity scores of 2-4 regarding IgE binding frequency and IgE levels to Blo t 5 and HDM allergens, we found that patients with more severe asthma reacted more often to Blo t 5 and to HDM allergens than patients with a low asthma severity score Figure 7The International Study of Asthma and Allergies in Childhood has completed phase 3 and it has now become clear that asthma has increased in Africa . For insOn the basis of clinical data, we observed a high rate of intermittent asthma and a rather low rate of more severe asthma (persistent and severe), which was different to functional data, which showed a high level of bronchial obstruction. However, reference values were not available for African children, the only reference values were established in American black children and showed lower values (minus 14%) in comparison to Caucasian children .B. tropicalis), but also respiratory virus infections have been shown as important trigger factors for wheezing attacks, especially in young children (D. pteronyssinus and/or D. farinae extracts. Previous studies from Zimbabwe reported HDM sensitization rates between 53% and 67.8% in atopic populations (Dermatophagoides species (Dermatophagoides species (sensitization rate: 18%) and Blomia tropicalis (15%)  .D. pteronyssinus allergen extract. Interestingly, we found the highest rate of sensitization for Blo t 5 from Blomia tropicalis (37%) with very high IgE levels (median 65.89 ISU), which agrees with results obtained by skin prick test to commercial B. tropicalis allergen extract in adjacent countries with the same tropical climate with high temperatures and high humidity [Nigeria  were much lower than the rBlo t 5 IgE levels , whereas the allergens of group 1 were less frequently recognized (11%). However, the highest median of IgE titers were found for rDer p 23 (30.54 ISU), followed by rDer p 2 (26 ISU) and nDer p 1 (21.65 ISU), confirming that these three allergens are important for this African population. In a European study with 105 asthmatic children, a higher rate of sensitization was observed for nDer p 1, rDer p 2 and rDer p 23 , but the median IgE titers were lower .Solenopsis africana, which represents an endemic red fire ant in Gabon) (The majority of patients (24 out of 35) had specific IgE to rVes v 5 (antigen 5) and 19 of the 24 rVes v 5-positive patients also showed IgE-reactivity to the cross-reactive allergen, rPol d 5. Thus, the patients may have been stung by wasps or may have been sensitized to allergens which cross-react with antigen 5  . Only thn Gabon) . It is fn Gabon) . TherefoSome discordances between skin prick tests and ImmunoCAP ISAC have been observed in our study, which might be explained by low quality of the skin prick test extracts , by IgE Blomia tropicalis; ii) much lower sensitization to Dermatophagoides pteronyssinus with a molecular sensitization profile which differs partly from European asthmatics; iii) low frequency of sensitization to most of the other tested allergens. Further investigations are needed to identify the relevant mite species in house dust samples and the clinically relevant mite allergens for this population, in order to improve diagnosis and immunotherapy in this area  a predominant sensitization to allergens of the tropical mite his area . Immunothis area –62. BaseThe original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.The study was approved by the Comité d’Ethique Institutionnel of the research center of Medical research of Lambaréné (Cermel) (CEI-MRU 01/2014) and by the Ethics Committee of the Medical University of Vienna (EK 1641/2014). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.GP contributed to the design of the study, interpreted the findings and wrote the manuscript. SV and RV designed the study and contributed to the interpretation of the findings, writing and revising the manuscript. CW, AR, CK, H-JH, ND’S, CL, JZ, RR, NA and LC performed the experiments, provided materials, read and revised the manuscript. All authors contributed to the article and approved the submitted version.


Lignocellulose is the most abundant renewable resource in the world and has attracted widespread attention. It can be hydrolyzed into sugars with the help of cellulases and hemicellulases that are secreted by filamentous fungi. Several studies have revealed that the Ras small GTPase superfamily regulates important cellular physiological processes, including synthesis of metabolites, sporulation, and cell growth and differentiation. However, it remains unknown how and to what extent Ras small GTPases participate in cellulase production.rsr1 (∆rsr1) significantly increased cellulase production and decreased the expression levels of ACY1-cAMP-protein kinase A (PKA) signaling pathway genes and the concentration of intracellular cyclic adenosine monophosphate (cAMP). Loss of acy1 based on ∆rsr1 (∆rsr1∆acy1) could further increase cellulase production and the expression levels of cellulase genes, while overexpression of acy1 based on ∆rsr1 (∆rsr1-OEacy1) significantly reduced cellulase production and transcriptional levels of cellulase genes. In addition, our results revealed that RSR1 negatively controlled cellulase production via the ACY1-cAMP-PKA pathway. Transcriptome analysis revealed significantly increased expression of three G-protein coupled receptors  and approximately two-fold higher expression of ACE3 and XYR1, which transcriptionally activated cellulases with the loss of rsr1. ∆rsr1∆ tre62462 exhibited a decrease in cellulase activity compared to ∆rsr1, while that of ∆rsr1∆tre58767 and ∆rsr1∆tre53238 showed a remarkable improvement compared to ∆rsr1. These findings revealed that GPCRs on the membrane may sense extracellular signals and transmit them to rsr1 and then to ACY1-cAMP-PKA, thereby negatively controlling the expression of the cellulase activators ACE3 and XYR1. These data indicate the crucial role of Ras small GTPases in regulating cellulase gene expression.In this study, we found that the putative Ras small GTPase RSR1 negatively regulated the expression of cellulases and xylanases. Deletion of Trichoderma reesei. Understanding the roles of these components in the regulation of cellulase gene transcription and the signaling processes in T. reesei can lay the groundwork for understanding and transforming other filamentous fungi.Here, we demonstrate that some GPCRs and Ras small GTPases play key roles in the regulation of cellulase genes in The online version contains supplementary material available at 10.1186/s13068-023-02341-z. Trichoderma reesei abundantly produces extracellular cellulases and hemicellulases, which catalyze the degradation of lignocellulose to monosaccharides , to form the rsr1 deletion plasmid (p∆rsr1). The deletion cassettes were transformed into QM6a strain by Agrobacterium-mediated transformation. The primers rsr1-CF/D70-4, Hg3.6/rsr1-CR and rsr1-OF/ rsr1-OR were used to verify the putative rsr1 disruption mutants (Δrsr1) by diagnostic PCR using with DNA sequencing. Hyg casseette were used as screening markers. B. To construct an RSR1 complementation strain, the 1.5-kb promoter fragment, gene coding sequence fragment, and 0.5-kb terminator fragment together were obtained from the T. reesei genome QM6a, which constituted 3245-bp and inserted into SwaI-linearized LML2.1 to turn into the re-complementation vector pRrsr1 [2]. The re-complementation cassettes were transformed into Δrsr1 strain by Agrobacterium-mediated transformation. Re-complementation strains (RC-rsr1) are screened by PCR using the primers rc-rsr1-CF and rc-rsr1-CR and DNA sequencing. The primers were used in this experiment in Table S3. C. Validated electrophoretic diagram to verify the knockout and complementation of rsr1. D. The skeleton schematic diagram to knockout acy1 in the knockout strain Δrsr1. E. The skeleton schematic diagram to overexpress acy1 in the knockout strain Δrsr1. F. The skeleton schematic diagram to knockout tre62462 in the knockout strain Δrsr1. G. The skeleton schematic diagram to knockout tre58767 in the knockout strain Δrsr1. H. The skeleton schematic diagram to knockout tre53238 in the knockout strain Δrsr1. I. Validated electrophoretic diagram to verify the deletion of tre62462, tre58767, tre53238, acy1 in the knockout strain Δrsr1, and the overexpression of acy1 in the knockout strain Δrsr1. 1: ∆rsr1∆tre62462-F; 2: ∆rsr1∆tre62462-R; 3: ∆rsr1∆tre58767-F; 4: ∆rsr1∆tre58767-R; 5: ∆rsr1∆tre53238-F; 6: ∆rsr1∆tre53238-R; 7: ∆rsr1∆acy1-F; 8: ∆rsr1∆acy1-R; 9: ∆rsr1-OEacy1-F; 10: ∆rsr1-OEacy1-R. J. Validated electrophoretic diagram to verify the deletion of acy1 in the original strain QM6a, and the overexpression of acy1 in the original strain QM6a. 1: ∆acy1-F; 2: ∆acy1-R; 3: ∆acy1-O; 4: QM6a-O; 5: QM6a-OEacy1-F; 6: QM6a-OEacy1-R. K. Validated electrophoretic diagram to verify the deletion of tre62462, tre58767, and tre53238 in the original strain QM6a. 1: ∆tre62462-F; 2: ∆tre62462-R; 3: ∆tre62462-O; 4: QM6a-O; 5: ∆tre58767-F; 6: ∆tre58767-R; 7: ∆tre58767-O; 8: QM6a-O; 9: ∆tre53238-F; 10: ∆tre53238-R; 11: ∆tre53238-O; 12: QM6a-O.Additional file 5: Table S2. Transcriptional level of total DEGs in Δrsr1 and QM6a strains.Additional file 6: Table S3. Comparison of cellulase and hemicellulase genes expression levels in Δrsr1 and QM6a strainsAdditional file 7: Table S4. Log2 fold change (Log2fc) of the top 10 genes differentially expressed in Δrsr1 and QM6a strainsAdditional file 8: Table S5. Log2fold change (Log2fc) of characterized transcriptional factors involved in the regulation of lignocellulase genes. NS represented not significant, P adjust > 0.05Additional file 9.Table S6 The changes of 876 predicted transporter genes in T. reesei QM6a genome were sorted outAdditional file 10: Table S7. The changes of 58 GPCR genes [1] in Δrsr1 and QM6a strains were sorted out, NS represented not significant, P adjust > 0.05Additional file 11: Table S8 Primers used in this studyAdditional file 12: Figure S4. The correlation between the biological replicates of each sample. The following heat map is used to show the correlation. Each value in the grid is the correlation coefficient between two samples. The larger the value, the greater the correlation between the two samples and the closer they are. These results indicate that the transcriptome data is more credible


For detecting genotype-phenotype association from case–control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype.In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case–control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher’s exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons.With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case–control GWAS. A central problem in genetics is determining which loci of a genome are responsible for the difference between two phenotypes of an organism. A typical approach uses the case–control study, which samples subjects with both phenotypes and looks for differences in the target variables between the two groups. In the case of genotype-phenotype association study specifically, this means looking for differences between the two groups in the frequencies of alleles. A case–control Genome-Wide Association Study (GWAS) does that across a large portion of the subjects’ genomes , which iY and aim to detect variants which affect Y based on case–control SNP data.For GWAS, a genome can be represented as a sequence of variant sites, i.e. genomic locations that may have different genotypes. The simplest type of variant site is a single nucleotide polymorphism (SNP), in which variants differ by a single base pair. We may also simplify analysis by considering only the most common version of a gene  and one rarer version . A diploid organism may have 0, 1 or 2 copies of a minor allele. In this paper, we will focus on a dichotomous phenotype p values, for instance by performing a Fisher’s exact test at each site with the null hypothesis that the given site has no effect on the phenotype, and then making a Bonferroni correction [p values and a more lenient correction to larger p values. This improves the sensitivity; but does not address the second criticism. Uffelmann et al. [Given case–control SNP data, the simplest GWAS methods study variant sites individually . A classrrection . But thirrection , 28 due rrection  address p values. The paper uses 4 such: SKAT [More recent algorithms for identifying influential variant sites take into account the spatial clustering . For insch: SKAT , SKAT-O ch: SKAT , burden ch: SKAT  and wtesch: SKAT . In thisYet another class of methods uses a deep-learning approach. For example,  applies We propose modelling the unknown genotype-phenotype association state sequence using a Hidden Markov model (HMM). HMM offers a different outlook on association state clustering to existing methods. Unlike some methods such as collapsing, it does not require knowing the locations of the blocks ahead of time. Furthermore, it attempts to offer more than ZFA by finding not just locations of blocks, but a model for their formation: Blocks form because the state of each variant site affects the state of the next site. Also, our model is designed to accommodate both rare and common variants, making it more versatile than methods that perform well on only rare or only common variants. HMM has already been applied successfully in a wide variety of settings including speech recognition , image cWe will regard variant sites as being in one of three possible unobserved states: Negative Influence, No Influence and Positive Influence. The influence of minor alleles on the phenotype will depend on which state the variant site is in. The states themselves will be governed by a Markov process, with the state of each variant site affecting the state of the next according to a transition probability matrix. The goal of the algorithm will be to determine the state of each variant site as accurately as possible. After describing the algorithm in detail, we will compare its performance to the Fisher’s exact test with Bonferroni correction, FDR and ZFA.n subjects, with each genotype consisting of p variant sites. Let N and M be the numbers of phenotype 0 (control) and phenotype 1 (case) subjects respectively. We assume that each site has one possible minor allele. The data will be organized in the form of a genotype matrixG of size i, j)th entry representing the number of minor allele copies of the jth variant that the ith subject has, and a phenotype vectory of length n, whose ith component is the phenotype of the ith subject.Suppose we have collected the genotypes and phenotypes of jth variant site being in each of these states respectively. The goal of HMM is to predict a state sequence jth site.A null distribution Influence strength parameters A, where k.Markov parameters For brevity, we write j and for each state x at that site.We assume that there are 3 possible states for a variant site: Negative Influence, No Influence and Positive Influence. We write A. It is necessary to ensure that predicted influential sites come in blocks, with higher threshold values leading to longer predicted blocks. We used and recommend In additional to the model parameters described above, our HMM uses thresholding parameters to guarantee biologically meaningful and practically identifiable states. The minimum influence strength threshold Make starting estimates for Evaluate the master objective functionUsing the values for Using the marginal state probabilities, update the parameters values with the Expectation-Maximization Algorithm.Enforce thresholds on self-transition probabilities Repeat steps 2–5 until the master objective function has increased by less than a threshold The forward jth site and being in state k at the jth site. jth site and given that the state is k at the jth site. The master objective function is calculated as HMM attempts to find the values of ained in . \documeWe may also report the marginal state probabilities mentclass2pt{minimWe will describe in detail the emission distribution functions, EM parameter re-estimation process and initial parameter estimates. We omit an explanation of Step 3 because that is already explained in detail in . As one Evaluation of our method, including descriptions of the simulations used to do so, are in the “jth contingency table Classical HMM assumes we have an observation sequence . For thai copies of the minor allele and i copies of the minor allele.where We assume that among the population of phenotype 0 individuals, there are underlying probabilities In the No Influence state, t iterations, i.e. t passes through steps 2–5 in the overall algorithm, and similarly for any other parameter or set of parameters. The purpose of the Expectation-Maximization step is to update the parameter values: Given Let In the E step, we find the function to be maximized. First, we find HMM’s likelihood function: The function to be maximized is:where In the M step, we find values of The last main component of HMM we will discuss is the choice of initial estimates for the model parameters. The EM algorithm is designed to converge to a local maximum, not necessarily the global maximum; hence HMM may likewise converge to a local but non-global maximum. In this subsection, we provide two choices of initial estimates called Default and Random. Default will always yield the same output, and hence need only be run once, while Random may produce a different output each time, and hence may be run multiple runs. The run with the highest final master objective value should be selected.j, recall that i copies of the minor allele. Hence a reasonable initial estimate is At variant site jth variant site as follows:In the “Appendix”, we see that:Next, we make initial estimates of kth row and A. The latter expression is the expected number of transitions from k to k before the final site, if the sites were independent.We then use as initial estimates: U, sort the set Alternatively, we can start the EM algorithm with random parameter values. To explain the random initial, we first define a random triple function: sample U. U. Then for each row, the remaining probability is distributed in proportion t is drawn from U.We carried out nine groups of simulations, each consisting of 20 trials, except for groups 7 and 8 which consisted of 40 trials each. Each trial consisted of running HMM together with 6 comparison methods on a synthesized dataset where the true state of each variant site is known. We recorded the sensitivity, specificity and MCC (Matthews correlation coefficient) of each method in each trial and calculated means. In trials with no true influential sites, sensitivity and MCC were marked as ‘NA’ and excluded from the mean. Unless otherwise stated, the trials consisted of 1000 phenotype 0 subjects and 1000 phenotype 1 subjects. Although HMM results distinguish between Negative Influence and Positive Influence, we collapsed these two categories into one for the purposes of calculating sensitivity and MCC averages. A table displaying the means is shown near the end of each of the following subsections. Standard deviations of MCCs are shown in parentheses.HMM was run with three p value methods: SKAT, SKATO, burden and wtest. The first two consisted of extracting a p value from the Fisher’s exact test separately for each variant site. Bonferroni marked the site as influential if the p value was below 0.05 divided by the number of variant sites. We use a version of FDR due to Benjamini and Hochberg [p value  and multiplies Bonferroni’s significance threshold by the p value ranking. The four ZFA methods were run from the package zfa in R; we used CommonRare_Cutoff = 0.5, fast.path = FALSE and called each of the four methods with the test argument.The comparison methods used were: Bonferroni, FDR and ZFA with four Hochberg , which rThe simulation groups we used are summarized in Table U. The lower bound prevented influences from being too weak.Each component of U and The diagonal entries were We then generated a true state sequence A used to generate As a preliminary test for our method, the first group of simulations generated data according to the HMM model itself. High performance for HMM on these datasets was thus expected. First, we generated values for j, using the true state At each variant site Lastly, we generated the genotypes and phenotypes as follows: The first 1000 subjects were phenotype 0 while the second 1000 were phenotype 1. Thus the phenotype vector consisted of 1000 0’s followed by 1000 1’s. As for the genotype matrix, we knew that the first 1000 rows would correspond to phenotype 0 subjects while the second 1000 rows would correspond to phenotype 1 subjects. We furthermore knew from the contingency tables, for each variant site, how many subjects of phenotype 0 had 0 copies of the minor allele, how many had 1, how many had 2, and the same for phenotype 1 subjects. To determine which phenotype 0 subjects had each genotype, we randomly shuffled a temporary copy of the list of subjects and filled the quota for genotype 0, followed by genotype 1, followed by genotype 2, according to the shuffled order. Then we did the same for phenotype 1 subjects. This process was repeated for each variant site. Results summarized in Table As expected, HMM outperforms all comparison methods. HMM does well under all three jth variant and The rest of the simulations generated data according to a block model, intended to be neutral between HMM and the comparison methods. Influential sites were assumed to come in blocks of defined length and position. Unlike in the HMM model, genotypes were fixed before phenotypes, with the former assumed to exert a causal effect on the latter. We modeled this causal effect with a logistic regression equation from Wu et al. : \documePrior to starting any of the individual synthesized datasets, we first fixed a distribution for number of influential sites as follows: We generated 10,000 datasets as in HMM Model Simulations, but saved only the number of influential sites, and excluded instances with more than 300 influential sites. Then, for each block model simulation, we sampled one number randomly from this list, and took this as our approximate number of influential sites.Next, we decided the influential blocks. For each simulation, we set the number of blocks as 1, 2, 3 or 4 with equal probability. Having decided this, for each block the length was sampled i.i.d. from j, the minor allele frequency q was q. Recall that in this group of simulations, a is the desired number of phenotype 0 subjects, b is the desired number of phenotype 1 subjects and Then we generated the genotypes and phenotypes. For each variant site ollowing , 32 we tThese simulations are conceptually more challenging than the HMM model simulations because whereas in the HMM model each influential site directly affects the corresponding column of the genotype matrix, in the block model, all such influences are mixed together under a sum, which is then further hidden behind a dichotomous variable. Thus, even before considering the specifics of a classification algorithm, lower classification performance is to be expected. Results summarized in Table HMM again outperforms all comparison methods. HMM does best with threshold 0.1; performance declines with increasing threshold value.ons from , but witNoise Terms. Following , we add Impurity: Sites within an influential block had a probability of reverting to No Influence.In the first additional simulation group, impurity was set to 0.25. In the second, it was set to 0.5. To compensate, the approximate number influential sites was scaled by 4/3 and 2 respectively. Results summarized in Tables We generated two more groups of block model simulations, but with the following modifications:HMM outperforms the comparison methods under both impurity levels. Different thresholds don’t change the performance very much. All methods tend to perform worse with the higher impurity level.Next, we generated two groups of block model simulations with imbalanced phenotypes, i.e. more phenotype 0 subjects than phenotype 1. Specifically, we used 1500 of the former and 500 of the latter. The first group featured just the imbalanced phenotypes and the uniform i.i.d. individual site strengths, with the simulations otherwise set up as in the first group of block model simulations. The second group added to this the noise terms and an impurity of 0.25 as in the preceding subsection. Results summarized in Tables HMM outperforms the comparison methods in both groups. All three threshold values show similar performance. All methods show worse performance in the noise and impurity group, except Bonferroni.Lastly, we generated two groups of block model simulations with small sample sizes. In the first group, we used 150 phenotype 0 subjects and 150 phenotype 1 subjects. In the second group, we used 200 phenotype 0 subjects and 100 phenotype 1 subjects. Due to the large variance in performance, the number of trials in each group was doubled to 40. Settings were as in the first group of block model simulations, except with uniform i.i.d. individual site strengths. Results summarized in Tables While all three HMM thresholds outperform the comparison methods, there is a clear preference for higher threshold values, with HMM 0.2 doing best.Lastly, bearing in mind that ZFA was primarily designed for testing rare variants, we generated a group of block model simulations with low MAFs. MAFs for each site were set i.i.d. to We again see a preference for higher HMM threshold values. HMM 0.15 outperformed the comparison methods in mean MCC, but at the cost of higher variance. HMM 0.2 achieved the highest mean MCC of them all, with lower variance than the ZFA methods.HMM is only designed to detect influential sites, not genes directly. The latter is generally the next step in studying genetic causal pathways for the phenotype . We suggBoth HMM and ZFA improve on individual site testing methods by using information from neighboring sites, on the presumption that influential sites come in clusters. However, HMM has several advantages. It offers greater versatility because it is designed for both rare and common variants, whereas ZFA is designed for just rare variants and wtest for common variants. Furthermore, ZFA predicts that influential sites come in (pure) blocks. This may be too idealized an assumption. HMM offers more flexibility because while it prefers purity, by disfavoring too many transitions to different states, it does not insist on it.We suggest four possible modifications to our method.hierarchical HMM model, using exponential distributions for site strengths; but found that the runtime was unacceptably high. However, with algorithmic improvements or more computational resources, a hierarchical HMM model could be a direction for future research.First, the method assumes that all sites in the same state exert the same influence strength on the phenotype, as summarized by two parameters  and more complex comparison methods . We anticipate that HMM may offer improved performance in classifying influential sites from GWAS, a strong first step in the study of genotype-phenotype causal relationships.Our model provides a mechanism for why influential sites should tend to cluster into blocks: Each site affects its successor because staying in the same state is a priori more probable than transiting to a different state. The model offers versatility, in being designed for both rare and common variants, and flexibility, in preferring influential sites to come in uninterrupted blocks but without insisting on it. Across diverse groups of simulations based on block models of influential sites, HMM consistently outperforms both simple comparison methods (Fisher’s exact test with corrections to the


The Southern Ocean upper-layer freshwater balance exerts a global climatic influence by modulating density stratification and biological productivity, and hence the exchange of heat and carbon between the atmosphere and the ocean interior. It is thus important to understand and quantify the time-varying freshwater inputs, which is challenging from measurements of salinity alone. Here we use seawater oxygen isotopes from samples collected between 2016 and 2021 along a transect spanning the Scotia and northern Weddell Seas to separate the freshwater contributions from sea ice and meteoric sources. The unprecedented retreat of sea ice in 2016 is evidenced as a strong increase in sea ice melt across the northern Weddell Sea, with surface values increasing approximately two percentage points between 2016 and 2018 and column inventories increasing approximately 1 to 2 m. Surface meteoric water concentrations exceeded 4% in early 2021 close to South Georgia due to meltwater from the A68 megaberg; smaller icebergs may influence meteoric water at other times also. Both these inputs highlight the importance of a changing cryosphere for upper-ocean freshening; potential future sea ice retreats and increases in iceberg calving would enhance the impacts of these freshwater sources on the ocean and climate.This article is part of a discussion meeting issue ‘Heat and carbon uptake in the Southern Ocean: the state of the art and future priorities’. MultiplFreshwater injection to the surface ocean modulates these processes: at low temperatures, density gradients, and thus upper-ocean stratification, are dominated by gradients in salinity. The concentration and distribution of freshwater thus exert a strong influence on the transfer of climatically important tracers between the atmosphere and the ocean interior. The freshwater that enters the Southern Ocean derives from multiple sources. The formation and melt of Antarctic sea ice is one of the most significant seasonal signals on the planet, with impacts on light levels within the ocean (and hence phytoplankton growth and carbon fluxes), stratification and water mass production and properties. Meltwater from glaciers and ice shelves is injected at the coast and can be a source of the micronutrients that stimulate productivity ,8. Glaci2) A68 iceberg that calved in 2017, and that exited the Weddell Sea and reached the shelf of South Georgia before disintegrating in 2021 [Freshwater input from these sources is changing, yielding a net Southern Ocean freshening over the past several decades . Underst in 2021 ,21.In this paper, we use data from five occupations of an oceanographic transect spanning the Scotia and northern Weddell Seas between 2016 and 2021. During each of these cruises, samples were collected for analysis of oxygen isotopes in seawater, and we use these data alongside salinity measurements to generate new quantitative information on the time-varying upper-ocean freshwater balance in this region. Special emphasis is placed on tracing the impact of the very rapid sea ice retreat that occurred at the start of our sequence, for which the Weddell Sea was a focal point, and tracing the impact of the A68 iceberg that traversed this region toward the end.. 2 (a)Data used here were collected along the A23 repeat hydrographic section between 2016 and 2021 ; table 1The A23 section is fortuitously positioned for studies of Southern Ocean freshwater changes. Within the Scotia Sea, it spans the southernmost front of the Antarctic Circumpolar Current  and also the Southern Boundary of the ACC . Farther (b)δ18O, the standardized ratio of H218O to H216O in seawater), using the CO2 equilibration method with an Isoprime 100 mass spectrometer plus Aquaprep device. Isotope measurements were calibrated against internal and international standards including VSMOW2 and VSLAP2. The long-term mean of all seawater replicates is better than 0.04‰ (1 s.d.).A SeaBird Scientific SBE 911plus Conductivity–Temperature–Depth (CTD) system was used on each of the ORCHESTRA/ENCORE cruises to obtain near full-depth profiles of temperature, salinity and other ocean variables. In each case, the CTD was mounted on a frame that also carried Niskin bottles; these were used to obtain discrete water samples for conductivity calibration of the CTD and the oxygen isotope samples. The exception to this modus operandi is the ACE cruise, for which only discrete near-surface samples of isotopes  and saliδ18O data, with coherence of profiles inspected and obvious fliers repeated or flagged as bad. While samples were all analysed in the same laboratory, the possibility of small offsets between cruises cannot be excluded: while factors such as the use of intermediate standards should give zero or very small changes, there is some evidence that they cannot be discounted a priori [δ18O of AABW on each section and use this to offset each cruise dataset so they have the same AABW means. (AABW is preferred to Circumpolar Deep Water (CDW) for this, despite being more recently formed at this location, since it is more voluminous and hence a more reliable mean can be obtained). Offset values are JR15006 , JR16004 (–0.03‰), JR17003 (–0.07‰), DY113 (–0.03‰) and JC211 (+0.01‰). The offsets used are typically very close to the precision of the data. Application of these adjustments ensures the results presented here are internally consistent, and the year-on-year changes identified are not data artefacts. It is accepted that this process will include any small natural variability in deep water properties as well as any methodologically-induced changes, and thus the upper-layer changes that we derive might be marginally smaller than otherwise. However, it is the safest approach.Quality control was conducted on the a priori . To accoThe sampling strategy on cruises was to enhance the vertical resolution of sample collection in the upper ocean, where gradients in overall freshwater and its constituent components are expected to be largest, and also enhance sampling density close to the seabed where bottom boundary layers can be important. Depending on water depth, typically between 8 and 20 samples were collected per cast, with nominally 6 of these being collected in the upper 200 m. (c)δ18O is set at the surface by freshwater exchanges with the atmosphere and cryosphere, and is a conservative tracer in the ocean interior. δ18O, when measured in addition to salinity, provides an extra degree of freedom that informs on the source of the freshwater inputs. On a planetary scale, the lighter H216O molecule evaporates preferentially in the tropical/subtropical regions, while the heavier H218O molecule rains out preferentially from the atmosphere. With a net movement of water vapour in the atmosphere towards the poles, this results in high-latitude precipitation having very low values of δ18O. Glacial and iceberg melt, which derives from this precipitation, has correspondingly very low values. Conversely, sea ice has a much higher value of δ18O, very close to the value of the seawater from which it formed  injection to the seawater sampled.Like salinity, o 1.0035 ,31). Consim, Fmet and Fcdw are the fractions of sea ice melt, meteoric water and CDW respectively; Ssim, Smet and Scdw are the salinities of the pure endmembers of the respective constituent waters; δsim, δmet and δcdw are corresponding δ18O values for these endmembers; and S, δ are the measured salinity and δ18O of the water sample.Quantitative information on different freshwater sources can be derived via implementation of a simple three-endmember mass balance. This approach was originally developed for the Arctic  and has δ18O were taken from direct measurements on the cruises, and are representative of values typically encountered on the A23 section , the temperature-minimum remnant of the winter mixed layer that persists into summer . We do nδ18O endmembers, and least sensitive to salinity, and the choice of meteoric water endmember was most influential overall. In total, uncertainties in the derived freshwater contributions were calculated as being less than ±1 percentage points of the water sampled.To quantify the impact of uncertainty in endmember selection and measurement uncertainty on freshwater percentage calculation, we conducted a sensitivity analysis as per . For thiIt should be noted that the system of equations used to derive the freshwater percentages can yield negative values for sea ice melt in the area sampled. This is consistent with the addition/removal of freshwater from the ocean by sea ice being a bidirectional process, with positive values denoting net sea ice melt having occurred into the waters sampled, and negative values denoting net sea ice production from the waters sampled. For meteoric water, negative values are not expected in the high-latitude Southern Ocean, with only a few exceptions (e.g. under the influence of strong evaporation driven by intense katabatic winds or cold air outbreaks).δ18O data, we cannot address seasonal aliasing of that signal directly; however, δ18O is relatively insensitive to sea ice melt/formation processes hence any seasonal changes are not expected to impact strongly on derived sea ice melt variability.Here we interpret the changes in freshwater fractions between cruises as primarily reflecting interannual variability, especially for the sea ice melt component. The cruises were all conducted in the austral summer season; however, there is the possibility of some aliasing of the seasonal signal, given the different months of occupation of the section . To addr (d)https://www.metoffice.gov.uk/hadobs/hadisst/ with updates. Iceberg trajectory data is obtained from the Brigham-Young University (BYU) database [https://www.scp.byu.edu/data/iceberg/database1.html. Iceberg outlines were traced from MODIS Terra images downloaded from NASA's Worldview Snapshots application (https://wvs.earthdata.nasa.gov), part of the Earth Observing System Data and Information System (EOSDIS).Interpretation of the cruise data is aided by cryospheric data from several sources. Sea ice concentration information is obtained from the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset, available at database , being a. 318O space, the role of different freshwater components in determining the water mass characteristics in the Weddell and Scotia Seas is apparent  is also offset horizontally, albeit by less than JR17003. These two clusters are the primary foci of this paper.Above WW lies Antarctic Surface Water (AASW), the most variable water mass in both salinity and a–c), then a return to surface salinities closer to those originally seen at the start of the sequence. During this period, the near-surface δ18O in the northern Weddell Sea showed only minimal changes .The two distinct clusters also show strongly contrasting spatial distributions. Near-surface salinity along the cruise track shows a strong freshening in the northern Weddell Sea from 2016 to 2018 a–c, then changes , consist8O space . The larrly 2021 e.Figure. 4δ18O anomalies are indicative of their origins, with the multi-year changes in the northern Weddell Sea being driven primarily by changes in the sea ice field, and the anomalies close to South Georgia in 2021 being caused by meteoric water input. To quantify these inputs, we implement the mass balance system given in equation (2.1). For sea ice melt in the northern Weddell Sea, the near-surface values in 2016 are as low as –1%, indicating that there had been some freshwater removal through net sea ice production from this water prior to it being sampled (a). During the next 2 years, the sea ice melt percentage increased strongly in the near-surface layers, reaching a maximum that exceeds 2% in early 2018 . There was then a decrease closer to previous values in the southernmost region, the exact timing of which is difficult to ascertain given the 2-year gap between JR17003 and DY113.The patterns of salinity and  sampled a. During figures b,c. Thera) could be seen as counterintuitive, given that this is a region where typically one would expect net sea ice melt, with strong ice production instead occuring further south and over the shelves of the Weddell Sea in particular [That there are some initially negative near-surface values of sea ice melt in the northern Weddell Sea a could brticular . It seemrticular ; it is ia,b). There is a temporal lag between the peaks of the signals, with the maximal oceanic imprint of the sea ice change occurring 1–2 years after the rapid retreat . After the 2-year gap in our sampling, sea ice in the region was a mixed pattern ; the ocean anomalies reflect this, with varied patterns of positive and negative sea ice melt along the A23 line  and intermediate mean sea ice melt values in the northern Weddell Sea . Interpreting this value requires caution: it is clearly related to the excess amount of anomalous sea ice melt that entered the ocean between these times, but it does not directly reflect the mean thickness of sea ice that melted. First, it should be noted that water is denser than ice, so there is a simple scaling (approximately 0.9) that leads to the ocean tracer value underestimating the thickness of solid sea ice that melted. Second, once the melt enters the ocean it mixes laterally, reducing the peak values. Third, the isotope tracer used here detects the impact of sea ice formed from the freezing of seawater: any snow accumulation that becomes compacted on top of the sea ice will not be included in this sea ice melt calculation. The third of these factors appears to be of comparatively low significance, given the relative invariance of δ18O in the northern Weddell Sea waters that showed the large changes in sea ice melt. The other two factors both reduce the ocean signal relative to the solid sea ice change, and thus the quantification should be interpreted as an approximate lower limit to the net sea ice thickness change associated with the rapid sea ice retreat.To gain quantitative information on the amounts of sea ice melt accumulated in the upper ocean during the unprecedented retreat, we calculate column inventories by integrating the sea ice melt percentages over the upper 100 m of the ocean. It should be noted that, while the absolute values for column integrals are sensitive to the choice of this thickness, the year-on-year changes in those values are not. By this measure, the change in height of freshwater derived from sea ice melt present in the northern Weddell Sea between early 2016 and early 2018 is approximately 1 to 2 m a–c. Inte. 5δ18O. The major exception is adjacent to South Georgia in early 2021 (e), which shows a small region with meteoric water values above 3%, significantly higher than the 1–2% background values seen more broadly and at other times.The other freshwater component that is quantified in the endmember mass balance equation is meteoric water, being the net input to the ocean from precipitation and glacial sources. Meteoric water is relatively invariant across the field area and across the years of sampling , as experly 2021 e, which d,e). By the time A68 had reached the vicinity of South Georgia, it had lost considerable mass [δ18O data points very close to iceberg fragments showing very little difference from values that would typically be expected along A23 in other years . In particular, short zonal and meridional sections to the west and south of the largest iceberg fragment show salinities of 33.8–34.0 and δ18O values around –0.3‰, not dissimilar to the general values for this location at other times .The source of this anomaly is meltwater from the giant iceberg A68, which originated in the Weddell Sea and traversed the Scotia Sea in the period preceding the timing of the 2021 A23 cruise blue in d,e. By tble mass  and had er years a,b. In per times . Conversroximity d.Figureδ18O sampling around A68 subsampled a significantly complex field of glacial meltwater. The strong gradients in salinity, δ18O and meteoric water (It seems clear that our ic water  point toic water .c), with values raised to approximately 3%. Comparison with the trajectories of icebergs around this time  icebergs are far more common in this vicinity . Althougfigure 9his time  shows thhis time  crossed . 6δ18O), with the extra degree of freedom that it provides, enables quantitative insight into the relative importance of the changing freshwater sources.The two events that are the central foci of this paper are remarkable in their magnitudes. The decline in Antarctic sea ice during 2016 is unprecedented in the satellite era, with rates of retreat dwarfing those seen even in the Arctic . The giaWe find that, in response to the rapid retreat of sea ice in the Weddell Sea following its record maximum in 2015, a large pulse of freshwater from sea ice melt passed through the area. A small temporal lag exists between the retreat of the solid sea ice and the peak sea ice melt contribution in the northern Weddell Sea, which we ascribe to the advective timescale for the affected waters to reach the sampling site, combined with a timescale of accumulation of sea ice melt in the ocean. We also note that our quasi-annual temporal sampling is sparse in this context.While the absolute values of the sea ice melt contribution presented here are sensitive to choices of endmembers, changes between times or locations are much less influenced. Nonetheless, it is interesting that some initial sea ice melt percentages in the northern Weddell Sea (before the freshwater pulse moved through) were negative, given that this locality is an area of predominant net sea ice melt. We tested the possibility of seasonal aliasing of the cruise data being a factor; while all the cruises were conducted in the austral summer, there were inevitable differences in the actual months of occupation of the sections. However, we found this to be negligible in terms of impact on the sea ice melt signal observed. It seems likely that the negative sea ice melt percentages, denoting net sea ice formation from the waters sampled, were a reflection of strong sea ice production prior to the record-length maximum in 2015, again combined with advection from the major ice production regions further south.In contrast with the sea ice melt anomalies, which covered a very broad area of the northern Weddell Sea, the melt anomalies from the giant A68 iceberg were much more localized. While the iceberg discharged a considerable quantity of freshwater to the ocean (802 ± 34 Gt of ice was lost from A68a in 3.5 y ), this sThe example of A68, being a marked source of isotopically-light waters locally to South Georgia, highlights the role that large icebergs can play in modulating the freshwater balance in ecologically-sensitive regions. The trajectory of A68 is not atypical for an iceberg exiting the Weddell Sea through ‘iceberg alley’, though a range of other trajectories both west and south of South Georgia also exists . Those tThere is an interesting possible linkage of two of the anomalous freshwater patterns observed here, with the exit of icebergs from the Weddell Sea in 2017–2018 likely to have been facilitated at least partially by the corresponding dramatic sea ice retreat. It is known that icebergs close to Antarctica can be held comparatively immobile in the ice pack for extended periods, thus it is feasible that a thinner and less extensive sea ice field permitted greater iceberg mobility. This is speculative but, if confirmed, would constitute a striking connection between meteoric and non-meteoric freshwater inputs to the ocean, which our oxygen isotope tracer is capable of decoupling.Each of the signals reported on here will inevitably change in future. Sea ice is expected to retreat in a warming world, and projections based on climate and Earth System Models generally depict strong reductions in Antarctic sea ice by the end of this century ,47. HoweProjections of glacial ice discharge from Antarctica appear more robust than projections of sea ice changes: it is known with higher confidence that the ice shelves, ice sheets and glaciers will contribute increasing amounts of freshwater to the Southern Ocean over the coming decades, though the absolute amounts will depend strongly on factors such as our success in limiting greenhouse gas emissions ,47. This