Accurate information on flood extent and exposure is critical for disaster management in data-scarce, vulnerable regions, such as Sub-Saharan Africa (SSA). However, uncertainties in flood extent affect flood exposure estimates. This study developed a framework to examine the spatiotemporal pattern of floods and to assess flood exposure through utilization of satellite images, ground-based participatory mapping of flood extent, and socio-economic data. Drawing on a case study in the White Volta basin in Western Africa, our results showed that synergetic use of multi-temporal radar and optical satellite data improved flood mapping accuracy , in comparison with existing global flood datasets  Near Real-Time (NRT) Global Flood Product). Increases in flood extent were observed according to our classified product, as well as two existing global flood products. Similarly, increased flood exposure was also observed, however its estimation remains highly uncertain and sensitive to the input dataset used. Population exposure varied greatly depending on the population dataset used, while the greatest farmland and infrastructure exposure was estimated using a composite flood map derived from three products, with lower exposure estimated from each flood product individually. The study shows that there is considerable scope to develop an accurate flood mapping system in SSA and thereby improve flood exposure assessment and develop mitigation and intervention plans. Growing population along with continued socio-economic changes such as urbanisation and farming expansion increase exposure to flooding5 and result in large increases in flood risk8. Flooding poses long-term challenges to livelihoods in SSA, not only through loss of lives, destruction of farmlands and infrastructure6, but also disease outbreaks and worsened food and water security1.Flood events occur frequently in many regions of SSA due to high climate variability and associated extreme precipitation events9, however has received little attention to date . The few flood exposure assessment studies either mainly assess broad land cover classes such as 'urban area'11 or only use population map layers10, whilst very few focus on infrastructures such as roads and water supplies. A comprehensive flood exposure assessment is fundamental for flood risk assessment and key for developing flooding mitigation strategies, but also particularly crucial in data-scarce SSA, the only global region showing increasing flood mortality rates since 199012. Accurate flood mapping and flood exposure assessment can help address this increase in mortality rates13.The damaging impacts on livelihoods can be reduced by proper flooding mitigation strategies that are guided by sufficient flood hazard assessment, monitoring, and early warning. However, lack of monitoring and information on flood extent and flood exposure hampers spatial targeting of effective mitigation strategies. Flood exposure, defined as population and assets located in flood-prone areas14. Flood hazard models have been implemented for flood forecasting and monitoring16. However, the accuracy of model-based flood hazard maps is restricted as it depends on the accuracy of various input data such as meteorological and topography data, thereby leading to greater uncertainty from error propagation11. In addition, these flood hazard models tend to focus on the national or regional scale that is not designed for local-scale estimation where the impacts are experienced and local-level decisions are required11. Global-scale flood datasets derived from satellite data are available, such as the MODIS NRT Global Flood Product17, the MODIS Global Flood Database18, and the Global Flood Detection System19. These datasets again have coarse spatial resolution and validation of them is highly challenging, particularly in data-sparse regions such as SSA14 where accurate data on flood extent at the local scale is greatly needed for risk management21.The availability of accurate historical and current information on flood hazard events is particularly limited in SSA22. Synthetic Aperture Radar (SAR) is particularly useful for flood mapping since it can provide frequent observations24 thanks to its capability to monitor land in almost any weather conditions26. Flooded areas generate a low backscatter signal and appear to be dark in SAR images, which makes them distinguishable from other land cover classes such as agricultural land or built-up areas. Many studies have used SAR images to map flood inundation27, using algorithms such as histogram thresholding or clustering28, change detection30, and time series analysis26. To successfully map inundation areas using such algorithms, defining a robust and objective threshold is critical as results can be sensitive to the thresholds used31. Currently, most studies are either based on a universal threshold value that may be unsuitable for specific sub-regions, or dependent on user-defined empirical analysis, which makes it difficult to apply in different study regions. Our study, however, proposes an approach that defines a threshold in an objective way, that is, use optical satellite images to define thresholds for SAR-based flood mapping techniques (Sect. 3.1.1). Optical images can detect water bodies34, but tend to underestimate flooded areas as they are subject to cloud cover during the rainy and flood season20. Spatial overlap of water bodies detected from optical images with SAR images can demonstrate the range of backscatter signal in SAR images, and can therefore be used to define thresholds objectively that are tailored for local scale application (Sect. 3.1.1). Alongside using a combination of optical images and SAR images to improve existing algorithms for flood mapping using SAR data, our study also derived flood extent and calculated total inundation area from both optical and SAR images, which provide benefits of long-term coverage of optical imagery while addressing cloud cover issues via the use of SAR.Progress has been made to monitor flood inundation at the local scale and over the long term, particularly with the development of Earth Observation (EO) systems with increased revisit frequency and higher spatial resolution that are increasingly used in operational disaster monitoring systems22, for example, by using optical images to evaluate the accuracy of flood mapping from radar images20. However, this approach carries limitations as optical imagery may be affected by high cloud occurrence. Alongside using high-resolution satellite data for assessing accuracy, this study also collected ground data through participatory mapping to assess the accuracy of satellite-based flood maps. Participatory mapping, which engages local knowledge and expertise and allows local communities to delineate flood-affected extent on provided basemaps, has been widely recognized as an effective tool to collect and understand flood extent on the ground35. Involving communities’ knowledge of floods through participatory mapping is critical in the data-scarce SSA context as communities experience flooding first hand. Despite the value of local experience and knowledge, very few attempts have been made to combine flood extent derived from satellite data and through participatory mapping37 for evaluating satellite-derived flood extent accuracy. Such a comparison can not only enable accuracy assessment of satellite-derived flood extent, but also indicate flood-prone areas associated with high impacts for local communities.In addition, the lack of local ground data that can be used to evaluate the accuracy and limitations of satellite data-derived flood extent has been another major drawback in previous studies. Most studies assess accuracy via inter-comparison of satellite-based flooding mapsMap flood area at fine spatial scale  using multi-source satellite images  and analyse flood area dynamics through comparison with existing global flood datasets  Global Flood Database, and JRC Global Surface Water dataset) over 2000–2020.Evaluate the accuracy of satellite-derived flood extent , through comparison with participatory mapping outputs.Estimate flood exposure by combining the satellite-derived flood datasets with socio-economic data including high-resolution population density (100 m and 30 m), land use (30 m) and key infrastructures.This research, for the first time, employed a combination of top-down approaches based on multi-source satellite images and a consultative approach via participatory mapping to map flooded areas and their dynamics. Further, it investigates the scale and severity of population, infrastructure and farmland exposure to flooding in the White Volta basin in Ghana. Specifically, this study aims to:38 in White Volta catchment in Western Africa. The study area White Volta catchment is located in Ghana  has not been informed by comprehensive data on flood extent at a high spatial resolution41.This study is a component of a wider program that employed a mixed-methods monitoring flooding events and examining its impacts on livelihoods, healthcare utilisation, and water point qualityana Fig. , a subbaWe firstly mapped the monthly maximum flood extent at a fine spatial scale (10 m) during the wet season (July–September) using Sentinel-1 radar data available from 2016 to 2020 and optical satellite data from Sentinel-2 and Landsat-8. Secondly, we assessed the spatiotemporal flood dynamics by combining outputs from this study with existing flood datasets . Thirdly, we used the high-resolution PlanetScope dataset and collected ground data through participatory mapping to evaluate the accuracy of flood extent from the satellite data, and finally assess flood exposure through integration with land use, population and infrastructure map layers.42. This global dataset has a 12 or 6 day revisit cycle depending on the availability of Sentinel-1B imagery43 with a spatial resolution of 10 m. The level-1 Ground Range Detected (GRD) pre-processed product in Google Earth Engine was accessed and processed for the White Volta Basin, comprising 2148 scenes from 2016–2020. Only descending pathways were available in the study area and VV polarized data were used because of higher accuracy in detecting floods compared to VH, based on previous studies45.We used the Sentinel-1 Synthetic Aperture Radar (SAR) C-band (5.4 GHz) data provided by the European Space Agency (ESA)47, the Change Detection and Thresholding (CDAT)48 and the Normalized Difference Flood Index (NDFI) algorithms26. The flooded areas identified consistently from both the CDAT and NDFI detection algorithm was found to achieve higher flood mapping accuracy than alone20. The CDAT algorithm uses multi-temporal SAR imagery and classifies flooded areas based on difference in backscatter between flood/post-flood and reference non-flooded images48. The NDFI also implements change detection principles, but combines a time series of images to compensate for changes in land cover and soil moisture, defined as in Eq. (1) below26. The image thresholding method was also examined in this study, as it is a straightforward approach based on a single image and setting pixels as flooded when the backscatter coefficient is lower than a certain threshold value47.We examined the combination of three algorithms to map flooded areas, namely image thresholding method49. We selected images in June given that this is a dry month with no flooding recorded in this area during 2016–2020 according to visual inspection of Sentinel-1 images. In addition, vegetation phenology in June is close to that in the flooding season starting from July, which minimizes the impact of seasonal variation in vegetation on backscattering. Mean value composites were generated for the reference month of June and a monthly minimal value composite image was generated for post-flood scenes to capture the maximum flood extent area.Selecting non-flood reference images is key for change detection techniques44. Normally, the threshold criterion is used to classify flooding when the values are less than the mean pixel value minus the standard deviation of the entire image, times a coefficient of 1.544. We examined the feasibility of this commonly used threshold in our study area, as well as proposed a new approach to objectively retrieve threshold fit to local case studies. We firstly introduced sub-basin boundaries, as our study area covers a relatively large spatial domain with heterogeneous land cover types. We then calculated the thresholds described above at sub-basin level for each month. In addition, to define a more objective threshold appropriate to this study, water bodies identified via optical satellite images Sentinel-2 and Landsat-8 indices, and the JRC Global Surface Water dataset  were used to define the thresholds for the Sentinel-1 images. Water bodies were classified using an algorithm with three widely used indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Modified Normalized Difference Water Index (mNDWI): Water = (mNDWI > EVI or mNDWI > NDVI) and EVI < 0.151. We calculated the distributions of these indices for detected water bodies for each of the three flood detection methods . For each index, the 95th percentile (5% percentile for NDFI distribution) was used as a threshold because this threshold enables the detection of 95% of water bodies derived from optical images. We examined histogram threshold criteria from previous studies44, as well as our own criterion based on optical images.Defining threshold values is another critical step for accurately mapping flood extent using the above three methods. For the change detection and NDFI methods, threshold values are normally determined by analysing a histogram of the distribution of backscatter difference or the NDFI index. The threshold criterion is defined based on various empirical analyses20. In addition, all clusters smaller than 10 pixels are excluded to reduce spurious flooded areas as the minimum mappable unit is clumps of approximately 10 pixels. Finally, any pixels falling on a slope of > 5° via NASA's Shuttle Radar Topography Mission (SRTM) elevation dataset52, where a flood would be unlikely, were excluded18. Flood mapping in urban areas, dense forest is challenging with Sentinel-1 data because of double-bounce effects, however, as the White Volta basin is dominated by savanna and crop fields that were normally submerged under floods, and flooded forest and urban area is not extensive therefore was not considered in this study.We applied a median 3 × 3 pixel filter to remove speckle effects in SAR images for a smoother image50), and two datasets based on the moderate-resolution imaging spectroradiometer (MODIS) images: the MODIS NRT Global Flood Product17 and the MODIS Global Flood Database18. The MODIS-based datasets both used the band ratio water detection algorithms to map flood area18. The MODIS NRT product is available daily from 2000–2020 at 250 m spatial resolution, however only data for 2016–2020 could be accessed for the White Volta Basin. Since the MODIS Global Flood Database mainly maps large flood events documented by the Dartmouth Flood Observatory (DFO) from 2000 to 201818, this dataset is available on an event basis. In this study, monthly and yearly maximum flood extent was generated using the MODIS NRT product from 2016–2020, while yearly maximum flood extent was generated using the MODIS Global Flood Database from 2000–2018. The JRC Global Surface Water dataset was generated using Landsat 5, 7, and 8 satellite imagery from 2000–2019 at a higher spatial resolution of 30 m . It maps the location and temporal distribution of seasonal surface water over this 20-year period at monthly level, forming a proxy for monthly flood extent. Yearly maximum flood area was also calculated based on this dataset for a longer period (2000–2020). We compared monthly and yearly maximum flood extent derived from the above flood products with the Sentinel-1 flood extent from this study, and further estimated changes in flood extent over 2000–2020. The statistical significance of flood extent trends at the basin level was assessed using the Mann–Kendall (MK) test53. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset was used to examine the correlation between precipitation and flood extent. This dataset is a quasi-global rainfall dataset with a spatial resolution of 0.05° covering over 30 years, derived from satellite imagery and combined with in-situ station data54. Specifically, yearly daily maximum precipitation summed over the White Volta basin was calculated, and a linear regression was fitted to quantify the relationship between flood extent and precipitation.Existing global satellite-derived flood datasets were compared with our Sentinel-1-derived flood extent data, firstly to evaluate their accuracy in flood detection in the White Volta basin, secondly to examine flood extent trends, and thirdly to generate a composite map based on the maximum flood extent from Sentinel-1 and global flood datasets. In addition, precipitation trends were analyzed to understand whether precipitation alone can explain the dynamics of changing flood extent. The global flood datasets are the JRC Global Surface Water dataset .Our flood extents delineated using Sentinel-1 data were validated through two different datasets: (1) participatory flood mapping outputs; (2) flooded and non-flooded extent classified using the PlanetScope dataset. A participatory mapping campaign for flood map accuracy assessment was conducted in September 2020 during the flood period in Northern Ghana. Eight severely flood-affected sites were selected based on consultation with the Disaster Management Organisation’s (NADMO) district office during the pre-field survey period ) with a spatial resolution of 3 m, were used as reference data to evaluate the accuracy of flood extent classified by Sentinel-1. Cloud-free PlanetScope images covering the Talensi site were acquired on 25th September 2020 and compared with a contemporary Sentinel-1 scene acquired on 22nd September 2020. PlanetScope images were classified into the flooded and non-flooded areas using an algorithm with three spectral indices described above58. The HRSL dataset is based on the identification of individual buildings from high-resolution satellite imagery and distribution of population census data among these buildings to produce population density maps at a spatial resolution of 30 m for the year 201559. The WorldPop dataset is based on multi-variate random forest models that disaggregate areal head counts of census data at a spatial scale of 90 m. WorldPop population layers from 2016–2020 were intersected with flood extent data for the same period, while fixed HRSL population data in 2015 were overlaid with flood maps. The spatial distribution of urban and agricultural land use was identified as follows (see Table 60. (2) Rural, semi-urban and urban land use areas were obtained from the Global Human Settlement Layer (GHSL), produced by JRC. (3) Cropland and urban classes for 2020 were derived from the Globeland30 global land cover dataset, which has relatively good accuracy over Africa62. Similarly, the spatial distribution of key infrastructures was also used to map flood exposure including: (1) roads, dams, and water points obtained from OpenStreetMap; (2) potential geographic occupancy of domestic water points of boreholes and water wells, produced using a maximum entropy modeling technique combining observational water point data with environmental covariates to adjust for incomplete feature coverage63; (3) health facilities of clinics and hospitals based on the dataset from the HealthSites initiative , though this dataset’s coverage might not be comprehensive. To examine the impact of using different flood datasets on exposure assessment, flood maps derived from Sentinel-1 and existing global flood datasets were intersected with population, land use, and key infrastructure map layers separately. To preserve spatial heterogeneity and give flood maps a common spatial domain, Landsat satellite data (30 m) and the MODIS flood product (250 m) were resampled to match the finest spatial resolution dataset, namely the 10 m resolution of Sentinel-1. Similarly, when estimating flood exposure, population, land use and key infrastructure layers and flood datasets were resampled to match the finest spatial resolution of any input dataset. All satellite data, population data, land use dataset were accessed and processed in the Google Earth Engine cloud-computing platform  while further statistical analyses were performed in R version 4.1.2.Spatial overlay between flood extent and map layers of population density, land use and key infrastructures were analyzed to estimate flood exposure. Two different gridded population density maps were used in this study: the High Resolution Settlement Layer (HRSL) and the WorldPop dataset44, we found that standalone implementation of the NDFI and CDAT algorithms gave larger flood extent estimates than combined algorithms, with a particularly high false detection rate for the NDFI algorithm  compared to each dataset separately, but still showed similar yearly variation , while the agreement was lower for the MODIS NRT flooding dataset  and lowest for the Global Surface Water data   have been exposed to at least one Sentinel-1 observed event since 2016, based on the WorldPop and HRSL datasets Fig. , the pop2 of rural area (7.12%) and 81.42 km2 of farmland (1.05%). Hotspots of flood exposure of urban areas  and key infrastructure such as schools and hospitals  were also detected, together with roads  and potential occupancy area of water points . These estimates are based on flood extent from the Sentinel-1 dataset, which are lower than using the composite flood map, which generates the highest flood exposure  and key infrastructure exposure for the largest flooding year 2020 showed that rural and farming areas are the most exposed to flooding, with 138.5 kmSpatially detailed mapping of flood extent and exposure assessment is generally challenging for data-poor areas such as SSA. Efficient and pragmatic methods are critical for flood mapping and helping to mitigate flood impacts, but the validation of the available data is also necessary. This study, for the first time, combines participatory mapping and multi-source satellite and socio-economic data to investigate flood extent dynamics and evaluate flood exposure in the White Volta basin in Ghana. Our results reveal both opportunities and challenges for monitoring floods using satellite data and suggest areas for further investigation. Our results further stress the need for implementing flood management strategies as we observed increasing flood hazards and exposures.20. Most studies defined the threshold based on either empirical analysis or applying values from other studies’ distributional analyses44. This criterion is suitable when a large part of the study area is flooded. However, where flooding is limited, it tends to overestimate flood extent in a relatively large spatial domain with heterogeneous land cover types, such as for the White Volta basin . Our study demonstrates a more objective threshold by using optical satellite data to define thresholds for SAR-based flood mapping techniques . Our study further suggests that many global scale flood estimates using Landsat and MODIS images underestimate flood area and exposure   Fig. , Table 268 and degradation69, and ecosystem services70, participatory mapping has been proven as an effective tool for flood risk assessment35 and enhancing community resilience by integrating their knowledge on the causes and mitigation strategies of flooding. In this study, local key participants tend to delineate a larger flood extent with a higher elevation than that detected via satellite imagery  than existing global flood product . Our results showed that existing global flood datasets , however, underestimated flood extent, indicating there is a considerable scope to develop an accurate flood mapping system in SSA. Using flood extent maps developed at fine resolution, we also demonstrated flood exposure assessment in the White Volta Basin in Ghana. We found increased population exposure and identified rural areas, farmland, and infrastructure that are prone to flooding where flood mitigation and adaptation should be prioritized. Our localized flood extent maps and flood exposure monitoring could help with flood risk assessment in SSA.However, satellite data could underestimate inundation area depending on the revisit frequency of satellite observation and recession day of flooding. In addition, we found flooding exposure assessments are sensitive to input datasets used, such as flood extent data and population layers. Moving forward, more frequent satellite observation is critical for flood mapping. Flood maps from both optical and radar satellite images with different revisit patterns, as well as flood maps from existing flood datasets should be combined for flood monitoring as it allows for more samples in time and therefore provides more complete monitoring of flood events. In addition, flood exposure uncertainty assessment should be considered for future studies that can guide end-users for decision-making.Supplementary Information.


HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data. Molecular transmission networks are built by connecting similar HIV-1 drug resistance viral genomes. Using such methods, approximately half of all sequences stay unlinked, and the remaining half fall into categories of many small clusters and a few large clusters showing fragmentary epidemiological relations. However, the unavailability of genetic data for the entire underlying transmission network and challenges of sampling completeness limits the reliance on creating transmission clusters. Here, we take the known relationship from genetic data and transform the problem into a hybrid multi-step unsupervised and supervised learning problem in which we use contextual data to create a model unwrapping the hidden epidemiological relation among positive cases in transmission networks beyond genetic data. The goal is not to provide a static feature set from metadata that can be used for all jurisdictions but can offer a dynamic classifier framework that can reveal the unique dynamics of the spread of HIV in each jurisdiction. Observations of closely related viral strains indicate that HIV transmission is occurring rapidly within a common network. Immediately following transmission of HIV between two people, the molecular sequence of the HIV strain in the recipient will be nearly identical to strains found in the transmitting person. As time passes, however, the strains infecting each person will change independently of one another and will look more different . After aα. The α is a distance threshold for genetic similarity defined experimentally by field experts, typically between 0.5-2% substitution per site .m individuals with viral sequences. If their pairwise genetic distances (generated by TN93  where fij denotes the value of the j-the meta feature for the i-the individual. Each instance Xi (meta feature vector) in i-the individual. The addition of these class labels to the metadata from these individuals results in obtaining a labeled training dataset, which is then fed into a supervised learning classification algorithm. Here we used decision tree, random forest (120 estimators), k-nearest-neighbors (n: number of clusters), and support vector machine classifiers. The process of training a machine-learning algorithm requires a distance metric for comparing different instances in L1 norm) in the meta feature space. Therefore, the distance between two individuals with meta instances Xi and Xj is computed as:We first generate a labeled dataset by using clustered or singleton individuals from genetically linked viral sequences of surveillance data. Assume that the dataset has a size of  by TN93 ) is smalK = 10 in which our entire dataset is split into 10. The first fold is treated as a validation set and the method is trained on the remaining 9 folds. In the next fold, another subgroup among 10 split data is held out as the test dataset and the remaining groups as the training set. In every fold, the model is fit using the training  and evaluated on the testing set (about 120 nodes). The procedure repeats until the last fold among 10-fold. Although 10-fold cross-validation is not susceptible to classification bias, we decided to conduct other methods to preclude any possibility of bias or overfitting. Therefore, we assessed the models again using repetitive and stratified cross-validation. We performed evaluations on all four models to measure whether the trained classifier can automatically assign a new positive case to the class labels it belongs to . In the Algorithm 1 we describe the step-by-step method.First, we evaluated the classifiers using train-test sets (70 to 30 ratio). Having in mind, the size of the dataset under study and the facts shown in Algorithm 1 Hybrid HIV transmission reconstruction modelRequire: Pairwise genetic distance of viral strains using TN93(1) Create graph G =  while  do (2) If WE >α, label the corresponding nodes (V) as singletons  (3) Cluster G into C = [C1..Cn] from all connected components: where WE ≤ α and label the corresponding nodes (V) as clustered  (4) Report binary labels (clustered vs. singletons) for all nodes  (5) Report cluster ids: C = [C1..Cn] for clustered nodes  end while for all nodes Vdo (6) List each Vi along with the corresponding binary label (clustered vs singleton)  (7) Incorporate the non-genetic features of Vi besides its binary class label to form metadata feature space  end for (8) Split data into training and testing sets (9) Train models using different classifiers to automatically map each infection event onto the clustered or singleton label from step (4) (10) Validate the accuracy of class assignments using the testing dataset In addition to binary classification (clustered vs. singletons) which is beneficial in recognizing related infection incidents, we trained classifiers with the genetic information of infected populations to identifying clusters. To test the extended hypothesis from binary classification for cluster identification, we thought clusters size 2 or 3 might not have enough nodes to exhibit an acceptable level of resolution among these clusters. Therefore, we decided to test it with larger clusters of size ≥ 5. San Diego cohort dataset clusters of size ≥ 5 comprise 43.8% of all clustered individuals residing in 29 clusters. We used cross-validation in multiple repetitions and also repeated the evaluation of the cluster identification using stratified cross-validation re-sampling method to avoid bias and overfitting possibilities.


Secondary hyperkalemic paralysis is a life-threatening manifestation of hyperkalemia seen with a potassium level of 7 or above 7 milliequivalents per liter (Meq/L) in an acute or chronic state. Standard hyperkalemic treatment should be initiated upon diagnosis with emergency dialysis in refractory cases. Here we present the case of a patient with end-stage renal disease (ESRD) compliant with dialysis three times a week. The patient presented with generalized ascending flaccid paralysis and was found to have serum potassium of 9.6 Meq/L. Spontaneous resolution of the paralysis was observed shortly after the completion of one hemodialysis session. The goal of this case report is to raise awareness of a life-threatening complication of electrolyte imbalances in ESRD even in patients that are compliant with dialysis. Hyperkalemia-induced ascending paralysis secondary to familial hyperkalemic periodic paralysis (HYPP) is well described; however, secondary hyperkalemic paralysis in the absence of an underlying genetic disorder has only been described in a handful of case reports . HYPP isThis is a 61-year-old male with a past medical history significant for hypertension and ESRD on dialysis thrice weekly for the past seven years who presented for evaluation of ascending lower and upper extremities weakness. The weakness was sudden in onset, progressive, and without remittance. It began shortly after he reported a mechanical fall. The patient’s last dialysis session was two days prior. Most notably, he endorsed an increased consumption of bananas over the past few days because he was experiencing lower extremity muscle cramps.On physical examination, there were diminished breath sounds throughout the entire lung fields, the abdomen was soft but moderately tender in all four quadrants, and there was complete paralysis of all four extremities (motor strength 0/5); areflexia but without cranial nerve or sensory deficits. His initial chemistry panel revealed a blood urea nitrogen of 41 milligrams per deciliter (mg/dl) 7-25), creatinine of 5.89 mg/dl (0.70-1.30), and potassium of 9.6 millimoles per liter (mmol/L) (3.5-5.1). The initial EKG revealed a junctional rhythm with an atypical left bundle branch block and prominent T waves  is seen in approximately 1% of hospitalized patients, with a significant risk of mortality. Excessive intake of potassium is a rare cause of symptomatic hyperkalemia given the body’s adaptive capacity to increase renal secretion; however, dietary intake can have a major effect in susceptible patients such as those with ESRD . The skeSecondary hyperkalemic paralysis is flaccid and ascending, mimicking Guillain-Barre. However, secondary hyperkalemic paralysis does not typically cause rectal tone dysfunction or respiratory muscle paralysis , which wA diagnosis of secondary hyperkalemic paralysis can be challenging given the wide variety of causes of flaccid paralysis, and early EKG changes and elevated serum potassium can assist in making the diagnosis, especially in a patient with chronic kidney disease. Although early EKG changes are often seen with profound serum potassium elevation, a case series revealed two cases of severe hyperkalemia with serum potassium above 9 mmol/L without expected EKG manifestations of hyperkalemia . Thus, iAll patients should receive standard hyperkalemic treatment consisting of calcium gluconate with EKG changes or severe hyperkalemia to stabilize cardiac myocytes. Beta-agonists or intravenous insulin should be given to promote the intracellular shift of potassium in conjunction with medications that promote the increase of potassium excretion from the body . HemodiaSecondary hyperkalemic paralysis is a rare manifestation of hyperkalemia. This case report reveals a diagnostic differential that should be considered as a potential etiology of ascending flaccid paralysis, especially in patients with impaired renal function and an elevated potassium level. We also emphasize the importance of educating patients with ESRD about maintaining a low potassium diet and compliance with dialysis to prevent life-threatening electrolyte imbalances.


Modern chemical and biological studies are undergoing a paradigm shift, where understanding the fate of individual cells, in an apparently homogeneous population, is becoming increasingly important. This has inculcated a growing demand for developing strategies that label individual cells with unique fluorescent signatures or barcodes so that their spatiotemporal trajectories can be mapped in real time. Among various approaches, light-regulated methods employing photocaged fluorophores have received particular attention, owing to their fine spatiotemporal control over labelling. However, their multiplexed use to barcode large numbers of cells for interrogating cellular libraries or complex tissues remains inherently challenging, due to the lack of multiple spectrally distinct photoactivated states in the currently available photocaged fluorophores. We report here an alternative multiplexable strategy based on optically controlled host–guest recognition in the cucurbit[7]uril (CB[7]) system that provides spatial control over the positioning of fluorophores to generate distinct barcodes in ‘user-defined’ cells. Using a combination of three spectrally distinct CB[7]-conjugated fluorophores and by sequentially performing cycles of photoactivation and fluorophore encoding, we demonstrate 10-color barcoding in microtubule-targeted fixed cells as well as 7-color barcoding in cell surface glycan targeted live MCF7 cells. Barcoding provides abilities to learn about individual species within an apparently homogeneous population. We describe a light-mediated multiplexed cellular barcoding strategy through spatial programming of cucurbit[7]uril molecular recognition. However, much of our current knowledge is based on ensemble measurements where population averaging tends to obscure key insights from individual cells. For example, ensemble measurements can miss rare cell types and their function or completely overlook variability between cells and the functional significance of the heterogeneity.7 This specifically designed class of fluorophores remains in a non-fluorescent state prior to activation by light but switches over to a fluorescence ON state upon illumination at an activating wavelength of light, thereby providing a means to highlight a selected cell with spatiotemporal precision. In general, access to a range of photocaged fluorophores was achieved by synthetic modifications that either hinder their ability to absorb excitation light or control their nonradiative deactivation process.6 However, an inherent challenge with the photocaged fluorophores is that they allow only single-color investigation, since photoactivatable fluorophores switch to a single fluorescent emissive state from an essentially non-emissive ‘OFF’ state upon light activation. A multicolor optical highlighting approach, generating a wide array of fluorescent profiles at spatiotemporal precision, can provide unique identification tags to many cells. This can be an extremely powerful tool for visualizing the dynamics of many cells within a given cellular population to address fundamentally different kinds of questions, including establishing interaction circuits between like cells and piecing together their lineage relationships over developmental, regeneration, or differentiation trajectories.3Traditionally light-regulated labeling requires specific fluorescent probes that are capable of exhibiting pronounced changes in their fluorescence emission in response to light stimulation. Towards this end, a class of photocaged fluorophores has been developed that has revolutionized the possibility of spatiotemporally controlled labeling.6 As a result of their ability to convert from one emissive state to another spectrally distinct emissive state upon light activation, these probes can improve multiplexing capability. While the photoconvertible probes based on proteins can be specifically expressed and possess distinct spectral features, the disadvantage of these probes, however, is that they are not suitable for continuous and long-term imaging as they are susceptible to photobleaching, and their color diminishes with protein turnover. On the other hand, the reports of synthetic fluorophores that display photoconvertible behavior are extremely rare.10 Optimizing the scaffold of fluorophores to achieve multiple distinguishable emission states from light-irradiation remains highly challenging and their ability to color-code many specifically targeted cells continues to be limited. These considerations prompted us to set a goal to develop a strategy that harnesses the benefits of light activation and concurrently provides scalable multiplexing with highly optimized synthetic fluorophores to enable barcoding of many cells in parallel.A smaller subset of light-responsive probes, called photoconvertible probes, which are more abundant among the family of fluorescent proteins, have been developed for multicolor optical highlighting.11 The photoinducible version of certain bioorthogonal reactions has also been recently reported in the literature; however, there were a few challenges in adopting them in our sequential multiplexing scheme.12 They suffer from sluggish reaction kinetics, and their activation requires an extensive amount of light irradiation. These constraints typically push each photolabeling step to be completed in hour timescale, requiring many hours to completely label a cellular library via sequential activation. In addition, each step's inefficient coupling may result in extensive mixing of color signatures between two consecutive steps. Hence, a critical challenge for the success of our scheme was to achieve highly efficient bioorthogonal photolabeling with substantially minimized time of activation and conjugation in each cycle. We postulated that this can be achieved if we employ non-covalent bioorthogonal pairs for labeling as their association operates with diffusion-controlled kinetics (kon ∼108 M−1 s−1).13 However, to be suitable in our labeling scheme, we required non-covalent bioorthogonal pairs that not only display photoresponsive association, but also simultaneously provide high affinity and high selectivity recognition for efficient target imaging. To this end, we first developed a photoinducible bioorthogonal coupling strategy based on non-covalent synthetic host–guest recognition in the cucurbit[n]uril (CB[n]) family of synthetic hosts.14 Specifically, the heptameric member of this family, cucurbit[7]uril (CB[7]), has been the highlight in recent studies for its exceptional molecular recognition property in the biological milieu towards rationally designed guest molecules.15 One remarkable example is 1-adamantylamine (ADA), which displays high affinity (Ka > 1012 M−1) and highly specific molecular recognition towards CB[7] through a combination of hydrophobic, dipolar, and electrostatic interactions.16 To place CB[7] host–guest molecular recognition under optical control, we designed a photocaged ADA derivative (CADA) based on theoretical insights from molecular dynamics simulation. The CADA was then used as a universal adaptor for spatially controlling the position of CB[7]-conjugated fluorophores (CB[7]-FL) in individual cells. Spatiotemporal control is first demonstrated in fixed cells by tethering CADA onto microtubule and actin filaments and subsequently labeling cytoskeletal filaments at sub-cellular resolution. Subsequently, utilizing a combination of three spectrally distinct CB[7]-FLs and combining it with the spatial encoding approach, we demonstrated 10-color barcoding in fixed cells. Finally, to show the photochemical encoding of barcodes in the living system, we paired light-mediated recognition with a metabolically incorporated reporter tag and showed 7-color barcoding in MCF7 cells.Here, to develop a light-mediated multiplexed barcoding strategy, rather than relying on the fluorophore's photophysical properties, we explored the possibility of spatially controlling the fluorophore's positioning by using a universal photoactivatable adaptor . This un17 In our molecular design of CADA, we thought to eliminate the impact of synergistic interplay that renders high-affinity binding between CB[7] and ADA by converting the amine residue of ADA to a carbamate functionality in CADA. This essentially blocks the ion–dipole stabilizing interaction with the carbonyl portal. Moreover, the formation of a bulky nitrogen center, with the large caging group in the vicinity, hinders the approach of the rigid CB[7] structure towards the hydrophobic ADA core of CADA, which additionally imposes a steric constraint for complex formation  across the range of the reaction coordinate, here taken as the distance between the center of mass (COM) between CB[7] and ADA or CADA. This approach makes sure that the free energy landscape is extensively sampled by the system and a quantitative measure of the free energy surface can be obtained. In the case of complexation between CB[7] and ADA, snapshots of the bound (0.0 nm) and unbound (1.0 nm) states are shown in −1 free energy stabilization for complexation between CB[7] and ADA at a COM distance of ∼0.0 nm. However, for the interaction between CB[7] and CADA, the extent of free energy stabilization is much lower, and importantly the CADA molecule is observed to lie at a distance of ∼0.1 nm away from CB[7]  indicated that the reaction proceeds without any undesired side products  analysis. Quantitative formation of desired photocleaved ADA products was observed from each CADA conjugate, indicating a clean and efficient photochemical transformation that is unperturbed by derivatization -based quenching experiment to investigate light-gated molecular recognition with the CB[7] host. For this experiment, CADA was derivatized with an Alexa Fluor 647 fluorescent dye (CADA-Alexa 647), and CB[7] was covalently conjugated with a BHQ3 Quencher (CB[7]–BHQ3) (CADA-Alexa 647 in PBS (pH = 7.4) was titrated against CB[7]–BHQ3. Alexa Fluor 647 fluorescence was recorded after each addition of CB[7]–BHQ3 aliquots, and the titration was conducted up to a total of 2 eq. of CB[7]–BHQ3 with respect to the concentration of CADA-Alexa 647. Notably, the emission spectra showed negligible fluorescence quenching throughout the course of titration  . To estiitration , effecti[7]–BHQ3 . This inCADA as the fluorescent reporter probe to gain temporal control over the labeling process. Post CB[7]-phalloidin staining, the cells were incubated with a solution of CADA-sCy5 and the fluorescence image was subsequently acquired using structured illumination microscopy (SIM). As shown in CADA-sCy5 was still floating in the solution. Interestingly, SIM images acquired after the light pulse showed a fluorescently labelled actin network throughout the cellular structure  cells . To thistructure , indicatCADA was devised. To this end, we have conjugated CADA with antibodies that ensure immobilization of the CADA on a specific target biomolecule , and a small square area of the visual window was illuminated by sequential line scanning with a 405 nm diode laser  was imaged under lower magnification and merged with a bright-field image  via the free carbonyl portal. In addition, the mass transport limitation in the crowded cellular environment can create a local retention zone for CB[7]-FL to allow dissociated CB[7]-FL molecules to rebind to an ADA site, contributing to the slow dissociation kinetics of the CB[7]–ADA complex. Next, we tested if this strategy can be expanded to other cytoskeleton proteins or even in tissues. In this direction, we have synthesized a CADA conjugated phalloidin derivative to label actin in a spatially controlled manner , in a selected cell was photoactivated using a 405 nm laser to uncage the CADA molecule . The 1st cycle resulted in a fluorescence signal in the 488 nm channel and a negligible signal in other channels  and ternary mixtures  of CB[7]-FLs. This process generated a larger number of color palettes and enhanced the number of possible barcodes from this strategy  provides a way to generate a unique color signature via controlled mixing of CB[7]-FLs. In this respect, we tested two microscope parameters to control the degree of CADA photoactivation: the activating laser power and the activation time. Selected ROIs in MEF cells, containing microtubule targeted CADA, were activated either by varying laser power or irradiation time and subsequently labeled with CB[7]-Alexa 647. Quantitative analysis of the microtubule fluorescence intensity from different ROIs demonstrated that the degree of labeling (∝CADA activation) can be efficiently controlled by varying these two microscope parameters .19 Cells expressing azido glycans were reacted with a DBCO derivative of CADA via a strain promoted cycloaddition reaction to integrate CADA in the cell surface glycan structure , CB[7]-fluorescein (2nd cycle) and CB[7]-TAMRA (3rd cycle)  to place this molecular recognition under optical control. We utilized this optical control over the CB[7] host–guest recognition to spatially control the positioning of fluorophores to generate distinct barcodes in ‘user-defined’ cells. We optimized and validated this barcoding strategy in a fixed cell setting by specifically targeting the photoresponsive CB[7] host–guest pairs to microtubules. We subsequently showed the successful translation of this barcoding strategy to live cells by targeting cell surface glycans. In addition, we established an approach to rapidly create an enhanced number of distinct optical signatures or barcodes in cells by synergistically combining spatial encoding with microscopic control over the degree of CADA photoactivation. Given our strategy is independent of the types of labels used, we envision that our strategy can be easily synergized with other multiplexable labels, such as geometrically encoded fluorescent DNA-origami labels, to enhance the barcoding power of our strategy.20 Besides application in cellular barcoding, our photoresponsive CB[7] system benefits from the diffusion-controlled association kinetics and represents one of the fastest bioorthogonal coupling tools that can be manipulated by light. We expect that these fundamentally beneficial characteristics of this system will additionally enable a gamut of new explorations for light-gated bioorthogonal coupling strategies in basic and synthetic biology as well as in biomedical and imaging-related fields.In conclusion, we have developed a light-regulated cellular barcoding strategy based on photocontrolled host–guest molecular recognition in the CB[7] macrocycle. Rather than controlling the fluorophore's photophysical properties, we developed a distinct approach to barcode cells by spatially controlling the positioning of fluorescent labels. This represents a unique strategy that harnesses the benefits of light-regulation and concurrently provides highly multiplexed barcoding of living cells with highly optimized synthetic fluorophores. Our strategy builds upon the exceptional molecular recognition property of CB[7], where based on molecular dynamics simulation we designed and synthesized a photocaged guest molecule (A. S. and S. S. A. conceived the study, designed and performed the experiments, analysed and interpreted the data and wrote the manuscript. M. P. performed the binding experiments. S. B. and J. M. performed the simulation studies and wrote the manuscript. N. D. S. and M. S. B. prepared cells for the imaging experiment and reviewed the manuscript. R. S. helped with the imaging experiments. S. S. A. supervised the overall study.There are no conflicts to declare.SC-012-D0SC06860H-s001


Scientific Reports 10.1038/s41598-021-02993-7, published online 07 January 2022Correction to: The original version of this Article contained an error in the Acknowledgments section.“We acknowledge financial support from the European infrastructure projects IPERION CH (654028) and IPERION HS (654028). The samples in this study originate from fresh parchment prepared from calfskin. They were provided by the colleagues Margrit Hundertmark, Julia Bispinck-Rossbacher, Everardus Overgauuw from the collection of the Staatsbibliothek zu Berlin (SBB)—Stiftung Preußischer Kulturbesitz (SPK) in the framework of the Mary of Guelders research program founded by the Ernst von Siemens Kunst Stiftung (Martin Hoernes). The SBB is thanked for having provided permission to include a photograph of the colorful illustrated folio of the Prayer book of Mary of Guelders. Raw mock-up samples were prepared by Ellen Egel, former Rathgen research laboratory (RRL), Staatliche Museen zu Berlin (SMB)-Stiftung Preußischer Kulturbesitz (SPK) now Brandenburgisches Landesamt für Denkmalpflege for the Mary of Guelder project. The RRL staff members, especially Sabrina Buchhorn and Sonja Tesche, are acknowledged for the administrative support of this research program. HZB BESSY II is thanked for providing beamtime at the IRIS beamline in the framework of the proposal no. 17104894-ST/R. Liljana Puskar, Ulrich Schade, the team of the IRIS beamline, as well as Ellen Egel, Cristina Lopes Aibéo (RRL) are kindly acknowledged for their support during beamtime at IRIS beamline. We thank Friederike Fless, president of the DAI, for the interesting discussions on this project.”now reads:“We acknowledge financial support from the European infrastructure projects IPERION CH (654028) and IPERION HS (871034). The samples in this study originate from fresh parchment prepared from calfskin. They were provided by the colleagues Margrit Hundertmark, Julia Bispinck-Rossbacher, Everardus Overgauuw from the collection of the Staatsbibliothek zu Berlin (SBB)—Stiftung Preußischer Kulturbesitz (SPK) in the framework of the Mary of Guelders research program founded by the Ernst von Siemens Kunst Stiftung (Martin Hoernes). The SBB is thanked for having provided permission to include a photograph of the colorful illustrated folio of the Prayer book of Mary of Guelders. Raw mock-up samples were prepared by Ellen Egel, former Rathgen research laboratory (RRL), Staatliche Museen zu Berlin (SMB)-Stiftung Preußischer Kulturbesitz (SPK) now Brandenburgisches Landesamt für Denkmalpflege for the Mary of Guelder project. The RRL staff members, especially Sabrina Buchhorn and Sonja Tesche, are acknowledged for the administrative support of this research program. HZB BESSY II is thanked for providing beamtime at the IRIS beamline in the framework of the proposal no. 17104894-ST/R. Liljana Puskar, Ulrich Schade, the team of the IRIS beamline, as well as Ellen Egel, Cristina Lopes Aibéo (RRL) are kindly acknowledged for their support during beamtime at IRIS beamline. We thank Friederike Fless, president of the DAI, for the interesting discussions on this project.”The original Article has been corrected.


Drug synergy is the combine effect of drug efficacy. Synergistic combinations of active ingredients have proven to be highly effective and more useful in therapeutics. In contrast, the individual effect of drug is usually undesirable and mostly used for selecting drug-resistant mutations. Purpose of this study was to check synergistic effects of both plants (Barbadensis miller and Marsdenia condurango) against liver and cervical cancer. 50 evaluation, viability assays , p53 ELISA and immunocytochemistry, MUSE analysis (count and viability), antioxidants , at the end RT-PCR was performed. Culturing of HeLa  and HepG2 (liver cancer cell line) cells, IC50 evaluation was done of each plant individually and with combination for synergistic effects, IC50 with plants combination (synergism) was applied on further viability assays  p53 ELISA and immunocytochemistry for evaluation of cellular apoptosis, antioxidants assays , and RT-PCR with proliferative and apoptotic markers along with internal control. ICAccording to current study it was observed that synergistic effect of these plants has more anticancer properties with minimum effective dose. It was also observed that extracts possess the ability to induce apoptosis, restrict proliferation and enhanced oxidative stress. It is used as a therapeutic agent for stomach cancer. Condurango extract (CE) is also used as an appetite stimulant and has relatively few side effects when taken at the recommended dose. In addition, CE has been shown to have anti-inflammatory effects in animal studies .Many people have envisioned drug combinations as promising methods of treating complex diseases such as inflammation, cancer and type 2 diabetes . However, when combined, the drugs interact in many unexpected ways they show a variety of different results . Among these interactions, drug synergy and antagonism have received particular attention. Drug synergy is the combined effect of drug . Synergistic combinations of active ingredients have proven to be highly effective and more specific as treatment therapies . In contrast, drug antagonism is usually undesirable, but can be useful in selecting drug-resistant mutations . Drug interactions can also be determined in this way, so that the biological network structure of the drug target under study can clarify the mode of action of the drug  and interact . Indeed, an early study theoretically proved that continuous inhibition of the enzyme chain can lead to drug synergy , mentioned by Fitzgerald et al . Different modes of collaborative combination have been studied in several typical network environments as mentioned by Lehar et al . Antitumor and cytotoxic potential of Barbadensis miller extract (BE) continues to be the center of interest for scientific research since 1980’s and has extensively been used as an anticancer potential drug  Marsdenia cundurango (Condurango), commonly known as condor vine. It is a medicinal plant whose bark extract (“Hepatocellular carcinoma (HCC) is the most common form of liver cancer and is often associated with a very poor prognosis  and the third most common cause of death worldwide. Mostly, HCC is associated with severe liver fibrosis and cirrhosis caused by chronic liver inflammation. Therefore, the risk of liver cancer depends on the background liver factor, where fibrosis is the main determinant. , this is due to the accumulation of reactive oxygen species (ROS) in damaged hepatocytes, an increase in oxidative stress in hepatocytes may explain the close relationship between liver fibrosis and hepato-carcinogenesis . Cervical cancer is the second most common malignancy among women worldwide . It is the commonest malignancy of the female genital tract in developing countries . It has a heavy global burden, with 530,000 new cases and 275,000 maternal deaths reported annually . Approximately 83% of these cases occur in developing countries, where it is the leading cause of cancer-related death among women.Plant preparationBarbadensis miller (whole plant) and Marsdenia cundurango (bark) preparation was done according to Abid F protocol  protocol. The final extract was dissolved in DMSO in different concentrations according to need.Culturing of HeLa and HepG2 Cell Lines2 with 95% humidity level. Complete culturing medium contained 10% FBS, 1X Penicillin/Streptomycin solution (Thermo Scientific Cat#15140122) in DMEM high glucose (Thermo Scientific Cat#12800017). Culture was expanded and stored both in -80 freezers and liquid nitrogen containers as well and taken out when needed. HeLa cell line was obtained from American Type Culture Collection (ATCC®). Culture was maintained at 37ºC and 5% COTreatment of HeLa and HepG2 cell lines with plant extracts50 calculation. Cultured HeLa and HepG2 cells were divided into seven groups in case of each plant while it was further reduced to five groups in case of synergistic effect of combined plant extracts for each cell line for IC50, In case of HeLa, ethanolic extract of Barbadensis miller (BE), UT is untreated group, B-H  was treated HeLa group, while in case of HepG2, UT is untreated group, B-HG  was treated HepG2 group. In case of HeLa, ethanolic extracts of Condurango (CE), UT is untreated group, C-H  was treated HeLa group, while in case of HepG2, UT is untreated group, C-HG  was treated HepG2 cells, In case of synergistic effects on HeLa, combination of ethanol extracts, UT is untreated and S-H  was treated HeLa group and for HepG2 UT is untreated, S-HG  was treated HepG2 group. After that IC50 of combined plant extracts, cells were divided into three groups in case of each cell line for analysis of further assays, viability assays , ELISA p53, MUSE analysis, antioxidant assay and Lactate dehydrogenase assay (LDH) were performed in 96 well plate while immunocytochemistry p53 was performed in 24 well plate.HeLa and HepG2 cells were cultured onto 96-well plate for ICIC50 Evaluation 50 was evaluated via 3--2,5-diphenyltetrazolium bromide (MTT) assay after 72 hrs treatment. Monolayer of cells was first washed with phosphate buffer saline (PBS) , further cells were incubated in 100 µl complete medium containing 25 µl MTT solution  for 2 hrs. MTT converted into purple colored formazan in living cells which was then solubilized with dimethyl sulphoxide (DMSO)  and absorbance of solution was taken at 570 nm.ICTrypan Blue AssayTrypan blue assay was performed according to maqbool T protocol .Crystal Violet AssayCell viability was also assessed by crystal violet staining method. This method was conducted in a 96-well plate accoding to maqbool T protocol Enzyme linked immunosorbent assay (ELISA)Solid phase sandwich ELISA was performed for p53  in a 96-well plate . Performed according to  protocol absorbance was taken at 450 nm by using the microtiter plate.ImmunostainingCells of experimental groups of HeLa and HepG2 cell lines, were plated in different wells of a 6-well plate for characterization of immunostaining following 72 hrs treatment according to  protocol.Muse analysis via count and viability kitEthanolic extracts in combination were applied on HeLa and HepG2 cells which were cultured in 6 well plate through count and viability kit (Cat. No MCH100102) by using the Muse™ automated cell analyzer (Merck-Millipore). Cells after treatment were centrifuged at 2000 RPM for 5 min, supernatant was discarded pellet was dissolved in cell and viability reagent and were counted via MUSE analyzer.Evaluation of antioxidative enzymes Glutathione reductase (GSH), Catalase (CAT) assay, Superoxide dismutase (SOD) assaysGSH, SOD, CAT were performed according to protocol .Estimation of gene expression profilingGene expression profilingAfter carefully analyzing all the previous experimental results, the most appropriate IC50 concentrations were applied on cell line groups. Total RNA was extracted from cancer cells HeLa, HepG2 cells of all experimental groups by using TRIZOL reagent . cDNA synthesis was done by Reverse Transcription System  according to the manufacturer’s protocol. Gene expression analysis was carried out by RT-PCR using SYBR Green PCR SuperMix (Fermentas) on BioRad System iQ5. For in-vitro experiments, expression of BAX, PCNA, p53 and Ki67 was estimated. GAPDH was used as an internal control.Statistical analysisAll data of experimental groups was expressed as mean ± SEM in triplicate experiments. For statistical analysis, group means were compared by one-way ANOVA and Bonferroni’s test was used to identify differences between groups. Quantitative data obtained from different experimental groups was statistically analyzed via graph pad software by using two ways ANOVA. P value less than 0.05 was considered as significant from statistical analysis. Endnote was used to insert reference.MTT Assay for IC50 EvaluationIC50 of ethanolic extracts of Barbadensis miller (BE) and Marsdenia cundurango (CE) extracts separately and combined plant extracts. 50 calculation was done against HeLa and HepG2 liver cancer cells with increasing concentrations. The IC50 value was found to be 403 µg/mL in case of ethanolic extracts of Barbadensis miller and 477 µg/mL in case of ethanolic extracts of Marsdenia cundurango in case of HepG2 cells, and 385 µg/mL in case of ethanolic extracts of Barbadensis miller and 459 µg/mL in case of Marsdenia cundurango in case of HeLa cells.ICWhile in case of combined cytotoxic effect of plants on HepG2 cells was 53µg/mL (1:1 of each plant) and on HeLa cells 49.9 µg/mL (1:1 of each plant).Trypan blue cell viability assayViability of cells was further assessed by trypan blue cell viability assay for detection and evaluation of dead and live cells as shown in HeLa and HepG2 cancer cells were treated with ethanolic extracts via staining with trypan blue. A significantly large number of blue colored cells were observed in treated HeLa and HepG2 cells indicating more dead cells as compared to untreated cells.Crystal violet cell viability assayCell viability was also estimated using crystal violet staining of HeLa and HepG2 cell line as displayed in Muse analysis via count and viability kitThe results of cell count and viability are shown in High apoptosis level in post treated HeLa and HepG2 cells via ELISAAfter treating HeLa and HepG2 cells with combined ethanolic extracts of Barbadensis miller and Marsdenia cundurango, more apoptosis was observed via p53 ELISA shown in Higher expression of apoptosis in post treated HeLa and HepG2 cells via immunocytochemistryAfter treating HeLa and HepG2 cells with ethanolic extracts of Barbadensis miller Marsdenia cundurango extracts, more apoptosis was seen via p53 immunocytochemistry shown in Decreased antioxidants level in treated HepG2 and HeLa groups The antioxidant potential using the catalase (CAT), superoxide dismutase (SOD) and glutathione reductase (GSH) assays which determines the free radical scavenging activity. It was observed that GSH, CAT and SOD activities were decreased in treated groups as compared to untreated groups as reflected in Gene expression profilingTo check the effect of combined plant extracts at the molecular level of cell proliferation and cell apoptosis, cellular proliferation markers (PCNA and Ki67) and apoptosis markers  levels were assessed via RT-PCR analyses. The results showed that level of proliferative markers was downregulated in treated groups of HeLa and HepG2 cell lines as compared to untreated groups while the level of apoptotic genes was upregulated in treated HeLa and HepG2 cell lines as compared to untreated. In both apoptosis and proliferation, GAPDH served as an internal control as the expression levels of each gene were normalized by GAPDH as shown in Multidrug therapy is a helpful strategy focused on the direct blocking or killing of damaging agents (such as cancer cells or pathogens) as well as on the activation of human body defenses or repair mechanisms. It derives from a gradual withdrawal of the previously adopted dogma of mono-drug therapy; for decades, pharmacological research was based on the identification of a single active principle . As regards phytotherapy research, only recently have the contributions of traditional Chinese medicine, Ayurveda and traditional Western phytomedicines begun to be scientifically confirmed and appreciated. Furthermore, during the last 20 years, the world has encountered an increasing rate of the use of conventional medicines combined with complementary and alternative medicine (CAM), which are represented not only by homeopathy, naturopathy, chiropractic, and energy medicine, among others, but also ethnopharmacology and phytotherapy . It is becoming evident that many diseases have a multifaceted etiology, which could be treated more successfully with a drug combination strategy than a single administration. In western countries, for multifactorial or complex disease treatment  in the present study activity of both plants were individually assessed using only MTT while combined plant extracts were analyzed using MTT and rest of assays are described below. 50 was very less which is a great sign as an anticancer agent against HeLa and HepG2 cell line which was 49.90 µg/mL (1:1 of each plant) on HeLa and 53 µg/mL (1:1 of each plant) on HepG2 -2,5-diphenyltetrazolium bromide] to an insoluble purple formazan by the action of mitochondrial reductase . Activity of ethanolic extracts of both plants Barbadensis miller (BE) and Marsdenia cundurango (CE) were assessed by MTT via application of different doses i.e., 10, 25, 50, 100, 500 and 1,000 µg/mL where BE and CE showed higher IC50 on HeLa and HepG2 which was 403 and 385 µg/mL and 477 and 459 µg/mL respectively while in case of synergistic effect ICon HepG2 . Accordi50 of combined plant extracts of BE+CE. The increased percentage of dead cells suggesting that disrupted cell membrane of hepatocellular carcinoma and cervical cancer and is very effective against hepatocellular carcinoma . In current study, evaluation of cell viability by the trypan blue method showed an increased trend against ICarcinoma . Furtherntreated .Count and viability assay are a fluorescent-based analysis using flow cytometry, which is more specific and reliable to quantify the number of viable cells. The DNA binding dyes present in the reagent differentially stain viable and non-viable cells based on their permeability and provide an accurate count of both the cells . Apoptosis induction is a useful strategy for anticancer drug development. Plant derived anti-cancer drugs exert cell death by inducing apoptosis in cancer cells. Many mechanisms responsible for apoptosis induced by plants and most of them induce apoptotic cell death by intrinsic or extrinsic pathway and p53 dependent or independent pathway, in our finding it was observed that plant extracts induced apoptosis in HepG2 and HeLa cells via p53 ELISA and Immunocytochemistry  and 5. AIn this study we also did the antioxidant evaluation of plants by SOD, catalase and GSH, as oxidative stress is a primary marker for cancer . Anti-oxidative enzymes effect the proliferation of cells in positive way. But when antioxidants are given with Anti-proliferative therapy it will enhance the effect of therapy and improve anticancer effect. The SOD, catalase and GSH activities were increased when treated with plant extracts .In conclusion, this study was based upon synergistic effects of ethanolic extracts of Barbadensis miller and Marsdenia cundurango upon HeLa and HepG2 cells. It was observed that combined plant extracts have more anticancer potential with low dose as compared with individual plant extracts. According to our finding it was observed that combined plant extract induce apoptosis, restrict proliferation and enhanced oxidative stress.This study was designed, directed and co-ordinated by Dr. Tahir Maqbool, Dr. Faheem Hadi, Dr. Sehrish Razzaq, Sadia Naz, Dr. Saira Aftab, Dr. Sana Javaid Awan, Ayesha Nawaz, Farah Abid and Prof. Dr. Arif Malik. The manuscript was written by Dr. Tahir Maqbool and Dr. Faheem Hadi.


ASIP gene controls both the temporal and spatial distribution of yellow and black pigments. Here, we identify independent regulatory modules for ventral and hair cycle ASIP expression, and we characterize their action and evolutionary origin. Structural variants define multiple alleles for each regulatory module and are combined in different ways to explain five distinctive dog colour patterns. Phylogenetic analysis reveals that the haplotype combination for one of these patterns is shared with Arctic white wolves and that its hair cycle-specific module probably originated from an extinct canid that diverged from grey wolves more than 2 million years ago. Natural selection for a lighter coat during the Pleistocene provided the genetic framework for widespread colour variation in dogs and wolves.Distinctive colour patterns in dogs are an integral component of canine diversity. Colour pattern differences are thought to have arisen from mutation and artificial selection during and after domestication from wolves but important gaps remain in understanding how these patterns evolved and are genetically controlled. In other mammals, variation at the  ASIP gene that explain five distinctive dog colour patterns and trace back the origin of one colour pattern to an extinct canid.Dogs exhibit remarkable variation in colour patterns. Here, the authors identify structural variants of independent regulatory modules for ventral and hair cycle expression of the  Agouti (ASIP), which encodes a paracrine signalling molecule and antagonist of the melanocortin 1 receptor (MC1R) that causes hair follicle melanocytes to switch from making eumelanin (black or brown pigment) to pheomelanin (yellow to nearly white pigment)4. In laboratory mice, Asip expression is controlled by alternative promoters in specific body regions and at specific times during hair growth and gives rise to the light-bellied agouti phenotype, with ventral hair that is yellow and dorsal hair that contains a mixture of black and yellow pigment4. Genetic variation in ASIP affects colour pattern in many mammals; however, in dogs, the situation is still unresolved, in large part due to the complexity of different pattern types, epistatic relationships with variants at other loci and challenges in distinguishing whether genetic association of one or more variants truly represents causal variation or just close linkage5.A central aspect of the amazing morphologic diversity among domestic dogs is their colours and colour patterns. In many mammals, specific colour patterns arise through differential regulation of ASIP regulatory modules and their effect on patterning phenotypes in domestic dogs. We expand our analysis to include modern and ancient wild canids and uncover an evolutionary history in which natural selection during the Pleistocene provided a molecular substrate for colour pattern diversity today.Here we investigate non-coding variation in ASIP promotes pheomelanin synthesis; therefore, ASIP alleles associated with a yellow colour are dominant to those associated with a black colour. Although dominant yellow (DY) is common in dogs from diverse geographic locations, the most common coat pattern of modern wolves is agouti (AG)6, in which the dorsum has banded hairs and the ventrum is light. Three additional colour patterns are recognizable but all have been described historically by different, inconsistent and sometimes overlapping names that predate genomic analysis; we refer to these as shaded yellow (SY), black saddle (BS) and black back (BB)  Fig. .Fig. 1CoASIP  data available from dogs of dominant yellow and black back patterns and identified three alternative untranslated first exons for dog SIP Fig. . As descses Fig.  reveal tses Fig.  did not ASIP locus to infer two VP haplotypes and five HCP haplotypes, consisting of multiple structural variants that lie within 1.5 kb of each transcriptional start site. VP1 contains a SINE element in reverse orientation relative to the transcription of ASIP and an A-rich expansion not found in VP2  to those from ten contemporary grey wolves  and grey wolf (n = 2) whole genome sequencing (WGS) data, dated 4–35 thousand years ago (ka)  were observed in various combinations  , in whicons Fig. . AncientASIP haplotypes suggests an evolutionary history in which multiple derivative haplotypes and associated colour patterns arose by recombination and mutation from two ancestral configurations corresponding to a white wolf (VP1-HCP1) and a grey wolf (VP2-HCP2), both present in the late Pleistocene  and is consistent with natural selection for light coat colour. A likely timeline for the origin of modules driving high levels of ASIP expression is depicted in Fig. Together with our phylogenetic results, comparative analysis of wolf and dog ene Fig. . The disASIP and dog colour pattern was recognized more than a century ago by Sewall Wright14 and explored in depth by the work of C. C. Little in the decades that followed15. Previous studies have reported molecular variation in or around the ASIP region associated with some dog colour patterns, including a 16-bp non-coding duplication associated with black saddle16, a SINE insertion associated with black back and black saddle16 and missense variants A82S and R83H associated with dominant yellow17 . Of particular interest is the Zhokov island dog from Siberia29. On the basis of a haplotype combination of VP2-HCP4, this sled dog that lived 9.5 ka exhibited a black back colour pattern, allowing it to be easily distinguished from white-coloured wolves in an Arctic environment.31; during the North American glacial retreat that followed, the VP1-HCP1 haplotype combination was selected for in today’s white-coloured Arctic wolves. Our results show how introgression, demographic history and the genetic legacy of extinct canids played key roles in shaping diversity in dogs and modern grey wolves.In wolves, natural selection for VP1 and HCP1 are a likely consequence of Pleistocene adaptation to Arctic environments and genetic exchange in glacial refugia, driven by canid and megafaunal dispersal during interglacial periods. Modern grey wolves are thought to have arisen from a single source ~25 ka close to the last glacial maximumAll animal experiments were done in accordance with the local regulations. Experiments were approved by the “Cantonal Committee For Animal Experiments” .Skin biopsies (6-mm punch) were recovered from three dogs (black back Miniature Pinscher and dominant yellow Border Terrier and Irish Terrier) at necropsy and/or surgery for reasons unrelated to this study. Biopsies were recovered from the ventral abdomen and dorsal thorax and are not matched for age or hair growth cycle. The biopsies were immediately put in RNAlater (Qiagen) for at least 24 h and then frozen at –20 °C. Before RNA extraction, the skin biopsies were homogenized mechanically with the TissueLyser II device from Qiagen. Total RNA was extracted from the homogenized tissue using the RNeasy Fibrous Tissue Mini Kit (Qiagen) according to the manufacturer’s instructions. RNA quality was assessed with a Fragment Analyzer (Agilent) and the concentration was measured using a Qubit Fluorometer (ThermoFisher Scientific).34.From each sample, 1 μg of high-quality total RNA (RNA integrity number > 9) was used for library preparation with the Illumina TruSeq Stranded mRNA kit. The libraries were individually barcoded and pooled and sequenced on an S1 flow cell with 2 × 50 bp paired-end sequencing using an Illumina NovaSeq 6000 instrument. On average, 31.5 million paired-end reads per sample were collected. One publicly available Beagle sample was used SRX1884098). All accession numbers and descriptive read statistics are given in Supplementary Table 84098. Al35. Three different alternate untranslated first exons with splice junctions to the coding exons of ASIP were defined on the basis of the visualizations of the read alignments in IGV on the basis of the RNA-seq data just described. These exact transcripts have not been documented in the National Center for Biotechnology Information (NCBI); however, the three transcripts of NCBI annotation release 105 are virtually identical except for minor differences regarding the length of the 5′ untranslated regions  22 nucelotides upstream compared to our annotation; NM_001007263.1, VP1-TSS 98 bp downstream of our annotation; XM_022408819.1 HCP-TSS 36 bp downstream of our annotation). Our visually curated gene models are given in Supplementary Table The STAR-aligned bam files were visualized in the integrated genomics viewer (IGV) browser36. SNVs and small indels were called as described36. The IGV software35 was used for visual inspection of the three promoter regions based on the transcripts identified in the RNA-seq data. Structural variants were identified and association with coat colour phenotypes was verified by visual inspection in IGV. The pattern of copy number variation at the third promoter did not associate with the coat patterns as defined in Fig. WGS data from 71 dogs and six wolves were used for variant discovery . The previously reported ASIP coding variants17 were genotyped by Sanger sequencing of PCR products. The previously reported RALY intronic duplication16 was genotyped by size differentiation of PCR products on a Fragment Analyzer (Agilent). Another primer pair was used for the amplification of the entire HCP  were required to unambiguously determine the VP and HCP haplotypes .Transcript data were generated from a second set of samples. Sample descriptions and colours are shown in Supplementary Table 41 was used to estimate the abundance and quantified as transcripts per million mapped reads (TPM) data on the basis of an index built from CanFam3.1 Ensembl transcriptome (release 99). The curated ASIP transcript isoform models based on alignment visualizations in the IGV browser35 were also included in the transcriptome. Results based on genotype of the promoter haplotypes are displayed in Extended Data Fig. The alignment-free quantification method Kallisto (v.0.46.0)PRJEB32865 and PRJNA448733. The VCFs for selected dogs were merged using BCFtools merge tool (http://samtools.github.io/bcftools/) with the parameter --missing-to-ref, which assumed genotypes at missing sites are homozygous reference type 0/0. Only dogs homozygous for ASIP haplotypes  were used to visualize haplotypes . SNVs within a 110-kb interval , which includes the ASIP transcriptional unit and regulatory sequences, were identified with Platypus (v.0.8.1)43 and filtered with VCFtools (v.0.1.15)44 to include 2,008 biallelic SNVs. Phasing was inferred with BEAGLE (v.4.1)45.Illumina whole genome sequence for 36 canids, including seven extant species and the dog, were downloaded from the NCBI short read archive as aligned (bam format) or unaligned  and a 16-kb region including the hair cycle first exon, extending to and including ASIP coding exons 2–4 . Consensus sequences of equal length were constructed for each inferred canid haplotype using BCFtools (v.1.9). Phylogenies were inferred using maximum likelihood method and Tamura–Nei model with 250 bootstrap replications, implemented in MEGAX47 and including 34 canids . Coverage depth for each sample ranged from 1 to 78× (Supplementary Table n = 2), the hair cycle promoter (n = 6) and the coding exons (n = 3) distinguished ventral and hair cycle promoter haplotypes , the phase of ventral and hair cycle promoter haplotypes was unambiguous. Seven wolves and one ancient dog were heterozygous with respect to both the ventral and hair cycle promoter haplotypes and, for these samples, haplotype phase was inferred on the basis of the linkage disequilibrium in the 67 unambiguous individuals.Further information on research design is available in the Supplementary InformationSupplementary text and references.Reporting SummaryPeer Review InformationSupplementary TableExcel file with 12 tabs, one for each table. Each table has a title.