PPMS: A framework to Profile Primary MicroRNAs from Single-cell RNA-sequencing datasets.

MOTIVATION: Single-cell/nuclei RNA-sequencing (scRNA-seq) technologies can simultaneously quantify gene expression in thousands of cells across the genome. However, the majority of the noncoding RNAs, such as microRNAs (miRNAs), cannot currently be profiled at the same scale. MiRNAs are a class of small noncoding RNAs and play an important role in gene regulation. MiRNAs originate from the processing of primary transcripts, known as primary-microRNAs (pri-miRNAs). The pri-miRNA transcripts, independent of their cognate miRNAs, can also function as long noncoding RNAs, code for micropeptides or even interact with DNA, acting like enhancers. Therefore, it is apparent that the significance of scRNA-seq pri-miRNA profiling expands beyond using pri-miRNA as proxies of mature miRNAs. However, there are no computational methods that allow profiling and quantification of pri-miRNAs at the single-cell-type resolution. RESULTS: We have developed a simple yet effective computational framework to profile pri-MiRNAs from single-cell RNA-sequencing datasets (PPMS). Based on user input, PPMS can profile pri-miRNAs at cell-type resolution. PPMS can be applied to both newly produced and publicly available datasets obtained via single cell or single-nuclei RNA-seq. It allows users to (i) investigate the distribution of pri-miRNAs across cell types and cell states and (ii) establish a relationship between the number of cells/reads sequenced and the detection of pri-miRNAs. Here, to demonstrate its efficacy, we have applied PPMS to publicly available scRNA-seq data generated from (i) individual chambers (ventricles and atria) of the human heart, (ii) human pluripotent stem cells during their differentiation into cardiomyocytes (the heart beating cells) and (iii) hiPSCs-derived cardiomyocytes infected with severe acute respiratory syndrome coronavirus 2.

HCG18, LEF1AS1 and lncCEACAM21 as biomarkers of disease severity in the peripheral blood mononuclear cells of COVID-19 patients.

BACKGROUND: Even after 3 years from SARS-CoV-2 identification, COVID-19 is still a persistent and dangerous global infectious disease. Significant improvements in our understanding of the disease pathophysiology have now been achieved. Nonetheless, reliable and accurate biomarkers for the early stratification of COVID-19 severity are still lacking. Long noncoding RNAs (LncRNAs) are ncRNAs longer than 200 nucleotides, regulating the transcription and translation of protein-coding genes and they can be found in the peripheral blood, thus holding a promising biomarker potential. Specifically, peripheral blood mononuclear cells (PBMCs) have emerged as a source of indirect biomarkers mirroring the conditions of tissues: they include monocytes, B and T lymphocytes, and natural killer T cells (NKT), being highly informative for immune-related events. METHODS: We profiled by RNA-Sequencing a panel of 2906 lncRNAs to investigate their modulation in PBMCs of a pilot group of COVID-19 patients, followed by qPCR validation in 111 hospitalized COVID-19 patients. RESULTS: The levels of four lncRNAs were found to be decreased in association with COVID-19 mortality and disease severity: HLA Complex Group 18-242 and -244 (HCG18-242 and HCG18-244), Lymphoid Enhancer Binding Factor 1-antisense 1 (LEF1-AS1) and lncCEACAM21 (i.e. ENST00000601116.5, a lncRNA in the CEACAM21 locus). Interestingly, these deregulations were confirmed in an independent patient group of hospitalized patients and by the re-analysis of publicly available single-cell transcriptome datasets. The identified lncRNAs were expressed in all of the PBMC cell types and inversely correlated with the neutrophil/lymphocyte ratio (NLR), an inflammatory marker. In vitro, the expression of LEF1-AS1 and lncCEACAM21 was decreased upon THP-1 monocytes exposure to a relevant stimulus, hypoxia. CONCLUSION: The identified COVID-19-lncRNAs are proposed as potential innovative biomarkers of COVID-19 severity and mortality.

Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor.

There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations.

Appraisal of historical trends in maximum and minimum temperature using multiple non-parametric techniques over the agriculture-dominated Narmada Basin, India.

In this study, the long-term trends in climatological parameters, viz., maximum temperature (TMAX) and minimum temperature (TMIN), are determined over 68 years (i.e., June 1951 to May 2019) using the gridded observation datasets (1° × 1° spatial resolution) of India Meteorological Department over the Narmada river basin, India. Multiple non-parametric techniques, viz., modified Mann-Kendall (MMK), Sen's slope (SS), and Spearman's rho (SR) tests, are used to determine monthly, seasonal, and annual trends over individual grids. The trends are also analyzed for the climatic variables spatially averaged over the entire basin to draw general conclusions on historical climate change. The results reveal a significant spatiotemporal variation in trends of TMAX and TMIN over the basin. In general, both the parameters are found to be increasing. Furthermore, the hottest months (April and May) have become hotter, and the coldest month (January) has become colder, implying a higher probability of increasing temperature extremes. Furthermore, the entire duration of 68 years is divided into two epochs of 34 years, i.e., 1951-1984 and 1985-2018, and the trend analysis of TMAX and TMIN is also carried out epoch-wise to better understand/assess the signals of climate change in recent years. In general, a relatively higher warming trend was observed in the latter epoch. As a majority of the basin area is dominated by agricultural lands, the implications of the temperature trends and their impacts on agriculture are succinctly discussed. The information reported in this study will be helpful for proper planning and management of water resources over the basin under the changing climatic conditions.

Impact of extreme weather events on cropland inundation over Indian subcontinent.

Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The "Gulab" cyclonic storm formed on the 24th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the "Gulab" cyclonic event. The Otsu's thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km2), followed by Telangana (~2040 km2) and Odisha (~1132 km2), and the least in Chhattisgarh (~93.75 km2). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years.

Genome-wide analysis of differential RNA editing in epilepsy.

The recoding of genetic information through RNA editing contributes to proteomic diversity, but the extent and significance of RNA editing in disease is poorly understood. In particular, few studies have investigated the relationship between RNA editing and disease at a genome-wide level. Here, we developed a framework for the genome-wide detection of RNA sites that are differentially edited in disease. Using RNA-sequencing data from 100 hippocampi from mice with epilepsy (pilocarpine-temporal lobe epilepsy model) and 100 healthy control hippocampi, we identified 256 RNA sites (overlapping with 87 genes) that were significantly differentially edited between epileptic cases and controls. The degree of differential RNA editing in epileptic mice correlated with frequency of seizures, and the set of genes differentially RNA-edited between case and control mice were enriched for functional terms highly relevant to epilepsy, including "neuron projection" and "seizures." Genes with differential RNA editing were preferentially enriched for genes with a genetic association to epilepsy. Indeed, we found that they are significantly enriched for genes that harbor nonsynonymous de novo mutations in patients with epileptic encephalopathy and for common susceptibility variants associated with generalized epilepsy. These analyses reveal a functional convergence between genes that are differentially RNA-edited in acquired symptomatic epilepsy and those that contribute risk for genetic epilepsy. Taken together, our results suggest a potential role for RNA editing in the epileptic hippocampus in the occurrence and severity of epileptic seizures.

Non-coding RNAs as therapeutic targets and biomarkers in ischaemic heart disease.

The adult heart is a complex, multicellular organ that is subjected to a series of regulatory stimuli and circuits and has poor reparative potential. Despite progress in our understanding of disease mechanisms and in the quality of health care, ischaemic heart disease remains the leading cause of death globally, owing to adverse cardiac remodelling, leading to ischaemic cardiomyopathy and heart failure. Therapeutic targets are urgently required for the protection and repair of the ischaemic heart. Moreover, personalized clinical biomarkers are necessary for clinical diagnosis, medical management and to inform the individual response to treatment. Non-coding RNAs (ncRNAs) deeply influence cardiovascular functions and contribute to communication between cells in the cardiac microenvironment and between the heart and other organs. As such, ncRNAs are candidates for translation into clinical practice. However, ncRNA biology has not yet been completely deciphered, given that classes and modes of action have emerged only in the past 5 years. In this Review, we discuss the latest discoveries from basic research on ncRNAs and highlight both the clinical value and the challenges underscoring the translation of these molecules as biomarkers and therapeutic regulators of the processes contributing to the initiation, progression and potentially the prevention or resolution of ischaemic heart disease and heart failure.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82-0.84) and a balanced accuracy of 0.78 (95% CI 0.77-0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40-0.74). Quantitative PCR validated LEF1-AS1's adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

Characterization and assessment of hydrological droughts using GloFAS streamflow data for the Narmada River Basin, India.

Hydrological droughts severely affect the demand of water for domestic water supply, irrigation, hydropower generation, and several other purposes. The pervasiveness and consequences of hydrological droughts necessitate a thorough investigation of their characteristics, which is hindered due to unavailability of continuous streamflow records at desirable resolutions. This study aims to assess the hydrological drought characteristics and their spatial distribution using high-resolution Global Flood Awareness System (GloFAS) v3.1 streamflow data for the period 1980 to 2020. Streamflow Drought Index (SDI) was used to characterize droughts at 3-, 6-, 9-, and 12-monthly timescales starting from June, i.e., the start of water year in India. GloFAS is found to capture the spatial distribution of streamflow and its seasonal characteristics. The number of hydrological drought years over the basin varied from 5 to 11 during the study duration, implying that the basin is prone to frequent abnormal water deficits. Interestingly, the hydrological droughts are more frequent in the eastern portion of the basin, i.e., the Upper Narmada Basin. The trend analysis of multi-scalar SDI series using non-parametric Spearman's Rho test exhibited increasing drying trends in the easternmost portions. The results were not similar for the middle and western portions of the basin, which may be due to presence of a large number of reservoirs in these regions and their systematic operations. This study highlights the importance of open-access global products that can be used for monitoring hydrological droughts, especially over ungauged catchments.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.

Short-Term Statistical Forecasts of COVID-19 Infections in India.

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.