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Multiple environmental stresses adversely affect plant growth and development. Plants under multiple stress condition trigger cascade of signals and show response unique to specific stress as well as shared responses, common to individual stresses. Here, we aim to identify common and unique genetic components during stress response mechanisms liable for cross-talk between stresses. Although drought and cold stress have been widely studied, insignificant information is available about how their combination affects plants. To that end, we performed meta-analysis and co-expression network comparison of drought and cold stress response in Arabidopsis thaliana by analyzing 390 microarray samples belonging to 29 microarray studies. We observed 6120 and 7079 DEGs (differentially expressed genes) under drought and cold stress respectively, using Rank Product methodology. Statistically, 28% (2890) DEGs were found to be common in both the stresses (i.e.; drought and cold stress) with most of them having similar expression pattern. Further, gene ontology-based enrichment analysis have identified shared biological processes and molecular mechanisms such as-'photosynthesis', 'respiratory burst', 'response to hormone', 'signal transduction', 'metabolic process', 'response to water deprivation', which were affected under cold and drought stress. Forty three transcription factor families were found to be expressed under both the stress conditions. Primarily, WRKY, NAC, MYB, AP2/ERF and bZIP transcription factor family genes were highly enriched in all genes sets and were found to regulate 56% of common genes expressed in drought and cold stress. Gene co-expression network analysis by WGCNA (weighted gene co-expression network analysis) revealed 21 and 16 highly inter-correlated gene modules with specific expression profiles under drought and cold stress respectively. Detection and analysis of gene modules shared between two stresses revealed the presence of four consensus gene modules.

MicroRNA-mediated gene regulation plays a crucial role in controlling drought tolerance. In the present investigation, 13 drought-associated miRNA families consisting of 65 members and regulating 42 unique target mRNAs were identified from drought-associated microarray expression data in maize and were subjected to structural and functional characterization. The largest number of members (14) was found in the zma-miR166 and zma-miR395 families, with several targets. However, zma-miR160, zma-miR390, zma-miR393, and zma-miR2275 each showed a single target. Twenty-three major drought-responsive -regulatory elements were found in the upstream regions of miRNAs. Many drought-related transcription factors, such as GAMYB, HD-Zip III, and NAC, were associated with the target mRNAs. Furthermore, two contrasting subtropical maize genotypes (tolerant: HKI-1532 and sensitive: V-372) were used to understand the miRNA-assisted regulation of target mRNA under drought stress. Approximately 35 and 31% of miRNAs were up-regulated in HKI-1532 and V-372, respectively. The up-regulation of target mRNAs was as high as 14.2% in HKI-1532 but was only 2.38% in V-372. The expression patterns of miRNA-target mRNA pairs were classified into four different types: Type I- up-regulation, Type II- down-regulation, Type III- neutral regulation, and Type IV- opposite regulation. HKI-1532 displayed 46 Type I, 13 Type II, and 23 Type III patterns, whereas V-372 had mostly Type IV interactions (151). A low level of negative regulations of miRNA associated with a higher level of mRNA activity in the tolerant genotype helped to maintain crucial biological functions such as ABA signaling, the auxin response pathway, the light-responsive pathway and endosperm expression under stress conditions, thereby leading to drought tolerance. Our study identified candidate miRNAs and mRNAs operating in important pathways under drought stress conditions, and these candidates will be useful in the development of drought-tolerant maize hybrids.

Tularemia caused by Gram-negative, coccobacillus bacterium, Francisella tularensis, is a highly infectious zoonotic disease. Human cases have been reported mainly from the United States, Nordic countries like Sweden and Finland, and some European and Asian countries. Naturally, the disease occurs in several vertebrates, particularly lagomorphs. Type A (subspecies tularensis) is more virulent and causes disease mainly in North America; type B (subspecies holarctica) is widespread, while subspecies mediasiatica is present in central Asia. F. tularensis is a possible bioweapon due to its lethality, low infectious dosage, and aerosol transmission. Small mammals like rabbits, hares, and muskrats are primary sources of human infections, but true reservoir of F. tularensis is unknown. Vector-borne tularemia primarily involves ticks and mosquitoes. The bacterial subspecies involved and mode of transmission determine the clinical picture. Early signs are flu-like illnesses that may evolve into different clinical forms of tularemia that may or may not include lymphadenopathy. Ulcero-glandular and glandular forms are acquired by arthropod bite or handling of infected animals, oculo-glandular form as a result of conjunctival infection, and oro-pharyngeal form by intake of contaminated food or water. Pulmonary form appears after inhalation of bacteria. Typhoidal form may occur after infection via different routes. Human-to-human transmission has not been known. Diagnosis can be achieved by serology, bacterial culture, and molecular methods. Treatment for tularemia typically entails use of quinolones, tetracyclines, or aminoglycosides. Preventive measures are necessary to avoid infection although difficult to implement. Research is underway for the development of effective live attenuated and subunit vaccines.

Asthma is a clinically and biologically heterogeneous syndrome with variable symptom patterns, severity, and treatment responses. Understanding this heterogeneity is important for developing personalized management strategies. In this study, we applied principal component analysis to multiple comparable baseline clinical features in three independent pediatric asthma cohorts, CAMP (N=1,041), PACT (N=230), and GACRS (N=1,165), to define reproducible endophenotypes as quintiles of the first principal component (PC1). Across cohorts, atopy, lung function, and demographic features were the greatest contributors to variation in PC1: CAMP (67%), PACT (49%), GACRS (60%). The extremal quintiles captured consistent clinical gradients: mean pre-bronchodilator FEV1% predicted declined from Q1 to Q5, while short-acting beta agonist (SABA) usage, IgE and eosinophil levels increased. We also observed that the derived endophenotypes enhanced the ability to predict longitudinal change in lung function that is treatment-specific (inhaled corticosteroid therapy (ICS) or not) in CAMP and PACT. Together, we have (1) a PCA-based aggregation of multiple common baseline asthma clinical features for an easily implementable definition of an endophenotype that (2) stratifies pediatric asthma into clinically meaningful lung function and atopy gradients across the endophenotype group quintiles which also (3) predicts ICS treatment response - warranting a case for their use in personalizing treatment decisions.

Rationale We previously validated five clinically distinct asthma metabo-endotypes (mechanistically derived asthma subgroups). We hypothesize that metabo-endotype membership may be partially driven by differences in serum microRNA profiles and their influence on metabolite levels. Objectives To determine whether serum miRNA levels can help understand the underlying drivers of metabolic dysregulation across metabo-endotypes. Method We compared expression levels of serum microRNAs across 1121 children grouped into five asthma metabo-endotypes using ANCOVA. A LASSO model was leveraged to determine the most important miRNAs for discriminating metabo-endotype membership. Finally, multiple linear regression models and two-sample t-tests were employed to determine whether serum microRNA ~ plasma metabolite relationships differed between individuals within different metabo-endotypes. Measurements and main results Of 317 serum miRNAs, 132 (41.6%) demonstrated significantly different expression across metabo-endotypes (FDR < 0.05), with miR-143-3p showing the greatest variation (FDR p  = 5.7 × 10 − 19 ). Most differences were driven by metabo-endotypes 2 and 3, the most and least severe. Enrichment analysis of microRNAs’ predicted target genes revealed critical asthma pathways, including Th17 and Th1/Th2 cell differentiation. A model based on 17 miRNAs was able to discriminate membership of metabo-endotype 2 versus 3 (AUC:81%, CI: 73%-88%). There was some evidence that relationships between specific miRNAs and metabolites differed between individuals in metabo-endotypes 2 and 3, which may suggest differential posttranscriptional regulation of pathways including eicosanoid and arginine metabolism. Conclusions The results provide some evidence to suggest differential miRNA regulated gene expression between biologically and clinically distinct asthma metabo-endotypes, with a potentially important role for miR-143-3p. Understanding these relationships may uncover novel therapeutic targets and guide more personalized treatment strategies.

Waterlogging causes extensive damage to maize crops in tropical and subtropical regions. The identification of tolerance genes and their interactions at the molecular level will be helpful to engineer tolerant genotypes. A whole-genome transcriptome assay revealed the specific role of genes in response to waterlogging stress in susceptible and tolerant genotypes. Genes involved in the synthesis of ethylene and auxin, cell wall metabolism, activation of G-proteins and formation of aerenchyma and adventitious roots, were upregulated in the tolerant genotype. Many transcription factors, particularly ERFs, MYB, HSPs, MAPK, and LOB-domain protein were involved in regulation of these traits. Genes responsible for scavenging of ROS generated under stress were expressed along with those involved in carbohydrate metabolism. The physical locations of 21 genes expressed in the tolerant genotype were found to correspond with the marker intervals of known QTLs responsible for development of adaptive traits. Among the candidate genes, most showed synteny with genes of sorghum and foxtail millet. Co-expression analysis of 528 microarray samples including 16 samples from the present study generated seven functional modules each in the two genotypes, with differing characteristics. In the tolerant genotype, stress genes were co-expressed along with peroxidase and fermentation pathway genes.

The lack of biomarkers to identify individuals at risk of asthma exacerbations remains a significant limitation to improving patient outcomes. To address this need, we analyze data from three asthma cohorts, combining up to 25 years of electronic medical records with sequential metabolomics studies, to develop and replicate a predictive model for asthma exacerbation risk. We identify asthma-associated biochemical pathways via global circulatory metabolomics and then apply targeted mass spectrometry methods to quantify selected steroids, sphingolipids, and microbial-derived metabolites. The sphingolipid-to-steroid ratios robustly associate with 5-year exacerbation risk (discovery p value = 1.63×10⁻ 26 -0.029; replication p value = 1.89×10⁻ 36 -0.033). Based upon these findings, we derive and replicate a simple 5-year predictive model of asthma exacerbations using 21 sphingolipid-to-steroid ratios that outperforms current clinical measures (discovery AUC = 0.90; replication AUC = 0.89). These findings underscore the value of metabolomic profiling to develop a practical, cost-effective clinical assay for asthma exacerbation risk that may improve patient care. Asthma exacerbations remain hard to predict with routine tests. Here, the authors show that simple blood sphingolipid-to-steroid ratios predict five-year exacerbation risk and can underpin a practical, low-cost assay that outperforms standard clinical measures.

IntroductionWith the global lymphatic filariasis (LF) elimination goal set to 2030, it is necessary to address challenges hindering the last-mile efforts. Never treated individuals are those who self-report that they have never taken the drugs for LF during any mass drug administration (MDA) rounds. Hence, it is necessary to identify these individuals and assess if they can be potential reservoirs of infection and understand the reasons for non-compliance.Methods and analysisThis mixed method study, proposed for a period of 2 years, will assess the filarial infection status of never treated individuals from four LF-endemic districts in India. A multi-stage cluster sampling design will be followed to select the health subcentres from one highly endemic block in each of the selected districts. A random sample of 2535 never treated individuals from each block will be assessed for filarial infection by a cross-sectional blood survey. Qualitative surveys, including in-depth interviews and focus group discussions, will be conducted to elicit the reasons for their non-compliance. The prevalence of filarial infection will be summarised as frequencies and percentages. Univariate and multivariate logistic regression analysis will be performed to find the factors associated with filarial infection. Exploring the various reasons, such as sociocultural, behavioural and programmatic drivers of non-participation, will enable the programme to design tailored communication and community engagement strategies to bring them under the umbrella of MDA and thereby support the ongoing LF elimination efforts.Ethics and disseminationThis study has been approved by the institutional ethics committee (IHEC 07-0824/N/F, dated 25 September 2024). After completion of the study, a workshop will be held with all stakeholders to disseminate the study findings.

Background MicroRNAs (miRNAs) are crucial post-transcriptional regulators involved in inflammatory diseases, such as asthma. Poor lung function and airflow issues in childhood are linked to the development of chronic obstructive pulmonary disease (COPD) in adulthood. Methods We analyzed small RNA-Seq data from 365 peripheral whole blood samples from the Genetics of Asthma in Costa Rica Study (GACRS) for association with airflow levels measured by FEV1/FVC. Differentially expressed (DE) miRNAs were identified using DESeq2 in R, adjusting for covariates and applying a 10% false discovery rate (FDR). The analysis included 361 samples and 649 miRNAs. The two DE miRNAs were further tested for association with airflow obstruction in a study of adult former smokers with and without COPD. Results We found 1 upregulated and 1 downregulated miRNA in participants with airflow below the threshold compared to those above it. In the adult study, the same miRNAs were upregulated and downregulated in individuals with FEV1/FVC < 0.7 versus those with FEV1/FVC > 0.7, showing suggestive statistical evidence. The target genes of these miRNAs were enriched for PI3K-Akt, Hippo, WNT, MAPK, and focal adhesion pathways. Conclusions Two differentially expressed miRNAs were associated with airflow levels in children with asthma and airflow obstruction in adults with COPD. This suggests that shared genetic regulatory systems may influence childhood airflow and contribute to adulthood airflow obstruction.

Temporal analysis of global cortical communication of cognitive tasks in coarse EEG information is still challenging due to the underlying complex neural mechanisms. This study proposes an attention-based time-series deep learning framework that processes fMRI functional connectivity optimized quasi-stable frequency microstates for classifying distinct temporal cortical communications of the cognitive task. Seventy volunteers were subjected to visual target detection tasks, and their electroencephalogram (EEG) and functional MRI (fMRI) were acquired simultaneously. At first, the acquired EEG information was preprocessed and bandpass to delta, theta, alpha, beta, and gamma bands and then subjected to quasi-stable frequency-microstate estimation. Subsequently, time-series elicitation of each frequency microstates is optimized with graph theory measures of simultaneously eliciting fMRI functional connectivity between frontal, parietal, and temporal cortices. The distinct neural mechanisms associated with each optimized frequency-microstate were analyzed using microstate-informed fMRI. Finally, these optimized, quasi-stable frequency microstates were employed to train and validate the attention-based Long Short-Term Memory (LSTM) time-series architecture for classifying distinct temporal cortical communications of the target from other cognitive tasks. The temporal, sliding input sampling windows were chosen between 180 to 750 ms/segment based on the stability of transition probabilities of the optimized microstates. The results revealed 12 distinct frequency microstates capable of deciphering target detections' temporal cortical communications from other task engagements. Particularly, fMRI functional connectivity measures of target engagement were observed significantly correlated with the right-diagonal delta ( r  = 0.31), anterior–posterior theta ( r  = 0.35), left–right theta ( r  = − 0.32), alpha ( r  = − 0.31) microstates. Further, neuro-vascular information of microstate-informed fMRI analysis revealed the association of delta/theta and alpha/beta microstates with cortical communications and local neural processing, respectively. The classification accuracies of the attention-based LSTM were higher than the traditional LSTM architectures, particularly the frameworks that sampled the EEG data with a temporal width of 300 ms/segment. In conclusion, the study demonstrates reliable temporal classifications of global cortical communication of distinct tasks using an attention-based LSTM utilizing fMRI functional connectivity optimized quasi-stable frequency microstates.