DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These peptides show vigorous antiviral activity against a diverse range of viruses by targeting different phases of the viral life cycle. Therefore, the accurate prediction of AVPs is an essential yet challenging task. Lately, many machine learning-based approaches have developed for this purpose; however, their limited capabilities in terms of feature engineering, accuracy, and generalization make these methods restricted. RESULTS: In the present study, we aim to develop an efficient machine learning-based approach for the identification of AVPs, referred to as DeepAVP-TPPred, to address the aforementioned problems. First, we extract two new transformed feature sets using our designed image-based feature extraction algorithms and integrate them with an evolutionary information-based feature. Next, these feature sets were optimized using a novel feature selection approach called binary tree growth Algorithm. Finally, the optimal feature space from the training dataset was fed to the deep neural network to build the final classification model. The proposed model DeepAVP-TPPred was tested using stringent 5-fold cross-validation and two independent dataset testing methods, which achieved the maximum performance and showed enhanced efficiency over existing predictors in terms of both accuracy and generalization capabilities. AVAILABILITY AND IMPLEMENTATION: https://github.com/MateeullahKhan/DeepAVP-TPPred.

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model.

BACKGROUND: Viral infections have been the main health issue in the last decade. Antiviral peptides (AVPs) are a subclass of antimicrobial peptides (AMPs) with substantial potential to protect the human body against various viral diseases. However, there has been significant production of antiviral vaccines and medications. Recently, the development of AVPs as an antiviral agent suggests an effective way to treat virus-affected cells. Recently, the involvement of intelligent machine learning techniques for developing peptide-based therapeutic agents is becoming an increasing interest due to its significant outcomes. The existing wet-laboratory-based drugs are expensive, time-consuming, and cannot effectively perform in screening and predicting the targeted motif of antiviral peptides. METHODS: In this paper, we proposed a novel computational model called Deepstacked-AVPs to discriminate AVPs accurately. The training sequences are numerically encoded using a novel Tri-segmentation-based position-specific scoring matrix (PSSM-TS) and word2vec-based semantic features. Composition/Transition/Distribution-Transition (CTDT) is also employed to represent the physiochemical properties based on structural features. Apart from these, the fused vector is formed using PSSM-TS features, semantic information, and CTDT descriptors to compensate for the limitations of single encoding methods. Information gain (IG) is applied to choose the optimal feature set. The selected features are trained using a stacked-ensemble classifier. RESULTS: The proposed Deepstacked-AVPs model achieved a predictive accuracy of 96.60%%, an area under the curve (AUC) of 0.98, and a precision-recall (PR) value of 0.97 using training samples. In the case of the independent samples, our model obtained an accuracy of 95.15%, an AUC of 0.97, and a PR value of 0.97. CONCLUSION: Our Deepstacked-AVPs model outperformed existing models with a ~ 4% and ~ 2% higher accuracy using training and independent samples, respectively. The reliability and efficacy of the proposed Deepstacked-AVPs model make it a valuable tool for scientists and may perform a beneficial role in pharmaceutical design and research academia.

MicroRNAs in shaping the resolution phase of inflammation.

Inflammation is a host defense mechanism orchestrated through imperative factors - acute inflammatory responses mediated by cellular and molecular events leading to activation of defensive immune subsets - to marginalize detrimental injury, pathogenic agents and infected cells. These potent inflammatory events, if uncontrolled, may cause tissue damage by perturbing homeostasis towards immune dysregulation. A parallel host mechanism operates to contain inflammatory pathways and facilitate tissue regeneration. Thus, resolution of inflammation is an effective moratorium on the pro-inflammatory pathway to avoid the tissue damage inside the host and leads to reestablishment of tissue homeostasis. Dysregulation of the resolution pathway can have a detrimental impact on tissue functionality and contribute to the diseased state. Multiple reports have suggested peculiar dynamics of miRNA expression during various pro- and anti-inflammatory events. The roles of miRNAs in the regulation of immune responses are well-established. However, understanding of miRNA regulation of the resolution phase of events in infection or wound healing models, which is sometimes misconstrued as anti-inflammatory signaling, remains limited. Due to the deterministic role of miRNAs in pro-inflammatory and anti-inflammatory pathways, in this review we have provided a broad perspective on the putative role of miRNAs in the resolution of inflammation and explored their imminent role in therapeutics.

Regulatory roles of MicroRNA in shaping T cell function, differentiation and polarization.

T lymphocytes are an integral component of adaptive immunity with pleotropic effector functions. Impairment of T cell activity is implicated in various immune pathologies including autoimmune diseases, AIDS, carcinogenesis, and periodontitis. Evidently, T cell differentiation and function are under robust regulation by various endogenous factors that orchestrate underlying molecular pathways. MicroRNAs (miRNA) are a class of noncoding, regulatory RNAs that post-transcriptionally control multiple mRNA targets by sequence-specific interaction. In this article, we will review the recent progress in our understanding of miRNA-gene networks that are uniquely required by specific T cell effector functions and provide miRNA-mediated mechanisms that govern the fate of T cells. A subset of miRNAs may act in a synergistic or antagonistic manner to exert functional suppression of genes and regulate pathways that control T cell activation and differentiation. Significance of T cell-specific miRNAs and their dysregulation in immune-mediated diseases is discussed. Exosome-mediated horizontal transfer of miRNAs from antigen presenting cells (APCs) to T cells and from one T cell to another T cell subset and their impact on recipient cell functions is summarized.

Non-coding RNA LINC01010 regulates macrophage polarization and innate immune functions by modulating NFκB signaling pathway.

Innate immune response is regulated by tissue resident or infiltrating immune cells such as macrophages (Mφ) that play critical role in tissue development, homeostasis, and repair of damaged tissue. However, the epigenetic mechanisms that regulate Mφ plasticity and innate immune functions are not well understood. Long non-coding RNA (lncRNA) are among the most abundant class of transcriptome but their function in myeloid cell biology is less explored. In this study, we deciphered the regulatory role of previously uncharacterized lncRNAs in Mφ polarization and innate immune responses. Two lncRNAs showed notable changes in their levels during M1 and M2 Mφ differentiation. Our findings indicate that LINC01010 expression increased and AC007032 expression decreased significantly. LINC01010 exhibit myeloid cell-specificity, while AC007032.1 is ubiquitous and expressed in both myeloid and lymphoid (T cells, B cells and NK cells) cells. Expression of these lncRNAs is dysregulated in periodontal disease (PD), a microbial biofilm-induced immune disease, and responsive to lipopolysaccharide (LPS) from different oral and non-oral bacteria. Knockdown of LINC01010 but not AC007032.1 reduced the surface expression of Mφ differentiation markers CD206 and CD68, and M1Mφ polarization markers MHCII and CD32. Furthermore, LINC01010 RNAi attenuated bacterial phagocytosis, antigen processing and cytokine secretion suggesting its key function in innate immunity. Mechanistically, LINC01010 knockdown Mφ treated with Escherichia coli LPS exhibit significantly reduced expression of multiple nuclear factor kappa B pathway genes. Together, our data highlight functional role of a PD-associated lncRNA LINC01010 in shaping macrophage differentiation, polarization, and innate immune activation.

Neuropsychiatric lupus in late- and early-onset systemic lupus erythematosus patients: a systematic review and meta-analysis.

OBJECTIVES: Late-onset SLE is usually milder and associated with lower frequency of LN and neuropsychiatric manifestations. The diagnosis of NPSLE is especially challenging in older patients because of increased incidence of neurological comorbidities. We performed a systematic review and meta-analysis to evaluate the differences in NPSLE manifestations in early-onset (<50-year-old) vs late-onset (≥50-year-old) SLE patients. METHODS: A literature search was performed using the PubMed, Web of Science and Cochrane Library databases. Studies available in English (1959-2022) including a late-onset SLE comparison group and evaluating the frequency of NPSLE were eligible. A forest plot was used to compare odds ratios (95% CI) of incidence and manifestations of NPSLE by age groups. Study heterogeneity was assessed using I2 statistics. RESULTS: A total of 44 studies, including 17 865 early-onset and 2970 late-onset SLE patients, fulfilled our eligibility criteria. CNS involvement was reported in 3326 patients. Cumulative NPSLE frequency was higher in the early-onset group than in the late-onset group (OR: 1.41, 95% CI: 1.24, 1.59, P < 0.0001). In early-onset SLE patients, seizures (OR: 1.68, 95% CI: 1.27, 2.22) and psychosis (OR: 1.72, 95% CI: 1.23, 2.41) were more common than in late-onset SLE patients (P values, 0.0003 and 0.0014, respectively). Peripheral neuropathy was more commonly reported in the late-onset SLE group than in the early-onset SLE group (OR: 0.64, 95% CI: 0.47, 0.86, P = 0.004). CONCLUSION: Our meta-analysis revealed that the frequencies of overall NPSLE, seizures, and psychosis were less common in late-onset SLE patients than in early-onset SLE patients. In contrast, peripheral neuropathy was more common in the late-onset SLE group.

Potential of UV-B radiation in drought stress resilience: A multidimensional approach to plant adaptation and future implications.

The escalating impact of climate change and ultraviolet (UV) radiation is subjecting plants to unique combinations of UV-B and drought stress. These combined stressors could have additive, synergistic, or antagonistic effects, but the precise nature of these impacts remains uncertain, hampering our ability to predict plant adaptations approach towards stressors. Our analysis of various studies shows that UV-B or drought conditions detrimentally influence plant growth and health metrics by the enhanced generation of reactive oxygen species causing damage to lipids, proteins, carbohydrates and DNA. Further reducing biomass accumulation, plant height, photosynthetic efficiency, leaf area, and water transpiration, while enhancing stress-related symptoms. In response to UV-B radiation and drought stress, plants exhibit a notable up-regulation of specific acclimation-associated metabolites, including proline, flavonoids, anthocyanins, unsaturated fatty acids, and antioxidants. These metabolites play a pivotal role in conferring protection against environmental stresses. Their biosynthesis and functional roles are potentially modulated by signalling molecules such as hydrogen peroxide, abscisic acid, jasmonic acid, salicylic acid, and ethylene, all of which have associated genetic markers that further elucidate their involvement in stress response pathways. In comparison to single stress, the combination of UV-B and drought induces the plant defence responses and growth retardation which are less-than-additive. This sub-additive response, consistent across different study environments, suggests the possibility of a cross-resistance mechanism. Our outlines imply that the adverse effects of increased drought and UV-B could potentially be mitigated by cross-talk between UV-B and drought regimes utilizing a multidimensional approach. This crucial insight could contribute significantly to refining our understanding of stress tolerance in the face of ongoing global climate change.

Designing future peanut: the power of genomics-assisted breeding.

KEY MESSAGE: Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

Investigating the chemo-preventive role of noscapine in lung carcinoma via therapeutic targeting of human aurora kinase B.

Lung carcinoma is the major contributor to global cancer incidence and one of the leading causes of cancer-related mortality worldwide. Irregularities in signal transduction events, genetic alterations, and mutated regulatory genes trigger cancer development and progression. Selective targeting of molecular modulators has substantially revolutionized cancer treatment strategies with improvised efficacy. The aurora kinase B (AURKB) is a critical component of the chromosomal passenger complex and is primarily involved in lung cancer pathogenesis. Since AURKB is an important therapeutic target, the design and development of its potential inhibitors are attractive strategies. In this study, noscapine was selected and validated as a possible inhibitor of AURKB using integrated computational, spectroscopic, and cell-based assays. Molecular docking analysis showed noscapine occupies the substrate-binding pocket of AURKB with strong binding affinity. Subsequently, MD simulation studies confirmed the formation of a stable AURKB-noscapine complex with non-significant alteration in various trajectories, including RMSD, RMSF, Rg, and SASA. These findings were further experimentally validated through fluorescence binding studies. In addition, dose-dependent noscapine treatment significantly attenuated recombinant AURKB activity with an IC50 value of 26.6 µM. Cell viability studies conducted on A549 cells and HEK293 cells revealed significant cytotoxic features of noscapine on A549 cells. Furthermore, Annexin-PI staining validated that noscapine triggered apoptosis in lung cancer cells, possibly via an intrinsic pathway. Our findings indicate that noscapine-based AURKB inhibition can be implicated as a potential therapeutic strategy in lung cancer treatment and can also provide a novel scaffold for developing next-generation AURKB-specific inhibitors.

Macrophage-enriched novel functional long noncoding RNAs LRRC75A-AS1 and GAPLINC regulate polarization and innate immune responses.

INTRODUCTION: Macrophages (Mφs) are functionally dynamic immune cells that bridge innate and adaptive immune responses; however, the underlying epigenetic mechanisms that control Mφ plasticity and innate immune functions are not well elucidated. OBJECTIVE: To identify novel functions of macrophage-enriched lncRNAs in regulating polarization and innate immune responses. METHODS: Total RNA isolated from differentiating monocyte-derived M1 and M2 Mφs was profiled for lncRNAs expression using RNAseq. Impact of LRRC75A-AS1, GAPLINC and AL139099.5 knockdown was examined on macrophage differentiation, polarization markers, phagocytosis, and antigen processing by flow cytometry and florescence microscopy. Cytokine profiles were examined by multiplex bead array and cytoskeletal signaling pathway genes were quantified by PCR-based array. Gingival biopsies were collected from periodontally healthy and diseased subjects to examine lncRNAs, M1/M2 marker expression. RESULTS: Transcriptome profiling of M1 and M2 Mφs identified thousands of differentially expressed known and novel lncRNAs. We characterized three Mφ-enriched lncRNAs LRRC75A-AS1, GAPLINC and AL139099.5 in polarization and innate immunity. Knockdown of LRRC75A-AS1 and GAPLINC downregulated the Mφ differentiation markers and skewed Mφ polarization by decreasing M1 markers without a significant impact on M2 markers. LRRC75A-AS1 and GAPLINC knockdown also attenuated bacterial phagocytosis, antigen processing and inflammatory cytokine secretion in Mφs, supporting their functional role in potentiating innate immune functions. Mechanistically, LRRC75A-AS1 and GAPLINC knockdown impaired Mφ migration by downregulating the expression of multiple cytoskeletal signaling pathways suggesting their critical role in regulating Mφ migration. Finally, we showed that LRRC75A-AS1 and GAPLINC were upregulated in periodontitis and their expression correlates with higher M1 markers suggesting their role in macrophage polarization in vivo. CONCLUSION: Our results show that polarized Mφs acquire a unique lncRNA repertoire and identified many previously unknown lncRNA sequences. LRRC75A-AS1 and GAPLINC, which are induced in periodontitis, regulate Mφ polarization and innate immune functions supporting their critical role in inflammation.

Unveiling Innovative Approaches to Mitigate Metals/Metalloids Toxicity for Sustainable Agriculture.

Due to anthropogenic activities, environmental pollution of heavy metals/metalloids (HMs) has increased and received growing attention in recent decades. Plants growing in HM-contaminated soils have slower growth and development, resulting in lower agricultural yield. Exposure to HMs leads to the generation of free radicals (oxidative stress), which alters plant morpho-physiological and biochemical pathways at the cellular and tissue levels. Plants have evolved complex defense mechanisms to avoid or tolerate the toxic effects of HMs, including HMs absorption and accumulation in cell organelles, immobilization by forming complexes with organic chelates, extraction via numerous transporters, ion channels, signaling cascades, and transcription elements, among others. Nonetheless, these internal defensive mechanisms are insufficient to overcome HMs toxicity. Therefore, unveiling HMs adaptation and tolerance mechanisms is necessary for sustainable agriculture. Recent breakthroughs in cutting-edge approaches such as phytohormone and gasotransmitters application, nanotechnology, omics, and genetic engineering tools have identified molecular regulators linked to HMs tolerance, which may be applied to generate HMs-tolerant future plants. This review summarizes numerous systems that plants have adapted to resist HMs toxicity, such as physiological, biochemical, and molecular responses. Diverse adaptation strategies have also been comprehensively presented to advance plant resilience to HMs toxicity that could enable sustainable agricultural production.

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity.

The escalating challenges posed by metal(loid) toxicity in agricultural ecosystems, exacerbated by rapid climate change and anthropogenic pressures, demand urgent attention. Soil contamination is a critical issue because it significantly impacts crop productivity. The widespread threat of metal(loid) toxicity can jeopardize global food security due to contaminated food supplies and pose environmental risks, contributing to soil and water pollution and thus impacting the whole ecosystem. In this context, plants have evolved complex mechanisms to combat metal(loid) stress. Amid the array of innovative approaches, omics, notably transcriptomics, proteomics, and metabolomics, have emerged as transformative tools, shedding light on the genes, proteins, and key metabolites involved in metal(loid) stress responses and tolerance mechanisms. These identified candidates hold promise for developing high-yielding crops with desirable agronomic traits. Computational biology tools like bioinformatics, biological databases, and analytical pipelines support these omics approaches by harnessing diverse information and facilitating the mapping of genotype-to-phenotype relationships under stress conditions. This review explores: (1) the multifaceted strategies that plants use to adapt to metal(loid) toxicity in their environment; (2) the latest findings in metal(loid)-mediated transcriptomics, proteomics, and metabolomics studies across various plant species; (3) the integration of omics data with artificial intelligence and high-throughput phenotyping; (4) the latest bioinformatics databases, tools and pipelines for single and/or multi-omics data integration; (5) the latest insights into stress adaptations and tolerance mechanisms for future outlooks; and (6) the capacity of omics advances for creating sustainable and resilient crop plants that can thrive in metal(loid)-contaminated environments.

Chromosome-level genome assembly and population genomic analysis provide novel insights into the immunity and evolution of Sogatella furcifera.

Sogatella furcifera is a destructive agricultural pest causing large threats to rice production in China and Southeast Asian countries. Despite recent breakthroughs in long-read sequencing, high quality genomic data are very limited in S. furcifera. In present study, a chromosome-level assembly of the S. furcifera genome was completed (0.64 GB), comprising 15 chromosomes covered 95.04% of the estimated genome size, along with other 624 small scaffolds making up the remaining 4.96% of the genome of S. furcifera. A total of 24,669 protein-coding genes, 1211 long noncoding RNA and 7595 circular RNA transcripts were predicted in this study. Comparative genomic analysis revealed rapidly evolved genes were associated with multiple immune-related pathways in S. furcifera. Genome resequencing of 44 individuals from 12 geographic populations revealed frequent gene flow among populations. The systemic genomic analysis will provide more insights into the understanding of the immunity and evolutionary adaptation of S. furcifera.

Soluble and insoluble expression of recombinant human interleukin-2 protein using pET expression vector in Escherichia coli.

Interleukin-2 has emerged as a potent protein-based drug to treat various cancers, AIDS, and autoimmune diseases. Despite its immense requirement, the production procedures are inefficient to meet the demand. Therefore, efficient production procedures must be adopted to improve protein yield and decrease procedural loss. This study analyzed cytoplasmic and periplasmic IL-2 expression for increased protein yield and significant biological activity. The study is focused on cloning IL-2 into a pET-SUMO and pET-28a vector that expresses IL-2 in soluble form and inclusion bodies, respectively. Both constructs were expressed into different E. coli expression strains, but the periplasmic and cytoplasmic expression of IL-2 was highest in overnight culture in Rosetta 2 (DE3). Therefore, E. coli Rosetta 2 (DE3) was selected for large-scale production and purification. Purified IL-2 was characterized by SDS-PAGE and western blotting, while its biological activity was determined using MTT bioassay. The results depict that the periplasmic and cytoplasmic IL-2 achieved adequate purification, yielding 0.86 and 0.51 mg/mL, respectively, with significant cytotoxic activity of periplasmic and cytoplasmic IL-2. Periplasmic IL-2 has shown better yield and significant biological activity in vitro which describes its attainment of native protein structure and function.

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.

Developing future heat-resilient vegetable crops.

Climate change seriously impacts global agriculture, with rising temperatures directly affecting the yield. Vegetables are an essential part of daily human consumption and thus have importance among all agricultural crops. The human population is increasing daily, so there is a need for alternative ways which can be helpful in maximizing the harvestable yield of vegetables. The increase in temperature directly affects the plants' biochemical and molecular processes; having a significant impact on quality and yield. Breeding for climate-resilient crops with good yields takes a long time and lots of breeding efforts. However, with the advent of new omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, the efficiency and efficacy of unearthing information on pathways associated with high-temperature stress resilience has improved in many of the vegetable crops. Besides omics, the use of genomics-assisted breeding and new breeding approaches such as gene editing and speed breeding allow creation of modern vegetable cultivars that are more resilient to high temperatures. Collectively, these approaches will shorten the time to create and release novel vegetable varieties to meet growing demands for productivity and quality. This review discusses the effects of heat stress on vegetables and highlights recent research with a focus on how omics and genome editing can produce temperature-resilient vegetables more efficiently and faster.

Primary hyperoxaluria: Comprehensive mutation screening of the disease causing genes and spectrum of disease-associated pathogenic variants.

The primary hyperoxalurias are rare disorders of glyoxylate metabolism. Accurate diagnosis is essential for therapeutic and management strategies. We conducted a molecular study on patients suffering from recurrent calcium-oxalate stones and nephrocalcinosis and screened primary hyperoxaluria causing genes in a large cohort of early-onset cases. Disease-associated pathogenic-variants were defined as missense, nonsense, frameshift-indels, and splice-site variants with a reported minor allele frequency <1% in controls. We found pathogenic-variants in 34% of the cases. Variants in the AGXT gene causing PH-I were identified in 81% of the mutation positive cases. PH-II-associated variants in the GRHPR gene are found in 15% of the pediatric PH-positive population. Only 3% of the PH-positive cases have pathogenic-variants in the HOGA1 gene, responsible to cause PH-III. A population-specific AGXT gene variant c.1049G>A; p.Gly350Asp accounts for 22% of the PH-I-positive patients. Pathogenicity of the identified variants was evaluated by in-silico tools and ACMG guidelines. We have devised a rapid and low-cost approach for the screening of PH by using targeted-NGS highlighting the importance of an accurate and cost-effective screening platform. This is the largest study in Pakistani pediatric patients from South-Asian region that also expands the mutation spectrum of the three known genes.

Plant lipid phosphate phosphatases: current advances and future outlooks.

Lipids are widely distributed in various tissues of an organism, mainly in plant storage organs (e.g., fruits, seeds, etc.). Lipids are vital biological substances that are involved in: signal transduction, membrane biogenesis, energy storage, and the formation of transmembrane fat-soluble substances. Some lipids and related lipid derivatives could be changed in their: content, location, or physiological activity by the external environment, such as biotic or abiotic stresses. Lipid phosphate phosphatases (LPPs) play important roles in regulating intermediary lipid metabolism and cellular signal response. LPPs can dephosphorylate lipid phosphates containing phosphate monolipid bonds such as: phosphatidic acid, lysophosphatidic acid (LPA), and diacylglycerol pyrophosphate, etc. These processes can change the contents of some important lipid signal mediation such as diacylglycerol and LPA, affecting lipid signal transmission. Here, we summarize the research progress of LPPs in plants, emphasizing the structural and biochemical characteristics of LPPs and their role in spatio-temporal regulation. In the future, more in-depth studies are required to boost our understanding of the key role of plant LPPs and lipid metabolism in: signal regulation, stress tolerance pathway, and plant growth and development.

Smart reprograming of plants against salinity stress using modern biotechnological tools.

Climate change gives rise to numerous environmental stresses, including soil salinity. Salinity/salt stress is the second biggest abiotic factor affecting agricultural productivity worldwide by damaging numerous physiological, biochemical, and molecular processes. In particular, salinity affects plant growth, development, and productivity. Salinity responses include modulation of ion homeostasis, antioxidant defense system induction, and biosynthesis of numerous phytohormones and osmoprotectants to protect plants from osmotic stress by decreasing ion toxicity and augmented reactive oxygen species scavenging. As most crop plants are sensitive to salinity, improving salt tolerance is crucial in sustaining global agricultural productivity. In response to salinity, plants trigger stress-related genes, proteins, and the accumulation of metabolites to cope with the adverse consequence of salinity. Therefore, this review presents an overview of salinity stress in crop plants. We highlight advances in modern biotechnological tools, such as omics (genomics, transcriptomics, proteomics, and metabolomics) approaches and different genome editing tools (ZFN, TALEN, and CRISPR/Cas system) for improving salinity tolerance in plants and accomplish the goal of "zero hunger," a worldwide sustainable development goal proposed by the FAO.

AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks.

Inflammation is a biologically resistant response to harmful stimuli, such as infection, damaged cells, toxic chemicals, or tissue injuries. Its purpose is to eradicate pathogenic micro-organisms or irritants and facilitate tissue repair. Prolonged inflammation can result in chronic inflammatory diseases. However, wet-laboratory-based treatments are costly and time-consuming and may have adverse side effects on normal cells. In the past decade, peptide therapeutics have gained significant attention due to their high specificity in targeting affected cells without affecting healthy cells. Motivated by the significance of peptide-based therapies, we developed a highly discriminative prediction model called AIPs-SnTCN to predict anti-inflammatory peptides accurately. The peptide samples are encoded using word embedding techniques such as skip-gram and attention-based bidirectional encoder representation using a transformer (BERT). The conjoint triad feature (CTF) also collects structure-based cluster profile features. The fused vector of word embedding and sequential features is formed to compensate for the limitations of single encoding methods. Support vector machine-based recursive feature elimination (SVM-RFE) is applied to choose the ranking-based optimal space. The optimized feature space is trained by using an improved self-normalized temporal convolutional network (SnTCN). The AIPs-SnTCN model achieved a predictive accuracy of 95.86% and an AUC of 0.97 by using training samples. In the case of the alternate training data set, our model obtained an accuracy of 92.04% and an AUC of 0.96. The proposed AIPs-SnTCN model outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value. The reliability and efficacy of our AIPs-SnTCN model make it a valuable tool for scientists and may play a beneficial role in pharmaceutical design and research academia.