Genome-wide Methylation Dynamics and Context-dependent Gene Expression Variability in Differentiating Preadipocytes.

Obesity, characterized by the accumulation of excess fat, is a complex condition resulting from the combination of genetic and epigenetic factors. Recent studies have found correspondence between DNA methylation and cell differentiation, suggesting a role of the former in cell fate determination. There is a lack of comprehensive understanding concerning the underpinnings of preadipocyte differentiation, specifically when cells are undergoing terminal differentiation (TD). To gain insight into dynamic genome-wide methylation, 3T3 L1 preadipocyte cells were differentiated by a hormone cocktail. The genomic DNA was isolated from undifferentiated cells and 4 hours, 2 days postdifferentiated cells, and 15 days TD cells. We employed whole-genome bisulfite sequencing (WGBS) to ascertain global genomic DNA methylation alterations at single base resolution as preadipocyte cells differentiate. The genome-wide distribution of DNA methylation showed similar overall patterns in pre-, post-, and terminally differentiated adipocytes, according to WGBS analysis. DNA methylation decreases at 4 hours after differentiation initiation, followed by methylation gain as cells approach TD. Studies revealed novel differentially methylated regions (DMRs) associated with adipogenesis. DMR analysis suggested that though DNA methylation is global, noticeable changes are observed at specific sites known as "hotspots." Hotspots are genomic regions rich in transcription factor (TF) binding sites and exhibit methylation-dependent TF binding. Subsequent analysis indicated hotspots as part of DMRs. The gene expression profile of key adipogenic genes in differentiating adipocytes is context-dependent, as we found a direct and inverse relationship between promoter DNA methylation and gene expression.

Genome-wide identification of microsatellites for mapping, genetic diversity and cross-transferability in wheat (Triticum spp).

Wheat (Triticum aestivum L.) is a crucial global staple crop, and is consistently being improved to enhance yield, disease resistance, and quality traits. However, the development of molecular markers is a challenging task due to its hexaploid genome. Molecular marker system such as simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) are helpful for breeding, but SNP has limitations due to its development cost and its conversion to breeder markers. The study proposed an in-silico approach, by utilizing the low-cost transcriptome sequencing of two parental lines, 'TAC 75' and 'WH 1105', to identify polymorphic SSRs for mapping in a recombinant inbred line (RIL) population. This study introduces a new approach to bridge wheat genetics intricacies and next-generation sequencing potential. It presents a comprehensive genome-wide SSR distribution using IWGSC CS RefSeq v2.1 genome assembly and to identify 189 polymorphic loci through in-silico strategy. Of these, 54.76% showed polymorphism between parents, surpassing the traditional low polymorphic success rate. A RIL population screening validated these markers, demonstrating the fitness of identified markers through chi-square tests. The designed SSRs were also validated for genetic diversity analysis in a subset of 37 Indian wheat genotypes and cross-transferability in the wild/relative wheat species. In diversity analysis, a subset of 38 markers revealed 95 alleles (2.5 allele/locus), indicating substantial genetic variation. Population structure analysis unveiled three distinct groups, supported by phylogenetic and PCoA analyses. Further the polymorphic SSRs were also analyzed for SSR-gene association using gene ontology analysis. By utilizing the developing seed transcriptome data within parental lines, the study has enhanced the polymorphic SSR identification precision and facilitated in the RIL population. The undertaken study pioneers the use of transcriptome sequencing and genetic mapping to overcome challenges posed by the intricate wheat genome. This approach offers a cost-effective, less labour-intensive alternative to conventional methods, providing a platform for advancing wheat breeding research.

Genome sequencing and assembly of Lathyrus sativus - a nutrient-rich hardy legume crop.

Grass pea (Lathyrus sativus) is a cool-season legume crop tolerant to drought, salinity, waterlogging, insects, and other biotic stresses. Despite these beneficial traits, this crop is not cultivated widely due to the accumulation of a neurotoxin - ß-N-oxalyl-L-α, ß-diaminopropionic acid (ß-ODAP) in the seeds and its association with neurolathyrism. In this study, we sequenced and assembled the genome of Lathyrus sativus cultivar Pusa-24, an elite Indian cultivar extensively used in breeding programs. The assembled genome of Lathyrus was 3.80 Gb in length, with a scaffold N50 of 421.39 Mb. BUSCO assessment indicated that 98.3% of highly conserved Viridiplantae genes were present in the assembly. A total of 3.17 Gb (83.31%) of repetitive sequences and 50,106 protein-coding genes were identified in the Lathyrus assembly. The Lathyrus genome assembly reported here thus provides a much-needed and robust foundation for various genetic and genomic studies in this vital legume crop.

A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.

Identification of protein-coding and non-coding transcripts is paramount for understanding their biological roles. Computational approaches have been addressing this task for over a decade; however, generalized and high-performance models are still unreliable. This benchmark study assessed the performance of 24 tools producing >55 models on the datasets covering a wide range of species. We have collected 135 small and large transcriptomic datasets from existing studies for comparison and identified the potential bottlenecks hampering the performance of current tools. The key insights of this study include lack of standardized training sets, reliance on homogeneous training data, gradual changes in annotated data, lack of augmentation with homology searches, the presence of false positives and negatives in datasets and the lower performance of end-to-end deep learning models. We also derived a new dataset, RNAChallenge, from the benchmark considering hard instances that may include potential false alarms. The best and least well performing models under- and overfit the dataset, respectively, thereby serving a dual purpose. For computational approaches, it will be valuable to develop accurate and unbiased models. The identification of false alarms will be of interest for genome annotators, and experimental study of hard RNAs will help to untangle the complexity of the RNA world.

Integrated transcriptional and metabolomics signature pattern of pigmented wheat to insight the seed pigmentation and other associated features.

Anthocyanin biosynthesis in plants is complex, especially in a polyploid monocot wheat plant. Using whole-genome sequencing, transcriptomics, and LC-MS/MS, we investigated anthocyanin pigmentation patterns in (black, blue, and purple) colored wheat seeds. According to differential gene expression profiling, 2AS-MYC, 7DL-MYB, and WD40 regulatory genes control purple pericarp coloration, 4DL-MYC, 2AS-MYC, 7DL-MYB, WD40 control blue aleurone coloration, and 4DL-MYC, 7DL-MYB, WD40 controls black aleurone color. We hypothesized that at least one MYC and MYB isoform is sufficient to regulate the anthocyanin synthesis in pericarp or aleurone. Transcriptomics and metabolomics revealed that the purple pericarp trait is associated with acylated anthocyanins compared to blue aleurone. Based upon the reduced expressions of the genes belonging to the 4D, SSR molecular marker mapping, variant calling using genome sequencing, and IGV browser gene structure visualization, it was inferred that the advanced black and blue wheat lines were substitution lines (4E{4D}), with very small recombinations. Pericarp anthocyanin pigmentation is controlled by a mutation in chromosome 2AS of purple wheat, and environmental variations influence pigmented pericarp trait. The expression patterns of anthocyanin structural and other genes varied in different colored wheat, corroborating differences in agronomical metrics. Ovate seed shape trait in black and blue wheat dragged with 4E chromosome.

A rare anomaly: duodenal Dieulafoy's lesion in a preterm baby.

Neonatal Dieulafoy's lesion is a rare but serious condition that can be life-threatening if not diagnosed and intervened in a timely manner. It presents with episodes of sudden acute gastrointestinal haemorrhage in the form of blood in vomit and/or blood in stool. In general, most of the lesions are successfully treated with endoscopic or angiographic intervention. Surgery is usually reserved for cases that fail endoscopic or angiographic intervention. We present a neonatal case of duodenal Dieulafoy's lesion that occurred in a 29-week-old male baby with birth weight of 1.2 kg. He developed melena and haematemesis at 4 weeks of life. He required normal saline boluses and transfusion of blood products for acute blood loss. The lesion was successfully treated with endoscopic intervention.

Identification of multiple RNAs using feature fusion.

Detection of novel transcripts with deep sequencing has increased the demand for computational algorithms as their identification and validation using in vivo techniques is time-consuming, costly and unreliable. Most of these discovered transcripts belong to non-coding RNAs, a large group known for their diverse functional roles but lacks the common taxonomy. Thus, upon the identification of the absence of coding potential in them, it is crucial to recognize their prime functional category. To address this heterogeneity issue, we divide the ncRNAs into three classes and present RNA classifier (RNAC) that categorizes the RNAs into coding, housekeeping, small non-coding and long non-coding classes. RNAC utilizes the alignment-based genomic descriptors to extract statistical, local binary patterns and histogram features and fuse them to construct the classification models with extreme gradient boosting. The experiments are performed on four species, and the performance is assessed on multiclass and conventional binary classification (coding versus no-coding) problems. The proposed approach achieved >93% accuracy on both classification problems and also outperformed other well-known existing methods in coding potential prediction. This validates the usefulness of feature fusion for improved performance on both types of classification problems. Hence, RNAC is a valuable tool for the accurate identification of multiple RNAs .