Comparative Small RNA and Degradome Sequencing Provides Insights into Antagonistic Interactions in the Biocontrol Fungus Clonostachys rosea.

Necrotrophic mycoparasitism is an intricate process involving recognition, physical mycelial contact, and killing of host fungi (mycohosts). During such interactions, mycoparasites undergo a complex developmental process involving massive regulatory changes of gene expression to produce a range of chemical compounds and proteins that contribute to the parasitism of the mycohosts. Small RNAs (sRNAs) are vital components of posttranscriptional gene regulation, although their role in gene expression regulation during mycoparasitisms remain understudied. Here, we investigated the role of sRNA-mediated gene regulation in mycoparasitism by performing sRNA and degradome tag sequencing of the mycoparasitic fungus Clonostachys rosea interacting with the plant-pathogenic mycohosts Botrytis cinerea and Fusarium graminearum at two time points. The majority of differentially expressed sRNAs were downregulated during the interactions with the mycohosts compared to a C. rosea self-interaction control, thus allowing desuppression (upregulation) of mycohost-responsive genes. Degradome analysis showed a positive correlation between high degradome counts and antisense sRNA mapping and led to the identification of 201 sRNA-mediated potential gene targets for 282 differentially expressed sRNAs. Analysis of sRNA potential gene targets revealed that the regulation of genes coding for membrane proteins was a common response against both mycohosts. The regulation of genes involved in oxidative stress tolerance and cellular metabolic and biosynthetic processes was exclusive against F. graminearum, highlighting common and mycohost-specific gene regulation of C. rosea. By combining these results with transcriptome data collected during a previous study, we expand the understanding of the role of sRNA in regulating interspecific fungal interactions and mycoparasitism. IMPORTANCE Small RNAs (sRNAs) are emerging as key players in pathogenic and mutualistic fungus-plant interactions; however, their role in fungus-fungus interactions remains elusive. In this study, we employed the necrotrophic mycoparasite Clonostachys rosea and the plant-pathogenic mycohosts Botrytis cinerea and Fusarium graminearum and investigated the sRNA-mediated gene regulation in mycoparasitic interactions. The combined approach of sRNA and degradome tag sequencing identified 201 sRNA-mediated putative gene targets for 282 differentially expressed sRNAs, highlighting the role of sRNA-mediated regulation of mycoparasitism in C. rosea. We also identified 36 known and 13 novel microRNAs (miRNAs) and their potential gene targets at the endogenous level and at a cross-species level in B. cinerea and F. graminearum, indicating a role of cross-species RNA interference (RNAi) in mycoparasitism, representing a novel mechanism in biocontrol interactions. Furthermore, we showed that C. rosea adapts its transcriptional response, and thereby its interaction mechanisms, based on the interaction stages and identity of the mycohost.

Computational Analysis Reveals Monomethylated Triazolopyrimidine as a Novel Inhibitor of SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp).

The human population is still facing appalling conditions due to several outbreaks of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) virus. The absence of specific drugs, appropriate vaccines for mutants, and knowledge of potential therapeutic agents makes this situation more difficult. Several 1, 2, 4-triazolo [1, 5-a] pyrimidine (TP)-derivative compounds were comprehensively studied for antiviral activities against RNA polymerase of HIV, HCV, and influenza viruses, and showed immense pharmacological interest. Therefore, TP-derivative compounds can be repurposed against the RNA-dependent RNA polymerase (RdRp) protein of SARS-CoV-2. In this study, a meta-analysis was performed to ensure the genomic variability and stability of the SARS-CoV-2 RdRp protein. The molecular docking of natural and synthetic TP compounds to RdRp and molecular dynamic (MD) simulations were performed to analyse the dynamic behaviour of TP compounds at the active site of the RdRp protein. TP compounds were also docked against other non-structural proteins (NSP1, NSP2, NSP3, NSP5, NSP8, NSP13, and NSP15) of SARS-CoV-2. Furthermore, the inhibition potential of TP compounds was compared with Remdesivir and Favipiravir drugs as a positive control. Additionally, TP compounds were analysed for inhibitory activity against SARS-CoV RdRp protein. This study demonstrates that TP analogues (monomethylated triazolopyrimidine and essramycin) represent potential lead molecules for designing an effective inhibitor to control viral replication. Furthermore, in vitro and in vivo studies will strengthen the use of these inhibitors as suitable drug candidates against SARS-CoV-2.

SARS-CoV-2 transcriptome analysis and molecular cataloguing of immunodominant epitopes for multi-epitope based vaccine design.

Genomics-led researches are engaged in tracing virus expression pattern, and induced immune responses in human to develop effective vaccine against COVID-19. In this study, targeted expression profiling and differential gene expression analysis of major histocompatibility complexes and innate immune system genes were performed through SARS-CoV-2 infected RNA-seq data of human cell line, and virus transcriptome was generated for T-and B-cell epitope prediction. Docking studies of epitopes with MHC and B-cell receptors were performed to identify potential T-and B-cell epitopes. Transcriptome analysis revealed the specific multiple allele expressions in cell line, genes for elicited induce immune response, and virus gene expression. Proposed T- and B-cell epitopes have high potential to elicit equivalent immune responses caused by SARS-CoV-2 infection which can be useful to provide links between elicited immune response and virus gene expression. This study will facilitate in vitro and in vivo vaccine related research studies in disease control.

Linking crop traits to transcriptome differences in a progeny population of tetraploid potato.

BACKGROUND: Potato is the third most consumed crop in the world. Breeding for traits such as yield, product quality and pathogen resistance are main priorities. Identifying molecular signatures of these and other important traits is important in future breeding efforts. In this study, a progeny population from a cross between a breeding line, SW93-1015, and a cultivar, Désirée, was studied by trait analysis and RNA-seq in order to develop understanding of segregating traits at the molecular level and identify transcripts with expressional correlation to these traits. Transcript markers with predictive value for field performance applicable under controlled environments would be of great value for plant breeding. RESULTS: A total of 34 progeny lines from SW93-1015 and Désirée were phenotyped for 17 different traits in a field in Nordic climate conditions and controlled climate settings. A master transcriptome was constructed with all 34 progeny lines and the parents through a de novo assembly of RNA-seq reads. Gene expression data obtained in a controlled environment from the 34 lines was correlated to traits by different similarity indices, including Pearson and Spearman, as well as DUO, which calculates the co-occurrence between high and low values for gene expression and trait. Our study linked transcripts to traits such as yield, growth rate, high laying tubers, late and tuber blight, tuber greening and early flowering. We found several transcripts associated to late blight resistance and transcripts encoding receptors were associated to Dickeya solani susceptibility. Transcript levels of a UBX-domain protein was negatively associated to yield and a GLABRA2 expression modulator was negatively associated to growth rate. CONCLUSION: In our study, we identify 100's of transcripts, putatively linked based on expression with 17 traits of potato, representing both well-known and novel associations. This approach can be used to link the transcriptome to traits. We explore the possibility of associating the level of transcript expression from controlled, optimal environments to traits in a progeny population with different methods introducing the application of DUO for the first time on transcriptome data. We verify the expression pattern for five of the putative transcript markers in another progeny population.

Charting oat (Avena sativa) embryo and endosperm transcription factor expression reveals differential expression of potential importance for seed development.

Uniquely, oat, among cereals, accumulates an appreciable amount of oil in the endosperm together with starch. Oat is also recognized for its soluble fibers in the form of ß-glucans. Despite high and increasing interest in oat yield and quality, the genetic and molecular understanding of oat grain development is still very limited. Transcription factors (TFs) are important regulatory components for plant development, product quality and yield. This study aimed to develop a workflow to determine seed tissue specificity of transcripts encoding transcription factors to reveal differential expression of potential importance for storage compound deposition and quality characters in oat. We created a workflow through the de novo assembly of sequenced seed endosperm and embryo, and publicly available oat seed RNAseq dataset, later followed by TF identification. RNAseq data were assembled into 33,878 transcripts with approximately 90% completeness. A total of 3875 putative TF encoding transcripts were identified from the oat hybrid assemblies. Members of the B3, bHLH, bZIP, C3H, ERF, NAC, MYB and WRKY families were the most abundant TF transcripts. A total of 514 transcripts which were differentially expressed between embryo and endosperm were identified with a threshold of 16-fold expression difference. Among those, 36 TF transcript homologs, belonging to 7 TF families, could be identified through similarity search in wheat embryo and endosperm EST libraries of NCBI Unigene database, and almost all the closest homologs were specifically expressed in seed when explored in WheatExp database. We verified our findings by cloning, sequencing and finally confirming differential expression of two TF encoding transcripts in oat seed embryo and endosperm. The developed workflow for identifying tissue-specific transcription factors allows further functional characterization of specific genes to increase our understanding of grain filling and quality.

Genome Sequence Resource for the Oomycete Taro Pathogen Phytophthora colocasiae.

Phytophthora colocasiae is a phytopathogenic oomycete that causes leaf blight and corm rot on taro (Colocasia esculenta), an important staple crop in the tropics. The impact of P. colocasiae is a serious concern for food security in Asian and Oceanic regions. Vietnamese strain 7290 of P. colocasiae was sequenced (Illumina) to assemble a draft genome of 56.6 Mb, comprised of 19,853 scaffolds and 19,984 predicted protein-coding genes. As in other Phytophthora species, P. colocasiae possesses numerous pathogenicity-related genes, such as the RxLR class of effectors. This draft genome sequence of P. colocasiae provides a resource to underpin the first steps in determining the molecular mechanisms of disease development in this pathosystem.

NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction.

UNLABELLED: The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms have explained the regulation of plant immunity, but still, we have limited understanding about plant defence against different pathogens. Identification of R-proteins and proteins having R-protein-like features across the genome, transcriptome and proteome would be highly useful to develop the global understanding of plant defence mechanisms, but it is laborious and time-consuming task. Therefore, we have developed a support vector machine-based high-throughput pipeline called NBSPred to differentiate NBSLRR and NBSLRR-like protein from Non-NBSLRR proteins from genome, transcriptome and protein sequences. The pipeline was tested and validated with input sequences from three dicot and two monocot plants including Arabidopsis thaliana, Boechera stricta, Brachypodium distachyon Solanum lycopersicum and Zea mays. AVAILABILITY AND IMPLEMENTATION: The NBSPred pipeline is available at http://soilecology.biol.lu.se/nbs/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: sandeep.kushwaha@biol.lu.se.

Potato tuber expression of Arabidopsis WRINKLED1 increase triacylglycerol and membrane lipids while affecting central carbohydrate metabolism.

Tuber and root crops virtually exclusively accumulate storage products in the form of carbohydrates. An exception is yellow nutsedge (Cyperus esculentus) in which tubers have the capacity to store starch and triacylglycerols (TAG) in roughly equal amounts. This suggests that a tuber crop can efficiently handle accumulation of energy dense oil. From a nutritional as well as economic aspect, it would be of interest to utilize the high yield capacity of tuber or root crops for oil accumulation similar to yellow nutsedge. The transcription factor WRINKLED1 from Arabidopsis thaliana, which in seed embryos induce fatty acid synthesis, has been shown to be a major factor for oil accumulation. WRINKLED1 was expressed in potato (Solanum tuberosum) tubers to explore whether this factor could impact tuber metabolism. This study shows that a WRINKLED1 transcription factor could induce triacylglycerol accumulation in tubers of transformed potato plants grown in field (up to 12 nmol TAG/mg dry weight, 1% of dry weight) together with a large increase in polar membrane lipids. The changes in metabolism further affected starch accumulation and composition concomitant with massive increases in sugar content.

MetCap: a bioinformatics probe design pipeline for large-scale targeted metagenomics.

BACKGROUND: Massive sequencing of genes from different environments has evolved metagenomics as central to enhancing the understanding of the wide diversity of micro-organisms and their roles in driving ecological processes. Reduced cost and high throughput sequencing has made large-scale projects achievable to a wider group of researchers, though complete metagenome sequencing is still a daunting task in terms of sequencing as well as the downstream bioinformatics analyses. Alternative approaches such as targeted amplicon sequencing requires custom PCR primer generation, and is not scalable to thousands of genes or gene families. RESULTS: In this study, we are presenting a web-based tool called MetCap that circumvents the limitations of amplicon sequencing of multiple genes by designing probes that are suitable for large-scale targeted metagenomics sequencing studies. MetCap provides a novel approach to target thousands of genes and genomic regions that could be used in targeted metagenomics studies. Automatic analysis of user-defined sequences is performed, and probes specifically designed for metagenome studies are generated. To illustrate the advantage of a targeted metagenome approach, we have generated more than 400,000 probes that match more than 300,000 [corrected] publicly available sequences related to carbon degradation, and used these probes for target sequencing in a soil metagenome study. The results show high enrichment of target genes and a successful capturing of the majority of gene families. MetCap is freely available to users from: http://soilecology.biol.lu.se/metcap/ . CONCLUSION: MetCap is facilitating probe-based target enrichment as an easy and efficient alternative tool compared to complex primer-based enrichment for large-scale investigations of metagenomes. Our results have shown efficient large-scale target enrichment through MetCap-designed probes for a soil metagenome. The web service is suitable for any targeted metagenomics project that aims to study several genes simultaneously. The novel bioinformatics approach taken by the web service will enable researchers in microbial ecology to tap into the vast diversity of microbial communities using targeted metagenomics as a cost-effective alternative to whole metagenome sequencing.

Vaccine design and development: Exploring the interface with computational biology and AI.

Computational biology involves applying computer science and informatics techniques in biology to understand complex biological data. It allows us to collect, connect, and analyze biological data at a large scale and build predictive models. In the twenty first century, computational resources along with Artificial Intelligence (AI) have been widely used in various fields of biological sciences such as biochemistry, structural biology, immunology, microbiology, and genomics to handle massive data for decision-making, including in applications such as drug design and vaccine development, one of the major areas of focus for human and animal welfare. The knowledge of available computational resources and AI-enabled tools in vaccine design and development can improve our ability to conduct cutting-edge research. Therefore, this review article aims to summarize important computational resources and AI-based tools. Further, the article discusses the various applications and limitations of AI tools in vaccine development.

The application of vaccines is one of the most promising treatments for numerous infectious diseases. However, the design and development of effective vaccines involve huge investments and resources, and only a handful of candidates successfully reach the market. Only relying on traditional methods is both time-consuming and expensive. Various computational tools and software have been developed to accelerate the vaccine design and development. Further, AI-enabled computational tools have revolutionized the field of vaccine design and development by creating predictive models and data-driven decision-making processes. Therefore, information and awareness of these AI-enabled computational resources will immensely facilitate the development of vaccines against emerging pathogens. In this review, we have meticulously summarized the available computational tools for each step of in-silico vaccine design and development, delving into the transformative applications of AI and ML in this domain, which would help to choose appropriate tools for each step during vaccine development, and also highlighting the limitations of these tools to facilitate the selection of appropriate tools for each step of vaccine design.

Genetic marker: a genome mapping tool to decode genetic diversity of livestock animals.

Genotyping is the process of determining the genetic makeup of an organism by examining its DNA sequences using various genetic markers. It has been widely used in various fields, such as agriculture, biomedical and conservation research, to study genetic diversity, inheritance, the genetic basis of disease-associated traits, evolution, adaptation, etc., Genotyping markers have evolved immensely and are broadly classified as random markers (RFLP, RAPD, AFLP, etc.) and functional markers (SCoT, CDDP, SRAP, etc.). However, functional markers are very limited in genotype studies, especially in animal science, despite their advantages in overcoming the limitations of random markers, which are directly linked with phenotypic traits, high specificity, and similar logistic requirements. The current review surveyed the available random and functional markers for genotyping applications, focusing on livestock including plant and microbe domains. This review article summarises the application, advantages, and limitations of developed markers and methods for genotyping applications. This review aims to make the reader aware of all available markers, their design principles, and methods, and we discuss the marker inheritance patterns of RLFP and AFLP. The review further outlines the marker selection for particular applications and endorses the application of functional markers in genotyping research.

Impact of transgenerational host switch on gut bacterial assemblage in generalist pest, Spodoptera littoralis (Lepidoptera: Noctuidae).

Diet composition is vital in shaping gut microbial assemblage in many insects. Minimal knowledge is available about the influence of transgenerational diet transition on gut microbial community structure and function in polyphagous pests. This study investigated transgenerational diet-induced changes in Spodoptera littoralis larval gut bacteriome using 16S ribosomal sequencing. Our data revealed that 88% of bacterial populations in the S. littoralis larval gut comprise Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. The first diet transition experiment from an artificial diet (F0) to a plant diet (F1), cabbage and cotton, caused an alteration of bacterial communities in the S. littoralis larval gut. The second transgenerational diet switch, where F1 larvae feed on the same plant in the F2 generation, displayed a significant variation suggesting further restructuring of the microbial communities in the Spodoptera larval gut. F1 larvae were also challenged with the plant diet transition at the F2 generation (cabbage to cotton or cotton to cabbage). After feeding on different plant diets, the microbial assemblage of F2 larvae pointed to considerable differences from other F2 larvae that continued on the same diet. Our results showed that S. littoralis larval gut bacteriome responds rapidly and inexplicably to different diet changes. Further experiments must be conducted to determine the developmental and ecological consequences of such changes. Nevertheless, this study improves our perception of the impact of transgenerational diet switches on the resident gut bacteriome in S. littoralis larvae and could facilitate future research to understand the importance of symbiosis in lepidopteran generalists better.

Prognostic model development for classification of colorectal adenocarcinoma by using machine learning model based on feature selection technique boruta.

Colorectal cancer (CRC) is the third most prevalent cancer type and accounts for nearly one million deaths worldwide. The CRC mRNA gene expression datasets from TCGA and GEO (GSE144259, GSE50760, and GSE87096) were analyzed to find the significant differentially expressed genes (DEGs). These significant genes were further processed for feature selection through boruta and the confirmed features of importance (genes) were subsequently used for ML-based prognostic classification model development. These genes were analyzed for survival and correlation analysis between final genes and infiltrated immunocytes. A total of 770 CRC samples were included having 78 normal and 692 tumor tissue samples. 170 significant DEGs were identified after DESeq2 analysis along with the topconfects R package. The 33 confirmed features of importance-based RF prognostic classification model have given accuracy, precision, recall, and f1-score of 100% with 0% standard deviation. The overall survival analysis had finalized GLP2R and VSTM2A genes that were significantly downregulated in tumor samples and had a strong correlation with immunocyte infiltration. The involvement of these genes in CRC prognosis was further confirmed on the basis of their biological function and literature analysis. The current findings indicate that GLP2R and VSTM2A may play a significant role in CRC progression and immune response suppression.

Unlocking the Potential of the CA2, CA7, and ITM2C Gene Signatures for the Early Detection of Colorectal Cancer: A Comprehensive Analysis of RNA-Seq Data by Utilizing Machine Learning Algorithms.

Colorectal cancer affects the colon or rectum and is a common global health issue, with 1.1 million new cases occurring yearly. The study aimed to identify gene signatures for the early detection of CRC using machine learning (ML) algorithms utilizing gene expression data. The TCGA-CRC and GSE50760 datasets were pre-processed and subjected to feature selection using the LASSO method in combination with five ML algorithms: Adaboost, Random Forest (RF), Logistic Regression (LR), Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM). The important features were further analyzed for gene expression, correlation, and survival analyses. Validation of the external dataset GSE142279 was also performed. The RF model had the best classification accuracy for both datasets. A feature selection process resulted in the identification of 12 candidate genes, which were subsequently reduced to 3 (CA2, CA7, and ITM2C) through gene expression and correlation analyses. These three genes achieved 100% accuracy in an external dataset. The AUC values for these genes were 99.24%, 100%, and 99.5%, respectively. The survival analysis showed a significant logrank p-value of 0.044 for the final gene signatures. The analysis of tumor immunocyte infiltration showed a weak correlation with the expression of the gene signatures. CA2, CA7, and ITM2C can serve as gene signatures for the early detection of CRC and may provide valuable information for prognostic and therapeutic decision making. Further research is needed to fully understand the potential of these genes in the context of CRC.

Herbal Therapeutics for Alzheimer's Disease: Ancient Indian Medicine System from the Modern Viewpoint.

Alzheimer's is a chronic neurodegenerative disease where amyloid-beta (Aß) plaques and neurofibrillary tangles are formed inside the brain. It is also characterized by progressive memory loss, depression, neuroinflammation, and derangement of other neurotransmitters. Due to its complex etiopathology, current drugs have failed to completely cure the disease. Natural compounds have been investigated as an alternative therapy for their ability to treat Alzheimer's disease (AD). Traditional herbs and formulations which are used in the Indian ayurvedic system are rich sources of antioxidant, anti-amyloidogenic, neuroprotective, and anti-inflammatory compounds. They promote quality of life by improving cognitive memory and rejuvenating brain functioning through neurogenesis. A rich knowledge base of traditional herbal plants (Turmeric, Gingko, Ashwagandha, Shankhpushpi, Giloy, Gotu kola, Garlic, Tulsi, Ginger, and Cinnamon) combined with modern science could suggest new functional leads for Alzheimer's drug discovery. In this article Ayurveda, the ancient Indian herbal medicine system based on multiple clinical and experimental, evidence have been reviewed for treating AD and improving brain functioning. This article presents a modern perspective on the herbs available in the ancient Indian medicine system as well as their possible mechanisms of action for AD treatment. The main objective of this research is to provide a systematic review of herbal drugs that are easily accessible and effective for the treatment of AD.

Use of an Integrated Multi-Omics Approach To Identify Molecular Mechanisms and Critical Factors Involved in the Pathogenesis of Leptospira.

Leptospirosis, a bacterial zoonosis caused by pathogenic Leptospira spp., is prevalent worldwide and has become a serious threat in recent years. Limited understanding of Leptospira pathogenesis and host response has hampered the development of effective vaccine and diagnostics. Although Leptospira is phagocytosed by innate immune cells, it resists its destruction, and the evading mechanism involved is unclear. In the present study, we used an integrative multi-omics approach to identify the critical molecular factors of Leptospira involved in pathogenesis during interaction with human macrophages. Transcriptomic and proteomic analyses were performed at 24 h postinfection of human macrophages (phorbol-12-myristate-13-acetate differentiated THP-1 cells) with the pathogenic Leptospira interrogans serovar Icterohaemorrhagiae strain RGA (LEPIRGA). Our results identified a total of 1,528 transcripts and 871 proteins that were significantly expressed with an adjusted P value of <0.05. The correlations between the transcriptomic and proteomic data were above average (r = 0.844), suggesting the role of the posttranscriptional processes during host interaction. The conjoint analysis revealed the expression of several virulence-associated proteins such as adhesins, invasins, and secretory and chemotaxis proteins that might be involved in various processes of attachment and invasion and as effectors during pathogenesis in the host. Further, the interaction of bacteria with the host cell (macrophages) was a major factor in the differential expression of these proteins. Finally, eight common differentially expressed RNA-protein pairs, predicted as virulent, outer membrane/extracellular proteins were validated by quantitative PCR. This is the first report using integrated multi-omics approach to identify critical factors involved in Leptospira pathogenesis. Validation of these critical factors may lead to the identification of target antigens for the development of improved diagnostics and vaccines against leptospirosis. IMPORTANCE Leptospirosis is a zoonotic disease of global importance. It is caused by a Gram-negative bacterial spirochete of the genus Leptospira. The current challenge is to detect the infection at early stage for treatment or to develop potent vaccines that can induce cross-protection against various pathogenic serovars. Understanding host-pathogen interactions is important to identify the critical factors involved in pathogenesis and host defense for developing improved vaccines and diagnostics. Utilizing an integrated multi-omics approach, our study provides important insight into the interaction of Leptospira with human macrophages and identifies a few critical factors (such as virulence-associated proteins) involved in pathogenesis. These factors can be exploited for the development of novel tools for the detection, treatment, or prevention of leptospirosis.

Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds.

The effect of breed on milk components-fat, protein, lactose, and water-has been observed to be significant. As fat is one of the major price-determining factors for milk, exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. Here, on whole-genome sequencing, 25 differentially expressed hub or bottleneck fat QTLs were explored for variations across indigenous breeds. Out of these, 20 genes were identified as having nonsynonymous substitutions. A fixed SNP pattern in high-milk-yielding breeds in comparison to low-milk-yielding breeds was identified in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E and, vice versa, in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The identified SNPs were ratified by pyrosequencing to prove that key differences exist in fat QTLs between the high- and low-milk-yielding breeds.

Correlating multi-functional role of cold shock domain proteins with intrinsically disordered regions.

Cold shock proteins (CSPs) are an ancient and conserved family of proteins. They are renowned for their role in response to low-temperature stress in bacteria and nucleic acid binding activities. In prokaryotes, cold and non-cold inducible CSPs are involved in various cellular and metabolic processes such as growth and development, osmotic oxidation, starvation, stress tolerance, and host cell invasion. In prokaryotes, cold shock condition reduces cell transcription and translation efficiency. Eukaryotic cold shock domain (CSD) proteins are evolved form of prokaryotic CSPs where CSD is flanked by N- and C-terminal domains. Eukaryotic CSPs are multi-functional proteins. CSPs also act as nucleic acid chaperons by preventing the formation of secondary structures in mRNA at low temperatures. In human, CSD proteins play a crucial role in the progression of breast cancer, colon cancer, lung cancer, and Alzheimer's disease. A well-defined three-dimensional structure of intrinsically disordered regions of CSPs family members is still undetermined. In this article, intrinsic disorder regions of CSPs have been explored systematically to understand the pleiotropic role of the cold shock family of proteins.

Anti-biofilm activity of caffeine against uropathogenic E. coli is mediated by curli biogenesis.

Biofilms are assemblages of sessile microorganisms that form an extracellular matrix around themselves and mediate attachment to surfaces. The major component of the extracellular matrix of Uropathogenic E. coli and other Enterobacteriaceae are curli fibers, making biofilms robust and resistant to antimicrobials. It is therefore imperative to screen antibiofilm compounds that can impair biofilm formation. In the present study, we investigated the curli-dependent antibiofilm activity of caffeine against UPEC strain CFT073 and commensal strain E. coli K-12MG1655.Caffeine significantly reduced the biofilm formation of both UPEC and E. coli K-12 by 86.58% and 91.80% respectively at 48 mM caffeine as determined by Crystal Violet assay. These results were further confirmed by fluorescence microscopy and Scanning Electron Microscope (SEM). Caffeine significantly reduced the cytotoxicity and survivability of UPEC. Molecular docking analysis revealed a strong interaction between caffeine and curli regulator protein (Csg D) of E. coli. The qRT-PCR data also showed significant downregulation in the expression of CsgBA and the CsgDEFG operon at both 24 mM and 48 mM caffeine. The findings revealed that caffeine could inhibit E. coli biofilm formation by regulating curli assembly and thus may be used as an alternative therapeutic strategy for the treatment of chronic E. coli biofilm-related infections.