Extent of Virulence and Antibiotic Resistance Genes in Helicobacter pylori and Campylobacteria.

Helicobacter pylori, a member of the clade campylobacteria, is the leading cause of chronic gastritis and gastric cancer. Virulence and antibiotic resistance of H. pylori are of great concern to public health. However, the relationship between virulence and antibiotic resistance genes in H. pylori in relation to other campylobacteria remains unclear. Using the virulence and comprehensive antibiotic resistance databases, we explored all available 354 complete genomes of H. pylori and compared it with 90 species of campylobacteria for virulence and antibiotic resistance genes/proteins. On average, H. pylori had 129 virulence genes, highest among Helicobacter spp. and 71 antibiotic resistance genes, one of the lowest among campylobacteria. Just 2.6% of virulence genes were shared by all campylobacterial members, whereas 9.4% were unique to H. pylori. The cytotoxin-associated genes (cags) seemed to be exclusive to H. pylori. Majority of the isolates from Asia and South America were cag2-negative and many antibiotic resistance genes showed isolate-specific patterns of occurrence. Just 15 (8.8%) antibiotic resistance genes, but 103 (66%) virulence genes including 25 cags were proteomically identified in H. pylori. Arcobacterial members showed large variation in the number of antibiotic resistance genes and there was a positive relation with the genome size. Large repository of antibiotic resistance genes in campylobacteria and a unique set of virulence genes might have important implications in shaping the course of virulence and antibiotic resistance in H. pylori.

Prevalence and heterogeneity of antibiotic resistance genes in Orientia tsutsugamushi and other rickettsial genomes.

Despite a million infections every year and an estimated one billion people at risk, scrub typhus is regarded as a neglected tropical disease. The causative bacterium Orientia tsutsugamushi, a member of rickettsiae, seems to be intrinsically resistant to several classes of antibiotics. The emergence of antibiotic-resistant scrub typhus is likely to become a global public health concern. Yet, it is unknown as to how common antibiotic resistance genes are in O. tsutsugamushi, and how variable these loci are among the genomes of rickettsiae. By using the comprehensive antibiotic resistance database, we explored 79 complete genomes from 24 species of rickettsiae for antibiotic resistance loci. There were 244 unique antibiotic resistance genes in rickettsiae. Both the total and unique antibiotic resistance genes in O. tsutsugamushi were significantly less compared to other members of rickettsiae. However, antibiotic resistance genes in O. tsutsugamushi genomes were more unique and highly variable. Many genes such as resistant variants of evgS, and vanS A/G were present in numerous copies. These results will have important implications in the context of antibiotic-resistant scrub typhus.

Molecular detection of monkeypox and related viruses: challenges and opportunities.

The recent widespread emergence of monkeypox (mpox), a rare and endemic zoonotic disease by monkeypox virus (MPXV), has made global headlines. While transmissibility (R0 ≈ 0.58) and fatality rate (0-3%) are low, as it causes prolonged morbidity, the World Health Organization has declared monkeypox as a public health emergency of international concern. Thus, effective containment and disease management require quick and efficient detection of MPXV. In this bioinformatic overview, we summarize the numerous molecular tests available for MPXV, and discuss the diversity of genes and primers used in the polymerase chain reaction-based detection. Over 90 primer/probe sets are used for the detection of poxviruses. While hemagglutinin and A-type inclusion protein are the most common target genes, tumor necrosis factor receptor and complement binding protein genes are frequently used for distinguishing Clade I and Clade II of MPXV. Problems and possibilities in the detection of MPXV have been discussed.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches.

Background: Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people. Objectives: In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making. Methods: An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots. Results: Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM. Conclusion: A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Amplicon-Based Nanopore Sequencing of Patients Infected by the SARS-CoV-2 Omicron (B.1.1.529) Variant in India.

We report the sequencing of SARS-CoV-2 Omicron variants from 75 patients, using nanopore long-read sequencing chemistry. These data show a range of mutations in spike glycoprotein that are both unique and common to other populations.

Perspectives on Rapid Antigen Tests for Downstream Validation and Development of Theranostics.

Point-of-care SARS-CoV-2 rapid antigen tests have proven to be useful over the years and have become more apparent to the public eye during COVID-19 pandemic due to their ease of use, rapid processing and result times, and low cost. Here, we have assessed the effectiveness and accuracy of rapid antigen tests in comparison to the standard real-time polymerase chain reaction analyses of the same samples.

Inferring Recombination Events in SARS-CoV-2 Variants In Silico.

Over the last 34 months, at least 10 severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) distinct variants have evolved. Among these, some were more infectious while others were not. These variants may serve as candidates for identification of the signature sequences linked to infectivity and viral transgressions. Based on our previous hijacking and transgression hypothesis, we aimed to investigate whether SARS-CoV-2 sequences associated with infectivity and trespassing of long noncoding RNAs (lncRNAs) provide a possible recombination mechanism to drive the formation of new variants. This work involved a sequence and structure-based approach to screen SARS-CoV-2 variants in silico, taking into account effects of glycosylation and links to known lncRNAs. Taken together, the findings suggest that transgressions involving lncRNAs may be linked with changes in SARS-CoV-2-host interactions driven by glycosylation events.

COVID-19: Impact on linguistic and genetic isolates of India.

The rapid expansion of coronavirus SARS-CoV-2 has impacted various ethnic groups all over the world. The burden of infectious diseases including COVID-19 are generally reported to be higher for the Indigenous people. The historical knowledge have also suggested that the indigenous populations suffer more than the general populations in the pandemic. Recently, it has been reported that the indigenous groups of Brazil have been massively affected by COVID-19. Series of studies have shown that many of the indigenous communities reached at the verge of extinction due to this pandemic. Importantly, South Asia also has several indigenous and smaller communities, that are living in isolation. Till date, despite the two consecutive waves in India, there is no report on the impact of COVID-19 for indigenous tribes. Since smaller populations experiencing drift may have greater risk of such pandemic, we have analysed Runs of Homozygosity (ROH) among South Asian populations and identified several populations with longer homozygous segments. The longer runs of homozygosity at certain genomic regions may increases the susceptibility for COVID-19. Thus, we suggest extreme careful management of this pandemic among isolated populations of South Asia.

SARS-CoV-2 transgressing LncRNAs uncovers the known unknowns.

SARS-CoV-2 harbors many known unknown regions in the form of hypothetical open reading frames (ORFs). Although the mechanisms underlying the disease pathogenesis are not clearly understood, molecules such as long noncoding RNAs (lncRNAs) play a key regulatory role in the viral pathogenesis from endocytosis. We asked whether or not the lncRNAs in the host are associated with the viral proteins and argue that lncRNA-mRNAs molecules related to viral infection may regulate SARS-CoV-2 pathogenesis. Toward the end of the perspective, we provide challenges and insights into investigating these transgression pathways.

Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery.

The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.

miRNA Regulatory Functions in Farm Animal Diseases, and Biomarker Potentials for Effective Therapies.

MicroRNAs (miRNAs) are small endogenous RNAs that regulate gene expression post-transcriptionally by targeting either the 3' untranslated or coding regions of genes. They have been reported to play key roles in a wide range of biological processes. The recent remarkable developments of transcriptomics technologies, especially next-generation sequencing technologies and advanced bioinformatics tools, allow more in-depth exploration of messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs), including miRNAs. These technologies have offered great opportunities for a deeper exploration of miRNA involvement in farm animal diseases, as well as livestock productivity and welfare. In this review, we provide an overview of the current knowledge of miRNA roles in major farm animal diseases with a particular focus on diseases of economic importance. In addition, we discuss the steps and future perspectives of using miRNAs as biomarkers and molecular therapy for livestock disease management as well as the challenges and opportunities for understanding the regulatory mechanisms of miRNAs related to disease pathogenesis.

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies.

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

Improvement of small seed for big nutritional feed.

Exploding global population, rapid urbanization, salinization of soils, decreasing arable land availability, groundwater resources, and dynamic climatic conditions pose impending damage to our food security by reducing the grain quality and quantity. This issue is further compounded in arid and semi-arid regions due to the shortage of irrigation water and erratic rainfalls. Millets are gluten (a family of proteins)-free and cultivated all over the globe for human consumption, fuel, feed, and fodder. They provide nutritional security for the under- and malnourished. With the deployment of strategies like foliar spray, traditional/marker-assisted breeding, identification of candidate genes for the translocation of important minerals, and genome-editing technologies, it is now tenable to biofortify important millets. Since the bioavailability of iron and zinc has been proven in human trials, the challenge is to make such grains accessible. This review encompasses nutritional benefits, progress made, challenges being encountered, and prospects of enriching millet crops with essential minerals.

Decoding SARS-CoV-2 hijacking of host mitochondria in COVID-19 pathogenesis.

Because of the ongoing pandemic around the world, the mechanisms underlying the SARS-CoV-2-induced COVID-19 are subject to intense investigation. Based on available data for the SARS-CoV-1 virus, we suggest how CoV-2 localization of RNA transcripts in mitochondria hijacks the host cell's mitochondrial function to viral advantage. Besides viral RNA transcripts, RNA also localizes to mitochondria. SARS-CoV-2 may manipulate mitochondrial function indirectly, first by ACE2 regulation of mitochondrial function, and once it enters the host cell, open-reading frames (ORFs) such as ORF-9b can directly manipulate mitochondrial function to evade host cell immunity and facilitate virus replication and COVID-19 disease. Manipulations of host mitochondria by viral ORFs can release mitochondrial DNA (mtDNA) in the cytoplasm and activate mtDNA-induced inflammasome and suppress innate and adaptive immunity. We argue that a decline in ACE2 function in aged individuals, coupled with the age-associated decline in mitochondrial functions resulting in chronic metabolic disorders like diabetes or cancer, may make the host more vulnerable to infection and health complications to mortality. These observations suggest that distinct localization of viral RNA and proteins in mitochondria must play essential roles in SARS-CoV-2 pathogenesis. Understanding the mechanisms underlying virus communication with host mitochondria may provide critical insights into COVID-19 pathologies. An investigation into the SARS-CoV-2 hijacking of mitochondria should lead to novel approaches to prevent and treat COVID-19.

Hypothetical Proteins as Predecessors of Long Non-coding RNAs.

Hypothetical Proteins [HP] are the transcripts predicted to be expressed in an organism, but no evidence of it exists in gene banks. On the other hand, long non-coding RNAs [lncRNAs] are the transcripts that might be present in the 5' UTR or intergenic regions of the genes whose lengths are above 200 bases. With the known unknown [KU] regions in the genomes rapidly existing in gene banks, there is a need to understand the role of open reading frames in the context of annotation. In this commentary, we emphasize that HPs could indeed be the predecessors of lncRNAs.

A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.

BACKGROUND: Hypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs. RESULTS: In our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67). CONCLUSION: With the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.

Gene selection for tumor classification using a novel bio-inspired multi-objective approach.

Identifying the informative genes has always been a major step in microarray data analysis. The complexity of various cancer datasets makes this issue still challenging. In this paper, a novel Bio-inspired Multi-objective algorithm is proposed for gene selection in microarray data classification specifically in the binary domain of feature selection. The presented method extends the traditional Bat Algorithm with refined formulations, effective multi-objective operators, and novel local search strategies employing social learning concepts in designing random walks. A hybrid model using the Fisher criterion is then applied to three widely-used microarray cancer datasets to explore significant biomarkers which reveal the effectiveness of the proposed method for genomic analysis. Experimental results unveil new combinations of informative biomarkers have association with other studies.

Making novel proteins from pseudogenes.

MOTIVATION: Recently, we made synthetic proteins from non-coding DNA of Escherichia coli. Encouraged by this, we asked: can we artificially express pseudogenes into novel and functional proteins? What kind of structures would be generated? Would these proteins be stable? How would the organism respond to the artificial reactivation of pseudogenes? RESULTS: To answer these questions, we studied 16 full-length protein equivalents of pseudogenes. The sequence-based predictions indicated interesting molecular and cellular functional roles for pseudogene-derived proteins. Most of the proteins were predicted to be involved in the amino acid biosynthesis, energy metabolism, purines and pyrimidine biosynthesis, central intermediary metabolism, transport and binding. Interestingly, many of the pseudogene-derived proteins were predicted to be enzymes. Furthermore, proteins showed strong evidence of stable tertiary structures. The prediction scores for structure, function and stability were found to be favorable in most of the cases. IMPACT: To our best knowledge, this is the first such report that predicts the possibility of making functional and stable proteins from pseudogenes. In future, it would be interesting to experimentally synthesize and validate these predictions.

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare.

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

Toxicity and tolerance of aluminum in plants: tailoring plants to suit to acid soils.

Aluminum (Al) stress is one of the serious limiting factors in plant productivity in acidic soils, which constitute about 50 % of the world's potentially arable lands and causes anywhere between 25 and 80 % of yield losses depending upon the species. The mechanism of Al toxicity and tolerance has been examined in plants, which is vital for crop improvement and enhanced food production in the future. Two mechanisms that facilitate Al tolerance in plants are Al exclusion from the roots and the ability to tolerate Al in the symplast or both. Although efforts have been made to unravel Al-resistant factors, many aspects remain unclear. Certain gene families such as MATE, ALMT, ASR, and ABC transporters have been implicated in some plants for resistance to Al which would enhance the opportunities for creating crop plants suitable to grow in acidic soils. Though QTLs have been identified related to Al-tolerance, no crop plant that is tolerant to Al has been evolved so far using breeding or molecular approaches. The remarkable changes that plants experience at the physiological, biochemical and molecular level under Al stress, the vast array of genes involved in Al toxicity-tolerance, the underlying signaling events and the holistic image of the molecular regulation, and the possibility of creating transgenics for Al tolerance are discussed in this review.