Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types.

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

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.

RB1 screening of retinoblastoma patients in Sri Lanka using targeted next generation sequencing (NGS) and gene ratio analysis copy enumeration PCR (GRACE-PCR).

BACKGROUND: Retinoblastoma (RB) a tumour affecting those under 5 years, has a prevalence of 1 in 20,000, with around twenty new diagnoses per year in Sri Lanka. Unilateral and bilateral RB presents around 24 and 15 months respectively. Approximately 10% are familial. Systematic genetic testing for germline pathogenic variants of RB1, the only gene associated with an inherited risk of RB, is unavailable in Sri Lanka. Genetic testing optimizes management of affected children and at-risk siblings. This study aimed to develop accessible genetic testing to identify children with a germline pathogenic variant of RB1 in Sri Lanka. METHODS: Targeted next generation sequencing (NGS) for detecting pathogenic sequence variants and Gene Ratio Analysis Copy Enumeration PCR (GRACE-PCR) for detecting RB1 copy number variations (CNVs) were performed for 49 consecutive RB patients treated between 2016 and 2020 at the designated RB care unit, Lady Ridgway hospital, Colombo. Patients (bilateral RB (n = 18; 37%), unilateral n = 31) were recruited following ethical clearance and informed consent. RESULTS: There were 26 (53%) females. Mean age at diagnosis was 18 months. Thirty-five patients (71%) had undergone enucleation. Germline pathogenic variants of RB1 identified in 22/49 (45%) patients including 18 (37%; 12 bilateral and 6 unilateral) detected by targeted NGS (2 missense, 7 stop gained, 1 splice donor, 8 frameshift variants). Six were previously undescribed, likely pathogenic frameshift variants. Four bilateral RB patients had GRACE-PCR detected CNVs including one whole RB1, two intragenic deletions (exon 12/13; exon 11 and 23) and a partial duplication of exon 27. The only familial case (affected mother and child) shared the duplication. Only 2 of 4 CNVs and 10 of 18 pathogenic variants were confirmed by whole exome sequencing and Sanger sequencing respectively, due to funding limitations. CONCLUSIONS: The study identified pathogenic or likely pathogenic germline RB1 sequence variants and copy number variants in 16/18 (89%) bilateral and 6/31(19%) unilateral cases, which is comparable to worldwide data (10-15% unilateral, 80-85% bilateral). Targeted NGS combined with GRACE-PCR significantly reduce the cost of RB1 testing in Sri Lanka, and may widen access for genetic diagnosis of RB patients in other low and middle income countries.

Whole Exome-Trio Analysis Reveals Rare Variants Associated with Congenital Pouch Colon.

Anorectal malformations (ARM) are individually common, but Congenital Pouch Colon (CPC) is a rare anorectal anomaly that causes a dilated pouch and communication with the genitourinary tract. In this work, we attempted to identify de novo heterozygous missense variants, and further discovered variants of unknown significance (VUS) which could provide insights into CPC manifestation. From whole exome sequencing (WES) performed earlier, the trio exomes were analyzed from those who were admitted to J.K. Lon Hospital, SMS Medical College, Jaipur, India, between 2011 and 2017. The proband exomes were compared with the unaffected sibling/family members, and we sought to ask whether any variants of significant interest were associated with the CPC manifestation. The WES data from a total of 64 samples including 16 affected neonates (11 male and 5 female) with their parents and unaffected siblings were used for the study. We examined the role of rare allelic variation associated with CPC in a 16 proband/parent trio family, comparing the mutations to those of their unaffected parents/siblings. We also performed RNA-Seq as a pilot to find whether or not the genes harboring these mutations were differentially expressed. Our study revealed extremely rare variants, viz., TAF1B, MUC5B and FRG1, which were further validated for disease-causing mutations associated with CPC, further closing the gaps of surgery by bringing intervention in therapies.

Towards Understanding the Key Signature Pathways Associated from Differentially Expressed Gene Analysis in an Indian Prostate Cancer Cohort.

Prostate cancer (PCa) is one of the most prevalent cancers among men in India. Although studies on PCa have dealt with genetics, genomics, and the environmental influence in the causality of PCa, not many studies employing the Next Generation Sequencing (NGS) approaches of PCa have been carried out. In our previous study, we identified some causal genes and mutations specific to Indian PCa using Whole Exome Sequencing (WES). In the recent past, with the help of different cancer consortiums such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), along with differentially expressed genes (DEGs), many cancer-associated novel non-coding RNAs have been identified as biomarkers. In this work, we attempt to identify differentially expressed genes (DEGs) including long non-coding RNAs (lncRNAs) associated with signature pathways from an Indian PCa cohort using the RNA-sequencing (RNA-seq) approach. From a cohort of 60, we screened six patients who underwent prostatectomy; we performed whole transcriptome shotgun sequencing (WTSS)/RNA-sequencing to decipher the DEGs. We further normalized the read counts using fragments per kilobase of transcript per million mapped reads (FPKM) and analyzed the DEGs using a cohort of downstream regulatory tools, viz., GeneMANIA, Stringdb, Cytoscape-Cytohubba, and cbioportal, to map the inherent signatures associated with PCa. By comparing the RNA-seq data obtained from the pairs of normal and PCa tissue samples using our benchmarked in-house cuffdiff pipeline, we observed some important genes specific to PCa, such as STEAP2, APP, PMEPA1, PABPC1, NFE2L2, and HN1L, and some other important genes known to be involved in different cancer pathways, such as COL6A1, DOK5, STX6, BCAS1, BACE1, BACE2, LMOD1, SNX9, CTNND1, etc. We also identified a few novel lncRNAs such as LINC01440, SOX2OT, ENSG00000232855, ENSG00000287903, and ENST00000647843.1 that need to be characterized further. In comparison with publicly available datasets, we have identified characteristic DEGs and novel lncRNAs implicated in signature PCa pathways in an Indian PCa cohort which perhaps have not been reported. This has set a precedent for us to validate candidates further experimentally, and we firmly believe this will pave a way toward the discovery of biomarkers and the development of novel therapies.

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.

Anastrozole-mediated modulation of mitochondrial activity by inhibition of mitochondrial permeability transition pore opening: an initial perspective.

The mitochondrial permeability transition pore (mtPTP) plays a vital role in altering the structure and function of mitochondria. Cyclophilin D (CypD) is a mitochondrial protein that regulates mtPTP function and a known drug target for therapeutic studies involving mitochondria. While the effect of aromatase inhibition on the mtPTP has been studied previously, the effect of anastrozole on the mtPTP has not been completely elucidated. The role of anastrozole in modulating the mtPTP was evaluated by docking, molecular dynamics and network-guided studies using human CypD data. The peripheral blood mononuclear cells (PBMCs) of patients with mitochondrial disorders and healthy controls were treated with anastrozole and evaluated for mitochondrial permeability transition pore (mtPTP) function and apoptosis using a flow cytometer. Spectrophotometry was employed for estimating total ATP levels. The anastrozole-CypD complex is more stable than cyclosporin A (CsA)-CypD. Anastrozole performed better than cyclosporine in inhibiting mtPTP. Additional effects included inducing mitochondrial membrane depolarization and a reduction in mitochondrial swelling and superoxide generation, intrinsic caspase-3 activity and cellular apoptosis, along with an increase in ATP levels. Anastrozole may serve as a potential therapeutic agent for mitochondrial disorders and ameliorate the clinical phenotype by regulating the activity of mtPTP. However, further studies are required to substantiate our preliminary findings.Communicated by Ramaswamy H. Sarma.

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.

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis.

Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI's oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.

Inferring bona fide Differentially Expressed Genes and Their Variants Associated with Vitamin K Deficiency Using a Systems Genetics Approach.

Systems genetics is key for integrating a large number of variants associated with diseases. Vitamin K (VK) is one of the scarcely studied disease conditions. In this work, we ascertained the differentially expressed genes (DEGs) and variants associated with individual subpopulations of VK disease phenotypes, viz., myocardial infarction, renal failure and prostate cancer. We sought to ask whether or not any DEGs harbor pathogenic variants common in these conditions, attempt to bridge the gap in finding characteristic biomarkers and discuss the role of long noncoding RNAs (lncRNAs) in the biogenesis of VK deficiencies.

Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks.

An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.

Systematic understanding of anti-tumor mechanisms of Tamarixetin through network and experimental analyses.

Tamarixetin, a flavonoid derived from Quercetin, was shown to possess anti-cancer properties in various types of cancer. However, the mechanism of action of this compound is not well understood. Observations from reverse docking and network pharmacology analysis, were validated by cell based studies to analyse the chemotherapeutic potential and elucidate the molecular mechanism of action of Tamarixetin in breast cancer. In silico analysis using reverse docking and PPI analysis clearly indicated that out of 35 proteins targeted by Tamarixetin, the top 3 hub genes, namely, AKT1, ESR1 and HSP90AA1, were upregulated in breast tumor tissues and more importantly showed strong negative correlation to breast cancer patient survival. Furthermore, the KEGG pathway analysis showed enrichment of target proteins of Tamarixetin in 33 pathways which are mainly involved in neoplastic signalling. In vitro cell-based studies demonstrated that Tamarixetin could inhibit cell proliferation, induce ROS and reduce mitochondrial membrane potential, leading to cell death. Tamarixetin induced cell cycle arrest at G2/M phase and inhibited the migration as well as the invasion of breast cancer cells. Taken together, the combination of in silico and in vitro approaches used in the present study clearly provides evidence for the chemotherapeutic potential of Tamarixetin in breast cancer.

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.

A Pilot Study on the Whole Exome Sequencing of Prostate Cancer in the Indian Phenotype Reveals Distinct Polymorphisms.

Prostate cancer (PCa) is the third most common cancer among men in India, and no next-generation sequencing (NGS) studies have been attempted earlier. Recent advances in NGS have heralded the discovery of biomarkers from Caucasian/European and Chinese ancestry, but not much is known about the Indian phenotype/variant of PCa. In a pilot study using the whole exome sequencing of benign/PCa patients, we identified characteristic mutations specific to the Indian sub-population. We observed a large number of mutations in DNA repair genes, viz. helicases, TP53, and BRCA besides the variants of unknown significance with a possibly damaging rare variant (rs730881069/chr19:55154172C/TR136Q) in the TNNI3 gene that has been previously reported as a semi-conservative amino acid substitution. Our pilot study attempts to bring an understanding of PCa prognosis and recurrence for the Indian phenotype.

Current Challenges and Implications of Proteogenomic Approaches in Prostate Cancer.

In the recent past, next-generation sequencing (NGS) approaches have heralded the omics era. With NGS data burgeoning, there arose a need to disseminate the omic data better. Proteogenomics has been vividly used for characterising the functions of candidate genes and is applied in ascertaining various diseased phenotypes, including cancers. However, not much is known about the role and application of proteogenomics, especially Prostate Cancer (PCa). In this review, we outline the need for proteogenomic approaches, their applications and their role in PCa.

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.

Systems Challenges of Hepatic Carcinomas: A Review.

Hepatocellular Carcinoma (HCC) is ubiquitous in its prevalence in most of the developing countries. In the era of systems biology, multi-omics has evinced an extensive approach to define the underlying mechanism of disease progression. HCC is a multifactorial disease and the investigation of progression of liver cirrhosis becomes much extensive with cultivating omics approaches. We have performed a comprehensive review about such challenges in multi-omics approaches that are concerned to identify the immunological, genetics and epidemiological factors associated with HCC.

Microbial Mutagenicity Assay: Ames Test.

The Microbial mutagenicity Ames test is a bacterial bioassay accomplished in vitro to evaluate the mutagenicity of various environmental carcinogens and toxins. While Ames test is used to identify the revert mutations which are present in strains, it can also be used to detect the mutagenicity of environmental samples such as drugs, dyes, reagents, cosmetics, waste water, pesticides and other substances which are easily solubilized in a liquid suspension. We present the protocol for conducting Ames test in the laboratory.

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.

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.