A large-scale targeted proteomics of serum and tissue shows the utility of classifying high grade and low grade meningioma tumors.

BACKGROUND: Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. METHODS: A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. RESULTS: A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. CONCLUSIONS: The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Body mass index associates with amyotrophic lateral sclerosis survival and metabolomic profiles.

INTRODUCTION/AIMS: Body mass index (BMI) is linked to amyotrophic lateral sclerosis (ALS) risk and prognosis, but additional research is needed. The aim of this study was to identify whether and when historical changes in BMI occurred in ALS participants, how these longer term trajectories associated with survival, and whether metabolomic profiles provided insight into potential mechanisms. METHODS: ALS and control participants self-reported body height and weight 10 (reference) and 5 years earlier, and at study entry (diagnosis for ALS participants). Generalized estimating equations evaluated differences in BMI trajectories between cases and controls. ALS survival was evaluated by BMI trajectory group using accelerated failure time models. BMI trajectories and survival associations were explored using published metabolomic profiling and correlation networks. RESULTS: Ten-year BMI trends differed between ALS and controls, with BMI loss in the 5 years before diagnosis despite BMI gains 10 to 5 years beforehand in both groups. An overall 10-year drop in BMI associated with a 27.1% decrease in ALS survival (P = .010). Metabolomic networks in ALS participants showed dysregulation in sphingomyelin, bile acid, and plasmalogen subpathways. DISCUSSION: ALS participants lost weight in the 5-year period before enrollment. BMI trajectories had three distinct groups and the group with significant weight loss in the past 10 years had the worst survival. Participants with a high BMI and increase in weight in the 10 years before symptom onset also had shorter survival. Certain metabolomics profiles were associated with the BMI trajectories. Replicating these findings in prospective cohorts is warranted.

Expanding the clinico-molecular spectrum of Angelman syndrome phenotype with the GABRG3 gene: Evidence from methylation and sequencing studies.

Angelman syndrome (AS) (OMIM#105830) is an imprinting disorder caused due to alterations in the maternal chr 15q11-13 region. Majority of cases can be diagnosed by methylation-specific polymerase chain reaction (MS-PCR) of SNRPN gene and by UBE3A sequencing, however, about 10% of cases with AS phenotype remain undiagnosed. Differential diagnoses of AS can be detected by chromosomal microarray (CMA) and clinical exome sequencing (CES). In this study, 30 cases with AS features were evaluated by MS-PCR, CMA, and CES. SNRPN MS-PCR confirmed AS in eight (26%), CMA and CES diagnosed nine (30%) cases. One case was identified with a novel variant c.1125C > T in GABRG3, located at 15q12 region, which is currently not associated with any syndrome. The GABRG3 gene is also speculated to be imprinted, a MS-PCR assay was designed to confirm its differential parental methylation status. This assay identified another case with altered GABRG3 methylation. The two cases with GABRG3 alteration-sequence change and methylation indicate that GABRG3 may be associated with a subtype of AS or a new related syndrome. Performing GABRG3 MS-PCR and sequencing of a larger group of patients with AS phenotype and normal SNPRN and UBE3A status will help in establishing exact genotype-phenotype correlation.

Calcium-dependent phosphorylation of Plasmodium falciparum serine repeat antigen 5 triggers merozoite egress.

The human malaria parasite Plasmodium falciparum proliferates in red blood cells following repeated cycles of invasion, multiplication, and egress. P. falciparum serine repeat antigen 5 (PfSERA5), a putative serine protease, plays an important role in merozoite egress. However, regulation of its activity leading to merozoite egress is poorly understood. In this study, we show that PfSERA5 undergoes phosphorylation prior to merozoite egress. Immunoprecipitation of parasite lysates using anti-PfSERA5 serum followed by MS analysis identified calcium-dependent protein kinase 1 (PfCDPK1) as an interacting kinase. Association of PfSERA5 with PfCDPK1 was corroborated by co-sedimentation, co-immunoprecipitation, and co-immunolocalization analyses. Interestingly, PfCDPK1 phosphorylated PfSERA5 in vitro in the presence of Ca2+ and enhanced its proteolytic activity. A PfCDPK1 inhibitor, purfalcamine, blocked the phosphorylation and activation of PfSERA5 both in vitroas well as in schizonts, which, in turn, blocked merozoite egress. Together, these results suggest that phosphorylation of PfSERA5 by PfCDPK1 following a rise in cytosolic Ca2+ levels activates its proteolytic activity to trigger merozoite egress.

Pedigree and BRCA gene analysis in breast cancer patients to identify hereditary breast and ovarian cancer syndrome to prevent morbidity and mortality of disease in Indian population.

Global burden of breast cancer is expected to increase to >2 million new cases every year by 2030 and 10% of these are likely to have hereditary breast and ovarian cancer syndrome. Identifying these individuals by pedigree and BRCA1/2 mutation analyses will enable us to offer targeted mutation testing and appropriate counseling. This study from a tertiary care hospital showed that of the 127 breast cancer patients on treatment during 2014-2015, 24 of them fulfilled the criteria of hereditary breast and ovarian cancer syndrome after detailed verbal autopsy and pedigree analysis, and BRCA1 and 2 next-generation sequencing done after pre-test counseling revealed mutations in 13 cases (54%), these included 9 BRCA1 mutations (69%) and 4 BRCA2 mutation (31%). Subsequent post-test counseling recommended targeted mutation analysis for 64 high-risk members in these 13 families with pathogenic mutations, which will help in surveillance for early detection, appropriate management, and prevention of the disease by decreasing the burden to both family and nation. Results from this preliminary study highlight the importance of genetic counseling, pedigree analysis, and genetic testing. It can be recommended that all oncology units should have a genetic counseling service for providing appropriate support to oncologists, patients, and families to prevent unnecessary testing; however, breast cancer screening program is incomplete without evaluating for hereditary breast and ovarian cancer syndrome.

A role for adaptor protein complex 1 in protein targeting to rhoptry organelles in Plasmodium falciparum.

The human malaria parasite Plasmodium falciparum possesses sophisticated systems of protein secretion to modulate host cell invasion and remodeling. In the present study, we provide insights into the function of the AP-1 complex in P. falciparum. We utilized GFP fusion constructs for live cell imaging, as well as fixed parasites in immunofluorescence analysis, to study adaptor protein mu1 (Pfµ1) mediated protein trafficking in P. falciparum. In trophozoites Pfµ1 showed similar dynamic localization to that of several Golgi/ER markers, indicating Golgi/ER localization. Treatment of transgenic parasites with Brefeldin A altered the localization of Golgi-associated Pfµ1, supporting the localization studies. Co-localization studies showed considerable overlap of Pfµ1 with the resident rhoptry proteins, rhoptry associated protein 1 (RAP1) and Cytoadherence linked asexual gene 3.1 (Clag3.1) in schizont stage. Immunoprecipitation experiments with Pfµ1 and PfRAP1 revealed an interaction, which may be mediated through an intermediate transmembrane cargo receptor. A specific role for Pfµ1 in trafficking was suggested by treatment with AlF4, which resulted in a shift to a predominantly ER-associated compartment and consequent decrease in co-localization with the Golgi marker GRASP. Together, these results suggest a role for the AP-1 complex in rhoptry protein trafficking in P. falciparum.

Effectiveness of immunological agents in non-small cell lung cancer.

BACKGROUND AND AIM: Non-small cell lung cancer (NSCLC) continues to claim millions of lives worldwide. Although its poor prognosis is largely attributed to the lack of adequate and precise detection technologies, cancer cells' suppression of the immune system adds on to the difficulty of identifying abnormal NSCLC tumors in their early stages. Therefore, cancer immunotherapy, which activates the immune system and helps it fight tumors, has recently become the most sought-after technique, especially in the advanced stages of NSCLC, where surgery or chemotherapy may or may not bring about the desired survival benefits in patients. METHODS: This review focuses on the various immunotherapeutic interventions and their efficacy in advanced NSCLC clinical trials. Monoclonal antibodies like anti-PD-1/PD-L1 agents and anti-CTLA-4 antibodies, cancer vaccines, oncolytic viruses and adoptive T cell therapy have been discussed in brief. Furthermore, the effects of gender, age, and race on the efficacy of immune checkpoint inhibitors and suggest plausible future approaches in the realm of immuno-oncology. RESULTS: Immunotherapy is used alone or in combination either with other immunological agents or with chemotherapy. However, the efficacy of these strategies depends extensively on various demographic variables, as some patients respond perfectly well to immunotherapy, while others do not benefit at all or experience disease progression. By targeting a "hallmark" of cancer (immune evasion), immunotherapy has transformed NSCLC management, though several barriers prevent its complete effectiveness. CONCLUSIONS: All these immunological strategies should be interpreted in the current setting of synergistic treatment, in which these agents can be combined with chemotherapy, radiotherapy, and, or surgery following patient and tumor characteristics to proportionate the best-individualized treatment and achieve superior results. To better pursue this goal, further investigations on cost-effectiveness and sex-gender, race, and age differences in immunotherapy are needed.

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

An overview of oral insulin delivery strategies (OIDS).

Despite tremendous efforts, the world continues its fight against the common chronic disease-diabetes. Diabetes is caused by elevated glucose levels in the blood, which can lead to several complications like glaucoma, cataract, kidney failure, diabetic ketoacidosis, heart attack, and stroke. According to recent statistics, China, India, and the US rank at the top three positions with regards to the number of patients affected by diabetes. Ever since its discovery, insulin is one of the major therapeutic molecules that is used to control the disease in the diabetic population, worldwide. The most common route of insulin administration has been the subcutaneous route. However, the limitations associated with this route have motivated global efforts to explore alternative strategies to deliver insulin, including pulmonary, transdermal, nasal, rectal, buccal, and oral routes. Oral insulin delivery is the most convenient and patient-centered route. However, the oral route is also associated with numerous drawbacks that present significant challenges to the scientific fraternity. The human physiological system acts as a formidable barrier to insulin, limiting its bioavailability. The present review covers the major barriers against oral insulin delivery and explains formulation strategies that have been adopted to overcome these barriers. The review focuses on oral insulin delivery strategies (OIDS) for increasing the bioavailability of oral insulin, including nanoparticles, microparticles, nano-in-microparticles, hydrogels, tablets, capsules, intestinal patches, and use of ionic liquids. It also highlights some of the notable recent advancements and clinical trials in oral insulin delivery. This formulation based OIDS may significantly improve patient compliance in the treatment of diabetes.

Utility of next-generation sequencing in genetic testing and counseling of disorders involving the musculoskeletal system-trends observed from a single genetic unit.

BACKGROUND: Disorders involving the musculoskeletal system are often identified with short stature and a range of orthopedic problems. The clinical and genetic heterogeneity of these diseases along with several characteristic overlaps makes definitive diagnosis difficult for clinicians. Hence, using molecular testing in addition to conventional tests becomes essential for appropriate diagnosis and management. METHODS: Comprehensive clinical examination, detailed pretest and posttest counseling, molecular diagnosis with next-generation sequencing (NGS), genotype-phenotype correlation and Sanger sequencing for targeted variant analysis. RESULTS: This manuscript reports a molecular spectrum of variants in 34 orthopedic cases referred to a single genetic unit attached to a tertiary care hospital. The diagnostic yield of NGS-based tests coupled with genetic counseling and segregation analysis was 79% which included 7 novel variants. In about 53% (i.e. 18/34 cases), molecular testing outcome was actionable since 8 of the 18 underwent prenatal diagnosis, as they were either in their early gestation or had planned a pregnancy subsequent to molecular testing, while ten cases were premaritally/prenatally counseled for the families to take informed decisions as they were in the reproductive age. CONCLUSIONS: The report highlights the importance of NGS-based tests even in a low resource setting as it helps patients, families and healthcare providers in reducing the economic, social and emotional burden of these disorders.

Alteration of methylation status in archival DNA samples: A qualitative assessment by methylation specific polymerase chain reaction.

DNA methylation is an epigenetic mechanism that regulates gene expression, which also facilitates genomic imprinting. Genomic imprinting is responsible for differential expression of genes based on parent of origin. Altered methylation of parental alleles results in imprinting disorders diagnosed by methylation specific polymerase chain reaction (MS-PCR) technique. With increasing evidence of genes under epigenetic influence, methylation studies are extensively performed on archival samples. To evaluate effect of storage and storage conditions on DNA methylation, a systematic MS-PCR based analysis was planned on an imprinted gene, SNRPN, located on chromosome 15q11.2. It was assessed by MS-PCR on fresh, 4 -20, and -80°C stored DNA samples for different time periods for systematic evaluation of methylation status. Technical factors like type of sample processing, method of DNA isolation, primer region polymorphism, sample heterogeneity were also evaluated. DNA methylation was observed to be altered for SNRPN gene after storage at -80°C from 2 months onwards. Long-term storage of DNA at -80°C results in altered DNA methylation status. This may lead to false MS-PCR diagnosis of imprinting disorders. Our proof of concept study should be followed with quantitative validation since the findings have critical implications in the present era of biobanking.

Familial Acute Necrotizing Encephalopathy: Evidence From Next Generation Sequencing of Digenic Inheritance.

Infection-induced acute encephalopathies (IIAEs) are a group of neurologic disorders caused post infection. They are of 8 types, 6 of which are herpes specific, whereas IIAE3 and IIAE4 can be triggered by infections additional to herpeslike influenza, enterovirus, etc. IIAE3 is also known as acute necrotizing encephalopathy type 1, which is a rare type of encephalopathy that occurs following an infection in infancy or early childhood. Symptoms include fever, cough, congestion, vomiting, and diarrhea followed by seizures, hallucination, ataxia, and abnormal muscle tone, and sometimes it leads to untimely death. Here, we describe a familial case where 3 siblings were clinically diagnosed with acute necrotizing encephalopathy 1. Genetic testing revealed 2 heterozygous variations: RANBP2 c.5249C>G, p.P1750 R, and CPT2 c.365C>T, p.S122F. Variants in RANBP2 and CPT2 have been individually known to be associated with IIAE3 and IIAE4, respectively. Segregation analysis revealed that the RANBP2 variant was inherited from the father and the CPT2 variant from the mother. This case qualifies to be the first of its kind where digenic inheritance (ie, DNA sequence variants in 2 genes are required for the pathogenic phenotypes) appears to cause a lethal class of acute necrotizing encephalopathy.

Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations.

Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.

Infectivity and Progression of COVID-19 Based on Selected Host Candidate Gene Variants.

Introduction: Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has spread around the globe. Susceptibility has been associated with age, biological sex, and other prior existing health conditions. However, host genes are involved in viral infectivity and pathogenicity, and polymorphisms in these could be responsible for the interethnic/interindividual variability observed in infection and progression of COVID-19. Materials and Methods: Clinical exome data of 103 individuals was analyzed to identify sequence variants in five selected candidate genes: ACE2, TMPRSS2, CD209, IFITM3, and MUC5B to assess their prevalence and role to understand the COVID-19 infectivity and progression in our population. Results: A total of 497 polymorphisms were identified in the five selected genes in the exomes analyzed. Thirty-eight polymorphisms identified in our cohort have been reported earlier in literature and have functional significance or association with health conditions. These variants were classified into three groups: protective, susceptible, and responsible for comorbidities. Discussion and Conclusion: The two polymorphisms described in literature as risk inducing are rs35705950 in MUC5B gene and TMPRSS2 haplotype (rs463727, rs34624090, rs55964536, rs734056, rs4290734, rs34783969, rs11702475, rs35899679, and rs35041537) were absent in our cohort explaining the slower infectivity of the disease in this part of India. The 38 functional variants identified can be used as a predisposition panel for the COVID-19 infectivity and progression and stratify individuals as "high or low risk," which would help in planning appropriate surveillance and management protocols. A larger study from different regions of India is warranted to validate these results.