Evaluating eukaryotic secreted protein prediction

BACKGROUND: Improvements in protein sequence annotation and an increase in the number of annotated protein databases has fueled development of an increasing number of software tools to predict secreted proteins. Six software programs capable of high throughput and employing a wide range of prediction methods, SignalP 3.0, SignalP 2.0, TargetP 1.01, PrediSi, Phobius, and ProtComp 6.0, are evaluated. RESULTS: Prediction accuracies were evaluated using 372 unbiased, eukaryotic, SwissProt protein sequences. TargetP, SignalP 3.0 maximum S-score and SignalP 3.0 D-score were the most accurate single scores (90–91% accurate). The combination of a positive TargetP prediction, SignalP 2.0 maximum Y-score, and SignalP 3.0 maximum S-score increased accuracy by six percent. CONCLUSION: Single predictive scores could be highly accurate, but almost all accuracies were slightly less than those reported by program authors. Predictive accuracy could be substantially improved by combining scores from multiple methods into a single composite prediction

Department of Health Sciences Research, Mayo Clinic

ABSTRACT Pathogenic variants in EBF3 were recently described in three back-to-back publications in association with a novel neurodevelopmental disorder characterized by intellectual disability, speech delay, ataxia, and facial dysmorphisms. In this report we describe an additional patient carrying a de novo show that our patient presents with phenotypes consistent with previously reported patients harboring EBF3 variants and expands the phenotypic spectrum of this newly identified disorder with the additional feature of a bicornuate uterus

Varenicline for smoking cessation: efficacy, safety, and treatment recommendations

Smoking is the leading preventable cause of morbidity and mortality in the US, and decreasing smoking prevalence is a public health priority. Patients achieve the greatest success when quit attempts involve behavioral therapy combined with pharmacotherapy. Varenicline is the most recent addition to the pharmacotherapeutic armamentarium for the treatment of tobacco dependence. Varenicline is efficacious and cost-effective. Smoking relapse and adverse treatment-related side effects may decrease medication adherence and patient satisfaction with varenicline. In the clinical setting, varenicline treatment can be optimized by reducing doses in patients who experience intolerable side effects, increasing the dose in partial responders, and providing long-term maintenance therapy for relapse prevention

AMOD: a morpholino oligonucleotide selection tool

AMOD is a web-based program that aids in the functional evaluation of nucleotide sequences through sequence characterization and antisense morpholino oligonucleotide (target site) selection. Submitted sequences are analyzed by translation initiation site prediction algorithms and sequence-to-sequence comparisons; results are used to characterize sequence features required for morpholino design. Within a defined subsequence, base composition and homodimerization values are computed for all putative morpholino oligonucleotides. Using these properties, morpholino candidates are selected and compared with genomic and transcriptome databases with the goal to identify target-specific enriched morpholinos. AMOD has been used at the University of Minnesota to design ∼200 morpholinos for a functional genomics screen in zebrafish. The AMOD web server and a tutorial are freely available to both academic and commercial users at

Assessing Human Genetic Variations in Glucose Transporter SLC2A10 and Their Role in Altering Structural and Functional Properties

Purpose: Demand is increasing for clinical genomic sequencing to provide diagnoses for patients presenting phenotypes indicative of genetic diseases, but for whom routine genetic testing failed to yield a diagnosis. DNA-based testing using high-throughput technologies often identifies variants with insufficient evidence to determine whether they are disease-causal or benign, leading to categorization as variants of uncertain significance (VUS).Methods: We used molecular modeling and simulation to generate specific hypotheses for the molecular effects of variants in the human glucose transporter, GLUT10 (SLC2A10). Similar to many disease-relevant membrane proteins, no experimentally derived 3D structure exists. An atomic model was generated and used to evaluate multiple variants, including pathogenic, benign, and VUS.Results: These analyses yielded detailed mechanistic data, not currently predictable from sequence, including altered protein stability, charge distribution of ligand binding surfaces, and shifts toward or away from transport-competent conformations. Consideration of the two major conformations of GLUT10 was important as variants have conformation-specific effects. We generated detailed molecular hypotheses for the functional impact of variants in GLUT10 and propose means to determine their pathogenicity.Conclusion: The type of workflow we present here is valuable for increasing the throughput and resolution with which VUS effects can be assessed and interpreted

Antiangiogenic Effects and Therapeutic Targets of Azadirachta indica Leaf Extract in Endothelial Cells

Azadirachta indica (common name: neem) leaves have been found to possess immunomodulatory, anti-inflammatory and anti-carcinogenic properties. The present study evaluates anti-angiogenic potential of ethanol extract of neem leaves (EENL) in human umbilical vein endothelial cells (HUVECs). Treatment of HUVECs with EENL inhibited VEGF induced angiogenic response in vitro and in vivo. The in vitro proliferation, invasion and migration of HUVECs were suppressed with EENL. Nuclear fragmentation and abnormally small mitochondria with dilated cristae were observed in EENL treated HUVECs by transmission electron microscopy. Genome-wide mRNA expression profiling after treatment with EENL revealed differentially regulated genes. Expression changes of the genes were validated by quantitative real-time polymerase chain reaction. Additionally, increase in the expression of HMOX1, ATF3 and EGR1 proteins were determined by immunoblotting. Analysis of the compounds in the EENL by mass spectrometry suggests the presence of nimbolide, 2′,3′-dehydrosalannol, 6-desacetyl nimbinene and nimolinone. We further confirmed antiproliferative activity of nimbolide and 2′,3′-dehydrosalannol in HUVECs. Our results suggest that EENL by regulating the genes involved in cellular development and cell death functions could control cell proliferation, attenuate the stimulatory effects of VEGF and exert antiangiogenic effects. EENL treatment could have a potential therapeutic role during cancer progression

Deep Phenotyping of Non-Alcoholic Fatty Liver Disease Patients with Genetic Factors for Insights into the Complex Disease

Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disorder characterized by the excessive accumulation of fat in the liver in individuals who do not consume significant amounts of alcohol, including risk factors like obesity, insulin resistance, type 2 diabetes, etc. We aim to identify subgroups of NAFLD patients based on demographic, clinical, and genetic characteristics for precision medicine. The genomic and phenotypic data (3,408 cases and 4,739 controls) for this study were gathered from participants in Mayo Clinic Tapestry Study (IRB#19-000001) and their electric health records, including their demographic, clinical, and comorbidity data, and the genotype information through whole exome sequencing performed at Helix using the Exome+$^\circledR$ Assay according to standard procedure (www$.$helix$.$com). Factors highly relevant to NAFLD were determined by the chi-square test and stepwise backward-forward regression model. Latent class analysis (LCA) was performed on NAFLD cases using significant indicator variables to identify subgroups. The optimal clustering revealed 5 latent subgroups from 2,013 NAFLD patients (mean age 60.6 years and 62.1% women), while a polygenic risk score based on 6 single-nucleotide polymorphism (SNP) variants and disease outcomes were used to analyze the subgroups. The groups are characterized by metabolic syndrome, obesity, different comorbidities, psychoneurological factors, and genetic factors. Odds ratios were utilized to compare the risk of complex diseases, such as fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), as well as liver failure between the clusters. Cluster 2 has a significantly higher complex disease outcome compared to other clusters. Keywords: Fatty liver disease; Polygenic risk score; Precision medicine; Deep phenotyping; NAFLD comorbidities; Latent class analysis.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 11 page

3' tag digital gene expression profiling of human brain and universal reference RNA using Illumina Genome Analyzer

<p>Abstract</p> <p>Background</p> <p>Massive parallel sequencing has the potential to replace microarrays as the method for transcriptome profiling. Currently there are two protocols: full-length RNA sequencing (RNA-SEQ) and 3'-tag digital gene expression (DGE). In this preliminary effort, we evaluated the 3' DGE approach using two reference RNA samples from the MicroArray Quality Control Consortium (MAQC).</p> <p>Results</p> <p>Using Brain RNA sample from multiple runs, we demonstrated that the transcript profiles from 3' DGE were highly reproducible between technical and biological replicates from libraries constructed by the same lab and even by different labs, and between two generations of Illumina's Genome Analyzers. Approximately 65% of all sequence reads mapped to mitochondrial genes, ribosomal RNAs, and canonical transcripts. The expression profiles of brain RNA and universal human reference RNA were compared which demonstrated that DGE was also highly quantitative with excellent correlation of differential expression with quantitative real-time PCR. Furthermore, one lane of 3' DGE sequencing, using the current sequencing chemistry and image processing software, had wider dynamic range for transcriptome profiling and was able to detect lower expressed genes which are normally below the detection threshold of microarrays.</p> <p>Conclusion</p> <p>3' tag DGE profiling with massive parallel sequencing achieved high sensitivity and reproducibility for transcriptome profiling. Although it lacks the ability of detecting alternative splicing events compared to RNA-SEQ, it is much more affordable and clearly out-performed microarrays (Affymetrix) in detecting lower abundant transcripts.</p

Novel pathogenic variant in TGFBR2 confirmed by molecular modeling is a rare cause of Loeys-Dietz syndrome

Loeys-Dietz syndrome (LDS) is a connective tissue disorder characterized by vascular findings of aneurysm and/or dissection of cerebral, thoracic, or abdominal arteries and skeletal findings. We report a case of a novel pathogenic variant in TGFBR2 and phenotype consistent with classic LDS. The proband was a 10-year-old presenting to the genetics clinic with an enlarged aortic root (Z-scores 5-6), pectus excavatum, and congenital contractures of the right 2nd and 3rd digit. Molecular testing of TGFBR2 was sent to a commercial laboratory and demonstrated a novel, likely pathogenic, variant in exon 4, c.1061T&gt;C, p.(L354P). Molecular modeling reveals alteration of local protein structure as a result of this pathogenic variant. This pathogenic variant has not been previously reported in LDS and thus expands the pathogenic variant spectrum of this condition

Adrenomedullin is up-regulated in patients with pancreatic cancer and causes insulin resistance in β cells and mice

New-onset diabetes in patients with pancreatic cancer is likely to be a paraneoplastic phenomenon caused by tumor-secreted products. We aimed to identify the diabetogenic secretory product(s) of pancreatic cancer. Methods: Using microarray analysis, we identified adrenomedullin as a potential mediator of diabetes in patients with pancreatic cancer. Adrenomedullin was up-regulated in pancreatic cancer cell lines, in which supernatants reduced insulin signaling in beta cell lines. We performed quantitative reverse-transcriptase polymerase chain reaction and immunohistochemistry on human pancreatic cancer and healthy pancreatic tissues (controls) to determine expression of adrenomedullin messenger RNA and protein, respectively. We studied the effects of adrenomedullin on insulin secretion by beta cell lines and whole islets from mice and on glucose tolerance in pancreatic xenografts in mice. We measured plasma levels of adrenomedullin in patients with pancreatic cancer, patients with type 2 diabetes mellitus, and individuals with normal fasting glucose levels (controls). Results: Levels of adrenomedullin messenger RNA and protein were increased in human pancreatic cancer samples compared with controls. Adrenomedullin and conditioned media from pancreatic cell lines inhibited glucose-stimulated insulin secretion from beta cell lines and islets isolated from mice; the effects of conditioned media from pancreatic cancer cells were reduced by small hairpin RNA-mediated knockdown of adrenomedullin. Conversely, overexpression of adrenomedullin in mice with pancreatic cancer led to glucose intolerance. Mean plasma levels of adrenomedullin (femtomoles per liter) were higher in patients with pancreatic cancer compared with patients with diabetes or controls. Levels of adrenomedullin were higher in patients with pancreatic cancer who developed diabetes compared those who did not. Conclusions: Adrenomedullin is up-regulated in patients with pancreatic cancer and causes insulin resistance in β cells and mice.Fil: Aggarwal, Gaurav. Mayo Clinic College of Medicine; Estados UnidosFil: Ramachandran, Vijaya. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Javeed, Naureen. Mayo Clinic College of Medicine; Estados UnidosFil: Arumugam, Thiruvengadam. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Dutta, Shamit. Mayo Clinic College of Medicine; Estados UnidosFil: Klee, George G.. Mayo Clinic College of Medicine; Estados UnidosFil: Klee, Eric W.. Mayo Clinic College of Medicine; Estados UnidosFil: Smyrk, Thomas C.. Mayo Clinic College of Medicine; Estados UnidosFil: Bamlet, William. Mayo Clinic College of Medicine; Estados UnidosFil: Han, Jing Jing. Mayo Clinic College of Medicine; Estados UnidosFil: Rumie Vittar, Natalia Belen. Mayo Clinic College of Medicine; Estados Unidos. Universidad Nacional de Río Cuarto. Facultad de Ciencias Exactas, Fisicoquímicas y Naturales. Departamento de Biología Molecular. Sección Química Biológica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: De Andrade, Mariza. Mayo Clinic College of Medicine; Estados UnidosFil: Mukhopadhyay, Debabrata. Mayo Clinic College of Medicine; Estados UnidosFil: Petersen, Gloria M.. Mayo Clinic College of Medicine; Estados UnidosFil: Fernandez Zapico, Martin Ernesto. Mayo Clinic College of Medicine; Estados UnidosFil: Logsdon, Craig D.. University of Texas Health Science Center at Houston. University of Texas Md Anderson Cancer Center; Estados UnidosFil: Chari, Suresh T.. Mayo Clinic College of Medicine; Estados Unido