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Colorado potato beetle (CPB, Leptinotarsa decemlineata ) is a major pest of potato and other solanaceous vegetables in the Northern Hemisphere. The insect feeds on leaves and can completely defoliate crops. Because of the repeated use of single insecticide classes without rotating active ingredients, many chemicals are no longer effective in controlling CPB. Ledprona is a sprayable double-stranded RNA biopesticide with a new mode of action that triggers the RNA interference pathway. Laboratory assays with second instar larvae fed Ledprona showed a dose–response where 25×10 −6 g/L of dsPSMB5 caused 90% mortality after 6days of initial exposure. We also showed that exposure to Ledprona for 6h caused larval mortality and decreased target messenger RNA (mRNA) expression. Decrease in PSMB5 protein levels was observed after 48h of larval exposure to Ledprona. Both PSMB5 mRNA and protein levels did not recover over time. Ledprona efficacy was demonstrated in a whole plant greenhouse trial and performed similarly to spinosad. Ledprona, currently pending registration at EPA, represents a new biopesticide class integrated pest management and insecticide resistance management programs directed against CPB.

Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.

Abstract High-coverage sequencing allows the study of variants occurring at low frequencies within samples, but is susceptible to false-positives caused by sequencing error. Ion Torrent has a very low single nucleotide variant (SNV) error rate and has been employed for the majority of human papillomavirus (HPV) whole genome sequences. However, benchmarking of intrahost SNVs (iSNVs) has been challenging, partly due to limitations imposed by the HPV life cycle. We address this problem by deep sequencing three replicates for each of 31 samples of HPV type 18 (HPV18). Errors, defined as iSNVs observed in only one of three replicates, are dominated by C→T (G→A) changes, independently of trinucleotide context. True iSNVs, defined as those observed in all three replicates, instead show a more diverse SNV type distribution, with particularly elevated C→T rates in CCG context (CCG→CTG; CGG→CAG) and C→A rates in ACG context (ACG→AAG; CGT→CTT). Characterization of true iSNVs allowed us to develop two methods for detecting true variants: (1) VCFgenie, a dynamic binomial filtering tool which uses each variant's allele count and coverage instead of fixed frequency cut-offs; and (2) a machine learning binary classifier which trains eXtreme Gradient Boosting models on variant features such as quality and trinucleotide context. Each approach outperforms fixed-cut-off filtering of iSNVs, and performance is enhanced when both are used together. Our results provide improved methods for identifying true iSNVs in within-host applications across sequencing platforms, specifically using HPV18 as a case study.

Limited understanding of the biology predisposing certain human papillomavirus-related (HPV+) oropharyngeal squamous cell carcinomas (OPSCCs) to relapse impedes therapeutic personalization. We aimed to identify molecular traits that distinguish recurrence-prone tumors. 50 HPV+ OPSCCs that later recurred (cases) and 50 non-recurrent controls matched for stage, therapy, and smoking history were RNA-sequenced. Groups were compared by gene set enrichment analysis, and select differences were validated by immunohistochemistry. Features discriminating groups were scored in each tumor using gene set variation analysis, and scores were evaluated for recurrence prediction ability. Cases downregulated pathways linked to anti-tumor immunity (FDR-adjusted p<.05) and contained fewer tumor-infiltrating lymphocytes (p<.001), including cytotoxic T-cells (p=.005). Cases also upregulated pathways related to cell division and other aspects of tumor progression. Upregulated and downregulated pathways were respectively used to define a tumor progression score (TPS) and immune suppression score (ISS) for each tumor. Correlation between TPS and ISS (r=.603, p<.001) was potentially explained by observed upregulation of DNA repair pathways in cases, which might enhance their progression directly and by limiting cytosolic DNA-induced inflammation. Accordingly, cases contained fewer double-strand breaks based on staining for phospho-RPA32 (p=.006) and γ-H2AX (p=.005) and downregulated pro-inflammatory components of the cytoplasmic DNA sensing pathway. A combined score derived from TPS and ISS optimized recurrence prediction and stratified survival in a manner generalizable to three external cohorts. We provide novel evidence that limiting genomic instability makes tumor-intrinsic and immune-mediated contributions to HPV+ OPSCC recurrence risk, opening opportunities to detect and target this treatment-resistant biology.

Understanding the effects of missense mutations on protein stability is a widely acknowledged significant biological problem. Genomic missense mutations may alter one or more amino acids leading to increased or decreased stability of the encoded proteins. In this study, we propose a novel approach - Protein Stability Prediction with a Gaussian Network Model (PSP-GNM) to study the effect of single amino acid substitutions on protein stability. Specifically, PSP-GNM employs a coarse-grained Gaussian Network Model (GNM) that has interactions between amino acids weighted by the Miyazawa-Jernigan (MJ) statistical potential. We use PSP-GNM to simulate partial unfolding of the wildtype and mutant structures and then, use the difference in energies of the unfolded wildtype and mutant protein structures to estimate the experimentally obtained unfolding free energy change (ΔΔG). We verify the extent of correspondence between the ΔΔG calculated by PSP-GNM and the ΔΔG obtained experimentally using three datasets: 350 forward mutations from 66 proteins, 2298 forward mutations from 126 proteins and 611 forward and reverse mutations from 66 proteins and observe Pearson correlation coefficient (PCC) as high as 0.58 and root mean-squared error (RMSE) as low as 1.24 kcal/mol. The performance is comparable to the existing state of the art methods. Importantly, we do observe an increase in the correlation to 0.73 and decrease in RMSE to 1.07 when considering only those measurements made close to 25°C and neutral pH, suggesting a strong dependence on temperature and pH. PSP-GNM is written in Python and is available as a free downloadable package at https://github.com/sambitmishra0628/PSP-GNM . Competing Interest Statement The authors have declared no competing interest. Footnotes * The previous version was missing the figure captions and the figures were of poor quality in the uploaded pdf. The revised version includes figure captions and high resolution figures added to the main text of the manuscript.

To better understand cervical cancer progression, we analyzed RNA from 262 biopsies from women referred for colposcopy We determined HPV type and analyzed the expression of 51 genes. HPV31 was significantly more prevalent in precancer than stage 1 cancer and invasive cancer (p < 0.0001) and HPV16 increased in invasive disease (p < 0.0001). CCNE1, MELTF, and ULBP2 were significantly increased in HPV16-positive compared to HPV31 precancers while NECTIN2 and HLA-E expression decreased. Markers of the innate immune system, DNA repair genes, and cell cycle genes are significantly increased during cancer progression (p = 0.0001). In contrast, the TP53 and RB1 tumor suppressor gene expression is significantly decreased in cancer cells. TheT cell markers CD28 and FLT3LG expression decreased in cancer while FOXP3, IDO1, and ULBP2 expression increased. There is a significantly higher survival rate in individuals with increased expression of CD28 (p = 0.0005), FOXP3 (p = 0.0002), IDO1 (p = 0.038), FLT3LG (p = 0.026), APOBEC3B (p = 0.0011), and RUNX3 (p = 0.019), and a significantly lower survival rate in individuals with increased expression of ULBP2 (p = 0.035). These results will help us understand the molecular factors influencing the progression of cervical precancer to cancer.

Protein-protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo- or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the protein docking challenge of distinguishing a natively bound complex from non-native forms. Current protein docking approaches address this problem by sampling multiple binding modes in proteins and scoring each of these modes, with the lowest-energy (or highest scoring) binding mode being regarded as a near-native complex. However, high-scoring modes often match poorly with the true bound form, suggesting a need for improvement of the scoring function. In this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein-protein interfaces. For a benchmark set of 53 bound complexes, KFC-E identifies a near-native binding mode as the top-scoring pose in 38% and in the top 5 in 55% of the complexes. For a set of 17 unbound complexes, KFC-E identifies a near-native pose in the top 10 ranked poses in more than 50% of the cases. The performance is considerably reduced upon excluding the information on hotspots and their coevolution. A scoring function that incorporates information on non-hotspots and their coevolution identifies near-native structures in fewer cases (top 1 in 8% and top 5 in 30% of the complexes) than one that incorporates information on hotspot coevolution (top 1 in 36% and top 5 in 47% of the complexes). Our study highlights the importance of incorporating the knowledge of putative hotspot residues along with their evolutionary and coevolutionary information in identifying natively bound complexes and suggests a novel approach for addressing the protein-protein docking problem.

Bladder cancer (BC) is characterized by significant histopathologic and molecular heterogeneity. The discovery of molecular pathways and knowledge of cellular mechanisms have grown exponentially and may allow for better disease classification, prognostication, and development of novel and more efficacious noninvasive detection and surveillance strategies, as well as selection of therapeutic targets, which can be used in BC, particularly in a neoadjuvant or adjuvant setting. This article outlines recent advances in the molecular pathology of BC with a better understanding and deeper focus on the development and deployment of promising biomarkers and therapeutic avenues that may soon make a transition into the domain of precision medicine and clinical management for patients with BC.

Low-grade oncocytic tumor (LOT) has been recently proposed as a unique renal tumor. However, we have encountered tumors with more oncocytoma-like morphology that show diffuse keratin 7 reactivity, which we sought to characterize molecularly. Eighteen tumors with a diffuse keratin 7 positive and KIT negative pattern were identified from 184 with predominantly oncocytoma-like histology. These tumors were subjected to detailed immunohistochemical evaluation and 14 were evaluated using the Illumina ® HiSeq 4000 platform for 324 cancer-associated genes. Patients’ ages ranged from 39 to 80 (median = 59.5 years) with a male to female ratio of 1.25:1. Morphology was predominantly oncocytoma-like with discrete nests, compared to the solid and edematous patterns described in LOT. Other than positive keratin 7 and negative KIT, the tumor cells were positive for PAX8, E-cadherin, AE1/AE3, Ber-EP4, AMACR, CD10, and MOC31, and were negative for other studied markers. FH and INI1 were normal. Eleven of 14 harbored genomic abnormalities, likely sporadic, primarily involving the MTOR pathway (73%). Overall, the alterations included MTOR activating mutation ( n  = 1), TSC1 inactivating mutation ( n  = 1), TSC2 mutation (p.X534 splice site, n  = 1), STK11 (a negative regulator of the MTOR pathway) mutation ( n  = 1), both STK11 and TSC1 mutations ( n  = 1), biallelic loss of PTEN and TSC1 deletion ( n  = 1), and MET amplification and TSC1 inactivating mutation ( n  = 1). Amplification of FGFR3 was identified in one additional tumor. Other alterations included FOXP1 loss ( n  = 1), NF2 E427 homozygous loss ( n  = 1), and PI3KCA activating mutation ( n  = 1). At a median follow-up of 68 months (2–147 months) for 15 patients, all were alive without disease. Oncocytic renal tumors with diffuse keratin 7 labeling show frequent alterations in the TSC/MTOR pathway, despite more oncocytoma-like morphology than initially described in LOT, likely expanding the morphologic spectrum of the latter.

Abstract Objectives Penile squamous cell carcinomas (PCs) are rare malignancies with a dismal prognosis in a metastatic setting; therefore, novel immunotherapeutic modalities are an unmet need. One such modality is the immune checkpoint molecule programmed cell death ligand 1 (PD-L1). We sought to analyze PD-L1 expression and its correlation with various clinicopathologic parameters in a contemporary cohort of 134 patients with PC. Methods A cohort of 134 patients with PC was studied for PD-L1 immunohistochemistry. The PD-L1 expression was evaluated using a combined proportion score with a cutoff of 1 or higher to define positivity. The results were correlated with various clinicopathologic parameters. Results Overall, 77 (57%) patients had positive PD-L1 expression. Significantly high PD-L1 expression was observed in high-grade tumors (P = .006). We found that 37% of human papillomavirus (HPV)–associated subtypes and 73% of other histotype tumors expressed PD-L1, while 63% of HPV-associated tumors and 27% of other histotype tumors did not (odds ratio, 1.35; P = .002 when compared for HPV-associated groups vs all others). Similarly, PD-L1–positive tumors had a 3.61-times higher chance of being node positive than PD-L1–negative tumors (P = .0009). In addition, PD-L1 high–positive tumors had a 5-times higher chance of being p16ink4a negative than PD-L1 low–positive tumors (P = .004). The PD-L1–positive tumors had a lower overall survival and cancer-specific survival than PD-L1–negative tumors. Conclusions Overall, PD-L1 expression is associated with high-grade and metastatic tumors. Lower PD-L1 expression is observed more frequently in HPV-associated (warty or basaloid) subtypes than in other, predominantly HPV-independent types. As a result, PD-L1 positivity, including higher expression, portends lower overall and cancer-specific survival. These data provide a rational for further investigating PD-L1–based immunotherapeutics in PC.