In Silico Predictions of Ecological Plasticity Mediated by Protein Family Expansions in Early-Diverging Fungi

Early-diverging fungi (EDF) are ubiquitous and versatile. Their diversity is reflected in their genome sizes and complexity. For instance, multiple protein families have been reported to expand or disappear either in particular genomes or even whole lineages. The most commonly mentioned are CAZymes (carbohydrate-active enzymes), peptidases and transporters that serve multiple biological roles connected to, e.g., metabolism and nutrients intake. In order to study the link between ecology and its genomic underpinnings in a more comprehensive manner, we carried out a systematic in silico survey of protein family expansions and losses among EDF with diverse lifestyles. We found that 86 protein families are represented differently according to EDF ecological features (assessed by median count differences). Among these there are 19 families of proteases, 43 CAZymes and 24 transporters. Some of these protein families have been recognized before as serine and metallopeptidases, cellulases and other nutrition-related enzymes. Other clearly pronounced differences refer to cell wall remodelling and glycosylation. We hypothesize that these protein families altogether define the preliminary fungal adaptasome. However, our findings need experimental validation. Many of the protein families have never been characterized in fungi and are discussed in the light of fungal ecology for the first time.

Premature Termination Codon in 5′ Region of Desmoplakin and Plakoglobin Genes May Escape Nonsense-Mediated Decay through the Reinitiation of Translation

Arrhythmogenic cardiomyopathy is a heritable heart disease associated with desmosomal mutations, especially premature termination codon (PTC) variants. It is known that PTC triggers the nonsense-mediated decay (NMD) mechanism. It is also accepted that PTC in the last exon escapes NMD; however, the mechanisms involving NMD escaping in 5′-PTC, such as reinitiation of translation, are less known. The main objective of the present study is to evaluate the likelihood that desmosomal genes carrying 5′-PTC will trigger reinitiation. HL1 cell lines were edited by CRISPR/Cas9 to generate isogenic clones carrying 5′-PTC for each of the five desmosomal genes. The genomic context of the ATG in-frame in the 5′ region of desmosomal genes was evaluated by in silico predictions. The expression levels of the edited genes were assessed by Western blot and real-time PCR. Our results indicate that the 5′-PTC in PKP2, DSG2 and DSC2 acts as a null allele with no expression, whereas in the DSP and JUP gene, N-truncated protein is expressed. In concordance with this, the genomic context of the 5′-region of DSP and JUP presents an ATG in-frame with an optimal context for the reinitiation of translation. Thus, 5′-PTC triggers NMD in the PKP2, DSG2* and DSC2 genes, whereas it may escape NMD through the reinitiation of the translation in DSP and JUP genes, with no major effects on ACM-related gene expression.

Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters

Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a1 and a2 positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification.

248 Empiric profiling of peripheral T cell recall responses to tumor mutanomes versus in silico predictions in NSCLC patients undergoing pembrolizumab treatment ± chemotherapy

BackgroundEffective immune checkpoint blockade (ICB) treatment is dependent on T-cell recognition of patient-specific mutations (neoantigens). Empirical identification of neoantigens ex vivo has revealed shortcomings of in silico predictions.1 To better understand the impact of ICB treatment on T cell responses and differences between in silico and in vitro methods, neoantigen-specific T cell responses were evaluated in patients with non-small cell lung cancer undergoing first-line therapy with pembrolizumab ± chemotherapy.MethodsTumor and whole blood samples were collected from 14 patients prior to and after immunotherapy; seven each in monotherapy and combination therapy cohorts. The ex vivo ATLAS™ platform was used to profile neoantigen-specific T-cell responses. Patient-specific tumor mutations identified by next-generation sequencing (NGS) were expressed individually as ATLAS clones, processed patient-specific autologous antigen presenting cells, and presented to their T cells in vitro. ATLAS-verified antigens were compared with epitope predictions made using algorithms.ResultsOn average, 150 (range 37–339) non-synonymous mutations were identified. Pre-treatment, ATLAS identified T cell responses to a median of 15% (9–25%) of mutations, with nearly equal proportions of neoantigens (8%, 5–15%) and Inhibigens™, targets of suppressive T cell responses (8%, 3–13%). The combination therapy cohort had more confirmed neoantigens (46, 20–103) than the monotherapy cohort (7, 6–79). After treatment, the median ratio of CD4:CD8 T cells doubled in the monotherapy but not combination cohort (1.2 to 2.4 v. 1.6 to 1.3). Upon non-specific stimulation, T cells from patients on combination therapy expanded poorly relative to monotherapy (24 v. 65-fold, p = 0.014); no significant differences were observed pre-treatment (22 v. 18-fold, p = 0.1578). Post-treatment, the median number of CD8 neoantigens increased in the combination therapy cohort (11 to 15) but in monotherapy were mostly unchanged (6 to 7). Across timepoints, 36% of ATLAS-identified responses overlapped. In silico analysis resulted in 1,895 predicted epitopes among 961 total mutations; among those, 30% were confirmed with ATLAS, although nearly half were Inhibigens, which could not be predicted. Moreover, 50% of confirmed neoantigens were missed by in silico prediction.ConclusionsMonotherapy and combination therapy had differential effects on CD4:CD8 T cell ratios and their non-specific expansion. A greater proportion of neoantigens was identified than previously reported in studies employing in silico predictions prior to empirical verification.2 Overlap between confirmed antigens and in silico prediction was observed, but in silico prediction continued to have a large false negative rate and could not characterize Inhibigens.AcknowledgementsWe would like to acknowledge and thank the patients and their families for participating in this study.ReferencesLam H, McNeil LK, Starobinets H, DeVault VL, Cohen RB, Twardowski P, Johnson ML, Gillison ML, Stein MN, Vaishampayan UN, DeCillis AP, Foti JJ, Vemulapalli V, Tjon E, Ferber K, DeOliveira DB, Broom W, Agnihotri P, Jaffee EM, Wong KK, Drake CG, Carroll PM, Davis TA, Flechtner JB. An empirical antigen selection method identifies neoantigens that either elicit broad antitumor T-cell responses or drive tumor growth. Cancer Discov 2021;11(3):696–713. doi: 10.1158/2159- 8290.CD-20-0377. Epub 2021 January 27. PMID: 33504579. Rosenberg SA. Immersion in the search for effective cancer immunotherapies. Mol Med 27,63(2021). https://doi.org/10.1186/s10020-021-00321-3

Combined in silico, ex vivo, and in vivo Assessment of L-17, a Thiadiazine Derivative With Putative Neuroprotective and Antidepressant-Like Effects

L-17 is a thiadiazine derivative with putative anti-inflammatory, neuroprotective, and antidepressant-like properties. In this study, we applied combined in silico, ex vivo, and in vivo electrophysiology techniques to reveal the potential mechanism of action of L-17. PASS 10.4 Professional Extended software suggested that L-17 might have pro-cognitive, antidepressant, and antipsychotic effects. Docking energy assessment with AutoDockVina predicted that the binding affinities of L-17 to the serotonin transporter (SERT) and serotonin receptors 3 and 1A (5-HT3 and 5-HT1A) receptors are compatible to the selective serotonin reuptake inhibitor (SSRI) fluoxetine and selective antagonists of 5-HT3 and 5-HT1A receptors, granisetron and WAY100135, respectively. Acute pre-treatment with L-17 robustly increased c-Fos immunoreactivity in the amygdala (central nucleus), suggesting increased neuronal excitability in this brain area after L-17 administration. Acute L-17 also dose-dependently inhibited of 5-HT neurons of the dorsal raphe nucleus (DRN). This inhibition was partially reversed by subsequent administration of WAY100135, suggesting the involvement of extracellular 5-HT. Based on in silico predictions, c-Fos immunohistochemistry, and in vivo electrophysiology, we suggest that L-17 is a potent 5-HT reuptake inhibitor and/or partial 5-HT1A receptor antagonist. Thus, L-17 might be a representative of a new class of antidepressant drugs. Since L-17 also possesses neuro- and cardio-protective properties, it can be useful in post-stroke and post-myocardial infarction (MI) depression. In general, combined in silico predictions and ex vivo neurochemical and in vivo electrophysiological assessment might be a useful strategy for early preclinical assessment of the affectivity and neural mechanism in action of the novel CNS drugs.