A Saturation Mutagenesis Approach to Understanding PTEN Lipid Phosphatase Activity and Genotype-Phenotype Relationships

Pleiotropic tumor suppressive functions of PTEN missense mutations during gliomagenesis

PTEN plays a crucial role in preventing the development of glioblastoma (GBM), a severe and untreatable brain cancer. In GBM, most PTEN deficiencies are missense mutations that have not been thoroughly examined. Here, we leveraged genetically modified mice and isogenic astrocyte cell cultures to investigate the role of clinically relevant mutations (G36E, L42R, C105F, and R173H) in the development of EGFR-driven GBM. We report that the loss of tumor suppression from these mutants is unrelated to their lipid phosphatase activity and rather relate to elevated localization at the cell membrane. Moreover, expression of these PTEN mutations heightened EGFR activity by sequestering EGFR within endomembranes longer and affected its signaling behavior. Through comprehensive studies on global protein phosphorylation and kinase library analyses in cells with the G36E and L42R PTEN mutations, we identified distinct cancer-promoting pathways activated by EGFR, offering targets for treating GBM with these PTEN alterations.

Systematic characterization of indel variants using a yeast-based protein folding sensor

Gene variants resulting in insertions or deletions of amino acid residues (indels) have important consequences for evolution and are often linked to disease, yet, compared to missense variants, the effects of indels are poorly understood and predicted. We developed a sensitive protein folding sensor based on the complementation of uracil auxotrophy in yeast by circular permutated orotate phosphoribosyltransferase (CPOP). The sensor reports on the folding of disease-linked missense variants and de-novo-designed proteins. Applying the folding sensor to a saturated library of single-residue indels in human dihydrofolate reductase (DHFR) revealed that most regions that tolerate indels are confined to internal loops, the termini, and a central α helix. Several indels are temperature sensitive, and folding is rescued upon binding to methotrexate. Rosetta and AlphaFold2 predictions correlate with the observed effects, suggesting that most indels destabilize the native fold and that these computational tools are useful for the classification of indels observed in population sequencing.

Cancer and disease profiles for PTEN pathogenic variants in Japanese population

A germline alteration in the PTEN gene causes a spectrum of disorders conceptualized as PTEN hamartoma tumor syndrome (PHTS), which show high risk of tumor development and a highly variable and complex phenotype. The diagnosis of PHTS is established in a proband by identification of a heterozygous germline PTEN pathogenic variant on molecular genetic testing. In this study, to understand more PTEN-associated clinical phenotype and PHTS in a Japanese population, we extracted 128 germline PTEN rare variants from 113,535 adult Japanese registered in Biobank Japan (BBJ), and categorized 29 pathogenic/likely pathogenic variants in 30 individuals (0.0264%) with ClinVar classifications and ACMG/AMP guideline for PTEN. We examined case-control association in 75,238 patients with various types of cancer and 38,297 non-cancer controls, and identified that PTEN pathogenic variants (PVs) were significantly associated with endometrial cancer (OR = 35.7, P = 9.73E-04) and marginally associated with female breast cancer (OR = 19.5, P = 3.92E-03), especially at young onset and with multiple cancers. We observed that among the 127 disease phenotypes the PTEN PV carriers had uterine fibroid, goiter, ovarian cyst, and epilepsy, which is consistent with PTEN-related phenotypes. We also found that weight/height were significantly higher in adult female carriers with PTEN PV (P = 3.1E-04 and P = 0.0014, respectively), which is consistent with overgrowth syndrome of PHTS. Our results indicate the phenotypical features associated with PTEN PVs in a Japanese population, especially female, and can contribute to the screening for PTEN variants and its associated several phenotypes.

Using multiplexed functional data to reduce variant classification inequities in underrepresented populations

BACKGROUND

Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS).

METHODS

We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN.

RESULTS

Using two orthogonal statistical approaches, we show a higher prevalence (p ≤ 5.95e - 06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation (p ≤ 2.5e - 05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were increased in individuals of European-like genetic ancestry (p ≤ 2.5e - 05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry (p = 9.1e - 03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency (p = 7.47e - 06) and computational predictor (p = 6.92e - 05) evidence codes for individuals of non-European-like genetic ancestry.

CONCLUSIONS

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

ESM-scan-A tool to guide amino acid substitutions

Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM-zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp.

Classification of PTEN germline non-truncating variants: a new approach to interpretation

BACKGROUND

PTEN hamartoma tumour syndrome (PHTS) encompasses distinct syndromes, including Cowden syndrome resulting from PTEN pathogenic variants. Missense variants account for 30% of PHTS cases, but their classification remains challenging. To address these difficulties, guidelines were published by the Clinical Genome Resource PTEN Variant Curation Expert Panel.

METHODS

Between 2010 and 2020, the Bergonie Institute reference laboratory identified 76 different non-truncating PTEN variants in 166 patients, 17 of which have not previously been reported. Variants were initially classified following the current guidelines. Subsequently, a new classification method was developed based on four main criteria: functional exploration, phenotypic features and familial segregation, in silico modelling, and allelic frequency.

RESULTS

This new method of classification is more discriminative and reclassifies 25 variants, including 8 variants of unknown significance.

CONCLUSION

This report proposes a revision of the current PTEN variant classification criteria which at present rely on functional tests evaluating only the phosphatase activity of PTEN and apply a particularly stringent clinical PHTS score.The classification of non-truncating variants of PTEN is facilitated by taking into consideration protein stability for variants with intact phosphatase activity, clinical and segregation criteria adapted to the phenotypic variability of PHTS and by specifying the allelic frequency of variants in the general population. This novel method of classification remains to be validated in a prospective cohort.

Impact of genomic background and developmental state on signaling pathways and response to therapy in glioblastoma patient-derived cells

Glioblastoma (GBM) tumors represents diverse genomic epigenomic, and transcriptional landscapes, with significant intratumoral heterogeneity that challenges standard of care treatments involving radiation (RT) and the DNA-alkylating agent temozolomide (TMZ). In this study, we employed targeted proteomics to assess the response of a genomically-diverse panel of GBM patient-derived cancer stem cells (CSCs) to astrocytic differentiation, growth factor withdrawal and traditional high fetal bovine serum culture. Our findings revealed a complex crosstalk and co-activation of key oncogenic signaling in CSCs and diverse patterns of response to these external stimuli. Using RNA sequencing and DNA methylation, we observed common adaptations in response to astrocytic differentiation of CSCs across genomically distinct models, including BMP-Smad pathway activation, reduced cholesterol biosynthesis, and upregulation of extracellular matrix components. Notably, we observed that these differentiated CSC progenies retained a subset of stemness genes and the activation of cell survival pathways. We also examined the impact of differentiation state and genomic background on GBM cell sensitivity and transcriptional response to TMZ and RT. Differentiation of CSCs increased resistance to TMZ but not to RT. While transcriptional responses to these treatments were predominantly regulated by p53 in wild-type p53 GBM cells, its transcriptional activity was modulated by the differentiation status and treatment modality. Both mutant and wild-type p53 models exhibited significant activation of a DNA-damage associated interferon response in CSCs and differentiated cells, suggesting this pathway may play a wider role in GBM response to TMZ and RT. Our integrative analysis of the impact of GBM cell developmental states, in the context of genomic and molecular diversity of patient-derived models, provides valuable insights for pre-clinical studies aimed at optimizing treatment strategies.

PX-478 Alleviated the Autism Spectrum Disorder Progression of Offspring Rats Induced by Prenatal Hypoxia

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction, communication, repetitive behaviors, and narrow interests. This study aimed to investigate the impact of the Hypoxia-inducible factor-1 alpha (HIF-1α) inhibitor (PX-478) on ASD-like behaviors in rat offspring exposed to prenatal hypoxia (PH).

METHODS

Pregnant rats were randomly assigned to control or PH groups, with the latter experiencing six hours of hypoxia on the 17th day of gestation. Offspring were further treated with PX-478 treatment initiated at one week (+1 w) or three weeks (+3 w) after birth. Hippocampal histology was assessed using hematoxylin and eosin (HE) staining, while protein levels of HIF-1α and phosphatase and tensin homolog (PTEN) were analyzed via western blotting. The concentration of vascular endothelial growth factor (VEGF) was measured using an Enzyme-Linked Immunosorbent Assay (ELISA) kit.

RESULTS

PX-478 treatment significantly improved spatial memory, learning, and social ability, while reducing anxiety-like behavior in PH-exposed offspring rats. HE staining revealed that PX-478 treatment decreased the number of hippocampal neurons necrosis in offspring. However, PX-478 treatment at one week post-birth led to decreased body weight and elevated levels of alkaline phosphatase (ALP) and Alanine aminotransferase (ALT) in offspring rats, whereas no significant effect was observed after three weeks of treatment. Additionally, PX-478 treatment resulted in reduced HIF-1α protein levels in the hippocampus and VEGF concentration in the serum of PH-exposed offspring rats, along with elevated PTEN protein levels.

CONCLUSIONS

The findings suggest that PX-478 treatment attenuated autism-like behavior in offspring. HIF-1α might play an important role in autism-like behavior induced by prenatal hypoxia, which may be realized by inhibiting PTEN activity.

Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect

BACKGROUND

A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.

RESULTS

BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.

CONCLUSIONS

An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants.

Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis

Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.

Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification

As the adoption and scope of genetic testing continue to expand, interpreting the clinical significance of DNA sequence variants at scale remains a formidable challenge, with a high proportion classified as variants of uncertain significance (VUSs). Genetic testing laboratories have historically relied, in part, on functional data from academic literature to support variant classification. High-throughput functional assays or multiplex assays of variant effect (MAVEs), designed to assess the effects of DNA variants on protein stability and function, represent an important and increasingly available source of evidence for variant classification, but their potential is just beginning to be realized in clinical lab settings. Here, we describe a framework for generating, validating and incorporating data from MAVEs into a semi-quantitative variant classification method applied to clinical genetic testing. Using single-cell gene expression measurements, cellular evidence models were built to assess the effects of DNA variation in 44 genes of clinical interest. This framework was also applied to models for an additional 22 genes with previously published MAVE datasets. In total, modeling data was incorporated from 24 genes into our variant classification method. These data contributed evidence for classifying 4043 observed variants in over 57,000 individuals. Genetic testing laboratories are uniquely positioned to generate, analyze, validate, and incorporate evidence from high-throughput functional data and ultimately enable the use of these data to provide definitive clinical variant classifications for more patients.

PHACTboost: A Phylogeny-Aware Pathogenicity Predictor for Missense Mutations via Boosting

Most algorithms that are used to predict the effects of variants rely on evolutionary conservation. However, a majority of such techniques compute evolutionary conservation by solely using the alignment of multiple sequences while overlooking the evolutionary context of substitution events. We had introduced PHACT, a scoring-based pathogenicity predictor for missense mutations that can leverage phylogenetic trees, in our previous study. By building on this foundation, we now propose PHACTboost, a gradient boosting tree-based classifier that combines PHACT scores with information from multiple sequence alignments, phylogenetic trees, and ancestral reconstruction. By learning from data, PHACTboost outperforms PHACT. Furthermore, the results of comprehensive experiments on carefully constructed sets of variants demonstrated that PHACTboost can outperform 40 prevalent pathogenicity predictors reported in the dbNSFP, including conventional tools, metapredictors, and deep learning-based approaches as well as more recent tools such as AlphaMissense, EVE, and CPT-1. The superiority of PHACTboost over these methods was particularly evident in case of hard variants for which different pathogenicity predictors offered conflicting results. We provide predictions of 215 million amino acid alterations over 20,191 proteins. PHACTboost is available at https://github.com/CompGenomeLab/PHACTboost. PHACTboost can improve our understanding of genetic diseases and facilitate more accurate diagnoses.

Functional analysis of ESRP1/2 gene variants and CTNND1 isoforms in orofacial cleft pathogenesis

Orofacial cleft (OFC) is a common human congenital anomaly. Epithelial-specific RNA splicing regulators ESRP1 and ESRP2 regulate craniofacial morphogenesis and their disruption result in OFC in zebrafish, mouse and humans. Using esrp1/2 mutant zebrafish and murine Py2T cell line models, we functionally tested the pathogenicity of human ESRP1/2 gene variants. We found that many variants predicted by in silico methods to be pathogenic were functionally benign. Esrp1 also regulates the alternative splicing of Ctnnd1 and these genes are co-expressed in the embryonic and oral epithelium. In fact, over-expression of ctnnd1 is sufficient to rescue morphogenesis of epithelial-derived structures in esrp1/2 zebrafish mutants. Additionally, we identified 13 CTNND1 variants from genome sequencing of OFC cohorts, confirming CTNND1 as a key gene in human OFC. This work highlights the importance of functional assessment of human gene variants and demonstrates the critical requirement of Esrp-Ctnnd1 acting in the embryonic epithelium to regulate palatogenesis.

PTEN hamartoma tumor syndrome: Clinical and genetic characterization in pediatric patients

OBJECTIVE

The aim of this study was to provide a full characterization of a cohort of 11 pediatric patients diagnosed with PTEN hamartoma tumor syndrome (PHTS).

PATIENTS AND METHODS

Eleven patients with genetic diagnostic of PHTS were recruited between February 2019 and April 2023. Clinical, imaging, demographic, and genetic data were retrospectively collected from their hospital medical history.

RESULTS

Regarding clinical manifestations, macrocephaly was the leading sign, present in all patients. Frontal bossing was the most frequent dysmorphism. Neurological issues were present in most patients. Dental malformations were described for the first time, being present in 27% of the patients. Brain MRI showed anomalies in 57% of the patients. No tumoral lesions were present at the time of the study. Regarding genetics, 72% of the alterations were in the tensin-type C2 domain of PTEN protein. We identified four PTEN genetic alterations for the first time.

CONCLUSIONS

PTEN mutations appear with a wide variety of clinical signs and symptoms, sometimes associated with phenotypes which do not fit classical clinical diagnostic criteria for PHTS. We recommend carrying out a genetic study to establish an early diagnosis in children with significant macrocephaly. This facilitates personalized monitoring and enables anticipation of potential PHTS-related complications.

Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease

Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We developed a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we showed that PreMode reached state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and optimization in protein engineering.

Defining and Reducing Variant Classification Disparities

Background

Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style data may help resolve variant classification disparities between populations, especially for variants of uncertain significance (VUS).

Methods

We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource's Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN .

Results

Using two orthogonal statistical approaches, we show a higher prevalence ( p ≤5.95e-06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation ( p ≤2.5e-05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were higher in individuals of European-like genetic ancestry ( p ≤2.5e-05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry ( p =9.1e-03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency ( p =7.47e-06) and computational predictor ( p =6.92e-05) evidence codes for individuals of non-European-like genetic ancestry.

Conclusions

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

A probabilistic graphical model for estimating selection coefficient of missense variants from human population sequence data

Accurately predicting the effect of missense variants is a central problem in interpretation of genomic variation. Commonly used computational methods does not capture the quantitative impact on fitness in populations. We developed MisFit to estimate missense fitness effect using biobank-scale human population genome data. MisFit jointly models the effect at molecular level ( d ) and population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . MisFit is a probabilistic graphical model that integrates deep neural network components and population genetics models efficiently with inductive bias based on biological causality of variant effect. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that s is informative in predicting frequency across ancestries and consistent with the fraction of de novo mutations given s . Finally, MisFit outperforms previous methods in prioritizing missense variants in individuals with neurodevelopmental disorders.

Cancer and Autism: How PTEN Mutations Degrade Function at the Membrane and Isoform Expression in the Human Brain

Mutations causing loss of PTEN lipid phosphatase activity can promote cancer, benign tumors (PHTS), and neurodevelopmental disorders (NDDs). Exactly how they preferentially trigger distinct phenotypic outcomes has been puzzling. Here, we demonstrate that PTEN mutations differentially allosterically bias P loop dynamics and its connection to the catalytic site, affecting catalytic activity. NDD-related mutations are likely to sample conformations of the functional wild-type state, while sampled conformations for the strong, cancer-related driver mutation hotspots favor catalysis-primed conformations, suggesting that NDD mutations are likely to be weaker, and our large-scale simulations show why. Prenatal PTEN isoform expression data suggest exons 5 and 7, which harbor NDD mutations, as cancer-risk carriers. Since cancer requires more than a single mutation, our conformational and genomic analysis helps discover how same protein mutations can foster different clinical manifestations, articulates a role for co-occurring background latent driver mutations, and uncovers relationships of splicing isoform expression to life expectancy.

Unraveling the Concealed Transcriptomic Landscape of PTEN in Human Malignancies

Background

Phosphatase and tensin homolog, widely known as PTEN, is a major negative regulator of the PI3K/AKT/mTOR signaling pathway, involved in the regulation of a variety of important cellular processes, including cell proliferation, growth, survival, and metabolism. Since most of the molecules involved in this biological pathway have been described as key regulators in cancer, the study of the corresponding genes at several levels is crucial.

Objective

Although previous studies have elucidated the physiological role of PTEN under normal conditions and its involvement in carcinogenesis and cancer progression, the transcriptional profile of PTEN has been poorly investigated.

Methods

In this study, instead of conducting the "gold-standard" direct RNA sequencing that fails to detect less abundant novel mRNAs due to the decreased sequencing depth, we designed and implemented a multiplexed PTEN-targeted sequencing approach that combined both short- and long-read sequencing.

Results

Our study has highlighted a broad spectrum of previously unknown PTEN mRNA transcripts and assessed their expression patterns in a wide range of human cancer and non-cancer cell lines, shedding light on the involvement of PTEN in cell cycle dysregulation and thus tumor development.

Conclusion

The identification of the described novel PTEN splice variants could have significant implications for understanding PTEN regulation and function, and provide new insights into PTEN biology, opening new avenues for monitoring PTEN-related diseases, including cancer.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Differential cell cycle checkpoint evasion by PTEN germline mutations associated with dichotomous phenotypes of cancer versus autism spectrum disorder

Individuals with a PTEN germline mutation receive the molecular diagnosis of PTEN hamartoma tumor syndrome (PHTS). PHTS displays a complex spectrum of clinical phenotypes including harmartomas, predisposition to cancers, and autism spectrum disorder (ASD). Clear-cut genotype-phenotype correlations are yet to be established due to insufficient information on the PTEN function being impacted by mutations. To fill this knowledge gap, we compared functional impacts of two selected missense PTEN mutant alleles, G132D and M134R, each respectively being associated with distinct clinical phenotype, ASD or thyroid cancer without ASD using gene-edited human induced pluripotent stem cells (hiPSCs). In homozygous hiPSCs, PTEN expression was severely reduced by M134R mutation due to shortened protein half-life. G132D suppressed PTEN expression to a lesser extent than Μ134R mutation without altering protein half-life. When challenged with γ-irradiation, G132D heterozygous cells exited radiation-induced G2 arrest earlier than wildtype and M134R heterozygous hiPSCs despite the similar DNA damage levels as the latter two. Immunoblotting analyses suggested that γ-irradiation induced apoptosis in G132D heterozygous cells to lesser degrees than in the hiPSCs of other genotypes. These data suggest that ASD-associated G132D allele promotes genome instability by premature cell cycle reentry with incomplete DNA repair.

Source, co-occurrence, and prognostic value of PTEN mutations or loss in colorectal cancer

Somatic PTEN mutations are common and have driver function in some cancer types. However, in colorectal cancers (CRCs), somatic PTEN-inactivating mutations occur at a low frequency (~8-9%), and whether these mutations are actively selected and promote tumor aggressiveness has been controversial. Analysis of genomic data from ~53,000 CRCs indicates that hotspot mutation patterns in PTEN partially reflect DNA-dependent selection pressures, but also suggests a strong selection pressure based on protein function. In microsatellite stable (MSS) tumors, PTEN alterations co-occur with mutations activating BRAF or PI3K, or with TP53 deletions, but not in CRC with microsatellite instability (MSI). Unexpectedly, PTEN deletions are associated with poor survival in MSS CRC, whereas PTEN mutations are associated with improved survival in MSI CRC. These and other data suggest use of PTEN as a prognostic marker is valid in CRC, but such use must consider driver mutation landscape, tumor subtype, and category of PTEN alteration.

Neurodevelopmental disorders and cancer networks share pathways, but differ in mechanisms, signaling strength, and outcome

Epidemiological studies suggest that individuals with neurodevelopmental disorders (NDDs) are more prone to develop certain types of cancer. Notably, however, the case statistics can be impacted by late discovery of cancer in individuals afflicted with NDDs, such as intellectual disorders, autism, and schizophrenia, which may bias the numbers. As to NDD-associated mutations, in most cases, they are germline while cancer mutations are sporadic, emerging during life. However, somatic mosaicism can spur NDDs, and cancer-related mutations can be germline. NDDs and cancer share proteins, pathways, and mutations. Here we ask (i) exactly which features they share, and (ii) how, despite their commonalities, they differ in clinical outcomes. To tackle these questions, we employed a statistical framework followed by network analysis. Our thorough exploration of the mutations, reconstructed disease-specific networks, pathways, and transcriptome levels and profiles of autism spectrum disorder (ASD) and cancers, point to signaling strength as the key factor: strong signaling promotes cell proliferation in cancer, and weaker (moderate) signaling impacts differentiation in ASD. Thus, we suggest that signaling strength, not activating mutations, can decide clinical outcome.

AlphaMissense is better correlated with functional assays of missense impact than earlier prediction algorithms

Missense variants that alter a single amino acid in the encoded protein contribute to many human disorders but pose a substantial challenge in interpretation. Though these variants can be reliably identified through sequencing, distinguishing the clinically significant ones remains difficult, such that "Variants of Unknown Significance" outnumber those classified as "Pathogenic" or "Likely Pathogenic." Numerous in silico approaches have been developed to predict the functional impact of missense variants to inform clinical interpretation, the latest being AlphaMissense, which uses artificial intelligence methods trained on predicted protein structure. To independently assess the performance of AlphaMissense and 38 other predictors of missense severity, we compared predictions to data from multiplexed assays of variant effect (MAVE). MAVE experiments generate almost every possible individual amino acid change in a gene and measure their functional impact using a high-throughput assay. Assessing 17,696 variants across five genes (DDX3X, MSH2, PTEN, KCNQ4, and BRCA1), we find that AlphaMissense is consistently one of the top five algorithms based on correlation with functional impact and is the best-correlated algorithm for two genes. We conclude that AlphaMissense represents the current best-in-class predictor by this metric; however, the improvement over other algorithms is modest. We note that multiple missense predictors, including AlphaMissense, appear to overcall variants as pathogenic despite minimal functional impact and that substantially more high-quality training data, including consistently analyzed patient cohorts and MAVE analyses, are required to improve accuracy.

Assigning credit where it's due: An information content score to capture the clinical value of Multiplexed Assays of Variant Effect

Background

A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, we hypothesized that this measure of utility underrepresents the information gained from MAVEs and that an information theory approach which includes data that does not reclassify variants will better reflect true information gain. We used this information theory approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information.

Results

BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification.

Conclusions

An information content approach will more accurately portray information gained through MAVE mapping efforts than counting the number of variants reclassified. This information content approach may also help define the impact of modifying information definitions used to classify many variants, such as guideline rule changes.

Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation

Defects in hydroxymethylbilane synthase (HMBS) can cause acute intermittent porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ∼⅓ of clinical HMBS variants are missense variants, and most clinically reported HMBS missense variants are designated as "variants of uncertain significance" (VUSs). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino acid substitutions. The resulting variant effect maps generally agreed with biochemical expectations and provide further evidence that HMBS can function as a monomer. Additionally, the maps implicated specific residues as having roles in active site dynamics, which was further supported by molecular dynamics simulations. Most importantly, these maps can help discriminate pathogenic from benign HMBS variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.

A New Frameshift Mutation of PTEN Gene Associated with Cowden Syndrome-Case Report and Brief Review of the Literature

Cowden syndrome (CS) is a rare disease that was first described in 1963 and later included in the large group of genodermatoses. It is the most common syndrome among the PTEN-associated hamartomatous tumor syndromes (PHTS). CS has an autosomal dominant inheritance pattern, with increased penetrance and variable expressivity, making early diagnosis difficult. Mutations in the PTEN gene (phosphatase and TENsin homolog) are involved in its pathogenesis, involving many organs and systems originating in the three embryonic layers (ectodermum, endodermum, and mesodermum). The consequence is the development of hamartomatous lesions in various organs (brain, intestines, thyroid, oropharyngeal cavity, colon, rectum, etc.). Multiple intestinal polyps are common in patients with CS, being identified in over 95% of patients undergoing colonoscopy. The authors describe the case of a patient who presented the first signs of the disease at 3 ½ years (tonsil polyp) but was diagnosed only at the age of 20 following a colonoscopy that revealed hundreds of intestinal polyps, suggesting further molecular testing. A heterozygous frameshift mutation was identified in the PTEN gene, classified as a potentially pathogenic variant (c.762del.p(Val255*)). The authors present this case to highlight the path taken by the patient from the first symptoms to the diagnosis and to emphasize the clinical aspects of this mutational variant that have still not been identified in other patients with this syndrome.

Classification of PTEN missense VUS through exascale simulations

Phosphatase and tensin homolog (PTEN), a tumor suppressor with dual phosphatase properties, is a key factor in PI3K/AKT signaling pathway. Pathogenic germline variation in PTEN can abrogate its ability to dephosphorylate, causing high cancer risk. Lack of functional evidence lets numerous PTEN variants be classified as variants of uncertain significance (VUS). Utilizing Molecular Dynamics (MD) simulations, we performed a thorough evaluation for 147 PTEN missense VUS, sorting them into 66 deleterious and 81 tolerated variants. Utilizing replica exchange molecular dynamic (REMD) simulations, we further assessed the variants situated in the catalytic core of PTEN's phosphatase domain and uncovered conformational alterations influencing the structural stability of the phosphatase domain. There was a high degree of agreement between our results and the variants classified by Variant Abundance by Massively Parallel Sequencing, saturation mutagenesis, multiplexed functional data and experimental assays. Our extensive analysis of PTEN missense VUS should benefit their clinical applications in PTEN-related cancer.

SIGNIFICANCE STATEMENT

Classification of PTEN variants affecting its lipid phosphatase activity is important for understanding the roles of PTEN variation in the pathogenesis of hereditary and sporadic malignancies. Of the 3000 variants identified in PTEN, 1296 (43%) were assigned as VUS. Here, we applied MD and REMD simulations to investigate the effects of PTEN missense VUS on the structural integrity of the PTEN phosphatase domain consisting the WPD, P and TI active sites. We classified a total of 147 missense VUS into 66 deleterious and 81 tolerated variants by referring to the control group comprising 54 pathogenic and 12 benign variants. The classification was largely in concordance with these classified by experimental approaches.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

PTEN-related risk classification models for predicting prognosis and immunotherapy response of hepatocellular carcinoma

INTRODUCTION

PTEN often mutates in tumors, and its manipulation is suggested to be used in the development of preclinical tools in cancer research. This study aims to explore the biological impact of gene expression related to PTEN mutations and to develop a prognostic classification model based on the heterogeneity of PTEN expression, and to explore its sensitivity as an indicator of prognosis and molecular and biologic features in hepatocellular carcinoma (HCC).

MATERIAL AND METHODS

RNA-seq data and mutation data of the LIHC cohort sample downloaded from The Cancer Genome Atlas (TCGA). The HCC samples were grouped according to the mean expression of PTEN, and the tumor microenvironment (TME) was evaluated by ESTIMATE and ssGSEA. The prognostic classification model related to PTEN were constructed by COX and LASSO regression analysis of differentially expressed genes (DEGs) between PTEN-high and -low expressed group.

RESULTS

The expression of PTEN was affected by copy number variation (CNV) and negatively correlated with immune score, IFNγ score and immune cell infiltration. 1281 DEGs were detected between PTEN-high and PTEN-low expressed group, 8 of the DEGs were finally filtered for developing a prognosis classification model. This model showed better prognostic value than other clinicopathological parameters, and the prediction accuracy of prognosis and ICB treatment for immunotherapy cohorts was better than that of TIDE model.

CONCLUSIONS

This study demonstrated the effect of CNV on PTEN expression and the negative immune correlation of PTEN, and constructed a classification model related to the expression of PTEN, which was of guiding significance for evaluating prognostic results of HCC patients and ICB treatment response of cancer immunotherapy cohorts.

Actionability classification of variants of unknown significance correlates with functional effect

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

Discovering functionally important sites in proteins

Proteins play important roles in biology, biotechnology and pharmacology, and missense variants are a common cause of disease. Discovering functionally important sites in proteins is a central but difficult problem because of the lack of large, systematic data sets. Sequence conservation can highlight residues that are functionally important but is often convoluted with a signal for preserving structural stability. We here present a machine learning method to predict functional sites by combining statistical models for protein sequences with biophysical models of stability. We train the model using multiplexed experimental data on variant effects and validate it broadly. We show how the model can be used to discover active sites, as well as regulatory and binding sites. We illustrate the utility of the model by prospective prediction and subsequent experimental validation on the functional consequences of missense variants in HPRT1 which may cause Lesch-Nyhan syndrome, and pinpoint the molecular mechanisms by which they cause disease.

SUNi mutagenesis: Scalable and uniform nicking for efficient generation of variant libraries

Multiplexed assays of variant effects (MAVEs) have made possible the functional assessment of all possible mutations to genes and regulatory sequences. A core pillar of the approach is generation of variant libraries, but current methods are either difficult to scale or not uniform enough to enable MAVEs at the scale of gene families or beyond. We present an improved method called Scalable and Uniform Nicking (SUNi) mutagenesis that combines massive scalability with high uniformity to enable cost-effective MAVEs of gene families and eventually genomes.

Structure-based network analysis predicts mutations associated with inherited retinal disease

With continued advances in gene sequencing technologies comes the need to develop better tools to understand which mutations cause disease. Here we validate structure-based network analysis (SBNA)1,2 in well-studied human proteins and report results of using SBNA to identify critical amino acids that may cause retinal disease if subject to missense mutation. We computed SBNA scores for genes with high-quality structural data, starting with validating the method using 4 well-studied human disease-associated proteins. We then analyzed 47 inherited retinal disease (IRD) genes. We compared SBNA scores to phenotype data from the ClinVar database and found a significant difference between benign and pathogenic mutations with respect to network score. Finally, we applied this approach to 65 patients at Massachusetts Eye and Ear (MEE) who were diagnosed with IRD but for whom no genetic cause was found. Multivariable logistic regression models built using SBNA scores for IRD-associated genes successfully predicted pathogenicity of novel mutations, allowing us to identify likely causative disease variants in 37 patients with IRD from our clinic. In conclusion, SBNA can be meaningfully applied to human proteins and may help predict mutations causative of IRD.

Enzyme Stability-Activity Trade-Off: New Insights from Protein Stability Weaknesses and Evolutionary Conservation

A general limitation of the use of enzymes in biotechnological processes under sometimes nonphysiological conditions is the complex interplay between two key quantities, enzyme activity and stability, where the increase of one is often associated with the decrease of the other. A precise stability-activity trade-off is necessary for the enzymes to be fully functional, but its weight in different protein regions and its dependence on environmental conditions is not yet elucidated. To advance this issue, we used the formalism that we have recently developed to effectively identify stability strength and weakness regions in protein structures and applied it to a large set of globular enzymes with known experimental structure and catalytic sites. Our analysis showed a striking oscillatory pattern of free energy compensation centered on the catalytic region. Indeed, catalytic residues are usually nonoptimal with respect to stability, but residues in the first shell around the catalytic site are, on the average, stability strengths and thus compensate for this lack of stability; residues in the second shell are weaker again, and so on. This trend is consistent across all enzyme families. It is accompanied by a similar, but less pronounced, pattern of residue conservation across evolution. In addition, we analyzed cold- and heat-adapted enzymes separately and highlighted different patterns of stability strengths and weaknesses, which provide insight into the longstanding problem of catalytic rate enhancement in cold environments. The successful comparison of our stability and conservation results with experimental fitness data, obtained by deep mutagenesis scanning, led us to propose criteria for improving catalytic activity while maintaining enzyme stability, a key goal in enzyme design.

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

One Sentence Summary

Deep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

Functional analysis of PTEN variants of unknown significance from PHTS patients unveils complex patterns of PTEN biological activity in disease

Heterozygous germline mutations in PTEN gene predispose to hamartomas and tumors in different tissues, as well as to neurodevelopmental disorders, and define at genetic level the PTEN Hamartoma Tumor Syndrome (PHTS). The major physiologic role of PTEN protein is the dephosphorylation of phosphatidylinositol (3,4,5)-trisphosphate (PIP3), counteracting the pro-oncogenic function of phosphatidylinositol 3-kinase (PI3K), and PTEN mutations in PHTS patients frequently abrogate PTEN PIP3 catalytic activity. PTEN also displays non-canonical PIP3-independent functions, but their involvement in PHTS pathogeny is less understood. We have previously identified and described, at clinical and genetic level, novel PTEN variants of unknown functional significance in PHTS patients. Here, we have performed an extensive functional characterization of these PTEN variants (c.77 C > T, p.(Thr26Ile), T26I; c.284 C > G, p.(Pro95Arg), P95R; c.529 T > A, p.(Tyr177Asn), Y177N; c.781 C > G, p.(Gln261Glu), Q261E; c.829 A > G, p.(Thr277Ala), T277A; and c.929 A > G, p.(Asp310Gly), D310G), including cell expression levels and protein stability, PIP3-phosphatase activity, and subcellular localization. In addition, caspase-3 cleavage analysis in cells has been assessed using a C2-domain caspase-3 cleavage-specific anti-PTEN antibody. We have found complex patterns of functional activity on PTEN variants, ranging from loss of PIP3-phosphatase activity, diminished protein expression and stability, and altered nuclear/cytoplasmic localization, to intact functional properties, when compared with PTEN wild type. Furthermore, we have found that PTEN cleavage at the C2-domain by the pro-apoptotic protease caspase-3 is diminished in specific PTEN PHTS variants. Our findings illustrate the multifaceted molecular features of pathogenic PTEN protein variants, which could account for the complexity in the genotype/phenotype manifestations of PHTS patients.

Hacking hematopoiesis - emerging tools for examining variant effects

Hematopoiesis is a continuous process of blood and immune cell production. It is orchestrated by thousands of gene products that respond to extracellular signals by guiding cell fate decisions to meet the needs of the organism. Although much of our knowledge of this process comes from work in model systems, we have learned a great deal from studies on human genetic variation. Considerable insight has emerged from studies on presumed monogenic blood disorders, which continue to provide key insights into the mechanisms critical for hematopoiesis. Furthermore, the emergence of large-scale biobanks and cohorts has uncovered thousands of genomic loci associated with blood cell traits and diseases. Some of these blood cell trait-associated loci act as modifiers of what were once thought to be monogenic blood diseases. However, most of these loci await functional validation. Here, we discuss the validation bottleneck and emerging methods to more effectively connect variant to function. In particular, we highlight recent innovations in genome editing, which have paved the path forward for high-throughput functional assessment of loci. Finally, we discuss existing barriers to progress, including challenges in manipulating the genomes of primary hematopoietic cells.

Systematically testing human HMBS missense variants to reveal mechanism and pathogenic variation

Defects in hydroxymethylbilane synthase (HMBS) can cause Acute Intermittent Porphyria (AIP), an acute neurological disease. Although sequencing-based diagnosis can be definitive, ~⅓ of clinical HMBS variants are missense variants, and most clinically-reported HMBS missense variants are designated as "variants of uncertain significance" (VUS). Using saturation mutagenesis, en masse selection, and sequencing, we applied a multiplexed validated assay to both the erythroid-specific and ubiquitous isoforms of HMBS, obtaining confident functional impact scores for >84% of all possible amino-acid substitutions. The resulting variant effect maps generally agreed with biochemical expectation. However, the maps showed variants at the dimerization interface to be unexpectedly well tolerated, and suggested residue roles in active site dynamics that were supported by molecular dynamics simulations. Most importantly, these HMBS variant effect maps can help discriminate pathogenic from benign variants, proactively providing evidence even for yet-to-be-observed clinical missense variants.

Mutational fitness landscape and drug resistance

Robust technology has been developed to systematically quantify fitness landscapes that provide valuable opportunities to improve our understanding of drug resistance and define new avenues to develop drugs with reduced resistance susceptibility. We outline the critical importance of drug resistance studies and the potential for fitness landscape approaches to contribute to this effort. We describe the major technical advancements in mutational scanning, which is the primary approach used to quantify protein fitness landscapes. There are many complex steps to consider in planning and executing mutational scanning projects including developing a selection scheme, generating mutant libraries, tracking the frequency of variants using next-generation sequencing, and processing and interpreting the data. Key experimental parameters impacting each of these steps are discussed to aid in planning fitness landscape studies. There is a strong need for improved understanding of drug resistance, and fitness landscapes provide a promising new approach.

Comparative Protein Structural Network Analysis Reveals C-Terminal Tail Phosphorylation Structural Communication Fingerprint in PTEN-Associated Mutations in Autism and Cancer

PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tightly regulated dual-specificity phosphatase and key regulator of the PI3K/AKT/mTOR signaling pathway. PTEN phosphorylation at its carboxy-terminal tail (CTT) serine/threonine cluster negatively regulates its tumor suppressor function by inducing a stable, closed, and inactive conformation. Germline PTEN mutations predispose individuals to PTEN hamartoma tumor syndrome (PHTS), a rare inherited cancer syndrome and, intriguingly, one of the most common causes of autism spectrum disorder (ASD). However, the mechanistic details that govern phosphorylated CTT catalytic conformational dynamics in the context of PHTS-associated mutations are unknown. Here, we utilized a comparative protein structure network (PSN)-based approach to investigate PTEN CTT phosphorylation-induced conformational dynamics specific to PTEN-ASD compared to PTEN-cancer phenotypes. Results from our study show differences in structural flexibility, inter-residue contacts, and allosteric communication patterns mediated by CTT phosphorylation, differentiating PTEN-ASD and PTEN-cancer phenotypes. Further, we identified perturbations among global metapaths and community network connections within the active site and inter-domain regions, indicating the significance of these regions in transmitting information across the PSN. Together, our studies provide a mechanistic underpinning of allosteric regulation through the coupled interplay of CTT phosphorylation conformational dynamics in PTEN-ASD and PTEN-cancer mutations. Importantly, the detailed atomistic interactions and structural consequences of PTEN variants reveal potential allosteric druggable target sites as a viable and currently unexplored treatment approach for individuals with different PHTS-associated mutations.

Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes

Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.

Shared Cancer Dataset Analysis Identifies and Predicts the Quantitative Effects of Pan-Cancer Somatic Driver Variants

Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations.

SIGNIFICANCE

A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.

Molecular dynamic simulation and functional analysis of pathogenic PTEN mutations in glioblastoma

PTEN, a dual-phosphatase and scaffold protein, is one of the most commonly mutated tumour suppressor gene across various cancer types in human. The aim of this study therefore was to investigate the stability, structural and functional effects, and pathogenicity of 12 missense PTEN mutations (R15S, E18G, G36R, N49I, Y68H, I101T, C105F, D109N, V133I, C136Y, R173C and N276S) found by next generation sequencing of the PTEN gene in tissue samples obtained from glioblastoma patients. Computational tools and molecular dynamic simulation programs were used to identify the deleterious effects of these mutations. Furthermore, PTEN mRNA and protein expression levels were evaluated by qRT-PCR, Western Blot, and immunohistochemistry staining methods. Various computational tools predicted strong deleterious effects for the G36R, C105F, C136Y and N276S mutations. Molecular dynamic simulation revealed a significant decrease in protein stability for the Y68H and N276S mutations when compared with the wild type protein; whereas, C105F, D109N, V133I and R173C showed partial stability reduction. Significant residual fluctuations were observed in the R15S, N49I and C136Y mutations and radius of gyration graphs revealed the most compact structure for D109N and least for C136Y. In summary, our study is the first one to show the presence of PTEN E18G, N49I, D109N and N276S mutations in glioblastoma patients; where, D109N is neutral and N276S is a damaging and disease-associated mutation.Communicated by Ramaswamy H. Sarma.

Integrating deep mutational scanning and low-throughput mutagenesis data to predict the impact of amino acid variants

BACKGROUND

Evaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.

RESULTS

In this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.

CONCLUSIONS

We show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.

Scalable Functional Assays for the Interpretation of Human Genetic Variation

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics

Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.