CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Dimethyl carbonate as a green alternative to acetonitrile in reversed-phase liquid chromatography. Part II: Purification of a therapeutic peptide

Preparative liquid chromatography in reversed phase conditions (RPLC) is the most common approach adopted in the downstream processing for the purification of therapeutic peptides at industrial level. Due to the strict requirements on the quality imposed by the Regulatory Agencies, routinary methods based on the use of aqueous buffers and acetonitrile (ACN) as organic modifier are commonly used, where ACN is practically the only available choice for the purification of peptide derivatives. However, ACN is known to suffers of many shortcomings, such as drastic shortage in the market, high costs and, most importantly, it shows unwanted toxicity for human health and environment, which led it among the less environmentally friendly ones. For this reason, the selection of a suitable alternative becomes crucial for the sustainable downstream processing of peptides and biopharmaceuticals in general. In this paper, a promising green solvent, namely dimethyl carbonate (DMC) has been used for the separation of a peptide not only in linear conditions but also for its purification through non-linear overloaded chromatography. The performance of the process has been compared to that achievable with the common method where ACN is used as organic modifier and to that obtained with two additional solvents (namely ethanol and isopropanol), already used as greener alternatives to ACN. This proof-of-concept study showed that, thanks to its higher elution strength, DMC can be considered a green alternative to ACN, since it allows to reduce method duration while reaching good purities and recoveries. Indeed, at a target purity fixed to 98.5 %, DMC led to the best productivity with respect to all the other solvents tested, confirming its suitability as a sustainable alternative to ACN for the purification of complex biopharmaceutical products.

Dimethyl carbonate as a green alternative to acetonitrile in reversed-phase liquid chromatography. Part I: Separation of small molecules

Nowadays, environmental problems are drawing the attention of governments and international organisations, which are therefore encouraging the transition to green industrial processes and approaches. In this context, chemists can help indicate a suitable direction. Beside the efforts focused on greening synthetic approaches, currently also analytical techniques and separations are under observation, especially those employing large volumes of organic solvents, such as reversed-phase liquid chromatography (RPLC). Acetonitrile has always been considered the best performing organic modifier for RPLC applications, due to its chemical features (complete miscibility in water, UV transparency, low viscosity etc); nevertheless, it suffers of severe shortcomings, and most importantly, it does not fully comply with Environmental, Health and Safety (EHS) requirements. For these reasons, alternative greener solvents are being investigated, especially easily available alcohols. In this work, chromatographic performance of the most common solvents used in reversed-phase chromatography, i.e., acetonitrile, ethanol and isopropanol, have been compared to a scarcely used solvent, dimethyl carbonate (DMC). The analytes of interest were two small molecules, caffeine and paracetamol, whose kinetics and retention behaviour obtained with the four solvents have been compared, and all contributions to band broadening have been assessed. Results about kinetic performance are very promising, indicating that a small amount (7 % v/v) of DMC is able to produce the same efficiency as a 2.5-times larger ACN volume (18 % v/v), and larger efficiency than alcohols. This paper reports, for the first time, fundamental studies concerning the mass transfer phenomena when DMC is used as an organic solvent in RPLC, and, together with the companion paper, represents the results of a research whose final aim was to discover whether DMC is suitable for chromatographic applications both in linear and preparative conditions.

Tixagevimab/Cilgavimab as pre-exposure prophylaxis against SARS-CoV-2 in patients with hematological malignancies

The approved combination of Tixagevimab/Cilgavimab has been shown to decrease the rate of symptomatic SARS-CoV-2 infection in patients at increased risk of inadequate response to vaccination. However, Tixagevimab/Cilgavimab was tested in a few studies that included patients with hematological malignancies, even if this population has shown an increased risk of unfavorable outcomes following infection (with high rates of hospitalization, intensive care unit admission, and mortality) and poor significant immunization following vaccines. We performed a real-life prospective cohort study to evaluate the rate of SARS-CoV-2 infection following pre-exposure prophylaxis with Tixagevimab/Cilgavimab in anti-spike seronegative patients compared to a cohort of seropositive patients who were observed or received a fourth vaccine dose. We recruited 103 patients with a mean age of 67 years: 35 (34%) received Tixagevimab/Cilgavimab and were followed from March 17, 2022, until November 15, 2022. After a median follow-up of 4.24 months, the 3-month cumulative incidence of infection was 20% versus 12% in the Tixagevimab/Cilgavimab and observation/vaccine groups respectively (HR 1.57; 95% CI: 0.65-3.56; p = 0.34). In this study, we report our experience with Tixagevimab/Cilgavimab and a tailored approach to SARS-CoV-2 infection prevention in patients with hematological malignancies during the SARS-CoV-2 omicron surge.

CK1α/RUNX2 Axis in the Bone Marrow Microenvironment: A Novel Therapeutic Target in Multiple Myeloma

Simple Summary

Multiple myeloma (MM) is an incurable disease for which novel therapeutic approaches targeting the malignant cells and the associated bone disease are urgently needed. CK1α is a protein kinase that plays a crucial role in the signaling network that sustains plasma cell (PC) survival and bone disease. This protein regulates Wnt/β-catenin signaling, which is fundamental for both MM cell survival and mesenchymal stromal cell (MSC) osteogenic differentiation. In this study, we investigated its involvement in MM–MSC cross-talk. We found that, by lowering CK1α expression levels in co-cultures of MM and MSC cells, expression of RUNX2—the master regulator of osteogenic differentiation—was regulated differently in the two cell types. Our data suggest the possibility of using a specific CK1α inhibitor as part of a novel therapeutic approach to selectively kill malignant PCs and overcome the blocking of osteogenic differentiation induced by MM cells in MSCs.

Abstract

Multiple myeloma (MM) is a malignant plasma cell (PC) neoplasm, which also displays pathological bone involvement. Clonal expansion of MM cells in the bone marrow causes a perturbation of bone homeostasis that culminates in MM-associated bone disease (MMABD). We previously demonstrated that the S/T kinase CK1α sustains MM cell survival through the activation of AKT and β-catenin signaling. CK1α is a negative regulator of the Wnt/β-catenin cascade, the activation of which promotes osteogenesis by directly stimulating the expression of RUNX2, the master gene regulator of osteoblastogenesis. In this study, we investigated the role of CK1α in the osteoblastogenic potential of mesenchymal stromal cells (MSCs) and its involvement in MM–MSC cross-talk. We found that CK1α silencing in in vitro co-cultures of MMs and MSCs modulated RUNX2 expression differently in PCs and in MSCs, mainly through the regulation of Wnt/β-catenin signaling. Our findings suggest that the CK1α/RUNX2 axis could be a potential therapeutic target for constraining malignant PC expansion and supporting the osteoblastic transcriptional program of MSCs, with potential for ameliorating MMABD. Moreover, considering that Lenalidomide treatment leads to MM cell death through Ikaros, Aiolos and CK1α proteasomal degradation, we examined its effects on the osteoblastogenic potential of MSC compartments.

SARS-CoV-2 variants preferentially emerge at intrinsically disordered protein sites helping immune evasion

The SARS‐CoV‐2 pandemic is maintained by the emergence of successive variants, highlighting the flexibility of the protein sequences of the virus. We show that experimentally determined intrinsically disordered regions (IDRs) are abundant in the SARS‐CoV‐2 viral proteins, making up to 28% of disorder content for the S1 subunit of spike and up to 51% for the nucleoprotein, with the vast majority of mutations occurring in the 13 major variants mapped to these IDRs. Strikingly, antigenic sites are enriched in IDRs, in the receptor‐binding domain (RBD) and in the N‐terminal domain (NTD), suggesting a key role of structural flexibility in the antigenicity of the SARS‐CoV‐2 protein surface. Mutations occurring in the S1 subunit and nucleoprotein (N) IDRs are critical for immune evasion and antibody escape, suggesting potential additional implications for vaccines and monoclonal therapeutic strategies. Overall, this suggests the presence of variable regions on S1 and N protein surfaces, which confer sequence and antigenic flexibility to the virus without altering its protein functions.

Protein Kinase CK1α Sustains B-Cell Receptor Signaling in Mantle Cell Lymphoma

Mantle Cell Lymphoma (MCL) is still an incurable B-cell malignancy characterized by poor prognosis and frequent relapses. B Cell Receptor (BCR) signaling inhibitors, in particular of the kinases BTK and PI3Kγ/δ, have demonstrated clinically meaningful anti-proliferative effects in B cell tumors. However, refractoriness to these drugs may develop, portending a dismal prognosis. Protein kinase CK1α is an emerging pro-growth enzyme in B cell malignancies. In multiple myeloma, this kinase sustains β-catenin and AKT-dependent survival and is involved in the activation of NF-κB in B cells. In this study, we analyzed the role of CK1α on MCL cell survival and proliferation, on the regulation of BCR-related BTK, NF-κB, PI3K/AKT signaling cascades and the effects of CK1α chemical inhibition or gene silencing in association with the BTK inhibitor Ibrutinib or the PI3Kγ/δ inhibitor Duvelisib. CK1α was found highly expressed in MCL cells as compared to normal B cells. The inactivation/loss of CK1α caused MCL cell apoptosis and proliferation arrest. CK1α sustained BCR signaling, in particular the NF-κB, AKT and BTK pathways by modulating the phosphorylation of Ser 652 on CARD11, Ser 536 p65 on NF-κB, Ser 473 on AKT, Tyr 223 on BTK, as well as the protein levels. We also provided evidence that CK1α-mediated regulation of CARD11 and BTK likely implicates a physical interaction. The combination of CK1α inhibition with Ibrutinib or Duvelisib synergistically increased cytotoxicity, leading to a further decrease of the activation of BCR signaling pathways. Therefore, CK1α sustains MCL growth through the regulation of BCR-linked survival signaling cascades and protects from Ibrutinib/Duvelisib-induced apoptosis. Thus, CK1α could be considered as a rational molecular target for the treatment of MCL, in association with novel agents.

PER2 C111G polymorphism, cognitive reserve and cognition in subjective cognitive decline and mild cognitive impairment: a 10-year follow-up study

BACKGROUND AND PURPOSE

CLOCK and PER2 genes have been implicated in sleep-wake cycle alterations and neurodegenerative diseases. Our aim was to evaluate the effect of CLOCK T3111C and PER2 C111G on cognitive functioning in subjective cognitive decline (SCD) patients and mild cognitive impairment (MCI) patients at the baseline of a longitudinal study, and the effect of these two polymorphisms on the progression to Alzheimer's disease (AD) of the two groups.

METHODS

Sixty-eight subjects (41 SCD and 27 MCI) who underwent clinical evaluation, neuropsychological assessment, CLOCK and PER2 genotyping at baseline and neuropsychological follow-up every 2 years for a mean time of 10 years were included. Subjects who developed AD (SCD-c and MCI-c) and non-converters (SCD-nc, MCI-nc) were considered.

RESULTS

CLOCK T3111C was detected in 47% of cases (21 SCD, 11 MCI) and PER2 C111G in 19% of cases (eight SCD and five MCI). PER2 G carriers presented lower premorbid intelligence score (P = 0.049), fewer years of education (P = 0.007) and a lower frequency of family history of AD (P = 0.04) than G non-carriers. MCI PER2 G carriers had worse performance in tests assessing memory, executive function, language and visuospatial abilities at baseline. During follow-up, two SCD and 15 MCI subjects progressed to AD: both of the SCD-c subjects presented the PER2 G allele, while none of the SCD PER2 G non-carriers converted to AD (P = 0.003).

CONCLUSION

PER2 seems to have a role in cognitive reserve and cognition in SCD and MCI patients. Nevertheless, further studies are needed to assess the role of PER2 C111G on the risk of progression to AD.

The implication of BDNF Val66Met polymorphism in progression from subjective cognitive decline to mild cognitive impairment and Alzheimer's disease: a 9-year follow-up study

Brain-derived natriuretic factor (BDNF) Val66Met polymorphism has been frequently reported to be associated with Alzheimer's disease (AD) with contrasting results. Numerous studies showed that Met allele increased the risk of AD only in women, while other studies have found worse cognitive performance in Val/Val carriers. We aimed to inquire the effects of Val66Met polymorphism on the progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and from MCI to AD and to ascertain if this effect is modulated by demographic and cognitive variables. For this purpose, we followed up 74 subjects (48 SCD, 26 MCI) for a mean time of 9 years. All participants underwent extensive neuropsychological assessment, cognitive reserve estimation, BDNF and apolipoprotein E (ApoE) genotype analysis at baseline. Personality traits and leisure activities were assessed in a subgroup. Each patient underwent clinical-neuropsychological follow-up, during which 18 out of 48 SCD subjects progressed to MCI and 14 out of 26 MCI subjects progressed to AD. We found that Val66Met increased the risk of progression from SCD to MCI and from MCI to AD only in women. Nevertheless, Val/Val carriers who progressed from SCD to MCI had a shorter conversion time compared to Met carriers. We concluded that Val66Met polymorphism might play different roles depending on sex and stage of the disease.

Influence of ApoE Genotype and Clock T3111C Interaction with Cardiovascular Risk Factors on the Progression to Alzheimer's Disease in Subjective Cognitive Decline and Mild Cognitive Impairment Patients

BACKGROUND

Some genes could interact with cardiovascular risk factors in the development of Alzheimer's disease. We aimed to evaluate the interaction between ApoE ε4 status, Clock T3111C and Per2 C111G polymorphisms with cardiovascular profile in Subjective Cognitive Decline (SCD) and Mild Cognitive Impairment (MCI).

METHODS

We included 68 patients who underwent clinical evaluation; neuropsychological assessment; ApoE, Clock and Per2 genotyping at baseline; and neuropsychological follow-up every 12-24 months for a mean of 13 years. We considered subjects who developed AD and non-converters.

RESULTS

Clock T3111C was detected in 47% of cases, Per2 C111G in 19% of cases. ApoE ε4 carriers presented higher risk of heart disease; Clock C-carriers were more frequently smokers than non C-carriers. During the follow-up, 17 patients progressed to AD. Age at baseline, ApoE ε 4 and dyslipidemia increased the risk of conversion to AD. ApoE ε4 carriers with history of dyslipidemia showed higher risk to convert to AD compared to ApoE ε4- groups and ApoE ε4+ without dyslipidemia patients. Clock C-carriers with history of blood hypertension had a higher risk of conversion to AD.

CONCLUSIONS

ApoE and Clock T3111C seem to interact with cardiovascular risk factors in SCD and MCI patients influencing the progression to AD.

In silico prediction of blood cholesterol levels from genotype data

In this work we present a framework for blood cholesterol levels prediction from genotype data. The predictor is based on an algorithm for cholesterol metabolism simulation available in literature, implemented and optimized by our group in the R language. The main weakness of the former simulation algorithm was the need of experimental data to simulate mutations in genes altering the cholesterol metabolism. This caveat strongly limited the application of the model in the clinical practice. In this work we present how this limitation could be bypassed thanks to an optimization of model parameters based on patient cholesterol levels retrieved from literature. Prediction performance has been assessed taking into consideration several scoring indices currently used for performance evaluation of machine learning methods. Our assessment shows how the optimization phase improved model performance, compared to the original version available in literature.

Patterns of recurrence after open partial horizontal laryngectomy types II and III: univariate and logistic regression analysis of risk factors

In choosing the best surgical treatment (total or partial laryngectomy) for patients affected by laryngeal squamous cell carcinoma (SCC), it is still necessary to identify a link between prognostic factors and oncological outcomes. A retrospective analysis of clinical outcomes of 819 patients affected by laryngeal cancer who underwent OPHL type II and III between 1995 to 2014 was carried out. Focusing on recurrence and its site (local, regional or distant), our cohort has been divided in two groups: patients showing recurrence (n = 108) vs those without recurrence (n = 711). Thirteen clinical-pathological parameters have been studied by univariate and multivariate analysis to identify possible correlations between recurrence and oncological outcomes (overall survival (OS), disease free survival (DFS), disease specific survival (DSS), laryngectomy free survival (LSF), laryngectomy free freedom (FFL). In multivariate analysis, we found 4 negative prognostic factors for recurrence: site of tumour (> supraglottic), cartilage invasion (> if present), perineural invasion (> if present) and type of OPHL (> in OPHL type III). The knowledge and detection of negative prognostic factors for the risk of recurrence (pN classification, cartilage involvement, perineural invasion, and thus the type of surgical treatment adopted) could increase the already well-established potentiality of OPHLs in treating cases with a safe indication after careful discussion in the tumour board.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BACKGROUND

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.

RESULTS

Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.

CONCLUSION

We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

P6547The energy cost of His bundle pacing can be curtailed

Introduction

His bundle pacing (HBP) allows physiological ventricular activation and prevents the electrical and mechanical desynchronization generally induced by myocardial stimulation, which can increase the risk of atrial fibrillation and heart failure. On the other hand, reliable HBP capture often requires higher energy than conventional myocardial pacing. This reduces the expected life of the stimulator and might limit the diffusion of HBP in the clinical practice.

Purpose

Decreasing HBP current drain by careful management of stimulation safety margin and pulse duration.

Methods

In 28 patients undergoing DDD pacing with HBP, a third lead was implanted in RV apex to provide back-up pacing on demand. HBP and apical leads were connected, respectively, to the V1 and V2 channels of a 3-chamber stimulator. When HBP was effective, apical sensing occurred within the VV delay and prevented V2 stimulation. In contrast, in case of HBP failure, V2 sensing was missing and apical back-up pacing was promptly delivered at the end of the VV delay. The availability of a back-up pulse on demand allowed reducing the HBP safety margin with no risk. Furthermore, the individual HBP strength-duration curve was derived in the aim of optimizing the Hisian pulse parameters, which are the major determinants of the device current drain.

Results

Correct back-up inhibition by successful HBP and stimulation in the event of capture loss was achieved in all the patients. The latency from Hisian pacing to apical sensing averaged 96±14 ms. According to the pacemaker counters, no back-up pulse was delivered in daily life in 59% of patients. In the remaining, the prevalence of back-up stimulation never exceeded 15% of paced ventricular cycles. The high HBP threshold was essentially due to an increased rheobase (1.2±0.6 V), while the chronaxie ranged from 0.30 to 0.53 ms in 71% of patients (median 0.44 ms), exceeding 0.6 ms only in 29% of the cases. An average current saving of 5.4±3.0 μA was obtained at the expense of a mild reduction in HBP safety margin (from 1.6±0.2 to 1.4±0.1 times).

HBP and apical back-up

Conclusions

Back-up stimulation on demand is a reliable option to decrease HBP current drain and prolong the stimulator service life with full safety. In most of the cases, significant saving can be achieved by pulse shortening, as the chronaxie time is in the same range as with myocardial stimulation and longer pulses are not required. A pulse duration exceeding 0.6 ms is indicated in less than 1/3 of the implants.

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the PCM1 protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance was evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab, used a neural-network-based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein.

Characterization of intellectual disability and autism comorbidity through gene panel sequencing

Intellectual disability (ID) and autism spectrum disorder (ASD) are clinically and genetically heterogeneous diseases. Recent whole exome sequencing studies indicated that genes associated with different neurological diseases are shared across disorders and converge on common functional pathways. Using the Ion Torrent platform, we developed a low-cost next-generation sequencing gene panel that has been transferred into clinical practice, replacing single disease-gene analyses for the early diagnosis of individuals with ID/ASD. The gene panel was designed using an innovative in silico approach based on disease networks and mining data from public resources to score disease-gene associations. We analyzed 150 unrelated individuals with ID and/or ASD and a confident diagnosis has been reached in 26 cases (17%). Likely pathogenic mutations have been identified in another 15 patients, reaching a total diagnostic yield of 27%. Our data also support the pathogenic role of genes recently proposed to be involved in ASD. Although many of the identified variants need further investigation to be considered disease-causing, our results indicate the efficiency of the targeted gene panel on the identification of novel and rare variants in patients with ID and ASD.

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Lessons from the CAGI-4 Hopkins clinical panel challenge

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI

Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype-phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.

Crohn disease risk prediction-Best practices and pitfalls with exome data

The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.

Reducing properties of 1,2-dipyridyl-1,2-disodioethanes: chemical validation of theoretical and electrochemical predictions

Theoretical calculations and electrochemical analysis were used to set up a relative scale for the reducing strength of the dianions of 1,2-dipyridylethenes, validated by studying their reactivity towards halogenated benzoic and arylacetic acids.

Viral nano-hybrids for innovative energy conversion and storage schemes

Typical rod-like viruses (the Tobacco Mosaic Virus (TMV) and the Bacteriophage M13) are biological nanostructures that couple a 1D mono-dispersed morphology with a precisely defined topology of surface spaced and orthogonal reactive domains. These biogenic scaffolds offer a unique alternative to synthetic nano-platforms for the assembly of functional molecules and materials. Spatially resolved 1D arrays of inorganic-organic hybrid domains can thus be obtained on viral nano-templates resulting in the functional arrangement of photo-triggers and catalytic sites with applications in light energy conversion and storage. Different synthetic strategies are herein highlighted depending on the building blocks and with a particular emphasis on the molecular design of viral-templated nano-interfaces holding great potential for the dream-goal of artificial photosynthesis.