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.

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 ß-catenin signaling. CK1α is a negative regulator of the Wnt/ß-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/ß-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 ß-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.

Linguistic profiles, brain metabolic patterns and rates of amyloid-ß biomarker positivity in patients with mixed primary progressive aphasia.

We aimed to detail language profiles, brain metabolic patterns and proportion of Alzheimer's disease biomarkers in a cohort of patients with mixed primary progressive aphasia (mPPA). We considered 58 patients with PPA: 10 with non-fluent/agrammatic variant (nfvPPA), 16 with semantic variant (svPPA), 21 with logopenic variant (lvPPA) and 9 with mPPA. Patients with mPPA were further classified as 4 nf/lvPPA (with prevailing features for nfvPPA and lvPPA) and 5 s/lvPPA (with prevailing features for svPPA and lvPPA). Nf/lvPPA patients were characterized by higher proportion of Naming impairment compared to nfvPPA and more frequent Grammatical Errors and Phonologic Errors than lvPPA. S/lvPPA had higher proportion of impairment in Sentences Repetition compared to svPPA and in Single-word Comprehension compared to lvPPA. 100% of nf/lvPPA and 40% of s/lvPPA had Aß positive biomarkers. Brain hypometabolic pattern in Nf/lvPPA was consistent with lvPPA, while s/lvPPA had a brain metabolism resembling svPPA. We concluded that nf/lvPPA patients might be considered as PPA variant due to Alzheimer's disease and s/lvPPA group mainly included patients with svPPA.

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.

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.

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.

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.

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.

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.

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.