Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018-2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances.

De novo and acquired drug resistance can limit the long-term efficacy of targeted cancer therapies such as tyrosine kinase inhibitors targeting key oncogenic drivers like EGFR and cMET. Mechanisms of resistance include secondary mutations of EGFR and cMET and other downstream oncogenic pathways such as KRAS and amplification of alternate growth factor receptors. MET amplification or protein overexpression has been established as the most common mechanism of clinical resistance to EGFR inhibitors such as osimertinib. AZD9592 is a first-in-class bispecific ADC designed to target EGFR and cMET, while overcoming pathway-mediated resistance mechanisms that limit other targeted agents. Here we describe the generation, characterization and preclinical evaluation of AZD9592. The ADC was constructed on the backbone of the clinically validated DuetMab monovalent bispecific IgG platform and was engineered with higher affinity for cMET compared to EGFR (>15 fold), with the aim of reducing EGFR-driven toxicity in normal tissues. The antibody is conjugated via a cleavable linker to a proprietary topoisomerase 1 inhibitor (TOP1i) payload (AZ14170132). The internalization and in vitro cytotoxicity (IC50 in the low nM range) of AZD9592 were found to be optimal when both EGFR and cMET were engaged. When EGFR alone was engaged, cytotoxicity was significantly reduced, consistent with the lower affinity for EGFR. Treatment of cells with AZD9592 induced multiple DNA damage response pathway markers (like ATM, ATR, γΗ2ΑX), consistent with the proposed primary mechanism of action (MOA) of direct tumor-cell killing caused by double strand DNA breaks. AZD9592 monotherapy showed activity in vivo in patient-derived xenograft (PDX) models representing multiple EGFR and cMET expressing tumor types, including both EGFR mutant (m) and wild-type NSCLC and head and neck squamous cell carcinoma. Responses (≥30% regression from baseline tumor volume) were observed across a wide range of clinically relevant dose levels, including a 41% response rate in EGFRm NSCLC tumors treated at the lowest tested dose of 2 mg/kg. AZD9592 combined with osimertinib also showed benefit in PDX models derived from patients who progressed on osimertinib alone, as well as models representing primary resistance (EGFR ex20ins). AZD9592 was well tolerated in cynomolgus monkeys over a 6-week period (dosing every 3 weeks). The key safety findings were limited hematological effects, consistent with the MOA of the TOP1i payload. Plasma pharmacokinetics in cynomolgus monkeys showed an acceptable profile at tolerated doses, in line with other EGFR and cMET directed antibodies. These results demonstrate that AZD9592 has a promising efficacy and safety profile in preclinical models representing diverse opportunities in multiple clinical settings. Citation Format: Frank Comer, Yariv Mazor, Elaine Hurt, Chunning Yang, Ryan Fleming, Harini Shandilya, Balakumar Vijayakrishnan, Meghan Sterba, Ruoyan Chen, Edward Rosfjord, Nicolas Floch, Anton I. Rosenbaum, Yue Huang, Jiaqi Yuan, Kevin Beaumont, Lisa Godfrey, Lara McGrath, Fernanda Arnaldez, Puja Sapra. AZD9592: An EGFR-cMET bispecific antibody-drug conjugate (ADC) targeting key oncogenic drivers in non-small-cell lung cancer (NSCLC) and beyond. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5736.

Background Immunoglobulin A nephropathy (IgAN) is one of the leading causes of end-stage kidney disease (ESKD). Many studies have shown the significance of pathological manifestations in predicting the outcome of patients with IgAN, especially T -score of Oxford classification. Evaluating prognosis may be hampered in patients without renal biopsy. Methods A baseline dataset of 690 patients with IgAN and an independent follow-up dataset of 1,168 patients were used as training and testing sets to develop the pathology T -score prediction ( T pre ) model based on the stacking algorithm, respectively. The 5-year ESKD prediction models using clinical variables (base model), clinical variables and real pathological T -score (base model plus T bio ), and clinical variables and T pre (base model plus T pre ) were developed separately in 1,168 patients with regular follow-up to evaluate whether T pre could assist in predicting ESKD. In addition, an external validation set consisting of 355 patients was used to evaluate the performance of the 5-year ESKD prediction model using T pre . Results The features selected by AUCRF for the T pre model included age, systolic arterial pressure, diastolic arterial pressure, proteinuria, eGFR, serum IgA, and uric acid. The AUC of the T pre was 0.82 (95% CI: 0.80–0.85) in an independent testing set. For the 5-year ESKD prediction model, the AUC of the base model was 0.86 (95% CI: 0.75–0.97). When the T bio was added to the base model, there was an increase in AUC [from 0.86 (95% CI: 0.75–0.97) to 0.92 (95% CI: 0.85–0.98); P = 0.03]. There was no difference in AUC between the base model plus T pre and the base model plus T bio [0.90 (95% CI: 0.82–0.99) vs . 0.92 (95% CI: 0.85–0.98), P = 0.52]. The AUC of the 5-year ESKD prediction model using T pre was 0.93 (95% CI: 0.87–0.99) in the external validation set. Conclusion A pathology T -score prediction ( T pre ) model using routine clinical characteristics was constructed, which could predict the pathological severity and assist clinicians to predict the prognosis of IgAN patients lacking kidney pathology scores.

Supplementary Materials and Methods, Supplementary Table S1. Sources of cell lines and cell growth media; Supplemental Table S2. Sources of antibodies for flow cytometry; Supplementary Table S3. CS-1 ADC Characteristics, Supplemental Table S4. Pharmacokinetic profile of the CS-1 ADC; Supplemental Figure S1. Characterization of the CS-1 ADC molecule; Supplemental Figure S2. CS-1 ADC effect on tumor cell growth; Figure S3. Detailed results of colony forming assays; Supplementary Figure S4. CS-1 mAb and elotuzumab bind to similar cells in bone marrow.

Abstract De novo and acquired drug resistance can limit the long-term efficacy of targeted cancer therapies such as tyrosine kinase inhibitors targeting key oncogenic drivers like EGFR and cMET. Mechanisms of resistance include secondary mutations of EGFR and cMET and other downstream oncogenic pathways such as KRAS and amplification of alternate growth factor receptors. MET amplification or protein overexpression has been established as the most common mechanism of clinical resistance to EGFR inhibitors such as osimertinib. AZD9592 is a first-in-class bispecific ADC designed to target EGFR and cMET, while overcoming pathway-mediated resistance mechanisms that limit other targeted agents. Here we describe the generation, characterization and preclinical evaluation of AZD9592. The ADC was constructed on the backbone of the clinically validated DuetMab monovalent bispecific IgG platform and was engineered with higher affinity for cMET compared to EGFR (>15 fold), with the aim of reducing EGFR-driven toxicity in normal tissues. The antibody is conjugated via a cleavable linker to a proprietary topoisomerase 1 inhibitor (TOP1i) payload (AZ14170132). The internalization and in vitro cytotoxicity (IC50 in the low nM range) of AZD9592 were found to be optimal when both EGFR and cMET were engaged. When EGFR alone was engaged, cytotoxicity was significantly reduced, consistent with the lower affinity for EGFR. Treatment of cells with AZD9592 induced multiple DNA damage response pathway markers (like ATM, ATR, γΗ2ΑX), consistent with the proposed primary mechanism of action (MOA) of direct tumor-cell killing caused by double strand DNA breaks. AZD9592 monotherapy showed activity in vivo in patient-derived xenograft (PDX) models representing multiple EGFR and cMET expressing tumor types, including both EGFR mutant (m) and wild-type NSCLC and head and neck squamous cell carcinoma. Responses (≥30% regression from baseline tumor volume) were observed across a wide range of clinically relevant dose levels, including a 41% response rate in EGFRm NSCLC tumors treated at the lowest tested dose of 2 mg/kg. AZD9592 combined with osimertinib also showed benefit in PDX models derived from patients who progressed on osimertinib alone, as well as models representing primary resistance (EGFR ex20ins). AZD9592 was well tolerated in cynomolgus monkeys over a 6-week period (dosing every 3 weeks). The key safety findings were limited hematological effects, consistent with the MOA of the TOP1i payload. Plasma pharmacokinetics in cynomolgus monkeys showed an acceptable profile at tolerated doses, in line with other EGFR and cMET directed antibodies. These results demonstrate that AZD9592 has a promising efficacy and safety profile in preclinical models representing diverse opportunities in multiple clinical settings. Citation Format: Frank Comer, Yariv Mazor, Elaine Hurt, Chunning Yang, Ryan Fleming, Harini Shandilya, Balakumar Vijayakrishnan, Meghan Sterba, Ruoyan Chen, Edward Rosfjord, Nicolas Floch, Anton I. Rosenbaum, Yue Huang, Jiaqi Yuan, Kevin Beaumont, Lisa Godfrey, Lara McGrath, Fernanda Arnaldez, Puja Sapra. AZD9592: An EGFR-cMET bispecific antibody-drug conjugate (ADC) targeting key oncogenic drivers in non-small-cell lung cancer (NSCLC) and beyond. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5736.

Background Serum uric acid (SUA) levels have been associated with an increased risk and recurrence of venous thromboembolism (VTE) in European populations, but the potential causal relationship remains unclear. Large-scale studies on the association between SUA and VTE in East Asian populations are lacking, despite the high prevalence of hyperuricemia in this region. To address this, we conducted a cohort analysis and a two-sample Mendelian randomization (MR) study in East Asian populations. Methods We collected data on VTE patients from the China Pulmonary Thromboembolism Registry Study (CURES) and compared them to controls obtained from the China Health and Retirement Longitudinal Survey (CHARLS). Propensity score matching (PSM) and cubic-spline models were applied to assess the effect of SUA on VTE risk while adjusting for multiple covariates. We also performed two-sample MR analyses to infer potential causality based on summary statistics from Genome-wide Association Studies (GWAS) of SUA and VTE in the East Asian population. Findings We found that the SUA levels were higher in VTE patients (317.95 mmol/L) compared to the general population (295.75 mmol/L), and SUA >= 325 mmol/L was associated with an increased risk of VTE recurrence (Pvalue = 0.0001). The univariable MR suggested a causal relationship between elevated SUA and higher VTE risk (P-inverse variance weighted < 0.05), and multivariable MR showed that elevated SUA levels continued to promote the development of VTE after adjusting for multiple covariates (P-multivariable residual < 0.05). Sensitivity analyses produced similar results for these estimations. Interpretation Our study provides evidence supporting a robust positive association between SUA and VTE in the East Asian population, and MR analyses suggest that this association is likely to be causal. Our findings underscore the importance of monitoring SUA levels in VTE prevention and call for urgent action to address the growing burden of hyperuricemia in the Asia-Pacific region.

Near-infrared (NIR) spectroscopy is a promising technique for field identification of substandard and falsified drugs because it is portable, rapid, nondestructive, and can differentiate many formulated pharmaceutical products. Portable NIR spectrometers rely heavily on chemometric analyses based on libraries of NIR spectra from authentic pharmaceutical samples. However, it is difficult to build comprehensive product libraries in many low- and middle-income countries due to the large numbers of manufacturers who supply these markets, frequent unreported changes in materials sourcing and product formulation by the manufacturers, and general lack of cooperation in providing authentic samples. In this work, we show that a simple library of lab-formulated binary mixtures of an active pharmaceutical ingredient (API) with two diluents gave good performance on field screening tasks, such as discriminating substandard and falsified formulations of the API. Six data analysis models, including principal component analysis and support-vector machine classification and regression methods and convolutional neural networks, were trained on binary mixtures of acetaminophen with either lactose or ascorbic acid. While the models all performed strongly in cross-validation (on formulations similar to their training set), they individually showed poor robustness for formulations outside the training set. However, a predictive algorithm based on the six models, trained only on binary samples, accurately predicts whether the correct amount of acetaminophen is present in ternary mixtures, genuine acetaminophen formulations, adulterated acetaminophen formulations, and falsified formulations containing substitute APIs. This data analytics approach may extend the utility of NIR spectrometers for analysis of pharmaceuticals in low-resource settings.

Answer selection task is an important task in question answering systems. In this work, we propose several deep learning methods to address answer selection task. Current answer selection tasks use LSTM networks to learn the contextual information of query and candidate answer sequences, but the LSTM network suffers from the problem of gradient instability and fail to extract local information. Aiming to solve these problems, we first introduce fusion layer with residual ideas to alleviate gradient instability. Then we further introduce CNN networks to capture local n-gram information. In addition, we introduce one-way and two-way attention mechanism respectively, in order to capture the interaction between query and candidate answer, and further improve model performance. Experimental results of two public datasets InsuranceQA and WikiQA show that our methods outperform baseline methods, which conclude the effectiveness of our methods proposed.

Object Detection Algorithms is widely used in transportation. With YOLOv3 however, it is impossible to achieve real-time detection. This paper made some adjustments to YOLOv3, and proposed a new light-scale model named MobileNetv1_yolov3lite. In our MobileNetv1_yolov3lite, we use MobileNetv1 instead of Darknet53 as our backbone network, and we use a newly proposed module yolov3lite for feature fusion. These adjustments achieve significant increases in detecting speed, and can achieve real-time detection. However, it suffers from accuracy loss. In order to improve detecting accuracy, we further modify loss function as well as training methods, which contributes to a higher accuracy.

Symptoms of coronavirus disease 2019 (COVID-19) range from asymptomatic to severe pneumonia and death. A deep understanding of the variation of biological characteristics in severe COVID-19 patients is crucial for the detection of individuals at high risk of critical condition for the clinical management of the disease. Herein, by profiling the gene expression spectrum deduced from DNA coverage in regions surrounding transcriptional start site in plasma cell-free DNA (cfDNA) of COVID-19 patients, we deciphered the altered biological processes in the severe cases and demonstrated the feasibility of cfDNA in measuring the COVID-19 progression. The up- and downregulated genes in the plasma of severe patient were found to be closely related to the biological processes and functions affected by COVID-19 progression. More importantly, with the analysis of transcriptome data of blood cells and lung cells from control group and cases with severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection, we revealed that the upregulated genes were predominantly involved in the viral and antiviral activity in blood cells, reflecting the intense viral replication and the active reaction of immune system in the severe patients. Pathway analysis of downregulated genes in plasma DNA and lung cells also demonstrated the diminished adenosine triphosphate synthesis function in lung cells, which was evidenced to correlate with the severe COVID-19 symptoms, such as a cytokine storm and acute respiratory distress. Overall, this study revealed tissue involvement, provided insights into the mechanism of COVID-19 progression, and highlighted the utility of cfDNA as a noninvasive biomarker for disease severity inspections.

Protein-truncating variants (PTVs) have important impacts on phenotype diversity and disease. However, their population genetics characteristics in more globally diverse populations are not well defined. Here, we describe patterns of PTVs in 1320 genes sequenced in 10,539 healthy controls and 9434 patients with psoriasis, all of Han Chinese ancestry. We identify 8720 PTVs, of which 77% are novel, and estimate 88% of all PTVs are deleterious and subject to purifying selection. Furthermore, we show that individuals with psoriasis have a significantly higher burden of PTVs compared to controls (P = 0.02). Finally, we identified 18 PTVs in 14 genes with unusually high levels of population differentiation, consistent with the action of local adaptation. Our study provides insights into patterns and consequences of PTVs.

Abstract Multiple myeloma is a hematologic cancer that disrupts normal bone marrow function and has multiple lines of therapeutic options, but is incurable as patients ultimately relapse. We developed a novel antibody–drug conjugate (ADC) targeting CS-1, a protein that is highly expressed on multiple myeloma tumor cells. The anti–CS-1 mAb specifically bound to cells expressing CS-1 and, when conjugated to a cytotoxic pyrrolobenzodiazepine payload, reduced the viability of multiple myeloma cell lines in vitro. In mouse models of multiple myeloma, a single administration of the CS-1 ADC caused durable regressions in disseminated models and complete regression in a subcutaneous model. In an exploratory study in cynomolgus monkeys, the CS-1 ADC demonstrated a half-life of 3 to 6 days; however, no highest nonseverely toxic dose was achieved, as bone marrow toxicity was dose limiting. Bone marrow from dosed monkeys showed reductions in progenitor cells as compared with normal marrow. In vitro cell killing assays demonstrated that the CS-1 ADC substantially reduced the number of progenitor cells in healthy bone marrow, leading us to identify previously unreported CS-1 expression on a small population of progenitor cells in the myeloid–erythroid lineage. This finding suggests that bone marrow toxicity is the result of both on-target and off-target killing by the ADC.

Background With the success of T cell checkpoint antagonists in treating cancer, we must better understand treatment response heterogeneity and develop more physiological preclinical models for evaluating the next wave of candidate therapeutics. Several hurdles limit the successful recapitulation of the cellular and molecular interactions between human T cells and tumor cells, not the least of which involves the challenge of access to – and ex vivo manipulation of – bona fide tumor antigen-specific T cells. Methods In order to improve on our understanding of checkpoint therapy using human model antigens, we developed an antigen-specific T cell-mediated cytotoxicity model using anti-viral human T cells co-cultured with a human tumor cell line expressing viral peptide epitopes. Results We found that anti-viral T cells could be used to model cytotoxic HLA-restricted anti-tumor responses and these responses varied by donor according to peptide antigen density, antigen quality, T cell numbers, and time. By identifying sub-optimal conditions in a donor-specific fashion, we demonstrated enhanced cytolytic function of T cells in vitro when combined with multiple disparate anti-tumor modalities, including immune checkpoint blockade, growth factor blockade, and chemotherapy. This in vitro model was then successfully adapted to an in vivo tumor model system that demonstrated control of tumor growth in an antigen-dependent manner that was responsive to checkpoint blockade. Conclusions These in vitro and in vivo systems represent a simple, yet elegant and robust platform for testing human T cell-directed immuno-oncology (IO) therapeutics and IO combinations. Ethics Approval All animal experiments were conducted in a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care in accordance with institutional animal care and use committee guidelines and after appropriate approvals.

Symptoms of coronavirus disease 2019 (COVID-19) range from asymptomatic to severe pneumonia and death. Detection of individuals at high risk for critical condition is crucial for control of the disease. Herein, for the first time, we profiled and analyzed plasma cell-free DNA (cfDNA) of mild and severe COVID-19 patients. We found that in comparison between mild and severe COVID-19 patients, Interleukin-37 signaling was one of the most relevant pathways; top significantly altered genes included POTEH, FAM27C, SPATA48, which were mostly expressed in prostate and testis; adrenal glands, small intestines and liver were tissues presenting most differentially expressed genes. Our data thus revealed potential tissue involvement, provided insights into mechanism on COVID-19 progression, and highlighted utility of cfDNA as a noninvasive biomarker for disease severity inspections.

The biological function of a protein stems from its 3-dimensional structure, which is thermodynamically determined by the energetics of interatomic forces between its amino acid building blocks (the order of amino acids, known as the sequence, defines a protein). Given the costs (time, money, human resources) of determining protein structures via experimental means such as X-ray crystallography, can we better describe and compare protein 3D structures in a robust and efficient manner, so as to gain meaningful biological insights? We begin by considering a relatively simple problem, limiting ourselves to just protein secondary structural elements. Historically, many computational methods have been devised to classify amino acid residues in a protein chain into one of several discrete secondary structures, of which the most well-characterized are the geometrically regular $\alpha$-helix and $\beta$-sheet; irregular structural patterns, such as 'turns' and 'loops', are less understood. Here, we present a study of Deep Learning techniques to classify the loop-like end cap structures which delimit $\alpha$-helices. Previous work used highly empirical and heuristic methods to manually classify helix capping motifs. Instead, we use structural data directly--including (i) backbone torsion angles computed from 3D structures, (ii) macromolecular feature sets (e.g., physicochemical properties), and (iii) helix cap classification data (from CAPS-DB)--as the ground truth to train a bidirectional long short-term memory (BiLSTM) model to classify helix cap residues. We tried different network architectures and scanned hyperparameters in order to train and assess several models; we also trained a Support Vector Classifier (SVC) to use as a baseline. Ultimately, we achieved 85% class-balanced accuracy with a deep BiLSTM model.

Background Heart failure is one of the leading causes of death in Western countries, and there is a need for new therapeutic approaches. Relaxin‐2 is a peptide hormone that mediates pleiotropic cardiovascular effects, including antifibrotic, angiogenic, vasodilatory, antiapoptotic, and anti‐inflammatory effects in vitro and in vivo. Methods and Results We developed RELAX10, a fusion protein composed of human relaxin‐2 hormone and the Fc of a human antibody, to test the hypothesis that extended exposure of the relaxin‐2 peptide could reduce cardiac hypertrophy and fibrosis. RELAX10 demonstrated the same specificity and similar in vitro activity as the relaxin‐2 peptide. The terminal half‐life of RELAX10 was 7 days in mouse and 3.75 days in rat after subcutaneous administration. We evaluated whether treatment with RELAX10 could prevent and reverse isoproterenol‐induced cardiac hypertrophy and fibrosis in mice. Isoproterenol administration in mice resulted in increased cardiac hypertrophy and fibrosis compared with vehicle. Coadministration with RELAX10 significantly attenuated the cardiac hypertrophy and fibrosis compared with untreated animals. Isoproterenol administration significantly increased transforming growth factor β1 (TGF‐β1)–induced fibrotic signaling, which was attenuated by RELAX10. We found that RELAX10 also significantly increased protein kinase B/endothelial NO synthase signaling and protein S‐nitrosylation. In the reversal study, RELAX10‐treated animals showed significantly reduced cardiac hypertrophy and collagen levels. Conclusions These findings support a potential role for RELAX10 in the treatment of heart failure.

Holoprosencephaly is a pathology of forebrain development characterized by high phenotypic heterogeneity. The disease presents with various clinical manifestations at the cerebral or facial levels. Several genes have been implicated in holoprosencephaly but its genetic basis remains unclear: different transmission patterns have been described including autosomal dominant, recessive and digenic inheritance. Conventional molecular testing approaches result in a very low diagnostic yield and most cases remain unsolved. In our study, we address the possibility that genetically unsolved cases of holoprosencephaly present an oligogenic origin and result from combined inherited mutations in several genes. Twenty-six unrelated families, for whom no genetic cause of holoprosencephaly could be identified in clinical settings [whole exome sequencing and comparative genomic hybridization (CGH)-array analyses], were reanalysed under the hypothesis of oligogenic inheritance. Standard variant analysis was improved with a gene prioritization strategy based on clinical ontologies and gene co-expression networks. Clinical phenotyping and exploration of cross-species similarities were further performed on a family-by-family basis. Statistical validation was performed on 248 ancestrally similar control trios provided by the Genome of the Netherlands project and on 574 ancestrally matched controls provided by the French Exome Project. Variants of clinical interest were identified in 180 genes significantly associated with key pathways of forebrain development including sonic hedgehog (SHH) and primary cilia. Oligogenic events were observed in 10 families and involved both known and novel holoprosencephaly genes including recurrently mutated FAT1, NDST1, COL2A1 and SCUBE2. The incidence of oligogenic combinations was significantly higher in holoprosencephaly patients compared to two control populations (P < 10-9). We also show that depending on the affected genes, patients present with particular clinical features. This study reports novel disease genes and supports oligogenicity as clinically relevant model in holoprosencephaly. It also highlights key roles of SHH signalling and primary cilia in forebrain development. We hypothesize that distinction between different clinical manifestations of holoprosencephaly lies in the degree of overall functional impact on SHH signalling. Finally, we underline that integrating clinical phenotyping in genetic studies is a powerful tool to specify the clinical relevance of certain mutations.

Introduction: Human relaxin-2 (hRelaxin-2), a vasoactive hormone with hemodynamic, anti-fibrotic, and cardioprotective effects, has been pursued as a potential therapy for acute decompensated heart failure. It binds to RXFP1, its cognate receptor, and to a lesser extent to RXFP2. In clinical trials, intravenous (IV) administration of recombinant hRelaxin-2 (Serelaxin) improved markers of cardiac, renal, and hepatic damage and reduced congestion. However, these effects diminished rapidly upon treatment termination. We hypothesized that the transient treatment benefits could be due to the short half-life (T1/2=4.6 hours) of Serelaxin since continuous infusion of hRelaxin-2 using subcutaneous implanted minipump has resulted in long term benefits in animal studies (Hypertension. 2014;64:315-322). To test this hypothesis, we constructed a novel hRelaxin-2 fusion (hRLX2) and evaluated its activity in vitro and its pharmacokinetic (PK) profile in vivo.

Rapid development of next generation sequencing (NGS) technology has substantially improved our ability to detect genomic variations. However, unlike other variations, such as point mutations, insertions, and deletions, which can be identified in high sensitivities and specificities based on NGS reads, most of inversions, especially those shorter than 1 kb, remain difficult to detect. Here we introduce a new framework, SRinversion, which was developed specifically for detection of inversions shorter than 1 kb by splitting and realigning poorly mapped or unmapped reads of the NGS data.