Correction: Whole exome sequencing reveals HSPA1L as a genetic risk factor for spontaneous preterm birth.

[This corrects the article DOI: 10.1371/journal.pgen.1007394.].

Whole exome sequencing reveals HSPA1L as a genetic risk factor for spontaneous preterm birth.

Preterm birth is a leading cause of morbidity and mortality in infants. Genetic and environmental factors play a role in the susceptibility to preterm birth, but despite many investigations, the genetic basis for preterm birth remain largely unknown. Our objective was to identify rare, possibly damaging, nucleotide variants in mothers from families with recurrent spontaneous preterm births (SPTB). DNA samples from 17 Finnish mothers who delivered at least one infant preterm were subjected to whole exome sequencing. All mothers were of northern Finnish origin and were from seven multiplex families. Additional replication samples of European origin consisted of 93 Danish sister pairs (and two sister triads), all with a history of a preterm delivery. Rare exonic variants (frequency <1%) were analyzed to identify genes and pathways likely to affect SPTB susceptibility. We identified rare, possibly damaging, variants in genes that were common to multiple affected individuals. The glucocorticoid receptor signaling pathway was the most significant (p<1.7e-8) with genes containing these variants in a subgroup of ten Finnish mothers, each having had 2-4 SPTBs. This pathway was replicated among the Danish sister pairs. A gene in this pathway, heat shock protein family A (Hsp70) member 1 like (HSPA1L), contains two likely damaging missense alleles that were found in four different Finnish families. One of the variants (rs34620296) had a higher frequency in cases compared to controls (0.0025 vs. 0.0010, p = 0.002) in a large preterm birth genome-wide association study (GWAS) consisting of mothers of general European ancestry. Sister pairs in replication samples also shared rare, likely damaging HSPA1L variants. Furthermore, in silico analysis predicted an additional phosphorylation site generated by rs34620296 that could potentially affect chaperone activity or HSPA1L protein stability. Finally, in vitro functional experiment showed a link between HSPA1L activity and decidualization. In conclusion, rare, likely damaging, variants in HSPA1L were observed in multiple families with recurrent SPTB.

Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods.

PURPOSE: We propose a framework with simple proxies to dissect the relative energy contributions responsible for standard drug discovery binding activity. METHODS: We explore a rule of thumb using hydrogen-bond donors, hydrogen-bond acceptors and rotatable bonds as relative proxies for the thermodynamic terms. We apply this methodology to several datasets (e.g., multiple small molecules profiled against kinases, Mycobacterium tuberculosis (Mtb) high throughput screening (HTS) and structure based drug design (SBDD) derived compounds, and FDA approved drugs). RESULTS: We found that Mtb active compounds developed through SBDD methods had statistically significantly larger PEnthalpy values than HTS derived compounds, suggesting these compounds had relatively more hydrogen bond donor and hydrogen bond acceptors compared to rotatable bonds. In recent FDA approved medicines we found that compounds identified via target-based approaches had a more balanced enthalpic relationship between these descriptors compared to compounds identified via phenotypic screens CONCLUSIONS: As it is common to experimentally optimize directly for total binding energy, these computational methods provide alternative calculations and approaches useful for compound optimization alongside other common metrics in available software and databases.

Computational prediction and validation of an expert's evaluation of chemical probes.

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Peroxynitrite mediates active site tyrosine nitration in manganese superoxide dismutase. Evidence of a role for the carbonate radical anion.

Protein tyrosine nitration has been observed in a variety of human diseases associated with oxidative stress, such as inflammatory, neurodegenerative, and cardiovascular conditions. However, the pathways leading to nitration of tyrosine residues are still unclear. Recent studies have shown that peroxynitrite (PN), produced by the reaction of superoxide and nitric oxide, can lead to protein nitration and inactivation. Tyrosine nitration may also be mediated by nitrogen dioxide produced by the oxidation of nitrite by peroxidases. Manganese superoxide dismutase (MnSOD), which plays a critical role in cellular defense against oxidative stress by decomposing superoxide within mitochondria, is nitrated and inactivated under pathological conditions. In this study, MnSOD is shown to catalyze PN-mediated self-nitration. Direct, spectroscopic observation of the kinetics of PN decay and nitrotyrosine formation (k(cat) = 9.3 × 10(2) M(-1) s(-1)) indicates that the mechanism involves redox cycling between Mn(2+) and Mn(3+), similar to that observed with superoxide. Distinctive patterns of tyrosine nitration within MnSOD by various reagents were revealed and quantified by MS/MS analysis of MnSOD trypsin digest peptides. These analyses showed that three of the seven tyrosine residues of MnSOD (Tyr34, Tyr9, and Tyr11) were the most susceptible to nitration and that the relative amounts of nitration of these residues varied widely depending upon the nature of the nitrating agent. Notably, nitration mediated by PN, in both the presence and absence of CO2, resulted in nitration of the active site tyrosine, Tyr34, while nitration by freely diffusing nitrogen dioxide led to surface nitration at Tyr9 and Tyr11. Flux analysis of the nitration of Tyr34 by PN-CO2 showed that the nitration rate coincided with the kinetics of the reaction of PN with CO2. These kinetics and the 20-fold increase in the efficiency of tyrosine nitration in the presence of CO2 suggest a specific role for the carbonate radical anion (•CO3(-)) in MnSOD nitration by PN. We also observed that the nitration of Tyr34 caused inactivation of the enzyme, while nitration of Tyr9 and Tyr11 did not interfere with the superoxide dismutase activity. The loss of MnSOD activity upon Tyr34 nitration implies that the responsible reagent in vivo is peroxynitrite, acting either directly or through the action of •CO3(-).

Data Mining and Computational Modeling of High-Throughput Screening Datasets.

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Studying human disease using human neurons.

Utilizing patient derived cells has enormous promise for discovering new drugs for diseases of the nervous system, a goal that has been historically quite challenging. In this review, we will outline the potential of human stem cell derived neuron models for assessing therapeutics and high-throughput screening and compare to more traditional drug discovery strategies. We summarize recent successes of the approach and discuss special considerations for developing human stem cell based assays. New technologies, such as genome editing, offer improvements to help overcome the challenges that remain. Finally, human neurons derived from patient cells have advantages for translational research beyond drug screening as they can also be used to identify individual efficacy and safety prior to clinical testing and for dissecting disease mechanisms. This article is part of a Special Issue entitled SI: Exploiting human neurons.

Single-Cell Analysis of SMN Reveals Its Broader Role in Neuromuscular Disease.

The mechanism underlying selective motor neuron (MN) death remains an essential question in the MN disease field. The MN disease spinal muscular atrophy (SMA) is attributable to reduced levels of the ubiquitous protein SMN. Here, we report that SMN levels are widely variable in MNs within a single genetic background and that this heterogeneity is seen not only in SMA MNs but also in MNs derived from controls and amyotrophic lateral sclerosis (ALS) patients. Furthermore, cells with low SMN are more susceptible to cell death. These findings raise the important clinical implication that some SMN-elevating therapeutics might be effective in MN diseases besides SMA. Supporting this, we found that increasing SMN across all MN populations using an Nedd8-activating enzyme inhibitor promotes survival in both SMA and ALS-derived MNs. Altogether, our work demonstrates that examination of human neurons at the single-cell level can reveal alternative strategies to be explored in the treatment of degenerative diseases.

Databases and collaboration require standards for human stem cell research.

Stem cell research is at an important juncture: despite significant potential for human health and several countries with key initiatives to expedite commercialization, there are gaps in capturing and exploiting the results of past and current research. Here, we propose a concerted plan that could be taken to foster a more collaborative approach and ensure that all research efforts can be leveraged across the community. The creation of a definitive centralized database repository, or at least harmonized data repositories, for stem cell groups in academia and industry, enabling secure selective sharing of data when needed, could provide the core structure that is sought globally and protect intellectual property. The development of minimum information about stem cell experiments (MIASCE) could be key to this development.

Parallel worlds of public and commercial bioactive chemistry data.

The availability of structures and linked bioactivity data in databases is powerfully enabling for drug discovery and chemical biology. However, we now review some confounding issues with the divergent expansions of public and commercial sources of chemical structures. These are associated with not only expanding patent extraction but also increasingly large vendor collections amassed via different selection criteria between SciFinder from Chemical Abstracts Service (CAS) and major public sources such as PubChem, ChemSpider, UniChem, and others. These increasingly massive collections may include both real and virtual compounds, as well as so-called prophetic compounds from patents. We address a range of issues raised by the challenges faced resolving the NIH probe compounds. In addition we highlight the confounding of prior-art searching by virtual compounds that could impact the composition of matter patentability of a new medicinal chemistry lead. Finally, we propose some potential solutions.

A brief review of recent Charcot-Marie-Tooth research and priorities.

This brief review of current research progress on Charcot-Marie-Tooth (CMT) disease is a summary of discussions initiated at the Hereditary Neuropathy Foundation (HNF) scientific advisory board meeting on November 7, 2014. It covers recent published and unpublished in vitro and in vivo research. We discuss recent promising preclinical work for CMT1A, the development of new biomarkers, the characterization of different animal models, and the analysis of the frequency of gene mutations in patients with CMT. We also describe how progress in related fields may benefit CMT therapeutic development, including the potential of gene therapy and stem cell research. We also discuss the potential to assess and improve the quality of life of CMT patients. This summary of CMT research identifies some of the gaps which may have an impact on upcoming clinical trials. We provide some priorities for CMT research and areas which HNF can support. The goal of this review is to inform the scientific community about ongoing research and to avoid unnecessary overlap, while also highlighting areas ripe for further investigation. The general collaborative approach we have taken may be useful for other rare neurological diseases.

Collaboration for rare disease drug discovery research.

Rare disease research has reached a tipping point, with the confluence of scientific and technologic developments that if appropriately harnessed, could lead to key breakthroughs and treatments for this set of devastating disorders. Industry-wide trends have revealed that the traditional drug discovery research and development (R&D) model is no longer viable, and drug companies are evolving their approach. Rather than only pursue blockbuster therapeutics for heterogeneous, common diseases, drug companies have increasingly begun to shift their focus to rare diseases. In academia, advances in genetics analyses and disease mechanisms have allowed scientific understanding to mature, but the lack of funding and translational capability severely limits the rare disease research that leads to clinical trials. Simultaneously, there is a movement towards increased research collaboration, more data sharing, and heightened engagement and active involvement by patients, advocates, and foundations. The growth in networks and social networking tools presents an opportunity to help reach other patients but also find researchers and build collaborations. The growth of collaborative software that can enable researchers to share their data could also enable rare disease patients and foundations to manage their portfolio of funded projects for developing new therapeutics and suggest drug repurposing opportunities. Still there are many thousands of diseases without treatments and with only fragmented research efforts. We will describe some recent progress in several rare diseases used as examples and propose how collaborations could be facilitated. We propose that the development of a center of excellence that integrates and shares informatics resources for rare diseases sponsored by all of the stakeholders would help foster these initiatives.

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation.

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.

Vascular and neurogenic rejuvenation of the aging mouse brain by young systemic factors.

In the adult central nervous system, the vasculature of the neurogenic niche regulates neural stem cell behavior by providing circulating and secreted factors. Age-related decline of neurogenesis and cognitive function is associated with reduced blood flow and decreased numbers of neural stem cells. Therefore, restoring the functionality of the niche should counteract some of the negative effects of aging. We show that factors found in young blood induce vascular remodeling, culminating in increased neurogenesis and improved olfactory discrimination in aging mice. Further, we show that GDF11 alone can improve the cerebral vasculature and enhance neurogenesis. The identification of factors that slow the age-dependent deterioration of the neurogenic niche in mice may constitute the basis for new methods of treating age-related neurodegenerative and neurovascular diseases.

Expression and characterization of the isolated glycosyltransferase module of Escherichia coli PBP1b.

The enzymes involved in the biosynthesis of peptidoglycan are targets for the development of new antibiotics. The bifunctional high molecular weight (HMW) penicillin-binding proteins (PBPs), which contain both glycosyltransferase (GTase) and transpeptidase (TPase) activities, are particularly attractive targets because of their extracellular location. However, there is limited mechanistic or structural information about the GTase modules of these enzymes. In this paper, we describe the overexpression and characterization of the GTase module of Escherichia coli PBP1b, a paradigm of the HMW PBPs. We define the C-terminal boundary of the GTase module and show that the isolated module can be overexpressed at significantly higher levels than the full-length protein. The catalytic efficiency and other characteristics of the isolated module are comparable in most respects to the full-length enzyme. This work lays the groundwork for mechanistic and structural analysis of GTase modules.