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PWAS (proteome-wide association study) is an innovative genetic association approach that complements widely used methods like GWAS (genome-wide association study). The PWAS approach involves consecutive phases. Initially, machine learning modeling and probabilistic considerations quantify the impact of genetic variants on protein-coding genes' biochemical functions. Secondly, for each individual, aggregating the variants per gene determines a gene-damaging score. Finally, standard statistical tests are activated in the case-control setting to yield statistically significant genes per phenotype. The PWAS Hub offers a user-friendly interface for an in-depth exploration of gene-disease associations from the UK Biobank (UKB). Results from PWAS cover 99 common diseases and conditions, each with over 10,000 diagnosed individuals per phenotype. Users can explore genes associated with these diseases, with separate analyses conducted for males and females. For each phenotype, the analyses account for sex-based genetic effects, inheritance modes (dominant and recessive), and the pleiotropic nature of associated genes. The PWAS Hub showcases its usefulness for asthma by navigating through proteomic-genetic analyses. Inspecting PWAS asthma-listed genes (a total of 27) provide insights into the underlying cellular and molecular mechanisms. Comparison of PWAS-statistically significant genes for common diseases to the Open Targets benchmark shows partial but significant overlap in gene associations for most phenotypes. Graphical tools facilitate comparing genetic effects between PWAS and coding GWAS results, aiding in understanding the sex-specific genetic impact on common diseases. This adaptable platform is attractive to clinicians, researchers, and individuals interested in delving into gene-disease associations and sex-specific genetic effects., (© 2024 Kelman et al.; Published by Cold Spring Harbor Laboratory Press.)

CRISPR-Cas9 genome editing has enabled advanced T cell therapies, but occasional loss of the targeted chromosome remains a safety concern. To investigate whether Cas9-induced chromosome loss is a universal phenomenon and evaluate its clinical significance, we conducted a systematic analysis in primary human T cells. Arrayed and pooled CRISPR screens revealed that chromosome loss was generalizable across the genome and resulted in partial and entire loss of the targeted chromosome, including in preclinical chimeric antigen receptor T cells. T cells with chromosome loss persisted for weeks in culture, implying the potential to interfere with clinical use. A modified cell manufacturing process, employed in our first-in-human clinical trial of Cas9-engineered T cells (NCT03399448), reduced chromosome loss while largely preserving genome editing efficacy. Expression of p53 correlated with protection from chromosome loss observed in this protocol, suggesting both a mechanism and strategy for T cell engineering that mitigates this genotoxicity in the clinic., Competing Interests: Declaration of interests C.A.T., J.A.D., and the Regents of the University of California have patents pending or issued related to the use of CRISPR genome editing technologies. R.B. is an employee of BioMarin Pharmaceutical Inc., J.L. is an employee of Altos Labs, and K.R.P. is a co-founder and employee of Cartography Biosciences. A.T.S. is a co-founder of Immunai and Cartography Biosciences. A.T.S. has received research support from Arsenal Biosciences, Allogene Therapeutics, and 10x Genomics. J.H.D.C. is a co-founder of Initial Therapeutics. J.E. is a co-founder of Mnemo Therapeutics, a scientific advisory board member of Cytovia Therapeutics, and a consultant for Casdin Capital, Resolution Therapeutics, IndeeLabs, and Treefrog Therapeutics. J.E. has received research support from Cytovia Therapeutics, Mnemo Therapeutics, and Takeda Pharmaceutical Company. J.A.F. has received research support from Tmunity. C.H.J. and the University of Pennsylvania have patents pending or issued related to the use of gene modification in T cells for adoptive T cell therapy. C.H.J. is a co-founder of Tmunity. H.Y.C. is a co-founder of Accent Therapeutics, Boundless Bio, Cartography Biosciences, and Orbital Therapeutics, and an advisor to 10x Genomics, Arsenal Biosciences, Chroma Medicine, Spring Discovery, and Vida Ventures. C.J.Y. is a co-founder of Survey Genomics, and a scientific advisory board member of Related Sciences and Immunai. C.J.Y. is a consultant for Maze Therapeutics, TReX Bio, ImYoo, and Santa Ana Bio. C.J.Y. has received research support from the Chan Zuckerberg Initiative, Chan Zuckerberg Biohub, Genentech, BioLegend, ScaleBio, and Illumina. J.A.D. is a co-founder of Editas Medicine, Intellia Therapeutics, Caribou Biosciences, Mammoth Biosciences, and Scribe Therapeutics, and a scientific advisory board member of Intellia Therapeutics, Caribou Biosciences, Mammoth Biosciences, Scribe Therapeutics, Vertex Pharmaceuticals, Felix Biosciences, The Column Group, Inari, and Isomorphic Labs. J.A.D. is the Chief Science Advisor at Sixth Street and a Director at Johnson & Johnson, Tempus, and Altos Labs. J.A.D. has sponsored research projects through Apple Tree Partners, Genentech, and Roche., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Predicting the effects of coding variants is a major challenge. While recent deep-learning models have improved variant effect prediction accuracy, they cannot analyze all coding variants due to dependency on close homologs or software limitations. Here we developed a workflow using ESM1b, a 650-million-parameter protein language model, to predict all ~450 million possible missense variant effects in the human genome, and made all predictions available on a web portal. ESM1b outperformed existing methods in classifying ~150,000 ClinVar/HGMD missense variants as pathogenic or benign and predicting measurements across 28 deep mutational scan datasets. We further annotated ~2 million variants as damaging only in specific protein isoforms, demonstrating the importance of considering all isoforms when predicting variant effects. Our approach also generalizes to more complex coding variants such as in-frame indels and stop-gains. Together, these results establish protein language models as an effective, accurate and general approach to predicting variant effects., (© 2023. The Author(s).)

Genetic studies of human traits have revolutionized our understanding of the variation between individuals, and yet, the genetics of most traits is still poorly understood. In this review, we highlight the major open problems that need to be solved, and by discussing these challenges provide a primer to the field. We cover general issues such as population structure, epistasis and gene-environment interactions, data-related issues such as ancestry diversity and rare genetic variants, and specific challenges related to heritability estimates, genetic association studies, and polygenic risk scores. We emphasize the interconnectedness of these problems and suggest promising avenues to address them., (© 2022. The Author(s).)

Introduction: Research on simplified antibiotic regimens for outpatient treatment of 'Possible Serious Bacterial Infection' (PSBI) and the subsequent World Health Organization (WHO) guidelines provide an opportunity to increase treatment coverage. This multi-country implementation research initiative aimed to learn how to implement the WHO guideline in diverse contexts. These experiences have been individually published; this overview paper provides a summary of results and lessons learned across sites., Methods Summary: A common mixed qualitative and quantitative methods protocol for implementation research was used in eleven sites in the Democratic Republic of Congo (Equateur province), Ethiopia (Tigray and Oromia regions), India (Haryana, Himachal Pradesh, Maharashtra, and Uttar Pradesh states), Malawi (Central Region), Nigeria (Kaduna and Oyo states), and Pakistan (Sindh province). Key steps in implementation research were: i) policy dialogue with the national government and key stakeholders, ii) the establishment of a 'Technical Support Unit' with the research team and district level managers, and iii) development of an implementation strategy and its refinement using an iterative process of implementation, programme learning and evaluation., Results Summary: All sites successfully developed and evaluated an implementation strategy to increase coverage of PSBI treatment. During the study period, a total of 6677 young infants from the study catchment area were identified and treated at health facilities in the study area as inpatients or outpatients among 88179 live births identified. The estimated coverage of PSBI treatment was 75.7% (95% CI 74.8% to 78.6%), assuming a 10% incidence of PSBI among all live births. The treatment coverage was variable, ranging from 53.3% in Lucknow, India to 97.3% in Ibadan, Nigeria. The coverage of inpatient treatment ranged from 1.9% in Zaria, Nigeria, to 33.9% in Tigray, Ethiopia. The outpatient treatment coverage ranged from 30.6% in Pune, India, to 93.6% in Zaria, Nigeria. Overall, the case fatality rate (CFR) was 14.6% (95% CI 11.5% to 18.2%) for 0-59-day old infants with critical illness, 1.9% (95% CI 1.5% to 2.4%) for 0-59-day old infants with clinical severe infection and 0.1% for fast breathing in 7-59 days old. Among infants treated as outpatients, CFR was 13.7% (95% CI 8.7% to 20.2%) for 0-59-day old infants with critical illness, 0.9% (95% CI 0.6% to 1.2%) for 0-59-day old infants with clinical severe infection, and 0.1% for infants 7-59 days old with fast breathing., Conclusion: Important lessons on how to conduct each step of implementation research, and the challenges and facilitators for implementation of PSBI management guideline in routine health systems are summarised and discussed. These lessons will be used to introduce and scale-up implementation in relevant Low- and middle-income countries., Competing Interests: The authors declare that no competing interests exist. Some of the authors are currently and/or were previously employed by not-for-profit organizations including: Save the Children, World Health Organization, BMGF, USAID, UNICEF, and ActionAid. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Summary: Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for text analysis. We introduce ProteinBERT, a deep language model specifically designed for proteins. Our pretraining scheme combines language modeling with a novel task of Gene Ontology (GO) annotation prediction. We introduce novel architectural elements that make the model highly efficient and flexible to long sequences. The architecture of ProteinBERT consists of both local and global representations, allowing end-to-end processing of these types of inputs and outputs. ProteinBERT obtains near state-of-the-art performance, and sometimes exceeds it, on multiple benchmarks covering diverse protein properties (including protein structure, post-translational modifications and biophysical attributes), despite using a far smaller and faster model than competing deep-learning methods. Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data., Availability and Implementation: Code and pretrained model weights are available at https://github.com/nadavbra/protein_bert., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Human genetic variation in coding regions is fundamental to the study of protein structure and function. Most methods for interpreting missense variants consider substitution measures derived from homologous proteins across different species. In this study, we introduce human-specific amino acid (AA) substitution matrices that are based on genetic variations in the modern human population. We analyzed the frequencies of >4.8M single nucleotide variants (SNVs) at codon and AA resolution and compiled human-centric substitution matrices that are fundamentally different from classic cross-species matrices (e.g. BLOSUM, PAM). Our matrices are asymmetric, with some AA replacements showing significant directional preference. Moreover, these AA matrices are only partly predicted by nucleotide substitution rates. We further test the utility of our matrices in exposing functional signals of experimentally-validated protein annotations. A significant reduction in AA transition frequencies was observed across nine post-translational modification (PTM) types and four ion-binding sites. Our results propose a purifying selection signal in the human proteome across a diverse set of functional protein annotations and provide an empirical baseline for interpreting human genetic variation in coding regions., (© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting., (© 2021. The Author(s).)

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores ( PRS ), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight ( n = 172,239) and comparative body size at age ten ( n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.

Natural language processing (NLP) is a field of computer science concerned with automated text and language analysis. In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress. Here, we review the success, promise and pitfalls of applying NLP algorithms to the study of proteins. Proteins, which can be represented as strings of amino-acid letters, are a natural fit to many NLP methods. We explore the conceptual similarities and differences between proteins and language, and review a range of protein-related tasks amenable to machine learning. We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models. In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models. Finally, we discuss trends and challenges in the intersection of NLP and protein research., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Ubiquitous overactivation of Hedgehog signaling in adult pituitaries results in increased expression of proopiomelanocortin (Pomc), growth hormone (Gh) and prolactin (Prl), elevated adrenocorticotropic hormone (Acth) production and proliferation of Sox2+ cells. Moreover, ACTH, GH and PRL-expressing human pituitary adenomas strongly express the Hedgehog target GLI1. Accordingly, Hedgehog signaling seems to play an important role in pathology and probably also in homeostasis of the adult hypophysis. However, the specific Hedgehog-responsive pituitary cell type has not yet been identified. We here investigated the Hedgehog pathway activation status and the effects of deregulated Hedgehog signaling cell-specifically in endocrine and non-endocrine pituitary cells. We demonstrate that Hedgehog signaling is unimportant for the homeostasis of corticotrophs, whereas it is active in subpopulations of somatotrophs and folliculo-stellate cells in vivo. Reinforcement of Hedgehog signaling activity in folliculo-stellate cells stimulates growth hormone production/release from somatotrophs in a paracrine manner, which most likely is mediated by the neuropeptide vasoactive intestinal peptide. Overall, our data show that Hedgehog signaling affects the homeostasis of pituitary hormone production via folliculo-stellate cell-mediated regulation of growth hormone production/secretion.

Contemporary catalogues of cancer driver genes rely primarily on high mutation rates as evidence for gene selection in tumors. Here, we present The Functional Alteration Bias Recovery In Coding-regions Cancer Portal, a comprehensive catalogue of gene selection in cancer based purely on the biochemical functional effects of mutations at the protein level. Gene selection in the portal is quantified by combining genomics data with rich proteomic annotations. Genes are ranked according to the strength of evidence for selection in tumor, based on rigorous and robust statistics. The portal covers the entire human coding genome (∼18,000 protein-coding genes) across 33 cancer types and pan-cancer. It includes a selected set of cross-references to the most relevant resources providing genomics, proteomics, and cancer-related information. We showcase the portal with known and overlooked cancer genes, demonstrating the utility of the portal via its simple visual interface, which allows users to pivot between gene-centric and cancer type views. The portal is available at fabric-cancer.huji.ac.il. SIGNIFICANCE: A new cancer portal quantifies and presents gene selection in tumor over the entire human coding genome across 33 cancer types and pan-cancer., (©2020 American Association for Cancer Research.)

Basal cell carcinoma (BCC) originate from Hedgehog/Patched signaling-activated epidermal stem cells. However, the chemically induced tumorigenesis of mice with a CD4Cre -mediated biallelic loss of the Hedgehog signaling repressor Patched also induces BCC formation. Here, we identified the cellular origin of CD4Cre -targeted BCC progenitors as rare Keratin 5 + epidermal cells and show that wildtype Patched offspring of these cells spread over the hair follicle/skin complex with increasing mouse age. Intriguingly, Patched mutant counterparts are undetectable in age-matched untreated skin but are getting traceable upon applying the chemical tumorigenesis protocol. Together, our data show that biallelic Patched depletion in rare Keratin 5 + epidermal cells is not sufficient to drive BCC development, because the spread of these cells is physiologically suppressed. However, bypassing the repression of Patched mutant cells, e.g., by exogenous stimuli, leads to an accumulation of BCC precursor cells and, finally, to tumor development.

We introduce Proteome-Wide Association Study (PWAS), a new method for detecting gene-phenotype associations mediated by protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein's function using machine learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. PWAS can capture complex modes of heritability, including recessive inheritance. A comparison with GWAS and other existing methods proves its capacity to recover causal protein-coding genes and highlight new associations. PWAS is available as a command-line tool.

Background: In recent years, research on cancer predisposition germline variants has emerged as a prominent field. The identity of somatic mutations is based on a reliable mapping of the patient germline variants. In addition, the statistics of germline variants frequencies in healthy individuals and cancer patients is the basis for seeking candidates for cancer predisposition genes. The Cancer Genome Atlas (TCGA) is one of the main sources of such data, providing a diverse collection of molecular data including deep sequencing for more than 30 types of cancer from > 10,000 patients., Methods: Our hypothesis in this study is that whole exome sequences from blood samples of cancer patients are not expected to show systematic differences among cancer types. To test this hypothesis, we analyzed common and rare germline variants across six cancer types, covering 2241 samples from TCGA. In our analysis we accounted for inherent variables in the data including the different variant calling protocols, sequencing platforms, and ethnicity., Results: We report on substantial batch effects in germline variants associated with cancer types. We attribute the effect to the specific sequencing centers that produced the data. Specifically, we measured 30% variability in the number of reported germline variants per sample across sequencing centers. The batch effect is further expressed in nucleotide composition and variant frequencies. Importantly, the batch effect causes substantial differences in germline variant distribution patterns across numerous genes, including prominent cancer predisposition genes such as BRCA1, RET, MAX, and KRAS. For most of known cancer predisposition genes, we found a distinct batch-dependent difference in germline variants., Conclusion: TCGA germline data is exposed to strong batch effects with substantial variabilities among TCGA sequencing centers. We claim that those batch effects are consequential for numerous TCGA pan-cancer studies. In particular, these effects may compromise the reliability and the potency to detect new cancer predisposition genes. Furthermore, interpretation of pan-cancer analyses should be revisited in view of the source of the genomic data after accounting for the reported batch effects.

Compiling the catalogue of genes actively involved in cancer is an ongoing endeavor, with profound implications to the understanding and treatment of the disease. An abundance of computational methods have been developed to screening the genome for candidate driver genes based on genomic data of somatic mutations in tumors. Existing methods make many implicit and explicit assumptions about the distribution of random mutations. We present FABRIC, a new framework for quantifying the selection of genes in cancer by assessing the effects of de-novo somatic mutations on protein-coding genes. Using a machine-learning model, we quantified the functional effects of ∼3M somatic mutations extracted from over 10 000 human cancerous samples, and compared them against the effects of all possible single-nucleotide mutations in the coding human genome. We detected 593 protein-coding genes showing statistically significant bias towards harmful mutations. These genes, discovered without any prior knowledge, show an overwhelming overlap with known cancer genes, but also include many overlooked genes. FABRIC is designed to avoid false discoveries by comparing each gene to its own background model using rigorous statistics, making minimal assumptions about the distribution of random somatic mutations. The framework is an open-source project with a simple command-line interface., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Viruses are the most prevalent infectious agents, populating almost every ecosystem on earth. Most viruses carry only a handful of genes supporting their replication and the production of capsids. It came as a great surprise in 2003 when the first giant virus was discovered and found to have a >1 Mbp genome encoding almost a thousand proteins. Following this first discovery, dozens of giant virus strains across several viral families have been reported. Here, we provide an updated quantitative and qualitative view on giant viruses and elaborate on their shared and variable features. We review the complexity of giant viral proteomes, which include functions traditionally associated only with cellular organisms. These unprecedented functions include components of the translation machinery, DNA maintenance, and metabolic enzymes. We discuss the possible underlying evolutionary processes and mechanisms that might have shaped the diversity of giant viruses and their genomes, highlighting their remarkable capacity to hijack genes and genomic sequences from their hosts and environments. This leads us to examine prominent theories regarding the origin of giant viruses. Finally, we present the emerging ecological view of giant viruses, found across widespread habitats and ecological systems, with respect to the environment and human health.

This paper examines the characteristics of implementation research (IR) efforts in low-income and middle-income countries (LMICs) by describing how key IR principles and concepts have been used in published health research in LMICs between 1998 and 2016, with focus on how to better apply these principles and concepts to support large-scale impact of health interventions in LMICs. There is a stark discrepancy between principles of IR and what has been published. Most IR studies have been conducted under conditions where the researchers have considerable influence over implementation and with extra resources, rather than in 'real world' conditions. IR researchers tend to focus on research questions that test a proof of concept, such as whether a new intervention is feasible or can improve implementation. They also tend to use traditional fixed research designs, yet the usual conditions for managing programmes demand continuous learning and change. More IR in LMICs should be conducted under usual management conditions, employ pragmatic research paradigm and address critical implementation issues such as scale-up and sustainability of evidence-informed interventions. This paper describes some positive examples that address these concerns and identifies how better reporting of IR studies in LMICs would include more complete descriptions of strategies, contexts, concepts, methods and outcomes of IR activities. This will help practitioners, policy-makers and other researchers to better learn how to implement large-scale change in their own settings., Competing Interests: Competing interests: None declared.

Information on the developmental stage of pear pre-harvest and in shelf-life is crucial to determine the optimum timing of harvest, post-harvest treatment, and time of consumption ensuring high eating quality. In the present study, CO 2 emission and fruit quality of European pear ( Pyrus communis L.) 'Conference' were analysed pre- and post-harvest with emphasis on shelf life for three years. Additionally, cytochrome and cyanide-resistant O 2 consumption were analysed in the last year of experiments. The respiration rate of pear showed typical climacteric rise of CO 2 emission in two years only, despite daily measurements. However, in each year the fruit quality in shelf life was closely linked to harvest date suggesting climacteric fruit response. Thus, the developmental stage of 'Conference' pear should be analysed by additional methods. Particularly, the cytochrome and cyanide-resistant O 2 consumption showed an encouraging potential to obtain data on characteristic respiratory patterns.

Implementation research is important in global health because it addresses the challenges of the know-do gap in real-world settings and the practicalities of achieving national and global health goals. Implementation research is an integrated concept that links research and practice to accelerate the development and delivery of public health approaches. Implementation research involves the creation and application of knowledge to improve the implementation of health policies, programmes, and practices. This type of research uses multiple disciplines and methods and emphasises partnerships between community members, implementers, researchers, and policy makers. Implementation research focuses on practical approaches to improve implementation and to enhance equity, efficiency, scale-up, and sustainability, and ultimately to improve people's health. There is growing interest in the principles of implementation research and a range of perspectives on its purposes and appropriate methods. However, limited efforts have been made to systematically document and review learning from the practice of implementation research across different countries and technical areas. Drawing on an expert review process, this Health Policy paper presents purposively selected case studies to illustrate the essential characteristics of implementation research and its application in low-income and middle-income countries. The case studies are organised into four categories related to the purposes of using implementation research, including improving people's health, informing policy design and implementation, strengthening health service delivery, and empowering communities and beneficiaries. Each of the case studies addresses implementation problems, involves partnerships to co-create solutions, uses tacit knowledge and research, and is based on a shared commitment towards improving health outcomes. The case studies reveal the complex adaptive nature of health systems, emphasise the importance of understanding context, and highlight the role of multidisciplinary, rigorous, and adaptive processes that allow for course correction to ensure interventions have an impact. This Health Policy paper is part of a call to action to increase the use of implementation research in global health, build the field of implementation research inclusive of research utilisation efforts, and accelerate efforts to bridge the gap between research, policy, and practice to improve health outcomes., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: Although maternal and newborn mortality have decreased 44 and 46% respectively between 1990 and 2015, achievement of ambitious Sustainable Development Goal targets requires accelerated progress. Mortality reduction requires a renewed focus on the continuum of maternal and newborn care from the household to the health facility. Although barriers to accessing skilled care are documented for specific contexts, there is a lack of systematic evidence on how women and families identify maternal and newborn illness and make decisions and subsequent care-seeking patterns. The focus of this multi-country study was to identify and describe illness recognition, decision-making, and care-seeking patterns across various contexts among women and newborns who survived and died to ultimately inform programmatic priorities moving forward., Methods: This study was conducted in seven countries-Ethiopia, Tanzania, Uganda, Nigeria, India, Indonesia, and Nepal. Mixed-methods were utilized including event narratives (group interviews), in-depth interviews (IDIs), focus group discussions (FDGs), rapid facility assessments, and secondary analyses of existing program data. A common protocol and tools were developed in collaboration with study teams and adapted for each site, as needed. Sample size was a minimum of five cases of each type (e.g., perceived postpartum hemorrhage, maternal death, newborn illness, and newborn death) for each study site, with a total of 84 perceived PPH, 45 maternal deaths, 83 newborn illness, 55 newborn deaths, 64 IDIs/FGDs, and 99 health facility assessments across all sites. Analysis included coding within and across cases, identifying broad themes on recognition of illness, decision-making, and patterns of care seeking, and corresponding contextual factors. Technical support was provided throughout the process for capacity building, quality assurance, and consistency across sites., Conclusion: This study provides rigorous evidence on how women and families recognize and respond to maternal and newborn illness. By using a common methodology and tools, findings not only were site-specific but also allow for comparison across contexts.

Almost three-quarters (74%) of all the noncommunicable disease burden is found within low- and middle-income countries. In September 2014, the National Heart, Lung, and Blood Institute held a Global Health Think Tank meeting to obtain expert advice and recommendations for addressing compelling scientific questions for late stage (T4) research-research that studies implementation strategies for proven effective interventions-to inform and guide the National Heart, Lung, and Blood Institute's global health research and training efforts. Major themes emerged in two broad categories: 1) developing research capacity; and 2) efficiently defining compelling scientific questions within the local context. Compelling scientific questions included how to deliver inexpensive, scalable, and sustainable interventions using alternative health delivery models that leverage existing human capital, technologies and therapeutics, and entrepreneurial strategies. These broad themes provide perspectives that inform an overarching strategy needed to reduce the heart, lung, blood, and sleep disorders disease burden and global health disparities., (Published by Elsevier B.V.)

Determining residue-level protein properties, such as sites of post-translational modifications (PTMs), is vital to understanding protein function. Experimental methods are costly and time-consuming, while traditional rule-based computational methods fail to annotate sites lacking substantial similarity. Machine Learning (ML) methods are becoming fundamental in annotating unknown proteins and their heterogeneous properties. We present ASAP (Amino-acid Sequence Annotation Prediction), a universal ML framework for predicting residue-level properties. ASAP extracts numerous features from raw sequences, and supports easy integration of external features such as secondary structure, solvent accessibility, intrinsically disorder or PSSM profiles. Features are then used to train ML classifiers. ASAP can create new classifiers within minutes for a variety of tasks, including PTM prediction (e.g. cleavage sites by convertase, phosphoserine modification). We present a detailed case study for ASAP: CleavePred, an ASAP-based model to predict protein precursor cleavage sites, with state-of-the-art results. Protein cleavage is a PTM shared by a wide variety of proteins sharing minimal sequence similarity. Current rule-based methods suffer from high false positive rates, making them suboptimal. The high performance of CleavePred makes it suitable for analyzing new proteomes at a genomic scale. The tool is attractive to protein design, mass spectrometry search engines and the discovery of new bioactive peptides from precursors. ASAP functions as a baseline approach for residue-level protein sequence prediction. CleavePred is freely accessible as a web-based application. Both ASAP and CleavePred are open-source with a flexible Python API.Database URL: ASAP's and CleavePred source code, webtool and tutorials are available at: https://github.com/ddofer/asap; http://protonet.cs.huji.ac.il/cleavepred., (© The Author(s) 2016. Published by Oxford University Press.)

Background: Viruses are the simplest replicating units, characterized by a limited number of coding genes and an exceptionally high rate of overlapping genes. We sought a unified evolutionary explanation that accounts for their genome sizes, gene overlapping and capsid properties., Results: We performed an unbiased statistical analysis of ~100 families within ~400 genera that comprise the currently known viral world. We found that the volume utilization of capsids is often low, and greatly varies among viral families. Furthermore, although viruses span three orders of magnitude in genome length, they almost never have over 1500 overlapping nucleotides, or over four significantly overlapping genes per virus., Conclusions: Our findings undermine the generality of the compression theory, which emphasizes optimal packing and length dependency to explain overlapping genes and capsid size in viral genomes. Instead, we propose that gene novelty and evolution exploration offer better explanations to size constraints and gene overlapping in all viruses., Reviewers: This article was reviewed by Arne Elofsson and David Kreil.

Hepatoma-derived growth factor (HDGF) is involved in diverse, apparently unrelated processes, such as cell proliferation, apoptosis, DNA-repair, transcriptional control, ribosome biogenesis and cell migration. Most of the interactions of HDGF with diverse molecules has been assigned to the hath region of HDGF. In this study we describe two previously unknown HDGF isoforms, HDGF-B and HDGF-C, generated via alternative splicing with structurally unrelated N-terminal regions of their hath region, which is clearly different from the well described isoform, HDGF-A. In silico modeling revealed striking differences near the PHWP motif, an essential part of the binding site for glycosaminoglycans and DNA/RNA. This observation prompted the hypothesis that these isoforms would have distinct interaction patterns with correspondingly diverse roles on cellular processes. Indeed, we discovered specific associations of HDGF-B and HDGF-C with cytoskeleton elements, such as tubulin and dynein, suggesting previously unknown functions of HDGF in retrograde transport, site directed localization and/or cytoskeleton organization. In contrast, the main isoform HDGF-A does not interact directly with the cytoskeleton, but via RNA with messenger ribonucleoprotein (mRNP) complexes. In summary, the discovery of HDGF splice variants with their discrete binding activities and subcellular distributions opened new avenues for understanding its biological function and importance.

Pneumonia, diarrhoea and malaria remain leading causes of death for children under 5 years of age and access to effective and appropriate treatment for sick children is extremely low where it is needed most. Integrated community case management (iCCM) enables community health workers to provide basic lifesaving treatment for sick children living in remote communities for these diseases. While many governments in sub-Saharan Africa recently changed policies to support iCCM, large variations in implementation remain. As a result, the collaboration represented in this supplement examined the policy processes underpinning iCCM through qualitative case study research in six purposively identified countries (Niger, Burkina Faso, Mali, Kenya, Malawi and Mozambique) and the global context. We introduce the supplement, by reviewing how policy analysis can inform: (a) how we frame iCCM and negotiate its boundaries, (b) how we tailor iCCM for national health systems and (c) how we foster accountability and learning for iCCM. In terms of framing, iCCM boundaries reflect how an array of actors use evidence to prioritize particular aspects of child mortality (lack of access to treatment), and how this underpins the ability to reach consensus and legitimate specific policy enterprises. When promoted at national level, contextual health system factors, such as the profile of CHWs and the history of primary health care, cannot be ignored. Adaptation to these contextual realities may lead to unintended consequences not forseen by technical or managerial expertize alone. Further scaling up of iCCM requires understanding of the political accountabilities involved, how ownership can be fostered and learning for improved policies and programs sustained. Collectively these articles demonstrate that iCCM, although often compartmentalized as a technical intervention, also reflects the larger and messier real world of health politics, policy and practice, for which policy analysis is vital, as an integral component of public health programming., (© The Author 2015. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Severe infections remain one of the main causes of neonatal deaths worldwide. Possible severe infection is diagnosed in young infants (aged 0-59 days) according to the presence of one or more clinical signs. The recommended treatment is hospital admission with 7-10 days of injectable antibiotic therapy. In low-income and middle-income countries, barriers to hospital care lead to delayed, inadequate, or no treatment for many young infants. We aimed to identify effective alternative antibiotic regimens to expand treatment options for situations where hospital admission is not possible., Methods: We did this randomised, open-label, equivalence trial in four urban hospitals and one rural field site in Bangladesh to determine whether two alternative antibiotic regimens with reduced numbers of injectable antibiotics combined with oral antibiotics had similar efficacy and safety to the standard regimen, which was also used as outpatient treatment. We randomly assigned infants who showed at least one clinical sign of severe, but not critical, infection (except fast breathing alone), whose parents refused hospital admission, to one of the three treatment regimens. We stratified randomisation by study site and age (<7 days or 7-59 days) using computer-generated randomisation sequences. The standard treatment was intramuscular procaine benzylpenicillin and gentamicin once per day for 7 days (group A). The alternative regimens were intramuscular gentamicin once per day and oral amoxicillin twice per day for 7 days (group B) or intramuscular procaine benzylpenicillin and gentamicin once per day for 2 days, then oral amoxicillin twice per day for 5 days (group C). The primary outcome was treatment failure within 7 days after enrolment. Assessors of treatment failure were masked to treatment allocation. Primary analysis was per protocol. We used a prespecified similarity margin of 5% to assess equivalence between regimens. This study is registered with ClinicalTrials.gov, number NCT00844337., Findings: Between July 1, 2009, and June 30, 2013, we recruited 2490 young infants into the trial. We assigned 830 infants to group A, 831 infants to group B, and 829 infants to group C. 2367 (95%) infants fulfilled per-protocol criteria. 78 (10%) of 795 per-protocol infants had treatment failure in group A compared with 65 (8%) of 782 infants in group B (risk difference -1.5%, 95% CI -4.3 to 1.3) and 64 (8%) of 790 infants in group C (-1.7%, -4.5 to 1.1). In group A, 14 (2%) infants died before day 15, compared with 12 (2%) infants in group B and 12 (2%) infants in group C. Non-fatal relapse rates were similar in all three groups (12 [2%] infants in group A vs 13 [2%] infants in group B and 10 [1%] infants in group C)., Interpretation: Our results suggest that the two alternative antibiotic regimens for outpatient treatment of clinical signs of severe infection in young infants whose parents refused hospital admission are as efficacious as the standard regimen. This finding could increase treatment options in resource-poor settings when referral care is not available or acceptable., (Copyright © 2015 Baqui et al. Open Access article distributed under the terms of CC BY. Published by .. All rights reserved.)

In Tanzania, the coverage of four or more antenatal care (ANC 4) visits among pregnant women has declined over time. We conducted an exploratory analysis to identify factors associated with utilization of ANC 4 and ANC 4 decline among pregnant women over time. We used data from 8035 women who delivered within two years preceding Tanzania Demographic and Health Surveys conducted in 1999, 2004/05 and 2010. Multivariate logistic regression models were used to examine the association between all potential factors and utilization of ANC 4; and decline in ANC 4 over time. Factors positively associated with ANC 4 utilization were higher quality of services, testing and counseling for HIV during ANC, receiving two or more doses of SP (Sulphadoxine Pyrimethamine)/Fansidar for preventing malaria during ANC and higher educational status of the woman. Negatively associated factors were residing in a zone other than Eastern zone, never married woman, reported long distance to health facility, first ANC visit after four months of pregnancy and woman's desire to avoid pregnancy. The factors significantly associated with decline in utilization of ANC 4 were: geographic zone and age of the woman at delivery. Strategies to increase ANC 4 utilization should focus on improvement in quality of care, geographic accessibility, early ANC initiation, and services that allow women to avoid pregnancy. The interconnected nature of the Tanzanian Health System is reflected in ANC 4 decline over time where introduction of new programs might have had unintended effects on existing programs. An in-depth assessment of the recent policy change towards Focused Antenatal Care and its implementation across different geographic zones, including its effect on the perception and understanding among women and performance and counseling by health providers can help explain the decline in ANC 4.

Health financing strategies that incorporate financial incentives are being applied in many low- and middle-income countries, and improving maternal and neonatal health is often a central goal. As yet, there have been few reviews of such programmes and their impact on maternal health. The US Government Evidence Summit on Enhancing Provision and use of Maternal Health Services through Financial Incentives was convened on 24-25 April 2012 to address this gap. This article, the final in a series assessing the effects of financial incentives--performance-based incentives (PBIs), insurance, user fee exemption programmes, conditional cash transfers, and vouchers--summarizes the evidence and discusses issues of context, programme design and implementation, cost-effectiveness, and sustainability. We suggest key areas to consider when designing and implementing financial incentive programmes for enhancing maternal health and highlight gaps in evidence that could benefit from additional research. Although the methodological rigor of studies varies, the evidence, overall, suggests that financial incentives can enhance demand for and improve the supply of maternal health services. Definitive evidence demonstrating a link between incentives and improved health outcomes is lacking; however, the evidence suggests that financial incentives can increase the quantity and quality of maternal health services and address health systems and financial barriers that prevent women from accessing and providers from delivering quality, lifesaving maternal healthcare.

Background: The current World Health Organization (WHO) recommendation for treatment of severe infection in young infants is hospitalization and parenteral antibiotic therapy. Hospital care is generally not available outside large cities in low- and middle-income countries and even when available is not acceptable or affordable for many families. Previous research in Bangladesh and India demonstrated that treatment outside hospitals may be possible., Research: A set of research studies with common protocols testing simplified antibiotic regimens that can be provided at the lowest-level health-care facility or at home are nearing completion. The studies are large individually randomized controlled trials that are set up in the context of a program, which provides home visits by community health workers to detect serious illness in young infants with assessment and treatment at an outpatient health facility near home. This article summarizes the policy implications of the research studies., Policy Implications: The studies are expected to result in information that would inform WHO guidelines on simple, safe and effective regimens for the treatment of clinical severe infection and pneumonia in newborns and young infants in settings where referral is not possible. The studies will also inform the inputs and process required to establish outpatient treatment of newborn and young infant infections at health facilities near the home. We expect that the information from research and the resulting WHO guidelines will form the basis of policy dialogue by a large number of stakeholders at the country level to implement outpatient treatment of neonatal infections and thereby reduce neonatal and infant mortality resulting from infection.

Background: Three randomized open-label clinical trials [Simplified Antibiotic Therapy Trial (SATT) Bangladesh, SATT Pakistan and African Neonatal Sepsis Trial (AFRINEST)] were developed to test the equivalence of simplified antibiotic regimens compared with the standard regimen of 7 days of parenteral antibiotics. These trials were originally conceived and designed separately; subsequently, significant efforts were made to develop and implement a common protocol and approach. Previous articles in this supplement briefly describe the specific quality control methods used in the individual trials; this article presents additional information about the systematic approaches used to minimize threats to validity and ensure quality across the trials., Methods: A critical component of quality control for AFRINEST and SATT was striving to eliminate variation in clinical assessments and decisions regarding eligibility, enrollment and treatment outcomes. Ensuring appropriate and consistent clinical judgment was accomplished through standardized approaches applied across the trials, including training, assessment of clinical skills and refresher training. Standardized monitoring procedures were also applied across the trials, including routine (day-to-day) internal monitoring of performance and adherence to protocols, systematic external monitoring by funding agencies and external monitoring by experienced, independent trial monitors. A group of independent experts (Technical Steering Committee/Technical Advisory Group) provided regular monitoring and technical oversight for the trials., Conclusions: Harmonization of AFRINEST and SATT have helped to ensure consistency and quality of implementation, both internally and across the trials as a whole, thereby minimizing potential threats to the validity of the trials' results.

Background: In pursuit of innovative approaches for the management of severe infections in young infants, which is a major cause of mortality, a multipartner research program was conceptualized to provide right care in the right place. The primary objective was to generate evidence and identify a simple, safe and effective treatment regimen for young infants with severe infections that can be provided closer to home by trained health workers where referral is not possible., Research: Published and nonpublished data on community-based approaches for the management of neonatal sepsis were critically reviewed by an independent expert panel convened in 2007 by the World Health Organization in collaboration with the United States Agency for International Development and Save the Children/Saving Newborn Lives. These stakeholders agreed to 1) undertake research to improve the specificity of a diagnostic algorithm and revise World Health Organization/United Nations International Children's Emergency Fund Integrated Management of Childhood Illness guidelines to identify sick young infants for referral, 2) develop research studies with common research designs (1 site in each Bangladesh and Pakistan and a multicentre site in Democratic Republic of Congo, Kenya and Nigeria) and oversight mechanisms to evaluate antibiotic regimens (when referral is not accepted by the family) that are safe and efficacious, appropriate to the severity of infection, and deployable on a large scale and 3) utilize existing program delivery structures incorporating community health workers, skilled health workers to deliver simple antibiotic treatment when referral is not possible., Conclusions: This research program facilitated innovative research in different geographical, cultural and administrative milieus to generate recommendations for policy.

Background: Newborns and young infants suffer high rates of infections in South Asia and sub-Saharan Africa. Timely access to appropriate antibiotic therapy is essential for reducing mortality. In an effort to develop community case management guidelines for young infants, 0-59 days old, with clinically diagnosed severe infections, or with fast breathing, 4 trials of simplified antibiotic therapy delivered in primary care clinics (Pakistan, Democratic Republic of Congo, Kenya and Nigeria) or at home (Bangladesh and Nigeria) are being conducted., Methods: This article describes the scientific rationale for these trials, which share major elements of trial design. All the trials are in settings of high neonatal mortality, where hospitalization is not feasible or frequently refused. All use procaine penicillin and gentamicin intramuscular injections for 7 days as reference therapy and compare this to various experimental arms utilizing comparatively simpler combination regimens with fewer injections and oral amoxicillin., Conclusion: The results of these trials will inform World Health Organization policy regarding community case management of young infants with clinical severe infections or with fast breathing.

Background: Because access to care is limited in settings with high mortality, exclusive reliance on the current recommendation of 7-10 days of parenteral antibiotic treatment is a barrier to provision of adequate treatment of newborn infections., Methods: We are conducting a trial to determine if simplified antibiotic regimens with fewer injections are as efficacious as the standard course of parenteral antibiotics for empiric treatment of young infants with clinical signs suggestive of severe infection in 4 urban hospitals and in a rural surveillance site in Bangladesh. The reference regimen of intramuscular procaine-benzyl penicillin and gentamicin given once daily for 7 days is being compared with (1) intramuscular gentamicin once daily and oral amoxicillin twice daily for 7 days and (2) intramuscular penicillin and gentamicin once daily for 2 days followed by oral amoxicillin twice daily for additional 5 days. All regimens are provided in the infant's home. The primary outcome is treatment failure (death or lack of clinical improvement) within 7 days of enrolment. The sample size is 750 evaluable infants enrolled per treatment group, and results will be reported at the end of 2013., Discussion: The trial builds upon previous studies of community case management of clinical severe infections in young infants conducted by our research team in Bangladesh. The approach although effective was not widely accepted in part because of feasibility concerns about the large number of injections. The proposed research that includes fewer doses of parenteral antibiotics if shown efficacious will address this concern.

Spreading manure containing antibiotics in agriculture is assumed to stimulate the dissemination of antibiotic resistance in soil bacterial populations. Plant roots influencing the soil environment and its microflora by exudation of growth substrates might considerably increase this effect. In this study, the effects of manure from pigs treated with sulfadiazine (SDZ), here called SDZ manure, on the abundance and transferability of sulfonamide resistance genes sul1 and sul2 in the rhizosphere of maize and grass were compared to the effects in bulk soil in a field experiment. In plots that repeatedly received SDZ manure, a significantly higher abundance of both sul genes was detected compared to that in plots where manure from untreated pigs was applied. Significantly lower abundances of sul genes relative to bacterial ribosomal genes were encountered in the rhizosphere than in bulk soil. However, in contrast to results for bulk soil, the sul gene abundance in the SDZ manure-treated rhizosphere constantly deviated from control treatments over a period of 6 weeks after manuring, suggesting ongoing antibiotic selection over this period. Transferability of sulfonamide resistance was analyzed by capturing resistance plasmids from soil communities into Escherichia coli. Increased rates of plasmid capture were observed in samples from SDZ manure-treated bulk soil and the rhizosphere of maize and grass. More than 97% of the captured plasmids belonged to the LowGC type (having low G+C content), giving further evidence for their important contribution to the environmental spread of antibiotic resistance. In conclusion, differences between bulk soil and rhizosphere need to be considered when assessing the risks associated with the spreading of antibiotic resistance.

The precise roles that oxidants play in lifespan and aging are still unknown. Here, we report the discovery that chronologically aging yeast cells undergo a sudden redox collapse, which affects over 80% of identified thiol-containing proteins. We present evidence that this redox collapse is not triggered by an increase in endogenous oxidants as would have been postulated by the free radical theory of aging. Instead it appears to be instigated by a substantial drop in cellular NADPH, which normally provides the electron source for maintaining cellular redox homeostasis. This decrease in NADPH levels occurs very early during lifespan and sets into motion a cascade that is predicted to down-regulate most cellular processes. Caloric restriction, a near-universal lifespan extending measure, increases NADPH levels and delays each facet of the cascade. Our studies reveal a time line of events leading up to the system-wide oxidation of the proteome days before cell death.DOI:http://dx.doi.org/10.7554/eLife.00306.001.

To understand and eventually predict the effects of changing redox conditions and oxidant levels on the physiology of an organism, it is essential to gain knowledge about its redoxome: the proteins whose activities are controlled by the oxidation status of their cysteine thiols. Here, we applied the quantitative redox proteomic method OxICAT to Saccharomyces cerevisiae and determined the in vivo thiol oxidation status of almost 300 different yeast proteins distributed among various cellular compartments. We found that a substantial number of cytosolic and mitochondrial proteins are partially oxidized during exponential growth. Our results suggest that prevailing redox conditions constantly control central cellular pathways by fine-tuning oxidation status and hence activity of these proteins. Treatment with sublethal H(2)O(2) concentrations caused a subset of 41 proteins to undergo substantial thiol modifications, thereby affecting a variety of different cellular pathways, many of which are directly or indirectly involved in increasing oxidative stress resistance. Classification of the identified protein thiols according to their steady-state oxidation levels and sensitivity to peroxide treatment revealed that redox sensitivity of protein thiols does not predict peroxide sensitivity. Our studies provide experimental evidence that the ability of protein thiols to react to changing peroxide levels is likely governed by both thermodynamic and kinetic parameters, making predicting thiol modifications challenging and de novo identification of peroxide sensitive protein thiols indispensable.