Allostatic load is a multiple biomarker measure of physiological ‘wear and tear’ that has shown some promise as marker of overall physiological health, but its power as a risk predictor for mortality and morbidity is less well known. This study has used data from the 2003 Scottish Health Survey (SHeS)  linked to mortality records to assess how well allostatic load predicts all-cause and cause-specific mortality. From the sample, data from 4,488 men and women were available with mortality status at 5 and 9.5 (rounded to 10) years after sampling in 2003. Cox proportional hazard models estimated the risk of death  according to allostatic load score. Multiple imputation was used to address missing values in the dataset. Analyses were also adjusted for potential confounders . There were 258 and 618 deaths over the 5-year and 10-year follow-up period, respectively. In the fully-adjusted model, higher allostatic load  was not associated with an increased risk of all-cause mortality after 5 years , but it was after 10 years . Allostatic load was not associated with specific causes of death over the same follow-up period. In conclusions, greater physiological wear and tear across multiple physiological systems, as measured by allostatic load, is associated with an increased risk of death, but may not be as useful as a predictor for specific causes of death. Allostatic Load (AL) is “the wear and tear on the body and brain resulting from chronic overactivity or inactivity of physiological systems that are normally involved in adaptation to environmental challenge”. This typAL has been shown to predict the risk of some major physical and mental health outcomes including heart disease and mortality.–6 HoweveThis study has used a large, nationally representative population study from ages 16 and up  to investigate the association between AL and mortality after five and ten years. In addition to all-cause mortality, this study has investigated AL’s ability to predict the risk of death for the five major killers in the population . Our hypoDetails of reporting for the STROBE checklist are available in The analysis used data from the 2003 Scottish Health Survey (SHeS) and administratively linked data from the Scottish Morbidity Records (SMR). The SHeSParticipants were asked to consent to have their survey data linked to the SMR data held at the Information Services Division (ISD) of NHS Scotland. SMR data contains details of deaths (including cause) and hospital admissions and is only available upon application to iSD. st May 2008) and 10 years (31st December 2012).The SHeS was carried out between June 2003 and December 2004. The actual date of interview was not provided in the dataset due to confidentiality, so 1st June 2003 was used as the start date for all participants in the analysis. The SMR data provided details of deaths up to 31st December 2012, therefore the maximum survival time for the mortality analysis was 3,501 days . The SMR data includes date of death and primary cause of death as per the death certificate, based on International Classification of Disease (ICD) 10th revision codes.[AL was constructed from eight biomarker variables, designed to summarise levels of physiological activity across a range of regulatory systems related to disease and mortality risk and used in previous validated AL constructs.,12 TheseIt was assumed that individuals taking medications had already experienced physiological dysregulation  so variaAL scores were calculated based on similar methods described elsewhere.,11 For eAge and sex variables were used in the adjusted analyses, as both are independently associated with risk of mortality as well as AL. To adjust for the confounding effect of socioeconomic deprivation, we used the 2012 Scottish Index of Multiple Deprivation (SIMD) in our analyses. The SIMD is the Scottish Government’s official measure of area based deprivation and is based on 31 indicators to provide a comprehensive picture of relative area deprivation. For thisOf the eight biomarker variables selected to create the AL score, 2,707 individuals had complete data, 1,781 had some data missing and 1,812 had data missing for all eight biomarkers. Analysis of the baseline characteristics of the three groups  revealedThe group  with incomplete biomarkers were slightly younger, more likely to be male and more deprived than the complete biomarker group. The proportion of deaths was higher (18% vs 11%) than in the complete biomarker group. This group was combined with the complete biomarker group to make a total analysis sample of 4,488.The Multiple Imputation (MI) procedure in Stata version 14  was used to impute values for the missing data. MI originated with Donald Rubin and is the method of choice to address problems due to missing values in complex surveys. The MI mCox Proportional Hazard Regression (CPHR) analyses, which take account of the time to events  were undertaken. Baseline (unadjusted) models (model 1) were estimated first, before additional adjustment for age (model 2), sex (model 3), deprivation (model 4) and finally fully adjusted for age, sex and deprivation (model 5). AL score was treated as a continuous measure in the primary analysis, with further analyses tested treating the score as a categorical variable. Interaction terms were included, but none were significant (p>0.05) and all were removed from the models. To assess whether the cumulative AL measure was more strongly associated with mortality compared to the individual AL components, additional survival analyses were conducted using single biomarkers.For comparison with results based on multiple imputation, CPHR analyses were also conducted on the complete-case dataset. All analyses were conducted in Stata version 14 .st June 2003. Equal proportions of men and women died after both 5 years (2.8–2.9%) and 10 years (6.9%). Deaths increased with age, with 0.1% of 16–24 year olds and 2.9% of 75+ year olds dying after 5 years. By 10 years, deaths in the youngest group had increased in number, but still only represented around 0.1% of the sample, while for the 75+ group the death rate was now at 6.6%. As expected deaths were also higher in more deprived groups, accounting for 1.5% (5 years) and 3.5% (10 years) of the sample.In the CPHR analysis, increasing age was associated with an increased risk of death at 5 years  and 10 years . Lower deprivation was associated with a reduced risk of dying, again in both the 5 year  and 10 year  models. Women had a lower risk of death compared to men after 10 years , but not 5 years .Unadjusted CPHR analysis with multiple imputation  showed tAnalyses examining the associations between the biomarkers that make up this AL score and all-cause mortality showed that after controlling for age more negative levels of five of these biomarkers  were associated with an increased risk of death within 10 years . The magFinally, CPHR analysis against cause-specific mortality  revealed that allostatic load was not associated with risk of death for any specific cause at 10 years .This study has found evidence amongst approximately 4,500 adults from the general population in Scotland that increasing allostatic load (AL), a marker of cumulative physiological burden, was associated with an increased risk of all-cause mortality, with a 1 unit increase associated with approximately a 45% increased risk of death by any cause within 5 and 10 years. Adjustment for sex and deprivation had very little effect on this association. However, adjustment for age fully attenuated the relationship in the 5-year model, but remained statistically significant over 10 years with a one-unit increase in AL associated with an 8% increased risk of death. The AL measure was a stronger predictor of mortality than the majority of individual biomarkers used to construct the AL measure , but is matched by (or exceed by) HDL cholesterol, HbA1c and waist:hip ratio in predicting risk in the fully adjusted 10-year models. Comparisons in terms of effect size are difficult, as the scores are not standardised to the same scale. When investigating the relationship between AL and the top five causes of death in the sample , AL was not associated with any of the causes investigated. This weakens the case for AL being a useful predictive tool if it cannot predict at the more focused scale where more tailored prevention measures can be trialled.Six notable studies have investigated the association between AL and mortality, although these have only used data from three cohorts/samples. Borrell et al (2010), Levine & Crimmins 2014) and Howard and Sparks (2016) used data from the third National Health and Nutrition Examination Survey (NHANES III) (1988–1994), a large, nationally representative study of around 40,000 U.S. children and adults (2months old and above).  and Howa,24 BorreThe second study utilised has been the MacArthur studies of successful ageing, a longitudinal study of relatively high functioning men and women aged 70 to 79 years living in the United States  sampled in 1988/89. Seeman et al (2001) investigated the association between AL and all-cause mortality over 7 years using a sub-sample of 720 people (153 who had died within the follow-up period). Using a Hwang et al (2014) used the Taiwanese Social Environment and Biomarkers of Aging Study (SEBAS), a nationally representative longitudinal survey of adults 54 years and older.[To the best of our knowledge, this is the first study to investigate the links between AL and mortality in a UK or European population. In common with a small number of studies in US and Taiwanese populations, this paper has identified increased risk of all-cause mortality associated with increasing AL. However, compared to these studies from the USA and Taiwan our adjusted hazard ratio after 10 years (HR = 1.08) is lower than that recorded elsewhere, except for Howard et al’s analysis on the NHANES III sample.(Howard & Sparks 2016) Differences in the biomarkers used to construct AL, for example, the inclusion/exclusion of neuroendocrine markers may explain some of the variation across studies, although there is no clear pattern with or without neuroendocrine markers being included. AL has also been operationalised differently across the studies, with clinical cut-offs , deciles , quartiles  and z-scores  used. Different follow-up times have also been used, ranging from 7 to 17 years, as well as a range of ages but largely focused on those in middle and older ages. In addition, various adjustments for confounders/covariates and analysis methods have been utilised across the literature. Given this variety and the small number of studies to date, it is difficult to be sure of the primary reasons for discrepancies between studies, but does highlight the issues across the AL literature. It is clear though that AL remains an independent predictor of all-cause mortality risk that may help provide additional risk prediction and understanding of the links between cumulative physiological burden and health.As seen previously there are clear links between increasing age and increasing AL, also reflected in our findings.,27 GivenOne of the strengths of this study was the use of the SHeS dataset, a large nationally representative population survey of Scottish adults (16+). With linkage to mortality records, this provided a prospective follow-up study for analysis across all age groups, both sexes and across socioeconomic groups for approximately 10 years. This study also builds on the limited evidence for the links between AL and mortality and risk prediction. Another strength was the use of multiple imputation for missing data. The use of MI increased the analysis sample size from 2,707 to 4,488, with all the missing variables being biomarkers for use in the AL measure. Compared to the complete-case analysis , the MI One potential limitation of this study was the choice of biomarkers used to construct the AL score. Allostatic load theory emphasises the importance of measuring dysregulation across physiological systems. Ideally this would include biomarkers from the cardiovascular, metabolic, immune and neuroendocrine systems. We were Another limitation is that the biomarker information was only a ‘snapshot’ at a particular time. Greater sensitivity in the prediction of mortality may be achieved by studying the change in biomarker levels (and AL) over time. Finally, there remains the issue of how best to operationalise AL, with the quartile construct used here. The quartile AL score is the most commonly used , but it does suffer from potential issues around oversimplification (by reducing risk to an arbitrary binary score for each biomarker) and being population-specific . This restricts the potential for comparing and pooling samples . Using a method such as clinical cut-offs does provide a more consistent and clinically-relevant measure, but even then clinical cut-offs will differ between populations and this type of measure risks diverting from the original theory that AL represents a sub-clinical dysregulation state.,32A key aspect of improving public health in the UK and elsewhere is through prevention strategies and early detection of morbidities in the general population. One method commonly employed are health screenings, typically focusing on cancer and newborn health. In recent years, the National Health Service in Scotland, as well as England and Wales, have introduced health check-ups for those turning 40 years of age. These check-ups were intended to help provide early risk predictions for major chronic conditions such as heart disease, diabetes and stroke and then provide further support and advice to help manage and reduce these risks.,34 TheseAL has been shown to be an effective marker of cumulative physiological dysregulation and burden across the body that can also be used as a risk predictor for mortality. However, there remain questions about how AL could be best integrated into practice to help predict risk at an individual level, as well as across populations and with different health outcomes.S1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableA. Hazard ratio for 5- and 10-year risk of death modelled against systolic blood pressure (multiple imputation).B. Hazard ratio for 5- and 10-year risk of death modelled against diastolic blood pressure (multiple imputation).C. Hazard ratio for 5- and 10-year risk of death modelled against pulse pressure (multiple imputation).D. Hazard ratio for 5- and 10-year risk of death modelled against total cholesterol (multiple imputation).E. Hazard ratio for 5- and 10-year risk of death modelled against HDL cholesterol (multiple imputation).F. Hazard ratio for 5- and 10-year risk of death modelled against glycated haemoglobin (HbA1c) (multiple imputation).G. Hazard ratio for 5- and 10-year risk of death modelled against waist:hip ratio (WHR) (multiple imputation).H. Hazard ratio for 5- and 10-year risk of death modelled against C-Reactive Protein (CRP) (multiple imputation).(DOCX)Click here for additional data file.


After the first reported case of Zika virus (ZIKV) in Brazil, in 2015, a significant increase in the reported cases of microcephaly was observed. Microcephaly is a neurological condition in which the infant’s head is significantly smaller with complications in brain development. Recently, two small membrane-associated interferon-inducible transmembrane proteins (IFITM1 and IFITM3) have been shown to repress members of the flaviviridae family which includes ZIKV. However, the exact mechanisms leading to the inhibition of the virus are yet unknown. Here, we assembled an interactome of IFITM1 and IFITM3 with known protein-protein interactions (PPIs) collected from publicly available databases and novel PPIs predicted using the High-confidence Protein-Protein Interaction Prediction (HiPPIP) model. We analyzed the functional and pathway associations of the interacting proteins, and found that there are several immunity pathways , neuronal pathways  and developmental pathways  that are associated with these interactors. Our novel PPIs associate cilia dysfunction in ependymal cells to microcephaly, and may also shed light on potential targets of ZIKV for host invasion by immunosuppression and cytoskeletal rearrangements. These results could help direct future research in elucidating the mechanisms underlying host defense to ZIKV and other flaviviruses. Until recently, ZIKV reports had been limited to Africa and Asia4 but it became a wide-spread ZIKV epidemic5. The virus spread rapidly across the Americas and was declared a ‘global emergency’ by the World Health Organization6. It is mostly transmitted by mosquitoes and clinical manifestations include rash, mild fever, arthralgia, conjunctivitis, myalgia, and headaches. In addition, the virus can be transmitted sexually, with the risk of infection persisting for several months after initial contact7. While the symptoms of ZIKV can be mild1, the virus has been linked to two more serious afflictions: Guillen-Barré syndrome (GBS)9 and microcephaly14, both of which are serious neurological conditions. Microcephaly results in reduced head circumference measurement in infants, exhibiting complications in brain development. Of particular concern is the attribution of microcephaly to infection with ZIKV occurring between the first two trimesters of pregnancy14. Evidence linking ZIKV to microcephaly includes detection of ZIKV RNA in tissue such as the placenta and amniotic fluid of pregnant women with ZIKV, as well as in the brains of stillborn infants with microcephaly15. In a study with human induced pluripotency stem cells, the mechanism of ZIKV related cell death has been elucidated. This study demonstrated that ZIKV infects human embryonic cortical neural progenitor cells (hNPCs), ultimately leading to attenuated population growth mediated by virally induced caspase-3-mediated apoptosis and cell-cycle dysregulation16. Furthermore, mice studies showed that ZIKV infection can lead to nerve degeneration, softening of the brain and porencephaly17. Additional studies have assessed the causal relation between ZIKV infection and birth defects, and the role of ZIKV as a cause of congenital defects and as a trigger of GBS has been established19.Zika virus (ZIKV) is a flavivirus that was initially isolated from rhesus monkeys in 1947 and was first reported in humans in 19527. The IFITM protein family belongs to a group of small (10 – 15kDa)20 interferon stimulated genes (ISGs), which are in turn produced by the interferon system21. The interferon system is the host’s primary response against infection which restricts entry and fusion from late endosomes22, with some ISGs being involved in suppressing the early stages of viral replication24. IFITMs are located at the cell plasma and endosomal membranes, which are the means of entry of many viruses25. This protein family protects the host against viral infection by directly restricting entry and fusion from late endosomes26.Recently, it was discovered that two small membrane-associated interferon inducible transmembrane proteins (IFITMs) IFITM1 and IFITM3 play a protective role against ZIKV infection by inhibiting replication of the virus and preventing cell death29. Mice that are lacking immune sensors, signaling pathways and effector molecules were found to be susceptible to flavivirus infection highlighting the importance of the host’s innate immune response to these viruses31. Recent literature has demonstrated that during ZIKV infection, the production of several types of interferon and ISGs is increased, and that IFITM1 and IFITM3 are involved in inhibiting ZIKV infection during the early stages of pathogenesis33. Enveloped viruses, such as ZIKV, have a membrane of host cell lipids that contains viral fusion proteins; these proteins mediate fusion between the virus and the endosomal membrane of the host cell, a necessary step for initiating infection34. The precise mechanisms by which IFITMs inhibit infection are yet to be described. However, IFITM3 has been shown to impact the function of vesicle-associated membrane protein (VAMP)-associated protein A (VAPA) and oxysterol-binding protein (OSBP) by directly interacting with VAPA. This leads to the accumulation of cholesterol in multivesicular bodies and late endosomes, preventing the fusion of the intraluminal virion-containing vesicles with endosomal membranes35. Additionally, IFITM1 has been shown to suppress cell to cell fusion (syncytia formation) by localizing to the plasma membrane after interferon induction21.Flaviviruses depend on the intracellular membrane of their host throughout their lifecycle36. Therefore, prior to the induction of ISGs, IFITMs may provide initial defense against the infection7. Furthermore, IFITMs have been found to restrict virus replication among other flaviviruses, including West Nile virus and Dengue virus38. Recently, the role of IFITM3 as an antiviral protein against West Nile virus has been exploredin vivo, and-/-Ifitm3 mice exhibited a greater susceptibility to lethal viral infection, with a greater accumulation of viral protein in peripheral organs and central nervous system tissues26. Additionally, IFITM1, IFITM2, and IFITM3 have been shown to have antiviral properties in humans. Polymorphisms in human IFITM3 correlate with the severity of influenza A infection38. These functional members are expressed across a variety of tissues in humans and out of the three, IFITM3 is thought to be the most potent line of defense against viral infection21.There are additional reasons to explore the role of IFITMs in ZIKV restriction. For instance, murine models of ZIKV infection require deficiency of type 1 interferon signaling, which suggests a role for ISGs in restricting infection39 using machine learning to classify features of protein-pairs such as colocalization, coexpression, shared molecular function and biological processes.HiPPIP was also instrumental in discovering thatoligoadenylate synthetase like protein (OASL) interacts withretinoic acid inducible gene I product (RIG-I) to activate the RIG-I immunity pathway during influenza viral infection inhibiting virus replication40. Functional studies initiated solely by this predicted PPI showed that human OASL binds to dsRNA to enhance RIG-I signaling, and that boosting OASL can help inhibit viral infection40. Using novel PPIs predicted with HiPPIP, we could explain the apparent discordance between modern and historical genetic basis of schizophrenia41, and the role of cilia in the pathogenesis of congenital heart disease42. PPIs predicted by our method revealed a molecular basis for the negative association between schizophrenia and rheumatoid arthritis43. These successes demonstrate that there is enormous potential for biomedical discovery buried in the largely-unexplored novel PPIs in the human interactome.As the mechanism through which IFITM1 and IFITM3 mediate restriction is unknown, computational methods could accelerate research by presenting testable hypotheses. Protein-protein interactions (PPIs) prove to be valuable in understanding the function of a protein, and specifically in how it plays a role in causing or preventing disease. Motivated by this, we had developed a computational model called ‘High-confidence Protein-Protein Interaction Prediction’ (HiPPIP) model that identifies novel PPIs in the human interactomeIn this work, we applied the HiPPIP model to discover novel PPIs of IFITM1 and IFITM3, to potentially accelerate the discovery of the mechanism by which they inhibit ZIKV and other viral infections.39 and collecting known PPIs from the Human Protein Reference Database (HPRD)44 and Biological General Repository for Interaction Datasets (BioGRID)45. HiPPIP uses a score cut-off of 0.5 to achieve a high precision of 97.5%, albeit successfully predicting only a few PPIs , when evaluated on a held-out test data. Thus, the novel PPIs predicted by HiPPIP are highly dependable to lead to successful experiments. Furthermore, predicted PPIs with scores ranging from 0.41 to 0.65 were experimentally validated and found to be true interacting pairs39. The HiPPIP model was also computationally evaluated on hub proteins and showed a better performance when compared to Qiet al.’s model46. HiPPIP is configured to present protein-pairs achieving a score of greater than 0.5 to be novel PPIs. Additional information regarding experimental validation of predicted interactions and model evaluation are available in prior work39.We assembled the PPIs of IFITM1 and IFITM3 (‘IFITM interactome’) by predicting novel PPIs with HiPPIP47. Pathways associated with proteins in the interactome were collected using Ingenuity Pathway Analysis® suite (www.ingenuity.com). Gene Ontology (GO) terms enriched in the interactome were computed using the BiNGO plugin of Cytoscape48.IFITM interactome figure was created using Cytoscape49, revealed two main categories: development and innate immunity.We assembled the PPIs of IFITM1 and IFITM3  found that DEAF1-/- deficient mice had lower levels of IFN-beta mRNA compared to the wild type controls, along with lower mRNA levels of other markers of viral infection52.FNDC3B, a membrane protein, was found to be associated with heart rate, height and corneal structure through genome-wide association studies. While its own functions are unknown, its known interactors are involved in regulation of glial cell apoptotic process, regulation of ion transport  and several cardiac processes.SPTA1 which encodes for Spectrin alpha, erythrocytic 1 is among the predicted interactors with functional annotations related to innate immunity. It is a molecular scaffold protein that is involved in linking the plasma membrane to the actin cytoskeleton, thereby guiding cell shape and other processes pertaining to the cell’s structure53. It is also involved in neural functions of actin filament organization, neurite outgrowth and axon guidance. Considering that IFITM1 and IFITM3 regulate permeability of the cell membrane, it is plausible that SPTA1 is also involved in aiding the host cell’s architecture to impede infection. Another predicted interactor that could be related to the host cell’s defense against infection isRASSF7, which is localized to the microtubule organizing center. While its function is unknown, it has known interactions with proteins that are involved in cell proliferation in the brain, regulation of neuroblast proliferation, nervous system development, synaptic vesicle fusion to presynaptic membrane, and viral budding and assembly.TSSC4,TLR7, andARPC1B. TSSC4 is predicted to interact with both IFITM1 and IFITM3. TSSC4’s functions are unknown but its known interactions suggest that it may be involved in viral penetration into host nucleus, protein import into nucleus and immune response signaling, among other processes. TLR7 is involved in several functions and pathways related to innate immunity. It belongs to the Toll-like receptor (TLR) protein family and is involved in the recognition of pathogen-associated molecular patterns (PAMPs), along with stimulating the innate immune response by producing cytokines54. Its known interactions have cellular components related to membrane components and protein binding. ARPC1B is part of actin related protein 2/3 complex; its known interactions suggest that it may be involved in processes of neuronal development such as axonogensis and development, neuron differentiation, nervous system development, and in immune related processessuch as the innate immune response, regulation of the immune response, etc. These functional annotations are sourced from Wiki-Pi49; for example, see DEAF1 athttp://severus.dbmi.pitt.edu/schizo-pi/index.php/gene/view/10522.Other predicted interactors include16. The study with eight samples (four infected and four control samples) showed that the infection of human neural progenitor cells (hNPCs) with the virus caused increased cell death and cell-cycle dysregulation16. We examined whether any of the interacting genes were differentially expressed in that study and found five genes that were differentially expressed with a small fold-change but with significant p-value (< 0.005)  : CD81, N 0.005) .Our goal here is to release the novel PPIs of IFITMs that we could predict computationally, recognizing their importance in mediating immune response in ZIKV infection. These predictions were obtained by using an experimentally and computationally validated algorithm HiPPIP, and are estimated to be highly accurate. Novel PPIs of IFITM1 and IFITM3 shed light on possible mechanisms of host invasion adopted by Zika virus including suppression of the immune system and cognitive and birth defects following infection. Based on the predicted protein-protein interactions, and the functions of the interacting proteins, we formulated three hypotheses about mechanisms of Zika virus infection.55. The fact that multiciliated ependyma is infected by a wide variety of viruses, possess motile cilia, micro villi and aherens junctions and also mediate cellular infiltration into the CNS indicate that it may be functioning as an immunological barrier57. This may point at the possibility that the multiciliated ependymal functions as a barrier which is also susceptible to the mechanisms of host invasion adopted by Zika virus and that cilia dysfunction may be a potential mechanism. In a recent study, delay in cilia disassembly was identified to be the cause of premature differentiation of neural progenitor cells (NPCs) in the ventricular zone which led to microcephaly58. In an earlier study, the same group linked premature differentiation of NPCs to infection by Zika virus and impaired brain cellularity and structural organization of the cortical plate that follows59.IFITM1 and three of the novel interactors  were found in cilia interactome (i.e. interactome of proteins of the organelle cilia), and seemed to be associated with processes that led to hydrocephalus, a condition frequently associated with ciliopathies, in which cerebrospinal fluid accumulate in the brain ventricle giving rise to an enlarged head, viz. dysfunction of ciliated ependymal cells in the brain (unpublished results)60. Moreover, RGCs constitute a barrier between brain parenchyma- a site of Zika infection- and ventricles from the embryonic stage61. Fetal mice infected with Zika virus exhibit a reduction in the size of lateral ventricles62. It has also been suggested that genes implicated in microcephaly may also influence the development of hydrocephalus. For example, ASPM which has undergone rapid evolution over the course of recent evolution of hominids is implicated in the size of cerebral cortex and neurogenesis and is also associated with primary microcephaly63. The identification of ASH domains in ASPM has indicated that it may also be implicated in cilia dysfunction and hydrocephalus64. ASH domains are normally found in proteins that localize to cilia, such as Hydin and OCRL which are associated with microcephaly and Lowe oculocerebrorenal syndrome respectively66. In this respect, it has also been observed that injection of pregnant mice with Zika virus, leads to infection of RGCs that are responsible for cortex development located in the dorsal ventricular zone of fetal mice67.While it has been established that defects in adhesion of ependymal cells and formation of cilia lead to hydrocephaly, its shared mechanisms with microcephaly, within the context of Zika infection has not been studied extensively. Interestingly, it has been reported that radial glial cells (RGCs), that give rise to ependymal cells, and astrocytes are more susceptible to infection by Zika virus than neurons68. It is induced during maturation stages of angiogenesis in vitro while in vivo it is stably expressed by microvascular endothelial cells which are quiescent68. A role for IFITM1 in formation of stable contacts between cells during lumen formation in endothelial tissue was revealed by a study. On knockdown of IFITM1, intercellular vacuoles failed to fuse and form a multicellular lumen due to the mislocalization of OCLN (occludin), which normally interacts with IFITM1 and localizes to tight junctions between endothelial cells68. It was speculated that the mechanism allowing IFITM1 to regulate assembly of tight junctions may be related to endosomal trafficking since internalized OCLN is returned to the plasma membrane from recycling endosomes during remodeling of tight junctions in endothelial cells. IFITM1 has also been shown to function in the endosomal pathway to inhibit viral infection69. Microcephaly may arise due to dysfunctional DNA repair systems that lead to increased apoptosis of neural progenitor cells having a low endurance for damage and under normal conditions, migrate to the cortical plate to form various structures in the brain70. Depletion of RAB80 or UIMC1- a novel interactor of IFITM1- which occurs in a complex with p73 when overexpressed, impairs translocation of BRCA1 to DNA damage sites resulting in defective control of cell cycle and repair of double strand breaks72. p73 has been implicated in the development and maintenance of ependymal cells and in animals deficient in p73, increased apoptosis and lack of differentiation of RGCs into ependymal cells accompanied by loss of motile cilia resulting in hydrocephalus and hippocampal dysgenesis were reported74. NME5 is highly expressed in ependymal cells and moderate to marked hydrocephalus along with ciliary dysfunction has been observed in mice homozygous for NME575. It is known that patients with HSV-2 (Herpes simplex virus-2) infection are more susceptible to the teratogenic effects induced by Zika virus, since HSV-2 infection enhances the sensitivity of placental tissue which facilitates the entry of Zika virus into cells mediated by TAM receptors that recognize pathogen associated molecular patterns (PAMPs)77. It has been shown that VP26, the capsid surface protein of HSV, interacts with DYNLT3 which is a component of the motor protein, cytoplasmic dynein 1, mediating retrograde movement of vesicles and organelles along microtubules within cells78.The expression of IFITM1 in endothelial cells of various organs such as the bladder, brain and stomach is correlated with the maturation of blood vessels80. RASSF7 is a centrosomal protein that regulates microtubule dynamics, the knockdown of which inXenopus has been reported to cause nuclear breakdown, apoptosis and loss of tissue architecture in the neural tube81. One of the novel interactors predicted for IFITM3 is DEAF1, mutations in which has been linked to white matter disease, microcephaly and syndromic intellectual diability using whole exome sequencing82. Exencephaly has been observed in mice homozygous for DEAF1 which is involved in the development of the neural tube83. TSSC4 is associated with Beckwith Wiedemann syndrome which is incidentally also characterized by microcephaly in addition to other physical manifestations and interacts with CEP76, a candidate gene associated with autosomal recessive congenital microcephaly and found in ciliated cells86. The novel interactor PHLDA2 is also associated with Beckwith Wiedemann syndrome87.RASSF7 (another novel interactor of IFITM1), has been shown to directly interact with DISC1 whose role in formation of astrocytes, which express primary cilia, in the embryonic brain via modulation of RAS/MEK/ERK signaling has been revealed in a study88. So, the novel interactions of TLR7 and CD81 with IFITM1 may also serve as potential targets of Zika virus by which it suppresses immune responses of the host.The novel interactions of TLR7 and CD81 with IFITM1 opens up the possibility that the inhibitory action of IFITM1 on Zika virus may be mediated by effector functions of natural killer (NK) cells. At the same time, it has been shown that Zika viruses evade anti-viral responses by inhibiting interferon-mediated signaling89. TLR7 (a novel interactor predicted for IFITM1) recognizes viral RNA in endosomes and mounts defense mechanisms against flaviviruses including JEV  by activating NK cells to stimulate secretion of pro-inflammatory cytokines, increase microglial activation and infiltration of peripheral immune cells in the brain91. Mice with TLR7 knocked out have been shown to be more susceptible to infection by JEV92. Another novel interactor of IFITM1- CD81 inhibits functions of NK cells when cross linked with HCV (Hepatitis C virus) and associates with GRP56 which negatively regulates effector responses of NK-92 cells dependent on IL-2 signaling including secretion of pro-inflammatory cytokines and cytolytic proteins93. Also, malformations associated with microcephaly syndrome have been reported in patients with null GPR5694.NK cells when activated combat infection by Zika virus95. The novel interactions of FNDC3B with IFITM3 and SPTA1, ARPC1B and AGTR2 with IFITM1 may shed light on mechanisms of host invasion underlying cytoskeletal re-arrangements that follow Zika virus infection.Infection by Zika virus has been known to cause drastic re-organization of cytoskeletal structures surrounding replication factories in the cell, which are regions of the endoplasmic reticulum re-modeled following viral infection to allow viral replication96. FNDC3B has been found to be upregulated in samples of hepatocellular carcinoma infected with HBV (Hepatitis B virus) or HCV and overexpressed in normal liver tissue infected with viral hepatitis97. It has also been reported that the expression of FNDC3B increases on induction of GDF-15 which is known to regulate expression of genes that orchestrate changes in cytoskeleton and adhesion junctions97. In this respect, it is interesting to note that IFITM3 has been suggested to inhibit early stages of Zika virus infection by directly altering the properties of cell and/or viral membranes and blocking formation of fusion pores98. SPTA1 is associated with spinocerebellar ataxia which also has microcephaly as one of its physical manifestations99. Spectrins form complexes with intracellular networks by interacting with actin, ankyrin and adducin99. Deletion of adducin gives rise to lethal hydrocephalus in mice100. Spectrins influence cell adhesion and spreading and are also found in the cortical cytoplasm of ependymal cells99. It has been reported that OSGEP and TP53RK, both of which are implicated in nephrotic syndrome with primary microcephaly, interact with components of the ARP2/3 complex including ARPC1B involved in actin remodeling at lamellipodia102. Lesions in white matter found in elderly people have been associated with polymorphisms in AGTR2, a novel interactor of IFITM1and an antagonist of AGTR1, which influences angiogenesis induced by VEGF103. Cytoskeletal events guided by VEGF signaling are known to orchestrate formation of vascular lumen, which might contribute to the development of microcephaly104.FNDC3B- a novel interactor of IFITM3 promotes cell migration by cooperating with ANXA2http://severus.dbmi.pitt.edu/schizo-pi orhttp://severus.dbmi.pitt.edu/wiki-pi for annotations compiled from various databases for each of the individual proteins.SeeAll pertaining data are provided in the manuscript. "Thus, the novel PPIs predicted by HiPPIP are highly dependable to lead to successful experiments" -I am not sure what this means, and"Furthermore, predicted PPIs with scores ranging from 0.41 to 0.65 were experimentally validated and found to be true interacting pairs" - I am not convinced about the experimental validation detailed in this prior publication as the presented immunoprecipitation data does not include appropriate negative controls, molecular weight markers, details about antibody sources or specificity etc.). Future studies of this nature would be greatly strengthened by experimental validation of computationally predicted protein-protein interactions and exploration of the importance of the candidate host factors for the ZIKV replication cycle .The changes included in this revised manuscript somewhat improve the description of the HiPPiP model and add some useful  discussion of the potential mechanisms of IFITM antiviral activities against ZIKV. However, I am still not entirely convinced of the validity of the HiPPiP model and some of the the statements supporting this model appear to be exaggerated  called High-confidence Protein-Protein Interaction Prediction (HiPPIP) to predict novel PPIs of two small membrane-associated interferon-inducible transmembrane proteins IFITM1 and IFITM3. The authors cite previously reported data on the role of IFITM1 and IFITM3 in inhibiting Zika virus (ZIKV) and make a valid argument that the novel PPIs of IFITM1 and IFITM3 from the HiPPIP model could possibly predict mechanisms of host invasion and immune suppression by Zika virus (ZIKV) and members of its familyI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Looks goodI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This manuscript presents a use model of the a protein-protein interaction method developed by the author along with other collaborators, focused on a very important pathogen (Zika) in the present circumstances. It is lucidly written and well presented. The biggest shortcoming of this manuscript is the failure to cite several previous work related to the interferon-inducible transmembrane protein 3 . Furthermore, the current manuscript and its stated methodology does not find two proteins (VAPA and OSBP) that have been shown to have interactions with IFITM3. This would have been a clincher. It would also be proper to mention other work related to IFITM mechanism  - and focus on the viral membrane hemifusion mentioned there."functional studies initiated solely" - capitalize functional.Widespread use of "we" with only a single author."Computationally discovered PPIs have been shown to be highly accurate by computational evaluations and experimental validations of a few PPIs'." - overstated and speculative.It is also speculative to correlate the expression changes of predicted interactors to these IFITMs, since there can be multiple other reasons for such expression changes . Minor comments:I have read this submission. I believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. 1 the HiPPIP model (and associated publications) was not referred to. The HiPPIP model should be more clearly described here, including some analysis/discussion of its success rate in predicting protein-protein interactions that have been experimentally validated.The report is largely based upon the predictions generated by the HiPPIP model. As this model is not well-described here and has not been extensively validated experimentally in the literature, it would be helpful if the criteria/rules employed by the model were described . In the Methods section it is simply stated that 'Computationally discovered PPIs have been shown to be highly accurate by computational evaluations and experimental validations of a few PPIs'. This statement is not well-justified and may be misleading. In support of the HiPPIP model, the author refers to the successful prediction of OASL interaction with RIG-I . However in the cited publication (on which Dr. Ganapathiraju is an author)As this report focuses on IFITM1 and IFITM3 antiviral functions and predicted interactions, it would be useful if the features and properties of these proteins were at least briefly described . This would help to interpret the significance of the predicted interactions.In the Results and Discussion some of the roles/properties of the predicted interacting partners are listed. It would be helpful if references were provided for these functions/properties (even if only to reviews). Furthermore, it would be helpful if these properties were discussed in the context of Zika virus and/or IFITM biology .The manuscript should be carefully edited to clarify interactions that are purely predicted and not supported by experimental evidence (e.g. statements like 'TSSC4 interacts with IFITM1 and IFITM3' are misleading).A conclusion/summary paragraph that briefly summarizes the major findings and their significance and potential future directions may be appropriate.   This manuscript reports a number of novel protein interactions of IFITM1 and IFITM3 proteins, as determined using a 'High-confidence Protein-Protein Interaction Prediction (HiPPIP)' computational prediction model of protein-protein interactions. Analysis of functional and pathway associations of the putative interacting proteins is also reported, as they may relate to IFITM-mediated restriction of Zika virus infection. The manuscript is generally well-written and the title is appropriate. However, there are several ways in which the study and manuscript could be improved:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. The title, abstract and article overall are well written and clear. The design, methods and analysis are mostly well described, although some detail could be added. In particular, given that the IFITM interactome contains a large number of previously unknown PPI’s, it would be useful to give a little more detail on the methodology on how these predictions were obtained, rather than just stating that HiPPIP was used. In particular, it would be useful to understand what cut-off was used and a brief mentioning of the prediction methodology in general. An estimate of false positive and false negative errors for the prediction in Figure 1 would be particularly helpful. In the analysis, the legend for Figure 2 needs expansion. It is not clear what the edges signify and in particular what is the meaning of directionality in the arrows. I object to the wording used on page 2 “which was validated to be a true PPI”, as any PPI evidence is debatable. I would reword to “which was experimentally validated”. I also object to the wording used on page 2 “Computationally discovered PPIs have been shown to be highly accurate”. Such a blanket statement is clearly not true, as there are many predictions out there that are highly inaccurate. A more specific example needs to be provided here. A minor suggestion is to replace “HiPPIP model” with “the HiPPIP model”.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Nutrition interventions may have favourable as well as unfavourable effects. The Maternal and Infant Nutrition Interventions in Matlab (MINIMat), with early prenatal food and micronutrient supplementation, reduced infant mortality and were reported to be very cost-effective. However, the multiple micronutrients (MMS) supplement was associated with an increased risk of stunted growth in infancy and early childhood. This unfavourable outcome was not included in the previous cost-effectiveness analysis. The aim of this study is to evaluate whether the MINIMat interventions remain cost-effective in view of both favourable  and unfavourable (increased stunting) outcomes.Pregnant women in rural Bangladesh, where food insecurity still is prevalent, were randomized to early (E) or usual (U) invitation to be given food supplementation and daily doses of 30 mg, or 60 mg iron with 400 μg of folic acid, or MMS with 15 micronutrients including 30 mg iron and 400 μg of folic acid. E reduced stunting at 4.5 years compared with U, MMS increased stunting at 4.5 years compared with Fe60, while the combination EMMS reduced infant mortality compared with UFe60. The outcome measure used was disability adjusted life years (DALYs), a measure of overall disease burden that combines years of life lost due to premature mortality  and years lived with disability (stunting). Incremental cost effectiveness ratios were calculated using cost data from already published studies.By incrementing UFe60 (standard practice) to EMMS, one DALY could be averted at a cost of US$24.When both favourable and unfavourable outcomes were included in the analysis, early prenatal food and multiple micronutrient interventions remained highly cost effective and seem to be meaningful from a public health perspective. Impaired antenatal and early life nutrition increases the risk of neonatal deaths and stunting. According to global estimates, maternal and child undernutrition causes 3.1 million child deaths annually . The chiReports from studies aimed at studying the efficacy and safety of multiple micronutrient (MMS) supplementation during pregnancy has varied; from encouraging results such as reduced prevalence of low birth weight,12 and sisrctn.org identifier ISRCTN16581394), prenatal MMS supplementation was combined with early or usual timing of food supplementation. Previous results have demonstrated substantial reduction in under-five year mortality with MMS combined with early (E) invitation to prenatal food supplementation (i.e. EMMS) compared with the standard 60 mg iron, 400 gm folic acid and invitation to food supplementation at the usual time in pregnancy (UFe60F)[In the randomized MINIMat trial . The pot (UFe60F). This coThe MINIMat interventions have been reported to be cost-effective in respect of the primary outcome of reduced infant mortality. Howevern = 738), EMMS (n = 740), EFe30F (n = 739), UFe60F (n = 738), UMMS (n = 740) and UFe30F (n = 741). In total, 4436 pregnant women participated, giving birth to 3625 live born children from April 2002 to June 2004. Children of mothers participating in the MINIMat trial were followed from birth to 5 years in regards to mortality and to 4.5 years in regards to stunting. The 4.5-year follow-up took place from April 2007 to February 2009.The data originate from the MINIMat trial , a factorial randomized trial carried out in Matlab, Bangladesh, a rural sub-district 57 km south-east of the capital Dhaka where food insecurity is still prevalent. At the time when the trial was implemented, the under-five mortality rate in Bangladesh was 74 per 1000 live births  and the Written informed consent was obtained from all parents of participating children . The Ethical Review Committee at the International Centre for Diarrhoeal Disease Research, Bangladesh and the Regional Ethical Review Board at Uppsala University, Sweden approved the study .Cost data were retrieved from a recent study by Shaheen et al., which eThe effect measure is presented as disability adjusted life years (DALYs) averted. DALYs are a measure of overall disease burden that combines years of life lost due to premature mortality (YLLs) and the time spent in an impaired health state, measured as years lived with disability (YLDs). One DALY lost is to be interpreted as one lost year of healthy life. Consequently, one DALY averted is equivalent to the gain of one year of healthy life. Calculating the cost per DALY averted facilitates comparison between different health interventions as well as enables the evaluation of an intervention’s effects on both premature death and long-term disability. The term disability here refers to loss of health due to stunting.DALYs are typically attributed to a specific disease, health state or age group. As the MINIMat interventions had an effect on both under-five deaths and stunting, DALYs were equal to YLL due to all-cause premature mortality plus YLD due to stunting. DALYs were calculated based on strictly empirical data from the trial for up to five years (DALY5s) and from a lifetime perspective based on secondary data (DALYs). To calculate YLL for DALY5s, we used the exact mortality age of the children who died and subtracted the age at death from 5 years. YLDs were calculated as the sum of months the children had been stunted from birth to 5 years  times a disability weight. The chiTo calculate DALYs from a lifetime perspective, the number of YLL and YLDs were created using the following assumptions. As most child deaths occurred in early infancy, the life expectancy at birth (LE) was used for YLLs. The remaining life expectancy (RLE) at 5 years was used for YLDs. LE and RLE at 5 years in the Matlab area were based on the demographic surveillance data from the year 2003 and 2008, estimated to 69.3 and 69.7 for girls and 67.8 and 66.9 years for boys,28. YLLsChildren were assumed to grow linearly. Height data were linearly interpolated if height measurements were missing in the age interval between birth and 2 years of age. From 2 to 4.5 years, height-for-age z-scores were imputed every 3 months.Duration of disability: Duration of stunting, for up to 5 years, was obtained from observed data. Children who were stunted at 4.5 years were assumed to be stunted at 5 years.Disability weights used: 0.002 (stunting) and 0.024 . Stunted children were assumed to be equally affected by the disability caused by stunting throughout infancy and childhood.Discount rate: No discounting.Duration of disability: Duration of stunting, for up to 5 years, was obtained from observed data. From 5 to 10 years, the average proportion of recovery and incidence of stunting in each intervention group between 4.5 and 10 were used. Children who were stunted at 4.5 years were assumed to be stunted at 5 years, and children who were stunted at 10 years were assumed to continue being stunted throughout life.Disability weights used: 0.002 (stunting) and 0.024 . Stunted children were assumed to be equally affected by the disability caused by stunting throughout infancy, childhood and adulthood.Discount rate: 3%.Life expectancy: LE at birth for YLL, and RLE at 5 years for YLD. Discounted LE and RLE for girls; 28.66 and 29.03 years. Discounted LE and RLE for boys; 28.16 and 28.84To obtain incremental cost effectiveness ratios (ICERs) for DALYs averted, we calculated the costs for supplementing 1 pregnant woman following the regimes in each intervention arm. These were then sorted according to ascending costs and dominating alternatives were excluded . This procedure resulted in three remaining intervention arms. Lastly, we calculated the increment in costs and DALYs across these three arms and divided the cost differences by the differences between DALYs, which gave the ICERs.There were 4,436 women enrolled into the MINIMat trial, of whom 845 were lost to follow-up before delivery, mainly due to fetal loss, outmigration or because they withdrew their consent. Of the 3,625 live born children 2,851 had anthropometry at 4.5 years . BackgroAdherence levels and costs for a hypothetical highest-cost delivery scenario are presented in Years lost to premature mortality (YLL), years lived with stunting (YLS), years lived with disability (YLD) and disability adjusted life years (DALYs) lost, for up to 5 years and calculated from a lifetime perspective, are presented in Tables Incremental costs, incremental DALYs and incremental cost-effectiveness ratios (ICER) are presented in When favourable  and unfavourable (increased stunting), outcomes of the MINIMat trial were included in the analysis the implementation of EMMS supplementation, when compared to U60Fe, remained highly cost-effective. The incremental cost effectiveness ratio per 5 years and lifetime DALY averted of US$187 and US$24 both fall well below Bangladesh’s per-capita gross national income (US$1190 in 2015) and compare favourably to corresponding interventions evaluated elsewhere in South Asia–33. In aThe main aim of this study was to evaluate the cost of switching from the routine prenatal supplementation of 60 mg iron and folate plus the usual timing (week 20) of daily food supplements (U60Fe) to the potentially superior multiple micronutrients plus an early invitation (week 9) to food supplementation (EMMS) including both favourable and unfavourable outcomes, but all the intervention arms were included in the analysis. It turned out that switching from U60Fe to U30Fe had the lowest ICER, at US$1.4 per DALY averted. As the cost for the two interventions were similar, the ICER for switching from U60Fe to U30Fe was mainly driven by the slightly lower DALYs in the U30Fe group due to primarily lower YLL but also lower YLD estimates. The fact that U30Fe would have a lower YLL estimate was somewhat expected as the U30Fe group had a lower mortality hazard ratio in the survival analysis by Persson et al.  than U60Although switching from U60fe to U30Fe had the lowest ICER, EMMS had the lowest DALY estimate. Switching from U60Fe to EMMS would avert twice as many DALYs as switching to U30Fe. As the ICER of US$24 can be considered highly cost-effective and affordable in a low-resource setting, switching from U60Fe to EMMS is viable both from a public health and economic perspective. In addition, as mentioned above, when modelled, the estimates that generated the lower YLLs and YLDs in the U30Fe intervention group were not statistical significant.The lower DALYs in the EMMS group was driven primarily by the lower YLLs as the difference in YLD between U60Fe and EMMS was minor. Meta-analyses of prenatal multiple micronutrient supplementation trials have reported limited increases in birth weight in comparison with iron-folic acid supplementation alone,12, and early pregnancy (UMMS) had the highest DALY estimates as a result of both the highest number of YLLs and YLDs. Further, in a recently published study the MINIMat trial children allocated to the multiple micronutrient supplementation displayed an unfavourable metabolic profile at 4,5 years with lower levels of IGF and HDL[What makes the MINIMat interventions different to the trials included in the above-mentioned studies is that the multiple micronutrient supplementation was combined with food supplementation early in pregnancy and the reduction in mortality was only seen as a combined effect of early food supplementation and MMS. MMS given without food supplementation in F and HDL. These rThe MINIMat trial was a community-based trial, conducted in an area with a well-established health and demographic surveillance system and an excellent research infrastructure that fulfils the prerequisites for obtaining high-quality data. The randomised design minimised the risk of potential confounding and double masking of the micronutrient intervention reduced the risk of reporting or observation bias.In the area where the trial was conducted, women are still frequently exposed to food insecurity and enter pregnancy deficient in several micronutrients. This makes the ICERs estimates valid to other rural areas in Bangladesh and to similar settings in other countries that struggle with problems of widespread maternal malnutrition. The cost data retrieved from the study by Shaheen et al. were collected within the same time-frame and in a situation similar to that of the MINIMat trial, which is why we believe they reasonably represent costs associated with the MINIMat interventions. Shaheen et al. presented three different delivery modes, resulting in three cost scenarios, NGO-run clinics, governmental-run clinics and a highest-cost delivery mode where the highest costs for different items from NGO- and government-run clinics were combined. As we used the highest cost scenario in our analysis, our generated ICERs might be somewhat overestimated.Most nutrition interventions have multiple objectives and outcomes. In this study, we included two outcomes; premature mortality, and disability caused by stunting, aware that this do not represent all potential benefits or harms of the intervention.When evaluating the cost-effectiveness of multiple outcomes, the issue of assigning values to the different outcomes becomes essential. The disability weight attributed to stunting by the global burden of disease study, 0.002, is small. In order to avoid underestimating the long-term effects of stunting, we chose to include the larger disability weight attributed to development delay due to malnutrition, although not all stunted children will suffer from cognitive impairment. When using the higher disability weight, the ICER only increased from US$24 to US$26 as differences in YLS between the intervention groups were small. The differences in YLS and YLL between the intervention group with the lowest and highest estimate were 3.2 YLS (UMMS-U30Fe) and 1.47 YLL (UMMS-EMMS) respectively. Thus, for the differences in YLS to change the ranking of the highest and lowest DALY estimate, the disability weight would have to be at least as high as 0.4 (1.47/3.2), equal to multiple sclerosis or bipolar disorder. FurtherAnother key assumption associated with the impact of stunting was that all children who were stunted at 10 years continued to be stunted throughout life, based on the fact that adult stature is strongly associated with pre-pubertal height . This asThe estimate of US$187 per DALY averted for up to 5 years was based on strictly observed data from the trial and was not subject to any assumptions except the disability weight for stunting. However, by not considering the number of life years lost to premature mortality and disability beyond the age of 5 years, the intervention is at a disadvantage when compared with alternatives, which are usually analysed from a lifetime perspective.Including stunting as an outcome did not alter the conclusion that the MINIMat interventions of early prenatal food and MMS supplementation were highly cost-effective in a population where maternal undernutrition is still common. The ICER of US$24 for switching from invitation to food supplementation at the usual time in pregnancy and iron-folic acid supplementation to an early initiation of food supplementation combined with MMS can be considered affordable in a low-resource setting and viable from both a public health and economic perspective. These results can hopefully inspire others to consider multiple outcomes of nutritional interventions in economic evaluations and help to support decision-makers in the prioritizing of financing for nutrition interventions targeting pregnant women’s health.S1 Table(DOCX)Click here for additional data file.S1 Dataset(SAV)Click here for additional data file.


Inference of demographic history from genetic data is a primary goal of population genetics of model and nonmodel organisms. Whole genome-based approaches such as the pairwise/multiple sequentially Markovian coalescent methods use genomic data from one to four individuals to infer the demographic history of an entire population, while site frequency spectrum (SFS)-based methods use the distribution of allele frequencies in a sample to reconstruct the same historical events. Although both methods are extensively used in empirical studies and perform well on data simulated under simple models, there have been only limited comparisons of them in more complex and realistic settings. Here we use published demographic models based on data from three human populations  as an empirical test case to study the behavior of both inference procedures. We find that several of the demographic histories inferred by the whole genome-based methods do not predict the genome-wide distribution of heterozygosity, nor do they predict the empirical SFS. However, using simulated data, we also find that the whole genome methods can reconstruct the complex demographic models inferred by SFS-based methods, suggesting that the discordant patterns of genetic variation are not attributable to a lack of statistical power, but may reflect unmodeled complexities in the underlying demography. More generally, our findings indicate that demographic inference from a small number of genomes, routine in genomic studies of nonmodel organisms, should be interpreted cautiously, as these models cannot recapitulate other summaries of the data. The pairwise sequentially Markovian coalescent (PSMC) and related methods have become a popular tool to estimate the history of a population from genetic variation data . These mPSMC plots have also become a cornerstone of many studies of nonmodel organisms lacking resources for the sequencing of numerous individuals, including archaic hominins . The SFS represents the distribution of alleles at different frequencies in a sample of individuals from a population . The disSFS-based and whole genome-based methods may have different strengths and weaknesses for demographic inference . TheoreteN (effective population size) generations ago and in its ability to detect population declines  information and the SFS . One of Due to anthropological and biomedical interest, humans have been extensively studied using numerous demographic inference methods and provide a means to quantitatively compare these demographic inference approaches using the same empirical populations. Here we leverage humans as a model system to perform an empirical comparison of the performance of whole genome, SFS, and combined methods of demographic inference. Specifically, we determine which published models of human demography described above  best fitWe find that the models inferred using the SFS or the combined method, SMC++, accurately recapitulate heterozygosity and the observed SFS. Among the MSMC models inferred by We determined which, if any, of the published models of human demography  describeThe first set of demographic models was jointly inferred for the three populations in ∂a∂i by File S1).The next nine models were inferred by File S1).To analyze their models with ∂a∂i, we converted these nine demographic models  into stepwise models of population size changes over small time intervals .The final set of models was inferred by File S1). While heterozygosity is a summary of the SFS, we considered it valuable to examine both statistics since information regarding the spatial correlation among SNPs along the genome is lost in the genome-wide SFS. The distribution of heterozygosity across windows of the genome retains some spatial information and is more similar to what is used by the MSMC inference approach.We compared the distribution of expected heterozygosity from data simulated under each demographic model to empirical 1000 Genomes data from the same populations to determine which models most accurately predict this broad summary of the data (File S1 for sequence IDs) were randomly chosen so that comparisons could be made with 1000 Genomes data from the CEU, CHB, and YRI populations were downloaded. 10 unrelated individuals per population (see Supplementary Note 3 in π) was calculated in nonoverlapping 100-kb windows from the whole genome data (Supplementary Note 3 in File S1) as:p is the frequency of one allele, L is the total number of callable sites in the window, and n is number of sampled chromosomes .Expected heterozygosity per site (File S1). The NRE is a useful tool that allows for the quick identification of putatively neutral regions that have high recombination rates and high B-values (indicating less linked selection). For the full set of parameters used in selection of putatively neutral regions, see Supplementary Note 3 in File S1.Because genetic variation can be affected by linked natural selection .To demonstrate that the use of the SMC′ approximation in the MaCS  .We used the diffusion approximation in ∂a∂i .To make sure our results were consistent with SFSs derived from other sequencing methodologies and different genomic regions, we also generated folded proportional genome-wide and neutral SFSs from the 1000 Genomes data described above [1000 Genomes (whole genome) and 1000 Genomes ] (File S1).Expected SFSs for a sample size of 10 diploid individuals were calculated in ∂a∂i . To testFile S1) and absolute  SFSs (File). The proportional SFS was calculated by dividing each bin of the SFS output by ∂a∂i by the sum of the bins. The absolute SFS was calculated by scaling the SFS output by ∂a∂i  in −8 mutations per base per generation, and −8 mutations per base per generation .We generated both the proportional . The fit of different models was compared by examining their decrease in log-likelihood compared to that of each of the observed SFSs to itself . Due to the uncertainty of singleton SNP calls using high-throughput sequencing data, log-likelihoods were calculated both with singletons and with the SFS renormalized without the singletons category when comparing to the 1000 Genomes SFSs .Log-likelihoods were calculated for each proportional SFS relative to each of the three observed SFSs  using a multinomial log-likelihood (File S1).Log-likelihoods were calculated for each absolute SFS (in terms of SNP counts) using a Poisson likelihood relative to the observed (Gutenkunst) SFS (Supplementary Note 4 and Table S3 in AN) in the MSMC trajectories would result in SFSs that better fit the observed SFS, we adjusted the CEU MSMC 2-haplotype model to have a variety of AN values. We also trimmed the model to remove ancient events (older than 225.5 KYA) to better match the time period (in years) encompassed by the File S1 describes the values of AN used when testing the trimmed and untrimmed models .To investigate whether changing the ancestral population size .Each simulated genome was then used for a separate MSMC inference, using the default parameters  Figure . To deteFile S1).We simulated data under more complex demographic histories, first to explore the relative abilities of the MSMC 2-haplotype and 8-haplotype models to infer extreme recent growth, and then to determine whether the addition of Neanderthal admixture may lead to MSMC trajectories resembling those inferred from real data by All code to simulate data under each demographic model, calculate heterozygosity, and generate the SFS from simulated and empirical data are available on GitHub: github.com/LohmuellerLab/Compare_Demographic_Models.We compared published models of demography for three human populations  inferred using different methods for demographic inference: (1) using the SFS in ∂a∂i (Gutenkunst) , and (3)The distribution of expected heterozygosity across 100- and 10-kb blocks was calculated from data simulated under each published demographic model for each of the three populations and compared to empirical distributions of heterozygosity based on whole genome and putatively neutral sequence data from the 1000 Genomes Project.File S1).We find that the Gutenkunst demographic model inferred from the SFS, the MSMC 2-haplotype model, and the SMC++ model all yielded distributions of heterozygosity that resemble the empirical whole genome distribution of heterozygosity, with MSMC 2-haplotype fitting the mean most closely . HoweverFile S1) , with mean whole genome heterozygosity distributions nearly seven times larger than that of the empirical whole genome distribution (File S1). The MSMC 8-haplotye model for YRI infers a similarly large ancestral size and has a similarly high mean heterozygosity as the 4-haplotype YRI model. The MSMC 8-haplotype models for CEU and CHB, however, infer much lower ancestral sizes   . Due to File S1; e.g., CEU mean heterozygosity per site for whole genome: 7.8 × 10−4vs. neutral: 9.4 × 10−4), suggesting that natural selection has directly and/or indirectly affected genome-wide patterns of heterozygosity. When the published demographic models were compared to the neutral heterozygosity distributions, we found similar trends to those seen for the whole genome data .When examining the 1000 Genomes data, we found that heterozygosity in the neutral regions was higher than that seen for the genome-wide distribution of heterozygosity calculated in 10-kb windows .We found that the lack of fit is not due to the use of the SMC′ approximation in the simulator MaCS , a coaleLastly, we examined which of the demographic models could match the SFS of the empirical data. To account for the possibility of overfitting the SFS-based Gutenkunst model to the SFS it was inferred from, we also compared all models to empirical SFSs based on low-coverage, high-throughput 1000 Genomes sequence data from the same three populations.File S1). The expected SFS based on the For each population, the SFSs predicted by the three MSMC models do not match the empirical proportional SFS from Interestingly, there is not a consistent improvement in fit to the observed SFS when increasing the number of individuals used for the MSMC inference. For each population, the 4-haplotype model has the worst fit . For CEUFile S1), and is only 9, 49, and 17 log-likelihood units below the best possible fits for CEU, CHB, and YRI models, respectively. The SMC++ models have the next-best fit to the absolute SFS, but come 86 (CEU), 176 (CHB), and 193 (YRI) log-likelihood units below the best possible fit; followed by MSMC 2-haplotype which fell 278 (CEU), 378 (CHB), and 455 (YRI) log-likelihood units below the optimal fit (Table S3 in File S1). In all three populations, the MSMC 4-haplotype and 8-haplotype models are thousands of log-likelihood units worse than the best possible fit, showing no improvement based on using a larger number of individuals in the inference (Table S3 in File S1). The overestimation of SNPs in the 4-haplotype model is due to the model’s extremely high predicted ancestral size  (Table S3 in File S1).The above comparisons considered the proportions of SNPs at specific frequencies in the sample. We also performed a comparison of the number of SNPs in each bin of the SFS, the absolute SFS, to the observed absolute SFS used in File S1).For both the proportional and absolute SFSs, we found that rescaling the models using a higher mutation rate did not produce large qualitative differences in how the MSMC models fit the observed (Gutenkunst) SFS . The fit to the empirical singletons bin was poor for all models, except for SMC++, which was, in part, fit to an SFS based on 1000 Genomes data. Calling singletons is notoriously difficult in low-coverage data, making that bin the least reliable in the 1000 Genomes data (File S1).To avoid giving the Gutenkunst model an unfair advantage by fitting all models to the SFS used to infer that particular model, we also compared all models to proportional folded SFSs based on whole genome and neutral data from the 1000 Genomes Project (mes data . We therFile S1). For CEU and CHB, if singletons are included, SMC++ fits the whole genome and neutral 1000 Genomes SFSs best. For CEU, the Gutenkunst model then fits second best, with the MSMC models far behind (File S1). For CHB, the MSMC 2-haplotype fits second best after SMC++, with the Gutenkunst model coming third, but both are >10,000 log-likelihood units below SMC++ (File S1). If singletons are excluded for CEU and CHB, then the Gutenkunst model fits best, with SMC++ coming in second, and the MSMC models all ranking far below (Table S4 in File S1).For YRI, the Gutenkunst model is the best-fitting model for the whole genome and neutral 1000 Genomes SFSs, both with and without singletons, with all other models having a much worse fit  , using the whole genome-based methods remains unclear . As discFile S1). We also truncated the MSMC trajectory to remove ancient events and better match the time period (in years) encompassed by the File S1).To test this hypothesis, we took the best fitting of the MSMC models, the CEU 2-haplotype model, and carried out a series of adjustment experiments to determine whether changes to the model could provide a better fit to the observed SFS. Without adjusting the time period encompassed by the model, we altered the ancestral population size to a variety of values including those inferred by File S1). The best-fit model with ancestral size (AN) equal to 12,300 was brought to within 38 log-likelihood units of the best possible likelihood , only 29 units below the Gutenkunst model. When repeating this procedure using the SFS based on counts, the SFSs under these adjusted models showed a different pattern of improvement. Here the untrimmed models that did not have ancient events >225 KYA trimmed away, but had a lowered ancestral population size of 7300–12,300, showed the most improvement . However, their fit was still >100 log-likelihood units worse than the Gutenkunst model .We found trimming away the ancient (older than ∼225 KYA) part of the demographic trajectory and lowering the ancestral population size to 10,000–12,300  dramatically improved the fit of the proportional SFSs predicted under these adjusted models to the observed (Gutenkunst) SFS , with the SFSs based on SNP counts showing a similar pattern (Table S3 in File S1). Therefore, if the Gutenkunst model is the true demographic model for human history, MSMC accurately captures the population size changes and produces an appropriate SFS.The SFSs predicted by the demographic models inferred using MSMC on the simulated data fit the SFS expected under the Gutenkunst model and the observed Gutenkunst SFSs better than the MSMC demographic models inferred by File S1). Unsurprisingly, MSMC 2-haplotype was not able to detect extreme recent growth. Its estimates of current population size were fairly accurate for the original Gutenkunst model (File S1). The results from 8-haplotype MSMC inference were most surprising. We found that for both models, MSMC 8-haplotype inferred extreme recent growth as many as four orders of magnitude beyond that in the underlying model, with a high degree of variance between replicates . Despite the high degree of variance, the average of the MSMC trajectories all showed a strong upward bias in estimates of the recent past . While the ability to detect recent growth is meant to be a feature of MSMC, our findings indicate that the magnitude of growth may not be estimated well.It is well established that two-haplotype, whole genome-based inference  is not able to detect recent demographic events . Howeverst model , but theFile S1).We had hypothesized that Neanderthal admixture could cause deviation between the MSMC and Gutenkunst demographic models, but found that the addition of Neanderthal admixture to our Gutenkunst model simulations did not substantively change the MSMC trajectories or expected SFSs . This result is at odds with the findings of File S1). It is worth noting that the model inferred in SMC++ used the same mutation rate as MSMC, yet fit the empirical SFSs much better (File S1), indicating that mutation rate differences between the whole genome- and SFS-based studies is not the source of the discrepancies.We found that the models based on MSMC inference from four or eight haplotypes did not improve the fit of the expected SFS compared to that based on two haplotypes; in fact, in most cases the 4- and 8-haplotype models fit much worse than the 2-haplotype models. The 4-haplotype models for CEU, CHB, and YRI and the 8-haplotype model for YRI appear to fit poorly due to their extremely high ancestral sizes and ancient humps of growth and decline . The expWe found that in addition to not fitting the empirical SFS, the MSMC 4-haplotype and 8-haplotype models did not predict the genome-wide distribution of heterozygosity , which mFile S1). However, the final bin of the model cannot explain all of the lack of fit of the MSMC models to the data as the CEU and CHB MSMC 8-haplotype trajectories do not show the extreme ancestral sizes in the last bin, yet these models also dramatically deviate from empirical heterozygosity and the SFS. In other words, simple exclusion of the final high ancestral size is not sufficient to improve model fit to other summaries of the data. Our trimming experiments were only made possible by the abundance of human sequence data and demographic models previously fit to the data. Since many MSMC trajectories are calculated for species for which there is no prior information about ancient demographic history, the “informed trimming” we carried out is not a practicable solution to improve the reliability of MSMC inference.Since the most ancient size in the MSMC trajectory will have a large influence on heterozygosity and the SFS and the most ancient bin of the MSMC trajectory may be unreliable , we explFile S1), indicating that MSMC is robust to small amounts of gene flow. The fact that the 2-haplotype model based on real data did not fit the observed SFS very well (File S1) suggests that the true underlying pattern of human demography is more complex than either type of inference (∂a∂i or MSMC) is capturing, potentially revealing weaknesses in both methods.While our results indicate that features of MSMC trajectories, particularly ancient events, should be regarded with caution, we also found that MSMC 2-haplotype is able to accurately recapitulate a complex demography  from simulated data, supporting the validity of the method, at least for use on simulated data . MigratieN. They therefore recommended only using samples with a mean genome coverage of ≥18× and <25% missing data, and employing a per-site sequencing depth filter of ≥10 , and number of pairwise differences et al. (−8 mutations/bp/generation), then the population sizes would have to be larger than those estimated by We found that no model was able to accurately recapitulate the empirical distribution of LD decay. The lack of fit of the SFS-based models is perhaps unsurprising, as Natural selection may affect both SFS- and whole genome-based methods of demographic inference. File S1). The fact that they are not seen in the observed SFS suggests that either the size changes did not occur, and the inferred size changes are artifacts, or instead, the true demography is more complex than currently modeled using either approach. Our conclusion of finding little evidence for the ancient population size changes is supported by the study of Our results have implications for understanding human demographic history. First, there has been controversy concerning the presence of ancient bottlenecks (>100 KYA) in human populations  tended tDeep ancestral structure has been put forward as explanation for the humps detected by the whole genome-based methods by the developers of PSMC and others . While BOur work provides a cautionary tale for understanding population history in nonmodel organisms. Our results argue against a literal interpretation of humps and other jumps in MSMC plots as reflecting population size changes. This problem is exacerbated for putative ancient size changes. Given the ever-increasing generation of genomic data from nonmodel taxa and the application of whole genome-based approaches to such data  are promTesting more complex demographic scenarios using multiple summaries of the data may help to resolve uncertainties about our own species’ history and will improve demographic inference for nonmodel organisms. Incorporating the potential complexity of possible demographic histories to produce models that better recapitulate the data may in fact present the greatest challenge.www.g3journal.org/lookup/suppl/doi:10.1534/g3.117.300259/-/DC1.Supplemental material is available online at Click here for additional data file.


Caenorhabditis elegans, we examined changes in response to hypergravity in the development of the 19 GABAergic DD/VD motor neurons that innervate body muscle. We found that a high gravity force above 10G significantly increases the number of animals with defects in the development of axonal projections from the DD/VD neurons. We showed that a critical period of hypergravity exposure during the embryonic/early larval stage was sufficient to induce defects. While characterizing the nature of the axonal defects, we found that in normal 1G gravity conditions, DD/VD axonal defects occasionally occurred, with the majority of defects occurring on the dorsal side of the animal and in the mid-body region, and a significantly higher rate of error in the 13 VD axons than the 6 DD axons. Hypergravity exposure increased the rate of DD/VD axonal defects, but did not change the distribution or the characteristics of the defects. Our study demonstrates that altering gravity can impact motor neuron development.As space flight becomes more accessible in the future, humans will be exposed to gravity conditions other than our 1G environment on Earth. Our bodies and physiology, however, are adapted for life at 1G gravity. Altering gravity can have profound effects on the body, particularly the development of muscles, but the reasons and biology behind gravity’s effect are not fully known. We asked whether increasing gravity had effects on the development of motor neurons that innervate and control muscle, a relatively unexplored area of gravity biology. Using the nematode model organism  Human anatomy and physiology is well adapted for survival and fitness on the planet Earth and its stable gravitational conditions. Altering gravity can have profound impacts on the human body. This is especially relevant with the possibility of long-term space travel and habitation and the associated changes in gravity in different space environments. Although some of the effects of decreased gravity on the human body are identified, such as muscle atrophy , the genOne of the cellular processes that may be constrained by gravity conditions is neuronal development. Previous work showed that microgravity induced a decrease in synaptic density in the hindlimb area of the motor cortex in the brains of rats that were aboard a space mission for 16 days . AlteratC. elegans is one of the premier metazoan genetic model organisms, and studies with the worm have led to seminal discoveries including RNA interference, microRNAs, and genes controlling programmed cell death unc-25::GFP), LG IV: evIs82a ((p)unc-129::GFP) and LG X: zdIs5 ((p)mec-4::GFP). The genetic background of the juIs76 strain was maintained by backcrossing this strain with the N2 wild-type strain, which was effective in maintaining a consistent axonal defect rate (see section Quantification of Defects).Animals were grown and maintained at 20 °C on Nematode Growth Medium (NGM) plates seeded with eviously . StrainsA total of 1 L NGM media was prepared similar to previous protocols , except E. coli bacteria, a single colony of OP50 strain bacteria was inoculated in LB broth and incubated in a shaker at 37 °C overnight, then concentrated by spinning down and removing the supernatant. The pellet was resuspended and 2 μl of bacteria was added to the surface of each tube and allowed to dry at room temperature for at least 24 h before usage.To create a small lawn of Eggs were harvested by bleaching gravid worms according to a standard protocol , and theAfter 60 h, animals were washed with M9 buffer and collected into 1.5 ml tubes and allowed to settle on the bottom of the tube. After removing the supernatant, animals were mounted onto glass slides with a dry 2% agarose pad. To immobilize the worms, 2 μl of 1 M sodium azide was added onto the agarose pad. An epi-fluorescent microscope (Olympus BX50) was used to visualize the GFP-labelled neurons. Imaging software (Nikon Elements) was used for extended depth of field (EDF) images, as well as all others images.juIs76 strain by backcrossing this strain with the N2 wild-type strain. Dorsal-ventral locations of defects were approximated by eye and scored as: Ventral—0 to 15% dorsal-ventral distance, Ventral sublateral—15% to 50% dorsal-ventral distance, Dorsal sublateral—50% to 85% dorsal-ventral distance, Dorsal—85% to 100% dorsal-ventral distance. Student’s T-test was performed (Microsoft Excel) to analyse the significance of data. For statistical analysis with multiple sets of data, single-factor ANOVA was performed (Microsoft Excel) to determine variance, and post-hoc Bonferroni correction was performed to determine significance.Defects were scored by researcher microscopic observations. Animals were scored as defective if one or more of the 19 DD/VD neurons showed any axonal defect . Occasionally, an abnormally high axonal defect rate (50% or more) was observed even in normal 1G conditions. We censured and discarded all the data from these days. We found that this abnormality may be due to random genetic abnormalities within this strain’s genome. Thus, we maintained the genetic background of the C. elegans biology, we used a tabletop refrigerated centrifuge to create a gravity force and designed a small worm cultivation tube from a 1.5 cm centrifuge tube filled with NGM agar and seeded with OP50 E. coli bacteria on top . It is important to note that embryos recovered by bleaching the mothers are not at a synchronous stage of development. Instead, these embryos are within an approximately 6 h range of development. This may explain why we observe an intermediate phenotype at 10 h hypergravity exposure  were located in the dorsal sub-lateral region which is defined as the area dorsal of the lateral mid-line, but ventral to the area of the dorsal nerve cord unc-25::GFP unc-47::GFP in the DD/VD neurons unc-25::GFP strain are valid, and this lends credence to the fact that DD/VD axon development may normally be error prone.Although hypergravity can increase axonal defects in the VD motor neurons, we still observe a range of DD/VD axonal defects in a portion of the control animals at normal gravity. This is consistent with observations from other groups that have studied DD/VD commissures using the -25::GFP . In addi neurons . Interes neurons . Howeverkgrounds . In addikgrounds , and thekgrounds . These sTwo major questions remain unanswered: what causes the errors in DD/VD axonal development, and how is hypergravity increasing these errors? Clues to answer the first question may lie in the distribution of the axonal defects. We find that commissures in the mid-body region tend to show more defects than the commissures at the ends of the animal. One possibility that may cause this to arise is that the length of the commissures are slightly longer in the mid-body area than the ends of the animal allowing for the possibility for more errors. Other differences in the mid-body compared to the ends of the animal are structures such as gonads, vulva, and muscle that could interfere with the guidance or migration of the axons. In fact, a previous study showed that axonal regeneration is less robust in the mid-body region likely due to interfering structures . We alsoC. elegans have the ability to repair and regenerate after damage (How does hypergravity affect DD/VD neuron axonal development? The force of gravity itself might be strong enough to break or damage the axons. However, an acute exposure and even a long exposure to hypergravity in adult animals was not sufficient to cause damage to the axons . In addir damage . In our We show an 18 h exposure early in development and during the 1st larval stage is sufficient to cause the axon defects  and 2C. C. elegans at the genetic level. These studies could clarify whether low gravity effects on motor neuron development can also contribute to the muscle weakness and atrophy observed in astronauts during long-term spaceflight and habitation.In this study, we have addressed the role that high gravity conditions have on DD/VD motor neuron development. What effect does low gravity have on motor neuron development? Previous studies in rats showed that development of the dendrites of spiny motor neurons during spaceflight could be altered . Similar10.7717/peerj.2666/supp-1Supplemental Information 1Click here for additional data file.