Rapid development of high throughput genome analysis technologies accompanied by significant reduction in costs has led to the accumulation of an incredible amount of data during the last decade. The emergence of big data has had a particularly significant impact in biomedical research by providing unprecedented, systems-level access to many disease states including cancer, and has created promising opportunities as well as new challenges. Arguably, the most significant challenge cancer research currently faces is finding effective ways to use big data to improve our understanding of molecular mechanisms underlying tumorigenesis and developing effective new therapies. Functional exploration of these datasets and testing predictions from computational approaches using experimental models to interrogate their biological relevance is a key step towards achieving this goal. Given the daunting scale and complexity of the big data available, experimental systems like Drosophila that allow large-scale functional studies and complex genetic manipulations in a rapid, cost-effective manner will be of particular importance for this purpose. Findings from these large-scale exploratory functional studies can then be used to formulate more specific hypotheses to be explored in mammalian models. Here, I will discuss several strategies for functional exploration of big cancer data using Drosophila cancer models. Cancer is a genetically complex and diverse disease. Most tumors carry alterations in a large number of cancer driver genes and individual tumors of the same type exhibit great diversity in their genomic landscapes . Tumor iSophisticated computational and statistical analyses of these datasets routinely identify specific molecular signatures that correlate with different tumor phenotypes, revealing common and unique features of tumors with different genomic landscapes or tissues of origin. While these correlative studies have been instrumental in identifying candidate biomarkers to predict disease outcome and drug response, establishing causal relationships between specific molecular alterations and tumor phenotypes or response to therapy is difficult to achieve computationally. Functional studies are essential to test predictions from these studies in experimental models in a comprehensive, systematic way to determine their relevance in tumor initiation and progression and to identify novel potential drug targets and other actionable vulnerabilities.Over the past hundred years, Drosophila—with its powerful genetic tools and practical advantages—has opened the door to understanding fundamental aspects of development, cell biology, and signal transduction. In addition to its contributions to basic science, Drosophila also has a strong track record as a useful disease model and a drug discovery platform . Around The history of Drosophila cancer research is rich and almost as long as the history of Drosophila as a model system itself . It starThese latter models were generated either by genetically manipulating Drosophila orthologs of genes altered in tumors  or directly introducing wildtype or altered versions of human genes into flies. Some models of cancer have targeted the Drosophila equivalent of the affected tissue in humans , while others used generic epithelia such as the imaginal discs where cancer relevant signaling pathways are highly conserved. Both approaches have been successful in capturing key hallmarks of tumorigenesis including overproliferation, evasion of apoptosis and senescence, epithelial–mesenchymal transition, migration, invasion, aspects of metastasis, as well as drug response.An extensive review of the Drosophila cancer literature is not the intended goal of this article; several excellent reviews that summarize contributions of Drosophila to cancer research are already available ,4,5,6,7.As massively parallel sequencing technologies became cost effective and, as a result, more widespread, the first tumor genome profiling efforts to emerge were those that sought to catalogue genetic alterations in tumor cells by comparing normal and tumor DNA sequences. These tumor sequence datasets still remain to be the most abundant big data in cancer research . These gMost of the novel and unique variants observed in human tumors will represent passenger mutations that perhaps reflect the unique mutational background of the cell from which the tumor originated or mutations that accumulated over the course of tumorigenesis that do not have any functional relevance. However, some may represent yet undiscovered, low frequency cancer driver genes or may impact tumor progression and drug response in subtle, context-dependent ways. In addition, DNA sequencing and copy number variation (CNV) analyses have revealed recurrent copy number changes associated with many tumor types, which usually include loss or amplification of chromosomal regions that may include dozens or hundreds of genes in some cases. For instance, sequencing studies have identified approximately 30 recurrently mutated, functionally validated cancer driver genes in colorectal tumors at the population level ,10. Of tIn summary, a common theme in big cancer data is the presence of large numbers of candidate genes or variants whose biological relevance is unknown. While sometimes, it is possible to prioritize candidate genes based on their functions, unbiased approaches for experimentally evaluating the impact of such genes using disease phenotypes would be useful to identify novel drivers of tumorigenesis and candidate drug targets. A genetic modifier screen approach could be modified to test a large number of such genes by genetically manipulating their Drosophila orthologs in tumor models with different genetic contexts. This is a scalable approach that can be adapted for both gain and loss of function studies and utilizes publicly available large-scale transgenic and mutant collections. Even though recurrently altered cancer drivers make up a small fraction of the tumor genome landscape, they are excellent starting points to build cancer models that can then be used to functionally interrogate the roles of the remaining uncharacterized genomic alterations observed in tumors to determine their potential contributions to tumorigenesis .Given the large number of known cancer driver alterations observed in tumors , even thGenetic screens that use complex, in vivo phenotypes as read-outs are a key strength of Drosophila as a model system. As previously discussed, many aspects of human cancer including proliferation, apoptosis, senescence, epithelial–mesenchymal transition, migration and dissemination have been captured in Drosophila models. Genetic and compound screens that use disease relevant phenotypes, imaging, or luciferase-based methods have been reported ,14,15. LFor instance, a Drosophila model of glioblastoma established by co-activating Epidermal Growth Factor Receptor (EGFR) and Phosphatidylinositol-3-Kinase (PI3K) pathways—commonly observed cancer driver alterations in these tumors—in glial cells and their precursors ,19 can bExploring roles for novel genes or variants observed in tumors requires introducing additional genetic manipulations into these already complex genetic backgrounds. Furthermore, such studies need to be large in scale to be able to explore the large amount of sequencing data available. Publicly available tools, resources, and practical advantages like low cost and speed allow functional studies in Drosophila at a scale that is not feasible in most genetically tractable model systems. For instance, several large-scale transgenic Drosophila resources that allow tissue specific and temporally controlled manipulation of almost every gene in the Drosophila genome are available ,13,14,15A related challenge is evaluating the functional impact of novel variants on protein activity. The vast majority of variants identified in tumor sequence studies are novel and do not fall into previously identified hotspot locations. Functional studies are required to determine whether these variants are deleterious and how they impact protein function . While there are excellent functional prediction algorithms that can be used to determine the likelihood that a mutation is deleterious ,22,23 anThere are a number of widely available gene editing and transgenic methods in Drosophila that can be scaled up to explore the functional impact of novel variants identified by sequencing studies. For instance, in cases where the particular mutated residue is conserved, the corresponding mutation in the Drosophila ortholog can be generated by the gene editing tool CRISPR/Cas9. Tissue-specific or conditional knock-out studies can be used to gain insights into the biological functions of such genes. As most variants observed in human tumors are heterozygous, large-scale genetic interaction experiments can be performed by introducing candidate variants as heterozygous alleles into cancer models to test their ability to modify tumor phenotypes. In addition to determining the functional relevance of novel variants in cancer, these studies can also provide experimentally validated datasets to improve the performance of functional prediction algorithms.In cases where the specific mutated residue is not conserved in the Drosophila ortholog, it is possible to directly introduce the mutant version of human genes observed in tumors into flies, while simultaneously knocking down or knocking out the Drosophila ortholog. Transgenic flies carrying the wildtype versions of these genes can be used as controls. This “humanization” strategy has been previously reported as a tool for functional exploration and characterization of variants observed in rare human genetic disorders ,25 and mA similar approach can also be used to model gene-fusion events that produce truncated proteins or novel fusion products with inappropriate or neomorphic activities. Such fusion events are being increasingly identified as recurrent drivers of tumorigenesis, especially by RNA sequencing studies . DrosophFinally, Drosophila also offers a platform for the exploration of any potential non-autonomous roles for germline variants in tumorigenesis. Each human tumor develops in a different germline background, with a large number of rare, potentially deleterious, and mostly heterozygous germline mutations; how these germline mutations interact with the somatic tumor genome landscape during tumorigenesis is not well-studied. A small number of these variants represent deleterious mutations in susceptibility genes that require a somatic mutation to inactivate the wildtype allele to initiate tumorigenesis, as proposed by Knudson in his two-hit theory of cancer causation ,30. SuchA functional exploration of how germline and somatic mutations interact during tumorigenesis is also complicated by the fact that, while somatic alterations are only present in tumor cells, germline alterations are found in both the tumor cells and their wildtype neighbors. Therefore, in addition to cell autonomous interactions with somatic mutations in tumor cells, germline mutations can also affect tumorigenesis in a cell non-autonomous fashion by altering the interactions between tumor cells and their non-tumor neighbors. These interactions can be captured and explored by introducing mutant alleles for Drosophila orthologs of candidate germline variants observed in human tumors while inducing somatic alterations in a tissue specific manner to establish tumors in these germline backgrounds. It is also possible to use orthogonal or intersecting expression systems  to inducIn addition to genomic alterations and chromosomal abnormalities, global changes at the transcriptional, translational, post-translational, and epigenetic levels have been documented in tumors . SophistIn recent years, gene expression datasets from thousands of patient tumors have also become available and led to a large number of gene expression signatures that correlate with patient outcomes and treatment responses. Such transcriptomic signatures have enormous potential as biomarkers in personalized cancer medicine and functional exploration of their roles in cancer could have a significant impact on our understanding of molecular mechanisms underlying tumorigenesis, in a way, providing “mechanisms of action” for these complex, multigenic molecular signatures. Given their importance as predictors of patient outcome, one or more of the genes in these signatures can also be promising drug target candidates. A systematic and comprehensive functional validation of candidate biomarkers can be challenging in vertebrate genetic models.Large transgenic Drosophila collections for gene overexpression and knock-down ,33,34,35In addition to changes in the expression levels of protein coding genes, sequencing studies also revealed global changes in other types of RNA in tumor cells, microRNAs, and long noncoding RNAs (lncRNA) in particular ,37. ThesFor instance, miR-21 is one of the most frequently upregulated microRNAs in tumors and serves as a useful prognostic biomarker for many tumor types . miR-21 Proteins are the final translational products of mRNA transcripts that mediate a variety of key cellular processes. As a result, alterations in protein levels and activity are key mediators of tumor initiation, progression, and drug response. Proteins are also the targets for the vast majority of targeted therapies in the clinic or under clinical development. As protein stability and activity is regulated by a wide number of posttranslational mechanisms, the correlation between protein expression levels and corresponding mRNA levels is relatively low ,46,47. IAs is the case for most big cancer data, these studies typically identify a large number of changes in tumors. Identifying alterations biologically most relevant to cancer through functional studies could help streamline drug discovery efforts by prioritizing candidate drug targets for drug development. The approaches I discuss in the previous sections can also be applied for large-scale functional exploration of these datasets by genetically manipulating Drosophila orthologs of genes encoding proteins with altered expression or activity levels in tumors. These approaches could include classic modifier screens performed by removing a functional copy of each gene by bringing in loss of function alleles, transgenic constructs that ectopically express wildtype, dominant negative, constitutively active versions of these proteins and long or short hairpin constructs for RNA interference (RNAi) mediated knockdown in Drosophila cancer models. Genes that alter tumor phenotypes can then be further explored in Drosophila to obtain mechanistic insights and formulate hypotheses that can be tested in mammalian models.Epigenomic alterations reported in tumors include global or gene-specific changes in DNA methylation and histone modification patterns . Some ofPosttranslational modification of histone proteins is a key epigenetic mechanism that governs regulation of gene expression and is maintained by an array of chromatin remodeling complexes . AlteratThe general relationship between chromatin state and gene expression as well as genes encoding key chromatin remodeling complexes are highly conserved in Drosophila . PotentiA complementary and perhaps more useful approach would be to focus on exploring the functional consequences of epigenomic changes observed in human tumors rather than trying to replicate these patterns in Drosophila cancer models. This could be achieved by targeted genetic modifier screens informed by big epigenome data, where Drosophila orthologs of genes that are epigenetically silenced or activated in human tumors are genetically manipulated in Drosophila cancer models to evaluate their effects on tumor phenotypes. Hits from these screens can then be tested in various combinations observed in human tumors to explore functional effects of more complex multi-gene epigenomic signatures.DNA methylation—especially of cytosines in CpG dinucleotides—is an essential mechanism for controlling gene expression and regulates many cancer-relevant processes ,57. WhilMulti-omics integration studies and network-based models use sophisticated computational approaches to integrate data obtained by different high-throughput omics technologies ,37,59. TAnother key advantage Drosophila brings as a functional validation platform for big cancer data is the ability to genetically manipulate multiple genes at once. Some of the tools developed for this purpose include the use of multigenic vectors, multiple short hairpin and guide RNA sequences, multicistronic expression systems that use self-cleaving peptides or Internal Ribosome Entry Sites (IRES), parallel genome editing, and multiplexable orthogonal and intersectional expression systems ,67,68,69Challenges associated with functional exploration of big cancer data in experimental models can be broadly grouped in two categories: (1) achieving the genetic complexity required to capture tumor genomic landscapes in cancer models, and (2) being able to perform additional genetic manipulations needed for functional studies in these already complex genetic backgrounds. Tools and resources already available in Drosophila serve as an excellent starting point to address both of these challenges. For instance, our multigenic vector platform allows expression of two proteins and eight short-hairpins from a single multigenic construct . This nuIt is also important to note that both the amount and the complexity of the big cancer data are growing at such a fast pace that increasing the number of genetic manipulations we can perform will not be sufficient to account for the genetic complexity of cancer. New methods that push the limits of Drosophila genetics even further in innovative ways will be necessary to increase our ability to simultaneously target different tissues or cell types in the same organism to build additional genetic complexity into our models.In addition to technical challenges, it is also important to acknowledge that not all aspects of human tumors will be fully captured in Drosophila models. For instance, while most cancer relevant genes and signaling pathways are highly conserved in Drosophila, some genes altered in human tumors will not have Drosophila orthologs. For example, it may be challenging to model hormone-dependent tumors  driven by alterations in nuclear hormone receptor activity in Drosophila. Nuclear hormone receptor function is broadly conserved in Drosophila, but there is significant divergence and specialization of functions and pathways . Of noteAnother limitation of Drosophila cancer models is the limited ability to capture and study tumor–stroma interactions. The tumor stroma is a complex environment that includes an extensive extracellular matrix, fibroblasts, immune cells, and vasculature, and impacts tumor progression and drug response . For insAs all model systems have their advantages and disadvantages, the most productive approach in any type of research is to play to the strengths of each experimental system. Therefore, it is important to recognize the limitations of Drosophila as a model system for cancer research and leverage its strengths to complement vertebrate models of cancer. We can achieve this by focusing our efforts on large-scale in vivo exploratory studies that embrace genetic complexity, an approach that may be particularly challenging in mammalian models and where Drosophila can make the biggest contribution to cancer research.Big cancer data are providing unprecedented access to global alterations in tumor cells at multiple levels and an impressive resolution. As our understanding of human tumor genomic and molecular landscapes becomes more sophisticated, there is an increasingly urgent need for genetically tractable cancer models to make sense of this large amount of data. With its sophisticated genetic tools and practical advantages, Drosophila offers a unique opportunity to address this important challenge in cancer research through genetic screens designed to functionally mine big cancer data and identify novel variants with cancer relevant roles. Big cancer data in return serve as a blueprint for building sophisticated and representative cancer models that are necessary for such genetics screens. Novel variants identified through such screens can then be incorporated into existing models to increase their genetic complexity in order to better capture the genomic landscape of cancer. Therefore, it is useful to think about the relationship between big cancer data and Drosophila cancer models as a dynamic and reciprocal one ; each noAs Drosophila researchers, we are a community of tool builders with a tradition of freely sharing reagents and resources. Large collections of publicly available transgenic and mutant lines that allow us to manipulate almost every gene in the Drosophila genome are a testament to this tradition. With our collective experience in technology development, our expertise in genetic modifier screens, and other complex in vivo genetic approaches that are routine in our laboratories, we are in a unique position to address this important challenge in cancer research brought upon by the postgenomic era.


Cancers focus on the role of RAS52RAD52 during MiDAS, this review emphases on the discovery of MiDAS and its molecular effectors. Faithful duplication of the human genome during the S phase of cell cycle and accurate segregation of sister chromatids in mitosis are essential for the maintenance of chromosome stability from one generation of cells to the next. Cells that are copying their DNA in preparation for division can suffer from ‘replication stress’ (RS) due to various external or endogenous impediments that slow or stall replication forks. RS is a major cause of pathologies including cancer, premature ageing and other disorders associated with genomic instability. It particularly affects genomic loci where progression of replication forks is intrinsically slow or problematic, such as common fragile site (CFS), telomeres, and repetitive sequences. Although the eukaryotic cell cycle is conventionally thought of as several separate steps, each of which must be completed before the next one is initiated, it is now accepted that incompletely replicated chromosomal domains generated in S phase upon RS at these genomic loci can result in late DNA synthesis in G2/M. In 2013, during investigations into the mechanism by which the specialized DNA polymerase eta (Pol η) contributes to the replication and stability of CFS, we unveiled that indeed some DNA synthesis was still occurring in early mitosis at these loci. This surprising observation of mitotic DNA synthesis that differs fundamentally from canonical semi-conservative DNA replication in S-phase has been then confirmed, called “MiDAS”and believed to counteract potentially lethal chromosome mis-segregation and non-disjunction. While other contributions in this Special Issue of  The duplication of chromosomes during S phase of the cell cycle in multicellular organisms contributes vastly to cell survival and evolution by ensuring the maintenance of genome integrity and the required adaptive responses to endogenous or external genotoxic stresses. The DNA replication process starts shortly after mitosis, during the G1 phase of the cell cycle, when daughter cells organize their genomes into large DNA replication domains containing multiple initiation sites that will be activated simultaneously in S phase. From these replication origins progress thereplication forks which ensure stable genetic and epigenetic inheritance. In human cells, the process takes about 10 h and involves the activation of roughly 50,000 replication origins . HoweverTo avoid an aberrant interruption of the cell cycle caused by the impediment of DNA replication, human cells have evolved multiple options to deal with the constant challenge of RS, depending on the source of the stress, the nature of the blockage and the level of accumulated stalled forks. Since stalled forks are frequently associated with large amounts of unwound single-stranded DNA (ssDNA) covered by the protein RPA, it is believed that the major signal for many responses to RS is the generation of this RPA-coated ssDNA. This is the case for the activation of the replication checkpoint, the main response that senses stalled forks in S phase, activates its cardinal kinase ATR, that in turn phosphorylates hundreds of substrates in order to stabilize and restart the stalled DNA forks . CompensIf active replication origins are lacking in the neighborhood of a stalled fork or if the responses to RS described above fail, this favors fork collapse and DNA double-strand break (DSB) are created at the stalled replication forks , a phenoThe failure of the RS response also increases the opportunity of incompletely replicated loci or unresolved replication intermediates and a fraction of these under-replicated genomic loci have been evidenced to enter into mitosis. When not correctly handled in the M phase, such DNA regions are converted into complex broken-DNA structures that are transmitted to G1 daughter cells where they are sequestered and shielded in nuclear compartments described as nuclear 53BP1 bodies ,18. To lTo date, the loci that most depend upon MiDAS seem to be the difficult-to-replicate DNA sequences such CFS and telomeres, very sensitive to even mild replicative stress . More geMiDAS has been documented to occur in several different stress contexts. We recently reported a two-fold increase in the percentage of cells with MiDAS in BRCA2-deficient compared to BRCA2-proficient mitotic cells B. We shoCollectively, these findings imply that any form of replicative stress, including DNA insults from external or internal sources but also genetic events that slow replication fork progression can all lead to MiDAS at hard-to-replicate DNA sequences.More than a late DNA replication mechanism, MiDAS could be better considered as a DNA repair process when conventional DNA replication forks are converted during mitosis into unprotected genomic DNA that can be targeted by repair factors. The attempted condensation of incompletely duplicated loci in early mitosis has been proposed to be the cause of this late DNA synthesis. It has been recently reported that the switch from conventional DNA replication to MiDAS could be facilitated by the TRAIP ubiquitin ligase that triggers replisome disassembly in response to unfinished DNA replication, providing access for nucleases and the MiDAS repair factors at unprocessed parental DNA . Upon miIn yeast, it is well established that Rad52 is a recombination factor that allows the exchange of RPA with Rad51 on ssDNA thus promoting homologous recombination (HR) . In humaThe contribution of RAD52 to DNA repair has also been described recently. When a sister chromatid is present as a donor, RAD51-mediated gene conversion generally occurs after passage of the replication fork while under-replicated DNA is located within the template ahead of the incoming fork and thus requires replication-coupled repair named break-induced replication (BIR), a form of conservative DNA synthesis that occurs when a broken chromosome invades its homologous duplex, initiates at broken or processed replication forks and proceeds along the length of the chromosome. RAD52, by promoting single-stranded DNA capture and through the creation of a higher-order structural form of RAD52, seems to hold an important role of DNA annealing in this process . It is lBecause RS is a highly relevant mechanism to explain genomic instability in cancer cells, its studies are at the forefront of cancer research today. Indeed, the persistence of stalled forks can lead to fork collapse and under-replicated regions that in turn leads to chromosomal breakage and chromosome instability (CIN). If we consider genomic instability and the resulting unstable cancer genome from an evolutionary perspective, CIN provides a continuing pool of variants upon which selection could act, and therefore constitutes a driving force of tumor heterogeneity and development. Similar to the Darwin’s evolutionary tree of speciation, clonal evolution in cancer can be explained by the multiple forms of selective pressure including physical barriers, environment and nutrients, oxygen concentration, immunity, and therapy, allowing some mutant sub clones to expand while others go extinct . CIN proSeveral years ago, we made the hypothesis that expression of specific genes that could neutralize excess RS in cancer cells might correlate with poor patient survival and we therefore explored the prognosis value of the expression of more than 100 genes involved in replication origins, fork stabilization, fork progression, replication checkpoint and repair of collapsed forks in large cohorts of solid cancer and hematological malignancies. Thus far, we found two categories of biomarkers as strong predictors of patient survival and both of them are indeed identified to limit excessive RS: the first category regroups the genes coding the checkpoint mediators Chk1, Claspin and Timeless known to stabilize the stalled replication forks upon RS ,44. The The concept of synthetic lethality by PARP inhibitors has been successfully used in therapy since 2015 for ovarian cancer. PARP inhibitors are now considered as a standard of care in patients with ovarian, breast and prostate cancers (this will be the case shortly for patients with pancreatic cancer) in the context of HR-deficiency . Despite interesting results, there is a high rate of primary or secondary resistance to PARP inhibitors. Several mechanisms involved in resistance might be linked to the responses to RS and targeting MiDAS, might be an interesting option to test. Interestingly, synthetic lethality can also be achieved by RAD52 depletion in HR-deficient human cancer cells . This syIn this review, we have analyzed the current views on how late unscheduled DNA synthesis, orchestrated by RAD52, can represent the last opportunity during one cell cycle to respond to replication stress and limit chromosome instability and transmission of DNA damage to the next cell generation. Whether MiDAS occurs through a break-induced replication (BIR)-like process needs further investigation. Besides stabilization of stalled DNA replication forks and repair process of broken forks, MiDAS is a critical response to replicative stress that should be considered as an important target in cancer therapy. The treatment of patients with MiDAS inhibitors, alone or in combination with other agents limiting excessive RS, might selectively target tumors exhibiting RS induced by oncogene activation.


We examine the effects of an uneven population distribution on the spread of the COVID-19 disease spread, using a diffusion model based on interpersonal contact networks. Taking into account spatial heterogeneity, the spread of COVID-19 is much “burstier” than in standard epidemiological models, with substantial local disparities in timing and severity and long lags between local outbreaks. We show that spatial heterogeneity may produce dramatic differences in social exposures to those with the illness, and may stress health care delivery systems in ways that are not well captured by standard SIR-like models. Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible–infectious–recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities. Since its emergence at the end of 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly to all portions of the globe, infecting over 20 million people as of mid-August 2020 , 4. By eCurrently, dominant approaches to COVID-19 modeling –8 are ba10In this paper, we examine the potential impact of local spatial heterogeneity on COVID-19, modeling the diffusion of SARS-CoV-2 in populations whose contacts are based on spatially plausible network structures. We focus here on the urban context, examining 19 different cities in the United States. We simulate the population of each city in detail , simulating hypothetical outbreaks on the contact network in each city in the absence of measures such as social distancing. Despite allowing the population to be well mixed in all other respects , we find that spatial heterogeneity alone is sufficient to induce substantial departures from spatially homogeneous SIR behavior. Among the phenomena observed are “long lag” outbreaks that appear in previously unharmed communities after the aggregate infection wave has largely subsided, frequently low correlations between infection timing in spatially adjacent communities, and distinct subpatterns of outbreaks found in some urban areas that are uncorrelated with the broader infection pattern. Gaps between infection peaks at the intraurban level can be large, for example, on the order of weeks or months in extreme cases, even for communities that are within kilometers of each other. Such heterogeneity is potentially consequential for the management of health care delivery services: As we show, using a simple “catchment” model of hospital demand, local variations in infection timing can easily overload hospitals in some areas, generating “hospital deserts” , while lWe note at the outset that the models used here are intended to probe the hypothetical impact of spatial heterogeneity on COVID-19 diffusion within particular scenarios, rather than to produce high-accuracy predictions or forecasts. For the latter applications, it is desirable to incorporate many additional features that are here simplified to facilitate insight into the phenomenon of central interest. In particular, we do not incorporate either demographic effects or social distancing , 22, allNetworks are generated using population distributions from the most recent US Census in 2010. Network construction followed the same methodology as Butts et al. . HospitaThe New York Times, based on reports from state and local health agencies . The SIF formally defines the relationship between two individuals based on spatial proximity. For example it has been shown that many social interaction patterns obey the Zipf law . Given these simulated networks, we then implement an individual-level SIR-like framework to examine COVID-19 diffusion. At each moment in time, each individual can be in a susceptible, infected but not infectious, infectious, deceased, or recovered state. The disease diffuses through the contact network, with currently infectious individuals infecting susceptible neighbors as a continuous time Poisson process with a rate estimated from mortality data (SI Appendix); recovered or deceased individuals are not considered infectious for modeling purposes. Upon infection, an individual’s transitions between subsequent states  are governed by waiting time distributions based on epidemiological data as described in SI Appendix. To begin each simulated trajectory, we randomly infect 25 individuals, with all others being considered susceptible. Simulation proceeds until no infectious individuals remain.We follow the protocol of refs. From the simulated trajectory data, we produce several metrics to assess spatial heterogeneity in disease outcomes. First, we present infection curves for illustrative cities, showing the detailed progress of the infection and its difference from what an SIR model would posit. We also present choropleth maps showing spatial variation in peak infection times, as well as the correlations between the infection trajectory within local areal units and the aggregate infection trajectory for the city as a whole. While an SIR model would predict an absence of systematic variation in the infection curves or the peak infection day for different areal units in the same city, geographically realistic models show considerable disparities in infection progress from one neighborhood to another. To quantify the degree of heterogeneity more broadly, we examine spatial variation in outcomes for each of our city networks. We show that large variations in peak infection days across tracts are typical (often spanning weeks or even months), and that overall correlations of within-tract infection trajectories with the aggregate urban trajectory are generally modest .social exposure of individuals to COVID-19, by computing the fraction of individuals with a personal contact who is respectively morbid or deceased. Our model shows considerable differences in these metrics over time, revealing that the pandemic can appear very different to those “on the ground”—evaluating its progress by its impact on their own personal contacts—than what would be suggested by aggregate statistics.In addition to these relatively abstract metrics, we also examine a simple measure of the potential load on the health care system in each city. Given the locations of each hospital in each city, we attribute infections to each hospital using a Voronoi tessellation . Examination of the potential hospital demand over time shows substantial differences in load, with some hospitals severely impacted while others have few cases. Finally, we consider the When taken over even moderately sized regions, aggregate infection curves can appear relatively smooth. Although this suggests homogeneous mixing , appearances can be deceiving. Bottom assesses this phenomenon over our entire sample, using two different consensus metrics. First, we simply compute the correlation between the infection curves in each pair of tracts (assessed at daily resolution), taking the mean for each tract of its correlation with all other tracts within the city; if the progress of the infection were uniform across the city, the mean correlations would be large and positive. Second, we provide a more direct assessment of the extent to which the set of infection curves can be summarized by a common pattern by taking the variance on the first principal component of the correlation matrix generated from the tract-level correlations discussed immediately above. As before, where different parts of the city experience similar patterns of growth and decline in infections, we expect the dimension of greatest shared variance to account for the overwhelming majority of variation in infection rates. Contrary to these expectations, however, SI Appendix, Fig. S4), in contrast to the hypothesized importance of similar delays in a cross-national context , but they can also create a false sense of security for those on the opposite side of the bridge (who may incorrectly assume that their area was passed over by the infection). Indeed, examining the time to peak infection across the cities of Seattle and Washington, DC  shows thSI Appendix, Fig. S2.) Although peak times in some cities are concentrated within an interval of several days to a week, it is more common for peak times to vary by several months or longer. Extreme delays in peak time arise from “slow burn” trajectories associated with very long infection chains, in which sequential transmission to only one or two alters at a time can sustain infection for greatly extended periods. Such gaps can arise naturally in inhomogeneous networks, but are far from what would be expected under uniform local mixing.The cases of Seattle and Washington, DC are not anomalous. Looking across multiple trajectories over our entire sample, SI Appendix). While hospitals draw from (and hence average across) areas that are larger than tracts, the heterogeneity shown in Variation in the timing of COVID-19 impacts across the urban landscape has potential ramifications for health care delivery, creating unequally distributed loads that overburden some providers while leaving others with excess resources. To obtain a sense of how spatial heterogeneity in the infection curve could potentially impact hospitals, we employ a simple “catchment” model in which seriously ill patients are taken to the nearest hospital, subsequently recovering and/or dying as assumed throughout our modeling framework. Based on prior estimates , we assuJust as hospital load varies, hospital capacities vary as well. As a simple measure of strain on hospital resources, we consider the difference between the number of COVID-19 hospitalizations and the total capacity of the hospital (in beds), truncating at zero when demand outstrips supply.  Using data on hospital locations and capacities, we show, in Looking across cities, we see the same high-variability patterns as observed in Seattle and Washington. In particular, we note that local variation in disease timing leads to a heavy-tailed distribution for the duration at which hospitals will be at capacity. It should be reiterated that the hospital load model used here is extremely simplified, and that we are employing a no-mitigation scenario. However, these results quite graphically demonstrate that the importance of curve-flattening interventions does not abate once geographical factors are taken into account. On the other hand, these results suggest that differences in hospital load may be substantially more profound than would be anticipated from uniform mixing models, creating logistical challenges and possibly exacerbating existing differences in resource levels across hospitals. At the same time, such heterogeneity implies that resource sharing and patient transfer arrangements could prove more effective as load management strategies than would be suggested by spatially homogeneous models, as hospitals are predicted to vary considerably in the timing of patient demand.subjective experience of the pandemic will potentially differ for individuals residing in different locations. In particular, social exposures to outcomes such as morbidity or mortality may shape individuals’ understandings of the risks posed by COVID-19, and their willingness to undertake protective actions to combat infection. Such exposures may furthermore act as stressors, with potential implications for physical and/or mental health. As a simple measure of social exposure, we consider the question of whether a focal individual (ego) either has experienced a negative outcome themself or has at least one personal contact  who has experienced the outcome in question. In addition to health care strain, the To examine how social exposure varies by location, we compute the fraction of individuals in each tract who are socially exposed to morbidity or mortality. By way of assessing the milieu within each tract, it is useful to consider the “cross-over” point at which at least half of the residents who will be socially exposed in a given tract have been socially exposed to either COVID-19 morbidity or mortality. Our simulation results all underscore the potential effects of local spatial heterogeneity on disease spread. The spatial heterogeneity driving these results occurs on a very small scale , operating well below the level of the city a whole. As the infection spreads, relatively small differences in local network connectivity and the prevalence of bridging ties driven by uneven population distribution can lead to substantial differences in infection timing and severity, leading different areas in each city to have vastly different experiences of the pandemic. Resources will be utilized differently in different areas, as some areas will experience the bulk of their infections far later than others, and the subjective experience of a given individual regarding the pandemic threat may differ substantially from someone in another area. These behaviors are in striking contrast to what is assumed by models based on the assumption of spatially homogeneous mixing, which posit uniform progress of the infection within local areas.As noted at the outset, our model is based on a no-mitigation scenario, and is not intended to capture the impact of social distancing. While distancing measures by definition limit transmission rates—and will hence slow diffusion—contacts occurring through spatially correlated networks like those modeled here are still likely to show patterns of heterogeneity like those described. One notable observation from our simulations is the long outbreak delay that some census tracts experience, even in the absence of social distancing. This would suggest that relaxation of mitigation measures leading to a resumption of “normal” diffusion may initially appear to have few negative effects, only to lead to deadly outbreaks weeks or months later. Public health messaging may need to stress that apparent lulls in disease progress are not necessarily indicators that the threat has subsided, and that areas “passed over” by past outbreaks could be impacted at any time.Finally, we stress that conventional diffusion models using locally homogeneous mixing have been of considerable value in both pandemic planning and scenario evaluation. Our findings should not be taken as an argument against the use of such models. However, the observation that incorporating geographical heterogeneity in contact rates leads to radically different local behavior would seem to suggest that there is value in including such effects in models intended to capture outcomes at the city or county level. Since these are the scales on which decisions regarding infrastructure management, health care logistics, and other policies are often made, improved geographical realism could potentially have a substantial impact on our ability to reduce lives lost to the COVID-19 pandemic.Supplementary File


Six of the top ten leading causes of death in low resource settings could potentially be prevented by vaccination. Development of vaccines for individuals in these populations is difficult because of the biological complexity of the prevalent pathogens and the challenges inherent to development of any vaccine. This review discusses those challenges and promising advances to address them and highlights recent progress in development of vaccines against several pathogens of interest. Hemophilus influenzae type b, pneumococcus, rotavirus, and rubella. The impact has been staggering including near eradication of poliovirus and dramatic reduction in childhood mortality with deaths cut by more than half. Despite the amazing progress, more breakthroughs are needed; approximately three million individuals still die of vaccine-preventable diseases each year and infectious diseases such as lower respiratory infections, tuberculosis (TB), HIV/AIDs, diarrheal diseases, malaria, and those associated with preterm birth make up six of the top ten leading causes of death in low-income countries  from the time the first vaccine was produced against smallpox, vaccination in low- and middle-income countries (LMIC) where mortality from infectious diseases is highest was not widespread until the World Health Organization implemented the Expanded Programme on Immunization (EPI) in 1974. Building on the success of the smallpox eradication efforts, the EPI recommended vaccines to protect against six diseases including tuberculosis, diphtheria, tetanus, pertussis, measles, and poliomyelitis. Five additional vaccines have since been recommended to prevent hepatitis B, Vaccine development is uniquely challenging when compared with development of other product modalities such as small molecules. The second hurdle in the valley of death is optimization of clinical laboratory assays to measure the immune response induced by the vaccine. Vaccine concentration and pharmacodynamics cannot be measured directly; the biological effect of interest in vaccine clinical trials is the ability of the vaccine to induce an immune response, typically antibody, that protects against infection and/or disease. Clinical laboratory assays are required to measure both antibody quantity and quality  as well as other measures of immune stimulation, such as CD8+ T cell activation. The immunologic assays are first evaluated in preclinical studies designed to show a correlation between vaccine dose level and immune response. Ideally, these preclinical studies would also show efficacy in challenge studies and support selection of a range of doses to be evaluated in clinical studies. Immunologic assay results are often highly variable, particularly those measuring functional responses, but they must be optimized to provide consistent results with appropriate positive and negative controls in order to receive regulatory approval for use in clinical trials.As candidate vaccines progress to clinical trials, another universal challenge is that the target populations are healthy individuals. Therefore, the benefit-risk assessment differs from that of a therapeutic agent that is being administered to an ill patient; the bar for demonstrating safety is high and the safety profile must be acceptable to regulators, ethics committees, policymakers, parents, and individuals receiving the vaccines. Large clinical trials with several thousand subjects are needed pre-licensure to establish the safety profile and inform the benefit-risk assessment for using the vaccine broadly. Further, safety must continue to be monitored post-licensure for detection of uncommon adverse events that may not have been detected in pre-licensure studies.Mycobacterium tuberculosis (Mtb), Plasmodium species, and Human Immunodeficiency Virus (HIV) with each having the ability to establish chronic, asymptomatic infection and presumably escaping immune detection for at least part of the cycle. Antigen selection is further complicated by genetic and antigenic variability across strains including geographic variability and in vivo evolution of strains after infection has been established, underscoring the need to search for conserved antigens or bioengineer mosaics that will cover the majority of strains. Although functional antibody is typically the mechanism by which vaccines induce protection, other immune responses may be important for these pathogens such as binding antibody and classical and non-classical T cell responses, and the desired response may differ by lifecycle stage. Further, for those vaccines which have shown at least limited efficacy in clinical field trials, protection is short-lived without sufficient effector memory. Improved antigen “quality” and/or novel adjuvants are needed to extend the duration of efficacy and clinical benefit.The challenges of vaccine development for low resource settings start with this rather daunting set of baseline challenges overlaid with technical challenges stemming from the significant biological complexity of the target pathogens of interest, particularly those causing TB, malaria, and HIV/AIDs. With rare exceptions, vaccines constructed from whole or modified organisms or surface proteins have had low or no efficacy against these diseases in clinical trials. As summarized in After the initial technical development challenges are addressed and favorable phase 1 safety and immunogenicity data generated, vaccine development typically progresses to a phase 2 “proof-of-concept” (PoC) study in the target population to further evaluate safety and to generate the initial evidence that the vaccine protects against the disease of interest. For TB, malaria, and HIV vaccines, the desired protective immune response is unclear. Therefore, large phase 2 studies designed to show a statistically significant signal of efficacy i.e., reduction in disease in vaccines as compared with controls are required to demonstrate PoC. Dependent on the disease burden, sample sizes of several hundred to thousands may be needed to accrue enough disease cases to make this comparison with statistical rigor. The silver lining is that these large PoC studies also present the opportunity to identify the vaccine-induced immune response(s) that correlate with disease prevention, which can significantly simply further development.As will be discussed in the sections that follow, strategies have been employed for these high-risk vaccine programs to reduce the probability of late stage development failures. For TB vaccines, PoC may be shown through smaller “prevention of infection” (POI) studies conducted utilizing a biomarker. Initial efficacy data for malaria vaccines may be shown in challenge studies using the Controlled Human Infection Model (CHIM). For HIV, immune responses similar to those previously identified to correlate with protection may gate progression to larger efficacy studies. Although each of these approaches provides supportive data for decisions on further development, ultimately large studies showing a vaccine prevents disease as compared with a control are required.Even in the best of circumstances, large studies with these safety, efficacy, and immunogenicity objectives are difficult to implement. Clinical trial sites with robust infrastructure, sophisticated laboratories for processing clinical samples, and highly trained personnel are needed. Further, the national regulatory authorities and ethics committee members who approve the studies to move forward must understand the approach to vaccine design and manufacturing, the implications of preclinical study results and immunologic assay performance, and the PoC study design rationale and intended outcomes to appropriately assess benefit versus risk for their populations. Finally, early engagement with local community leaders is essential to address potential concerns about study conduct and avoid myths and rumors that may circulate once the study has started. Each of these challenges is amplified even further for pivotal, confirmatory phase 3 trials, which are larger still (tens of thousands).Although initially conduct of large PoC and pivotal phase 3 studies in low resource settings may seem an impossible task, they are feasible with the right partners and planning. Stimulated by the emergence of HIV and further supported by global public health malaria and TB research initiatives, several academic, government, and non-government organizations have built clinical trial site and laboratory capacity in low resource settings and additional efforts to close research capacity gaps are ongoing. Further, approaches have been defined for following and retaining participants considered high risk because of co-morbidities not encountered in high resource settings or complex social circumstances. The WHO provides guidance, training, and support for national regulatory authorities in its Member States to review and approve clinical trial applications . BecauseCreation of a sustainable model for development, introduction and commercialization of products intended almost solely for individuals in low resource settings is also an ongoing unmet medical need. Vaccine development is expensive with estimated costs from research and discovery to registration between 200 and 500 million US dollars . This esVaccine development costs may be further amplified by post-approval requirements for additional studies before broad recommendations are made. Large safety and effectiveness studies are increasingly being required for vaccines with modest efficacy to more fully assess the benefit-risk profile. For vaccines requiring multiple doses or given outside of existing schedules, an evaluation of how well the vaccine integrates into existing immunization programs is a good practice. Although critical, these activities may be as costly as full development programs. To reduce timelines for broad vaccine introduction and manage ever-increasing vaccine development costs, a system for the World Health Organization and associated vaccine advisory groups to provide early feedback on the data necessary to support recommendations is needed so that these considerations can be incorporated into pre-approval phase 3 studies when feasible.Assuming funding is available and these vaccines progress favorably through phase 3 studies and regulatory approval, commercialization partners will be needed for manufacturing and widespread distribution. It is possible that commercialization partners may include large pharmaceutical companies but are more likely to be vaccine companies emerging from low- and middle-income countries with a business model that embraces vaccines sold at high volumes but with a low profit margin. The manufactured vaccine must then be made available to those who need it most, individuals in high prevalence countries. Again, an innovative business model will be needed to support countries, such as the one utilized by Gavi, the Vaccine Alliance, in which countries contribute a proportion of the vaccine costs and receive supplemental support from Gavi until they can fully support the vaccine costs on their own .The sections that follow summarize the status of development of vaccines against TB, malaria, HIV, and other diseases. Pathogen-specific technical challenges and recent technological advances and clinical trial results that provide optimism for the future are discussed.Mycobacterium tuberculosis (Mtb) has evolved with humankind over the last 40,000 years, harnessing the immune system to its advantage. An estimated one quarter of the world's population is infected with Mtb. Although only 10% of those infected develop symptomatic disease, because of the high Mtb prevalence, this translates to an estimated 10 million cases and 1.4 million deaths annually  and prevention of disease (POD) shape the current global TB vaccine development strategy as scientists push to simultaneously understand Mtb immunopathogenesis and develop safe and efficacious vaccines. While a dual approach is appropriate given the magnitude of TB morbidity and mortality globally, in practice it means conducting large and complex clinical trials. Although a validated biomarker, Mtb-specific gamma interferon release, simplifies studies evaluating vaccines for POI, currently the only means to show a vaccine prevents disease is through evaluation of clinical TB disease cases in controlled clinical trials, which require many thousands of subjects from low resource areas where the prevalence of TB is high.Despite the technical and clinical trial complexities, the biggest challenge for TB vaccine development may be non-technical. Because TB was nearly eliminated by the middle of the twentieth century in high- and middle-income countries through improved sanitation and drug therapy, resources for development of better TB vaccines have been significantly limited, with some estimates suggest a funding shortage of ~150 million US dollars annually . AlthougNew interventions will be needed to achieve the World Health Organization's End TB goal of reducing deaths by 95% by 2035 (as compared with 2015). A vaccine that prevents disease in adolescents and young adults, the primary transmitters driving the TB epidemic in low income countries, is essential to reaching this goal. As summarized in Mycobacterium bovis (M. bovis) that cross-protects against Mtb. The vaccine is indicated for prevention of disseminated TB in infants and young children. Since it was first administered to infants in 1921, several studies have been conducted showing variable results with efficacies ranging from 0 to 99% depending on the endpoint of the study. A meta-analysis of these studies suggests the efficacy in newborns and young children is ~50% but wanes in the first two decades of life leaving older adolescents and young adults vulnerable to infection and disease , BCG revaccination, and a placebo was conducted in adolescents vaccinated with BCG at birth in the Western Cape of South Africa to evaluate efficacy to prevent Mtb infection as measured by QuantIFERON testing. Although the primary endpoint of the study, prevention of initial infection, was not met, BCG revaccination was 45% efficacious in preventing sustained infection defined as lack of QuantiFERON conversion over a 6-month period . It is a live, attenuated  disease . Accordih period . These rM. obuense, previously identified as M. vaccae). This vaccine is highlighted because of the results of a previous phase 3 study of a parent strain of this vaccine conducted in HIV-infected individuals with a CD4 count of > 200 who had received BCG at birth. Although the study did not meet the primary endpoint of preventing disseminated TB or a secondary endpoint of “probable” TB, the study did meet a secondary endpoint preventing 39% of “definite” TB  formulated with AS01E adjuvant containing QS21 and MPL in a liposomal formulation. A phase 2b randomized (1:1), placebo-controlled study was conducted in 3,575 HIV-uninfected subjects 18 to 50 years of age in Kenya, South Africa, and Zambia to evaluate the efficacy of two doses of the vaccine to prevent disease in subjects already infected with Mtb as demonstrated by a positive QuantiFERON test . An initBecause protection with intradermal (ID) BCG administration is incomplete, alternative routes of administration have been studied. A recent study in rhesus macaques compared the immunogenicity and efficacy of BCG administered by ID and intravenous (IV) injection, or as an intratracheal mucosal boost (ID + IT). The study showed that BCG administered intravenously induced protection surpassing that achieved by all other routes with 5 of 6 monkeys protected . AlthougAnother promising preclinical TB vaccine candidate is an attenuated, replication-competent CMV vector-based vaccine selected because of the ability to induce and maintain robust CD4+ and CD8+ T-cell memory responses. Rhesus cytomegalovirus (RhCMV) vectors coding for a variety of Mtb antigens were evaluated in two independent Mtb challenge studies in rhesus macaques . These cIn summary, although the life cycle and immunopathogenesis of Mtb remains to be clarified, data from recent preclinical and clinical studies provide hope that an approved vaccine may be on the horizon in the next decade. Simultaneously with clinical trials, it is imperative that scientists continue to understand the Mtb life cycle, identify new target antigens and clarify the immunologic mechanisms of protection against infection, subclinical and clinical disease to ensure “next generation” vaccines that are even more promising than those currently moving through the TB vaccine pipeline. Sufficient funding for this work is an unquestionable roadblock; the significant mortality associated with Mtb calls for stakeholders to come together with creative funding solutions and business models to work toward the goal of ending the TB epidemic globally.Plasmodium parasites, transmitted to humans from the female Anopheles mosquito. The Plasmodium life cycle is illustrated in Plasmodium infections are typically asymptomatic or associated with uncomplicated disease including fever, chills, headache, nausea and vomiting, and fatigue. Severe disease occurs when infected erythrocytes sequester in blood vessels leading to major organ failure. Individuals at highest risk of infection and severe malaria are infants and young children <5 years old, pregnant women, individuals with HIV/AIDs, and non-immune migrants or travelers. Currently, prevention is best provided through vector control agents such as indoor residual spraying with insecticides and pyrethroid insecticide-treated bed nets. Antimicrobials are also available for chemoprophylaxis. In those already infected, artemisinin-based combinations are the best available therapy. These tools combined with environmental management have significantly reduced malaria morbidity and mortality. However, resistance to insecticides and antimicrobials has been a recurring problem throughout history. To ensure malaria elimination, a vaccine is needed.Malaria occurs from infection with Plasmodium falciparum pre-erythrocytic circumsporozoite protein (CSP) fused to the hepatitis B virus surface antigen—formulated with ASO1 adjuvant containing QS21  and monophosphoryl lipid A (MPL). The vaccine was ~50% efficacious against clinical malaria in infants 5 to 17 months of age in the initial analysis of a large phase 3 study conducted in southern Africa, but additional data showed the efficacy is low in young infants and wanes over time  . The vacver time . The mecPlasmodium species is complex and the preferred antigen(s) for vaccines are ill defined. The mechanism of immunity to Plasmodium species is as complex as the parasite's life cycle. Host factors are important for protection; in highly endemic areas, alterations in hemoglobin structures or certain erythrocyte-associated enzymes provide at least partial protection against infection and severe disease  Development of vaccines that will reduce transmission of the parasite, which are suitable for administration in mass campaigns  Licensure of vaccines against ampaigns . In the What is the path forward given the ambitious goals but technical uncertainties? First, it is important to build on the knowledge that has been generated from RTS,S/AS01 vaccine studies. It appears that functional antibody against the NANP repeats of CSP are important for protection, and that very high antibody titers persisting for several malaria seasons will be necessary to eliminate disease. Improvements in antibody quantity and quality through the use of novel adjuvants, structure-guided immunogen design, and optimized vaccine regimens will be important for “next-generation” pre-erythrocytic CSP vaccines. Further, given the evidence that antibody may also be important for blood-stage and sexual-stage (transmission blocking) vaccines, taking a similarly rigorous approach in antigen identification and design and using novel adjuvants early may be important lessons learned from pre-erythrocytic vaccines. For liver-stage vaccine candidates, ensuring that the vaccine reaches the hepatocytes that are infected and induces sufficient antigen-specific CD8+ T cell responses will be important, recognizing that novel formulations and routes of administration  may be required. Regardless of the approach, rigor in the development and optimization of assays be they for antigen down-selection or interrogating the immune response are critical. Further, engaging early with experts in protein expression systems and manufacturing process development and scale-up may help the vaccinologist “get ahead” in the development process and be better positioned to move forward robustly with CHIM studies and clinical development when preclinical data are favorable.Since the beginning of the epidemic, 75 million people have been infected with human immunodeficiency virus (HIV) and 32 million people have died . Africa The technical challenges of HIV vaccine development have a similar root cause as the challenges for TB and malaria vaccines. First is the virus life cycle. It rapidly establishes chronic infection integrating into the host genome as a DNA provirus, allowing it to escape immune detection. Second, it is tropic for T helper cells which facilitate spread and persistence of the infection. Further, the immunologic mechanism of protection is still being defined. For most viruses, neutralizing antibody is important to prevent entry into the cell. In the case of HIV, each virus is unique and only antibody directed against epitopes conserved across strains will be broadly effective. No vaccine has induced broadly neutralizing antibodies to date .Other challenges not often addressed in the scientific literature are those common to all vaccines in development. For example, expressing trimers of HIV's envelope (Env) glycoprotein (gp) in the proper conformation and manufacturing without proteolytic damage at sufficient yields for supplying clinical trials has been a significant challenge for clinical HIV vaccine research. Further, availability of novel adjuvants to increase the magnitude, breadth, and duration of antibody responses has been limited.Among its counterpart vaccines discussed in this paper, more progress has been made on HIV vaccine development than in any other area. The scientific learnings from early efficacy trials were sobering but valuable, showing that a subunit Env vaccine, such as the bivalent gp120, was not efficacious and that an adenovirus vector-based vaccine increased the risk of HIV infection ostensibly through activation of CD4+ T cells for which the virus is tropic . HoweverWhat is the path forward for HIV vaccines? First, the RV144 efficacy study is being repeated in South Africa with an appropriately matched Clade C vaccine and an additional booster dose. This will provide an opportunity to prospectively confirm the immunologic responses correlated with protection afforded by the vaccine. An efficacy study of another vectored vaccine, adenovirus 26 expressing different mosaic Env/Gag/Pol antigens intended to increase the breadth of immune response followed by a gp140 Env boost is also underway.An exciting evolution in the field is the identification of a growing number of broadly neutralizing antibodies (bnAbs) that neutralize a large proportion of HIV libraries. Two studies evaluating the efficacy of passively transfused bnAbs against VRC01 to prevent HIV infection are underway. Additional work to further increase breadth, potency, and half-life are ongoing. If the data from the efficacy studies are favorable, bnAbs could both be given to prevent HIV infection and help inform the design of future vaccines with the appropriate antigens needed for protection. To avoid delays, multiple forms of Env are being evaluated for induction of bnAbs and pnnAbs in parallel.Although the HIV field has a leg up on that of TB and malaria given the insights on immune responses that correlate with disease risk, the fundamental challenges are the same. For transfused bnAbs, will the cost of manufacturing and the need for skilled personnel to administer them preclude widespread use in the regions with the greatest HIV burden? For new HIV vaccines, what is the right antigen(s) needed to induce the desired, broadly neutralizing antibody profile? What are the antigenic conformations, vaccine regimens and/or adjuvants that can ensure that response is long-lived? It is hoped that the results of the aforementioned clinical HIV vaccine and bnAbs trials will shed more light on these questions and surprise us with new knowledge in the ongoing search for an affordable and pragmatic approach for HIV vaccination globally.Despite the introduction of pneumococcal conjugate and rotavirus vaccines, lower respiratory tract infections and diarrheal diseases are still in the top ten causes of mortality in low resource settings, with the greatest impact on infants and young children <5 years old . In regiStreptococcus pneumoniae is respiratory syncytial virus (RSV), estimated to cause 33 million cases and 118,000 deaths annually  Development of vaccines for maternal immunization during pregnancy leading to trans-placental antibody transfer and prevention of severe RSV disease in neonates and young infants; and (2) Development of vaccines to prevent RSV disease in infants and young children . The firVaccines being pursued for older infants and young children include attenuated live viruses and vector-based vaccines coding for surface glycoproteins. In clinical trials to date, live virus vaccines have not been associated with enhanced RSV disease; data for vector-based vaccines are still outstanding. Several other vaccine approaches are being utilized for older adults.RSV vaccine development is not fraught with the same challenges as TB, malaria, and HIV vaccines in that it has a straightforward life cycle, the target antigen is well-defined, and the protective immune response, neutralizing antibody, is well-known. However, RSV vaccine development carries a different set of challenges including those associated with vaccinating pregnant women. Although substantial progress has been made with maternal immunization programs over the last decade and the WHO and many national health authorities recommend maternal immunization based on the clear benefits for mothers and their infants, vaccine coverage rates remain low in some areas. A recent literature review identified several reasons for low vaccine acceptance among pregnant women, with unfounded concerns about maternal and fetal safety being primary among them . ConcernShigella is the second leading cause of diarrheal disease deaths behind rotavirus in low income countries, estimated to cause over 200,000 deaths annually . The majShigella sonnei O-specific polysaccharide conjugated to Pseudomonas aeruginosa recombinant exoprotein A (S. sonnei-r EPA). The vaccine was ~74% efficacious in adults with lower efficacy in young children and no efficacy in those between 1 to 2 years of age  of disease in young children  by which immunity is generated. Although data from the study of the elopment . FurtherFinally, requirements for clinical trials and regulatory approval are being evaluated . PivotalShigella to support widespread use will be available within the next decade. Further, long-acting therapeutics and monoclonal antibodies against malaria, HIV, and RSV that are highly potent, easy to administer, and affordable for low income countries will also likely exist. Our future challenge may be developing a people-centered care approach that sufficiently addresses the needs of individuals and their communities. Simplified manufacturing approaches with modular bioprocess and fill/finish units may make regional manufacturing an affordable model and reduce the need for large capital expenditures. This progress does not mean that basic research on life cycles, antigen selection, and immunopathogenesis should be slowed; in fact, the push should continue to simultaneously develop product candidates that have potential for higher efficacy similar to that observed with other routine childhood vaccines. Nonetheless, it is motivating to reflect on the fact that the tools exist today to move diseases that are currently an everyday fear of individuals living in low resources areas to their rightful place in history–eliminated.The development of vaccines against diseases that are prevalent almost solely in low resource settings is incredibly challenging, combining the traditional complexities of biologics manufacturing and immunologic characterization with the technical complexities of pathogens with elaborate life cycles and unclear immunology. The vaccines must be studied in populations who live in areas without sufficient clinical trial and laboratory site infrastructure. Further, the global funding envelop for each of these vaccines is limited. This is not an effort for the faint of heart. However, technological advances in high throughput antigen and antibody screening, immuno-profiling, and advanced manufacturing techniques put us in a better position today than ever before to achieve success. In fact, when one looks at the global health vaccine pipeline and the vaccine candidates currently in clinical trials, it is quite feasible that vaccines with sufficient efficacy against TB, malaria, HIV, RSV, and PH wrote, revised, reviewed, and approved the final manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The usual definition of the difference between climacteric and non-climacteric fruit relies on the fact that climacteric fruit ripens with concomitant increases of respiration and ethylene production, whereas barely any change in these two metabolisms occurs in non-climacteric fruit  and phosphoglucan water dikinase (PWD). For detailed mechanisms, see the review articles cited above.Starch degradation is more complex than starch synthesis, as it follows different pathways according to the plant organ where they occur (Kötting et al., From general knowledge, outlined in Robinson et al.  showed tNowadays with increasing availability of large data sets, regarding the transcriptomes or the proteomes, further research regarding the starch synthesis pathway could be initiated comparing some model fruit. Regarding tomato, one tool has recently been published, TomExpress (Zouine et al., Regarding starch degradation, the mining of such large data sets may also reveal some differences between these two fruit classes, that have not yet been studied. I believe more is to be uncovered in the coming decade, regarding starch metabolism in climacteric vs. non-climacteric fruit.CC wrote the article and created the illustration.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Network interventions for HIV prevention represent a potential area for growth in a globalizing world, where persons are more easily connected to one another through social media and networking applications. The basic tenets of network interventions such as (1) selection of a change agent, (2) segmentation, (3) induction, and (4) alteration represent myriad ways to structure network interventions for HIV prevention with the potential for large public health impact.Recent studies have employed the use of social networking websites such as Facebook to identify key persons to recruit others and disseminate information aimed at decreasing HIV transmission and improving safe sex practices among groups who are more vulnerable to HIV acquisition. Many of these interventions have successfully decreased HIV risk behaviors as well as decreased the spread of HIV among intervention cohorts.Network interventions for HIV prevention provide more opportunities to reach populations who have not been reached through typical efforts employed in clinical and public health settings, though they are not currently widely employed by the public health community and other stakeholders. Social networks as a unit of social structure represent an essential feature of public health and an opportunity for interventionists to engage networks around HIV prevention through network characterization, visualization, analysis, and intervention. The goal of network analysis is to explain the behavior of groups of individuals within their context and the systems and interrelationships that exist as a result of the connections between said groups . RelatioThe majority of network interventions for health improvement, especially as pertains to disease prevention, provide methodologies for implementing behavioral change strategies that include a role for social influence, norms and peer influences . In thisA central tenet of network interventions is the notion that behavior change is enacted through explicit or implicit mechanisms of influence among individuals that are close to one another . The intResearch on the diffusion of health behaviors within the context of network interventions has been active in areas such as fertility preferences  and pharIn his seminal work, Thomas Valente describes four typologies of network interventions . These fPerhaps the most classic and intuitive network intervention is the peer change agent intervention. Peer change agent interventions identify influential individuals within a target population and train them to champion a desired behavior within their networks. The act of championing a behavior can take many forms—e.g., educating peers about the benefits of a behavior, modeling the behavior itself, and providing support to peers who are in the process of adopting the behavior—and has been applied to encourage HIV-related behavioral or attitudinal changes in dyadic , personaAlthough change agents can be chosen on the basis of a variety of salient characteristics including their desire to help, aspects of their personality , or their experience with the desired behavior, what grants a peer change agent their credibility rests largely in their social standing in organic social networks among members of the target population , 33. ThuIn the context of HIV prevention, the Type I change agent interventions are the most commonly utilized and reported on with documented efficacy in rigorously conducted studies. The prototypical peer change agent intervention is the Popular Opinion Leader (POL) model , which dThat said, some POL studies have shown more mixed results. In a randomized trial across five countries, the deployment of “natural leaders” led to a 33% reduction in HIV risk behaviors among individuals assigned to the intervention. However, these reductions were experienced mostly among those at highest-risk  rather than the general population . Taken tThe POL model may also be hampered by its over-reliance on change agents who are most “popular” in a network, a position that often requires an individual to maintain conformity to the status quo rather than change it . This haAlthough the intentional use of network bridges remains an underutilized peer change agent intervention strategy , several studies reveal important clues about their potential efficacy for promoting HIV prevention. In a study involving Indian MSM , candidaa priori as is the case when an innovation is introduced into distinct communities of practice like House Ball Community (HBC) [In contrast to interventions that employ individual change agents as the proponents of behavior change, segmentation interventions identify groups of people to change at the same time . Groups ty (HBC) , which iFor example, Hosek et al. (2015) tailored an evidence-based intervention for use within the HBC called Promoting Ovahness through Safer Sex Education (POSSE); the intervention recruited and trained leaders from within the HBC to deliver risk reduction messages to their peers, resulting in statistically significant declines in self-reported partner concurrency, condomless anal intercourse, and intercourse with partners with unknown HIV status . And, inGroup segmentation can also be based on a particular attribute of the individuals in the target population. A promising terrain in which to employ attribute-based segmentation in the service of HIV prevention and care engagement is the “hook-up” or dating application (app). “Hook-up” apps for MSM  allow users to signal their partner sero-sorting preferences in their profiles. An intervention that clusters these sero-sorters on the basis of their negative or positive HIV status would allow researchers to test the effectiveness of status-neutral approaches to testing and treatment engagement in both behavioral communities . Given tFinally, unlike the previous segmentation strategies that draw on preexisting categorical distinctions to define groups, segmentation can also be artificially imposed on a target population in the service of an intervention that aims to encourage group-based interactions and social learning. The HOPE intervention study implemented in Peru and later in Los Angeles used this segmentation approach toward promoting HIV testing among MSM, and is evidence of the effectiveness of a Type II network intervention as an HIV prevention intervention. Specifically, MSM participants were assigned to secret Facebook groups with designated peer leaders who were trained to encourage group members to test for HIV, which resulted in greater numbers of intervention group members testing for HIV and using condoms , 51. TheNetwork mobilization or network induction is an intervention strategy that stimulates peer-to-peer interaction to create information cascades or behavioral diffusion through existing social pathways among network members . The TypAn example of how induction works as both a recruitment and intervention strategy is next-generation social network strategies (SNS) for HIV testing , 53. PasBuilding upon classical partner services and the Social Network Strategy, the Transmission Reduction Intervention Project (TRIP) conducted network-based recruiting, counseling, and testing in Odessa, Ukraine; Athens, Greece; and Chicago, Illinois. TRIP’s primary goal was to increase early detection of recently HIV-infected individuals . A secon#TruvadaWhore social media campaign exemplifies what virality can do for HIV prevention awareness and advocacy. Launched in response to the way in which PrEP users in the gay community were being stigmatized as promiscuous “Truvada Whores” [#TruvadaWhore campaign defiantly reappropriated this “slut shaming” to be worn instead as a badge of pride. On Twitter alone, the use of the Truvada Whore hashtag resulted in thousands of tweets that brought attention to PrEP as a viable HIV prevention innovation that strove to destigmatize its use and that generated political activism. Grassroots campaigns like this can offer lessons for interventionists who want to generate interest and enthusiasm for different prevention modalities through peer-to-peer messaging.Inducing HIV prevention information cascades can also be achieved in nonclinical, naturalized social environments. Social networking platforms like Facebook, Twitter, Instagram, and YouTube enable rapid content sharing and electronic Word-of-Mouth (eWOM) cascades that can be leveraged for health promotion campaigns . The #Tr Whores” , the #TrChanging the network is likely the most difficult network intervention to implement and potentially the strategy with the most ethical challenges, whereby networks and social spheres are manipulated for the potential to illicit behavior change. This is not unlike the ways in which Facebook or Grindr may suggest particular “friends” or potential partners for users on their platforms . IntervePrEP Chicago where digital network data from Facebook was used to provide a more expansive definition of a person’s social network [The decision to use network interventions within the HIV prevention cascade involves a complex set of features aimed at targeting multiple persons for maximum public health impact. Clearly, implementation science can contribute to network intervention effectiveness, though how and when we utilize said interventions depends largely on inner and outer contexts, and the desired outcome, in addition to the desired scope of prevention efforts and ability to “scale-out” these interventions , 66. For network •. An addLarger scale HIV prevention interventions can be organized through the process of segmentation, which can take on geographic, sociodemographic, psychographic, and sociometric characteristics in order to reach a subset of the population at specific risk for HIV . The utiThe process of social network recruitment is rooted in peer-based recruitment, and can serve to identify populations at risk for HIV who are less likely to interface with healthcare settings due to discrimination or isolation, such as young Black MSM in the United States , 72. TheAlteration may be the most difficult network intervention to characterize within the context of HIV prevention efforts as it is less well described and utilized. Alteration as an HIV prevention strategy represents an area for further development within the context of network interventions. Though given its potential for desired outcomes in HIV-positive persons, there is potential to develop studies to test its feasibility within the HIV prevention cascade.Network interventions represent a growing area for HIV prevention efforts surrounding PrEP and other biomedical and behavioral interventions aimed at stemming new infections. Understanding groups who are most vulnerable to HIV infection, change makers within their social spheres, and methodologies for increasing both education and thereby, decision-making capacity may broaden the success of public health interventions to reduce HIV transmission. Implementation science becomes increasingly important if network interventions are to be successful, particularly with close attention paid to (1) stakeholder buy-in and context given that network data collection can be extensive, sensitive, and (2) that most public health HIV researchers and service providers have been dominated by psychological underpinnings of prevention and focused primarily at the individual level. Understanding the networks therein may allow for more successful outcomes for HIV prevention by emphasizing the influence of social systems and the environment on behaviors related to sexual health.