Poverty, Disease, and the Ecology of Complex Systems

Understanding why some human populations remain persistently poor remains a significant challenge for both the social and natural sciences. The extremely poor are generally reliant on their immediate natural resource base for subsistence and suffer high rates of mortality due to parasitic and infectious diseases. Economists have developed a range of models to explain persistent poverty, often characterized as poverty traps, but these rarely account for complex biophysical processes. In this Essay, we argue that by coupling insights from ecology and economics, we can begin to model and understand the complex dynamics that underlie the generation and maintenance of poverty traps, which can then be used to inform analyses and possible intervention policies. To illustrate the utility of this approach, we present a simple coupled model of infectious diseases and economic growth, where poverty traps emerge from nonlinear relationships determined by the number of pathogens in the system. These nonlinearities are comparable to those often incorporated into poverty trap models in the economics literature, but, importantly, here the mechanism is anchored in core ecological principles. Coupled models of this sort could be usefully developed in many economically important biophysical systems—such as agriculture, fisheries, nutrition, and land use change—to serve as foundations for deeper explorations of how fundamental ecological processes influence structural poverty and economic development

Persisting Viral Sequences Shape Microbial CRISPR-based Immunity

Well-studied innate immune systems exist throughout bacteria and archaea, but a more recently discovered genomic locus may offer prokaryotes surprising immunological adaptability. Mediated by a cassette-like genomic locus termed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), the microbial adaptive immune system differs from its eukaryotic immune analogues by incorporating new immunities unidirectionally. CRISPR thus stores genomically recoverable timelines of virus-host coevolution in natural organisms refractory to laboratory cultivation. Here we combined a population genetic mathematical model of CRISPR-virus coevolution with six years of metagenomic sequencing to link the recoverable genomic dynamics of CRISPR loci to the unknown population dynamics of virus and host in natural communities. Metagenomic reconstructions in an acid-mine drainage system document CRISPR loci conserving ancestral immune elements to the base-pair across thousands of microbial generations. This ‘trailer-end conservation’ occurs despite rapid viral mutation and despite rapid prokaryotic genomic deletion. The trailer-ends of many reconstructed CRISPR loci are also largely identical across a population. ‘Trailer-end clonality’ occurs despite predictions of host immunological diversity due to negative frequency dependent selection (kill the winner dynamics). Statistical clustering and model simulations explain this lack of diversity by capturing rapid selective sweeps by highly immune CRISPR lineages. Potentially explaining ‘trailer-end conservation,’ we record the first example of a viral bloom overwhelming a CRISPR system. The polyclonal viruses bloom even though they share sequences previously targeted by host CRISPR loci. Simulations show how increasing random genomic deletions in CRISPR loci purges immunological controls on long-lived viral sequences, allowing polyclonal viruses to bloom and depressing host fitness. Our results thus link documented patterns of genomic conservation in CRISPR loci to an evolutionary advantage against persistent viruses. By maintaining old immunities, selection may be tuning CRISPR-mediated immunity against viruses reemerging from lysogeny or migration

epidemiological network Clusters of poverty and disease emerge from feedbacks on an "Data Supplement" References Subject collections Clusters of poverty and disease emerge from feedbacks on an epidemiological network

The distribution of health conditions is characterized by extreme inequality. These disparities have been alternately attributed to disease ecology and the economics of poverty. Here, we provide a novel framework that integrates epidemiological and economic growth theory on an individual-based hierarchically structured network. Our model indicates that, under certain parameter regimes, feedbacks between disease ecology and economics create clusters of low income and high disease that can stably persist in populations that become otherwise predominantly rich and free of disease. Surprisingly, unlike traditional poverty trap models, these localized disease-driven poverty traps can arise despite homogeneity of parameters and evenly distributed initial economic conditions

Inferring Social Network Structure from Bacterial Sequence Data

Using DNA sequence data from pathogens to infer transmission networks has traditionally been done in the context of epidemics and outbreaks. Sequence data could analogously be applied to cases of ubiquitous commensal bacteria; however, instead of inferring chains of transmission to track the spread of a pathogen, sequence data for bacteria circulating in an endemic equilibrium could be used to infer information about host contact networks. Here, we show— using simulated data—that multilocus DNA sequence data, based on multilocus sequence typing schemes (MLST), from isolates of commensal bacteria can be used to infer both local and global properties of the contact networks of the populations being sampled. Specifically, for MLST data simulated from small-world networks, the small world parameter controlling the degree of structure in the contact network can robustly be estimated. Moreover, we show that pairwise distances in the network—degrees of separation—correlate with genetic distances between isolates, so that how far apart two individuals in the network are can be inferred from MLST analysis of their commensal bacteria. This result has important consequences, and we show an example from epidemiology: how this result could be used to test for infectious origins o

Molecular Markers of Sulfadoxine-Pyrimethamine Resistance in Samples from Children with Uncomplicated Plasmodium falciparum at Three Sites in Angola in 2019

Funding Information: The study was funded by the U.S. President’s Malaria Initiative and the Advanced Molecular Detection program at CDC. Funding Information: We thank all study staff and participants, Venceslau Mambi Pelenda, José Bumba da Cunha, Oliveira Kiatoko, Felismina Caquece, Luzala Elisabeth Armando Garcia, Djos Kialanda, Garcia Pembele, Domingos Jandondo, and Belmira José Bondo. The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the U.S. Centers for Disease Control and Prevention or the U.S. Agency for International Development. The study was funded by the U.S. President’s Malaria Initiative and the Advanced Molecular Detection program at CDC. P.R.D., C.M.F., B.N.A., S.L., R.H., J.F.M.M., F.F., J.F.M., and M.M.P. designed and participated in the specimen collection. P.R.D., A.L.M.C., S.R.R., and J.K. performed the laboratory analysis. P.R.D., A.L.M.C., J.K., S.R.R., J.-H.M.O., and E.T. analyzed the sequencing data. S.R.R. and M.M.P. wrote the manuscript. All authors read and approved the final version of the manuscript. Publisher Copyright: Copyright © 2023 Rosillo et al.Sulfadoxine-pyrimethamine (SP) is used for prevention of malaria in pregnant women in Angola. We sequenced the Plasmodium falciparum dihydrofolate reductase (pfdhfr) and dihydropteroate synthase (pfdhps) genes, implicated in SP resistance, in samples collected during a 2019 study of artemisinin-based combination therapy efficacy in Benguela, Lunda Sul, and Zaire provinces. A total of 90 day 0 and day of failure samples were individually sequenced, while 508 day 0 samples from participants without recurrent parasitemia were pooled after DNA extraction into 61 pools. The N51I, C59R, and S108N pfdhfr mutations and A437G pfdhps mutations were present at high proportions in all provinces (weighted allele frequencies, 62% to 100%). The K540E pfdhps mutation was present at lower proportions (10% to 14%). The A581G pfdhps mutation was only observed in Zaire, at a 4.6% estimated prevalence. The I431V and A613S mutations were also only observed in Zaire, at a prevalence of 2.8% to 2.9%. The most common (27% to 66%) reconstructed haplotype in all three provinces was the canonical quadruple pfdhfr pfdhps mutant. The canonical quintuple mutant was absent in Lunda Sul and Benguela and present in 7.9% of samples in Zaire. A single canonical sextuple (2.6%) mutant was observed in Zaire Province. Proportions of the pfdhps K540E and A581G mutations were well below the World Health Organization thresholds for meaningful SP resistance (prevalence of 95% for K540E and 10% for A581G). Samples from therapeutic efficacy studies represent a convenient source of samples for monitoring SP resistance markers.publishersversionpublishe

Persisting viral sequences shape microbial CRISPR-based immunity.

Well-studied innate immune systems exist throughout bacteria and archaea, but a more recently discovered genomic locus may offer prokaryotes surprising immunological adaptability. Mediated by a cassette-like genomic locus termed Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), the microbial adaptive immune system differs from its eukaryotic immune analogues by incorporating new immunities unidirectionally. CRISPR thus stores genomically recoverable timelines of virus-host coevolution in natural organisms refractory to laboratory cultivation. Here we combined a population genetic mathematical model of CRISPR-virus coevolution with six years of metagenomic sequencing to link the recoverable genomic dynamics of CRISPR loci to the unknown population dynamics of virus and host in natural communities. Metagenomic reconstructions in an acid-mine drainage system document CRISPR loci conserving ancestral immune elements to the base-pair across thousands of microbial generations. This 'trailer-end conservation' occurs despite rapid viral mutation and despite rapid prokaryotic genomic deletion. The trailer-ends of many reconstructed CRISPR loci are also largely identical across a population. 'Trailer-end clonality' occurs despite predictions of host immunological diversity due to negative frequency dependent selection (kill the winner dynamics). Statistical clustering and model simulations explain this lack of diversity by capturing rapid selective sweeps by highly immune CRISPR lineages. Potentially explaining 'trailer-end conservation,' we record the first example of a viral bloom overwhelming a CRISPR system. The polyclonal viruses bloom even though they share sequences previously targeted by host CRISPR loci. Simulations show how increasing random genomic deletions in CRISPR loci purges immunological controls on long-lived viral sequences, allowing polyclonal viruses to bloom and depressing host fitness. Our results thus link documented patterns of genomic conservation in CRISPR loci to an evolutionary advantage against persistent viruses. By maintaining old immunities, selection may be tuning CRISPR-mediated immunity against viruses reemerging from lysogeny or migration

Multiple infections cause the appearance and expansion of the basin of attraction of poverty traps.

<p>For graphs (a) and (b) (dashed blue line) and (solid dark green line). Graph (b) is a magnified version of the initial portion of graph (a), while graph (c) is a magnified version of the initial portion of graph (b) showing a stable positive poverty trap. The filled circles denote stable equilibria while the open circle denotes an unstable equilibrium. Graph (e) is a magnified version of the initial portion of graph (d), while graph (f) is a magnified version of the initial portion of graph (e). Each curve in graphs (d–f) represents the structure of capital accumulation for different numbers of pathogens.</p

(a–c) are examples of nonlinear production functions extracted from the economics literature [2].

<p>The x-axis is the stock of capital. The blue line represents the rate of capital accumulation (i.e., savings) and the red line represents that rate of capital depreciation. Income (generated from capital) will necessarily fall when the red line is above the blue line, and will rise when the reverse is true. (c) is the canonical depiction of a poverty trap, but (a–c) all have stable equilibria in the basin of attraction of a poverty trap, and unstable equilibria that represent a critical threshold of capital necessary for growth. These models are speculative, based on hypothetical scenarios, but are useful for demonstrating a range of theoretical possibilities. The scientific community should contribute to our understanding of how such nonlinearities might emerge from, or be nested within, real world biophysical systems.</p

Disease and food systems exhibit bistability.

<p>Phase plots of (a) human capital against disease prevalence, (b) capital against plant density, and (c) human capital against nutrition showing two stable equilibria (solid circles), and one unstable equilibrium (open circle) in between. Sample trajectories that converge to the good equilibrium (solid blue circle) are denoted by blue lines, while sample trajectories that converge to the bad equilibrium (solid red circle) are denoted by red lines.</p