Biology subjects

Thompson, J.

Publications and source records attributed to Thompson, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2023

Atlas of fetal metabolism during mid-to-late gestation and diabetic pregnancy

Mounting evidence supports an instructive role for metabolism in stem cell fate decisions. However, much is yet unknown about how fetal metabolism changes during mammalian development and how altered maternal metabolism shapes fetal metabolism. Here, we present a descriptive atlas of in vivo fetal murine metabolism during mid-to-late gestation in normal and diabetic pregnancy. Using 13C-glucose and LC-MS, we profiled the metabolism of fetal brains, hearts, livers, and placentas harvested from pregnant dams between embryonic days (E)10.5 and 18.5. Comparative analysis of our large metabolomics dataset revealed metabolic features specific to fetal tissues developed under a hyperglycemic environment as well as metabolic signatures that may denote developmental transitions during euglycemic development. We observed sorbitol accumulation in fetal tissues and altered neurotransmitter levels in fetal brains isolated from dams with maternal hyperglycemia. Tracing 13C-glucose revealed disparate nutrient sourcing in fetuses depending on maternal glycemic states. Regardless of glycemic state, histidine-derived metabolites accumulated during late development in fetal tissues and maternal plasma. Our rich dataset presents a comprehensive overview of in vivo fetal tissue metabolism and alterations occurring as a result of maternal hyperglycemia.

cell biology↗

bioRxiv2023

High Maternal Adiposity During Pregnancy Programs an Imbalance in the Lipidome and Predisposes to Diet-induced Hepatosteatosis in the Offspring

BackgroundExposure to high maternal adiposity in utero is a significant risk factor for the later-life development of metabolic syndrome (MetS), including non-alcoholic fatty liver disease (NAFLD). We have previously shown that high pre-pregnancy adiposity programs adipose tissue dysfunction in the offspring, leading to spillover of fatty acids into the circulation, a key pathogenic event in obesity-associated MetS. Herein, we hypothesized that programming of adipose tissue dysfunction in offspring born to overweight dams increases the risk for developing NAFLD. ResultsFemales heterozygous for leptin receptor deficiency (Hetdb) were used as a model of high pre-pregnancy adiposity. Wild-type (Wt) offspring born to Hetdb pregnancies gained significantly more body fat following high fat/fructose diet (HFFD) compared to Wt offspring born to Wt dams. HFFD increased circulating free fatty acids (FFA) in male offspring of control dams, while FFA levels were similar in HFFD-fed offspring from Wt dams compared to CD or HFFD-Wt offspring from Hetdb dams. Despite female-specific protection from diet-induced FFA spillover, both male and female offspring from Hetdb. dams were more susceptible to diet-induced hepatosteatosis. Lipidomic analysis revealed that CD-offspring of overweight dams had decreased hepatic PUFA levels compared to control offspring. Changes to saturated fatty acids (SFA) and the de novo lipogenic (DNL) index were diet driven; however, there was a significant effect of the intrauterine environment on FA elongation and {Delta}9 desaturase activity. ConclusionHigh maternal adiposity during pregnancy programs a susceptibility to diet-induced hepatosteatosis.

developmental biology↗

bioRxiv2023

Predicting response to cytotoxic chemotherapy

Cytotoxic chemotherapies have been a crucial part of cancer treatment for over 40 years. While their primary target is cancer cells, they can also harm normal cells, resulting in dose-limiting toxicity. Most chemotherapies were approved before the advent of precision biomarkers, as such, many patients experience severe toxic side effects without any benefit. To address this challenge, we have developed three precision biomarkers to predict response to platins, taxanes, and anthracyclines. Based on chromosomal instability (CIN) signatures, these biomarkers can be computed from a single genomic test. For platins and taxanes, we used CIN signatures related to impaired homologous recombination, while for anthracyclines, we discovered a CIN signature representing micronuclei induction which predicts resistance. In a clinical study involving 41 high-grade serous ovarian cancers, patients predicted to be sensitive by these biomarkers showed significantly prolonged progression-free survival. To further validate the effectiveness of the taxane and anthracycline predictors, we conducted a retrospective randomised control study involving 182 ovarian and 219 breast cancer patients. Patients predicted as resistant showed increased risk of time to treatment failure compared to standard of care, hazard ratios of 1.73 (95%CI=0.98-3.07) for taxane in ovarian, 3.67 (95%CI=2.12-6.34) for taxane in breast, and 1.93 (95%CI=1.22-3.04) for doxorubicin in ovarian. We also found that liquid biopsies can be used to make these predictions in up to 30% of ovarian cancer patients. Our findings highlight the clinical value of CIN signatures in predicting treatment response to various chemotherapies across multiple different types of cancer. The ability to quantify multiple CIN signature biomarkers using a single genomic test offers a unified approach to guide treatment decisions for cytotoxic chemotherapies. Ultimately, this has the potential to transform the current one-size-fits-all chemotherapy approach into a more precise and tailored form of medicine.

cancer biology↗

bioRxiv2022

Integrating a tailored recurrent neural network with Bayesian experimental design to optimize microbial community functions

Microbiomes interact dynamically with their environment to perform exploitable functions such as production of valuable metabolites and degradation of toxic metabolites for a wide range of applications in human health, agriculture, and environmental cleanup. Developing computational models to predict the key bacterial species and environmental factors to build and optimize such functions are crucial to accelerate microbial community engineering. However, there is an unknown web of interactions that determine the highly complex and dynamic behaviors of these systems, which precludes the development of models based on known mechanisms. By contrast, entirely data-driven machine learning models can produce physically unrealistic predictions and often require significant amounts of experimental data to learn system behavior. We develop a physically constrained recurrent neural network that preserves model flexibility but is constrained to produce physically consistent predictions and show that it outperforms existing machine learning methods in the prediction of experimentally measured species abundance and metabolite concentrations. Further, we present an experimental design algorithm to select a set of experimental conditions that simultaneously maximize the expected gain in information and target microbial community functions. Using a bioreactor case study, we demonstrate how the proposed framework can be used to efficiently navigate a large design space to identify optimal operating conditions. The proposed methodology offers a flexible machine learning approach specifically tailored to optimize microbiome target functions through the sequential design of informative experiments that seek to explore and exploit community functions. 1 Author summaryThe functions performed by microbiomes hold tremendous promise to address grand challenges facing society ranging from improving human health to promoting plant growth. To design their properties, flexible computational models that can predict the temporally changing behaviors of microbiomes in response to key environmental parameters are needed. When considering bottom-up design of microbiomes, the number of possible communities grows exponentially with the number of organisms and environmental factors, which makes it challenging to navigate the microbiome function landscape. To overcome these challenges, we present a physically constrained machine learning model for microbiomes and a Bayesian experimental design framework to efficiently navigate the space of possible communities and environmental factors.

systems biology↗

bioRxiv2022

A Suite of Advanced Tutorials for the WESTPA 2.0 Rare-Events Sampling Software

We present six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for enabling applications of the weighted ensemble (WE) path sampling strategy to even larger systems and/or slower processes. The tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling at the system biology level. Applications of the tutorials range from atomistic to residue-level to non-spatial models, and include complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics simulations and completed the previous suite of WESTPA tutorials.

biophysics↗

bioRxiv2022

Potentiation of Adipogenesis by Reactive Oxygen Species is a Unifying Mechanism in the Pro-adipogenic Properties of Bisphenol A and its New Structural Analogues.

AimsStructural analogues of bisphenol A (BPA), including BPS and BPF, are emerging environmental toxicants as their presence in the environment is rising since new regulatory restrictions were placed on BPA-containing infant products. The adipogenesis-enhancing effect of bisphenols may explain the link between human exposure and metabolic disease; however, underlying molecular pathways remain unresolved. ResultsExposure to BPS, BPF, BPA or ROS generators enhanced lipid droplet formation and expression of adipogenic markers after induction of differentiation in adipose-derived progenitors isolated from mice. RNAseq analysis in BPS-exposed progenitors revealed modulation in pathways regulating adipogenesis and responses to oxidative stress. ROS was higher in bisphenol-exposed cells, while co-treatment with antioxidants attenuated adipogenesis and abolished the effect of BPS. There was a loss of mitochondria membrane potential in BPS-exposed cells and mitochondria-derived ROS contributed to potentiation of adipogenesis by BPS and its analogues. Male mice exposed to BPS during gestation had higher whole-body adiposity, as measured by TD-NMR, while postnatal exposure had no impact on adiposity in either sex. InnovationThese findings support existing evidence showing a role for ROS in regulating adipocyte differentiation and are the first to highlight ROS as a unifying mechanism that explains the pro-adipogenic properties of BPA and its structural analogues. ConclusionROS act as signaling molecules in the regulation of adipocyte differentiation and mediate bisphenol-induced potentiation of adipogenesis.

molecular biology↗

bioRxiv2022

Loss of retinogeniculate synaptic function in the DBA/2J mouse model of glaucoma.

BackgroundRetinal ganglion cell (RGC) axons comprise the optic nerve and carry information to the dorsolateral geniculate nucleus (dLGN) that is relayed to the cortex for conscious vision. Glaucoma is a blinding neurodegenerative disease that commonly results from intraocular pressure (IOP)-associated injury leading to RGC axonal pathology, disruption of RGC outputs to the brain, and eventual apoptotic loss of RGC somata. The consequences of elevated IOP and glaucomatous pathology on RGC signaling to the dLGN are largely unknown and likely to be important contributors to visual system dysfunction in glaucoma. Thus, the goal of this study was to determine how glaucoma affects RGC outputs to the dLGN. MethodsWe used a combination of anatomical and physiological approaches to study the structure and function of retinogeniculate synapses in male and female DBA/2J mice at multiple ages before and after IOP elevation. These included measures of anterograde axonal transport, immunofluorescence staining of RGC axon terminals, patch-clamp recording retinogeniculate (RG) synapses in living brain slices, Sholl analysis of thalamocortical relay neuron dendrites, measurements of RGC somatic density, and treatment with a topical ophthalmic alpha-2 adrenergic agonist (brimonidine). ResultsDBA/2J mice showed progressive loss of anterograde optic tract transport to the dLGN and vGlut2 labeling of RGC axon terminals. Patch-clamp measurements of RG synaptic function showed that the strength of synaptic transmission was lower in 9 and 12-month DBA/2J mice and that this was the result of loss of individual RGC axon contributions. TC neuron dendrites showed a reduction in complexity at 12 months, suggestive of a delayed reorganization following reduced synaptic input. There was no detectable change in RGC soma density in 11-12m DBA/2J retinas indicating that observed effects occurred prior to RGC somatic loss. Finally, treatment with brimonidine eye drops prevented the loss of vGlut2-labeled RGC terminals in the dLGN. ConclusionsThese findings identify glaucoma- and IOP-associated functional deficits in an important subcortical RGC projection target. This sheds light on the processes linking IOP to vision loss and will be critical for informing future diagnostic approaches and vision-restoration therapies.

neuroscience↗

bioRxiv2022

Exploring oxidative stress and endothelial dysfunction as a mechanism linking bisphenol S exposure to vascular disease in human umbilical vein endothelial cells and a mouse model of postnatal exposure.

BackgroundStructural analogues used to replace bisphenol A (BPA) since the introduction of new regulatory restrictions are considered emerging environmental toxicants and remain understudied with respect to their biological actions and health effects. Studies reveal a link between BPA exposure and vascular disease in human populations, whereas the vascular effects of BPA substitutes remain largely unknown. ObjectivesTo determine the effect of BPS, a commonly used BPA substitute, on redox balance, nitric oxide (NO) availability and microvascular NO-dependent dilation. MethodsIn human umbilical vein endothelial cells (HUVEC), production of reactive oxygen species (ROS) and NO after exposure to BPS was measured using fluorescent probes for DCFDA and DAF-FM diacetate, respectively. The contribution of endothelial NO synthase (eNOS) uncoupling to ROS generation was determined by measuring ROS in the presence or absence of an eNOS inhibitor (L-NAME) or eNOS co-factor, BH4, while the contribution of mitochondria-derived ROS was determined by treating cells with mitochondria-specific antioxidants prior to BPS exposure. Bioenergetic profiles were assessed using Seahorse extracellular flux analysis and mitochondria membrane polarization was measured with TMRE and JC-1 assays. In a mouse model of low dose BPS exposure, NO-mediated endothelial function was assessed in pressurized microvessels by inducing endothelium-dependent dilation in the presence or absence of L-NAME. ResultsBPS exposure ([≥] 25 nM) reduced NO and increased ROS production in HUVEC, the latter corrected by treating cells with L-NAME or BH4. BPS exposure led to a loss of mitochondria membrane potential but had no impact on bioenergetic parameters except for a decrease in the spare respiratory capacity. Treatment of HUVEC with mitochondria-specific antioxidants abolished the effect of BPS on NO and ROS. NO-mediated vasodilation was impaired in male mice exposed to BPS. DiscussionExposure to BPS may promote cardiovascular disease by perturbing NO-mediated vascular homeostasis through the induction of oxidative stress.

physiology↗

bioRxiv2022

Growth limitation via chemically complex carbohydrates reduces sensitivity of synthetic human gut communities to perturbations

Dietary fiber affects the composition and functions of microbial communities that reside in the human gut. However, we lack a detailed and quantitative understanding of how these nutrients shape microbial community dynamics, interaction networks and systems-level properties. Using synthetic human gut communities coupled to computational modeling, we dissect the effects of varied fiber types or their constituent sugars on community assembly and sensitivity to perturbations. By quantifying carbohydrate chemical complexity, we demonstrate that microbial growth decreases as a function of complexity. We further demonstrate that the balance of species occupying distinct metabolic niches is altered by the presence of chemically complex carbohydrates. The frequency of negative inter-species interactions is reduced in the presence of complex carbohydrates. Communities grown in complex carbohydrates reproducibly assemble from a wide range of initial species abundances and display reduced sensitivity to invasion. Resource competition is identified as a key mechanism influencing the response of communities to perturbations. The strength of resource competition can promote sensitivity of community assembly to variations in initial species proportions and impact community resistance to invasion. By limiting microbial growth, complex carbohydrates promote the expansion of species occupying niches beyond carbohydrate utilization, shape the distribution of inter-species interactions, which in turn determines the communitys response to perturbations.

ecology↗

bioRxiv2022

Paired Associates Learning is Disrupted After Unilateral Parietal Lobe Controlled Cortical Impact in Rats: A Trial-by-Trial Behavioral Analysis

Approximately 60-70 million people suffer from a traumatic brain injury (TBI) each year. As animal models continue to be paramount in understanding and treating cognitive impairment following TBI, the necessity of testing intervention strategies in clinically relevant settings cannot be ignored. This study used a unilateral parietal lobe controlled cortical impact (CCI) model of TBI and tested rats on a touchscreen-based associative learning task, Paired Associates Learning (PAL). In humans, PAL has been used to assess cognitive deficits in stimulus-location association in a multitude of disease states, including TBI. To date, the extent to which a rat model of TBI produces deficits in PAL has not yet been reported, although the usage of PAL will be important for understanding the clinical consequences of cognitive impairment post-injury and throughout intervention treatment. This study details the behavioral and histological consequences of the CCI injury model and closes a translational research gap between basic and clinical TBI research. HIGHLIGHTSO_LIPAL performance declines in a rat model of TBI. C_LIO_LIResponse-driven bias in PAL becomes elevated after TBI. C_LIO_LIInflammatory microglial response in the thalamus correlates with PAL deficit. C_LI

animal behavior and cognition↗

bioRxiv2022

Model-guided design of the diversity of a synthetic human gut community

Microbial communities have tremendous potential as therapeutics. However, a major bottleneck is manufacturing high-diversity microbial communities with desired species compositions. We develop a two-stage, model-guided framework to produce microbial communities with target species compositions. We apply this method to optimize the diversity of a synthetic human gut community. The first stage exploits media components to enable uniform growth responses of individual species and the second stage uses a design-test-learn cycle with initial species abundance as a control point to manipulate community composition. Our designed culture conditions yield 91% of the maximum possible diversity. Leveraging these data, we construct a dynamic ecological model to guide the design of lower-order communities with desired temporal properties over a longer timescale. In sum, a deeper understanding of how microbial community assembly responds to changes in environmental factors, initial species abundances, and inter-species interactions can enable the predictable design of community dynamics.

systems biology↗

bioRxiv2022

Therapeutic targeting of differentiation state-dependent metabolic vulnerabilities in DIPG

H3K27M diffuse intrinsic pontine gliomas (DIPG) exhibit cellular heterogeneity comprising less-differentiated, stem-like glioma cells that resemble oligodendrocyte precursors (OPC) and more differentiated astrocyte (AC)-like cells. H3K27M DIPG stem-like cells exhibit tumor-seeding capabilities in vivo, a feature lost or greatly diminished in the more differentiated AC-like cells. In this study, we established isogenic in vitro models of DIPG that closely recapitulated the OPC-like and AC-like phenotypes of DIPG cells. Using these tools, we performed transcriptomics, metabolomics, and bioenergetic profiling to identify metabolic programs operative in the different cellular states. From this, we defined new strategies to selectively target metabolic vulnerabilities within the specific tumor populations. Namely, we showed that the AC-like cells exhibited a more mesenchymal phenotype and were thus sensitized to ferroptotic cell death. In contrast, OPC-like cells upregulated cholesterol metabolism and mitochondrial oxidative phosphorylation (OXPHOS) and were accordingly more sensitive to statins and OXPHOS inhibitors. Additionally, statins and OXPHOS inhibitors showed efficacy and extended survival in preclinical orthotopic models established with stem-like H3K27M DIPG cells. Together, this study demonstrates that cellular subtypes within DIPGs harbor distinct metabolic vulnerabilities that can be uniquely and selectively targeted for therapeutic gain.

cancer biology↗

bioRxiv2021

WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

The weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and "binless" WE strategies. Table of Contents/Abstract ImageFor the manuscript "WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications" by Russo et al. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/471280v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@575710org.highwire.dtl.DTLVardef@151c3dorg.highwire.dtl.DTLVardef@1f0f525org.highwire.dtl.DTLVardef@6e5956_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Deep Learning Enables Design of Multifunctional Synthetic Human Gut Microbiome Dynamics

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current ordinary differential equation-based models fail to capture complex behaviors that fall outside of a predetermined ecological theory and do not scale well with increasing community complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of deep learning, the LSTM learns a high dimensional data-driven non-linear dynamical system model used to design communities with desired metabolite profiles. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model. We build methods decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to identify communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

systems biology↗

bioRxiv2021

Skeletal Muscle Transcriptome Alterations Related to Physical Function Decline in Older Mice

One inevitable consequence of aging is the gradual deterioration of physical function and exercise capacity, driven in part by the adverse effect of age on muscle tissue. Our primary purpose was to determine the relationship between patterns of gene expression in skeletal muscle and this loss of physical function. We hypothesized that some genes changing expression with age would correlate with functional decline, or conversely with preservation of function. Male C57BL/6 mice (6-months old, 6m, 24-months, 24m, and 28+-months, 28m; all n=8) were tested for physical ability using a comprehensive functional assessment battery (CFAB). CFAB is a composite scoring system comprised of five functional tests: rotarod (overall motor function), grip strength (fore-limb strength), inverted cling (4-limb strength/endurance), voluntary wheel running (activity rate/volitional exercise), and treadmill (endurance). We then extracted total RNA from the tibialis anterior muscle, analyzed with Next Generation Sequencing RNAseq to determine differential gene expression during aging, and compared these changes to physical function. Aging resulted in gene expression differences >[boxv]1.0[boxv] log2 fold change (multiple comparison adjusted p<0.05) in 219 genes in the 24m and in 6587 genes in the 28m. Linear regression with CFAB determined 253 differentially expressed genes strongly associated (R>0.70) with functional status in the 28m, and 22 genes in the 24m. We conclude that specific age-related transcriptomic changes are associated with declines in physical function, providing mechanistic clues. Future work will establish the underlying cellular mechanisms and the physiological relevance of these genes to age-related loss of physical function. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/444371v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@ad7362org.highwire.dtl.DTLVardef@17267e7org.highwire.dtl.DTLVardef@38600org.highwire.dtl.DTLVardef@15283a7_HPS_FORMAT_FIGEXP M_FIG C_FIG RNA sequencing of skeletal muscle from young and old mice were compared to physical function status obtained by performing a comprehensive functional assessment battery of tests. Between adulthood (6-months) and older age (28-months), 6707 genes were differentially expressed with 253 of these genes being significantly associated with physical function. Specific age-related changes to the skeletal muscle transcriptome are associated with a decline in physical function.

physiology↗

bioRxiv2021

Measuring the unknown: an estimator and simulation study for assessing case reporting during epidemics

The fraction of cases reported, known as reporting, is a key performance indicator in an outbreak response, and an essential factor to consider when modelling epidemics and assessing their impact on populations. Unfortunately, its estimation is inherently difficult, as it relates to the part of an epidemic which is, by definition, not observed. We introduce a simple statistical method for estimating reporting, initially developed for the response to Ebola in Eastern Democratic Republic of the Congo (DRC), 2018-2020. This approach uses transmission chain data typically gathered through case investigation and contact tracing, and uses the proportion of investigated cases with a known, reported infector as a proxy for reporting. Using simulated epidemics, we study how this method performs for different outbreak sizes and reporting levels. Results suggest that our method has low bias, reasonable precision, and despite sub-optimal coverage, usually provides estimates within close range (5-10%) of the true value. Being fast and simple, this method could be useful for estimating reporting in real-time in settings where person-to-person transmission is the main driver of the epidemic, and where case investigation is routinely performed as part of surveillance and contact tracing activities. Author summaryWhen responding to epidemics of infectious diseases, it is essential to estimate how many cases are not being reported. Unfortunately reporting, the proportion of cases actually observed, is difficult to estimate during an outbreak, as it typically requires large surveys to be conducted on the affected populations. Here, we introduce a method for estimating reporting from case investigation data, using the proportion of cases with a known, reported infector. We used simulations to test the performance of our approach by mimicking features of a recent Ebola epidemic in the Democratic Republic of the Congo. We found that despite some uncertainty in smaller outbreaks, our approach can be used to obtain informative ballpark estimates of reporting under most settings. This method is simple and computationally inexpensive, and can be used to inform the response to any epidemic in which transmission events can be uncovered by case investigation.

bioinformatics↗

bioRxiv2021

Predicted distribution of a rare and understudied forest carnivore: Humboldt martens (Martes caurina humboldtensis)

BackgroundA suite of mammalian species have experienced range contractions following European settlement and post-settlement development of the North American continent. For example, while North American martens (American marten, Martes americana; Pacific marten, M. caurina) generally have a broad range across northern latitudes, local populations have experienced substantial reductions in distribution and some extant populations are small and geographically isolated. The Humboldt marten (M. c. humboldtensis), a subspecies of Pacific marten that occurs in coastal Oregon and northern California, was recently designated as federally threatened in part due to its reduced distribution. To inform strategic conservation actions, we assessed Humboldt marten occurrence by compiling all known records from their range. MethodsWe compiled Humboldt marten locations since their rediscover to present (1,692 marten locations, 1996-2020). We spatially-thinned locations to 500-m to assess correlations with variables across contemporary Humboldt marten distribution (n=384). Using maximum entropy modeling (Maxent), we created distribution models with variables optimized for spatial scale; pre-selected scales were associated with marten ecology (50 to 1170 m radius). Marten locations were most correlated with abiotic factors (e.g., precipitation), which are unalterable and therefore uninformative within the context of restoration or management actions. Thus, we created variables to focus on hypothesized marten habitat relationships, including understory conditions such as predicted suitability of shrub species. ResultsHumboldt marten locations were positively associated with increased shrub cover (salal (Gautheria shallon), mast producing trees), increased pine (Pinus sp) overstory cover and precipitation at home-range spatial scales, areas with low and high amounts of canopy cover and slope, and cooler August temperatures. Unlike other recent literature on the species, we found little evidence that Humboldt marten locations were associated with old growth structural indices, perhaps because of a potential mismatch in the association between this index and shrub cover. As with any species distribution model, there were gaps in predicted distribution where Humboldt martens have been located during more recent surveys, for instance the southeastern portion of Oregons coast range. Conservation efforts and our assessment of potential risks to Humboldt marten populations would benefit from additional information on range extent, population sizes, and fine-scale habitat use. Like many rare and lesser-known species, this case study provides an example of how limited information can provide differing interpretations, emphasizing the need for study-level replication in ecology.

ecology↗

bioRxiv2021

Does land use matter? Carbon consequences of alternative land use futures in New England

Globally, forests play an important role in climate change mitigation. However, land-use impacts the ability of forests to sequester and store carbon. Here we quantify the impacts of five divergent future land-use scenarios on aboveground forest carbon stocks and fluxes throughout New England. These scenarios, four co-designed with stakeholders from throughout the region and the fifth a continuation of recent trends in land use, were simulated by coupling a land cover change model with a mechanistic forest growth model to produce estimates of aboveground carbon over 50 years. Future carbon removed through harvesting and development was tracked using a standard carbon accounting methodology, modified to fit our modeling framework. Of the simulated changes in land use, changes in harvesting had the most profound and immediate impacts on carbon stocks and fluxes. In one of the future land-use scenarios including a rapid expansion of harvesting for biomass energy, this changed New Englands forests from a net carbon sink to a net carbon source in 2060. Also in these simulations, relatively small reductions in harvest intensities (e.g., 10% reduction), coupled with an increased percent of wood going into longer-term storage, led to substantial reductions in net carbon emissions (909 MMtCO2eq) as compared to a continuation of recent trends in land use. However, these projected gains in carbon storage and reduction in emissions from less intense harvesting regimes can only be realized if it is paired with a reduction in the consumption of the timber products, and their replacements, that otherwise would result in additional emissions from leakage and substitution.

ecology↗