Cristae formation is a mechanical buckling event controlled by the inner mitochondrial membrane lipidome.

Cristae are high-curvature structures in the inner mitochondrial membrane (IMM) that are crucial for ATP production. While cristae-shaping proteins have been defined, analogous lipid-based mechanisms have yet to be elucidated. Here, we combine experimental lipidome dissection with multi-scale modeling to investigate how lipid interactions dictate IMM morphology and ATP generation. When modulating phospholipid (PL) saturation in engineered yeast strains, we observed a surprisingly abrupt breakpoint in IMM topology driven by a continuous loss of ATP synthase organization at cristae ridges. We found that cardiolipin (CL) specifically buffers the inner mitochondrial membrane against curvature loss, an effect that is independent of ATP synthase dimerization. To explain this interaction, we developed a continuum model for cristae tubule formation that integrates both lipid and protein-mediated curvatures. This model highlighted a snapthrough instability, which drives IMM collapse upon small changes in membrane properties. We also showed that cardiolipin is essential in low-oxygen conditions that promote PL saturation. These results demonstrate that the mechanical function of cardiolipin is dependent on the surrounding lipid and protein components of the IMM.

Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes.

To advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) simulations and Markov state models (MSMs) has enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increases exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called independent Markov decomposition (IMD) that leverages weak coupling between subsystems to compute a global kinetic model without requiring the sampling of all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD models can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.

Spatiotemporal modelling reveals geometric dependence of AMPAR dynamics on dendritic spine morphology.

The modification of neural circuits depends on the strengthening and weakening of synaptic connections. Synaptic strength is often correlated to the density of the ionotropic, glutamatergic receptors, AMPARs, (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors) at the postsynaptic density (PSD). While AMPAR density is known to change based on complex biological signalling cascades, the effect of geometric factors such as dendritic spine shape, size and curvature remain poorly understood. In this work, we developed a deterministic, spatiotemporal model to study the dynamics of AMPARs during long-term potentiation (LTP). This model includes a minimal set of biochemical events that represent the upstream signalling events, trafficking of AMPARs to and from the PSD, lateral diffusion in the plane of the spine membrane, and the presence of an extrasynaptic AMPAR pool. Using idealized and realistic spine geometries, we show that the dynamics and increase of bound AMPARs at the PSD depends on a combination of endo- and exocytosis, membrane diffusion, the availability of free AMPARs and intracellular signalling interactions. We also found non-monotonic relationships between spine volume and the change in AMPARs at the PSD, suggesting that spines restrict changes in AMPARs to optimize resources and prevent runaway potentiation. KEY POINTS: Synaptic plasticity involves dynamic biochemical and physical remodelling of small protrusions called dendritic spines along the dendrites of neurons. Proper synaptic functionality within these spines requires changes in receptor number at the synapse, which has implications for downstream neural functions, such as learning and memory formation. In addition to being signalling subcompartments, spines also have unique morphological features that can play a role in regulating receptor dynamics on the synaptic surface. We have developed a spatiotemporal model that couples biochemical signalling and receptor trafficking modalities in idealized and realistic spine geometries to investigate the role of biochemical and biophysical factors in synaptic plasticity. Using this model, we highlight the importance of spine size and shape in regulating bound AMPA receptor dynamics that govern synaptic plasticity, and predict how spine shape might act to reset synaptic plasticity as a built-in resource optimization and regulation tool.

Benchmarking ensemble docking methods in D3R Grand Challenge 4.

The discovery of new drugs is a time consuming and expensive process. Methods such as virtual screening, which can filter out ineffective compounds from drug libraries prior to expensive experimental study, have become popular research topics. As the computational drug discovery community has grown, in order to benchmark the various advances in methodology, organizations such as the Drug Design Data Resource have begun hosting blinded grand challenges seeking to identify the best methods for ligand pose-prediction, ligand affinity ranking, and free energy calculations. Such open challenges offer a unique opportunity for researchers to partner with junior students (e.g., high school and undergraduate) to validate basic yet fundamental hypotheses considered to be uninteresting to domain experts. Here, we, a group of high school-aged students and their mentors, present the results of our participation in Grand Challenge 4 where we predicted ligand affinity rankings for the Cathepsin S protease, an important protein target for autoimmune diseases. To investigate the effect of incorporating receptor dynamics on ligand affinity rankings, we employed the Relaxed Complex Scheme, a molecular docking method paired with molecular dynamics-generated receptor conformations. We found that Cathepsin S is a difficult target for molecular docking and we explore some advanced methods such as distance-restrained docking to try to improve the correlation with experiments. This project has exemplified the capabilities of high school students when supported with a rigorous curriculum, and demonstrates the value of community-driven competitions for beginners in computational drug discovery.

Development of a simple and effective online training for health workers: results from a pilot in Nigeria.

BACKGROUND: Health workers (HWs) in Africa face challenges accessing and learning from existing online training opportunities. To address these challenges, we developed a modular, self-paced, mobile-ready and work-relevant online course covering foundational infection prevention and control (IPC) concepts. Here, we evaluate the first pilot of this course, conducted with HWs in Nigeria. METHODS: We used a learner-centered design and prototyping process to create a new approach to delivering online training for HWs. The resulting course comprised 10 self-paced modules optimized for use on mobile devices. Modules presented IPC vignettes in which learning was driven by short assessment questions with feedback. Learners were recruited by distributing a link to the training through Nigeria-based email lists, WhatsApp groups and similar networks of HWs, managers and allied professionals. The course was open to learners for 8 weeks. We tracked question responses and time on task with platform analytics and assessed learning gains with pre- and post-testing. Significance was evaluated with the Wilcoxon signed-rank test, and effect size was calculated using Cohen's d. RESULTS: Three hundred seventy-two learners, with roles across the health system, enrolled in the training; 59% completed all 10 modules and earned a certificate. Baseline knowledge of foundational IPC concepts was low, as measured by pre-test scores (29%). Post-test scores were significantly higher at 54% (effect size 1.22, 95% confidence interval 1.00-1.44). Learning gains were significant both among learners with low pre-test scores and among those who scored higher on the pre-test. We used the Net Promoter Score (NPS), a common user experience metric, to evaluate the training. The NPS was + 62, which is slightly higher than published scores of other self-paced online learning experiences. CONCLUSIONS: High completion rates, significant learning gains and positive feedback indicate that self-paced, mobile-ready training that emphasizes short, low-stakes assessment questions can be an effective, scalable way to train HWs who choose to enroll. Low pre-test scores suggest that there are gaps in IPC knowledge among this learner population.

Structural basis for ligand modulation of the CCR2 conformational landscape.

CC chemokine receptor 2 (CCR2) is a part of the chemokine receptor family, an important class of therapeutic targets. These class A G-protein coupled receptors (GPCRs) are involved in mammalian signaling pathways and control cell migration toward endogenous CC chemokine ligands, named for the adjacent cysteine motif on their N terminus. Chemokine receptors and their associated ligands are involved in a wide range of diseases and thus have become important drug targets. CCR2, in particular, promotes the metastasis of cancer cells and is also implicated in autoimmunity-driven type-1 diabetes, diabetic nephropathy, multiple sclerosis, asthma, atherosclerosis, neuropathic pain, and rheumatoid arthritis. Although promising, CCR2 antagonists have been largely unsuccessful to date. Here, we investigate the effect of an orthosteric and an allosteric antagonist on CCR2 dynamics by coupling long-timescale molecular dynamics simulations with Markov-state model theory. We find that the antagonists shift CCR2 into several stable inactive conformations that are distinct from the crystal structure conformation and disrupt a continuous internal water and sodium ion pathway, preventing transitions to an active-like state. Several metastable conformations present a cryptic drug-binding pocket near the allosteric site that may be amenable to targeting with small molecules. Without antagonists, the apo dynamics reveal intermediate conformations along the activation pathway that provide insight into the basal dynamics of CCR2 and may also be useful for future drug design.

SamQL: a structured query language and filtering tool for the SAM/BAM file format.

BACKGROUND: The Sequence Alignment/Map Format Specification (SAM) is one of the most widely adopted file formats in bioinformatics and many researchers use it daily. Several tools, including most high-throughput sequencing read aligners, use it as their primary output and many more tools have been developed to process it. However, despite its flexibility, SAM encoded files can often be difficult to query and understand even for experienced bioinformaticians. As genomic data are rapidly growing, structured, and efficient queries on data that are encoded in SAM/BAM files are becoming increasingly important. Existing tools are very limited in their query capabilities or are not efficient. Critically, new tools that address these shortcomings, should not be able to support existing large datasets but should also do so without requiring massive data transformations and file infrastructure reorganizations. RESULTS: Here we introduce SamQL, an SQL-like query language for the SAM format with intuitive syntax that supports complex and efficient queries on top of SAM/BAM files and that can replace commonly used Bash one-liners employed by many bioinformaticians. SamQL has high expressive power with no upper limit on query size and when parallelized, outperforms other substantially less expressive software. CONCLUSIONS: SamQL is a complete query language that we envision as a step to a structured database engine for genomics. SamQL is written in Go, and is freely available as standalone program and as an open-source library under an MIT license, https://github.com/maragkakislab/samql/ .

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries.

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the finite element method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.

From lionizing to protecting health care workers during and after COVID-19-systems solutions for human tragedies.

During the COVID-19 pandemic, health care workers (HCWs) have been lauded as heroes, yet both before and during the pandemic, they lacked the protections needed to keep them safe. We summarize data on HCW infections and deaths during previous epidemics, the costs of the failure to protect them, and provide recommendations for strengthening HCW protections by investments in and implementation of infection prevention and control and water, sanitation, and hygiene programs, training and career development, and national and global monitoring of HCW infections. We must move from placing individuals at undue risk to accepting collective responsibility and accountability for the well-being of our HCWs and take concrete actions to protect HCWs who risk their lives to protect patients and populations.

An Open-Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries.

Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naïve user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.

Identifying and Interrupting Superspreading Events-Implications for Control of Severe Acute Respiratory Syndrome Coronavirus 2.

It appears inevitable that severe acute respiratory syndrome coronavirus 2 will continue to spread. Although we still have limited information on the epidemiology of this virus, there have been multiple reports of superspreading events (SSEs), which are associated with both explosive growth early in an outbreak and sustained transmission in later stages. Although SSEs appear to be difficult to predict and therefore difficult to prevent, core public health actions can prevent and reduce the number and impact of SSEs. To prevent and control of SSEs, speed is essential. Prevention and mitigation of SSEs depends, first and foremost, on quickly recognizing and understanding these events, particularly within healthcare settings. Better understanding transmission dynamics associated with SSEs, identifying and mitigating high-risk settings, strict adherence to healthcare infection prevention and control measures, and timely implementation of nonpharmaceutical interventions can help prevent and control severe acute respiratory syndrome coronavirus 2, as well as future infectious disease outbreaks.

Detection of Avian Influenza A(H7N2) Virus Infection Among Animal Shelter Workers Using a Novel Serological Approach-New York City, 2016-2017.

BACKGROUND: In 2016, an influenza A(H7N2) virus outbreak occurred in cats in New York City's municipal animal shelters. One human infection was initially detected. METHODS: We conducted a serological survey using a novel approach to rule out cross-reactive antibodies to other seasonal influenza viruses to determine whether additional A(H7N2) human infections had occurred and to assess exposure risk. RESULTS: Of 121 shelter workers, one had serological evidence of A(H7N2) infection, corresponding to a seroprevalence of 0.8% (95% confidence interval, .02%-4.5%). Five persons exhibited low positive titers to A(H7N2) virus, indicating possible infection; however, we could not exclude cross-reactive antibody responses to seasonal influenza viruses. The remaining 115 persons were seronegative. The seropositive person reported multiple direct cat exposures without using personal protective equipment and mild illness with subjective fever, runny nose, and sore throat. CONCLUSIONS: We identified a second case of A(H7N2) infection from this outbreak, providing further evidence of cat-to-human transmission of A(H7N2) virus.

Increase in Infant Measles Deaths During a Nationwide Measles Outbreak-Mongolia, 2015-2016.

BACKGROUND: Surveillance data from a large measles outbreak in Mongolia suggested increased case fatality ratio (CFR) in the second of 2 waves. To confirm the increase in CFR and identify risk factors for measles death, we enhanced mortality ascertainment and conducted a case-control study among infants hospitalized for measles. METHODS: We linked national vital records with surveillance data of clinically or laboratory-confirmed infant (aged <12 months) measles cases with rash onset during March-September 2015 (wave 1) and October 2015-June 2016 (wave 2). We abstracted medical charts of 95 fatal cases and 273 nonfatal cases hospitalized for measles, matched by age and sex. We calculated adjusted matched odds ratios (amORs) and 95% confidence intervals (CIs) for risk factors. RESULTS: Infant measles deaths increased from 3 among 2224 cases (CFR: 0.13%) in wave 1 to 113 among 4884 cases (CFR: 2.31%) in wave 2 (P < .001). Inpatient admission, 7-21 days before measles rash onset, for pneumonia or influenza (amOR: 4.5; CI, 2.6-8.0), but not other diagnoses, was significantly associated with death. DISCUSSION: Measles infection among children hospitalized with respiratory infections likely increased deaths due to measles during wave 2. Preventing measles virus nosocomial transmission likely decreases measles mortality.

The Implementation of the Colored Abstract Simplicial Complex and its Application to Mesh Generation.

We introduce CASC: a new, modern, and header-only C++ library which provides a data structure to represent arbitrary dimension abstract simplicial complexes (ASC) with user-defined classes stored directly on the simplices at each dimension. This is accomplished by using the latest C++ language features including variadic template parameters introduced in C++11 and automatic function return type deduction from C++14. Effectively CASC decouples the representation of the topology from the interactions of user data. We present the innovations and design principles of the data structure and related algorithms. This includes a metadata aware decimation algorithm which is general for collapsing simplices of any dimension. We also present an example application of this library to represent an orientable surface mesh.

Long-Term Supportive Housing is Associated with Decreased Risk for New HIV Diagnoses Among a Large Cohort of Homeless Persons in New York City.

It is unknown whether providing housing to persons experiencing homelessness decreases HIV risk. Housing, including access to preventive services and counseling, might provide a period of transition for persons with HIV risk factors. We assessed whether the new HIV diagnosis rate was associated with duration of supportive housing. We linked data from a cohort of 21,689 persons without a previous HIV diagnosis who applied to a supportive housing program in New York City (NYC) during 2007-2013 to the NYC HIV surveillance registry. We used time-dependent Cox modeling to compare new HIV diagnoses among recipients of supportive housing (defined a priori, for program evaluation purposes, as persons who spent > 7 days in supportive housing; n = 6447) and unplaced applicants (remainder of cohort), after balancing the groups on baseline characteristics with propensity score weights. Compared with unplaced applicants, persons who received ≥ 3 continuous years of supportive housing had decreased risk for new HIV diagnosis (HR 0.10; CI 0.01-0.99). Risk of new HIV diagnosis decreased with longer duration placement in supportive housing. Supportive housing might aid in primary HIV prevention.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Disparities in Zika Virus Testing and Incidence Among Women of Reproductive Age-New York City, 2016.

CONTEXT: The New York City Department of Health and Mental Hygiene (NYC DOHMH) performs surveillance for reportable diseases, including Zika virus (ZIKV) infection and disease, to inform public health responses. Incidence rates of other mosquito-borne diseases related to international travel are associated with census tract poverty level in NYC, suggesting that high poverty areas might be at higher risk for ZIKV infections. OBJECTIVES: We assessed ZIKV testing rates and incidence of travel-associated infection among reproductive age women in NYC to identify areas with high incidence and low testing rates and assess the effectiveness of public health interventions. DESIGN: We analyzed geocoded ZIKV surveillance data collected by NYC DOHMH. Women aged 15 to 44 years tested during January-July 2016 (n = 4733) were assigned to census tracts, which we grouped by poverty level and quartile of the number of persons born in countries or territories with mosquito-borne ZIKV transmission as a proxy for risk of travel to these areas. We calculated crude ZIKV testing rates, incidence rates, and incidence rate ratios (IRRs). SETTING: New York City. RESULTS: Eight percent of patients (n = 376) tested had evidence of ZIKV infection. Cumulative incidence was higher both in areas with higher versus lower poverty levels (IRR = 2.4; 95% confidence interval [CI], 2.0-3.0) and in areas with the largest versus smallest populations of persons born in countries or territories with mosquito-borne ZIKV transmission (IRR = 11.3; 95% CI, 6.2-20.7). Initially, ZIKV testing rates were lowest in higher poverty areas with the largest populations of persons born in countries or territories with mosquito-borne ZIKV transmission (15/100 000), but following targeted interventions, testing rates were highest in these areas (80/100 000). CONCLUSIONS: Geocoded data enabled us to identify communities with low testing but high ZIKV incidence rates, intervene to promote testing and reduce barriers to testing, and measure changes in testing rates.

Exascale Computing: A New Dawn for Computational Biology.

As biologists discover and learn to embrace the complexity of biological systems, computational data analysis and modeling have become critical for furthering our understanding. Exascale computing will enable the development of new predictive multiscale models, transforming how we study the behaviors of organisms and ecosystems, ultimately leading to new innovations and discoveries.

Outbreak of Influenza A(H7N2) Among Cats in an Animal Shelter With Cat-to-Human Transmission-New York City, 2016.

We describe the first case of cat-to-human transmission of influenza A(H7N2), an avian-lineage influenza A virus, that occurred during an outbreak among cats in New York City animal shelters. We describe the public health response and investigation.