How Do Cells Adapt? Stories Told in Landscapes

PyCoM: a python library for large-scale analysis of residue-residue coevolution data

MOTIVATION

Computational methods to detect correlated amino acid positions in proteins have become a valuable tool to predict intra- and inter-residue protein contacts, protein structures, and effects of mutation on protein stability and function. While there are many tools and webservers to compute coevolution scoring matrices, there is no central repository of alignments and coevolution matrices for large-scale studies and pattern detection leveraging on biological and structural annotations already available in UniProt.

RESULTS

We present a Python library, PyCoM, which enables users to query and analyze coevolution matrices and sequence alignments of 457 622 proteins, selected from UniProtKB/Swiss-Prot database (length ≤ 500 residues), from a precompiled coevolution matrix database (PyCoMdb). PyCoM facilitates the development of statistical analyses of residue coevolution patterns using filters on biological and structural annotations from UniProtKB/Swiss-Prot, with simple access to PyCoMdb for both novice and advanced users, supporting Jupyter Notebooks, Python scripts, and a web API access. The resource is open source and will help in generating data-driven computational models and methods to study and understand protein structures, stability, function, and design.

AVAILABILITY AND IMPLEMENTATION

PyCoM code is freely available from https://github.com/scdantu/pycom and PyCoMdb and the Jupyter Notebook tutorials are freely available from https://pycom.brunel.ac.uk.

Evidence of zoonotic pathogens through biophysically induced genomic variance

Zoonoses are infectious agents that are transmissible between animals and humans. Up to 60% of known infectious diseases and 75% of emergent diseases are zoonotic. Genomic variation between homeostatic populations provides a novel window into the effect of environmental pathogens on allelic distributions within the populations. Genodynamics is a biophysical approach utilizing developed metrics on biallelic single-nucleotide polymorphisms (SNPs) that can be used to quantify the adaptive influences due to pathogens. A genomic free energy that is minimized when overall population health is optimized describes the influence of environmental agents upon genomic variation. A double-blind exploration of over 100 thousand SNPs searching for smooth functional dependencies upon four zoonotic pathogens carried by four possible hosts amidst populations that live in their ancestral environments has been conducted. Exemplars that infectious agents can have significant adaptive influence on human populations are presented. One discussed SNP is likely associated with both adaptive and innate immune regulation. The adaptive response of another SNP suggests an intriguing connection between zoonoses and human cancers. The adaptive forces of the presented pathogens upon the human genome have been quantified.

Subcutaneous Administration of Monoclonal Antibodies: Pharmacology, Delivery, Immunogenicity, and Learnings From Applications to Clinical Development

Subcutaneous (s.c.) administration of monoclonal antibodies (mAbs) can reduce treatment burden for patients and healthcare systems compared with intravenous (i.v.) infusion through shorter administration times, made possible by convenient, patient-centric devices. A deeper understanding of clinical pharmacology principles related to efficacy and safety of s.c.-administered mAbs over the past decade has streamlined s.c. product development. This review presents learnings from key constituents of the s.c. mAb development pathway, including pharmacology, administration variables, immunogenicity, and delivery devices. Restricted mAb transportation through the hypodermis explains their incomplete absorption at a relatively slow rate (pharmacokinetic (PK)) and may impact mAb-cellular interactions and/or onset and magnitude of physiological responses (pharmacodynamic). Injection volumes, formulation, rate and site of injection, and needle attributes may affect PKs and the occurrence/severity of adverse events like injection-site reactions or pain, with important consequences for treatment adherence. A review of immunogenicity data for numerous compounds reveals that incidence of anti-drug antibodies (ADAs) is generally comparable across i.v. and s.c. routes, and complementary factors including response magnitude (ADA titer), persistence over time, and neutralizing antibody presence are needed to assess clinical impact. Finally, four case studies showcase how s.c. biologics have been clinically developed: (i) by implementation of i.v./s.c. bridging strategies to streamline PD-1/PD-L1 inhibitor development, (ii) through co-development with i.v. presentations for anti-severe acute respiratory syndrome-coronavirus 2 antibodies to support rapid deployment of both formulations, (iii) as the lead route for bispecific T cell engagers (BTCEs) to mitigate BTCE-mediated cytokine release syndrome, and (iv) for pediatric patients in the case of dupilumab.

Adaptive DNA amplification of synthetic gene circuit opens a way to overcome cancer chemoresistance

Drug resistance continues to impede the success of cancer treatments, creating a need for experimental model systems that are broad, yet simple, to allow the identification of mechanisms and novel countermeasures applicable to many cancer types. To address these needs, we investigated a set of engineered mammalian cell lines with synthetic gene circuits integrated into their genome that evolved resistance to Puromycin. We identified DNA amplification as the mechanism underlying drug resistance in 4 out of 6 replicate populations. Triplex-forming oligonucleotide (TFO) treatment combined with Puromycin could efficiently suppress the growth of cell populations with DNA amplification. Similar observations in human cancer cell lines suggest that TFOs could be broadly applicable to mitigate drug resistance, one of the major difficulties in treating cancer.

Is cancer an intelligent species?

Some relevant emerging properties of intelligent systems are "adaptation to a changing environment," "reaction to unexpected situations," "capacity of problem solving," and "ability to communicate." Single cells have remarkable abilities to adapt, make adequate context-dependent decision, take constructive actions, and communicate, thus theoretically meeting all the above-mentioned requirements. From a biological point of view, cancer can be viewed as an invasive species, composed of cells that move from primary to distant sites, being continuously exposed to changes in the environmental conditions. Blood represents the first hostile habitat that a cancer cell encounters once detached from the primary site, so that cancer cells must rapidly carry out multiple adaptation strategies to survive. The aim of this review was to deepen the adaptation mechanisms of cancer cells in the blood microenvironment, particularly referring to four adaptation strategies typical of animal species (phenotypic adaptation, metabolic adaptation, niche adaptation, and collective adaptation), which together define the broad concept of biological intelligence. We provided evidence that the required adaptations (either structural, metabolic, and related to metastatic niche formation) and "social" behavior are useful principles allowing putting into a coherent frame many features of circulating cancer cells. This interpretative frame is described by the comparison with analog behavioral traits typical of various animal models.

Evidence of cancer-linked rodent zoonoses from biophysical genomic variations

As a mechanism to explore the role of environmental adaptation in establishing the optimal distribution of single nucleotide polymophisms (SNPs) within resident homeostatic populations, relationships between quantified environmental parameters and the frequencies of the variants are being explored. We have performed sequential double-blind scans on more than 30% of chromosome 3 in an attempt to discover possible relationships using simple mathematical functions that are indicative of "adaptive forces" on the variants due to specific quantified environmental agents. We have found an association of rs13071758 with rodent zoonotic diseases. This variant is within the FHIT gene, which spans the most fragile of the common fragile sites in human lymphoblasts. FHIT, which is highly sensitive to environmental carcinogens, is partially lost in most human cancers. This finding is consistent with other studies postulating an association between rodent zoonoses and cancer. We quantify the adaptive force on the T allele as 0.28 GEUs per unit of zoonotic rodent host richness.

Coupled Mutual Inhibition and Mutual Activation Motifs as Tools for Cell-Fate Control

Multistability is central to biological systems. It plays a crucial role in adaptation, evolvability, and differentiation. The presence of positive feedback loops can enable multistability. The simplest of such feedback loops are (a) a mutual inhibition (MI) loop, (b) a mutual activation (MA) loop, and (c) self-activation. While it is established that all three motifs can give rise to bistability, the characteristic differences in the bistability exhibited by each of these motifs is relatively less understood. Here, we use dynamical simulations across a large ensemble of parameter sets and initial conditions to study the bistability characteristics of these motifs. Furthermore, we investigate the utility of these motifs for achieving coordinated expression through cyclic and parallel coupling amongst them. Our analysis revealed that MI-based architectures offer discrete and robust control over gene expression, multistability, and coordinated expression among multiple genes, as compared to MA-based architectures. We then devised a combination of MI and MA architectures to improve coordination and multistability. Such designs help enhance our understanding of the control structures involved in robust cell-fate decisions and provide a way to achieve controlled decision-making in synthetic systems.

Functional Resilience of Mutually Repressing Motifs Embedded in Larger Networks

Elucidating the design principles of regulatory networks driving cellular decision-making has important implications for understanding cell differentiation and guiding the design of synthetic circuits. Mutually repressing feedback loops between 'master regulators' of cell fates can exhibit multistable dynamics enabling "single-positive" phenotypes: (high A, low B) and (low A, high B) for a toggle switch, and (high A, low B, low C), (low A, high B, low C) and (low A, low B, high C) for a toggle triad. However, the dynamics of these two motifs have been interrogated in isolation in silico, but in vitro and in vivo, they often operate while embedded in larger regulatory networks. Here, we embed these motifs in complex larger networks of varying sizes and connectivity to identify hallmarks under which these motifs maintain their canonical dynamical behavior. We show that an increased number of incoming edges onto a motif leads to a decay in their canonical stand-alone behaviors. We also show that this decay can be exacerbated by adding self-inhibition but not self-activation loops on the 'master regulators'. These observations offer insights into the design principles of biological networks containing these motifs and can help devise optimal strategies for the integration of these motifs into larger synthetic networks.

Quantification of adaptive forces on SNP rs1010211 due to viral zoonotic pathogens

Widespread genotyping of human populations in environmental homeostasis has created opportunities to quantify how environmental parameters affect the genomic distribution of variants in healthy populations. This represents an ongoing natural experiment upon the human species that can only be understood through developing models of adaptation. By examining the information dynamics of optimal SNP distributions within such populations, "adaptive forces" on genomic variants can be quantified through comparisons between different populations. To this end, we are performing double-blind scans of SNPs in order to ascertain any potential smooth functional relationships between the frequencies of the variants and changes in quantified environmental parameters. At present, we have sequentially examined more than twenty thousand SNPs (on chromosome 3) of nine homeostatic native populations for potential adaptive flagging of the variants as functions of 15 environmental parameters. Our first significant flag has related rs1010211 to viral pathogens in mammalian hosts. Such pathogens present a significant risk for the emergence of new infectious diseases in humans. This genomic variant is within the gene TNIK, which is a germinal center kinase (GCK). GCKs are participants in both adaptive and innate immune regulation. However, the function of TNIK is not fully understood. We quantify the adaptive force on the C allele due to the pathogens as 0.04 GEU's/viral species.

Fundamental behaviors emerge from simulations of a living minimal cell

We present a whole-cell fully dynamical kinetic model (WCM) of JCVI-syn3A, a minimal cell with a reduced genome of 493 genes that has retained few regulatory proteins or small RNAs. Cryo-electron tomograms provide the cell geometry and ribosome distributions. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demands of its metabolism, genetic information processes, and growth, and offer insight into the principles of life for this minimal cell. The energy economy of each process including active transport of amino acids, nucleosides, and ions is analyzed. WCM reveals how emergent imbalances lead to slowdowns in the rates of transcription and translation. Integration of experimental data is critical in building a kinetic model from which emerges a genome-wide distribution of mRNA half-lives, multiple DNA replication events that can be compared to qPCR results, and the experimentally observed doubling behavior.

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

The Protein Folding Problem: The Role of Theory

The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.

Recent advances in understanding the role of proteostasis

Maintenance of a functional proteome is achieved through the mechanism of proteostasis that involves precise coordination between molecular machineries assisting a protein from its conception to demise. Although each organelle within a cell has its own set of proteostasis machinery, inter-organellar communication and cell non-autonomous signaling bring forth the multidimensional nature of the proteostasis network. Exposure to extrinsic and intrinsic stressors can challenge the proteostasis network, leading to the accumulation of aberrant proteins or a decline in the proteostasis components, as seen during aging and in several diseases. Here, we summarize recent advances in understanding the role of proteostasis and its regulation in aging and disease, including monogenetic and infectious diseases. We highlight some of the emerging as well as unresolved questions in proteostasis that need to be addressed to overcome pathologies associated with damaged proteins and to promote healthy aging.

Coupled Feedback Loops Involving PAGE4, EMT and Notch Signaling Can Give Rise to Non-genetic Heterogeneity in Prostate Cancer Cells

Non-genetic heterogeneity is emerging as a crucial factor underlying therapy resistance in multiple cancers. However, the design principles of regulatory networks underlying non-genetic heterogeneity in cancer remain poorly understood. Here, we investigate the coupled dynamics of feedback loops involving (a) oscillations in androgen receptor (AR) signaling mediated through an intrinsically disordered protein PAGE4, (b) multistability in epithelial-mesenchymal transition (EMT), and c) Notch-Delta-Jagged signaling mediated cell-cell communication, each of which can generate non-genetic heterogeneity through multistability and/or oscillations. Our results show how different coupling strengths between AR and EMT signaling can lead to monostability, bistability, or oscillations in the levels of AR, as well as propagation of oscillations to EMT dynamics. These results reveal the emergent dynamics of coupled oscillatory and multi-stable systems and unravel mechanisms by which non-genetic heterogeneity in AR levels can be generated, which can act as a barrier to most existing therapies for prostate cancer patients.