A Meta-Analysis of Bioelectric Data in Cancer, Embryogenesis, and Regeneration

Pranjal Srivastava; Anna Kane; Christina Harrison; Michael Levin

doi:10.1089/bioe.2019.0034

. 2021 Mar 16;3(1):42–67. doi: 10.1089/bioe.2019.0034

A Meta-Analysis of Bioelectric Data in Cancer, Embryogenesis, and Regeneration

Pranjal Srivastava ¹, Anna Kane ², Christina Harrison ², Michael Levin ^2,^✉

PMCID: PMC8370481 PMID: 34476377

Abstract

Developmental bioelectricity is the study of the endogenous role of bioelectrical signaling in all cell types. Resting potentials and other aspects of ionic cell physiology are known to be important regulatory parameters in embryogenesis, regeneration, and cancer. However, relevant quantitative measurement and genetic phenotyping data are distributed throughout wide-ranging literature, hampering experimental design and hypothesis generation. Here, we analyze published studies on bioelectrics and transcriptomic and genomic/phenotypic databases to provide a novel synthesis of what is known in three important aspects of bioelectrics research. First, we provide a comprehensive list of channelopathies—ion channel and pump gene mutations—in a range of important model systems with developmental patterning phenotypes, illustrating the breadth of channel types, tissues, and phyla (including man) in which bioelectric signaling is a critical endogenous aspect of embryogenesis. Second, we perform a novel bioinformatic analysis of transcriptomic data during regeneration in diverse taxa that reveals an electrogenic protein to be the one common factor specifically expressed in regeneration blastemas across Kingdoms. Finally, we analyze data on distinct V_mem signatures in normal and cancer cells, revealing a specific bioelectrical signature corresponding to some types of malignancies. These analyses shed light on fundamental questions in developmental bioelectricity and suggest new avenues for research in this exciting field.

Keywords: cancer, development, channelopathy, V_mem, resting potential, regeneration, ion channel

Introduction

Bioelectrical signaling is an ancient modality by which cells and tissues exchange information necessary for coordinating development, regeneration, and cancer suppression.^1–4 Many details have now been uncovered about the ion channels and pumps that determine cell membrane resting potential (V_mem) and about the electrical synapses known as gap junctions that propagate those states across distances in vivo.⁵ Using a range of model species, modern developmental biology and genetics efforts have identified roles of bioelectrical signaling in cell migration,^6,7 organ morphogenesis,^8–10 regenerative axial polarity,¹¹ size control,¹² and many other important metazoan phenomena. The implications of these results stretch from basic evolutionary developmental biology^13,14 to many aspects of biomedicine.^15–19

However, this exciting emerging field has been hampered by the fact that, due to its highly interdisciplinary nature, important data are spread across publications in numerous subfields and have not been analyzed en masse. Thus, we undertook a synthesis and meta-analysis of data from published studies to ask several fundamental questions in three deeply related subfields: development, regeneration, and cancer.

How important is bioelectricity in embryogenesis? It is often thought that the roles of ion channels are only apparent from a handful of focused studies,^10,20–22 and that genetics does not offer a convenient entry point to discover novel bioelectric controls. Thus, we analyzed databases of developmental phenotypes across a number of popular model systems to identify a comprehensive list of electrogenic genes that have been implicated in embryonic patterning.

Cancer can be thought of as a breakdown of the normal processes that orchestrate cells into cooperating toward production and maintenance of large-scale anatomical structures during development and adulthood.^23–26 Interestingly, classical^27,28 and recent^29–32 functional data suggest that control of bioelectric signaling can not only induce cancer phenotypes but can also be used to suppress/normalize tumors. Can bioelectric parameters be used to distinguish between normal somatic cells and those that have defected back into a unicellular-like state (transformed cancer cells)? We thus attempted to identify every published study of resting potential in cells and analyzed them to derive a physiological signature of the transformed state across different tissue types.

Regeneration is a process by which complex organs and appendages are rebuilt to a specific target morphology from various starting states (injuries).³³ The evolutionary origins of this capability across taxa are poorly understood,³⁴ and it is unclear what molecular-genetic and biophysical components underlie regeneration of different types of body architectures. Thus, we analyzed transcriptomic datasets comparing regenerating versus intact adult tissues across Kingdoms, asking what types of regeneration-induced gene expression changes they might share. A small number of genes turned out to be a common feature of regeneration in all animals, from flatworms to mammals. Remarkably, analysis taking into account plants revealed only one component that all regenerating blastemas have in common: an ancient electrogenic protein.

Development, regeneration, and cancer have a profound connection. The first two concern the ability of cells to work together to, respectively, build and rebuild complex anatomical structures. The third, cancer, illustrates the effects of individual somatic cells abandoning multicellularity and reverting to a unicellular mode of existence that treats the rest of the body as its environment. In all of these cases, communication and interaction among cell groups are paramount. Evolution discovered very early how to exploit biophysical forces to coordinate information across space and time in living cells and tissues.^35–37 It is now essential, for biomedicine and for evolutionary developmental biology, to understand how the molecular mechanisms of bioelectricity enable cooperative cell function in normal morphogenesis and its disruption during disease.^38,39 Together, the following meta-analyses establish a rigorous starting point for novel work in this field, revealing a range of physiological and genetic targets for future investigation and shedding light on foundational concepts in developmental bioelectricity.

Materials and Methods

Literature search for V_mem values in somatic and cancerous cells

Literature search was completed in two distinct phases: first, the keywords “Vmem,” “Electrical Potential,” “Electrophysiological properties,” and “Membrane Potential” were used singly and in combination in PubMed searches. Published V_mem values and their sources were documented in a database to avoid duplication. After all potential papers that could be found using this method were exhausted, existing meta-analyses such as those found in previous studies^40,41 were mined for any additional papers that contained V_mem values. These published studies were then themselves included in our analysis. We decided to only include data from rodent and human cells in our analysis because the vast majority of the papers focused on these models and data from other systems did not provide sufficient power for analysis.

Statistical analysis

Meta-analyses were performed to assess relationships between reported V_mem in cancerous and noncancerous tissues. A random-effects meta-analysis was performed to assess the relationship between reported mean V_mem in cancerous tissues and their corresponding somatic tissue type. Statistics were only performed on tissues for which there was both somatic and cancerous measurements taken in the same report. Analysis was performed with the rma function from the Metafor (v2.0-0) R package. p-value was considered significant at p < 0.05, and tests for heterogeneity were performed in each case.

To assess the relationship between the V_mem of a somatic tissue and the difference in V_mem between it and the V_mem of a corresponding cancerous tissue, a mixed-effect regression model was used. We modeled the change in voltage from somatic tissue to cancerous tissue as a linear function of the mean somatic voltage, and included all studies that provided estimates of both means and variability for both cell types. The rma function from the Metafor (v2.0-0) R package was used for this analysis. A least-squares regression was done as sensitivity analysis that included additional studies where estimates of within-study variation were not provided. All studies were equally weighted in this analysis. The lm function in the stats (v3.5.3) R package was used to estimate the linear regression line. p-value was considered significant at p < 0.05, and tests for heterogeneity were performed in each case.

Statistics were performed by the Tufts Biostatistics, Epidemiology, and Research Design Center. R version 3.5.3 (2019-03-1) run through R Studio v1.1.463 (c) 2009–2018, R Studio, Inc. was used to perform the meta-analyses. Raw p-values are reported without adjustment for multiple testing.

Channelopathy identification database and literature search

To identify as many ion channel-associated developmental and morphological phenotypes as possible, we searched species-specific databases that report phenotypes associated with genetic mutations. We primarily focused on phenotype databases to ensure that we had the broadest phenotype report possible, since these databases do not require that the phenotypes induced by genetic mutation be interesting enough to rise to the level of publication. In addition to ion pumps and channels, both of which establish bioelectrical potentials, gap junctions are important because they enable cells to communicate that bioelectric state to each other,^5,42 and because their voltage-based and other gating modalities enable cell collectives to form networks, which process bioelectrical information in complex ways, such as forming feedback loops and even computational circuits.^43,44 Thus, here we broaden the term “channelopathy” to include those induced by mutations in ion pumps and gap junctions, not only strictly ion channel proteins, because of the similar, crucial roles of these other two types of proteins in regulating the bioelectric state of cells.

Human channelopathies were identified from long-term, manual curation of publications that assess developmental phenotypes linked to genetic screens or clinically identified syndromes. Mouse channelopathies were identified through keyword searches in the MGI database⁴⁵ for terms, including “channel,” “pump,” “transporter,” “antiporter,” “gap junction,” and “connexin” (and manually filtered to include only electrically relevant targets). Each entry was searched for morphogenesis and developmental phenotypes in the “Mutations, Alleles and Phenotypes” section, and the phenotype was verified in the reported primary literature. To identify zebrafish channelopathies, the database Zfin⁴⁶ was used. Keyword searches for “Pannexin,” “Innexin,” “Ion Channel,” and “Ion Junction” were carried out to identify channelopathies, and morphogenesis phenotypes were identified in the “Disease Ontology” and “Gene Ontology” sections of each entry. Each phenotype identified was verified in the reported primary literature. Flybase⁴⁷ was used to identify channelopathies in Drosophila. Keyword searches for “Ion Channel,” “Pannexin,” “Ion Pump,” and “Ion Transporter” were performed, and each entry was searched for developmental and morphogenesis phenotypes in the “Phenotype” section of the entry.

Additional genes mentioned in known papers (previously identified through PubMed searches) on ion channels in Drosophila were also included (and referenced accordingly), particularly, Smith et al.⁸ which contained a long list of wing-related Drosophila channelopathies. Each phenotype was verified in the reported primary literature. Genes identified in the Flybase search were also assessed using the Bristle Screen Online Database,⁴⁸ which provided more detailed analyses of morphogenesis defects than Flybase alone. For the Caenorhabditis elegans, we created an XML code that searched Wormbase (release WS271) for patterning defects. The code was provided by one of the curators of the database, Christian A. Grove.

The results that gave multiple patterning defects for one gene were condensed into one entry. Identified entries were then individually assessed for ion channel activity and morphogenesis or developmental defects and confirmed in the reported primary literature.

Bioinformatics

Datasets containing lists of up- and downregulated transcripts in regeneration blastemas of axolotl (Ambystoma mexicanum, NCBI SRA064951),⁴⁹ planarian head and tail fragments (Schmidtea mediterranea, NCBI SRP002478),⁵⁰ deer antler (Cervus nippon, BioProject IDs: PRJNA397466 and PRJNA404007), and plant (Arabidopsis thaliana, NCBI GSE19863) were obtained from published supplementary data,^49,50 NCBI SRA,⁵¹ or NCBI GEO.⁵² Annotation for the planaria dataset was obtained from eggNOG-mapper.^53,54

Some of the datasets only provided F-tests for significant results but did not provide information about direction of fold change. Thus, all significantly altered transcripts, both up- and downregulated, were combined and considered as one pool. Genes with false discovery rate (FDR) <0.25 were considered significantly altered. All significantly altered genes in each dataset were then crossreferenced to identify any transcripts that were commonly differentially expressed across model systems.

For gene set enrichment analysis, “background” genes were assigned in each model organism based on protein homology to human protein-coding gene sequences obtained from BioMart. Gene Ontology sets were acquired from the MSigDB Collections.⁵⁵ Gene sets related to ion channels and cell junctions or learning, memory, and cognition were selected, and Fisher's exact tests were performed to determine enrichment. FDR <0.25 was considered significant enrichment.

Results and Discussion

Numerous channelopathies reveal roles for bioelectrics in model systems

A number of studies have used gain-of-function approaches (misexpression of specific channel or pump proteins) to evaluate a role of bioelectric states in patterning processes such as development,^22,56,57 regeneration,^58,59 and tumor suppression/normalization.^31,60–68 Some studies also identified endogenous roles for bioelectrical gradients.^{9,10,12,69,70} It is critical to identify the native conductances responsible for the salient bioelectrical prepatterns in vivo to place V_mem into pathways, understand the evolution of bioelectric control mechanisms, and provide candidate targets for biomedical intervention.

We searched databases of several popular model systems (mouse, zebrafish, Drosophila, and C. elegans) as well as literature, including human channelopathies, to identify known electrogenic genes with developmental phenotypes that include structural malformations. Tables 1–5 show channels, pumps, ion transporters, and gap junctions (and a few direct regulators of these) that are known to cause not just physiological disease conditions but also anatomical malformations (birth defects).

Table 1.

Human Channelopathies

Protein (gene)	UniProt number	Channel type	Mutant phenotype	Reference
V-ATPase, (TCIRG1/VATB1)	Q13488/P15313	Vacuolar proton pump	Facial dysmorphism, dense bones	^89,90
Kir3.2 (kcnj6)	P48542	Voltage-gated K⁺ channel	Keppen-Lubinsky syndrome—craniofacial defects and microcephaly	⁹¹
K_v10.1 (kcnh1) and VATB2	O95259 and P21281	K⁺ channel and vacuolar proton pump	Zimmermann-Laband and Temple-Baraitser syndrome—craniofacial and brain defects, dysplasia/aplasia of nails of thumb and great toe	^92,93
GLRa4	Q5JXX5	Ligand-gated Cl⁻ channel	Craniofacial defects	⁹⁴
K_ir6.1 (kcnj8)	Q15842	K⁺ inwardly rectifying channel	Cantu syndrome—face, heart, skeleton, brain defects	^95–97
NALCN	Q8IZF0	Na⁺ leak channel	Freeman-Sheldon syndrome—congenital contractures of face and limbs, cerebral and cerebellar atrophy, small pituitary	⁹⁸
CFTR	P13569	Cl⁻ channel transmembrane conductance regulator	Bilateral absence of vas deferens	^99,100
SCN3A	Q9NY46	Voltage-gated Na⁺ channel	Polymicrogyria	¹⁰¹
TRPV1, TRPV4	Q8NER1, Q9HBA0	Transient receptor potential cation channels	Temperature-induced face, heart defects	¹⁰²
K_v3.1 (kcnc1)	P48547	Voltage-gated K⁺ channel	Head/face dysmorphias	¹⁰³
K_T3.2 (kcnk9)	Q9NPC2	K⁺ two-pore domain channel	Birk-Barel dysmorphism syndrome—craniofacial defects, cortical patterning defects	^104–106
K_ir6.2 (kcnj 11)	Q14654	Inwardly rectifying K⁺ channel	Craniofacial defects, neural differentiation	¹⁰⁷
K_v1.9 (kcnq1) (via epigenetic regulation)	P51787	Voltage-gated K⁺ channel	Hypertrophy of tongue, liver, spleen, pancreas, kidneys, adrenals, genitalia	^108–111
K_v1.9 (kcnq1)	P51787	Voltage-gated K⁺ channel	Jervell and Lange-Nielsen syndrome—dysmorphism of inner ear	^112–114
K_v3.3 (kcnc3)	O43525	Voltage-gated K⁺ channel	Cerebellar dysplasia	⁵⁶
K_ir2.1 (kcnj2)	P63252	Inwardly rectifying K⁺ channel	Andersen-Tawil syndrome—craniofacial defects, abnormal limb patterning, gracile ribs, and long bones	^115–117
Na_v1.2 (scn2a)	Q99250	Voltage-gated Na⁺ channel	Laterality defects	¹¹⁸
Ca_v1.2 (cac1c)	Q13936	Voltage-gated Ca²⁺ channel	Timothy syndrome-webbed fingers, dysmorphic facial features, heart development defects, small teeth	¹¹⁹
Cx43 (gja1)	P17302	Gap junction	Heart defects (outflow tract and conotruncal), ODDD, visceroatrial heterotaxia	^120–122

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ODDD, oculodentodigital dysplasia.

Table 2.

Mouse Channelopathies

Protein (Gene)	UniProt number	Channel type	Mutant phenotype	Reference
K_ir7.1 (kcnj13)	P86046	Inwardly rectifying K⁺ channel	Cleft palate, delayed lung development	¹²³
HCN1	O88704	Hyperpolarization-activated cyclic nucleotide-gated K⁺ channel	Reduced brain size, loss of interneuron populations	^124,125
CIC-3 (clcn3)	P51791	H⁺/Cl⁻ exchange transporter	Brain patterning defects	¹²⁶
K_v3.1 (kcnc1)	P15388	Voltage-gated K⁺ channel	Growth deficits	¹²⁴
TWIK-1 (kcnk1)	O08581	K⁺ two-pore domain channel	atrial dilation	¹²⁷
K_v1.1 (kcna1)	P16388	Voltage-gated K⁺ channel	Megencephaly	¹²⁸
K_ir6.2 (kcnj11)	Q61743	Inwardly rectifying K⁺ channel	Craniofacial defects, negatively regulates neural differentiation	¹²⁹
K_v1.9 (kcnq1) (via epigenetic regulation)	P97414	Voltage-gated K⁺ channel	Hypertrophy of tongue, liver, spleen, pancreas, kidneys, adrenals, genitalia—Beckwith-Wiedemann syndrome; craniofacial and limb defects	^108–110
K_v1.9 (kcnq1)	P97414	Voltage-gated K⁺ channel	Jervell and Lange-Nielsen syndrome—dysmorphism of inner ear and limb, abnormalities in rectum, pancreas, and stomach	^{108,112–114,130,131}
K_v3.3 (kcnc3)	Q63959	Voltage-gated K⁺ channel	Cerebellar dysplasia (human), eye and wing defects (Drosophila)	⁵⁶
K_ir2.1 (kcnj2)	P35561	Inwardly rectifying K⁺ channel	Andersen-Tawil syndrome—craniofacial defects (narrow maxilla, cleft secondary palate), abnormal limb patterning, gracile ribs, and long bones	^{115–117,132}
GABA-A receptor (gabrb3)	P63080	Ligand-gated Cl⁻ channel	Angelman syndrome—cleft palate, cerebellar vermis hypoplasia, abnormal cochlear development and morphology	^133–137
ANO1	Q8BHY3	Ca²⁺-activated Cl⁻ channel	Tracheomalacia with cartilage ring defects, abnormal trachealis muscle development	¹³⁸
K_ir3.2 (kcnj6)	P48542	G-protein coupled inwardly rectifying K⁺ channel	Cerebellar development defects	^139–142
K_v11.1 (kcnh2)	O35219	Voltage-gated inwardly rectifying K⁺ channel	Cardiac, craniofacial patterning defects	¹⁴³
K_vb1.3 (kcnab1)	P63143	Voltage-gated K⁺ channel subunit	Cardiac hypertrophy	¹⁴⁴
5-HT3B (htr3b)	Q9JHJ5	Ligand-gated cation channel	Increased bone density and body length in females, decreased bone density and body length in males	¹⁴⁵
CIC-2 (clcn2)	Q9R0A1	Voltage-gated Cl⁻ channel	Abnormal brain and eye morphology	¹⁴⁶
CIC-5 (clcn5)	Q9WVD4	H+/Cl⁻ exchange transporter	Renal tubular defects, kyphosis, abnormal tooth development	¹⁴⁷
nAChRα7 (chrna7)	P49582	Ligand-gated cation channel	Abnormal bone structure, abnormal cerebral cortex morphology, abnormal myeloblast development	^148–150
AchR (chrng)	P04760	Ligand-gated cation channel	Abnormal skeletal muscle morphology, abnormal neuromuscular junction morphology	¹⁵¹
Cx50 (gja8)	P28236	Gap junction	Abnormal lens development, micropthalmia	^152,153
Cx45 (gjc1)	P28229	Gap junction	Cardiac defects (cushion patterning) and impaired hematopoiesis	^154–157
Cx43 (gja1)	P23242	Gap junction	Deficits in myogenesis, abnormal osteoblast differentiation, left/right asymmetry randomization, heart defects (outflow tract and conotruncal), neural tube defects, delayed ossification of clavicles, ribs, vertebrae, and limbs, syndactyly and limb defects, craniofrontonasal syndrome	^158–164
Cx37 (gja4)	P28235	Gap junction	Lymphatic system patterning	^165,166
Cx26 (gjb2)	Q00977	Gap junction	Cochlear development defects	¹⁶⁷
Cx40 (gja5)	Q01231	Gap junction	Malformed bone in wrists, digits, and sternum joints	¹⁶⁸
PANX3	Q8CEG0	Gap junction	Delayed hypertrophic chondrocyte and osteoblast differentiation and delayed initiation of bone mineralization	¹⁶⁹
NMDAR2D (grin2d)	O15399	Ligand-gated cation channel	Abnormal bone mineralization and structure, reduced heart size	IMPC^a
GLRB	P48168	Ligand-gated Cl⁻ channel	Abnormal vertebral column morphology and abnormal intervertebral disk morphology	¹⁷⁰
K_ir7.1 (kcnj13)	P86046	Inwardly rectifying K⁺ channel	Cleft palate, abnormal lung development, abnormal tracheal development	^123,171
SCNN1B	Q9WU38	Sodium-permeable, nonvoltage-sensitive Na⁺ channel	Abnormal pelvic girdle bone morphology, decreased rib number, abnormal kidney morphology	IMPC^b ¹⁷²
Ca_v1.2 (cacna1c)	Q01815	Voltage-gated Ca²⁺ channel	Abnormal brain morphology, enlarged lateral ventricles, cardiac hypertrophy	^173,174

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^{^a}

https://www.mousephenotype.org/data/genes/MGI:95823

^{^b}

https://www.mousephenotype.org/data/genes/MGI:104696

Table 3.

Zebrafish Channelopathies

Protein (gene)	UniProt number	Channel type	Mutant phenotype	Reference
PANX3	E7F7V4	Gap junction	Delayed hypertrophic chondrocyte and osteoblast differentiation and delayed initiation of bone mineralization	¹⁶⁹
Na_v1.4a (scn4aa)	A0A0R4IJX4	Voltage-gated Na⁺ channel	Abnormal caudal fin structure, small head and trunk, disorganized skeletal muscle	¹⁷⁵
Na_v1.5 (scn12aa)	F1R3Q5	Voltage-gated Na⁺ channel	Abnormal cardiac ventricle and atrium morphology	^176,177
GluR2A (gria2a)	A0A0R4ING5	Ligand-activated cation channel	Abnormal cranial cartilage, achondrogenesis, absent ethmoid cartilage, enlarged fourth ventricle	¹⁷⁸
SLC8A4A	A4UQV6	Ca²⁺/Na⁺ antiporter	Decreased length, abnormal digestive tract development, heart mislocalized abnormal liver development	^179,180
SLC24A5 (nckx5)	B0V3S7	Na⁺/K⁺/Ca²⁺ exchanger	Delayed development of melanin pigmentation (“golden” phenotype)	¹⁸¹
SLC26A2	E7F9I7	So²⁻ transporter	Defective otolith patterning, abnormal semicircular canal morphology	¹⁸²
MCU	F1QT29	Ca²⁺ uniporter	Abnormal anterior/posterior axis specification; abnormal cell migration during gastrulation, abnormal notochord	¹⁸³
CACNB2	B2XY76	Voltage-gated Ca²⁺ channel	Small cardiac ventricle, heart is malformed and edematous, fragile heart tube	¹⁸⁴
P2RX3A	B3DG55	ATP-gated cation channel, purinoceptor	Hypotrophic ceratobranchial cartilage, abnormal ceratohyal cartilage, pharyngeal and ventral mandibular arches malformed	¹⁸⁵
PIEZO1	E7FD74	Mechanosensitive cation channel	Small head, caudal fin curled, erythrocyte deformities	^186,187
SLC8A1	Q32SG8	Ca²⁺/Na⁺ antiporter	Small head, heart abnormalities	^188,189
CLIC6	A0A286YBT4	Cl⁻ intracellular channel	Abnormal otolith morphology, hydrocephalus, left/right defects, and curved body axis	¹⁹⁰
TASK1 (kcnk3)	Q5TZ59	K⁺ leak channel	Abnormal heart morphology	¹⁹¹
TASK2 (kcnk5)	A0A2R8PYU3	K⁺ two-pore domain channel	Long anal fin; abnormal caudal fin	¹²
CNGA2A (cnga5)	Q0GFG2	Cyclic nucleotide-gated cation channel	Malformed otolith, right/left symmetry defects	¹⁹²
K_v2.1 (kcnb1)	A0A2R8Q685	Voltage-gated K⁺ channel	Small fourth ventricle; disrupted gastrulation	¹⁹³
Kir4.1 (kcjn10)	E7FD27	Inwardly rectifying K⁺ channel	Swim bladder absent, pronephric duct dilated	¹⁹⁴
Ca_v1.2 (cacna1c)	Q5TZF1	Voltage-gated Ca²⁺ channel	Timothy syndrome, hydrocephalus; abnormal heart morphology, small heart, dilated pronephridic duct, cystic kidney, abnormal mandibular arch	^195–197
TRPC1	E1U7G1	Transient receptor potential cation channel	Microphthalmia; disrupted angiogenesis, increased curvature in postvent region	¹⁹⁸
Kir7.1 (kcnj13)	Q0KIZ7	Inwardly rectifying K⁺ channel	Pigmentation issues on anal and caudal fins	^199,200
MagT1	Q7ZV50	Mg²⁺ transporter	Small, malformed head	²⁰¹
H⁺ V-ATPase	Multiple	Proton pump	Left–right asymmetry defects, muscle and nerve repair	^58,76
CFTR	A0A0R4ID63	cAMP-dependent Cl⁻ channel	Primordial germ cell development	²⁰²
Cx41.8 (gja5)	F1QL21	Gap junction	“leo” phenotype, pigmentation pattern defects	^203,204
Cx39.4 (luchs)	Q1LWG0	Gap junction	Loss of trunk striping, pigmentation pattern defects	²⁰³
Cx43 (gja1)	O57474	Gap junction	Small fin size, failure of joint morphogenesis, and abnormal pattern regulation	^205–207
FXYD6	F1QK04	Na⁺/K⁺ ATPase channel modulator	Abnormal Meckel's and ceratohyal cartilage, small head, abnormal skeletal muscle	²⁰⁸

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Table 4.

Drosophila Channelopathies

Protein (gene)	UniProt number	Channel type	Misexpression phenotype	Reference
LD30634p (ATP6AP2)	Q9VHG4	V-ATPase proton pump	Abnormal wing hair and bristle patterning, pigmentation and brain patterning, small size, small head size, blistered wings	^90,209–211
BEST1	Q9V3J6	Ca²⁺-activated Cl⁻ channel	Bifurcation of posterior crossvein	⁸
BEST2	Q9VRW4	Ca²⁺-activated Cl⁻ channel	Wings small and severely malformed	⁸
BEST3	Q9VUM7	Ca²⁺-activated Cl⁻ channel	Small narrow wings (male), missing anterior and incomplete posterior crossover vein, L2 vein bifurcated	⁸
BIB	P23645	Nonselective cation channel, aquaporin	Defects in neural development, hyperplasia of neuroblasts, sensillum precursors and peripheral glia, small wing size, failure of heart differentiation, increased macrochaetae and supernumary bristles, notum malformation and color defects	^48,212–215
BRV2	M9PFT4	Transient receptor potential channel	Bifurcation of posterior crossvein	⁸
GluClα	Q94900	Glutamate-gated Cl⁻ channel	Bristle defects	⁸
GluRIIB	Q9VMP3	Ionotropic glutamate receptor	Bifurcation of posterior crossvein	⁸
GPHR	Q3ZAN1	Voltage-gated Cl⁻ channel	Abnormally small body and wing blades	²¹⁶
iINAF-A; B; C	A8JUT0, A8WH7, Q6IIF2	Transient receptor potential channel modulator	Bifurcation of posterior crossvein	⁸
INX2	Q9V427	Gap junction	Small eye size, failure of epithelial patterning, loss of bristles, bristle morphology defects, abnormal foregut development, failed germline differentiation, and spermatogenesis	^48,217–221
INX3	Q9VAS7	Gap junction	Cuticle defects, and irregular denticle belts, loss of epithelial organization, polarity defects in epidermis, dorsal closure defects, incomplete formation of posterior crossvein and L5 vein, bifurcation of L4 and L5 veins	^219,222
INX4	Q9VRX6	Gap junction	Failure of germline differentiation and spermatogenesis	²²¹
IR67A	Q9VT09	Ionotropic glutamate receptor	Thick veins, bifurcation of posterior crossvein, and L3 vein	⁸
IR76A	Q9VW39	Ionotropic receptor	Incomplete posterior crossvein, bifurcated L5 vein	⁸
IR7B	Q9W3P4	Ionotropic receptor	Incomplete or bifurcated posterior crossvein, bifurcated L5 vein	⁸
Ir84a	Q9VIA5	Ionotropic glutamate receptor	Thick veins, bifurcated posterior crossvein	⁸
Ir92a	Q9VDN3	Ionotropic glutamate receptor	Bristle defects, abnormal vein pigment	⁸
Ir94g	Q9VCM1	Ionotropic receptor	Bristle defects, abnormal vein pigment	⁸
Ir94h	Q9VCM0	Ionotropic receptor	Bristle defects	⁸
K_ir2.1 (Irk1)	Q95UP7	Inwardly rectifying K⁺ channel	Thick veins, loss of anterior crossvein, L5 and L4 bifurcation, notum malformation, bristle morphology defects	^8,48
K_ir2.2 (Irk2)	Q8WQ82	Inwardly rectifying K⁺ channel	Wing patterning defects, bristle defects, L5 and L4 bifurcations, loss of anterior crossvein, thick veins	¹¹⁶
K_ir2.3 (Irk3)	X2JAW9	Inwardly rectifying K⁺ channel	Small wings, severe wing vein defects L5 and L4 bifurcations, loss of anterior crossvein, thick veins	¹¹⁶
JYα	A8QI34	Cation-transporting P-type ATPase	Defects in spermatogenesis and sperm motility	²²³
KCNQ	Q8IT87	Voltage-gated K⁺ channel	Slow rate of development, enclosure failure, abnormal heart function, posterior crossvein bifurcation	^111,224
α1U (na)	Q8I877	Na⁺ leak channel	Small size, long cylindrical abdomen, bristle defects, ectopic vein, notum malformation	^8,48,225
nAChRα5 (CHRNA5)	Q8T5F5	Ligand-gated cation channel	Bifurcated posterior crossvein, notum malformation	^8,48
nAChRα6 (CHRNA6)	M9PFD8	Ligand-gated cation channel	Bifurcated posterior crossvein	⁸
nAChRα7 (CHRNA7)	Q86MN7	Ligand-gated cation channel	Bifurcated posterior crossvein	⁸
NaCP60E	Q9W0Y8	Voltage-gated Na⁺ channel	Bifurcated posterior crossvein, pigment abnormality	⁸
Nan	Q9VUD5	Transient receptor potential channel	Incomplete or bifurcated L5 wing vein	⁸
Nhe2	Q9NGZ4	Na⁺/H⁺ exchanger	Epithelial patterning, abnormal gut morphology, defects in fat body development, defects in eye morphology	^93,226
nrv2	Q24048	Na⁺/K⁺-transporting P-type ATPase	Cardiac lumen collapse, defective blood–brain barrier formation, tracheal tube size defects, bristle loss	^48,227,228
Ogre	P27716	Gap junction	Small, disorganized optic lobes	²²⁹
olf186-F	Q9U6B8	Ca²⁺ release-activated Ca²⁺ channel	Small body size, wing defects, abnormal abdominal dorsal multidendritic neurons	^230–232
Or47a	P81921	Ligand-gated cation channel	Posterior crossvein bifurcation	⁸
ppk	O44940	Degenerin/epithelial Na⁺ channel	Large body size, bifurcated posterior crossvein	^8,233
ppk17	Q9VJI4	Degenerin/epithelial Na⁺ channel	Bifurcated posterior crossvein, L2 vein, and L3 vein	⁸
ppk25	A1Z6S4	Degenerin/epithelial Na⁺ channel	Bifurcated posterior crossvein and L5 vein	⁸
ppk30	Q9VAJ5	Degenerin/epithelial Na⁺ channel	Bifurcated L4 and L5 veins, incomplete posterior crossvein and L5 vein	⁸
Rdl	P25123	GABA-gated Cl⁻ channel	L2 incomplete, ectopic bristles, pigment defect	⁸
rpk	O46342	Degenerin/epithelial Na⁺ channel	Small wings, incomplete L5 formation, bifurcation of L4 and L5 veins	^8,234
SERCA	Q8STG9	Ca²⁺-transporting P-Type ATPase	Ectopic veins, ectopic bristles, pigment defect, cardiac dilation, wing notches, malformed legs, reduced eye number, abnormal eyes	^8,235,236
sh	P08510	Voltage-gated K⁺ channel	Abnormal pigmentation, abnormal wing expansion, abnormal abdominal muscles, head eversion defects, foreshortened wings and legs, bifurcated posterior crossvein	^8,237,238
SLO2	A8DY93	Ca²⁺-activated K⁺ channel	Incomplete posterior crossvein	⁸
Stim	P83094	CRAC channel regulator	Loss of bristles, hair cell duplication, small larval size, small abnormal eyes, notched wings, small wings, blistered wings, ectopic wing veins	^8,48,232,239
Task6	Q9VFS9	Two-pore domain K⁺ channel	Bifurcated posterior crossvein, notum malformation	^8,48
Teh1	Q9VH54	Voltage-gated Na⁺ channel	Black spots on wing below L5 vein	⁸
THADA	Q9VWB9	sarco(endo)plasmic reticulum Ca²⁺-ATPase regulator	Gain/loss of bristles, malformed notum	⁴⁸
TRPM	A8DYE2	Transient receptor potential channel	Decreased cell size, small salivary glands, shortened malpighian tubules	^240,241
Trpml	Q9VW35	Transient receptor potential channel	Reduced neuromuscular junction boutons, crumpled wings, gain of bristles, notum malformation	^48,242,243
unc79	Q06AJ1	Na⁺ leak channel complex regulator	Bifurcated posterior crossvein, cylindrical abdomen	^8,225
unc80	Q9VB11	Na⁺ leak channel complex regulator	Bifurcated posterior crossvein, ectopic veins, pigment defects	⁸
wtrw	Q9VHY7	Transient receptor potential channel	Incomplete posterior crossvein	⁸
CG18549	Q9VG64	Ion channel regulatory protein	Posterior crossover vein bifurcation, bristle morphology defects, notum malformation	^8,48

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CRAC, calcium release-activated channel; TRP, transient receptor potential channel.

Table 5.

Caenorhabditis elegans Channelopathies

Protein (gene)	UniProt number	Channel type	Mutant phenotype	Reference
acr-2	P48182	Ligand-gated cation channel	Increased length and girth, abnormal gonad morphology	⁷¹
acr-6	Q9N4M3	Ligand-gated cation channel	Increased girth	⁷¹
acr-7	P45963	Ligand-gated cation channel	Increased length	⁷¹
acr-9	Q18556	Ligand-gated cation channel	Reduced girth	⁷¹
acr-10	Q21645	Ligand-gated cation channel	Increased girth	⁷¹
acr-14	Q22224	Ligand-gated cation channel	Increased girth	⁷¹
acr-15	O16926	Ligand-gated cation channel	Reduced girth and length	⁷¹
acr-18	G5EG72	Ligand-gated cation channel	Reduced girth and length	⁷¹
acr-19	B3WFZ2	Ligand-gated cation channel	Decreased body length and increased girth	⁷¹
acr-21	A0A3P6MY71	Ligand-gated cation channel	Increased girth	⁷¹
acr-23	G5EG88	Ligand-gated cation channel	Increased girth	⁷¹
che-6	O61827	Cyclic nucleotide-gated channel	Abnormal sensillum morphology	²⁴⁴
eat-4	P34644	Glu/Na⁺ symporter	Abnormal pharynx morphology	²⁴⁵
eat-6	P90735	Na⁺/K⁺ ATPase	Abnormal sarcomere morphology, decreased girth and body length, abnormal pharynx	^245–247
egl-2	A0A0K3AVF4	Voltage-gated K⁺ channel	Increased length and girth, abnormal chemosensory neurons	^71,248
egl-19	Q8MQA1	Voltage-gated Ca²⁺ channel	Variable body length, decreased girth, blocked anus, tail morphology defects, pigmentation defects	^{71,245,246,249–251}
egl-23	C0Z3L1	K⁺ two-pore domain channel	Decreased body length and increased girth	^71,246
egl-36	G5EFC3	Voltage-gated K⁺ channel	Increased body length and girth	⁷¹
exc-4	Q8WQA4	Voltage-gated Cl⁻ channel	Defects in excretory canal, pigmentation defects	²⁵²
exp-2	H2KZQ6	Voltage-gated K⁺ channel	Abnormal pharyngeal muscles, pharyngeal disorganization	^246,253,
flr-1	G5EGI5	Degenerin/epithelial Na⁺ channel	Pigmentation defects, reduced body length, reduced girth	^71,249
lev-1	Q27218	Ligand-gated cation channel	Reduced girth and body length	⁷¹
lev-8	Q93329	Ligand-gated cation channel	Increased body length and girth	⁷¹
lov-1	Q09624	Transient receptor potential channel	Increased girth	^71,254
mec-10	P34886	Epithelial Na⁺ channel	Variable body length and increased girth	⁷¹
mod-1	Q9GQ00	Ligand-gated Cl⁻ channel	Increased body length and girth	⁷¹
unc-77	V6CKM5	Na⁺ leak channel	Decreased body length and girth	⁷¹
ocr-3	Q22374	Transient receptor potential channel	Increased girth	⁷¹
ocr-4	Q9N3Y9	Transient receptor potential channel	Decreased body length and increased girth	⁷¹
osm-9	G5EBV8	Transient receptor potential channel	Reduced girth and decreased body length, abnormal uterine seam cells	^71,255
sup-9	O17185	K⁺ two-pore domain channel	Increased girth and length	⁷¹
tax-4	Q03611	Cyclic nucleotide-gated channel	Failure of left/right asymmetric neuronal fate specification, abnormal sensory neurons	^256,257
trp-1	P34586	Transient receptor potential channel	Increased girth	⁷¹
trp-2	Q8I6Y9	Transient receptor potential channel	Decreased body length and increased girth	⁷¹
trp-4	Q9GRV5	Transient receptor potential channel	Increased girth	⁷¹
unc-110	Q18120	Outwardly rectifying K⁺ channel	Reduced girth	⁷¹
unc-2	A0A3B1E663	Voltage-gated Ca²⁺ channel	Reduced girth and length, abnormal axon branching, failure of left/right asymmetric neuronal fate specification	^{71,246,248,258}
unc-7	Q03412	Gap junction	Variable body width, short body length	⁷¹
unc-8	Q21974	Epithelial stretch-gated Na⁺ channel	Reduced girth and body length, swollen ventral nerve cord	^71,259
unc-9	O01393	Gap junction	Small male fan, short body length, reduced girth	⁷¹
unc-17	P34711	VAChT	Reduced body size and girth	^246,260
unc-32	P30628	V-Type ATPase	Abnormal body wall muscle morphology, pigmentation defects, small size and increased girth, abnormal pharynx, narrow rachis	^{71,245,246,261}
unc-36	P34374	Voltage-gated Ca²⁺ channel	Increased length, reduced girth, loss of left/right asymmetry	^245,246,262
unc-38	Q23022	Ligand-gated cation channel	Reduced girth and decreased body length	⁷¹
unc-49	G5ECD3	Ligand-gated Cl⁻ channel	Reduced body size	²⁶³
unc-58	Q22271	K⁺ two-pore domain channel	Reduced body length and girth	^264,265
unc-63	Q9N587	Ligand-gated cation channel	Decreased body length and increased girth	⁷¹
unc-68	A0A2C9C3E8	Ca²⁺ release channel	Reduced girth and body length, abnormal pharyngeal axons	^71,266
unc-77	V6CKM5	Na⁺ leak channel	Reduced girth, decreased body length. Abnormal vulva morphogenesis	^71,267
unc-80	Q9XV66	Na⁺ leak channel	Small eggs	²⁶⁸
unc-103	G5EFJ9	Voltage-gated K⁺ channel	Reduced girth, variable body length, abnormal sarcomere morphology, abnormal body wall musculature	^71,265,268
unc-105	Q09274	Epithelial Na⁺ channel	Animals are shorter and thinner, abnormal body wall musculature, abnormal body morphology, protruding vulva	^71,246,265
trpa-1	Q18297	Transient receptor potential channel	Variable body length and increased girth	⁷¹
delm-1	O45402	Degenerin/epithelial Na⁺ channel	Reduced girth and decreased body length	⁷¹
trpa-2	Q21517	Transient receptor potential channel	Increased girth and decreased body length	⁷¹
acc-4	Q9U358	Ligand-gated Cl⁻ channel	Reduced girth and body length	⁷¹
asic-2	Q22851	Epithelial Na⁺ channel	Decreased body length and increased girth	⁷¹
egas-3	Q9XTS9	Epithelial Na⁺ channel	Abnormal body proportions	⁷¹
egas-2	Q9U1T8	Epithelial Na⁺ channel	Reduced girth and body length	⁷¹
del-9	Q18077	Acid-sensing ion channel	Abnormal body proportions	⁷¹
delm-2	P91103	Degenerin/epithelial Na⁺ channel	Reduced girth and body length, abnormal ventral nerve cord patterning	^71,269
acd-2	P91100	Degenerin/epithelial Na⁺ channel	Reduced girth	⁷¹
del-7	Q18651	Epithelial Na⁺ channel	Decreased body length	⁷¹
acd-5	O01664	Degenerin/epithelial Na⁺ channel	Reduced girth and body length compared	⁷¹
asic-1	O01635	Epithelial Na⁺ channel	Reduced girth and body length	⁷¹

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A large number of channelopathies were identified across taxa (Fig. 1A), with an outsize representation of channelopathies in the less complex organisms, particularly Drosophila melanogaster and C. elegans (Fig. 1B). While the large number of channelopathies identified may reflect the use of these model organisms in broad mutation screens (e.g., wing and bristle formation in Drosophila,⁸ or length and girth in C. elegans⁷¹), it may also be the case that the large number of identified mutations in these species is due to smaller individual-to-individual variation than is seen in mammals, allowing for more narrowly defined “normal” phenotypes.

Channelopathies appear to primarily affect size and organization of tissues. The phenotypes created by channelopathies that we identified were mostly limited to alterations in size (31.6% of identified phenotypes), body patterning (i.e., left/right asymmetry, craniofacial patterning, limb patterning, etc., 31.9% of identified phenotypes), or tissue patterning (i.e., organ structure, 26.1% of identified phenotypes) (Fig. 1C). Due to variation in organ structure and function between species, we could not directly compare organs between all organisms, but we did detect a wide variety of organs affected in mammals (Fig. 1D). Craniofacial defects (14.7%), neural defects (16%), skeletal defects (12%), limb defects (10%), and heart defects (10%) were particularly common (Fig. 1D). These data reveal that numerous aspects of pattern formation, especially including the skeletal, neural, craniofacial, and cardiac systems, depend on the function of gene products that regulate bioelectric state.

Thus, the unexpected identification of ion channels in unbiased screens is a means by which many laboratories are brought into the field of bioelectricity. It should be noted that the lists in these Tables are a significant underestimate of the true prevalence of bioelectrical controls in development because single-gene knockouts/mutants are often masked by compensation and redundancy of functionality among channel family members or even completely different types of conductances.^72,73 Future work will examine the phenotypes of combinatorial knockouts and knock-ins of dominant negative proteins targeting entire subclasses of electrogenic proteins. Despite the difficulty of probing physiological mechanisms with gene targeting approaches, it is seen that classical genetic strategies have already revealed a striking number of bioelectric components in development of model systems with widely diverging developmental architectures, including man.

Electrogenic proteins are an ancient common factor in regeneration across Kingdoms

Regeneration is a process that implements morphogenesis in complex body organs or appendages. It is similar to development, except that unlike embryogenesis, which always begins with the same reliable state (fertilized egg), regeneration builds species-specific target morphologies from diverse starting states (injury conditions with variable amounts and locations of missing tissue). A critical tissue in all instances of regeneration is the blastema—early cells at the site of injury, which must make decisions about differentiation, proliferation, and spatial patterning that determine the fate of the wound (scarring or functional regeneration). We thus used bioinformatics to compare published transcriptomic analyses of transcriptional changes occurring in regeneration blastemas (Fig. 2A). We specifically sought to compare extremely distant taxa (crossing Kingdoms of life), to identify components that were associated with regeneration per se, and not the specifics of any one body-plan architecture or physiological/ecological lifestyle.

FIG. 2. — Bioelectric signature of transcriptional profile in regeneration. **(A)** A schematic representation of the analysis performed: published transcriptomic profiles of regeneration blastemas from planaria, axolotl, deer antlers, and plants (*Arabidopsis*) were compared to determine common DEGs across these highly diverse samples. **(B)** Twenty-three differentially expressed genes are common to regeneration among taxa as diverse as mammals (deer antlers), amphibians (axolotl), and invertebrate flatworms (planaria). These are listed in **(B′),** and include two subunits of the proton pump complex and a voltage-gated chloride channel. **(C)** Remarkably, when a plant (*Arabidopsis*) dataset is included, only one DEG is common in regeneration across Kingdoms, and thus across independent origins of multicellularity: an evolutionarily ancient and highly conserved protein that is a key subunit of both, the V-ATPase proton pump and gap junctions (C′), which have both been implicated in regeneration, wound healing, and developmental patterning.^{58,74,77,78,205,270–277} DEGs, differentially expressed genes.

Common to animal regeneration (planarian body fragments, frog limb, axolotl limb, and deer antler) were 10 transcripts listed in Table 6 and Figure 2B. These 10 genes define a regenerative signature across regeneration modes, including those based on adult stem cells, dedifferentiation, and tissue renewal.

Table 6.

Transcripts Common to Blastemas Across Kingdoms

Gene name	Type of protein
POLA1	DNA polymerase alpha 1, catalytic subunit
DPP10	Dipeptidyl peptidase like 10
GRN	Granulin precursor
TPM1	Tropomyosin 1
PSAP	Prosaposin
CTSB	Cathepsin B
ITGB2	Integrin subunit beta 2
CD151	CD151 molecule (Raph blood group)
ATP6V0C	ATPase H⁺-transporting V0 subunit c
PLD3	Phospholipase D family member 3

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Remarkably, when crossreferenced with transcriptomes from plant (Arabidopsis) regeneration, one common gene remains: the transmembrane ring protein component of the proton-transporting V-ATPase (Fig. 2C). This fascinating structure is related to both gap junctions (a.k.a., electrical synapses)⁷⁴ and ion pumps that polarize cells, and has already been functionally implicated in embryonic left–right patterning in chick, zebrafish, and frog,^75,76 zebrafish eye morphogenesis,⁷⁷ frog tail regeneration,⁵⁸ wound healing in Drosophila,⁷⁸ and stem cell regulation in the mouse brain.⁷⁹ In some of these cases (e.g., chick and frog), the V-ATPase pump is known to function on the cell surface, generating a significant hyperpolarization of cells by the efflux of positive charges.^58,76,80 It is also a regulator of pH, at both the cellular and organelle level,⁸¹ and the conserved c subunit has been proposed to function as a gap junction.^74,82–86

It is a remarkable fact that an ancient precursor to widely conserved proteins that set resting potential (ion pumps) and distribute it across tissue networks (gap junctions) is the one common factor in regeneration across Kingdoms. Moreover, because of the distinct origins of multicellularity in plants and animals, it is interesting to note that evolution pressed this highly versatile protein complex into service for tissue regeneration at least twice independently. This finding is consistent with the central importance of bioelectrical cellular states and communication in regenerative patterning processes.

V_mem differences among normal and transformed cell types in human and rodents

Seminal work by Binggeli and Weinstein⁴¹ building on the work of Cone^27,28,87,88 hypothesized that resting membrane potential, V_mem, predictably varied across cell types according to cell type and cell cycle stage. They performed a meta-analysis of literature that reported V_mem in a variety of cell types and stages, showing that proliferative cells and cancer cells both were more depolarized than differentiated cells, and that a boundary existed at around −36 mV that differentiated proliferating and nonproliferating cells.⁴¹

Binggeli and Weinstein were only able to find V_mem values for a few examples in each cell type, however, in the ensuing 34 years electrophysiological determination of V_mem has become a standard laboratory practice, providing ample examples for further assessment of V_mem as a function of cell cycle state. Thus, we revisited the Binggeli and Weinstein study,⁴¹ and analyzed all of the V_mem measurements we could find in a thorough literature search. We found V_mem values for 41 cancer cell types across 3 species from 18 publications and V_mem values for 70 noncancer cell types across 9 species from 24 publications (Supplementary Table S1). Our list did also include the publications described in Binggeli and Weinstein.⁴¹

We found that V_mem was generally more hyperpolarized in normal differentiated tissues than in their cancerous counterparts for both rodent and human tissues (Fig. 3A), a finding consistent with the conclusions in Binggeli and Weinstein. A meta-analysis using a random-effects model comparing data which had both cancer and somatic tissue V_mem values in the same study showed a significant decrease in V_mem in somatic tissue versus cancer (human cancer-somatic delta = 16.0, 95% confidence interval [CI]: 1.1–30.8, p = 0.0343, rodent cancer-somatic delta = 32.3, 95% CI: 8.8–55.7, p = 0.0068), although significant heterogeneity between studies was observed in both human and rodent studies (Tables 7 and 8).

FIG. 3. — Meta-analysis of bioelectric data in cancer. **(A)** Violin plots show membrane potential (V_mem) values for somatic and cancerous tissues in human and rodent cell types. Rodent cancerous cells were significantly more depolarized than somatic cells (95% CI for change in voltage: −30.3 to −11.2, p < 0.0001), but V_mem in human cells overall was not significantly different in somatic and cancerous cells. Data were collected from reports in the literature (Supplementary Table S1) and analyzed by meta-regression. Papers that contained values for both somatic and cancerous tissues are shown as paired, with a line connecting the values. When only considering paired samples, a random-effects model found that somatic cells were significantly more hyperpolarized than cancerous cells derived from the same cell type. For human somatic cells, the average was 16 mV more hyperpolarized than comparable cancer cells (95% CI: 1.1–30.8, p = 0.0343). Rodent somatic cells were on average 32.3 mV more hyperpolarized than the corresponding cancer cells (95% CI: 8.8–55.7, p = 0.0068). Heterogeneity between the studies was significant (p < 0.05) for both human and rodent studies. *p < 0.05, **p < 0.01. **(B)** There is an inverse relationship between the V_mem of somatic cells and the amount of change between the somatic and cancerous cells of the same type, suggesting that cells that start more hyperpolarized have larger increases in depolarization than those that start with V_mem closer to 0. A mixed-effects model identified this relationship as significant, with a 0.85-fold reduction in V_mem change for every 1 mV change in the somatic V_mem. p < 0.0001. **(C, D)** When considered in broad tissue-specific categories, the majority of cancerous cells appear to be more depolarized than the somatic tissues from which they are derived in rodent model systems (C) and human model systems (D). In this analysis, only data for which both somatic and cancerous cell types were represented in our dataset could be used, (grey shaded data in Table 7 for human cells and 8 for rodents) resulting in an insufficient number of studies that could be included in a meta-analysis. Thus, we were unable to determine the significance of this relationship. **(E–H)** Data were grouped into broad categories to show the types of somatic (**E, G**) and cancer (**F, H**) tissues from which we were able to collect V_mem values in both human and rodent model systems. CI, confidence interval.

Table 7.

Human Cell V_mem Measurements

graphic file with name bioe.2019.0034_figure4.jpg

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NA, not available.

Table 8.

Rodent Cell V_mem Measurements

graphic file with name bioe.2019.0034_figure5.jpg

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Furthermore, cancer cells seemed to fall within a narrower V_mem range than somatic cells such that the cancer cells derived from more hyperpolarized somatic cells showed a greater change in V_mem than those that were derived from less hyperpolarized cells (Fig. 3B, mixed-effects model y = −16.9 − 0.85x, where y is the difference between cancer and somatic V_mem and x is the somatic V_mem, p < 0.0001). While this relationship appears to be maintained when the cells are grouped by tissue type, we did not have a sufficient number of studies to determine whether this is a statistically significant relationship or not. The studies report that V_mem values in rodent cancer lines between −38 and −15 mV (a 23 mV difference) compared with a −96 to −30 mV variance in somatic tissues (a 66 mV difference) (Fig. 3C).

Interestingly, while the variance was broad, all rodent cancer cells had about half the V_mem of their corresponding somatic cells (Table 8), suggesting a linear relationship between somatic and cancerous cell V_mem. In human tissues, cancer cells may have a more depolarized V_mem (Fig. 3D), but insufficient published data are available to give significance. Regardless, the relationship between cancer and somatic cells was complicated, with some tissue types showing hyperpolarization in cancer (thyroid) (Fig. 3D) and more variation in the fold change over somatic V_mem (Table 7). While it is as yet unknown why thyroid cancer contradicts the general trend, such exceptions could be informative; for example, it may be due to the fact that most thyroid cancers are differentiated cancers, which may be expected to exhibit the more hyperpolarized values of differentiated cells. Overall, we find that a comprehensive analysis of V_mem measurements reveal a consistent hyperpolarized relationship between somatic tissue and cancer, with variation dependent on tissue type and model system.

Because this is a meta-analysis, we are limited by the types of V_mem characterization performed in the literature. Moreover, these values were determined by workers using diverse electrophysiological techniques, which may not be uniform across the dataset. Figure 2E–H shows the distribution of cell subtypes from which we were able to collect V_mem information. The rodent dataset lacks V_mem values for many cancer subtypes that have corresponding somatic tissue values and thyroid is over-represented, but otherwise the cell types cover a broad range of cancerous and somatic tissues. The values found for human somatic and cancerous tissues were distributed across many tissue types, and we found similar representation between somatic and cancerous tissue types, indicating that the data in human tissues might be the most reflective of general cancer versus somatic tissue phenotypes. It is clear however that comprehensive profiling of diverse cancer types, from multiple patients/cell lines in each category, using identical electrode types and extracellular media, will be an important component of future work.

Conclusion

Developmental bioelectricity is currently at a very exciting point, because physiology data are beginning to be integrated with molecular genetics. This is enabling a better understanding of the controls of growth and form at many scales, from single-cell behavior to multicell cooperation in organ morphogenesis.

The meta-analysis presented here uncovered some important patterns. First, a large number of channelopathies reveal the genetic basis for some bioelectric disorders. This list will certainly continue to grow as more model systems' genomes receive better coverage, but this will always be an underestimate because of the rich ability of physiological networks to drive complex dynamics even when the protein profile of cells is not altered. A comparison of phenotypes arising from genetic versus physiological stress will shed welcome light on the relationship between genome and anatomy. Many other genomic and profiling large datasets should be mined for interesting patterns of ion channel gene involvement.

Second, we reveal the remarkable fact that a small core of genes is strongly conserved with respect to expression in the regeneration blastema across Phyla. One target, the V-ATPase, which is conserved even across Kingdoms, is likely to be a critical part of repair. It was either harnessed at least twice by independent origins of multicellularity, or was already utilized by the unicellular ancestor for similar functions. This aspect, and the V-ATPase function in unicellular and multicellular morphogenesis, represents an important area for future research.

Finally, we report a higher resolution study of the bioelectric signature of cancer. The patterns we identified point to the urgent need for more data: many more normal and transformed cell types need to be profiled for their bioelectric state, to better understand the physiological controls of cellular cooperation in morphogenesis and defection by cancer cells. It is clear that physiomic profiling is a gap and opportunity for inclusion in genomic, transcriptomic, proteomic, and epigenetic global profiling efforts.

Because of its central role in neural function and embryogenesis, progress in bioelectricity has huge implications not only for basic understanding of evolution and developmental biology but also for regenerative medicine and synthetic bioengineering that impact numerous areas of the biosciences.

Supplementary Material

Supplemental data

Supp_Table1.pdf^{(72.1KB, pdf)}

Author Contributions

P.S., A.K., C.H., and M.L. analyzed data, wrote text, and made figures. All coauthors have reviewed and approved the article before submission.

Author Disclosure Statement

The authors confirm there is no conflict of interest, actual or potential, for each listed author. No competing financial interests exist.

Funding Information

The authors gratefully acknowledge support of the Allen Discovery Center program through The Paul G. Allen Frontiers Group (12171) and the Barton Family Foundation.

Supplementary Material

Supplementary Table S1

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PERMALINK

A Meta-Analysis of Bioelectric Data in Cancer, Embryogenesis, and Regeneration

Pranjal Srivastava

Anna Kane, PhD

Christina Harrison

Michael Levin, PhD

Abstract

Introduction

Materials and Methods

Literature search for Vmem values in somatic and cancerous cells

Statistical analysis

Channelopathy identification database and literature search

Bioinformatics

Results and Discussion

Numerous channelopathies reveal roles for bioelectrics in model systems

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

FIG. 1.

Electrogenic proteins are an ancient common factor in regeneration across Kingdoms

FIG. 2.

Table 6.

Vmem differences among normal and transformed cell types in human and rodents

FIG. 3.

Table 7.

Table 8.

Conclusion

Supplementary Material

Author Contributions

Author Disclosure Statement

Funding Information

Supplementary Material

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Literature search for V_mem values in somatic and cancerous cells

V_mem differences among normal and transformed cell types in human and rodents